Experiment: dtufc_elic-featurecoding_sd35_individual Log file: output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log DTUFCCodecConfig: arch: elic-featurecoding handler: sd35 checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar transform_type: kmeans transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json bit_depth: 8 device: cuda:0 Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar Checkpoint epoch: 255 Loaded elic-featurecoding (1-channel) on cuda:0 Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json Loaded per-key mappings: model=sd35 Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] ---------------- -------------------------------------------------------------------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar Transform type kmeans Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features Output output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond ---------------- -------------------------------------------------------------------------------------------------------------------- Files found: 100 ---------------------------------------------------------------------- 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.007s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 528B, BPFP=5.5000 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,724B, BPFP=0.7744 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,588B, BPFP=0.7782 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,996B, BPFP=0.7101 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,540B, BPFP=0.3439 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,540B, BPFP=0.3439 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,264B, BPFP=0.2522 ⌛️ [2/4] FRONTEND: Frontend time: 3.148s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.652s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.89898698 text_encoder-item0.clip_prompt_embeds 0.00025464 23.86213981 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.80910292 text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.11696513 text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00323178 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00635250 2.03506470 vae.encoder_f1 0.00635834 2.03520370 vae.decoder 0.00019940 0.05257336 ------------------------------------------------------------------------------------- TOTAL 0.00300073 2.20472717 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 126360 BPFP 0.4471 bits/point EBPFP 0.8942 equivalent bits/point MSE 2.204727 ---------------------- -------------------------------------------------------- Time: 4.807s Load: 0.007s, Pack+Encode: 3.148s, Decode+Unpack: 1.652s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2047 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000002153.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,504B, BPFP=0.7446 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,540B, BPFP=0.7744 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,712B, BPFP=0.6522 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,824B, BPFP=0.2415 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,808B, BPFP=0.2412 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,384B, BPFP=0.1948 ⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.601s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.83065629 text_encoder-item0.clip_prompt_embeds 0.00022609 23.87031165 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.86720915 text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.11626295 text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00273593 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.01130640 2.23257685 vae.encoder_f1 0.01130902 2.23122382 vae.decoder 0.00020860 0.04597215 ------------------------------------------------------------------------------------- TOTAL 0.00529919 2.29533932 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 108496 BPFP 0.3839 bits/point EBPFP 0.7678 equivalent bits/point MSE 2.295339 ---------------------- -------------------------------------------------------- Time: 3.772s Load: 0.009s, Pack+Encode: 2.162s, Decode+Unpack: 1.601s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2953 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000002431.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.007s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,664B, BPFP=0.6310 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 880B, BPFP=5.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,048B, BPFP=0.6532 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,084B, BPFP=0.5855 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,412B, BPFP=0.1284 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,416B, BPFP=0.1284 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,716B, BPFP=0.1439 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.598s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.83327031 text_encoder-item0.clip_prompt_embeds 0.00022402 23.87856382 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 1.01564064 text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.11217485 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00238347 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 1.19630027 5.33651543 vae.encoder_f1 1.19630098 5.33478737 vae.decoder 0.00023596 0.03710392 ------------------------------------------------------------------------------------- TOTAL 0.55486265 3.73380528 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 87152 BPFP 0.3084 bits/point EBPFP 0.6167 equivalent bits/point MSE 3.733805 ---------------------- -------------------------------------------------------- Time: 3.750s Load: 0.007s, Pack+Encode: 2.144s, Decode+Unpack: 1.598s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.7338 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000003661.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,268B, BPFP=0.7127 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 840B, BPFP=5.2500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,140B, BPFP=0.7419 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,764B, BPFP=0.6535 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,160B, BPFP=0.2924 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,172B, BPFP=0.2925 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,660B, BPFP=0.3253 ⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.596s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.84760300 text_encoder-item0.clip_prompt_embeds 0.00030342 23.84673887 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.80720968 text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.12316440 text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00270603 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00586287 1.52932823 vae.encoder_f1 0.00587438 1.53019440 vae.decoder 0.00017677 0.07601663 ------------------------------------------------------------------------------------- TOTAL 0.00277565 1.97284496 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118936 BPFP 0.4208 bits/point EBPFP 0.8417 equivalent bits/point MSE 1.972845 ---------------------- -------------------------------------------------------- Time: 3.750s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.596s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9728 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000011149.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,508B, BPFP=0.6098 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 868B, BPFP=5.4250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,896B, BPFP=0.6409 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,960B, BPFP=0.5824 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,432B, BPFP=0.2202 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,440B, BPFP=0.2203 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,748B, BPFP=0.1754 ⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.597s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.81513079 text_encoder-item0.clip_prompt_embeds 0.00024120 23.82504946 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.89479332 text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.11036035 text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00227786 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00779453 1.83153474 vae.encoder_f1 0.00779802 1.83182740 vae.decoder 0.00023829 0.04389901 ------------------------------------------------------------------------------------- TOTAL 0.00367359 2.10799533 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 99788 BPFP 0.3531 bits/point EBPFP 0.7062 equivalent bits/point MSE 2.107995 ---------------------- -------------------------------------------------------- Time: 3.757s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.597s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1080 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000023937.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,012B, BPFP=0.6780 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 836B, BPFP=5.2250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,808B, BPFP=0.7149 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,976B, BPFP=0.6082 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,136B, BPFP=0.3378 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,132B, BPFP=0.3377 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,884B, BPFP=0.2101 ⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.599s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.85076857 text_encoder-item0.clip_prompt_embeds 0.00025651 23.86635256 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.86871529 text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.10857085 text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00254635 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00655775 1.95896149 vae.encoder_f1 0.00656268 1.95900297 vae.decoder 0.00020283 0.04692890 ------------------------------------------------------------------------------------- TOTAL 0.00309620 2.16842191 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118716 BPFP 0.4200 bits/point EBPFP 0.8401 equivalent bits/point MSE 2.168422 ---------------------- -------------------------------------------------------- Time: 3.758s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1684 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000027620.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,256B, BPFP=0.5758 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 828B, BPFP=5.1750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,244B, BPFP=0.5880 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 21,164B, BPFP=0.5368 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,340B, BPFP=0.3256 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,364B, BPFP=0.3260 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,000B, BPFP=0.2441 ⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.612s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.85979358 text_encoder-item0.clip_prompt_embeds 0.00022242 23.87803115 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.88060017 text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.10052381 text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00225001 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00593415 1.72519755 vae.encoder_f1 0.00594307 1.72388017 vae.decoder 0.00018992 0.06470372 ------------------------------------------------------------------------------------- TOTAL 0.00280571 2.06167852 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 113124 BPFP 0.4003 bits/point EBPFP 0.8005 equivalent bits/point MSE 2.061679 ---------------------- -------------------------------------------------------- Time: 3.767s Load: 0.009s, Pack+Encode: 2.147s, Decode+Unpack: 1.612s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0617 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000030504.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,388B, BPFP=0.7289 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,244B, BPFP=0.7503 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,800B, BPFP=0.6291 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,880B, BPFP=0.2881 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,892B, BPFP=0.2883 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,488B, BPFP=0.2285 ⌛️ [2/4] FRONTEND: Frontend time: 2.165s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.593s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.83178202 text_encoder-item0.clip_prompt_embeds 0.00022110 23.77549843 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.85183525 text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.11839837 text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00263971 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00641770 1.72982085 vae.encoder_f1 0.00642053 1.73170066 vae.decoder 0.00017498 0.03963344 ------------------------------------------------------------------------------------- TOTAL 0.00305947 2.05978335 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114412 BPFP 0.4048 bits/point EBPFP 0.8096 equivalent bits/point MSE 2.059783 ---------------------- -------------------------------------------------------- Time: 3.767s Load: 0.008s, Pack+Encode: 2.165s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0598 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000031248.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,292B, BPFP=0.5806 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 836B, BPFP=5.2250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,948B, BPFP=0.6451 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,888B, BPFP=0.6313 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,660B, BPFP=0.2390 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,664B, BPFP=0.2390 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,592B, BPFP=0.2622 ⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.595s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.84371090 text_encoder-item0.clip_prompt_embeds 0.00021654 23.85337189 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.88945980 text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.11294892 text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00308358 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00577698 1.51141524 vae.encoder_f1 0.00578348 1.51252270 vae.decoder 0.00017559 0.05942889 ------------------------------------------------------------------------------------- TOTAL 0.00273280 1.96249631 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 106820 BPFP 0.3780 bits/point EBPFP 0.7559 equivalent bits/point MSE 1.962496 ---------------------- -------------------------------------------------------- Time: 3.764s Load: 0.008s, Pack+Encode: 2.162s, Decode+Unpack: 1.595s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9625 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000055072.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 520B, BPFP=5.4167 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,964B, BPFP=0.6715 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 796B, BPFP=4.9750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,648B, BPFP=0.7019 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,568B, BPFP=0.6485 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,640B, BPFP=0.2844 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,640B, BPFP=0.2844 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,052B, BPFP=0.1847 ⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.592s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.89337071 text_encoder-item0.clip_prompt_embeds 0.00022160 23.86023953 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.77966385 text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.10053646 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00282335 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00668450 1.67627096 vae.encoder_f1 0.00668875 1.67985725 vae.decoder 0.00023059 0.04887169 ------------------------------------------------------------------------------------- TOTAL 0.00315742 2.03785887 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 112208 BPFP 0.3970 bits/point EBPFP 0.7940 equivalent bits/point MSE 2.037859 ---------------------- -------------------------------------------------------- Time: 3.755s Load: 0.008s, Pack+Encode: 2.155s, Decode+Unpack: 1.592s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0379 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000060932.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 568B, BPFP=5.9167 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,416B, BPFP=0.7327 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 776B, BPFP=4.8500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,964B, BPFP=0.7276 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,068B, BPFP=0.6612 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,432B, BPFP=0.2507 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,436B, BPFP=0.2508 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,172B, BPFP=0.1273 ⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.600s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.83033673 text_encoder-item0.clip_prompt_embeds 0.00023190 23.79822147 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.73408380 text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.10797548 text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00294005 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.04018118 2.56911898 vae.encoder_f1 0.04018488 2.56824017 vae.decoder 0.00016201 0.03003616 ------------------------------------------------------------------------------------- TOTAL 0.01868571 2.44738536 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 107212 BPFP 0.3793 bits/point EBPFP 0.7587 equivalent bits/point MSE 2.447385 ---------------------- -------------------------------------------------------- Time: 3.767s Load: 0.009s, Pack+Encode: 2.158s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4474 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000062025.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 524B, BPFP=5.4583 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,636B, BPFP=0.6272 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,888B, BPFP=0.6403 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,524B, BPFP=0.6221 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,860B, BPFP=0.2878 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,844B, BPFP=0.2875 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,188B, BPFP=0.2499 ⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.602s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.90388664 text_encoder-item0.clip_prompt_embeds 0.00023140 23.88304079 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.81231508 text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.10921607 text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00260228 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.04874706 2.71420360 vae.encoder_f1 0.04875064 2.72208214 vae.decoder 0.00019641 0.03884537 ------------------------------------------------------------------------------------- TOTAL 0.02266071 2.52001771 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 112652 BPFP 0.3986 bits/point EBPFP 0.7972 equivalent bits/point MSE 2.520018 ---------------------- -------------------------------------------------------- Time: 3.755s Load: 0.009s, Pack+Encode: 2.145s, Decode+Unpack: 1.602s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.5200 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000064718.