> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Cosmos3

export const DiffusionModelTags = ({tags = []}) => {
  const normalizedTags = Array.isArray(tags) ? tags : [tags];
  return <div className="not-prose sgd-model-tags">
      {normalizedTags.map(tag => <span key={tag} className="sgd-chip">
          {tag}
        </span>)}
    </div>;
};

<DiffusionModelTags tags={["image", "video", "sound/action", "world model", "policy"]} />

## 1. Model Introduction

[NVIDIA Cosmos3](https://huggingface.co/collections/nvidia/cosmos3) is an omnimodal world-model family for image, video, sound, and action generation. SGLang Diffusion serves the public checkpoints with the native `Cosmos3OmniDiffusersPipeline`.

| Model                              | Status    | Notes                                                        |
| ---------------------------------- | --------- | ------------------------------------------------------------ |
| `nvidia/Cosmos3-Nano`              | Supported | T2I, T2V, I2V, V2V, joint sound, and action                  |
| `nvidia/Cosmos3-Super`             | Supported | T2I, T2V, I2V, and V2V; use multi-GPU for the 64B checkpoint |
| `nvidia/Cosmos3-Super-Text2Image`  | Supported | T2I-specialized checkpoint                                   |
| `nvidia/Cosmos3-Super-Image2Video` | Supported | I2V-specialized checkpoint                                   |
| `nvidia/Cosmos3-Nano-Policy-DROID` | Supported | DROID policy action generation                               |

Sound and action generation require the corresponding checkpoint heads. SGLang uses the flow-native `FlowUniPCMultistepScheduler` for Cosmos3 even if the checkpoint metadata names another scheduler. The default `flow_shift` is `3.0` for T2I and `10.0` for video and action modes.

## 2. Installation

Install SGLang with the diffusion dependencies:

```bash Command theme={null}
pip install -e "python[diffusion]"
```

Cosmos3 guardrails are enabled by default when the package is available:

```bash Command theme={null}
pip install "cosmos-guardrail==0.3.1"
```

`cosmos-guardrail` downloads gated NVIDIA guardrail weights, so pass a Hugging Face token if your environment needs one. If the package is not installed, SGLang skips Cosmos3 guardrails and logs a warning. To disable Cosmos3 guardrails for local experiments, set `SGLANG_DISABLE_COSMOS3_GUARDRAILS=1` before starting the server.

## 3. Serve Cosmos3

Serve `Cosmos3-Nano` directly from the Hugging Face model ID:

```bash Command theme={null}
sglang serve \
  --model-path nvidia/Cosmos3-Nano \
  --num-gpus 1
```

For `Cosmos3-Super`, split the model across multiple GPUs:

```bash Command theme={null}
sglang serve \
  --model-path nvidia/Cosmos3-Super \
  --num-gpus 4
```

The server also accepts the specialized `nvidia/Cosmos3-Super-Text2Image` and `nvidia/Cosmos3-Super-Image2Video` checkpoint IDs.

## 4. OpenAI-Compatible Requests

### Text to image

Cosmos3 text-to-image uses `/v1/images/generations`. The default Cosmos3 image response is `b64_json`, matching vLLM-Omni's examples.

```bash Command theme={null}
curl -sS -X POST http://127.0.0.1:30010/v1/images/generations \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A warehouse robot folds a blue cloth on a clean workbench.",
    "size": "1280x720",
    "n": 1,
    "num_inference_steps": 35,
    "guidance_scale": 6.0,
    "flow_shift": 3.0,
    "seed": 0,
    "extra_args": {
      "use_resolution_template": false,
      "guardrails": true
    }
  }'
```

### Text to video with sound

Use `/v1/videos` to create an asynchronous job, then poll the job and download the completed MP4. Set `generate_sound=true` to generate and mux a stereo 48 kHz audio track; omit it for a silent video.

```bash Command theme={null}
job_id=$(curl -sS -X POST http://127.0.0.1:30010/v1/videos \
  --form-string "prompt=A small warehouse robot moves a blue box across a clean floor." \
  --form-string "negative_prompt=blurry, distorted, low quality" \
  --form-string "size=1280x720" \
  --form-string "num_frames=81" \
  --form-string "fps=24" \
  --form-string "num_inference_steps=35" \
  --form-string "guidance_scale=4.0" \
  --form-string "flow_shift=10.0" \
  --form-string "generate_sound=true" \
  --form-string "seed=42" \
  --form-string 'extra_params={"guardrails":true,"use_resolution_template":false,"use_duration_template":false}' \
  | python -c 'import json, sys; print(json.load(sys.stdin)["id"])')

while true; do
  status=$(curl -sS "http://127.0.0.1:30010/v1/videos/${job_id}" \
    | python -c 'import json, sys; print(json.load(sys.stdin)["status"])')
  [ "$status" = "completed" ] && break
  [ "$status" = "failed" ] && exit 1
  sleep 1
done

curl -sS -L "http://127.0.0.1:30010/v1/videos/${job_id}/content" \
  -o cosmos3_t2v.mp4
```

