Experiment: dtufc_elic-featurecoding_sd35_individual Log file: output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log DTUFCCodecConfig: arch: elic-featurecoding handler: sd35 checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_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_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar Checkpoint epoch: 286 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_lambda0.001_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/lambda0.001/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: 392B, BPFP=4.0833 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: 4,252B, BPFP=0.5752 Using 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: 584B, BPFP=3.6500 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: 3,864B, BPFP=0.3136 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: 23,412B, BPFP=0.5939 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,804B, BPFP=0.1038 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,804B, BPFP=0.1038 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,020B, BPFP=0.0616 ⌛️ [2/4] FRONTEND: Frontend time: 3.142s (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: 1.649s [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 0.98285135 text_encoder-item0.clip_prompt_embeds 0.00025464 24.13778409 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.89578266 text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.28113096 text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.01094694 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00635250 5.63317633 vae.encoder_f1 0.00635834 5.63302755 vae.decoder 0.00019940 0.08836941 ------------------------------------------------------------------------------------- TOTAL 0.00300073 4.48315194 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 82248 BPFP 0.2910 bits/point EBPFP 0.5820 equivalent bits/point MSE 4.483152 ---------------------- -------------------------------------------------------- Time: 4.799s Load: 0.007s, Pack+Encode: 3.142s, Decode+Unpack: 1.649s ---------------------- -------------------------------------------------------- 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 4.4832 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst to output-fixed/sd35/lambda0.001/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: 368B, BPFP=3.8333 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: 4,204B, 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: 596B, BPFP=3.7250 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: 4,620B, BPFP=0.3750 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: 22,712B, BPFP=0.5761 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,076B, BPFP=0.0927 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,076B, BPFP=0.0927 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,296B, BPFP=0.0701 ⌛️ [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: 1.593s [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 0.00020777 0.97053838 text_encoder-item0.clip_prompt_embeds 0.00022609 24.15992374 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.93540077 text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.30117869 text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00944030 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.01130640 5.49005890 vae.encoder_f1 0.01130902 5.49318361 vae.decoder 0.00020860 0.06828294 ------------------------------------------------------------------------------------- TOTAL 0.00529919 4.41646989 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81064 BPFP 0.2868 bits/point EBPFP 0.5737 equivalent bits/point MSE 4.416470 ---------------------- -------------------------------------------------------- Time: 3.744s Load: 0.009s, Pack+Encode: 2.142s, 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 4.4165 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 368B, BPFP=3.8333 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: 3,548B, BPFP=0.4800 Using 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: 580B, BPFP=3.6250 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: 5,488B, BPFP=0.4455 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: 21,832B, BPFP=0.5538 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,432B, BPFP=0.0676 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,432B, BPFP=0.0676 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,916B, BPFP=0.0585 ⌛️ [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: 1.591s [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 0.00020323 1.00154018 text_encoder-item0.clip_prompt_embeds 0.00022402 24.16632212 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 1.15194073 text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.28200844 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.01141422 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 1.19630027 7.48225260 vae.encoder_f1 1.19630098 7.48225975 vae.decoder 0.00023596 0.05342209 ------------------------------------------------------------------------------------- TOTAL 0.55486265 5.33768006 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 76712 BPFP 0.2714 bits/point EBPFP 0.5429 equivalent bits/point MSE 5.337680 ---------------------- -------------------------------------------------------- Time: 3.735s Load: 0.008s, Pack+Encode: 2.137s, 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 5.3377 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst to output-fixed/sd35/lambda0.001/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: 388B, BPFP=4.0417 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: 3,992B, BPFP=0.5400 Using 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: 576B, BPFP=3.6000 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: 5,336B, BPFP=0.4331 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: 22,168B, BPFP=0.5623 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,780B, BPFP=0.0882 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,776B, BPFP=0.0881 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,220B, BPFP=0.0677 ⌛️ [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: 1.591s [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 0.00018694 0.95913498 text_encoder-item0.clip_prompt_embeds 0.00030342 24.14815848 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.91258974 text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.29200099 text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.01045617 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00586287 3.61977649 vae.encoder_f1 0.00587438 3.62048197 vae.decoder 0.00017677 0.11855990 ------------------------------------------------------------------------------------- TOTAL 0.00277565 3.55377791 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80352 BPFP 0.2843 bits/point EBPFP 0.5686 equivalent bits/point MSE 3.553778 ---------------------- -------------------------------------------------------- Time: 3.751s Load: 0.008s, Pack+Encode: 2.152s, 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 3.5538 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst to output-fixed/sd35/lambda0.001/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: 0.007s ------------------------------------------------------------ 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: 384B, BPFP=4.0000 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: 3,120B, BPFP=0.4221 Using 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: 600B, BPFP=3.7500 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: 4,972B, BPFP=0.4036 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: 20,784B, BPFP=0.5272 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,912B, BPFP=0.0750 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,904B, BPFP=0.0748 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,936B, BPFP=0.0591 ⌛️ [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: 1.592s [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 0.00027243 0.92672022 text_encoder-item0.clip_prompt_embeds 0.00024120 24.18158778 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 1.01336870 text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.28575180 text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00857447 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00779453 5.37561560 vae.encoder_f1 0.00779802 5.37735939 vae.decoder 0.00023829 0.06519622 ------------------------------------------------------------------------------------- TOTAL 0.00367359 4.36251926 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 75728 BPFP 0.2679 bits/point EBPFP 0.5359 equivalent bits/point MSE 4.362519 ---------------------- -------------------------------------------------------- Time: 3.746s Load: 0.007s, Pack+Encode: 2.147s, 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 4.3625 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst to output-fixed/sd35/lambda0.001/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: 376B, BPFP=3.9167 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: 4,024B, BPFP=0.5444 Using 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: 588B, BPFP=3.6750 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: 4,472B, BPFP=0.3630 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: 21,596B, BPFP=0.5478 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,532B, BPFP=0.1149 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,524B, BPFP=0.1148 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,200B, BPFP=0.0671 ⌛️ [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 0.00036702 0.97398551 text_encoder-item0.clip_prompt_embeds 0.00025651 24.14724745 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.97922897 text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.29912858 text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.01175591 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00655775 6.36966753 vae.encoder_f1 0.00656268 6.35528946 vae.decoder 0.00020283 0.07316829 ------------------------------------------------------------------------------------- TOTAL 0.00309620 4.82084021 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 82428 BPFP 0.2917 bits/point EBPFP 0.5833 equivalent bits/point MSE 4.820840 ---------------------- -------------------------------------------------------- Time: 3.731s Load: 0.008s, 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 4.8208 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst to output-fixed/sd35/lambda0.001/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: 376B, BPFP=3.9167 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: 3,676B, BPFP=0.4973 Using 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: 580B, BPFP=3.6250 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: 4,848B, BPFP=0.3935 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: 20,708B, BPFP=0.5253 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,024B, BPFP=0.1072 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,036B, BPFP=0.1074 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,056B, BPFP=0.0627 ⌛️ [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 0.00036856 0.99713278 text_encoder-item0.clip_prompt_embeds 0.00022242 24.16396527 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.96840534 text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.25489526 text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.01025587 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00593415 4.29291248 vae.encoder_f1 0.00594307 4.28838587 vae.decoder 0.00018992 0.09623761 ------------------------------------------------------------------------------------- TOTAL 0.00280571 3.86096808 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80420 BPFP 0.2845 bits/point EBPFP 0.5691 equivalent bits/point MSE 3.860968 ---------------------- -------------------------------------------------------- Time: 3.727s Load: 0.009s, 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 3.8610 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 384B, BPFP=4.0000 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: 4,092B, BPFP=0.5536 Using 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: 584B, BPFP=3.6500 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: 3,328B, BPFP=0.2701 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: 22,320B, BPFP=0.5662 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,976B, BPFP=0.1064 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,972B, BPFP=0.1064 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,556B, BPFP=0.0780 ⌛️ [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: 1.591s [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 0.00036736 0.94957813 text_encoder-item0.clip_prompt_embeds 0.00022110 24.14593479 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.94416571 text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.31809568 text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00961290 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00641770 5.19424725 vae.encoder_f1 0.00642053 5.19433546 vae.decoder 0.00017498 0.06966200 ------------------------------------------------------------------------------------- TOTAL 0.00305947 4.27913113 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81328 BPFP 0.2878 bits/point EBPFP 0.5755 equivalent bits/point MSE 4.279131 ---------------------- -------------------------------------------------------- Time: 3.748s Load: 0.009s, Pack+Encode: 2.148s, 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 4.2791 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 380B, BPFP=3.9583 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: 2,812B, BPFP=0.3804 Using 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: 576B, BPFP=3.6000 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: 4,304B, BPFP=0.3494 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: 22,084B, BPFP=0.5602 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,816B, BPFP=0.0735 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,820B, BPFP=0.0735 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,892B, BPFP=0.0577 ⌛️ [2/4] FRONTEND: Frontend time: 2.125s (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 0.00030751 0.95419860 text_encoder-item0.clip_prompt_embeds 0.00021654 24.15813760 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 1.02003555 text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.28719415 text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00998060 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00577698 3.55745482 vae.encoder_f1 0.00578348 3.55677867 vae.decoder 0.00017559 0.09001155 ------------------------------------------------------------------------------------- TOTAL 0.00273280 3.52128905 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 75800 BPFP 0.2682 bits/point EBPFP 0.5364 equivalent bits/point MSE 3.521289 ---------------------- -------------------------------------------------------- Time: 3.725s Load: 0.009s, Pack+Encode: 2.125s, 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 3.5213 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 380B, BPFP=3.9583 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: 3,696B, BPFP=0.5000 Using 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: 584B, BPFP=3.6500 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: 4,764B, BPFP=0.3867 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: 22,824B, BPFP=0.5789 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,704B, BPFP=0.1023 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,704B, BPFP=0.1023 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,772B, BPFP=0.0541 ⌛️ [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: 1.595s [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 0.00030339 0.99890939 text_encoder-item0.clip_prompt_embeds 0.00022160 24.16883751 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.85725489 text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.28117813 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00968985 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00668450 4.13008165 vae.encoder_f1 0.00668875 4.13034678 vae.decoder 0.00023059 0.07225477 ------------------------------------------------------------------------------------- TOTAL 0.00315742 3.78491471 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81544 BPFP 0.2885 bits/point EBPFP 0.5770 equivalent bits/point MSE 3.784915 ---------------------- -------------------------------------------------------- Time: 3.736s Load: 0.009s, Pack+Encode: 2.133s, 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 3.7849 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 376B, BPFP=3.9167 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: 3,644B, BPFP=0.4930 Using 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: 596B, BPFP=3.7250 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: 4,336B, BPFP=0.3519 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: 23,252B, BPFP=0.5898 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,748B, BPFP=0.1182 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,748B, BPFP=0.1182 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,504B, BPFP=0.0459 ⌛️ [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: 1.594s [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 0.00017240 0.95598094 text_encoder-item0.clip_prompt_embeds 0.00023190 24.17785275 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.79597254 text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.29217989 text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.01025546 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.04018118 6.26009989 vae.encoder_f1 0.04018488 6.26013756 vae.decoder 0.00016201 0.04441109 ------------------------------------------------------------------------------------- TOTAL 0.01868571 4.77021326 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 83320 BPFP 0.2948 bits/point EBPFP 0.5896 equivalent bits/point MSE 4.770213 ---------------------- -------------------------------------------------------- Time: 3.732s Load: 0.008s, Pack+Encode: 2.129s, 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 4.7702 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 380B, BPFP=3.9583 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: 3,656B, BPFP=0.4946 Using 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: 576B, BPFP=3.6000 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: 4,948B, BPFP=0.4016 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: 21,852B, BPFP=0.5543 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,420B, BPFP=0.1132 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,416B, BPFP=0.1132 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,144B, BPFP=0.0959 ⌛️ [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: 1.594s [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 0.00038474 0.98965502 text_encoder-item0.clip_prompt_embeds 0.00023140 24.16728812 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.91015978 text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.27612243 text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.01009127 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.04874706 7.68919706 vae.encoder_f1 0.04875064 7.68055105 vae.decoder 0.00019641 0.06903116 ------------------------------------------------------------------------------------- TOTAL 0.02266071 5.43290075 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 83508 BPFP 0.2955 bits/point EBPFP 0.5909 equivalent bits/point MSE 5.432901 ---------------------- -------------------------------------------------------- Time: 3.739s Load: 0.008s, Pack+Encode: 2.137s, 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 5.4329 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,760B, BPFP=0.5087 Using 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: 604B, BPFP=3.7750 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: 