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,940B, BPFP=0.6683 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,364B, BPFP=0.6789 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,620B, BPFP=0.5738 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 23,108B, BPFP=0.3526 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 23,108B, BPFP=0.3526 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,684B, BPFP=0.1124 ⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.599s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.83327127 text_encoder-item0.clip_prompt_embeds 0.00030893 23.86768212 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.84102917 text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.11524017 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00238493 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.01360236 2.51335573 vae.encoder_f1 0.01360807 2.51030731 vae.decoder 0.00023006 0.03687144 ------------------------------------------------------------------------------------- TOTAL 0.00637132 2.42393107 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 115568 BPFP 0.4089 bits/point EBPFP 0.8178 equivalent bits/point MSE 2.423931 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4239 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000070739.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.007s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,384B, BPFP=0.7284 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,120B, BPFP=0.7403 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,772B, BPFP=0.6791 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,700B, BPFP=0.1328 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,708B, BPFP=0.1329 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,208B, BPFP=0.0674 ⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.85753433 text_encoder-item0.clip_prompt_embeds 0.00024198 23.88177675 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.86356831 text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.11647375 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00252041 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 1.67190456 5.24764824 vae.encoder_f1 1.67190480 5.24779749 vae.decoder 0.00017417 0.01783886 ------------------------------------------------------------------------------------- TOTAL 0.77542609 3.69100588 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 90624 BPFP 0.3207 bits/point EBPFP 0.6413 equivalent bits/point MSE 3.691006 ---------------------- -------------------------------------------------------- Time: 3.742s Load: 0.007s, Pack+Encode: 2.141s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.6910 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000074646.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,984B, BPFP=0.6742 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 780B, BPFP=4.8750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,688B, BPFP=0.7052 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,540B, BPFP=0.6732 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 23,020B, BPFP=0.3513 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 23,016B, BPFP=0.3512 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,952B, BPFP=0.2427 ⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.602s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.82514389 text_encoder-item0.clip_prompt_embeds 0.00025129 23.85197891 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.86528921 text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.10805144 text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00303503 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00621760 1.93848717 vae.encoder_f1 0.00622505 1.94073749 vae.decoder 0.00025114 0.05572411 ------------------------------------------------------------------------------------- TOTAL 0.00294689 2.16011744 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 123908 BPFP 0.4384 bits/point EBPFP 0.8768 equivalent bits/point MSE 2.160117 ---------------------- -------------------------------------------------------- Time: 3.754s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.602s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1601 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000085157.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,488B, BPFP=0.6071 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 848B, BPFP=5.3000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,084B, BPFP=0.6562 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,488B, BPFP=0.5704 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 24,992B, BPFP=0.3813 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 24,984B, BPFP=0.3812 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,060B, BPFP=0.2460 ⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.600s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.88287481 text_encoder-item0.clip_prompt_embeds 0.00020838 23.78097310 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.85427189 text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.10807241 text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00237591 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00675961 2.29238510 vae.encoder_f1 0.00676652 2.29024053 vae.decoder 0.00021373 0.05880725 ------------------------------------------------------------------------------------- TOTAL 0.00319201 2.32164757 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 122860 BPFP 0.4347 bits/point EBPFP 0.8694 equivalent bits/point MSE 2.321648 ---------------------- -------------------------------------------------------- Time: 3.757s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3216 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000089648.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,912B, BPFP=0.6645 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,808B, BPFP=0.7149 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,088B, BPFP=0.7125 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 10,976B, BPFP=0.1675 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 10,980B, BPFP=0.1675 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 14,708B, BPFP=0.4489 ⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.598s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.82256802 text_encoder-item0.clip_prompt_embeds 0.00021387 23.87456245 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.83016224 text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.11205436 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00278659 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00596338 1.16257071 vae.encoder_f1 0.00596322 1.16077435 vae.decoder 0.00018207 0.08803513 ------------------------------------------------------------------------------------- TOTAL 0.00281657 1.80378972 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 108184 BPFP 0.3828 bits/point EBPFP 0.7656 equivalent bits/point MSE 1.803790 ---------------------- -------------------------------------------------------- Time: 3.745s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.598s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8038 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000093965.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,868B, BPFP=0.6585 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,276B, BPFP=0.6718 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,344B, BPFP=0.5668 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,036B, BPFP=0.1226 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,036B, BPFP=0.1226 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,260B, BPFP=0.2826 ⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.595s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.79396216 text_encoder-item0.clip_prompt_embeds 0.00022138 23.86529145 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.83283739 text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.11532161 text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00283245 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00552804 0.88623357 vae.encoder_f1 0.00552758 0.88619089 vae.decoder 0.00018040 0.07049032 ------------------------------------------------------------------------------------- TOTAL 0.00261550 1.67390397 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 90560 BPFP 0.3204 bits/point EBPFP 0.6409 equivalent bits/point MSE 1.673904 ---------------------- -------------------------------------------------------- Time: 3.741s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.6739 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000094852.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.007s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,520B, BPFP=0.6115 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 772B, BPFP=4.8250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,996B, BPFP=0.6490 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,736B, BPFP=0.5767 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 11,236B, BPFP=0.1714 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 11,244B, BPFP=0.1716 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,476B, BPFP=0.1671 ⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.595s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.88816810 text_encoder-item0.clip_prompt_embeds 0.00024507 23.68089658 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.89486389 text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.11706748 text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00249598 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00721525 1.55690527 vae.encoder_f1 0.00721777 1.55419445 vae.decoder 0.00018707 0.03931358 ------------------------------------------------------------------------------------- TOTAL 0.00340651 1.97598018 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 92916 BPFP 0.3288 bits/point EBPFP 0.6575 equivalent bits/point MSE 1.975980 ---------------------- -------------------------------------------------------- Time: 3.741s Load: 0.007s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9760 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000117914.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,324B, BPFP=0.5850 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 836B, BPFP=5.2250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,136B, BPFP=0.5792 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 20,628B, BPFP=0.5232 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,400B, BPFP=0.2808 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,412B, BPFP=0.2809 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,568B, BPFP=0.2310 ⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.584s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.80998604 text_encoder-item0.clip_prompt_embeds 0.00046272 23.86759334 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.89944487 text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.10592090 text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00214407 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.01999603 2.31108427 vae.encoder_f1 0.01999529 2.30996251 vae.decoder 0.00024882 0.05044583 ------------------------------------------------------------------------------------- TOTAL 0.00933711 2.33172762 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 106244 BPFP 0.3759 bits/point EBPFP 0.7518 equivalent bits/point MSE 2.331728 ---------------------- -------------------------------------------------------- Time: 3.741s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.584s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3317 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000123321.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,408B, BPFP=0.5963 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 848B, BPFP=5.3000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,988B, BPFP=0.6484 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 21,272B, BPFP=0.5396 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,876B, BPFP=0.3033 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,876B, BPFP=0.3033 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,680B, BPFP=0.1428 ⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.85347859 text_encoder-item0.clip_prompt_embeds 0.00020334 23.86152471 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.77089238 text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.10375704 text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00207544 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.01341345 2.26554680 vae.encoder_f1 0.01341645 2.26532221 vae.decoder 0.00018350 0.03078478 ------------------------------------------------------------------------------------- TOTAL 0.00627332 2.30821666 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 107868 BPFP 0.3817 bits/point EBPFP 0.7633 equivalent bits/point MSE 2.308217 ---------------------- -------------------------------------------------------- Time: 3.739s Load: 0.009s, Pack+Encode: 2.136s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3082 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000127182.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,712B, BPFP=0.6374 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,520B, BPFP=0.6916 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,032B, BPFP=0.6349 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,860B, BPFP=0.3183 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,860B, BPFP=0.3183 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 11,404B, BPFP=0.3480 ⌛️ [2/4] FRONTEND: Frontend time: 2.128s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.586s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.83899617 text_encoder-item0.clip_prompt_embeds 0.00022316 23.83282814 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.86385822 text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.11506623 text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00278886 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00606298 1.63249540 vae.encoder_f1 0.00607096 1.63216829 vae.decoder 0.00023408 0.06543185 ------------------------------------------------------------------------------------- TOTAL 0.00287331 2.01851054 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 121112 BPFP 0.4285 bits/point EBPFP 0.8571 equivalent bits/point MSE 2.018511 ---------------------- -------------------------------------------------------- Time: 3.723s Load: 0.008s, Pack+Encode: 2.128s, Decode+Unpack: 1.586s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0185 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000127394.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,580B, BPFP=0.6196 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 824B, BPFP=5.1500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,472B, BPFP=0.6065 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,196B, BPFP=0.6137 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,540B, BPFP=0.2982 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,528B, BPFP=0.2980 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,924B, BPFP=0.3029 ⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.84158524 text_encoder-item0.clip_prompt_embeds 0.00023597 23.82383404 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.84774380 text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.09653800 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00239982 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00653100 1.79484057 vae.encoder_f1 0.00653745 1.79495299 vae.decoder 0.00020026 0.05541398 ------------------------------------------------------------------------------------- TOTAL 0.00308450 2.09163606 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114984 BPFP 0.4068 bits/point EBPFP 0.8137 equivalent bits/point MSE 2.091636 ---------------------- -------------------------------------------------------- Time: 3.742s Load: 0.009s, Pack+Encode: 2.139s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0916 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000133969.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,984B, BPFP=0.6742 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,452B, BPFP=0.6860 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,768B, BPFP=0.5775 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,164B, BPFP=0.3077 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,152B, BPFP=0.3075 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,120B, BPFP=0.1562 ⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.597s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.83108862 text_encoder-item0.clip_prompt_embeds 0.00022433 23.81951772 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.86956081 text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.10507975 text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00241413 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00869686 2.33466864 vae.encoder_f1 0.00870063 2.33805871 vae.decoder 0.00021246 0.04184572 ------------------------------------------------------------------------------------- TOTAL 0.00408877 2.34144824 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 111368 BPFP 0.3941 bits/point EBPFP 0.7881 equivalent bits/point MSE 2.341448 ---------------------- -------------------------------------------------------- Time: 3.738s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.597s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3414 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000140270.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,184B, BPFP=0.7013 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,976B, BPFP=0.7286 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,980B, BPFP=0.6336 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,672B, BPFP=0.3459 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,680B, BPFP=0.3461 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,620B, BPFP=0.2020 ⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.597s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.85156735 text_encoder-item0.clip_prompt_embeds 0.00022433 23.86026701 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.81878805 text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.10433752 text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00259712 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00626512 2.13447738 vae.encoder_f1 0.00626949 2.13307738 vae.decoder 0.00018936 0.04572481 ------------------------------------------------------------------------------------- TOTAL 0.00295827 2.24898211 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 120840 BPFP 0.4276 bits/point EBPFP 0.8551 equivalent bits/point MSE 2.248982 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.009s, Pack+Encode: 2.147s, Decode+Unpack: 1.597s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2490 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000146358.