Cosmos
Diffusers
Safetensors
cosmos3_omni
nvidia
cosmos3
vllm
vllm-omni
text, image, video, audio, and action generation
omnimodel
Instructions to use nvidia/Cosmos3-Super with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use nvidia/Cosmos3-Super with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Diffusers
How to use nvidia/Cosmos3-Super with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nvidia/Cosmos3-Super", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Add SGLang serving instructions
#11
by MickJ - opened
- README.md +65 -45
- sound_tokenizer.ckpt +3 -0
- sound_tokenizer.json +42 -0
README.md
CHANGED
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@@ -10,54 +10,11 @@ tags:
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- cosmos3
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- vllm
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- vllm-omni
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- diffusers
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- text, image, video, audio, and action generation
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- omnimodel
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countDownloads:
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- checkpoint.json
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- vision_encoder/config.json
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- vision_encoder/model.safetensors
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---
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# **Cosmos 3: Omnimodal World Models for Physical AI**
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Cosmos3 outputs should not be treated as physically accurate simulation, reliable ground-truth reasoning, or safety-certified decision making. Applications involving robotics control, autonomous systems, scientific simulation, or safety-critical planning require additional validation, external constraints, system-level safety analysis, and domain-specific guardrails before deployment.
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## Inference
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**Acceleration Engine:** [PyTorch](https://pytorch.org/), [vLLM](https://github.com/vllm-project/vllm), [vLLM-Omni](https://github.com/vllm-project/vllm-omni), [Hugging Face Diffusers](https://github.com/huggingface/diffusers)
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- cosmos3
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- vllm
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- vllm-omni
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- sglang
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- sglang-diffusion
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- diffusers
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- text, image, video, audio, and action generation
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- omnimodel
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---
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# **Cosmos 3: Omnimodal World Models for Physical AI**
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Cosmos3 outputs should not be treated as physically accurate simulation, reliable ground-truth reasoning, or safety-certified decision making. Applications involving robotics control, autonomous systems, scientific simulation, or safety-critical planning require additional validation, external constraints, system-level safety analysis, and domain-specific guardrails before deployment.
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### SGLang
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SGLang-Diffusion can serve `nvidia/Cosmos3-Super` through OpenAI-compatible image and video endpoints. Install SGLang from the main branch with diffusion dependencies, then start the server:
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```bash
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git clone --branch main https://github.com/sgl-project/sglang.git
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cd sglang
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pip install -e "python[diffusion]"
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pip install "cosmos-guardrail==0.3.1"
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sglang serve \
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--model-path nvidia/Cosmos3-Super \
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--num-gpus 4
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```
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Cosmos 3 support in SGLang Diffusion currently requires the SGLang main branch. Switch to a stable SGLang release once Cosmos 3 support is included there.
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For the video-specialized checkpoint:
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```bash
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sglang serve \
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--model-path nvidia/Cosmos3-Super-Image2Video \
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--num-gpus 4
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```
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Supported SGLang endpoints:
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| Mode | Endpoint | Notes |
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| --- | --- | --- |
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| Text to image | `POST /v1/images/generations` | Returns base64 image data by default |
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| Text to video | `POST /v1/videos` | Creates an async job; poll `GET /v1/videos/{id}` and download `/content` |
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| Image to video | `POST /v1/videos` | Upload the conditioning image with `input_reference` |
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Example text-to-video request:
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```bash
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job_id=$(curl -sS -X POST http://localhost:30000/v1/videos \
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--form-string "prompt=A small warehouse robot moves a blue box across a clean floor." \
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--form-string "negative_prompt=blurry, distorted, low quality" \
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--form-string "size=1280x720" \
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--form-string "num_frames=81" \
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--form-string "fps=24" \
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--form-string "num_inference_steps=35" \
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--form-string "guidance_scale=4.0" \
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--form-string "flow_shift=10.0" \
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--form-string "seed=42" \
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--form-string 'extra_params={"guardrails":true,"use_resolution_template":false,"use_duration_template":false}' \
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| python -c 'import json, sys; print(json.load(sys.stdin)["id"])')
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while true; do
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status=$(curl -sS "http://localhost:30000/v1/videos/${job_id}" \
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| python -c 'import json, sys; print(json.load(sys.stdin)["status"])')
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[ "$status" = "completed" ] && break
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[ "$status" = "failed" ] && exit 1
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sleep 1
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done
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curl -sS -L "http://localhost:30000/v1/videos/${job_id}/content" \
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-o cosmos3_super_t2v_output.mp4
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```
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Video-to-video, video-with-sound, and action generation are not supported by SGLang yet.
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## Inference
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**Acceleration Engine:** [PyTorch](https://pytorch.org/), [vLLM](https://github.com/vllm-project/vllm), [vLLM-Omni](https://github.com/vllm-project/vllm-omni), [Hugging Face Diffusers](https://github.com/huggingface/diffusers)
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sound_tokenizer.ckpt
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:6daeb68a219f3e86c0918f616d78b9ebf073f3d700df63ff1c02d214c081d72d
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size 1985246007
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sound_tokenizer.json
ADDED
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{
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"model_type": "autoencoder_v2",
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"sampling_rate": 48000,
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"stereo": true,
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"use_wav_as_input": true,
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"normalize_volume": true,
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"hop_size": 1920,
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"input_channels": 1,
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"enc_type": "spec_convnext",
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"enc_dim": 192,
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"enc_intermediate_dim": 768,
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"enc_num_layers": 12,
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"enc_num_blocks": 2,
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"enc_n_fft": 64,
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"enc_hop_length": 16,
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"enc_latent_dim": 128,
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"enc_c_mults": [1, 2, 4],
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"enc_strides": [4, 5, 6],
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"enc_identity_init": false,
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"enc_use_snake": true,
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"dec_type": "oobleck",
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"dec_dim": 320,
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"dec_c_mults": [1, 2, 4, 8, 16],
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"dec_strides": [2, 4, 5, 6, 8],
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"dec_use_snake": true,
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"dec_final_tanh": false,
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"dec_out_channels": 2,
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"dec_anti_aliasing": false,
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"dec_use_nearest_upsample": false,
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"dec_use_tanh_at_final": false,
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"bottleneck_type": "vae",
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"bottleneck": {"type": "vae"},
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"activation": "snakebeta",
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"snake_logscale": true,
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"anti_aliasing": false,
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"use_cuda_kernel": false,
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"causal": false,
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"padding_mode": "zeros",
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"vocoder_input_dim": 64,
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"latent_mean": null,
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"latent_std": null
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}
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