Add files using upload-large-folder tool

.gitattributes

@@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/nvidia_example_caption_bf16.png filter=lfs diff=lfs merge=lfs -text
+examples/nvidia_example_caption_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text
+examples/01_metro_archive_reading_room_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text
+examples/02_arctic_greenhouse_night_shift_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text
+examples/03_control_room_restoration_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text
+examples/04_rain_market_cross_section_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text
+examples/05_manuscript_restoration_table_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text
+examples/06_robotic_assembly_line_signage_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text
+examples/07_kitchen_storm_chess_table_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text
+examples/08_orbital_cockpit_cyrillic_ui_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text
+examples/09_flood_command_center_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text
+examples/10_cyrillic_newspaper_press_sdnq_int8.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+base_model: nvidia/Cosmos3-Super-Text2Image
+library_name: diffusers
+pipeline_tag: text-to-image
+tags:
+  - cosmos3
+  - diffusers
+  - sdnq
+  - text-to-image
+  - int8
+license: other
+license_name: openmdw1.1-license
+license_link: https://openmdw.ai/license/1-1/
+---
+
+# Cosmos3-Super-Text2Image SDNQ INT8 Transformer
+
+This repository contains a transformer-only SDNQ quantization for [nvidia/Cosmos3-Super-Text2Image](https://huggingface.co/nvidia/Cosmos3-Super-Text2Image).
+
+It does not repeat the original model card. Read NVIDIA's model card, prompt-format guidance, license, and safety notes here:
+[nvidia/Cosmos3-Super-Text2Image](https://huggingface.co/nvidia/Cosmos3-Super-Text2Image).
+
+Only `transformer/` is provided as a weight artifact. The VAE, scheduler, tokenizers, safety checker, and other components are loaded from the base model.
+
+The quantization format comes from [Disty0/sdnq](https://github.com/Disty0/sdnq). SD.Next's quantization overview is here:
+[vladmandic/sdnext Quantization](https://github.com/vladmandic/sdnext/wiki/Quantization).
+
+## Recipe
+
+| Setting | Value |
+| --- | --- |
+| Weights dtype | `int8` |
+| Static quantization | `True` |
+| Dynamic quantization | `False` |
+| SVD | `False` |
+| SVD rank / steps | `32` / `8` |
+| Quantized matmul | `True` |
+| Dequantize FP32 | `True` |
+| Quantized conv / embedding | `False` / `False` |
+
+Quantization run: 20.90s; save time: 85.31s; transformer safetensors: 61.17 GiB.
+
+## Assemble The Pipeline
+
+```python
+import json
+import torch
+from diffusers import Cosmos3OmniPipeline, Cosmos3OmniTransformer
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+from huggingface_hub import snapshot_download
+from sdnq.loader import load_sdnq_model
+
+snapshot_path = snapshot_download("WaveCut/Cosmos3-Super-Text2Image-SDNQ-Int8-Transformer")
+transformer = load_sdnq_model(
+    f"{snapshot_path}/transformer",
+    model_cls=Cosmos3OmniTransformer,
+    dtype=torch.bfloat16,
+    device=torch.device("cuda"),
+    dequantize_fp32=True,
+    use_quantized_matmul=True,
+)
+
+pipe = Cosmos3OmniPipeline.from_pretrained(
+    "nvidia/Cosmos3-Super-Text2Image",
+    transformer=transformer,
+    torch_dtype=torch.bfloat16,
+    device_map="cuda",
+    enable_safety_checker=True,
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=3.0)
+pipe.to("cuda")
+
+json_caption = {
+    "subjects": [],
+    "background_setting": "A concise scene description.",
+    "comprehensive_t2i_caption": "A detailed natural-language caption.",
+    "resolution": {"H": 1024, "W": 1024},
+    "aspect_ratio": "1,1",
+}
+
+result = pipe(
+    prompt=json.dumps(json_caption),
+    negative_prompt="",
+    num_frames=1,
+    height=1024,
+    width=1024,
+    num_inference_steps=50,
+    guidance_scale=4.0,
+    generator=torch.Generator(device="cuda").manual_seed(1143),
+)
+result.video[0].save("cosmos3_sdnq_int8.png")
+```
+
+`load_sdnq_model` expects a local path. Download this repository first, or use `huggingface_hub.snapshot_download("WaveCut/Cosmos3-Super-Text2Image-SDNQ-Int8-Transformer")` and pass `snapshot_path + "/transformer"`.
