Videos API¶
vLLM-Omni provides an OpenAI-compatible video generation API for diffusion video models. The API supports asynchronous video jobs through /v1/videos and a synchronous benchmark-oriented endpoint through /v1/videos/sync.
Each server instance runs a single model specified at startup with vllm serve <model> --omni.
Quick Start¶
Start the Server¶
Create a Video Job¶
create_response=$(curl -s http://localhost:8091/v1/videos \
-F "prompt=A cinematic tracking shot of a mountain lake at sunrise" \
-F "width=1280" \
-F "height=720" \
-F "num_frames=80" \
-F "fps=16" \
-F "num_inference_steps=40")
video_id=$(echo "${create_response}" | jq -r '.id')
Poll and Download¶
curl -s "http://localhost:8091/v1/videos/${video_id}" | jq .
curl -L "http://localhost:8091/v1/videos/${video_id}/content" -o output.mp4
API Reference¶
Endpoints¶
| Endpoint | Method | Description |
|---|---|---|
/v1/videos | POST | Create an asynchronous video generation job |
/v1/videos/sync | POST | Generate a video synchronously and return raw video bytes |
/v1/videos/{video_id} | GET | Retrieve job status and metadata |
/v1/videos | GET | List stored video jobs |
/v1/videos/{video_id}/content | GET | Download generated video content |
/v1/videos/{video_id} | DELETE | Delete a video job and stored output |
Request Parameters¶
POST /v1/videos and POST /v1/videos/sync accept multipart/form-data.
OpenAI-style fields¶
| Parameter | Type | Default | Description |
|---|---|---|---|
prompt | string | required | Text prompt for video generation |
model | string | server's model | Optional model name |
seconds | string | null | Requested clip duration in seconds |
size | string | null | Requested output size in WIDTHxHEIGHT format |
user | string | null | Optional user identifier |
vLLM-Omni extension fields¶
| Parameter | Type | Default | Description |
|---|---|---|---|
input_reference | file | null | Uploaded reference image or video for image-to-video/video-to-video requests |
control_reference | file | null | Optional uploaded image/video control, up to 512 MiB, for models that declare control-upload support |
control_type | string | null | Model control name associated with control_reference; currently Cosmos3 supports edge, blur, depth, seg, and wsm |
image_reference | string | null | JSON-encoded reference image payload; do not combine with input_reference or video_reference |
video_reference | string | null | JSON-encoded reference video payload; do not combine with input_reference or image_reference |
audio_reference | string | null | JSON-encoded audio reference for speech-to-video: {"audio_url": "..."} — supports HTTP(s) URLs or base64 data URLs |
width | integer | model default | Output video width |
height | integer | model default | Output video height |
num_frames | integer | 1 | Number of generated frames |
fps | integer | model default | Output frames per second |
num_inference_steps | integer | model default | Number of diffusion steps |
guidance_scale | number | null | CFG guidance scale for the low-noise stage |
guidance_scale_2 | number | null | CFG guidance scale for the high-noise stage |
boundary_ratio | number | null | Boundary split ratio for multi-stage denoising |
flow_shift | number | null | Scheduler flow-shift value |
true_cfg_scale | number | null | True CFG scale when supported by the model |
seed | integer | null | Random seed for reproducibility |
generate_sound | boolean | false | Request model-generated audio for video models that support sound generation |
sound_duration | number | null | Duration in seconds for generated audio; defaults to generated video duration |
negative_prompt | string | null | Text describing what to avoid in the generated video |
enable_frame_interpolation | boolean | null | Enable post-generation frame interpolation |
frame_interpolation_exp | integer | null | Interpolation exponent; 1=2x, 2=4x, and so on |
frame_interpolation_scale | number | null | RIFE inference scale |
frame_interpolation_model_path | string | null | Local path or Hugging Face repo for the interpolation model |
lora | string | null | JSON-encoded LoRA configuration object |
extra_params | string | null | JSON-encoded object for additional model-specific parameters |
Create Response¶
POST /v1/videos returns a job record:
The final content is available from /v1/videos/{video_id}/content after the job status becomes completed.
Synchronous Response¶
POST /v1/videos/sync blocks until generation finishes and returns raw video bytes. It is useful for benchmarks and simple scripts that do not need job storage or polling.