5,056B, BPFP=0.4104 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: 21,408B, BPFP=0.5430 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,344B, BPFP=0.1273 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,336B, BPFP=0.1272 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,332B, BPFP=0.0406 ⌛️ [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: 1.598s [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 0.00017774 0.94525846 text_encoder-item0.clip_prompt_embeds 0.00030893 24.13466839 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.93691378 text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.27483487 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00895961 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.01360236 7.97659874 vae.encoder_f1 0.01360807 7.94816160 vae.decoder 0.00023006 0.04976268 ------------------------------------------------------------------------------------- TOTAL 0.00637132 5.55829812 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 83344 BPFP 0.2949 bits/point EBPFP 0.5898 equivalent bits/point MSE 5.558298 ---------------------- -------------------------------------------------------- Time: 3.743s Load: 0.008s, Pack+Encode: 2.138s, 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 5.5583 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst to output-fixed/sd35/lambda0.001/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: 392B, BPFP=4.0833 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: 4,304B, BPFP=0.5823 Using 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: 568B, BPFP=3.5500 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: 3,960B, BPFP=0.3214 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: 23,152B, BPFP=0.5873 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 3,412B, BPFP=0.0521 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 3,412B, BPFP=0.0521 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 816B, BPFP=0.0249 ⌛️ [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 0.00059206 0.96439727 text_encoder-item0.clip_prompt_embeds 0.00024198 24.18045057 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.95836315 text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.30282508 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00968835 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 1.67190456 10.07607651 vae.encoder_f1 1.67190480 10.07609940 vae.decoder 0.00017417 0.02470559 ------------------------------------------------------------------------------------- TOTAL 0.77542609 6.53820109 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 74132 BPFP 0.2623 bits/point EBPFP 0.5246 equivalent bits/point MSE 6.538201 ---------------------- -------------------------------------------------------- Time: 3.743s Load: 0.007s, 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 6.5382 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst to output-fixed/sd35/lambda0.001/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: 384B, BPFP=4.0000 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: 3,464B, BPFP=0.4686 Using 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: 600B, BPFP=3.7500 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: 4,688B, BPFP=0.3805 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,044B, BPFP=0.1075 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,044B, BPFP=0.1075 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,548B, BPFP=0.0778 ⌛️ [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: 1.593s [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 0.00021898 0.91452130 text_encoder-item0.clip_prompt_embeds 0.00025129 24.13571471 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.93395958 text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.27811345 text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.01013602 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00621760 5.59225368 vae.encoder_f1 0.00622505 5.58920479 vae.decoder 0.00025114 0.08633561 ------------------------------------------------------------------------------------- TOTAL 0.00294689 4.46296465 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 83004 BPFP 0.2937 bits/point EBPFP 0.5874 equivalent bits/point MSE 4.462965 ---------------------- -------------------------------------------------------- Time: 3.748s Load: 0.008s, Pack+Encode: 2.147s, 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 4.4630 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 368B, BPFP=3.8333 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: 2,872B, BPFP=0.3885 Using 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: 584B, BPFP=3.6500 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: 4,412B, BPFP=0.3581 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: 21,204B, BPFP=0.5378 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,468B, BPFP=0.1292 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,468B, BPFP=0.1292 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,464B, BPFP=0.0752 ⌛️ [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: 1.590s [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 0.00241962 0.98168969 text_encoder-item0.clip_prompt_embeds 0.00020838 24.14105832 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.95412331 text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.29061814 text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.01149948 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00675961 7.46221828 vae.encoder_f1 0.00676652 7.46320009 vae.decoder 0.00021373 0.09170915 ------------------------------------------------------------------------------------- TOTAL 0.00319201 5.33266157 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 82956 BPFP 0.2935 bits/point EBPFP 0.5870 equivalent bits/point MSE 5.332662 ---------------------- -------------------------------------------------------- Time: 3.740s Load: 0.009s, Pack+Encode: 2.141s, 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 5.3327 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 2,840B, BPFP=0.3842 Using 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: 596B, BPFP=3.7250 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: 3,836B, BPFP=0.3114 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: 22,824B, BPFP=0.5789 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 3,284B, BPFP=0.0501 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 3,284B, BPFP=0.0501 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,456B, BPFP=0.0750 ⌛️ [2/4] FRONTEND: Frontend time: 2.124s (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 0.00020005 0.94553932 text_encoder-item0.clip_prompt_embeds 0.00021387 24.14571496 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.90956221 text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.30240479 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00943748 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00596338 2.12444806 vae.encoder_f1 0.00596322 2.12444282 vae.decoder 0.00018207 0.14637701 ------------------------------------------------------------------------------------- TOTAL 0.00281657 2.86359380 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 73616 BPFP 0.2605 bits/point EBPFP 0.5209 equivalent bits/point MSE 2.863594 ---------------------- -------------------------------------------------------- Time: 3.723s Load: 0.008s, Pack+Encode: 2.124s, 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 2.8636 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,176B, BPFP=0.4297 Using 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: 600B, BPFP=3.7500 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: 4,152B, BPFP=0.3370 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: 20,252B, BPFP=0.5137 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 2,896B, BPFP=0.0442 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 2,896B, BPFP=0.0442 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,640B, BPFP=0.0500 ⌛️ [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: 1.595s [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 0.90837034 text_encoder-item0.clip_prompt_embeds 0.00022138 24.15707648 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.92459707 text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.30336490 text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00951084 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00552804 1.53075325 vae.encoder_f1 0.00552758 1.53074861 vae.decoder 0.00018040 0.10295933 ------------------------------------------------------------------------------------- TOTAL 0.00261550 2.58356841 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 70108 BPFP 0.2481 bits/point EBPFP 0.4961 equivalent bits/point MSE 2.583568 ---------------------- -------------------------------------------------------- Time: 3.746s Load: 0.008s, Pack+Encode: 2.143s, 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 2.5836 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 380B, BPFP=3.9583 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: 4,092B, BPFP=0.5536 Using 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: 584B, BPFP=3.6500 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: 4,548B, BPFP=0.3692 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: 21,444B, BPFP=0.5439 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,376B, BPFP=0.0668 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,380B, BPFP=0.0668 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,560B, BPFP=0.0476 ⌛️ [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 0.00019161 0.98175756 text_encoder-item0.clip_prompt_embeds 0.00024507 24.17634985 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.97799969 text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.28350961 text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.01141413 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00721525 3.21476436 vae.encoder_f1 0.00721777 3.21655512 vae.decoder 0.00018707 0.06028912 ------------------------------------------------------------------------------------- TOTAL 0.00340651 3.35998735 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 75480 BPFP 0.2671 bits/point EBPFP 0.5341 equivalent bits/point MSE 3.359987 ---------------------- -------------------------------------------------------- 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 3.3600 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,368B, BPFP=0.4556 Using 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: 584B, BPFP=3.6500 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: 4,544B, BPFP=0.3688 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: 21,260B, BPFP=0.5393 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,284B, BPFP=0.1111 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,284B, BPFP=0.1111 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,784B, BPFP=0.0850 ⌛️ [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: 1.592s [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 0.00018740 0.95827532 text_encoder-item0.clip_prompt_embeds 0.00046272 24.17621669 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 1.01535149 text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.26486900 text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.01174744 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.01999603 7.05560398 vae.encoder_f1 0.01999529 7.05458021 vae.decoder 0.00024882 0.08242677 ------------------------------------------------------------------------------------- TOTAL 0.00933711 5.14240396 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81604 BPFP 0.2887 bits/point EBPFP 0.5775 equivalent bits/point MSE 5.142404 ---------------------- -------------------------------------------------------- Time: 3.732s Load: 0.008s, Pack+Encode: 2.132s, 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 5.1424 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 380B, BPFP=3.9583 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: 3,392B, BPFP=0.4589 Using 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: 576B, BPFP=3.6000 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: 4,592B, BPFP=0.3727 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: 20,936B, BPFP=0.5310 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,692B, BPFP=0.1174 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,688B, BPFP=0.1173 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,708B, BPFP=0.0521 ⌛️ [2/4] FRONTEND: Frontend time: 2.131s (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 0.00062140 0.95718765 text_encoder-item0.clip_prompt_embeds 0.00020334 24.13443165 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.88893328 text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.28126173 text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.01117417 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.01341345 6.90047216 vae.encoder_f1 0.01341645 6.90155268 vae.decoder 0.00018350 0.04894200 ------------------------------------------------------------------------------------- TOTAL 0.00627332 5.06653422 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81080 BPFP 0.2869 bits/point EBPFP 0.5738 equivalent bits/point MSE 5.066534 ---------------------- -------------------------------------------------------- Time: 3.737s Load: 0.008s, Pack+Encode: 2.131s, 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 5.0665 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst to output-fixed/sd35/lambda0.001/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: 384B, BPFP=4.0000 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: 3,400B, BPFP=0.4600 Using 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: 592B, BPFP=3.7000 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: 4,696B, BPFP=0.3812 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,068B, BPFP=0.1078 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,068B, BPFP=0.1078 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,416B, BPFP=0.1042 ⌛️ [2/4] FRONTEND: Frontend time: 2.127s (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 0.00063926 0.93914278 text_encoder-item0.clip_prompt_embeds 0.00022316 24.16632212 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.95646887 text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.27522250 text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00972775 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00606298 4.22687149 vae.encoder_f1 0.00607096 4.21651030 vae.decoder 0.00023408 0.11043759 ------------------------------------------------------------------------------------- TOTAL 0.00287331 3.83148142 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 83084 BPFP 0.2940 bits/point EBPFP 0.5879 equivalent bits/point MSE 3.831481 ---------------------- -------------------------------------------------------- Time: 3.728s Load: 0.008s, Pack+Encode: 2.127s, 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 3.8315 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 384B, BPFP=4.0000 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: 3,512B, BPFP=0.4751 Using 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: 596B, BPFP=3.7250 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: 3,328B, BPFP=0.2701 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: 21,400B, BPFP=0.5428 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,044B, BPFP=0.0922 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,044B, BPFP=0.0922 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,044B, BPFP=0.0929 ⌛️ [2/4] FRONTEND: Frontend time: 2.127s (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 0.00054317 0.93302290 text_encoder-item0.clip_prompt_embeds 0.00023597 24.11996922 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.96720829 text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.27595112 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.01204853 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00653100 4.80642462 vae.encoder_f1 0.00653745 4.80588913 vae.decoder 0.00020026 0.09623204 ------------------------------------------------------------------------------------- TOTAL 0.00308450 4.10003822 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 78468 BPFP 0.2776 bits/point EBPFP 0.5553 equivalent bits/point MSE 4.100038 ---------------------- -------------------------------------------------------- Time: 3.731s Load: 0.008s, Pack+Encode: 2.127s, 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 4.1000 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst to output-fixed/sd35/lambda0.001/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: 392B, BPFP=4.0833 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: 3,620B, BPFP=0.4897 Using 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: 580B, BPFP=3.6250 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: 3,920B, BPFP=0.3182 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: 21,240B, BPFP=0.5388 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,556B, BPFP=0.1153 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,556B, BPFP=0.1153 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,688B, BPFP=0.0515 ⌛️ [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 0.00018905 0.94877529 text_encoder-item0.clip_prompt_embeds 0.00022433 24.17899207 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.96323977 text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.27324577 text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00997633 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00869686 7.33744669 vae.encoder_f1 0.00870063 7.33895016 vae.decoder 0.00021246 0.06088101 ------------------------------------------------------------------------------------- TOTAL 0.00408877 5.27135955 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80668 BPFP 0.2854 bits/point EBPFP 0.5709 equivalent bits/point MSE 5.271360 ---------------------- -------------------------------------------------------- Time: 3.745s Load: 0.008s, 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 5.2714 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 396B, BPFP=4.1250 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: 4,012B, BPFP=0.5427 Using 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: 584B, BPFP=3.6500 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: 4,956B, BPFP=0.4023 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: 22,456B, BPFP=0.5696 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,788B, BPFP=0.1188 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,788B, BPFP=0.1188 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,148B, BPFP=0.0656 ⌛️ [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: 1.601s [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 0.00020560 0.97669188 text_encoder-item0.clip_prompt_embeds 0.00022433 24.13874586 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.91679993 text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.26567811 text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00970865 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00626512 6.14828539 vae.encoder_f1 0.00626949 6.14746189 vae.decoder 0.00018936 0.07031621 ------------------------------------------------------------------------------------- TOTAL 0.00295827 4.71898213 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 84244 BPFP 0.2981 bits/point EBPFP 0.5962 equivalent bits/point MSE 4.718982 ---------------------- -------------------------------------------------------- Time: 3.738s Load: 0.008s, Pack+Encode: 2.128s, 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 4.7190 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst to output-fixed/sd35/lambda0.001/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: 372B, BPFP=3.8750 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: 4,012B, BPFP=0.5427 Using 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: 