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,328B, BPFP=0.7208 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 824B, BPFP=5.1500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,096B, BPFP=0.7383 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,776B, BPFP=0.6538 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,600B, BPFP=0.2380 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,596B, BPFP=0.2380 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,964B, BPFP=0.1515 ⌛️ [2/4] FRONTEND: Frontend time: 2.160s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.596s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.94862636 text_encoder-item0.clip_prompt_embeds 0.00026137 23.90143906 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.78788176 text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.11860948 text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00280564 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.35915655 3.59348392 vae.encoder_f1 0.35915723 3.60629416 vae.decoder 0.00024181 0.03622481 ------------------------------------------------------------------------------------- TOTAL 0.16663024 2.92956004 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 106112 BPFP 0.3755 bits/point EBPFP 0.7509 equivalent bits/point MSE 2.929560 ---------------------- -------------------------------------------------------- Time: 3.764s Load: 0.008s, Pack+Encode: 2.160s, Decode+Unpack: 1.596s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.9296 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000148662.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,132B, BPFP=0.5590 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,104B, BPFP=0.5766 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 21,448B, BPFP=0.5440 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,100B, BPFP=0.1083 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,096B, BPFP=0.1083 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,340B, BPFP=0.1019 ⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.609s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.86648409 text_encoder-item0.clip_prompt_embeds 0.00021656 23.83416193 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.88154650 text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.10318220 text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00221144 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.29031765 2.69674039 vae.encoder_f1 0.29031771 2.72031808 vae.decoder 0.00019965 0.04827992 ------------------------------------------------------------------------------------- TOTAL 0.13469251 2.51508359 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 79960 BPFP 0.2829 bits/point EBPFP 0.5658 equivalent bits/point MSE 2.515084 ---------------------- -------------------------------------------------------- Time: 3.765s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.609s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.5151 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000151051.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,056B, BPFP=0.5487 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,224B, BPFP=0.5864 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 20,412B, BPFP=0.5178 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,512B, BPFP=0.2367 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,520B, BPFP=0.2368 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 12,968B, BPFP=0.3958 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.588s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.86361790 text_encoder-item0.clip_prompt_embeds 0.00025451 24.08609392 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.81475792 text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.10149928 text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00212644 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00595764 1.25990105 vae.encoder_f1 0.00596395 1.25871062 vae.decoder 0.00019845 0.07239348 ------------------------------------------------------------------------------------- TOTAL 0.00281886 1.85224101 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 105436 BPFP 0.3731 bits/point EBPFP 0.7461 equivalent bits/point MSE 1.852241 ---------------------- -------------------------------------------------------- Time: 3.740s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.588s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8522 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000155443.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,136B, BPFP=0.6948 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,936B, BPFP=0.7253 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,964B, BPFP=0.6586 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,248B, BPFP=0.1869 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,248B, BPFP=0.1869 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,384B, BPFP=0.1338 ⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.589s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.86115360 text_encoder-item0.clip_prompt_embeds 0.00026157 23.79347817 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.85674801 text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.11716515 text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00293698 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.40456498 4.01680756 vae.encoder_f1 0.40456539 4.01192045 vae.decoder 0.00020503 0.03556418 ------------------------------------------------------------------------------------- TOTAL 0.18768128 3.11884465 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 98632 BPFP 0.3490 bits/point EBPFP 0.6980 equivalent bits/point MSE 3.118845 ---------------------- -------------------------------------------------------- Time: 3.726s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.589s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.1188 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000159458.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,956B, BPFP=0.6705 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,520B, BPFP=0.6104 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,072B, BPFP=0.5599 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,488B, BPFP=0.3126 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,492B, BPFP=0.3127 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,504B, BPFP=0.2900 ⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.586s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.81812358 text_encoder-item0.clip_prompt_embeds 0.00027179 23.83814216 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.88814993 text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.10115708 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00221671 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00673531 2.21116519 vae.encoder_f1 0.00673732 2.21292996 vae.decoder 0.00020129 0.06308398 ------------------------------------------------------------------------------------- TOTAL 0.00317768 2.28655151 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114776 BPFP 0.4061 bits/point EBPFP 0.8122 equivalent bits/point MSE 2.286552 ---------------------- -------------------------------------------------------- Time: 3.725s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.586s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2866 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000161128.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,164B, BPFP=0.6986 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,756B, BPFP=0.7107 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,348B, BPFP=0.6683 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 17,024B, BPFP=0.2598 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 17,012B, BPFP=0.2596 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,412B, BPFP=0.1346 ⌛️ [2/4] FRONTEND: Frontend time: 2.129s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.588s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.86110552 text_encoder-item0.clip_prompt_embeds 0.00023057 23.85795666 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.87620592 text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.11877416 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00283515 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00881784 2.13825560 vae.encoder_f1 0.00882136 2.13471913 vae.decoder 0.00017598 0.03071065 ------------------------------------------------------------------------------------- TOTAL 0.00418676 2.24913597 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 108460 BPFP 0.3838 bits/point EBPFP 0.7675 equivalent bits/point MSE 2.249136 ---------------------- -------------------------------------------------------- Time: 3.725s Load: 0.008s, Pack+Encode: 2.129s, Decode+Unpack: 1.588s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2491 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000168458.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,812B, BPFP=0.6510 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 792B, BPFP=4.9500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,388B, BPFP=0.6808 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,928B, BPFP=0.6069 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,128B, BPFP=0.2461 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,120B, BPFP=0.2460 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,704B, BPFP=0.2656 ⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.597s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.84246858 text_encoder-item0.clip_prompt_embeds 0.00025208 23.86141902 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.85281019 text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.12709424 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00264685 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00582247 1.32126212 vae.encoder_f1 0.00582996 1.32210803 vae.decoder 0.00016099 0.06810691 ------------------------------------------------------------------------------------- TOTAL 0.00279351 1.87599985 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 107804 BPFP 0.3814 bits/point EBPFP 0.7629 equivalent bits/point MSE 1.876000 ---------------------- -------------------------------------------------------- Time: 3.745s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.597s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8760 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000171788.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 576B, BPFP=6.0000 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,604B, BPFP=0.6228 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 796B, BPFP=4.9750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,128B, BPFP=0.6597 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,972B, BPFP=0.5827 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,808B, BPFP=0.3480 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,796B, BPFP=0.3478 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,960B, BPFP=0.2124 ⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.84359614 text_encoder-item0.clip_prompt_embeds 0.00020809 23.82893669 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.85488710 text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.11978471 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00224678 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00602745 2.09905267 vae.encoder_f1 0.00603159 2.10350680 vae.decoder 0.00017526 0.05372984 ------------------------------------------------------------------------------------- TOTAL 0.00288782 2.23466164 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118020 BPFP 0.4176 bits/point EBPFP 0.8352 equivalent bits/point MSE 2.234662 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2347 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000179265.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 564B, BPFP=5.8750 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,556B, BPFP=0.6163 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,960B, BPFP=0.6461 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,428B, BPFP=0.5689 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 23,464B, BPFP=0.3580 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 23,460B, BPFP=0.3580 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,692B, BPFP=0.2958 ⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.593s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.79352101 text_encoder-item0.clip_prompt_embeds 0.00020908 23.86343767 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.87245092 text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.11140109 text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00228995 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00634616 2.08246422 vae.encoder_f1 0.00635208 2.08311605 vae.decoder 0.00022721 0.05524236 ------------------------------------------------------------------------------------- TOTAL 0.00300000 2.22679799 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 121292 BPFP 0.4292 bits/point EBPFP 0.8583 equivalent bits/point MSE 2.226798 ---------------------- -------------------------------------------------------- Time: 3.750s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2268 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000189752.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,364B, BPFP=0.5904 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 848B, BPFP=5.3000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,516B, BPFP=0.6101 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 21,620B, BPFP=0.5484 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 10,520B, BPFP=0.1605 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 10,528B, BPFP=0.1606 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,712B, BPFP=0.1133 ⌛️ [2/4] FRONTEND: Frontend time: 2.155s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.605s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.86047808 text_encoder-item0.clip_prompt_embeds 0.00022947 23.86655548 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.92292900 text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.10354838 text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00213873 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.05448642 1.88902473 vae.encoder_f1 0.05448771 1.89111555 vae.decoder 0.00017748 0.02701620 ------------------------------------------------------------------------------------- TOTAL 0.02531999 2.13391746 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 88040 BPFP 0.3115 bits/point EBPFP 0.6230 equivalent bits/point MSE 2.133917 ---------------------- -------------------------------------------------------- Time: 3.768s Load: 0.008s, Pack+Encode: 2.155s, Decode+Unpack: 1.605s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1339 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000222118.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.007s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,596B, BPFP=0.6218 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,232B, BPFP=0.6682 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,708B, BPFP=0.6014 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,568B, BPFP=0.2375 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,580B, BPFP=0.2377 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,432B, BPFP=0.1353 ⌛️ [2/4] FRONTEND: Frontend time: 2.137s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.595s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.84439135 text_encoder-item0.clip_prompt_embeds 0.00020169 23.83392096 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.83469687 text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.10580166 text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00224194 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.06876971 2.67141151 vae.encoder_f1 0.06877109 2.65985513 vae.decoder 0.00023999 0.02570955 ------------------------------------------------------------------------------------- TOTAL 0.03194988 2.49265109 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 101860 BPFP 0.3604 bits/point EBPFP 0.7208 equivalent bits/point MSE 2.492651 ---------------------- -------------------------------------------------------- Time: 3.740s Load: 0.007s, Pack+Encode: 2.137s, Decode+Unpack: 1.595s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4927 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000222825.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 568B, BPFP=5.9167 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,744B, BPFP=0.6418 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,520B, BPFP=0.6916 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,632B, BPFP=0.5741 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,772B, BPFP=0.2254 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,760B, BPFP=0.2252 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,796B, BPFP=0.3295 ⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.591s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.81693935 text_encoder-item0.clip_prompt_embeds 0.00025253 23.80943714 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.90380545 text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.10884162 text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00261749 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00595097 1.26289248 vae.encoder_f1 0.00595882 1.26325762 vae.decoder 0.00020134 0.07577533 ------------------------------------------------------------------------------------- TOTAL 0.00281645 1.84756835 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 105984 BPFP 0.3750 bits/point EBPFP 0.7500 equivalent bits/point MSE 1.847568 ---------------------- -------------------------------------------------------- Time: 3.742s Load: 0.009s, Pack+Encode: 2.142s, Decode+Unpack: 1.591s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8476 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000227478.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,860B, BPFP=0.6575 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,600B, BPFP=0.6981 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,460B, BPFP=0.5951 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,752B, BPFP=0.1946 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,764B, BPFP=0.1948 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,912B, BPFP=0.3025 ⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.595s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.85106452 text_encoder-item0.clip_prompt_embeds 0.00022201 23.76763942 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.87833796 text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.12368905 text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00263870 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00831743 1.68298304 vae.encoder_f1 0.00831926 1.67834437 vae.decoder 0.00028593 0.04657605 ------------------------------------------------------------------------------------- TOTAL 0.00392223 2.03740136 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 102068 BPFP 0.3611 bits/point EBPFP 0.7223 equivalent bits/point MSE 2.037401 ---------------------- -------------------------------------------------------- Time: 3.742s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0374 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000239843.