+
+## Benchmarks
+
+Measured on one RunPod NVIDIA B200 instance with local container storage, cached model files, PyTorch `2.9.1+cu130`, 1024x1024 image generation, 50 inference steps, guidance scale 4.0, `flow_shift=3.0`, system prompt enabled.
+
+### Transformer Component Load
+
+| Variant | Load to CUDA | VRAM after load | Torch allocated | Torch reserved | Transformer safetensors |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| BF16 base transformer | 22.87s | 122,760 MiB | 122,121 MiB | 122,132 MiB | 119.21 GiB |
+| SDNQ INT8 transformer | 16.50s | 63,920 MiB | 63,018 MiB | 63,200 MiB | 61.17 GiB |
+
+### Full Pipeline Generation
+
+The stress set is ten handwritten JSON-caption prompts designed to stress Cyrillic text, reflections, multi-object composition, anatomy, small details, and scene-following.
+
+| Variant | Full pipeline load | VRAM after load | Torch allocated after load | Avg generation time | Min / max generation time | Peak sampled VRAM | Images |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| BF16 base pipeline | 31.31s | 125,134 MiB | 124,386 MiB | 16.05s | 15.51s / 17.97s | 141,104 MiB | 10 |
+| SDNQ INT8 pipeline | 26.79s | 67,268 MiB | 66,528 MiB | 25.51s | 21.57s / 36.53s | 83,202 MiB | 10 |
+
+### Original NVIDIA Example Caption
+
+The original model repository provides [`assets/example_caption.json`](https://huggingface.co/nvidia/Cosmos3-Super-Text2Image/blob/main/assets/example_caption.json). The images below are generated locally with the same JSON-caption, seed 1143, 1024x1024, 50 steps, guidance scale 4.0.
+
+| Variant | Pipeline load | Generation time | Peak sampled VRAM |
+| --- | ---: | ---: | ---: |
+| BF16 base pipeline | 35.41s | 18.01s | 141,098 MiB |
+| SDNQ INT8 pipeline | 25.79s | 66.05s | 83,218 MiB |
+
+BF16 reference output:
+
+![BF16 output for NVIDIA example caption](examples/nvidia_example_caption_bf16.png)
+
+SDNQ INT8 output:
+
+![SDNQ INT8 output for NVIDIA example caption](examples/nvidia_example_caption_sdnq_int8.png)
+
+## Stress Prompt Examples
+
+The following ten images use the same handwritten stress prompt set and seeds as the benchmark table.
+
+![01 metro archive reading room](examples/01_metro_archive_reading_room_sdnq_int8.png)
+![02 arctic greenhouse night shift](examples/02_arctic_greenhouse_night_shift_sdnq_int8.png)
+![03 control room restoration](examples/03_control_room_restoration_sdnq_int8.png)
+![04 rain market cross section](examples/04_rain_market_cross_section_sdnq_int8.png)
+![05 manuscript restoration table](examples/05_manuscript_restoration_table_sdnq_int8.png)
+![06 robotic assembly line signage](examples/06_robotic_assembly_line_signage_sdnq_int8.png)
+![07 kitchen storm chess table](examples/07_kitchen_storm_chess_table_sdnq_int8.png)
+![08 orbital cockpit cyrillic ui](examples/08_orbital_cockpit_cyrillic_ui_sdnq_int8.png)
+![09 flood command center](examples/09_flood_command_center_sdnq_int8.png)
+![10 cyrillic newspaper press](examples/10_cyrillic_newspaper_press_sdnq_int8.png)
+
+## Notes
+
+This repository is an independent transformer-only quantization artifact. NVIDIA's original card states that Cosmos3-Super-Text2Image was tested in BF16; this SDNQ artifact should be treated as an experimental deployment variant and evaluated for each workload.

transformer/config.json

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+{
+  "_class_name": "Cosmos3OmniTransformer",
+  "_diffusers_version": "0.39.0.dev0",
+  "_name_or_path": "nvidia/Cosmos3-Super-Text2Image",
+  "action_dim": 32,
+  "action_gen": false,
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "base_fps": 24,
+  "dtype": "bfloat16",
+  "enable_fps_modulation": true,
+  "freeze_und": false,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 25600,
+  "joint_attn_implementation": "two_way",
+  "latent_channel": 48,
+  "latent_patch_size": 2,
+  "max_action_dim": 32,
+  "max_position_embeddings": 262144,
+  "model_type": "qwen3_vl_text",
+  "num_attention_heads": 64,
+  "num_embodiment_domains": 32,
+  "num_hidden_layers": 64,
+  "num_key_value_heads": 8,
+  "patch_latent_dim": 192,
+  "position_embedding_type": "unified_3d_mrope",
+  "qk_norm": false,
+  "qk_norm_for_diffusion": true,
+  "qk_norm_for_text": true,
+  "quantization_config": {
+    "add_skip_keys": false,
+    "dequantize_fp32": true,
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