588B, BPFP=3.6750 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: 5,736B, BPFP=0.4656 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: 22,532B, BPFP=0.5715 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,884B, BPFP=0.1050 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,884B, BPFP=0.1050 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,992B, BPFP=0.0608 ⌛️ [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: 1.591s [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 0.01261352 1.04373590 text_encoder-item0.clip_prompt_embeds 0.00026137 24.17198069 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.89443398 text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.31664159 text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00990835 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.35915655 8.26363373 vae.encoder_f1 0.35915723 8.26258850 vae.decoder 0.00024181 0.05565530 ------------------------------------------------------------------------------------- TOTAL 0.16663024 5.70139081 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 83116 BPFP 0.2941 bits/point EBPFP 0.5882 equivalent bits/point MSE 5.701391 ---------------------- -------------------------------------------------------- Time: 3.739s Load: 0.008s, Pack+Encode: 2.140s, 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 5.7014 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst to output-fixed/sd35/lambda0.001/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: 0.007s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,220B, BPFP=0.4356 Using 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: 576B, BPFP=3.6000 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: 4,320B, BPFP=0.3506 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: 21,088B, BPFP=0.5349 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 3,052B, BPFP=0.0466 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 3,052B, BPFP=0.0466 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,112B, BPFP=0.0339 ⌛️ [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: 1.591s [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 0.00032602 0.99022396 text_encoder-item0.clip_prompt_embeds 0.00021656 24.17749129 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.96365452 text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.25703064 text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.01184711 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.29031765 5.63706017 vae.encoder_f1 0.29031771 5.63705492 vae.decoder 0.00019965 0.05911309 ------------------------------------------------------------------------------------- TOTAL 0.13469251 4.48174885 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 70924 BPFP 0.2509 bits/point EBPFP 0.5019 equivalent bits/point MSE 4.481749 ---------------------- -------------------------------------------------------- Time: 3.738s Load: 0.007s, Pack+Encode: 2.140s, 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 4.4817 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,480B, BPFP=0.4708 Using 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: 584B, BPFP=3.6500 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: 4,780B, BPFP=0.3880 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: 20,868B, BPFP=0.5293 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,164B, BPFP=0.0788 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,164B, BPFP=0.0788 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,892B, BPFP=0.0883 ⌛️ [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: 1.593s [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 0.00199158 0.98243602 text_encoder-item0.clip_prompt_embeds 0.00025451 24.12516699 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.89878654 text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.26460450 text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.01177369 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00595764 2.71336341 vae.encoder_f1 0.00596395 2.71329355 vae.decoder 0.00019845 0.12846886 ------------------------------------------------------------------------------------- TOTAL 0.00281886 3.13276979 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 77428 BPFP 0.2740 bits/point EBPFP 0.5479 equivalent bits/point MSE 3.132770 ---------------------- -------------------------------------------------------- Time: 3.729s Load: 0.008s, Pack+Encode: 2.128s, 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 3.1328 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,600B, BPFP=0.4870 Using 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: 588B, BPFP=3.6750 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: 5,440B, BPFP=0.4416 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,048B, BPFP=0.0923 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,048B, BPFP=0.0923 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,568B, BPFP=0.0479 ⌛️ [2/4] FRONTEND: Frontend time: 2.121s (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 0.97413627 text_encoder-item0.clip_prompt_embeds 0.00026157 24.17626953 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.93506718 text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.28657381 text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.01200903 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.40456498 9.66795921 vae.encoder_f1 0.40456539 9.66797733 vae.decoder 0.00020503 0.05276664 ------------------------------------------------------------------------------------- TOTAL 0.18768128 6.35167772 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80216 BPFP 0.2838 bits/point EBPFP 0.5677 equivalent bits/point MSE 6.351678 ---------------------- -------------------------------------------------------- Time: 3.717s Load: 0.008s, Pack+Encode: 2.121s, 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 6.3517 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst to output-fixed/sd35/lambda0.001/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: 376B, BPFP=3.9167 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: 4,184B, BPFP=0.5660 Using 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: 584B, BPFP=3.6500 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: 4,100B, BPFP=0.3328 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: 20,580B, BPFP=0.5220 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,912B, BPFP=0.1055 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,912B, BPFP=0.1055 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,512B, BPFP=0.0767 ⌛️ [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: 1.591s [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 0.00063306 0.94801625 text_encoder-item0.clip_prompt_embeds 0.00027179 24.13834001 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.96691256 text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.26443014 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00911699 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00673531 6.90369320 vae.encoder_f1 0.00673732 6.90369320 vae.decoder 0.00020129 0.10174251 ------------------------------------------------------------------------------------- TOTAL 0.00317768 5.07302185 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80276 BPFP 0.2840 bits/point EBPFP 0.5681 equivalent bits/point MSE 5.073022 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.008s, Pack+Encode: 2.154s, 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 5.0730 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst to output-fixed/sd35/lambda0.001/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: 0.007s ------------------------------------------------------------ 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: 372B, BPFP=3.8750 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: 4,192B, BPFP=0.5671 Using 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: 576B, BPFP=3.6000 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: 5,324B, BPFP=0.4321 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: 22,792B, BPFP=0.5781 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,124B, BPFP=0.0934 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,124B, BPFP=0.0934 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,472B, BPFP=0.0449 ⌛️ [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: 1.587s [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 0.97805532 text_encoder-item0.clip_prompt_embeds 0.00023057 24.14864888 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.97835817 text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.29856651 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00969472 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00881784 7.19752216 vae.encoder_f1 0.00882136 7.19769430 vae.decoder 0.00017598 0.04846763 ------------------------------------------------------------------------------------- TOTAL 0.00418676 5.20500843 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81092 BPFP 0.2869 bits/point EBPFP 0.5739 equivalent bits/point MSE 5.205008 ---------------------- -------------------------------------------------------- Time: 3.733s Load: 0.007s, Pack+Encode: 2.140s, Decode+Unpack: 1.587s ---------------------- -------------------------------------------------------- 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 5.2050 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst to output-fixed/sd35/lambda0.001/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: 388B, BPFP=4.0417 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: 3,180B, BPFP=0.4302 Using 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: 592B, BPFP=3.7000 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: 3,916B, BPFP=0.3179 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: 22,076B, BPFP=0.5600 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,244B, BPFP=0.0800 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,244B, BPFP=0.0800 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,332B, BPFP=0.0712 ⌛️ [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: 1.581s [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 0.96925656 text_encoder-item0.clip_prompt_embeds 0.00025208 24.14671689 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.92413445 text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.31092278 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00921072 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00582247 2.87002826 vae.encoder_f1 0.00582996 2.87001085 vae.decoder 0.00016099 0.09940404 ------------------------------------------------------------------------------------- TOTAL 0.00279351 3.20430335 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 77088 BPFP 0.2728 bits/point EBPFP 0.5455 equivalent bits/point MSE 3.204303 ---------------------- -------------------------------------------------------- Time: 3.737s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.581s ---------------------- -------------------------------------------------------- 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 3.2043 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 376B, BPFP=3.9167 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: 3,404B, BPFP=0.4605 Using 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: 596B, BPFP=3.7250 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: 5,172B, BPFP=0.4198 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: 21,460B, BPFP=0.5443 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 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: 7,100B, BPFP=0.1083 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,776B, BPFP=0.0542 ⌛️ [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: 1.587s [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 0.97845586 text_encoder-item0.clip_prompt_embeds 0.00020809 24.14396053 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.95081921 text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.30021483 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.01013761 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00602745 6.73076916 vae.encoder_f1 0.00603159 6.73078632 vae.decoder 0.00017526 0.08131354 ------------------------------------------------------------------------------------- TOTAL 0.00288782 4.99231111 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81100 BPFP 0.2870 bits/point EBPFP 0.5739 equivalent bits/point MSE 4.992311 ---------------------- -------------------------------------------------------- Time: 3.738s Load: 0.009s, Pack+Encode: 2.142s, Decode+Unpack: 1.587s ---------------------- -------------------------------------------------------- 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 4.9923 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,364B, BPFP=0.4551 Using 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: 600B, BPFP=3.7500 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: 5,488B, BPFP=0.4455 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: 21,388B, BPFP=0.5425 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,872B, BPFP=0.1201 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,864B, BPFP=0.1200 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,420B, BPFP=0.1044 ⌛️ [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: 1.587s [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 0.95042785 text_encoder-item0.clip_prompt_embeds 0.00020908 24.12595331 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.94384518 text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.27970044 text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.01180029 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00634616 6.50589800 vae.encoder_f1 0.00635208 6.51646519 vae.decoder 0.00022721 0.09216511 ------------------------------------------------------------------------------------- TOTAL 0.00300000 4.89058081 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 84492 BPFP 0.2990 bits/point EBPFP 0.5979 equivalent bits/point MSE 4.890581 ---------------------- -------------------------------------------------------- Time: 3.742s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.587s ---------------------- -------------------------------------------------------- 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 4.8906 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst to output-fixed/sd35/lambda0.001/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: 0.007s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,720B, BPFP=0.5032 Using 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: 580B, BPFP=3.6250 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: 4,112B, BPFP=0.3338 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: 21,352B, BPFP=0.5416 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,744B, BPFP=0.0724 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,744B, BPFP=0.0724 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,488B, BPFP=0.0454 ⌛️ [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 0.00020745 0.97543661 text_encoder-item0.clip_prompt_embeds 0.00022947 24.14017265 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 1.03031588 text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.27264353 text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.01071929 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.05448642 4.57257557 vae.encoder_f1 0.05448771 4.57258606 vae.decoder 0.00017748 0.03957417 ------------------------------------------------------------------------------------- TOTAL 0.02531999 3.98539302 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 75244 BPFP 0.2662 bits/point EBPFP 0.5325 equivalent bits/point MSE 3.985393 ---------------------- -------------------------------------------------------- Time: 3.724s Load: 0.007s, 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 3.9854 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 372B, BPFP=3.8750 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: 3,024B, BPFP=0.4091 Using 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: 588B, BPFP=3.6750 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: 5,244B, BPFP=0.4256 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: 21,420B, BPFP=0.5433 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,768B, BPFP=0.1033 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,772B, BPFP=0.1033 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,836B, BPFP=0.0560 ⌛️ [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: 1.585s [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 0.00026664 0.95922645 text_encoder-item0.clip_prompt_embeds 0.00020169 24.17121339 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.92427931 text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.27222938 text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.01221111 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.06876971 8.14709091 vae.encoder_f1 0.06877109 8.17547798 vae.decoder 0.00023999 0.04139964 ------------------------------------------------------------------------------------- TOTAL 0.03194988 5.65086736 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80140 BPFP 0.2836 bits/point EBPFP 0.5671 equivalent bits/point MSE 5.650867 ---------------------- -------------------------------------------------------- Time: 3.737s Load: 0.008s, Pack+Encode: 2.144s, 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 5.6509 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 404B, BPFP=4.2083 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: 3,656B, BPFP=0.4946 Using 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: 596B, BPFP=3.7250 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: 3,776B, BPFP=0.3065 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: 21,452B, BPFP=0.5441 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,760B, BPFP=0.0726 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,760B, BPFP=0.0726 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,404B, BPFP=0.0734 ⌛️ [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: 1.593s [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 0.00028326 0.95127519 text_encoder-item0.clip_prompt_embeds 0.00025253 24.14249146 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.97660885 text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.28540099 text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.01078810 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00595097 2.56366968 vae.encoder_f1 0.00595882 2.56342936 vae.decoder 0.00020134 0.11723247 ------------------------------------------------------------------------------------- TOTAL 0.00281645 3.06325997 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 75924 BPFP 0.2686 bits/point EBPFP 0.5373 equivalent bits/point MSE 3.063260 ---------------------- -------------------------------------------------------- Time: 3.747s Load: 0.008s, Pack+Encode: 2.146s, 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 3.0633 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst to output-fixed/sd35/lambda0.001/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: 384B, BPFP=4.0000 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: 3,824B, BPFP=0.5173 Using 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: 600B, BPFP=3.7500 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: 4,436B, BPFP=0.3601 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: 21,364B, BPFP=0.5419 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,864B, BPFP=0.0742 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,868B, BPFP=0.0743 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,792B, BPFP=0.0852 ⌛️ [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: 1.588s [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 0.00029404 0.96323331 text_encoder-item0.clip_prompt_embeds 0.00022201 24.13651371 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.95058393 text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.31551034 