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 564B, BPFP=5.8750 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,156B, BPFP=0.6975 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,796B, BPFP=0.7140 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,860B, BPFP=0.6559 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,508B, BPFP=0.3434 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,496B, BPFP=0.3433 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,772B, BPFP=0.2372 ⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.597s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.82926146 text_encoder-item0.clip_prompt_embeds 0.00026808 23.69417106 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.88939524 text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.11118768 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00280324 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00606586 2.09060049 vae.encoder_f1 0.00607066 2.09040308 vae.decoder 0.00019664 0.04843540 ------------------------------------------------------------------------------------- TOTAL 0.00286987 2.22524207 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 122340 BPFP 0.4329 bits/point EBPFP 0.8657 equivalent bits/point MSE 2.225242 ---------------------- -------------------------------------------------------- Time: 3.752s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.597s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2252 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000240250.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 508B, BPFP=5.2917 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,192B, BPFP=0.7024 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,344B, BPFP=0.7584 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,176B, BPFP=0.6640 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 17,296B, BPFP=0.2639 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 17,300B, BPFP=0.2640 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,136B, BPFP=0.1873 ⌛️ [2/4] FRONTEND: Frontend time: 2.167s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.87524303 text_encoder-item0.clip_prompt_embeds 0.00023198 23.88777352 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.92565374 text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.11598617 text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00278255 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.05216765 2.57749414 vae.encoder_f1 0.05216896 2.57889390 vae.decoder 0.00017960 0.04614798 ------------------------------------------------------------------------------------- TOTAL 0.02424513 2.45645906 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 111120 BPFP 0.3932 bits/point EBPFP 0.7863 equivalent bits/point MSE 2.456459 ---------------------- -------------------------------------------------------- Time: 3.770s Load: 0.008s, Pack+Encode: 2.167s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4565 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000258793.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,828B, BPFP=0.6531 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 832B, BPFP=5.2000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,344B, BPFP=0.6773 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,032B, BPFP=0.5588 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 23,844B, BPFP=0.3638 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 23,832B, BPFP=0.3636 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,924B, BPFP=0.2113 ⌛️ [2/4] FRONTEND: Frontend time: 2.159s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.599s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.84631562 text_encoder-item0.clip_prompt_embeds 0.00023125 23.81021079 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.89352951 text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.11355732 text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00233871 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00620361 1.87585378 vae.encoder_f1 0.00620966 1.87887168 vae.decoder 0.00020748 0.05436635 ------------------------------------------------------------------------------------- TOTAL 0.00293402 2.13016425 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 119560 BPFP 0.4230 bits/point EBPFP 0.8461 equivalent bits/point MSE 2.130164 ---------------------- -------------------------------------------------------- Time: 3.768s Load: 0.009s, Pack+Encode: 2.159s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1302 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000270402.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,868B, BPFP=0.6585 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 772B, BPFP=4.8250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,568B, BPFP=0.6955 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,488B, BPFP=0.5958 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,712B, BPFP=0.3008 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,716B, BPFP=0.3008 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,596B, BPFP=0.2623 ⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.603s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.81775260 text_encoder-item0.clip_prompt_embeds 0.00023066 23.87635916 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.81223221 text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.10868941 text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00248118 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.03159856 2.04221058 vae.encoder_f1 0.03160188 2.03632188 vae.decoder 0.00018417 0.04994386 ------------------------------------------------------------------------------------- TOTAL 0.01470700 2.20621924 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114660 BPFP 0.4057 bits/point EBPFP 0.8114 equivalent bits/point MSE 2.206219 ---------------------- -------------------------------------------------------- Time: 3.761s Load: 0.009s, Pack+Encode: 2.149s, Decode+Unpack: 1.603s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2062 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000274272.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,892B, BPFP=0.6618 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 760B, BPFP=4.7500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,228B, BPFP=0.6679 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,884B, BPFP=0.6312 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 25,000B, BPFP=0.3815 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 25,000B, BPFP=0.3815 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,180B, BPFP=0.1886 ⌛️ [2/4] FRONTEND: Frontend time: 2.147s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.600s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.81913519 text_encoder-item0.clip_prompt_embeds 0.00024948 23.84188777 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.82428102 text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.11033662 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00258513 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.03490865 3.06251311 vae.encoder_f1 0.03491008 3.07223701 vae.decoder 0.00028462 0.05688715 ------------------------------------------------------------------------------------- TOTAL 0.01625440 2.68302035 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 123880 BPFP 0.4383 bits/point EBPFP 0.8766 equivalent bits/point MSE 2.683020 ---------------------- -------------------------------------------------------- Time: 3.755s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.6830 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000280891.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,360B, BPFP=0.5898 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 780B, BPFP=4.8750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,108B, BPFP=0.6581 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,632B, BPFP=0.6755 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 9,160B, BPFP=0.1398 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 9,156B, BPFP=0.1397 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,700B, BPFP=0.2960 ⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.600s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.81055625 text_encoder-item0.clip_prompt_embeds 0.00021560 23.86761660 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.81359434 text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.11391029 text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00264211 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00544735 0.96533358 vae.encoder_f1 0.00544843 0.96576279 vae.decoder 0.00018632 0.07063036 ------------------------------------------------------------------------------------- TOTAL 0.00257940 1.71068118 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 96828 BPFP 0.3426 bits/point EBPFP 0.6852 equivalent bits/point MSE 1.710681 ---------------------- -------------------------------------------------------- Time: 3.758s Load: 0.009s, Pack+Encode: 2.150s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.7107 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000285788.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,564B, BPFP=0.6174 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 796B, BPFP=4.9750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,256B, BPFP=0.6701 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,852B, BPFP=0.5796 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,748B, BPFP=0.3166 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,748B, BPFP=0.3166 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,800B, BPFP=0.2686 ⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.597s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.82143164 text_encoder-item0.clip_prompt_embeds 0.00022698 23.83763909 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.89304085 text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.10548583 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00230795 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00630479 1.72101164 vae.encoder_f1 0.00631430 1.72081423 vae.decoder 0.00018596 0.04986764 ------------------------------------------------------------------------------------- TOTAL 0.00298001 2.05743876 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 115684 BPFP 0.4093 bits/point EBPFP 0.8186 equivalent bits/point MSE 2.057439 ---------------------- -------------------------------------------------------- Time: 3.764s Load: 0.009s, Pack+Encode: 2.158s, Decode+Unpack: 1.597s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0574 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000287291.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,520B, BPFP=0.6115 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 844B, BPFP=5.2750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,960B, BPFP=0.6461 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 20,944B, BPFP=0.5312 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,216B, BPFP=0.2932 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,200B, BPFP=0.2930 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,984B, BPFP=0.3047 ⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.593s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.89633878 text_encoder-item0.clip_prompt_embeds 0.00024643 23.85439495 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.90740814 text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.10691226 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00243166 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00612578 1.57670975 vae.encoder_f1 0.00613243 1.57759356 vae.decoder 0.00018179 0.05700742 ------------------------------------------------------------------------------------- TOTAL 0.00289482 1.99214592 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 111596 BPFP 0.3949 bits/point EBPFP 0.7897 equivalent bits/point MSE 1.992146 ---------------------- -------------------------------------------------------- Time: 3.754s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9921 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000289343.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,676B, BPFP=0.7679 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,848B, BPFP=0.8805 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,036B, BPFP=0.7111 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,964B, BPFP=0.0757 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,964B, BPFP=0.0757 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,248B, BPFP=0.3127 ⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.588s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.84829632 text_encoder-item0.clip_prompt_embeds 0.00024049 23.84893085 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.88501120 text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.11812148 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00306104 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00526071 0.47408646 vae.encoder_f1 0.00526072 0.47405764 vae.decoder 0.00016981 0.06622580 ------------------------------------------------------------------------------------- TOTAL 0.00248947 1.48204608 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 94464 BPFP 0.3342 bits/point EBPFP 0.6685 equivalent bits/point MSE 1.482046 ---------------------- -------------------------------------------------------- Time: 3.737s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.588s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.4820 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000304545.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,588B, BPFP=0.6207 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,020B, BPFP=0.6510 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,796B, BPFP=0.5782 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,984B, BPFP=0.3202 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,992B, BPFP=0.3203 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,456B, BPFP=0.3191 ⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.592s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.78728628 text_encoder-item0.clip_prompt_embeds 0.00022843 23.81535571 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.85366268 text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.10225165 text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00243368 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00622977 1.77213204 vae.encoder_f1 0.00623684 1.77645731 vae.decoder 0.00019755 0.05546091 ------------------------------------------------------------------------------------- TOTAL 0.00294358 2.08210384 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 117564 BPFP 0.4160 bits/point EBPFP 0.8319 equivalent bits/point MSE 2.082104 ---------------------- -------------------------------------------------------- Time: 3.743s Load: 0.009s, Pack+Encode: 2.142s, Decode+Unpack: 1.592s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0821 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000310622.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,968B, BPFP=0.6721 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,092B, BPFP=0.6568 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,060B, BPFP=0.5849 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,100B, BPFP=0.2151 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,100B, BPFP=0.2151 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,368B, BPFP=0.1943 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.593s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.88458975 text_encoder-item0.clip_prompt_embeds 0.00026004 23.88727256 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.93533411 text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.10601220 text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00247532 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00725303 1.37044764 vae.encoder_f1 0.00725507 1.36633348 vae.decoder 0.00017991 0.03903699 ------------------------------------------------------------------------------------- TOTAL 0.00341494 1.89408424 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 100428 BPFP 0.3553 bits/point EBPFP 0.7107 equivalent bits/point MSE 1.894084 ---------------------- -------------------------------------------------------- Time: 3.745s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8941 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000311394.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,924B, BPFP=0.6661 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 852B, BPFP=5.3250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,580B, BPFP=0.6964 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,936B, BPFP=0.6071 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,748B, BPFP=0.2403 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,756B, BPFP=0.2404 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,432B, BPFP=0.1353 ⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.89151080 text_encoder-item0.clip_prompt_embeds 0.00031748 23.82373258 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.91414862 text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.12005825 text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00252777 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.42111695 3.28145599 vae.encoder_f1 0.42111716 3.28101444 vae.decoder 0.00019827 0.03377866 ------------------------------------------------------------------------------------- TOTAL 0.19535708 2.77953902 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 103160 BPFP 0.3650 bits/point EBPFP 0.7300 equivalent bits/point MSE 2.779539 ---------------------- -------------------------------------------------------- Time: 3.750s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.7795 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000316015.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,144B, BPFP=0.6959 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 824B, BPFP=5.1500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,008B, BPFP=0.7312 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,940B, BPFP=0.6833 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,140B, BPFP=0.2768 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,144B, BPFP=0.2769 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,616B, BPFP=0.1714 ⌛️ [2/4] FRONTEND: Frontend time: 2.150s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.596s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.80660391 text_encoder-item0.clip_prompt_embeds 0.00024951 23.86809642 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.80819740 text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.10573315 text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00288148 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.10376993 3.59593558 vae.encoder_f1 0.10377157 3.59168553 vae.decoder 0.00019787 0.03864446 ------------------------------------------------------------------------------------- TOTAL 0.04817852 2.92556204 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 112740 BPFP 0.3989 bits/point EBPFP 0.7978 equivalent bits/point MSE 2.925562 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.596s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.9256 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000323571.