### Image to video

This mirrors the official `nvidia/Cosmos3-Nano` Hugging Face image-to-video example:

```python Python theme={null}
import json
import time
from pathlib import Path

import requests
from huggingface_hub import snapshot_download

base_url = "http://127.0.0.1:30010"
model_dir = Path(snapshot_download("nvidia/Cosmos3-Nano"))
asset_dir = model_dir / "assets"

prompt = json.dumps(json.loads((asset_dir / "example_i2v_prompt.json").read_text()))
negative_prompt = json.dumps(
    json.loads((asset_dir / "negative_prompt.json").read_text())
)

data = {
    "prompt": prompt,
    "negative_prompt": negative_prompt,
    "size": "1280x720",
    "num_frames": "189",
    "fps": "24",
    "num_inference_steps": "35",
    "guidance_scale": "6.0",
    "max_sequence_length": "4096",
    "flow_shift": "10.0",
    "seed": "1111",
    "extra_params": json.dumps(
        {
            "use_resolution_template": False,
            "use_duration_template": False,
            "guardrails": True,
        }
    ),
}

with (asset_dir / "example_i2v_input.jpg").open("rb") as image:
    response = requests.post(
        f"{base_url}/v1/videos",
        data=data,
        files={"input_reference": ("example_i2v_input.jpg", image, "image/jpeg")},
        timeout=60,
    )
response.raise_for_status()
video_id = response.json()["id"]

while True:
    job = requests.get(f"{base_url}/v1/videos/{video_id}", timeout=30).json()
    if job["status"] == "completed":
        break
    if job["status"] == "failed":
        raise RuntimeError(job.get("error") or "Video generation failed")
    time.sleep(1)

response = requests.get(f"{base_url}/v1/videos/{video_id}/content", timeout=300)
response.raise_for_status()
Path("cosmos3_i2v.mp4").write_bytes(response.content)
```

### Video to video

Upload a source video with `video_reference`. Cosmos3 keeps latent frames `[0, 1]` by default and generates the remaining frames. Use `condition_frame_indexes` to select different latent frames, and `condition_video_keep` to take conditioning frames from the start or end of the source.

```bash Command theme={null}
job_id=$(curl -sS -X POST http://127.0.0.1:30010/v1/videos \
  --form-string "prompt=A robotic arm pours liquid into a glass on a white tabletop." \
  --form "video_reference=@robot_pouring.mp4;type=video/mp4" \
  --form-string "size=1280x704" \
  --form-string "num_frames=45" \
  --form-string "fps=24" \
  --form-string "num_inference_steps=35" \
  --form-string "guidance_scale=6.0" \
  --form-string 'condition_frame_indexes=[0,1]' \
  --form-string "condition_video_keep=first" \
  | python -c 'import json, sys; print(json.load(sys.stdin)["id"])')
```

Poll and download this job with the same status and content endpoints used by the T2V example.

### Action generation

For DROID policy generation, start a single-GPU server with the policy checkpoint. Cosmos3 action generation does not currently support CFG or sequence parallelism.

```bash Command theme={null}
sglang serve \
  --model-path nvidia/Cosmos3-Nano-Policy-DROID \
  --num-gpus 1
```

The following request predicts a 16-step action chunk from one observation. The chunk length is `num_frames - 1`, and the completed job's `action` field contains the tensor data, shape, mode, and active action dimension.

```bash Command theme={null}
job_id=$(curl -sS -X POST http://127.0.0.1:30010/v1/videos \
  --form-string "prompt=Put the pot to the left of the purple item." \
  --form "input_reference=@observation.png;type=image/png" \
  --form-string "size=832x480" \
  --form-string "num_frames=17" \
  --form-string "fps=5" \
  --form-string "num_inference_steps=30" \
  --form-string "guidance_scale=1.0" \
  --form-string "action_mode=policy" \
  --form-string "domain_name=droid_lerobot" \
  | python -c 'import json, sys; print(json.load(sys.stdin)["id"])')

# After the job reaches "completed":
curl -sS "http://127.0.0.1:30010/v1/videos/${job_id}" \
  | python -c 'import json, sys; print(json.dumps(json.load(sys.stdin)["action"], indent=2))'
```

The other action modes are `forward_dynamics` (condition on an observation and an `action` JSON array to generate video) and `inverse_dynamics` (condition on a full video to predict action). Select the embodiment head with `domain_name` or `domain_id`; set `raw_action_dim` explicitly when it cannot be inferred from the domain name.

## 5. Cosmos3 Parameters

Cosmos3 supports the standard SGLang video and image fields such as `size`, `num_frames`, `fps`, `num_inference_steps`, `guidance_scale`, `negative_prompt`, and `seed`.

Top-level Cosmos3 request fields:

* `max_sequence_length`: maximum text token length used by the Cosmos3 tokenizer.
* `flow_shift`: per-request scheduler shift. If omitted, SGLang uses `--flow-shift`, then the mode default (`3.0` for T2I and `10.0` for video/action).

Cosmos3 omnimodal fields are accepted as extra JSON fields or multipart form fields:

* `generate_sound`: generate a sound track whose duration follows `num_frames / fps`.
* `sound_duration`: explicit sound duration in seconds; takes precedence over the derived duration.
* `condition_frame_indexes`: V2V latent-frame indexes to keep from the source video; defaults to `[0, 1]`.
* `condition_video_keep`: use the `first` or `last` source frames for V2V conditioning.
* `action_mode`: `policy`, `forward_dynamics`, or `inverse_dynamics`.
* `domain_name` / `domain_id`: select the action embodiment head.
* `raw_action_dim`: number of active action dimensions; inferred for known domain names.
* `action`: action array with shape `[T, D]`, required by `forward_dynamics`.
* `action_fps`: action-token frame rate for temporal mRoPE; defaults to the video FPS.
* `action_view_point`: viewpoint used in the structured action caption.
* `action_normalization`: dataset normalization mode, such as `quantile`, `meanstd`, or `minmax`.

Put model-specific compatibility knobs in `extra_params` for video requests, or `extra_args` for image requests:

* `use_duration_template`: whether to append SGLang's generated duration suffix to video prompts.
* `use_resolution_template`: accepted for vLLM-Omni request compatibility.
* `use_system_prompt`: whether to add the Cosmos3 system prompt to the chat template.
* `guardrails` or `use_guardrails`: per-request guardrail toggle when the server started with guardrails enabled.