text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.01204036 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00831743 4.00871325 vae.encoder_f1 0.00831926 4.00987434 vae.decoder 0.00028593 0.09777850 ------------------------------------------------------------------------------------- TOTAL 0.00392223 3.73281471 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 77248 BPFP 0.2733 bits/point EBPFP 0.5466 equivalent bits/point MSE 3.732815 ---------------------- -------------------------------------------------------- Time: 3.739s Load: 0.008s, Pack+Encode: 2.143s, 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 3.7328 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 380B, BPFP=3.9583 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: 3,988B, BPFP=0.5395 Using 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: 572B, BPFP=3.5750 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: 5,268B, BPFP=0.4276 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: 22,656B, BPFP=0.5747 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,568B, BPFP=0.1155 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,564B, BPFP=0.1154 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,712B, BPFP=0.0828 ⌛️ [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: 1.587s [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 0.95511007 text_encoder-item0.clip_prompt_embeds 0.00026808 24.17341171 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.98641386 text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.29782747 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.01015500 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00606586 6.03439093 vae.encoder_f1 0.00607066 6.03482962 vae.decoder 0.00019664 0.07593013 ------------------------------------------------------------------------------------- TOTAL 0.00286987 4.66950754 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 84824 BPFP 0.3001 bits/point EBPFP 0.6003 equivalent bits/point MSE 4.669508 ---------------------- -------------------------------------------------------- Time: 3.733s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.587s ---------------------- -------------------------------------------------------- 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 4.6695 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst to output-fixed/sd35/lambda0.001/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: 372B, BPFP=3.8750 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: 2,984B, BPFP=0.4037 Using 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: 580B, BPFP=3.6250 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: 4,820B, BPFP=0.3912 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: 22,756B, BPFP=0.5772 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,968B, BPFP=0.1063 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,960B, BPFP=0.1062 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,748B, BPFP=0.0533 ⌛️ [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: 1.583s [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 0.95938007 text_encoder-item0.clip_prompt_embeds 0.00023198 24.14481027 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 1.01815853 text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.31159081 text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.01154226 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.05216765 7.95160484 vae.encoder_f1 0.05216896 7.96446323 vae.decoder 0.00017960 0.07039291 ------------------------------------------------------------------------------------- TOTAL 0.02424513 5.56095299 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81304 BPFP 0.2877 bits/point EBPFP 0.5754 equivalent bits/point MSE 5.560953 ---------------------- -------------------------------------------------------- Time: 3.734s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.583s ---------------------- -------------------------------------------------------- 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 5.5610 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 392B, BPFP=4.0833 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: 3,612B, BPFP=0.4886 Using 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: 604B, BPFP=3.7750 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: 4,160B, BPFP=0.3377 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: 20,520B, BPFP=0.5205 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,540B, BPFP=0.1151 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,540B, BPFP=0.1151 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,676B, BPFP=0.0511 ⌛️ [2/4] FRONTEND: Frontend time: 2.127s (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 0.00048242 0.97162390 text_encoder-item0.clip_prompt_embeds 0.00023125 24.17374357 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.99705505 text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.27529554 text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.01163550 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00620361 5.52033615 vae.encoder_f1 0.00620966 5.51930571 vae.decoder 0.00020748 0.08015860 ------------------------------------------------------------------------------------- TOTAL 0.00293402 4.43049955 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80160 BPFP 0.2836 bits/point EBPFP 0.5673 equivalent bits/point MSE 4.430500 ---------------------- -------------------------------------------------------- Time: 3.719s Load: 0.008s, Pack+Encode: 2.127s, 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 4.4305 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst to output-fixed/sd35/lambda0.001/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: 384B, BPFP=4.0000 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: 3,788B, BPFP=0.5124 Using 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: 588B, BPFP=3.6750 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: 5,204B, BPFP=0.4224 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: 21,284B, BPFP=0.5399 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,140B, BPFP=0.1089 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,144B, BPFP=0.1090 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,416B, BPFP=0.0737 ⌛️ [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: 1.586s [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 0.00022540 0.96582389 text_encoder-item0.clip_prompt_embeds 0.00023066 24.15312585 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.87871065 text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.30394767 text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.01171645 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.03159856 5.44408846 vae.encoder_f1 0.03160188 5.43522310 vae.decoder 0.00018417 0.09091341 ------------------------------------------------------------------------------------- TOTAL 0.01470700 4.39522050 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 82064 BPFP 0.2904 bits/point EBPFP 0.5807 equivalent bits/point MSE 4.395221 ---------------------- -------------------------------------------------------- Time: 3.730s Load: 0.009s, Pack+Encode: 2.135s, 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 4.3952 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 380B, BPFP=3.9583 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: 3,800B, BPFP=0.5141 Using 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: 588B, BPFP=3.6750 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: 4,416B, BPFP=0.3584 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: 22,388B, BPFP=0.5679 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,184B, BPFP=0.2164 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,220B, BPFP=0.2170 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,068B, BPFP=0.0631 ⌛️ [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: 1.583s [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 0.94024833 text_encoder-item0.clip_prompt_embeds 0.00024948 24.13725142 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.87916470 text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.27680060 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00990191 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.03490865 15.41311550 vae.encoder_f1 0.03491008 15.52715778 vae.decoder 0.00028462 0.08123853 ------------------------------------------------------------------------------------- TOTAL 0.01625440 9.04405587 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 96160 BPFP 0.3402 bits/point EBPFP 0.6805 equivalent bits/point MSE 9.044056 ---------------------- -------------------------------------------------------- Time: 3.727s Load: 0.009s, Pack+Encode: 2.135s, Decode+Unpack: 1.583s ---------------------- -------------------------------------------------------- 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 9.0441 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,748B, BPFP=0.5070 Using 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: 572B, BPFP=3.5750 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: 5,764B, BPFP=0.4679 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: 22,348B, BPFP=0.5669 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 3,208B, BPFP=0.0490 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 3,208B, BPFP=0.0490 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,824B, BPFP=0.0557 ⌛️ [2/4] FRONTEND: Frontend time: 2.125s (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: 1.583s [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 0.95092694 text_encoder-item0.clip_prompt_embeds 0.00021560 24.13799758 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.88915510 text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.28563005 text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00996447 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00544735 1.65627050 vae.encoder_f1 0.00544843 1.65626514 vae.decoder 0.00018632 0.10638367 ------------------------------------------------------------------------------------- TOTAL 0.00257940 2.64096174 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 75168 BPFP 0.2660 bits/point EBPFP 0.5319 equivalent bits/point MSE 2.640962 ---------------------- -------------------------------------------------------- Time: 3.717s Load: 0.009s, Pack+Encode: 2.125s, Decode+Unpack: 1.583s ---------------------- -------------------------------------------------------- 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 2.6410 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst to output-fixed/sd35/lambda0.001/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: 372B, BPFP=3.8750 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: 3,520B, BPFP=0.4762 Using 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: 568B, BPFP=3.5500 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: 5,188B, BPFP=0.4211 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: 21,456B, BPFP=0.5442 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,344B, BPFP=0.1121 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,348B, BPFP=0.1121 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,368B, BPFP=0.0723 ⌛️ [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: 1.581s [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 0.95997572 text_encoder-item0.clip_prompt_embeds 0.00022698 24.17862005 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.96773348 text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.29299903 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.01156755 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00630479 4.77083540 vae.encoder_f1 0.00631430 4.77235079 vae.decoder 0.00018596 0.09035213 ------------------------------------------------------------------------------------- TOTAL 0.00298001 4.08554642 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 82280 BPFP 0.2911 bits/point EBPFP 0.5823 equivalent bits/point MSE 4.085546 ---------------------- -------------------------------------------------------- Time: 3.733s Load: 0.009s, Pack+Encode: 2.143s, Decode+Unpack: 1.581s ---------------------- -------------------------------------------------------- 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 4.0855 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst to output-fixed/sd35/lambda0.001/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: 372B, BPFP=3.8750 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: 4,208B, BPFP=0.5693 Using 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: 588B, BPFP=3.6750 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: 4,220B, BPFP=0.3425 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: 21,072B, BPFP=0.5345 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,752B, BPFP=0.1030 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,756B, BPFP=0.1031 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,304B, BPFP=0.0703 ⌛️ [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: 1.586s [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 0.00074171 1.00735593 text_encoder-item0.clip_prompt_embeds 0.00024643 24.15340064 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 1.02788153 text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.30867658 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.01002570 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00612578 4.12350368 vae.encoder_f1 0.00613243 4.12557459 vae.decoder 0.00018179 0.10162801 ------------------------------------------------------------------------------------- TOTAL 0.00289482 3.78662964 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80388 BPFP 0.2844 bits/point EBPFP 0.5689 equivalent bits/point MSE 3.786630 ---------------------- -------------------------------------------------------- Time: 3.730s Load: 0.008s, Pack+Encode: 2.136s, 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 3.7866 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,976B, BPFP=0.5379 Using 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: 576B, BPFP=3.6000 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: 5,452B, BPFP=0.4425 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: 22,096B, BPFP=0.5605 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 2,160B, BPFP=0.0330 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 2,160B, BPFP=0.0330 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,412B, BPFP=0.0736 ⌛️ [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: 1.592s [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 0.00018845 0.94895299 text_encoder-item0.clip_prompt_embeds 0.00024049 24.12631899 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.97266550 text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.29587642 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.01095127 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00526071 0.76434702 vae.encoder_f1 0.00526072 0.76434392 vae.decoder 0.00016981 0.10841784 ------------------------------------------------------------------------------------- TOTAL 0.00248947 2.22787790 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 73336 BPFP 0.2595 bits/point EBPFP 0.5190 equivalent bits/point MSE 2.227878 ---------------------- -------------------------------------------------------- Time: 3.737s Load: 0.009s, Pack+Encode: 2.136s, 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 2.2279 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst to output-fixed/sd35/lambda0.001/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: 376B, BPFP=3.9167 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: 3,552B, BPFP=0.4805 Using 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: 588B, BPFP=3.6750 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: 3,788B, BPFP=0.3075 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: 21,440B, BPFP=0.5438 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,424B, BPFP=0.1133 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,424B, BPFP=0.1133 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,832B, BPFP=0.0864 ⌛️ [2/4] FRONTEND: Frontend time: 2.125s (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 0.00063331 0.89194369 text_encoder-item0.clip_prompt_embeds 0.00022843 24.15569831 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.93505363 text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.26063419 text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.01143221 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00622977 5.09166050 vae.encoder_f1 0.00623684 5.09419441 vae.decoder 0.00019755 0.10067534 ------------------------------------------------------------------------------------- TOTAL 0.00294358 4.23369712 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81540 BPFP 0.2885 bits/point EBPFP 0.5770 equivalent bits/point MSE 4.233697 ---------------------- -------------------------------------------------------- Time: 3.727s Load: 0.009s, Pack+Encode: 2.125s, 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 4.2337 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst to output-fixed/sd35/lambda0.001/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: 384B, BPFP=4.0000 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: 4,032B, BPFP=0.5455 Using 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: 584B, BPFP=3.6500 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: 4,216B, BPFP=0.3422 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,240B, BPFP=0.0800 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,236B, BPFP=0.0799 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,436B, BPFP=0.0743 ⌛️ [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 0.00019653 0.96127407 text_encoder-item0.clip_prompt_embeds 0.00026004 24.13257153 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 1.05211229 text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.26906255 text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.01177141 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00725303 3.55454302 vae.encoder_f1 0.00725507 3.55718231 vae.decoder 0.00017991 0.06072289 ------------------------------------------------------------------------------------- TOTAL 0.00341494 3.51612297 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 78004 BPFP 0.2760 bits/point EBPFP 0.5520 equivalent bits/point MSE 3.516123 ---------------------- -------------------------------------------------------- Time: 3.722s 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 3.5161 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst to output-fixed/sd35/lambda0.001/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: 368B, BPFP=3.8333 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: 4,336B, BPFP=0.5866 Using 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: 580B, BPFP=3.6250 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: 4,580B, BPFP=0.3718 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: 21,468B, BPFP=0.5445 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,076B, BPFP=0.1080 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,076B, BPFP=0.1080 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,792B, BPFP=0.0547 ⌛️ [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: 1.589s [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 0.00056072 1.02742020 text_encoder-item0.clip_prompt_embeds 0.00031748 24.15739778 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 1.04859800 text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.28781970 text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00842214 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.42111695 8.91638947 vae.encoder_f1 0.42111716 8.90591717 vae.decoder 0.00019827 0.05010825 ------------------------------------------------------------------------------------- TOTAL 0.19535708 5.99952560 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81392 BPFP 0.2880 bits/point EBPFP 0.5760 