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,744B, BPFP=0.6418 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,584B, BPFP=0.6968 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,196B, BPFP=0.5884 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,348B, BPFP=0.2800 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,340B, BPFP=0.2798 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,508B, BPFP=0.1681 ⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.590s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.87016829 text_encoder-item0.clip_prompt_embeds 0.00022350 23.66391031 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.82552557 text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.11215879 text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00230287 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.01346414 2.26896644 vae.encoder_f1 0.01346933 2.26732516 vae.decoder 0.00019243 0.03726929 ------------------------------------------------------------------------------------- TOTAL 0.00629858 2.30549188 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 108444 BPFP 0.3837 bits/point EBPFP 0.7674 equivalent bits/point MSE 2.305492 ---------------------- -------------------------------------------------------- Time: 3.743s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.590s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3055 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000325483.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,824B, BPFP=0.6526 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 828B, BPFP=5.1750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,732B, BPFP=0.6276 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,040B, BPFP=0.6605 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,092B, BPFP=0.2455 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,096B, BPFP=0.2456 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,332B, BPFP=0.1322 ⌛️ [2/4] FRONTEND: Frontend time: 2.161s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.83262197 text_encoder-item0.clip_prompt_embeds 0.00024958 23.86288809 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.93778582 text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.11261843 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00278923 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.11196710 2.98739457 vae.encoder_f1 0.11196851 2.98860264 vae.decoder 0.00023459 0.03995568 ------------------------------------------------------------------------------------- TOTAL 0.05198575 2.64499105 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 104864 BPFP 0.3710 bits/point EBPFP 0.7421 equivalent bits/point MSE 2.644991 ---------------------- -------------------------------------------------------- Time: 3.763s Load: 0.008s, Pack+Encode: 2.161s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.6450 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000325991.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,212B, BPFP=0.7051 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,780B, BPFP=0.7127 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,444B, BPFP=0.5693 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,036B, BPFP=0.3362 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,028B, BPFP=0.3361 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,528B, BPFP=0.1687 ⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.584s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.86567243 text_encoder-item0.clip_prompt_embeds 0.00025929 23.89005428 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.87604914 text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.13376304 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00217431 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00675017 2.06889892 vae.encoder_f1 0.00675421 2.06949663 vae.decoder 0.00023635 0.05259768 ------------------------------------------------------------------------------------- TOTAL 0.00320042 2.22186901 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 115772 BPFP 0.4096 bits/point EBPFP 0.8193 equivalent bits/point MSE 2.221869 ---------------------- -------------------------------------------------------- Time: 3.728s Load: 0.008s, Pack+Encode: 2.135s, Decode+Unpack: 1.584s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2219 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000329319.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 564B, BPFP=5.8750 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,596B, BPFP=0.6218 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,988B, BPFP=0.6484 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,304B, BPFP=0.6418 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 26,360B, BPFP=0.4022 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 26,360B, BPFP=0.4022 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,584B, BPFP=0.1704 ⌛️ [2/4] FRONTEND: Frontend time: 2.133s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.593s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.79724352 text_encoder-item0.clip_prompt_embeds 0.00064775 23.86399993 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.86867237 text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.10379906 text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00277175 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00728993 2.57273340 vae.encoder_f1 0.00729572 2.57288599 vae.decoder 0.00026488 0.05151551 ------------------------------------------------------------------------------------- TOTAL 0.00345536 2.45337099 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 125956 BPFP 0.4457 bits/point EBPFP 0.8913 equivalent bits/point MSE 2.453371 ---------------------- -------------------------------------------------------- Time: 3.735s Load: 0.009s, Pack+Encode: 2.133s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4534 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000335081.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,712B, BPFP=0.6374 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,524B, BPFP=0.6919 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,976B, BPFP=0.6335 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,580B, BPFP=0.3293 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,592B, BPFP=0.3295 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,708B, BPFP=0.2047 ⌛️ [2/4] FRONTEND: Frontend time: 2.145s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.86993941 text_encoder-item0.clip_prompt_embeds 0.00023188 23.79517976 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.84015083 text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.10951506 text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00259632 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00613207 1.95740008 vae.encoder_f1 0.00613899 1.95685625 vae.decoder 0.00023812 0.05253877 ------------------------------------------------------------------------------------- TOTAL 0.00290239 2.16638943 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 117828 BPFP 0.4169 bits/point EBPFP 0.8338 equivalent bits/point MSE 2.166389 ---------------------- -------------------------------------------------------- Time: 3.746s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1664 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000342186.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,120B, BPFP=0.6926 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,680B, BPFP=0.7045 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,064B, BPFP=0.6104 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,828B, BPFP=0.2873 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,844B, BPFP=0.2875 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,448B, BPFP=0.2273 ⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.595s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.82205447 text_encoder-item0.clip_prompt_embeds 0.00023678 23.81970373 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.91614742 text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.10901198 text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00246406 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00636537 1.97112763 vae.encoder_f1 0.00636991 1.96216762 vae.decoder 0.00025538 0.04914599 ------------------------------------------------------------------------------------- TOTAL 0.00301360 2.17103868 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 112716 BPFP 0.3988 bits/point EBPFP 0.7976 equivalent bits/point MSE 2.171039 ---------------------- -------------------------------------------------------- Time: 3.741s Load: 0.009s, Pack+Encode: 2.138s, Decode+Unpack: 1.595s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1710 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000343976.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,476B, BPFP=0.6055 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,732B, BPFP=0.6276 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 21,760B, BPFP=0.5519 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,280B, BPFP=0.2789 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,276B, BPFP=0.2789 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,396B, BPFP=0.1342 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.589s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.87674459 text_encoder-item0.clip_prompt_embeds 0.00023432 23.64254642 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.76246672 text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.10463917 text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00248310 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.23155926 3.41490650 vae.encoder_f1 0.23156048 3.42219496 vae.decoder 0.00018572 0.03526046 ------------------------------------------------------------------------------------- TOTAL 0.10744199 2.83788522 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 104640 BPFP 0.3702 bits/point EBPFP 0.7405 equivalent bits/point MSE 2.837885 ---------------------- -------------------------------------------------------- Time: 3.741s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.589s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.8379 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000351362.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,504B, BPFP=0.6093 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,760B, BPFP=0.6299 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,136B, BPFP=0.5869 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,600B, BPFP=0.3448 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,608B, BPFP=0.3450 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,664B, BPFP=0.2339 ⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.602s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.84665179 text_encoder-item0.clip_prompt_embeds 0.00022528 23.85208249 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.86906490 text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.10725464 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00236752 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00729824 2.12695503 vae.encoder_f1 0.00730369 2.12878275 vae.decoder 0.00019938 0.06144282 ------------------------------------------------------------------------------------- TOTAL 0.00343853 2.24797218 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118000 BPFP 0.4175 bits/point EBPFP 0.8350 equivalent bits/point MSE 2.247972 ---------------------- -------------------------------------------------------- Time: 3.751s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.602s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2480 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000357816.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,492B, BPFP=0.6077 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,932B, BPFP=0.6438 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 21,192B, BPFP=0.5375 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,448B, BPFP=0.2205 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,448B, BPFP=0.2205 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,892B, BPFP=0.3019 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.592s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.81944537 text_encoder-item0.clip_prompt_embeds 0.00022149 23.79888731 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.85795994 text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.12099975 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00220059 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00564371 1.38803446 vae.encoder_f1 0.00565042 1.38727534 vae.decoder 0.00019980 0.06874166 ------------------------------------------------------------------------------------- TOTAL 0.00270919 1.90469981 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 102148 BPFP 0.3614 bits/point EBPFP 0.7229 equivalent bits/point MSE 1.904700 ---------------------- -------------------------------------------------------- Time: 3.744s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.592s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9047 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000361180.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,016B, BPFP=0.6786 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 812B, BPFP=5.0750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,576B, BPFP=0.6961 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 21,980B, BPFP=0.5575 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,628B, BPFP=0.2385 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,640B, BPFP=0.2386 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,616B, BPFP=0.3240 ⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.595s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.82752085 text_encoder-item0.clip_prompt_embeds 0.00022173 23.84761609 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.86172676 text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.12256636 text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00232320 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00576096 1.25992489 vae.encoder_f1 0.00576981 1.25845253 vae.decoder 0.00019592 0.06197868 ------------------------------------------------------------------------------------- TOTAL 0.00276400 1.84570194 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 107184 BPFP 0.3792 bits/point EBPFP 0.7585 equivalent bits/point MSE 1.845702 ---------------------- -------------------------------------------------------- Time: 3.743s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.595s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8457 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000361268.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.007s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,504B, BPFP=0.7446 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,560B, BPFP=0.7760 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,660B, BPFP=0.6762 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,824B, BPFP=0.1957 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,836B, BPFP=0.1959 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,048B, BPFP=0.1541 ⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.592s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.87499952 text_encoder-item0.clip_prompt_embeds 0.00025917 23.89076451 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.88335619 text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.12153882 text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00293281 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00594818 1.41173971 vae.encoder_f1 0.00595328 1.41017866 vae.decoder 0.00023462 0.04507460 ------------------------------------------------------------------------------------- TOTAL 0.00281845 1.91532551 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 102148 BPFP 0.3614 bits/point EBPFP 0.7229 equivalent bits/point MSE 1.915326 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.007s, Pack+Encode: 2.153s, Decode+Unpack: 1.592s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9153 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000367228.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,668B, BPFP=0.7668 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,832B, BPFP=0.8792 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,876B, BPFP=0.7071 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,032B, BPFP=0.1836 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,028B, BPFP=0.1835 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,956B, BPFP=0.1207 ⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.595s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.79689956 text_encoder-item0.clip_prompt_embeds 0.00022579 23.79212747 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.84955587 text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.12068535 text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00289565 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.85445058 4.50288010 vae.encoder_f1 0.85445166 4.50870657 vae.decoder 0.00025257 0.02084281 ------------------------------------------------------------------------------------- TOTAL 0.39632643 3.34513354 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 102132 BPFP 0.3614 bits/point EBPFP 0.7227 equivalent bits/point MSE 3.345134 ---------------------- -------------------------------------------------------- Time: 3.741s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.595s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.3451 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000369503.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,720B, BPFP=0.6385 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,236B, BPFP=0.6685 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,840B, BPFP=0.5793 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 25,080B, BPFP=0.3827 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 25,076B, BPFP=0.3826 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,440B, BPFP=0.3186 ⌛️ [2/4] FRONTEND: Frontend time: 2.154s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.602s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.85892725 text_encoder-item0.clip_prompt_embeds 0.00025458 23.89327778 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.87224464 text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.10994049 text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00224977 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00628510 1.98974931 vae.encoder_f1 0.00629234 1.99006224 vae.decoder 0.00023521 0.06762198 ------------------------------------------------------------------------------------- TOTAL 0.00297516 2.18588979 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 126128 BPFP 0.4463 bits/point EBPFP 0.8925 equivalent bits/point MSE 2.185890 ---------------------- -------------------------------------------------------- Time: 3.766s Load: 0.009s, Pack+Encode: 2.154s, Decode+Unpack: 1.602s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1859 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000370486.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,620B, BPFP=0.6250 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 832B, BPFP=5.2000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,016B, BPFP=0.6506 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,128B, BPFP=0.6120 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,208B, BPFP=0.2473 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,212B, BPFP=0.2474 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,096B, BPFP=0.2471 ⌛️ [2/4] FRONTEND: Frontend time: 2.143s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.600s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.88652237 text_encoder-item0.clip_prompt_embeds 0.00022807 23.87192023 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.93182621 text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.10664738 text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00267171 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00573429 1.39480186 vae.encoder_f1 0.00574192 1.39376414 vae.decoder 0.00017875 0.05230338 ------------------------------------------------------------------------------------- TOTAL 0.00271248 1.90728267 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 107028 BPFP 0.3787 bits/point EBPFP 0.7574 equivalent bits/point MSE 1.907283 ---------------------- -------------------------------------------------------- Time: 3.751s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9073 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000377635.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,464B, BPFP=0.8745 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 12,156B, BPFP=0.9867 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 49,412B, BPFP=1.2533 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,528B, BPFP=0.3285 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,512B, BPFP=0.3282 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,156B, BPFP=0.2489 ⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.599s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.86784180 text_encoder-item0.clip_prompt_embeds 0.00027120 23.89640617 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.86646910 text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.13512083 text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00450018 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00781570 2.31340528 vae.encoder_f1 0.00781878 2.31722593 vae.decoder 0.00029724 0.06009343 ------------------------------------------------------------------------------------- TOTAL 0.00369190 2.33742476 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 148944 BPFP 0.5270 bits/point EBPFP 1.0540 equivalent bits/point MSE 2.337425 ---------------------- -------------------------------------------------------- Time: 3.756s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3374 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000377814.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 572B, BPFP=5.9583 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,908B, BPFP=0.6640 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 776B, BPFP=4.8500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,896B, BPFP=0.7221 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,856B, BPFP=0.6051 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,480B, BPFP=0.2972 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,488B, BPFP=0.2974 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 12,584B, BPFP=0.3840 ⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.596s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.86577765 text_encoder-item0.clip_prompt_embeds 0.00022930 23.79028215 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.79752951 text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.11036105 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00242895 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00577752 1.44430304 vae.encoder_f1 0.00578475 1.44395792 vae.decoder 0.00024190 0.06737972 ------------------------------------------------------------------------------------- TOTAL 0.00273964 1.93005804 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118940 BPFP 0.4208 bits/point EBPFP 0.8417 equivalent bits/point MSE 1.930058 ---------------------- -------------------------------------------------------- Time: 3.744s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.596s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9301 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000379800.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,680B, BPFP=0.7684 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 824B, BPFP=5.1500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,352B, BPFP=0.7591 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,604B, BPFP=0.7002 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,504B, BPFP=0.3281 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,492B, BPFP=0.3279 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,860B, BPFP=0.1178 ⌛️ [2/4] FRONTEND: Frontend time: 2.149s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.595s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.91702930 text_encoder-item0.clip_prompt_embeds 0.00028764 23.78179535 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.89763489 text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.10751669 text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00283596 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.03343784 3.06175947 vae.encoder_f1 0.03344063 3.06419373 vae.decoder 0.00016139 0.02751608 ------------------------------------------------------------------------------------- TOTAL 0.01555870 2.67599027 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 119248 BPFP 0.4219 bits/point EBPFP 0.8439 equivalent bits/point MSE 2.675990 ---------------------- -------------------------------------------------------- Time: 3.752s Load: 0.009s, Pack+Encode: 2.149s, Decode+Unpack: 1.595s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.6760 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000384808.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 568B, BPFP=5.9167 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,428B, BPFP=0.5990 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,160B, BPFP=0.6623 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,116B, BPFP=0.5863 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 25,176B, BPFP=0.3842 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 25,148B, BPFP=0.3837 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,400B, BPFP=0.2869 ⌛️ [2/4] FRONTEND: Frontend time: 2.146s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.596s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.81506666 text_encoder-item0.clip_prompt_embeds 0.00023094 23.88841188 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.83805981 text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.10716456 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00254681 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00637455 2.02687764 vae.encoder_f1 0.00637988 2.02865243 vae.decoder 0.00020059 0.06053424 ------------------------------------------------------------------------------------- TOTAL 0.00301333 2.20238485 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 125192 BPFP 0.4430 bits/point EBPFP 0.8859 equivalent bits/point MSE 2.202385 ---------------------- -------------------------------------------------------- Time: 3.751s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.596s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2024 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000396338.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 568B, BPFP=5.9167 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,996B, BPFP=0.6759 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 832B, BPFP=5.2000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,680B, BPFP=0.7045 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,664B, BPFP=0.6510 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,848B, BPFP=0.2876 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,860B, BPFP=0.2878 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,316B, BPFP=0.2843 ⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.593s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.88210058 text_encoder-item0.clip_prompt_embeds 0.00025217 23.80840351 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.94590969 text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.10911267 text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00293017 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00581597 1.45285082 vae.encoder_f1 0.00582356 1.45070982 vae.decoder 0.00019494 0.05927231 ------------------------------------------------------------------------------------- TOTAL 0.00275264 1.93324480 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 116144 BPFP 0.4109 bits/point EBPFP 0.8219 equivalent bits/point MSE 1.933245 ---------------------- -------------------------------------------------------- Time: 3.741s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9332 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000397303.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.007s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 524B, BPFP=5.4583 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,256B, BPFP=0.5758 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 6,980B, BPFP=0.5666 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 21,776B, BPFP=0.5524 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 10,896B, BPFP=0.1663 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 10,896B, BPFP=0.1663 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,288B, BPFP=0.1614 ⌛️ [2/4] FRONTEND: Frontend time: 2.133s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.590s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.89496152 text_encoder-item0.clip_prompt_embeds 0.00026975 23.88629600 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.88568592 text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.11179326 text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00215576 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 1.11695218 4.51543188 vae.encoder_f1 1.11695278 4.49766922 vae.decoder 0.00019720 0.04865539 ------------------------------------------------------------------------------------- TOTAL 0.51806274 3.35073526 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 89812 BPFP 0.3178 bits/point EBPFP 0.6356 equivalent bits/point MSE 3.350735 ---------------------- -------------------------------------------------------- Time: 3.730s Load: 0.007s, Pack+Encode: 2.133s, Decode+Unpack: 1.590s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.3507 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000402473.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,884B, BPFP=0.6607 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,072B, BPFP=0.6552 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,680B, BPFP=0.6514 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,416B, BPFP=0.2963 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,436B, BPFP=0.2966 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,244B, BPFP=0.2516 ⌛️ [2/4] FRONTEND: Frontend time: 2.151s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.607s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.84131535 text_encoder-item0.clip_prompt_embeds 0.00025545 23.79525374 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.76980619 text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.10998962 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00260895 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.01535016 2.41759777 vae.encoder_f1 0.01535382 2.41793728 vae.decoder 0.00021460 0.05269147 ------------------------------------------------------------------------------------- TOTAL 0.00717511 2.38001182 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 115464 BPFP 0.4085 bits/point EBPFP 0.8171 equivalent bits/point MSE 2.380012 ---------------------- -------------------------------------------------------- Time: 3.766s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.607s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3800 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000409211.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.010s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,256B, BPFP=0.7110 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,292B, BPFP=0.7542 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,128B, BPFP=0.6627 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,220B, BPFP=0.2322 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,212B, BPFP=0.2321 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 11,012B, BPFP=0.3361 ⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.599s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.84618576 text_encoder-item0.clip_prompt_embeds 0.00022628 23.79591746 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.90268078 text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.11732530 text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00276397 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00589589 1.24995816 vae.encoder_f1 0.00590398 1.24855781 vae.decoder 0.00017838 0.07825442 ------------------------------------------------------------------------------------- TOTAL 0.00278687 1.84149380 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 111832 BPFP 0.3957 bits/point EBPFP 0.7914 equivalent bits/point MSE 1.841494 ---------------------- -------------------------------------------------------- Time: 3.773s Load: 0.010s, Pack+Encode: 2.163s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8415 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000427500.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,604B, BPFP=0.6228 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 796B, BPFP=4.9750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,028B, BPFP=0.6516 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 21,872B, BPFP=0.5548 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 23,528B, BPFP=0.3590 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 23,532B, BPFP=0.3591 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,824B, BPFP=0.1472 ⌛️ [2/4] FRONTEND: Frontend time: 2.162s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.601s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.82687449 text_encoder-item0.clip_prompt_embeds 0.00031548 23.78195389 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.86474438 text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.11164038 text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00215357 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00725484 2.50404501 vae.encoder_f1 0.00725992 2.50725889 vae.decoder 0.00019960 0.04165804 ------------------------------------------------------------------------------------- TOTAL 0.00342155 2.41919999 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 116124 BPFP 0.4109 bits/point EBPFP 0.8218 equivalent bits/point MSE 2.419200 ---------------------- -------------------------------------------------------- Time: 3.772s Load: 0.009s, Pack+Encode: 2.162s, Decode+Unpack: 1.601s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4192 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000435208.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,204B, BPFP=0.5687 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 872B, BPFP=5.4500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 6,868B, BPFP=0.5575 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 20,064B, BPFP=0.5089 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,176B, BPFP=0.3079 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,172B, BPFP=0.3078 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,200B, BPFP=0.1892 ⌛️ [2/4] FRONTEND: Frontend time: 2.157s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.593s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.84536695 text_encoder-item0.clip_prompt_embeds 0.00021831 23.88167529 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 1.01574459 text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.10301062 text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00194961 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00923516 2.21725321 vae.encoder_f1 0.00923823 2.21425867 vae.decoder 0.00019521 0.03566433 ------------------------------------------------------------------------------------- TOTAL 0.00433552 2.28635588 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 107484 BPFP 0.3803 bits/point EBPFP 0.7606 equivalent bits/point MSE 2.286356 ---------------------- -------------------------------------------------------- Time: 3.759s Load: 0.008s, Pack+Encode: 2.157s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2864 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000435880.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 520B, BPFP=5.4167 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,696B, BPFP=0.6353 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,344B, BPFP=0.6773 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,736B, BPFP=0.6274 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,648B, BPFP=0.3303 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,672B, BPFP=0.3307 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,904B, BPFP=0.2107 ⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.597s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.85955516 text_encoder-item0.clip_prompt_embeds 0.00062166 23.80995925 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.84212265 text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.10869741 text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00253471 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00831779 2.25304627 vae.encoder_f1 0.00832197 2.25870442 vae.decoder 0.00023271 0.04630571 ------------------------------------------------------------------------------------- TOTAL 0.00392639 2.30455608 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 117700 BPFP 0.4165 bits/point EBPFP 0.8329 equivalent bits/point MSE 2.304556 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.597s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3046 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000439593.