equivalent bits/point MSE 5.999526 ---------------------- -------------------------------------------------------- Time: 3.726s Load: 0.008s, Pack+Encode: 2.130s, 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 5.9995 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst to output-fixed/sd35/lambda0.001/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: 388B, BPFP=4.0417 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: 3,240B, BPFP=0.4383 Using 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: 588B, BPFP=3.6750 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: 4,236B, BPFP=0.3438 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: 22,860B, BPFP=0.5798 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,816B, BPFP=0.1345 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,808B, BPFP=0.1344 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,164B, BPFP=0.0660 ⌛️ [2/4] FRONTEND: Frontend time: 2.127s (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 0.92983150 text_encoder-item0.clip_prompt_embeds 0.00024951 24.16093835 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.88890400 text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.29964241 text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.01059524 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.10376993 10.30218410 vae.encoder_f1 0.10377157 10.29944420 vae.decoder 0.00019787 0.05978681 ------------------------------------------------------------------------------------- TOTAL 0.04817852 6.64591569 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 85216 BPFP 0.3015 bits/point EBPFP 0.6030 equivalent bits/point MSE 6.645916 ---------------------- -------------------------------------------------------- Time: 3.732s Load: 0.008s, Pack+Encode: 2.127s, 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 6.6459 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst to output-fixed/sd35/lambda0.001/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: 384B, BPFP=4.0000 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: 3,416B, BPFP=0.4621 Using 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: 584B, BPFP=3.6500 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: 5,876B, BPFP=0.4769 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: 21,208B, BPFP=0.5379 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,312B, BPFP=0.1116 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,312B, BPFP=0.1116 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,792B, BPFP=0.0547 ⌛️ [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: 1.596s [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 0.00035723 0.96114167 text_encoder-item0.clip_prompt_embeds 0.00022350 24.15164409 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.90963230 text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.30824664 text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.01018230 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.01346414 7.21030617 vae.encoder_f1 0.01346933 7.21060085 vae.decoder 0.00019243 0.05820503 ------------------------------------------------------------------------------------- TOTAL 0.00629858 5.21261831 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 82000 BPFP 0.2901 bits/point EBPFP 0.5803 equivalent bits/point MSE 5.212618 ---------------------- -------------------------------------------------------- Time: 3.733s Load: 0.008s, Pack+Encode: 2.129s, 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 5.2126 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,460B, BPFP=0.4681 Using 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: 568B, BPFP=3.5500 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: 4,120B, BPFP=0.3344 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: 23,280B, BPFP=0.5905 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,096B, BPFP=0.1235 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,100B, BPFP=0.1236 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,744B, BPFP=0.0532 ⌛️ [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: 1.592s [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 0.00021921 0.94934503 text_encoder-item0.clip_prompt_embeds 0.00024958 35.17991156 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 1.06550341 text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.27788023 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.01005422 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.11196710 9.13459969 vae.encoder_f1 0.11196851 9.16445351 vae.decoder 0.00023459 0.05491724 ------------------------------------------------------------------------------------- TOTAL 0.05198575 6.39870318 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 83864 BPFP 0.2967 bits/point EBPFP 0.5935 equivalent bits/point MSE 6.398703 ---------------------- -------------------------------------------------------- Time: 3.729s Load: 0.008s, Pack+Encode: 2.129s, 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 6.3987 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,936B, BPFP=0.5325 Using 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: 596B, BPFP=3.7250 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: 4,472B, BPFP=0.3630 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: 20,852B, BPFP=0.5289 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,664B, BPFP=0.1169 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,668B, BPFP=0.1170 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,724B, BPFP=0.0526 ⌛️ [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: 1.591s [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 0.00021756 0.96865447 text_encoder-item0.clip_prompt_embeds 0.00025929 24.16070160 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.97039089 text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.31650626 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00858178 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00675017 5.67633343 vae.encoder_f1 0.00675421 5.67159033 vae.decoder 0.00023635 0.07392344 ------------------------------------------------------------------------------------- TOTAL 0.00320042 4.50227554 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81408 BPFP 0.2880 bits/point EBPFP 0.5761 equivalent bits/point MSE 4.502276 ---------------------- -------------------------------------------------------- Time: 3.735s Load: 0.008s, Pack+Encode: 2.135s, 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 4.5023 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 380B, BPFP=3.9583 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: 3,656B, BPFP=0.4946 Using 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: 588B, BPFP=3.6750 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: 4,808B, BPFP=0.3903 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: 22,328B, BPFP=0.5664 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,600B, BPFP=0.1312 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,604B, BPFP=0.1313 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,216B, BPFP=0.0676 ⌛️ [2/4] FRONTEND: Frontend time: 2.131s (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 0.00017133 0.93132957 text_encoder-item0.clip_prompt_embeds 0.00064775 24.17539020 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.94909220 text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.28435497 text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00963636 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00728993 8.14774799 vae.encoder_f1 0.00729572 8.13980198 vae.decoder 0.00026488 0.07145484 ------------------------------------------------------------------------------------- TOTAL 0.00345536 5.64651492 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 85296 BPFP 0.3018 bits/point EBPFP 0.6036 equivalent bits/point MSE 5.646515 ---------------------- -------------------------------------------------------- Time: 3.736s Load: 0.008s, Pack+Encode: 2.131s, 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 5.6465 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 4,016B, BPFP=0.5433 Using 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: 580B, BPFP=3.6250 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: 5,060B, BPFP=0.4107 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: 22,052B, BPFP=0.5594 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,396B, BPFP=0.0976 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,392B, BPFP=0.0975 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,764B, BPFP=0.0538 ⌛️ [2/4] FRONTEND: Frontend time: 2.126s (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 0.00042462 0.98835055 text_encoder-item0.clip_prompt_embeds 0.00023188 24.15074997 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.94887018 text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.27951496 text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.01080692 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00613207 5.33283043 vae.encoder_f1 0.00613899 5.33905888 vae.decoder 0.00023812 0.07666980 ------------------------------------------------------------------------------------- TOTAL 0.00290239 4.34426445 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80756 BPFP 0.2857 bits/point EBPFP 0.5715 equivalent bits/point MSE 4.344264 ---------------------- -------------------------------------------------------- Time: 3.722s Load: 0.008s, Pack+Encode: 2.126s, 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 4.3443 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 380B, BPFP=3.9583 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: 3,540B, BPFP=0.4789 Using 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: 584B, BPFP=3.6500 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: 5,596B, BPFP=0.4542 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: 21,700B, BPFP=0.5504 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,816B, BPFP=0.1040 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,816B, BPFP=0.1040 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,236B, BPFP=0.0682 ⌛️ [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: 1.593s [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 0.00019246 0.92564432 text_encoder-item0.clip_prompt_embeds 0.00023678 24.13347411 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.98479233 text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.29839497 text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.01202309 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00636537 5.49132109 vae.encoder_f1 0.00636991 5.49502754 vae.decoder 0.00025538 0.08133584 ------------------------------------------------------------------------------------- TOTAL 0.00301360 4.41826339 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81784 BPFP 0.2894 bits/point EBPFP 0.5787 equivalent bits/point MSE 4.418263 ---------------------- -------------------------------------------------------- Time: 3.732s Load: 0.008s, Pack+Encode: 2.132s, 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 4.4183 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 4,136B, BPFP=0.5595 Using 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: 592B, BPFP=3.7000 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: 4,416B, BPFP=0.3584 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: 21,436B, BPFP=0.5437 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 9,560B, BPFP=0.1459 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 9,560B, BPFP=0.1459 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,628B, BPFP=0.0497 ⌛️ [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: 1.592s [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 0.00036983 0.96621561 text_encoder-item0.clip_prompt_embeds 0.00023432 24.15544465 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.82158737 text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.24971313 text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.01199322 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.23155926 11.17222118 vae.encoder_f1 0.23156048 11.17229271 vae.decoder 0.00018572 0.05221728 ------------------------------------------------------------------------------------- TOTAL 0.10744199 7.04703452 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 85824 BPFP 0.3037 bits/point EBPFP 0.6073 equivalent bits/point MSE 7.047035 ---------------------- -------------------------------------------------------- Time: 3.740s Load: 0.008s, Pack+Encode: 2.140s, 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 7.0470 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,332B, BPFP=0.4508 Using 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: 592B, BPFP=3.7000 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: 4,132B, BPFP=0.3354 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: 21,208B, BPFP=0.5379 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,808B, BPFP=0.1191 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,804B, BPFP=0.1191 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,380B, BPFP=0.0726 ⌛️ [2/4] FRONTEND: Frontend time: 2.126s (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 0.00020740 0.96037022 text_encoder-item0.clip_prompt_embeds 0.00022528 24.16626082 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.96937428 text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.26572761 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.01141842 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00729824 6.10242367 vae.encoder_f1 0.00730369 6.10720778 vae.decoder 0.00019938 0.09225003 ------------------------------------------------------------------------------------- TOTAL 0.00343853 4.70254082 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81752 BPFP 0.2893 bits/point EBPFP 0.5785 equivalent bits/point MSE 4.702541 ---------------------- -------------------------------------------------------- Time: 3.725s Load: 0.008s, Pack+Encode: 2.126s, 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 4.7025 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,792B, BPFP=0.5130 Using 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: 580B, BPFP=3.6250 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: 4,660B, BPFP=0.3782 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: 20,480B, BPFP=0.5195 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,012B, BPFP=0.0765 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,012B, BPFP=0.0765 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,748B, BPFP=0.0839 ⌛️ [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: 1.590s [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 0.00021207 0.96659390 text_encoder-item0.clip_prompt_embeds 0.00022149 24.15715681 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.94884129 text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.29630470 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.01024016 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00564371 3.10920548 vae.encoder_f1 0.00565042 3.11011815 vae.decoder 0.00019980 0.10449638 ------------------------------------------------------------------------------------- TOTAL 0.00270919 3.31582472 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 76780 BPFP 0.2717 bits/point EBPFP 0.5433 equivalent bits/point MSE 3.315825 ---------------------- -------------------------------------------------------- Time: 3.749s Load: 0.008s, Pack+Encode: 2.151s, 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 3.3158 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,524B, BPFP=0.4767 Using 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: 584B, BPFP=3.6500 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: 4,180B, BPFP=0.3393 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: 20,640B, BPFP=0.5235 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,580B, BPFP=0.0851 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,580B, BPFP=0.0851 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,732B, BPFP=0.0834 ⌛️ [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: 1.592s [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 0.00017717 0.93153310 text_encoder-item0.clip_prompt_embeds 0.00022173 24.15649308 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.93922348 text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.32157938 text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00851468 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00576096 2.83458281 vae.encoder_f1 0.00576981 2.83469629 vae.decoder 0.00019592 0.10638905 ------------------------------------------------------------------------------------- TOTAL 0.00276400 3.18932396 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 77324 BPFP 0.2736 bits/point EBPFP 0.5472 equivalent bits/point MSE 3.189324 ---------------------- -------------------------------------------------------- Time: 3.735s Load: 0.009s, Pack+Encode: 2.134s, 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 3.1893 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 400B, BPFP=4.1667 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: 3,948B, BPFP=0.5341 Using 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: 580B, BPFP=3.6250 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: 4,988B, BPFP=0.4049 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: 23,584B, BPFP=0.5982 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,384B, BPFP=0.0669 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,384B, BPFP=0.0669 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,656B, BPFP=0.0505 ⌛️ [2/4] FRONTEND: Frontend time: 2.125s (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 0.00024069 0.97611380 text_encoder-item0.clip_prompt_embeds 0.00025917 24.17278392 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.97948751 text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.30749662 text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.01019107 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00594818 3.30464721 vae.encoder_f1 0.00595328 3.30526686 vae.decoder 0.00023462 0.06399571 ------------------------------------------------------------------------------------- TOTAL 0.00281845 3.40261102 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 78040 BPFP 0.2761 bits/point EBPFP 0.5523 equivalent bits/point MSE 3.402611 ---------------------- -------------------------------------------------------- Time: 3.723s Load: 0.008s, Pack+Encode: 2.125s, 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 3.4026 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst to output-fixed/sd35/lambda0.001/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: 372B, BPFP=3.8750 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: 3,952B, BPFP=0.5346 Using 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: 576B, BPFP=3.6000 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: 5,292B, BPFP=0.4295 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: 22,544B, BPFP=0.5718 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,260B, BPFP=0.1260 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,260B, BPFP=0.1260 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,912B, BPFP=0.0583 ⌛️ [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: 1.595s [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 0.92722686 text_encoder-item0.clip_prompt_embeds 0.00022579 24.14732988 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.92799759 text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.32195133 text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.01057473 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.85445058 9.13271046 vae.encoder_f1 0.85445166 9.13290882 vae.decoder 0.00025257 0.03514840 ------------------------------------------------------------------------------------- TOTAL 0.39632643 6.10201075 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 85284 BPFP 0.3018 bits/point EBPFP 0.6035 equivalent bits/point MSE 6.102011 ---------------------- -------------------------------------------------------- Time: 3.732s Load: 0.008s, Pack+Encode: 2.130s, 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 6.1020 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,776B, BPFP=0.5108 Using 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: 580B, BPFP=3.6250 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: 4,652B, BPFP=0.3776 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: 21,456B, BPFP=0.5442 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,048B, BPFP=0.1228 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,052B, BPFP=0.1229 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,036B, BPFP=0.0927 ⌛️ [2/4] FRONTEND: Frontend time: 2.126s (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 0.00057152 0.97264632 text_encoder-item0.clip_prompt_embeds 0.00025458 24.13907561 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.95527134 text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.30388049 text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00856284 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00628510 5.68247128 vae.encoder_f1 0.00629234 5.67587757 vae.decoder 0.00023521 0.11058363 ------------------------------------------------------------------------------------- TOTAL 0.00297516 4.50781756 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 84104 BPFP 0.2976 bits/point EBPFP 0.5952 equivalent bits/point MSE 4.507818 ---------------------- -------------------------------------------------------- Time: 3.729s Load: 0.008s, Pack+Encode: 2.126s, 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 4.5078 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 368B, BPFP=3.8333 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: 4,300B, BPFP=0.5817 Using 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: 572B, BPFP=3.5750 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: 4,388B, BPFP=0.3562 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: 22,100B, BPFP=0.5606 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,524B, BPFP=0.0843 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,524B, BPFP=0.0843 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,960B, BPFP=0.0598 ⌛️ [2/4] FRONTEND: Frontend time: 2.126s (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 0.00037564 1.01778857 text_encoder-item0.clip_prompt_embeds 0.00022807 24.18131933 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 1.05928259 text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.26216450 text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00985099 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00573429 3.25785160 vae.encoder_f1 0.00574192 3.25603700 vae.decoder 0.00017875 0.08471111 ------------------------------------------------------------------------------------- TOTAL 0.00271248 3.38100507 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 78852 BPFP 0.2790 bits/point EBPFP 0.5580 equivalent bits/point MSE 3.381005 ---------------------- -------------------------------------------------------- Time: 3.728s Load: 0.009s, Pack+Encode: 2.126s, 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 3.3810 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst to output-fixed/sd35/lambda0.001/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: 384B, BPFP=4.0000 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: 4,052B, BPFP=0.5482 Using 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: 580B, BPFP=3.6250 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: 5,956B, BPFP=0.4834 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: 21,544B, BPFP=0.5465 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,352B, BPFP=0.1122 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,352B, BPFP=0.1122 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,920B, BPFP=0.0891 ⌛️ [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: 1.593s [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 0.00017150 0.95441008 text_encoder-item0.clip_prompt_embeds 0.00027120 24.16207555 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.94560156 text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.33970258 text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.01118011 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00781570 6.64535427 vae.encoder_f1 0.00781878 6.64542580 vae.decoder 0.00029724 0.09041872 ------------------------------------------------------------------------------------- TOTAL 0.00369190 4.95609611 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 84256 BPFP 0.2981 bits/point EBPFP 0.5962 equivalent bits/point MSE 4.956096 ---------------------- -------------------------------------------------------- Time: 3.746s Load: 0.008s, Pack+Encode: 2.145s, 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 4.9561 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst to output-fixed/sd35/lambda0.001/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: 388B, BPFP=4.0417 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: 3,588B, BPFP=0.4854 Using 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: 604B, BPFP=3.7750 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: 4,740B, BPFP=0.3847 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,708B, BPFP=0.1024 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,708B, BPFP=0.1024 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,140B, BPFP=0.0958 ⌛️ [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: 1.590s [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 0.00018216 0.98884956 text_encoder-item0.clip_prompt_embeds 0.00022930 46.21215080 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.84591675 text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.31043457 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.01165429 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00577752 3.47511506 vae.encoder_f1 0.00578475 3.46991324 vae.decoder 0.00024190 0.12345528 ------------------------------------------------------------------------------------- TOTAL 0.00273964 4.06391067 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81864 BPFP 0.2897 bits/point EBPFP 0.5793 equivalent bits/point MSE 4.063911 ---------------------- -------------------------------------------------------- Time: 3.728s Load: 0.008s, Pack+Encode: 2.129s, 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 4.0639 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 4,140B, BPFP=0.5601 Using 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: 588B, BPFP=3.6750 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: 3,904B, BPFP=0.3169 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: 23,216B, BPFP=0.5889 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 9,908B, BPFP=0.1512 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 9,900B, BPFP=0.1511 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,352B, BPFP=0.0413 ⌛️ [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 0.00047293 1.02349051 text_encoder-item0.clip_prompt_embeds 0.00028764 24.16832809 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 1.02637243 text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.29136158 text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.01032913 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.03343784 10.24435043 vae.encoder_f1 0.03344063 10.24082565 vae.decoder 0.00016139 0.04162013 ------------------------------------------------------------------------------------- TOTAL 0.01555870 6.61671081 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 87504 BPFP 0.3096 bits/point EBPFP 0.6192 equivalent bits/point MSE 6.616711 ---------------------- -------------------------------------------------------- Time: 3.756s Load: 0.009s, 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 6.6167 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst to output-fixed/sd35/lambda0.001/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: 372B, BPFP=3.8750 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: 2,884B, BPFP=0.3902 Using 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: 580B, BPFP=3.6250 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: 4,140B, BPFP=0.3360 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: 21,508B, BPFP=0.5456 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,460B, BPFP=0.1291 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,460B, BPFP=0.1291 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,372B, BPFP=0.0724 ⌛️ [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 0.00559742 0.95037746 text_encoder-item0.clip_prompt_embeds 0.00023094 24.15797061 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.91504002 text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.27412335 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.01108877 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00637455 5.95586634 vae.encoder_f1 0.00637988 5.95685101 vae.decoder 0.00020059 0.09865063 ------------------------------------------------------------------------------------- TOTAL 0.00301333 4.63450229 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 82892 BPFP 0.2933 bits/point EBPFP 0.5866 equivalent bits/point MSE 4.634502 ---------------------- -------------------------------------------------------- Time: 3.741s Load: 0.008s, 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 4.6345 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst to output-fixed/sd35/lambda0.001/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: 372B, BPFP=3.8750 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: 3,964B, BPFP=0.5363 Using 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: 592B, BPFP=3.7000 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: 4,924B, BPFP=0.3997 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: 22,280B, BPFP=0.5651 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,356B, BPFP=0.0970 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,348B, BPFP=0.0969 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,536B, BPFP=0.0774 ⌛️ [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: 1.589s [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 0.00036729 1.02612638 text_encoder-item0.clip_prompt_embeds 0.00025217 35.17419169 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 1.05486755 text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.28089651 text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.01011079 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00581597 3.52911067 vae.encoder_f1 0.00582356 3.52979302 vae.decoder 0.00019494 0.09564219 ------------------------------------------------------------------------------------- TOTAL 0.00275264 3.79702327 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81488 BPFP 0.2883 bits/point EBPFP 0.5767 equivalent bits/point MSE 3.797023 ---------------------- -------------------------------------------------------- Time: 3.748s Load: 0.008s, Pack+Encode: 2.150s, 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 3.7970 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst to output-fixed/sd35/lambda0.001/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: 372B, BPFP=3.8750 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: 4,112B, BPFP=0.5563 Using 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: 588B, BPFP=3.6750 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: 4,020B, BPFP=0.3263 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: 20,276B, BPFP=0.5143 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,572B, BPFP=0.0698 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,572B, BPFP=0.0698 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,916B, BPFP=0.0585 ⌛️ [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: 1.598s [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 0.00799810 1.01147318 text_encoder-item0.clip_prompt_embeds 0.00026975 24.15747176 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.97764053 text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.27652287 text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.01144176 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 1.11695218 8.75459480 vae.encoder_f1 1.11695278 8.76026630 vae.decoder 0.00019720 0.06748445 ------------------------------------------------------------------------------------- TOTAL 0.51806274 5.93013363 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 74544 BPFP 0.2638 bits/point EBPFP 0.5275 equivalent bits/point MSE 5.930134 ---------------------- -------------------------------------------------------- Time: 3.759s Load: 0.007s, Pack+Encode: 2.154s, 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 5.9301 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst to output-fixed/sd35/lambda0.001/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: 384B, BPFP=4.0000 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: 4,096B, BPFP=0.5541 Using 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: 592B, BPFP=3.7000 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: 3,912B, BPFP=0.3175 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: 22,364B, BPFP=0.5673 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,264B, BPFP=0.1108 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,256B, BPFP=0.1107 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,456B, BPFP=0.0750 ⌛️ [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: 1.589s [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 0.00023525 0.95998669 text_encoder-item0.clip_prompt_embeds 0.00025545 24.13845627 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.84299898 text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.25763054 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.01227235 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.01535016 6.54986954 vae.encoder_f1 0.01535382 6.56168842 vae.decoder 0.00021460 0.08730482 ------------------------------------------------------------------------------------- TOTAL 0.00717511 4.91007710 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 82440 BPFP 0.2917 bits/point EBPFP 0.5834 equivalent bits/point MSE 4.910077 ---------------------- -------------------------------------------------------- Time: 3.737s Load: 0.008s, Pack+Encode: 2.140s, 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 4.9101 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 372B, BPFP=3.8750 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: 3,996B, BPFP=0.5406 Using 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: 576B, BPFP=3.6000 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: 5,704B, BPFP=0.4630 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: 22,984B, BPFP=0.5830 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,264B, BPFP=0.0803 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,268B, BPFP=0.0804 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,304B, BPFP=0.0703 ⌛️ [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: 1.588s [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 0.00020648 0.96616332 text_encoder-item0.clip_prompt_embeds 0.00022628 24.13085092 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.96885643 text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.29224776 text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.01033304 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00589589 2.72922421 vae.encoder_f1 0.00590398 2.72745180 vae.decoder 0.00017838 0.12147257 ------------------------------------------------------------------------------------- TOTAL 0.00278687 3.14010642 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80584 BPFP 0.2851 bits/point EBPFP 0.5703 equivalent bits/point MSE 3.140106 ---------------------- -------------------------------------------------------- Time: 3.731s Load: 0.009s, Pack+Encode: 2.135s, 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 3.1401 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 384B, BPFP=4.0000 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: 3,552B, BPFP=0.4805 Using 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: 584B, BPFP=3.6500 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: 4,752B, BPFP=0.3857 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: 20,912B, BPFP=0.5304 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,052B, BPFP=0.1229 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,048B, BPFP=0.1228 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,728B, BPFP=0.0527 ⌛️ [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 0.00045802 0.94329683 text_encoder-item0.clip_prompt_embeds 0.00031548 24.13910097 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.96054459 text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.29402383 text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.01144404 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00725484 7.91458797 vae.encoder_f1 0.00725992 7.91479015 vae.decoder 0.00019960 0.05961195 ------------------------------------------------------------------------------------- TOTAL 0.00342155 5.53863413 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 82128 BPFP 0.2906 bits/point EBPFP 0.5812 equivalent bits/point MSE 5.538634 ---------------------- -------------------------------------------------------- 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 5.5386 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 4,188B, BPFP=0.5666 Using 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: 600B, BPFP=3.7500 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: 4,340B, BPFP=0.3523 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: 20,188B, BPFP=0.5121 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,928B, BPFP=0.1210 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,932B, BPFP=0.1210 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,248B, BPFP=0.0686 ⌛️ [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: 1.596s [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 0.00061068 0.95821150 text_encoder-item0.clip_prompt_embeds 0.00021831 24.18370367 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 1.14677715 text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.27219215 text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00865965 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00923516 6.93028307 vae.encoder_f1 0.00923823 6.93796730 vae.decoder 0.00019521 0.06005288 ------------------------------------------------------------------------------------- TOTAL 0.00433552 5.08386799 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81928 BPFP 0.2899 bits/point EBPFP 0.5798 equivalent bits/point MSE 5.083868 ---------------------- -------------------------------------------------------- Time: 3.752s Load: 0.009s, Pack+Encode: 2.147s, 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 5.0839 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,312B, BPFP=0.4481 Using 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: 584B, BPFP=3.6500 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: 4,564B, BPFP=0.3705 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: 21,860B, BPFP=0.5545 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,876B, BPFP=0.1202 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,876B, BPFP=0.1202 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,404B, BPFP=0.0734 ⌛️ [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: 1.596s [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 0.00028585 0.96280154 text_encoder-item0.clip_prompt_embeds 0.00062166 24.16884597 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.91867857 text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.30200766 text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.01208247 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00831779 7.81896353 vae.encoder_f1 0.00832197 7.81080103 vae.decoder 0.00023271 0.07497999 ------------------------------------------------------------------------------------- TOTAL 0.00392639 5.49532671 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 82980 BPFP 0.2936 bits/point EBPFP 0.5872 equivalent bits/point MSE 5.495327 ---------------------- -------------------------------------------------------- Time: 3.732s Load: 0.008s, Pack+Encode: 2.128s, 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 5.4953 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst to output-fixed/sd35/lambda0.001/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: 376B, BPFP=3.9167 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: 4,092B, BPFP=0.5536 Using 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: 580B, BPFP=3.6250 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: 4,360B, BPFP=0.3539 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: 22,200B, BPFP=0.5631 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,836B, BPFP=0.0891 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,836B, BPFP=0.0891 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,384B, BPFP=0.0422 ⌛️ [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: 1.604s [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 0.00019770 1.00037177 text_encoder-item0.clip_prompt_embeds 0.00022938 24.15152995 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.93341351 text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.27195645 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00976513 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00626977 4.38248730 vae.encoder_f1 0.00627489 4.38251734 vae.decoder 0.00017842 0.06801786 ------------------------------------------------------------------------------------- TOTAL 0.00295919 3.90062610 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 78780 BPFP 0.2787 bits/point EBPFP 0.5575 equivalent bits/point MSE 3.900626 ---------------------- -------------------------------------------------------- Time: 3.763s Load: 0.009s, Pack+Encode: 2.150s, 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 3.9006 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 364B, BPFP=3.7917 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: 3,472B, BPFP=0.4697 Using 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: 588B, BPFP=3.6750 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: 4,524B, BPFP=0.3672 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: 21,740B, BPFP=0.5514 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,404B, BPFP=0.0825 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,396B, BPFP=0.0823 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,832B, BPFP=0.0559 ⌛️ [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: 1.603s [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 0.00022406 0.92109386 text_encoder-item0.clip_prompt_embeds 0.00022180 24.15337316 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.92966967 text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.29880153 text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00969595 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00585720 3.57751822 vae.encoder_f1 0.00586586 3.57990003 vae.decoder 0.00016520 0.10538548 ------------------------------------------------------------------------------------- TOTAL 0.00276807 3.53336465 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 77436 BPFP 0.2740 bits/point EBPFP 0.5480 equivalent bits/point MSE 3.533365 ---------------------- -------------------------------------------------------- Time: 3.746s Load: 0.008s, Pack+Encode: 2.135s, 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 3.5334 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst to output-fixed/sd35/lambda0.001/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: 388B, BPFP=4.0417 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: 3,172B, BPFP=0.4291 Using 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: 588B, BPFP=3.6750 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: 4,772B, BPFP=0.3873 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: 21,520B, BPFP=0.5459 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,180B, BPFP=0.0790 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,180B, BPFP=0.0790 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,684B, BPFP=0.0514 ⌛️ [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: 1.601s [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 0.00265765 0.96322942 text_encoder-item0.clip_prompt_embeds 0.00025784 24.12292850 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.78232827 text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.26830375 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.01088269 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00734802 5.58563185 vae.encoder_f1 0.00734987 5.58566189 vae.decoder 0.00018093 0.06228124 ------------------------------------------------------------------------------------- TOTAL 0.00345989 4.45709151 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 76600 BPFP 0.2710 bits/point EBPFP 0.5421 equivalent bits/point MSE 4.457092 ---------------------- -------------------------------------------------------- Time: 3.745s Load: 0.008s, Pack+Encode: 2.136s, 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 4.4571 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 364B, BPFP=3.7917 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: 4,436B, BPFP=0.6001 Using 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: 588B, BPFP=3.6750 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: 4,236B, BPFP=0.3438 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: 21,600B, BPFP=0.5479 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,096B, BPFP=0.1235 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,096B, BPFP=0.1235 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,800B, BPFP=0.0549 ⌛️ [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: 1.596s [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 0.00019649 0.99233309 text_encoder-item0.clip_prompt_embeds 0.00023510 24.17182004 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.90250998 text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.27631380 text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00939288 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00637359 6.47539616 vae.encoder_f1 0.00637830 6.47597313 vae.decoder 0.00018566 0.08386888 ------------------------------------------------------------------------------------- TOTAL 0.00300937 4.87386360 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 83332 BPFP 0.2949 bits/point EBPFP 0.5897 equivalent bits/point MSE 4.873864 ---------------------- -------------------------------------------------------- Time: 3.744s Load: 0.009s, Pack+Encode: 2.139s, 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 4.8739 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst to output-fixed/sd35/lambda0.001/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: 384B, BPFP=4.0000 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: 3,252B, BPFP=0.4399 Using 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: 592B, BPFP=3.7000 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: 5,208B, BPFP=0.4227 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: 21,732B, BPFP=0.5512 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,632B, BPFP=0.1012 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,628B, BPFP=0.1011 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,308B, BPFP=0.0399 ⌛️ [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: 1.592s [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 0.95382945 text_encoder-item0.clip_prompt_embeds 0.00026418 24.22591146 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.92822065 text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.28834538 text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.01218202 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.01530954 6.68733883 vae.encoder_f1 0.01531230 6.68650866 vae.decoder 0.00017892 0.05056966 ------------------------------------------------------------------------------------- TOTAL 0.00715252 4.97029855 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 79852 BPFP 0.2825 bits/point EBPFP 0.5651 equivalent bits/point MSE 4.970299 ---------------------- -------------------------------------------------------- Time: 3.732s Load: 0.008s, Pack+Encode: 2.132s, 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 4.9703 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 368B, BPFP=3.8333 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: 3,984B, BPFP=0.5390 Using 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: 580B, BPFP=3.6250 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: 4,340B, BPFP=0.3523 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: 22,748B, BPFP=0.5770 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,232B, BPFP=0.0951 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,236B, BPFP=0.0952 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,644B, BPFP=0.0807 ⌛️ [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: 1.588s [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 0.00018183 0.91811895 text_encoder-item0.clip_prompt_embeds 0.00021481 24.15174344 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.85924768 text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.29697937 text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.01000579 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00591154 5.97916651 vae.encoder_f1 0.00591973 5.97626686 vae.decoder 0.00025286 0.08810807 ------------------------------------------------------------------------------------- TOTAL 0.00280398 4.64382498 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81248 BPFP 0.2875 bits/point EBPFP 0.5750 equivalent bits/point MSE 4.643825 ---------------------- -------------------------------------------------------- Time: 3.737s Load: 0.009s, Pack+Encode: 2.139s, 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 4.6438 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,948B, BPFP=0.5341 Using 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: 576B, BPFP=3.6000 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: 4,804B, BPFP=0.3899 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: 23,308B, BPFP=0.5912 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,012B, BPFP=0.0765 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,012B, BPFP=0.0765 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,416B, BPFP=0.0737 ⌛️ [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 0.00017556 0.98005207 text_encoder-item0.clip_prompt_embeds 0.00023458 24.14234350 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.93691788 text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.30844245 text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.01038304 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00588703 2.35378885 vae.encoder_f1 0.00589573 2.35293531 vae.decoder 0.00053402 0.10076332 ------------------------------------------------------------------------------------- TOTAL 0.00289910 2.96480362 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 79580 BPFP 0.2816 bits/point EBPFP 0.5632 equivalent bits/point MSE 2.964804 ---------------------- -------------------------------------------------------- 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 2.9648 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst to output-fixed/sd35/lambda0.001/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: 392B, BPFP=4.0833 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: 3,176B, BPFP=0.4297 Using 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: 580B, BPFP=3.6250 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: 4,268B, BPFP=0.3464 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: 22,416B, BPFP=0.5686 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,968B, BPFP=0.1063 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,980B, BPFP=0.1065 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,872B, BPFP=0.0571 ⌛️ [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: 1.591s [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 0.00027559 0.94054683 text_encoder-item0.clip_prompt_embeds 0.00022882 24.16442818 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.97531271 text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.28244720 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00941696 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00659691 6.09226131 vae.encoder_f1 0.00660300 6.08515358 vae.decoder 0.00023739 0.06292050 ------------------------------------------------------------------------------------- TOTAL 0.00311972 4.69206813 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80768 BPFP 0.2858 bits/point EBPFP 0.5716 equivalent bits/point MSE 4.692068 ---------------------- -------------------------------------------------------- Time: 3.735s Load: 0.008s, Pack+Encode: 2.136s, 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 4.6921 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst to output-fixed/sd35/lambda0.001/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: 0.007s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,556B, BPFP=0.4811 Using 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: 604B, BPFP=3.7750 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: 4,176B, BPFP=0.3390 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: 21,752B, BPFP=0.5517 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 3,612B, BPFP=0.0551 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 3,612B, BPFP=0.0551 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,304B, BPFP=0.0703 ⌛️ [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: 1.596s [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 0.00098754 0.96876772 text_encoder-item0.clip_prompt_embeds 0.00023928 24.16528637 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.95575962 text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.28898823 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00933623 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00583864 2.40074611 vae.encoder_f1 0.00583800 2.40073729 vae.decoder 0.00018889 0.10966457 ------------------------------------------------------------------------------------- TOTAL 0.00276073 2.98742164 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 74120 BPFP 0.2623 bits/point EBPFP 0.5245 equivalent bits/point MSE 2.987422 ---------------------- -------------------------------------------------------- Time: 3.750s Load: 0.007s, Pack+Encode: 2.147s, 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 2.9874 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,552B, BPFP=0.4805 Using 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: 588B, BPFP=3.6750 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: 4,200B, BPFP=0.3409 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: 23,552B, BPFP=0.5974 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 3,844B, BPFP=0.0587 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 3,844B, BPFP=0.0587 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,112B, BPFP=0.0645 ⌛️ [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: 1.590s [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 0.00032508 0.97860638 text_encoder-item0.clip_prompt_embeds 0.00024821 24.12517756 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.77763100 text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.28745137 text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.01195499 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00570467 1.78178096 vae.encoder_f1 0.00570488 1.78130674 vae.decoder 0.00017302 0.08262503 ------------------------------------------------------------------------------------- TOTAL 0.00269931 2.69627416 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 76196 BPFP 0.2696 bits/point EBPFP 0.5392 equivalent bits/point MSE 2.696274 ---------------------- -------------------------------------------------------- Time: 3.737s Load: 0.008s, Pack+Encode: 2.139s, 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 2.6963 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,748B, BPFP=0.5070 Using 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: 564B, BPFP=3.5250 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: 4,000B, BPFP=0.3247 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: 21,676B, BPFP=0.5498 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,288B, BPFP=0.0654 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,292B, BPFP=0.0655 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,424B, BPFP=0.0435 ⌛️ [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: 1.596s [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 0.00022393 0.97391391 text_encoder-item0.clip_prompt_embeds 0.00021458 24.15819467 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.83255119 text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.30696439 text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.01200779 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00914783 5.39435387 vae.encoder_f1 0.00914958 5.39376926 vae.decoder 0.00017527 0.04661561 ------------------------------------------------------------------------------------- TOTAL 0.00429285 4.36922072 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 74488 BPFP 0.2636 bits/point EBPFP 0.5271 equivalent bits/point MSE 4.369221 ---------------------- -------------------------------------------------------- Time: 3.751s Load: 0.008s, Pack+Encode: 2.147s, 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 4.3692 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,796B, BPFP=0.5135 Using 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: 572B, BPFP=3.5750 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: 4,432B, BPFP=0.3597 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 4,948B, BPFP=0.0755 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 4,952B, BPFP=0.0756 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,552B, BPFP=0.0779 ⌛️ [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: 1.591s [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 0.97272031 text_encoder-item0.clip_prompt_embeds 0.00022150 24.13124831 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.96863070 text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.31229516 text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00995455 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00578482 2.38086987 vae.encoder_f1 0.00579739 2.38033247 vae.decoder 0.00017668 0.11858881 ------------------------------------------------------------------------------------- TOTAL 0.00273588 2.97933640 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 78808 BPFP 0.2788 bits/point EBPFP 0.5577 equivalent bits/point MSE 2.979336 ---------------------- -------------------------------------------------------- Time: 3.737s Load: 0.009s, Pack+Encode: 2.138s, 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 2.9793 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst to output-fixed/sd35/lambda0.001/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: 372B, BPFP=3.8750 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: 3,360B, BPFP=0.4545 Using 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: 600B, BPFP=3.7500 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: 3,872B, BPFP=0.3143 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: 21,400B, BPFP=0.5428 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,072B, BPFP=0.0927 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,076B, BPFP=0.0927 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,984B, BPFP=0.0605 ⌛️ [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: 1.600s [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 0.00085811 0.97386734 text_encoder-item0.clip_prompt_embeds 