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,700B, BPFP=0.6358 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 784B, BPFP=4.9000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,900B, BPFP=0.6412 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,188B, BPFP=0.6135 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 17,696B, BPFP=0.2700 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 17,684B, BPFP=0.2698 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,556B, BPFP=0.1390 ⌛️ [2/4] FRONTEND: Frontend time: 2.138s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.599s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.85385847 text_encoder-item0.clip_prompt_embeds 0.00022938 23.86612850 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.84596195 text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.10649480 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00259210 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00626977 1.64644837 vae.encoder_f1 0.00627489 1.64967585 vae.decoder 0.00017842 0.05190464 ------------------------------------------------------------------------------------- TOTAL 0.00295919 2.02470216 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 106432 BPFP 0.3766 bits/point EBPFP 0.7532 equivalent bits/point MSE 2.024702 ---------------------- -------------------------------------------------------- Time: 3.745s Load: 0.009s, Pack+Encode: 2.138s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0247 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000441286.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,984B, BPFP=0.6742 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,876B, BPFP=0.7205 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,580B, BPFP=0.6235 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,340B, BPFP=0.2798 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,352B, BPFP=0.2800 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,676B, BPFP=0.2953 ⌛️ [2/4] FRONTEND: Frontend time: 2.165s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.604s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.80110741 text_encoder-item0.clip_prompt_embeds 0.00022180 23.89366672 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.82348394 text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.11178571 text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00278335 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00585720 1.50724125 vae.encoder_f1 0.00586586 1.50810945 vae.decoder 0.00016520 0.07080744 ------------------------------------------------------------------------------------- TOTAL 0.00276807 1.96273382 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114552 BPFP 0.4053 bits/point EBPFP 0.8106 equivalent bits/point MSE 1.962734 ---------------------- -------------------------------------------------------- Time: 3.779s Load: 0.009s, Pack+Encode: 2.165s, Decode+Unpack: 1.604s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9627 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000445365.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,656B, BPFP=0.6299 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 784B, BPFP=4.9000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,196B, BPFP=0.6653 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,476B, BPFP=0.5955 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,140B, BPFP=0.2310 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,132B, BPFP=0.2309 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,436B, BPFP=0.1964 ⌛️ [2/4] FRONTEND: Frontend time: 2.165s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.593s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.85782488 text_encoder-item0.clip_prompt_embeds 0.00025784 23.86219266 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.72089043 text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.10208406 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00249874 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00734802 1.80048048 vae.encoder_f1 0.00734987 1.80229974 vae.decoder 0.00018093 0.03117982 ------------------------------------------------------------------------------------- TOTAL 0.00345989 2.09303023 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 102748 BPFP 0.3636 bits/point EBPFP 0.7271 equivalent bits/point MSE 2.093030 ---------------------- -------------------------------------------------------- Time: 3.766s Load: 0.008s, Pack+Encode: 2.165s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0930 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000449996.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,196B, BPFP=0.7029 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,540B, BPFP=0.6932 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,788B, BPFP=0.6034 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 24,124B, BPFP=0.3681 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 24,128B, BPFP=0.3682 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,708B, BPFP=0.2047 ⌛️ [2/4] FRONTEND: Frontend time: 2.148s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.598s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.84612719 text_encoder-item0.clip_prompt_embeds 0.00023510 23.88338110 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.82510290 text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.11328020 text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00252396 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00637359 2.02584791 vae.encoder_f1 0.00637830 2.02433681 vae.decoder 0.00018566 0.05852472 ------------------------------------------------------------------------------------- TOTAL 0.00300937 2.20104740 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 122216 BPFP 0.4324 bits/point EBPFP 0.8649 equivalent bits/point MSE 2.201047 ---------------------- -------------------------------------------------------- Time: 3.754s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.598s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2010 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000451714.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,648B, BPFP=0.6288 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 792B, BPFP=4.9500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,616B, BPFP=0.6994 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,424B, BPFP=0.6195 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 17,136B, BPFP=0.2615 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 17,124B, BPFP=0.2613 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,108B, BPFP=0.1254 ⌛️ [2/4] FRONTEND: Frontend time: 2.142s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.592s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.85656230 text_encoder-item0.clip_prompt_embeds 0.00026418 23.86710506 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.84959106 text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.11786935 text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00258271 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.01530954 2.36900806 vae.encoder_f1 0.01531230 2.36636662 vae.decoder 0.00017892 0.03581875 ------------------------------------------------------------------------------------- TOTAL 0.00715252 2.35709941 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 105768 BPFP 0.3742 bits/point EBPFP 0.7485 equivalent bits/point MSE 2.357099 ---------------------- -------------------------------------------------------- Time: 3.743s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.592s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3571 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000464358.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,276B, BPFP=0.7137 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,784B, BPFP=0.7130 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,380B, BPFP=0.6438 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,280B, BPFP=0.3247 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,280B, BPFP=0.3247 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,260B, BPFP=0.2521 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.80594444 text_encoder-item0.clip_prompt_embeds 0.00021481 23.85526371 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.76831908 text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.10204204 text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00293791 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00591154 2.05325294 vae.encoder_f1 0.00591973 2.05202270 vae.decoder 0.00025286 0.05627364 ------------------------------------------------------------------------------------- TOTAL 0.00280398 2.21234776 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 119988 BPFP 0.4245 bits/point EBPFP 0.8491 equivalent bits/point MSE 2.212348 ---------------------- -------------------------------------------------------- Time: 3.746s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2123 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000466256.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,380B, BPFP=0.7278 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,368B, BPFP=0.7604 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,444B, BPFP=0.6961 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 13,036B, BPFP=0.1989 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 13,024B, BPFP=0.1987 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,868B, BPFP=0.3011 ⌛️ [2/4] FRONTEND: Frontend time: 2.139s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.598s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.86000013 text_encoder-item0.clip_prompt_embeds 0.00023458 23.80487563 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.87244720 text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.12158368 text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00308179 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00588703 1.11888659 vae.encoder_f1 0.00589573 1.12214541 vae.decoder 0.00053402 0.06059280 ------------------------------------------------------------------------------------- TOTAL 0.00289910 1.78019149 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 107836 BPFP 0.3816 bits/point EBPFP 0.7631 equivalent bits/point MSE 1.780191 ---------------------- -------------------------------------------------------- Time: 3.745s Load: 0.009s, Pack+Encode: 2.139s, Decode+Unpack: 1.598s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.7802 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000467848.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,320B, BPFP=0.7197 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,976B, BPFP=0.7286 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,948B, BPFP=0.6582 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,852B, BPFP=0.3029 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,828B, BPFP=0.3026 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,556B, BPFP=0.1696 ⌛️ [2/4] FRONTEND: Frontend time: 2.134s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.602s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.80988900 text_encoder-item0.clip_prompt_embeds 0.00022882 23.85597183 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.89758492 text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.11316407 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00266589 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00659691 2.01204205 vae.encoder_f1 0.00660300 2.01439023 vae.decoder 0.00023739 0.03950044 ------------------------------------------------------------------------------------- TOTAL 0.00311972 2.19266045 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 115228 BPFP 0.4077 bits/point EBPFP 0.8154 equivalent bits/point MSE 2.192660 ---------------------- -------------------------------------------------------- Time: 3.744s Load: 0.008s, Pack+Encode: 2.134s, Decode+Unpack: 1.602s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1927 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000468501.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 536B, BPFP=5.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,856B, BPFP=0.6569 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 824B, BPFP=5.1500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,324B, BPFP=0.6756 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,028B, BPFP=0.5841 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,848B, BPFP=0.1960 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,848B, BPFP=0.1960 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,484B, BPFP=0.3199 ⌛️ [2/4] FRONTEND: Frontend time: 2.158s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.599s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.85501154 text_encoder-item0.clip_prompt_embeds 0.00023928 23.86912583 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.86221590 text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.10139849 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00244076 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00583864 1.23455906 vae.encoder_f1 0.00583800 1.23455381 vae.decoder 0.00018889 0.07040647 ------------------------------------------------------------------------------------- TOTAL 0.00276073 1.83492127 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 102128 BPFP 0.3614 bits/point EBPFP 0.7227 equivalent bits/point MSE 1.834921 ---------------------- -------------------------------------------------------- Time: 3.764s Load: 0.008s, Pack+Encode: 2.158s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8349 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000468632.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,144B, BPFP=0.6959 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 780B, BPFP=4.8750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,880B, BPFP=0.7208 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,108B, BPFP=0.7130 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 10,920B, BPFP=0.1666 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 10,924B, BPFP=0.1667 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,064B, BPFP=0.2156 ⌛️ [2/4] FRONTEND: Frontend time: 2.156s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.593s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.87523301 text_encoder-item0.clip_prompt_embeds 0.00024821 23.87079782 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.68934870 text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.11116969 text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00336807 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00570467 0.98040038 vae.encoder_f1 0.00570488 0.98036331 vae.decoder 0.00017302 0.05788405 ------------------------------------------------------------------------------------- TOTAL 0.00269931 1.71609936 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 100740 BPFP 0.3564 bits/point EBPFP 0.7129 equivalent bits/point MSE 1.716099 ---------------------- -------------------------------------------------------- Time: 3.758s Load: 0.009s, Pack+Encode: 2.156s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.7161 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000471087.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,132B, BPFP=0.6943 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 804B, BPFP=5.0250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,664B, BPFP=0.7032 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,196B, BPFP=0.6137 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 11,928B, BPFP=0.1820 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 11,924B, BPFP=0.1819 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,104B, BPFP=0.1252 ⌛️ [2/4] FRONTEND: Frontend time: 2.152s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.606s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.85147603 text_encoder-item0.clip_prompt_embeds 0.00021458 23.79433425 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.74903307 text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.10901402 text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00253028 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00914783 1.98385429 vae.encoder_f1 0.00914958 1.98993611 vae.decoder 0.00017527 0.03275422 ------------------------------------------------------------------------------------- TOTAL 0.00429285 2.17778954 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 95676 BPFP 0.3385 bits/point EBPFP 0.6771 equivalent bits/point MSE 2.177790 ---------------------- -------------------------------------------------------- Time: 3.765s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.606s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1778 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000482477.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 560B, BPFP=5.8333 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,748B, BPFP=0.7776 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 780B, BPFP=4.8750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,372B, BPFP=0.8419 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,592B, BPFP=0.7252 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 13,944B, BPFP=0.2128 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 13,944B, BPFP=0.2128 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 11,904B, BPFP=0.3633 ⌛️ [2/4] FRONTEND: Frontend time: 2.157s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.591s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.84658464 text_encoder-item0.clip_prompt_embeds 0.00022150 23.86567404 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.89634161 text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.11764080 text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00277769 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00578482 1.18322086 vae.encoder_f1 0.00579739 1.18316317 vae.decoder 0.00017668 0.07167547 ------------------------------------------------------------------------------------- TOTAL 0.00273588 1.81192842 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114224 BPFP 0.4042 bits/point EBPFP 0.8083 equivalent bits/point MSE 1.811928 ---------------------- -------------------------------------------------------- Time: 3.757s Load: 0.009s, Pack+Encode: 2.157s, Decode+Unpack: 1.591s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8119 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000499768.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 568B, BPFP=5.9167 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,760B, BPFP=0.6439 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 800B, BPFP=5.0000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,096B, BPFP=0.6571 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,036B, BPFP=0.5843 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,084B, BPFP=0.2302 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,088B, BPFP=0.2302 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,496B, BPFP=0.2288 ⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.85621293 text_encoder-item0.clip_prompt_embeds 0.00023894 23.78523657 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.85915022 text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.10646548 text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00226350 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00958025 1.82702529 vae.encoder_f1 0.00958229 1.82239795 vae.decoder 0.00019995 0.05499873 ------------------------------------------------------------------------------------- TOTAL 0.00449688 2.10483074 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 103308 BPFP 0.3655 bits/point EBPFP 0.7311 equivalent bits/point MSE 2.104831 ---------------------- -------------------------------------------------------- Time: 3.765s Load: 0.008s, Pack+Encode: 2.163s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1048 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000499775.