0.00023894 24.14007331 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.94399881 text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.27335681 text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00921076 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00958025 4.55756855 vae.encoder_f1 0.00958229 4.55541420 vae.decoder 0.00019995 0.08479691 ------------------------------------------------------------------------------------- TOTAL 0.00449688 3.98294313 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 77852 BPFP 0.2755 bits/point EBPFP 0.5509 equivalent bits/point MSE 3.982943 ---------------------- -------------------------------------------------------- Time: 3.748s Load: 0.008s, Pack+Encode: 2.140s, 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 3.9829 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 4,064B, BPFP=0.5498 Using 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: 580B, BPFP=3.6250 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: 4,404B, BPFP=0.3575 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: 22,480B, BPFP=0.5702 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 3,128B, BPFP=0.0477 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 3,136B, BPFP=0.0479 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,148B, BPFP=0.0656 ⌛️ [2/4] FRONTEND: Frontend time: 2.131s (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 0.00017781 0.94032621 text_encoder-item0.clip_prompt_embeds 0.00023387 24.15529246 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.97719250 text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.30554940 text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.01105296 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00567713 1.81585181 vae.encoder_f1 0.00567905 1.81483209 vae.decoder 0.00019376 0.12489425 ------------------------------------------------------------------------------------- TOTAL 0.00268802 2.71840014 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 74444 BPFP 0.2634 bits/point EBPFP 0.5268 equivalent bits/point MSE 2.718400 ---------------------- -------------------------------------------------------- Time: 3.732s Load: 0.009s, Pack+Encode: 2.131s, 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 2.7184 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst to output-fixed/sd35/lambda0.001/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: 388B, BPFP=4.0417 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: 3,824B, BPFP=0.5173 Using 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: 584B, BPFP=3.6500 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: 4,524B, BPFP=0.3672 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: 23,304B, BPFP=0.5911 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,580B, BPFP=0.0851 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,580B, BPFP=0.0851 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,244B, BPFP=0.0685 ⌛️ [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: 1.587s [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 0.93971992 text_encoder-item0.clip_prompt_embeds 0.00024281 24.15887108 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.92595015 text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.28448035 text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.01106680 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.02387581 4.48511505 vae.encoder_f1 0.02387858 4.48227644 vae.decoder 0.00018648 0.06837785 ------------------------------------------------------------------------------------- TOTAL 0.01112583 3.94849281 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80144 BPFP 0.2836 bits/point EBPFP 0.5671 equivalent bits/point MSE 3.948493 ---------------------- -------------------------------------------------------- Time: 3.734s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.587s ---------------------- -------------------------------------------------------- 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 3.9485 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 388B, BPFP=4.0417 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: 3,360B, BPFP=0.4545 Using 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: 592B, BPFP=3.7000 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: 4,912B, BPFP=0.3987 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: 21,536B, BPFP=0.5463 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 9,900B, BPFP=0.1511 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 9,900B, BPFP=0.1511 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,104B, BPFP=0.0642 ⌛️ [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: 1.587s [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 0.96470555 text_encoder-item0.clip_prompt_embeds 0.00022399 24.15408338 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.88285122 text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.28227099 text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00890273 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.01169517 9.91565418 vae.encoder_f1 0.01169969 9.90596390 vae.decoder 0.00021186 0.05080292 ------------------------------------------------------------------------------------- TOTAL 0.00548058 6.46283793 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 86808 BPFP 0.3072 bits/point EBPFP 0.6143 equivalent bits/point MSE 6.462838 ---------------------- -------------------------------------------------------- Time: 3.744s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.587s ---------------------- -------------------------------------------------------- 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 6.4628 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 384B, BPFP=4.0000 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: 4,064B, BPFP=0.5498 Using 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: 592B, BPFP=3.7000 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: 4,232B, BPFP=0.3435 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: 23,252B, BPFP=0.5898 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 11,872B, BPFP=0.1812 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 11,872B, BPFP=0.1812 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 3,780B, BPFP=0.1154 ⌛️ [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: 1.593s [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 0.00018108 0.93982601 text_encoder-item0.clip_prompt_embeds 0.00022123 24.13732752 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.89572620 text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.27945576 text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.01033183 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.32749966 17.18148804 vae.encoder_f1 0.32750070 17.18149376 vae.decoder 0.00039956 0.09390083 ------------------------------------------------------------------------------------- TOTAL 0.15195981 9.83938244 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 94164 BPFP 0.3332 bits/point EBPFP 0.6664 equivalent bits/point MSE 9.839382 ---------------------- -------------------------------------------------------- Time: 3.749s Load: 0.008s, Pack+Encode: 2.148s, 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 9.8394 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst to output-fixed/sd35/lambda0.001/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: 368B, BPFP=3.8333 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: 3,464B, BPFP=0.4686 Using 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: 584B, BPFP=3.6500 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: 4,420B, BPFP=0.3588 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: 21,344B, BPFP=0.5414 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,104B, BPFP=0.0779 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,096B, BPFP=0.0778 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,220B, BPFP=0.0677 ⌛️ [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: 1.591s [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 0.00109564 0.93710287 text_encoder-item0.clip_prompt_embeds 0.00024675 24.14999324 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.86483965 text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.28546594 text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00931416 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00566967 2.96904898 vae.encoder_f1 0.00567867 2.96605110 vae.decoder 0.00017839 0.09390683 ------------------------------------------------------------------------------------- TOTAL 0.00268303 3.24784346 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 76716 BPFP 0.2714 bits/point EBPFP 0.5429 equivalent bits/point MSE 3.247843 ---------------------- -------------------------------------------------------- Time: 3.742s Load: 0.008s, Pack+Encode: 2.143s, 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 3.2478 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst to output-fixed/sd35/lambda0.001/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: 380B, BPFP=3.9583 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: 3,404B, BPFP=0.4605 Using 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: 576B, BPFP=3.6000 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: 3,484B, BPFP=0.2828 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: 21,692B, BPFP=0.5502 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 3,324B, BPFP=0.0507 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 3,324B, BPFP=0.0507 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,728B, BPFP=0.0527 ⌛️ [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: 1.587s [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 0.93948046 text_encoder-item0.clip_prompt_embeds 0.00022364 24.17564597 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 1.04120474 text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.27518049 text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.01033872 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00580750 2.24277925 vae.encoder_f1 0.00580664 2.24277520 vae.decoder 0.00018044 0.12620236 ------------------------------------------------------------------------------------- TOTAL 0.00274301 2.91592750 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 72028 BPFP 0.2549 bits/point EBPFP 0.5097 equivalent bits/point MSE 2.915927 ---------------------- -------------------------------------------------------- Time: 3.734s Load: 0.008s, Pack+Encode: 2.139s, Decode+Unpack: 1.587s ---------------------- -------------------------------------------------------- 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 2.9159 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 372B, BPFP=3.8750 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: 3,700B, BPFP=0.5005 Using 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: 584B, BPFP=3.6500 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: 4,108B, BPFP=0.3334 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: 22,068B, BPFP=0.5598 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,020B, BPFP=0.1071 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,020B, BPFP=0.1071 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,628B, BPFP=0.0497 ⌛️ [2/4] FRONTEND: Frontend time: 2.126s (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 0.00038620 0.95873952 text_encoder-item0.clip_prompt_embeds 0.00030118 24.18624231 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.85420990 text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.26169961 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.01194719 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.03869025 6.47439384 vae.encoder_f1 0.03869358 6.47456455 vae.decoder 0.00021614 0.06178448 ------------------------------------------------------------------------------------- TOTAL 0.01800198 4.87080175 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 80616 BPFP 0.2852 bits/point EBPFP 0.5705 equivalent bits/point MSE 4.870802 ---------------------- -------------------------------------------------------- Time: 3.725s Load: 0.008s, Pack+Encode: 2.126s, 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 4.8708 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 392B, BPFP=4.0833 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: 3,800B, BPFP=0.5141 Using 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: 584B, BPFP=3.6500 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: 5,316B, BPFP=0.4315 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: 21,796B, BPFP=0.5529 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 9,392B, BPFP=0.1433 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 9,380B, BPFP=0.1431 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,708B, BPFP=0.0521 ⌛️ [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 0.00084877 0.95586721 text_encoder-item0.clip_prompt_embeds 0.00023260 35.14480181 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 1.00513020 text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.28820478 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.01158732 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00839879 9.46686268 vae.encoder_f1 0.00840224 9.43346596 vae.decoder 0.00019463 0.05594161 ------------------------------------------------------------------------------------- TOTAL 0.00394849 6.53796180 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 86484 BPFP 0.3060 bits/point EBPFP 0.6120 equivalent bits/point MSE 6.537962 ---------------------- -------------------------------------------------------- Time: 3.752s Load: 0.008s, 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 6.5380 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst to output-fixed/sd35/lambda0.001/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: 0.009s ------------------------------------------------------------ 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: 384B, BPFP=4.0000 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: 3,904B, BPFP=0.5281 Using 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: 568B, BPFP=3.5500 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: 4,548B, BPFP=0.3692 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: 21,408B, BPFP=0.5430 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,684B, BPFP=0.1325 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,680B, BPFP=0.1324 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,072B, BPFP=0.0632 ⌛️ [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: 1.592s [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 0.00017723 0.95738284 text_encoder-item0.clip_prompt_embeds 0.00023544 24.11800342 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.96476297 text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.29987732 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00905925 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.01160815 8.07672787 vae.encoder_f1 0.01161249 8.07806015 vae.decoder 0.00021720 0.06268868 ------------------------------------------------------------------------------------- TOTAL 0.00544054 5.61382609 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 84364 BPFP 0.2985 bits/point EBPFP 0.5970 equivalent bits/point MSE 5.613826 ---------------------- -------------------------------------------------------- Time: 3.748s Load: 0.009s, Pack+Encode: 2.147s, 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 5.6138 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst to output-fixed/sd35/lambda0.001/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: 0.008s ------------------------------------------------------------ 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: 368B, BPFP=3.8333 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: 3,724B, BPFP=0.5038 Using 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: 596B, BPFP=3.7250 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: 4,740B, BPFP=0.3847 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: 21,856B, BPFP=0.5544 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 5,380B, BPFP=0.0821 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 5,384B, BPFP=0.0822 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: 2.149s (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: 1.601s [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 0.00017755 0.94985604 text_encoder-item0.clip_prompt_embeds 0.00022923 24.15210067 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.94070024 text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.27212120 text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.01161925 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.02989292 6.11934137 vae.encoder_f1 0.02989391 6.11396694 vae.decoder 0.00034944 0.05734404 ------------------------------------------------------------------------------------- TOTAL 0.01393319 4.70390060 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 78372 BPFP 0.2773 bits/point EBPFP 0.5546 equivalent bits/point MSE 4.703901 ---------------------- -------------------------------------------------------- Time: 3.758s Load: 0.008s, Pack+Encode: 2.149s, 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 4.7039 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst to output-fixed/sd35/lambda0.001/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: 376B, BPFP=3.9167 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: 3,876B, BPFP=0.5244 Using 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: 560B, BPFP=3.5000 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: 3,576B, BPFP=0.2903 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: 22,092B, BPFP=0.5604 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: 368B, BPFP=3.8333 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: 4,228B, BPFP=0.5720 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: 572B, BPFP=3.5750 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: 7,016B, BPFP=0.5695 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: 21,932B, BPFP=0.5563 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,316B, BPFP=0.1116 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,316B, BPFP=0.1116 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 1,824B, BPFP=0.0557 ⌛️ [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: 1.598s [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 0.00020076 0.99389807 text_encoder-item0.clip_prompt_embeds 0.00024627 24.18461259 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 1.01237488 text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.30276398 text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.01228809 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.06353617 text_encoder-item3.clip_prompt_embeds 0.00023247 46.01054349 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.69900036 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.20872611 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00898032 vae.encoder_f0 0.00613025 4.50184011 vae.encoder_f1 0.00613536 4.50274754 vae.decoder 0.00018697 0.08069747 ------------------------------------------------------------------------------------- TOTAL 0.00289634 3.96025434 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 81052 BPFP 0.2868 bits/point EBPFP 0.5736 equivalent bits/point MSE 3.960254 ---------------------- -------------------------------------------------------- Time: 3.747s Load: 0.009s, Pack+Encode: 2.140s, 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 3.9603 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst to output-fixed/sd35/lambda0.001/elic-featurecoding-8bit-individual/sd35cond/000000575243.zst ------------------------ ---------------------------- TOTAL PROCESSING SUMMARY ------------------------ ---------------------------- Total files 100 Avg BPFP 0.2846 bits/point Avg EBPFP 0.5691 equivalent bits/point Avg MSE 4.583636 Avg Time 3.748s ------------------------ ----------------------------