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 516B, BPFP=5.3750 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,684B, BPFP=0.7689 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,436B, BPFP=0.8471 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,784B, BPFP=0.7301 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 9,428B, BPFP=0.1439 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 9,424B, BPFP=0.1438 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 11,560B, BPFP=0.3528 ⌛️ [2/4] FRONTEND: Frontend time: 2.153s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.593s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.84457429 text_encoder-item0.clip_prompt_embeds 0.00023387 23.85029846 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.84625320 text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.12610566 text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00366840 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00567713 1.04932296 vae.encoder_f1 0.00567905 1.04924321 vae.decoder 0.00019376 0.07859966 ------------------------------------------------------------------------------------- TOTAL 0.00268802 1.75069060 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 105032 BPFP 0.3716 bits/point EBPFP 0.7433 equivalent bits/point MSE 1.750691 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.008s, Pack+Encode: 2.153s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.7507 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000506454.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,872B, BPFP=0.6591 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 820B, BPFP=5.1250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,448B, BPFP=0.6857 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,684B, BPFP=0.6768 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,020B, BPFP=0.2292 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,024B, BPFP=0.2292 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,192B, BPFP=0.2195 ⌛️ [2/4] FRONTEND: Frontend time: 2.160s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.603s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.81208944 text_encoder-item0.clip_prompt_embeds 0.00024281 23.87982151 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.80562038 text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.11302992 text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00303450 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.02387581 1.74501491 vae.encoder_f1 0.02387858 1.74432397 vae.decoder 0.00018648 0.03915877 ------------------------------------------------------------------------------------- TOTAL 0.01112583 2.06869547 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 106996 BPFP 0.3786 bits/point EBPFP 0.7572 equivalent bits/point MSE 2.068695 ---------------------- -------------------------------------------------------- Time: 3.770s Load: 0.008s, Pack+Encode: 2.160s, Decode+Unpack: 1.603s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0687 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000515828.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.010s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 552B, BPFP=5.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,852B, BPFP=0.6564 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 788B, BPFP=4.9250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,288B, BPFP=0.6727 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,840B, BPFP=0.6047 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 24,268B, BPFP=0.3703 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 24,256B, BPFP=0.3701 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,960B, BPFP=0.1514 ⌛️ [2/4] FRONTEND: Frontend time: 2.163s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.603s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.85396155 text_encoder-item0.clip_prompt_embeds 0.00022399 23.71671021 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.81667871 text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.10594823 text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00248443 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.01169517 2.66622782 vae.encoder_f1 0.01169969 2.67071462 vae.decoder 0.00021186 0.03367514 ------------------------------------------------------------------------------------- TOTAL 0.00548058 2.49185840 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 120184 BPFP 0.4252 bits/point EBPFP 0.8505 equivalent bits/point MSE 2.491858 ---------------------- -------------------------------------------------------- Time: 3.776s Load: 0.010s, Pack+Encode: 2.163s, Decode+Unpack: 1.603s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4919 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000517056.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,872B, BPFP=0.6591 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 796B, BPFP=4.9750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,300B, BPFP=0.6737 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,644B, BPFP=0.7266 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,592B, BPFP=0.2837 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,588B, BPFP=0.2836 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,780B, BPFP=0.2985 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.591s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.83546487 text_encoder-item0.clip_prompt_embeds 0.00022123 23.86700360 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.83138943 text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.11580614 text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00286291 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.32749966 4.39152384 vae.encoder_f1 0.32750070 4.38916254 vae.decoder 0.00039956 0.05304505 ------------------------------------------------------------------------------------- TOTAL 0.15195981 3.29706897 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118496 BPFP 0.4193 bits/point EBPFP 0.8385 equivalent bits/point MSE 3.297069 ---------------------- -------------------------------------------------------- Time: 3.744s Load: 0.009s, Pack+Encode: 2.144s, Decode+Unpack: 1.591s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.2971 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000523100.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,136B, BPFP=0.6948 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 776B, BPFP=4.8500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,976B, BPFP=0.7286 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,676B, BPFP=0.5752 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,372B, BPFP=0.2346 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,372B, BPFP=0.2346 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,288B, BPFP=0.2834 ⌛️ [2/4] FRONTEND: Frontend time: 2.132s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.585s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.81364536 text_encoder-item0.clip_prompt_embeds 0.00024675 23.79457099 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.80297699 text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.11245878 text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00244412 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00566967 1.29679966 vae.encoder_f1 0.00567867 1.29620922 vae.decoder 0.00017839 0.05451323 ------------------------------------------------------------------------------------- TOTAL 0.00268303 1.86029313 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 106508 BPFP 0.3769 bits/point EBPFP 0.7537 equivalent bits/point MSE 1.860293 ---------------------- -------------------------------------------------------- Time: 3.726s Load: 0.008s, Pack+Encode: 2.132s, Decode+Unpack: 1.585s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8603 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000526751.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 572B, BPFP=5.9583 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,244B, BPFP=0.5741 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 836B, BPFP=5.2250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,684B, BPFP=0.6237 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,952B, BPFP=0.6075 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 11,556B, BPFP=0.1763 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 11,556B, BPFP=0.1763 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 12,236B, BPFP=0.3734 ⌛️ [2/4] FRONTEND: Frontend time: 2.130s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.588s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.77733270 text_encoder-item0.clip_prompt_embeds 0.00022364 23.88428791 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.94515018 text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.10688800 text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00230403 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00580750 1.19199264 vae.encoder_f1 0.00580664 1.19193065 vae.decoder 0.00018044 0.08077567 ------------------------------------------------------------------------------------- TOTAL 0.00274301 1.81700674 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 101016 BPFP 0.3574 bits/point EBPFP 0.7148 equivalent bits/point MSE 1.817007 ---------------------- -------------------------------------------------------- Time: 3.726s Load: 0.008s, Pack+Encode: 2.130s, Decode+Unpack: 1.588s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8170 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000535578.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 544B, BPFP=5.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,644B, BPFP=0.6282 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 816B, BPFP=5.1000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,132B, BPFP=0.6601 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,804B, BPFP=0.5784 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,540B, BPFP=0.2524 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,536B, BPFP=0.2523 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,708B, BPFP=0.1437 ⌛️ [2/4] FRONTEND: Frontend time: 2.140s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.588s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.82641276 text_encoder-item0.clip_prompt_embeds 0.00030118 23.85233191 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.76949177 text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.10923803 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00269960 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.03869025 2.56461692 vae.encoder_f1 0.03869358 2.55490947 vae.decoder 0.00021614 0.04531607 ------------------------------------------------------------------------------------- TOTAL 0.01800198 2.44647727 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 103104 BPFP 0.3648 bits/point EBPFP 0.7296 equivalent bits/point MSE 2.446477 ---------------------- -------------------------------------------------------- Time: 3.736s Load: 0.009s, Pack+Encode: 2.140s, Decode+Unpack: 1.588s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4465 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000546325.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 532B, BPFP=5.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 4,432B, BPFP=0.5996 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 828B, BPFP=5.1750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 7,936B, BPFP=0.6442 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 22,556B, BPFP=0.5721 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 23,676B, BPFP=0.3613 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 23,672B, BPFP=0.3612 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,628B, BPFP=0.1412 ⌛️ [2/4] FRONTEND: Frontend time: 2.135s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.594s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.86203583 text_encoder-item0.clip_prompt_embeds 0.00023260 23.83115826 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.88921719 text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.11090948 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00270570 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00839879 2.36535692 vae.encoder_f1 0.00840224 2.36318493 vae.decoder 0.00019463 0.04074376 ------------------------------------------------------------------------------------- TOTAL 0.00394849 2.35488387 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 116640 BPFP 0.4127 bits/point EBPFP 0.8254 equivalent bits/point MSE 2.354884 ---------------------- -------------------------------------------------------- Time: 3.738s Load: 0.009s, Pack+Encode: 2.135s, Decode+Unpack: 1.594s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3549 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000551780.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 556B, BPFP=5.7917 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,612B, BPFP=0.7592 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 860B, BPFP=5.3750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,140B, BPFP=0.8231 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,124B, BPFP=0.6373 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,756B, BPFP=0.3320 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,764B, BPFP=0.3321 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,756B, BPFP=0.1757 ⌛️ [2/4] FRONTEND: Frontend time: 2.136s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.600s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.85033369 text_encoder-item0.clip_prompt_embeds 0.00023544 23.77243769 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.84690228 text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.11714505 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00324178 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.01160815 2.37187171 vae.encoder_f1 0.01161249 2.37318802 vae.decoder 0.00021720 0.03907328 ------------------------------------------------------------------------------------- TOTAL 0.00544054 2.35730326 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 119948 BPFP 0.4244 bits/point EBPFP 0.8488 equivalent bits/point MSE 2.357303 ---------------------- -------------------------------------------------------- Time: 3.744s Load: 0.008s, Pack+Encode: 2.136s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3573 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000555009.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.007s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 548B, BPFP=5.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,072B, BPFP=0.6861 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 808B, BPFP=5.0500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,652B, BPFP=0.7023 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,084B, BPFP=0.6109 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 13,284B, BPFP=0.2027 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 13,288B, BPFP=0.2028 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,364B, BPFP=0.1637 ⌛️ [2/4] FRONTEND: Frontend time: 2.157s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.597s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.80088584 text_encoder-item0.clip_prompt_embeds 0.00022923 23.88239609 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.85791693 text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.10917158 text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00255345 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.02989292 2.56563020 vae.encoder_f1 0.02989391 2.56470323 vae.decoder 0.00034944 0.03552894 ------------------------------------------------------------------------------------- TOTAL 0.01393319 2.44865294 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 99480 BPFP 0.3520 bits/point EBPFP 0.7040 equivalent bits/point MSE 2.448653 ---------------------- -------------------------------------------------------- Time: 3.762s Load: 0.007s, Pack+Encode: 2.157s, Decode+Unpack: 1.597s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4487 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000565469.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 540B, BPFP=5.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,232B, BPFP=0.7078 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 828B, BPFP=5.1750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 8,748B, BPFP=0.7101 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,584B, BPFP=0.6236 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 664B, BPFP=6.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 3,488B, BPFP=0.4719 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,056B, BPFP=6.6000 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,304B, BPFP=0.5117 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 16,868B, BPFP=0.4279 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,824B, BPFP=0.3330 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,824B, BPFP=0.3330 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,740B, BPFP=0.2057 ⌛️ [2/4] FRONTEND: Frontend time: 2.141s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 1.596s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.88776310 text_encoder-item0.clip_prompt_embeds 0.00024627 23.81219773 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.89471006 text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.10257251 text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00272501 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.86823312 text_encoder-item3.clip_prompt_embeds 0.00023247 23.65835532 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.54551582 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.11028878 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00131667 vae.encoder_f0 0.00613025 1.69518030 vae.encoder_f1 0.00613536 1.69508851 vae.decoder 0.00018697 0.05500416 ------------------------------------------------------------------------------------- TOTAL 0.00289634 2.04536827 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118700 BPFP 0.4200 bits/point EBPFP 0.8400 equivalent bits/point MSE 2.045368 ---------------------- -------------------------------------------------------- Time: 3.746s Load: 0.009s, Pack+Encode: 2.141s, Decode+Unpack: 1.596s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0454 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst to output-fixed/sd35/lambda0.004/elic-featurecoding-8bit-individual/sd35cond/000000575243.zst ------------------------ ---------------------------- TOTAL PROCESSING SUMMARY ------------------------ ---------------------------- Total files 100 Avg BPFP 0.3901 bits/point Avg EBPFP 0.7801 equivalent bits/point Avg MSE 2.246105 Avg Time 3.761s ------------------------ ----------------------------