Examples¶
Image-to-Video¶
curl -s http://localhost:8091/v1/videos \
-F "prompt=animate this image with subtle camera movement" \
-F "[email protected]" \
-F "width=1280" \
-F "height=720" \
-F "num_frames=80" \
-F "fps=16"
Video-to-Video¶
For models that support video conditioning, upload the reference video with input_reference:
curl -s http://localhost:8091/v1/videos \
-F "prompt=continue this motion with consistent subjects and lighting" \
-F "[email protected];type=video/mp4" \
-F "width=1280" \
-F "height=720" \
-F "num_frames=80" \
-F "fps=16"
You can also pass a JSON-safe video URL or data:video/...;base64,... payload through video_reference. Do not send video_reference together with input_reference or image_reference.
curl -s http://localhost:8091/v1/videos \
-F "prompt=continue this motion with consistent subjects and lighting" \
-F 'video_reference={"video_url":"https://example.com/input.mp4"}' \
-F "width=1280" \
-F "height=720" \
-F "num_frames=80" \
-F "fps=16"
JSON references currently support image_url/video_url; file_id references are not implemented yet. Models may expose additional V2V controls through extra_params. For example, Cosmos3 supports condition_frame_indexes_vision and condition_video_keep to select which decoded reference frames are used as clean conditioning. Cosmos3 transfer mode also accepts edge, blur, depth, seg, or wsm control hints. Each hint may specify its own control_path and control_weight. Request-level transfer options include control_guidance, control_guidance_interval, emphasize_control_in_prompt, num_video_frames_per_chunk, num_conditional_frames, show_control_condition, and show_input. Transfer uses its transfer-specific system prompt and, by default, appends a control-adherence directive to the positive prompt. It adds duration/FPS and resolution metadata to both CFG branches but does not add a negative prompt automatically. The Cosmos3 recipe includes an optional reference negative prompt and shows how to pass it. Set emphasize_control_in_prompt, use_duration_template, or use_resolution_template to false to disable the corresponding addition. negative_metadata_mode accepts same, inverse, or none and defaults to same for transfer. See the Cosmos3 recipe for complete examples.
A client that cannot place the control on the server filesystem can upload one control with control_reference and identify it with control_type. The API streams the upload to request-scoped storage, supplies its path to the model, and removes it after synchronous or asynchronous generation completes. Other options for the selected control can remain in extra_params; do not also set control or control_path there. Uploads larger than 512 MiB are rejected.
curl -s http://localhost:8091/v1/videos/sync \
-F "prompt=Preserve the scene while following the world-state control" \
-F "[email protected];type=video/mp4" \
-F "[email protected];type=video/mp4" \
-F "control_type=wsm" \
-F 'extra_params={"wsm":{"control_weight":1.0}}' \
-o output.mp4
HTTP redirects for image_reference.image_url follow vLLM's VLLM_MEDIA_URL_ALLOW_REDIRECTS setting. Before starting the server, set it to 1 (the default) to allow redirects or 0 to reject them. A redirect target can differ from the original host, and vLLM-Omni does not yet fully implement upstream vLLM's media URL allowlist protection. Deployments should therefore treat remote media URLs as untrusted and choose this setting as part of their URL access policy.
Speech-to-Video¶
For models that support audio-driven generation (e.g., Wan2.2-S2V), pass both an image reference and an audio reference. The audio_reference field accepts a JSON string with audio_url pointing to an HTTP(s) URL or base64 data URL.
curl -s http://localhost:8091/v1/videos \
-F "prompt=A person singing" \
-F 'image_reference={"image_url": "https://example.com/face.png"}' \
-F 'audio_reference={"audio_url": "https://example.com/speech.mp3"}' \
-F "width=832" \
-F "height=480" \
-F "num_inference_steps=40" \
-F "guidance_scale=4.5" \
-F "fps=16"
Synchronous Generation¶
curl -X POST http://localhost:8091/v1/videos/sync \
-F "prompt=A small robot walking through a neon city" \
-F "width=854" \
-F "height=480" \
-F "num_frames=80" \
-F "fps=16" \
-o output.mp4
Output Encoding¶
These extra_params control how the server turns decoded frames into MP4 bytes.
| Field | Type | Default | Description |
|---|---|---|---|
preencode_mp4 | boolean | false | Encode the MP4 on the worker while the VAE is still decoding, instead of after the full video is materialized |
video_codec_options | object | null | Encoder options passed through to the H.264 encoder, such as {"preset": "ultrafast", "threads": "0"} |
With preencode_mp4 enabled, each committed VAE chunk leaves the accelerator and is encoded while later chunks are still decoding, so host transfer and CPU encoding overlap the remaining decode instead of following it. The response is unchanged: the same complete MP4, byte-for-byte equivalent frames.
curl -X POST http://localhost:8091/v1/videos/sync \
-F "prompt=A small robot walking through a neon city" \
-F 'extra_params={"preencode_mp4": true, "video_codec_options": {"preset": "ultrafast"}}' \
-o output.mp4
preencode_mp4 applies to the complete-MP4 response paths only. The /v1/realtime/video WebSocket endpoint rejects it, because that path already overlaps encoding through its own incremental fragmented-MP4 encoder.
Support is per model: MiniMax-H3 implements it, and other models ignore the flag and take the full-decode path.
Storage¶
Set VLLM_OMNI_SERVER_STORAGE__PATH to control where asynchronous video outputs are stored:
VLLM_OMNI_STORAGE_PATHis deprecated and will be removed in a future release; useVLLM_OMNI_SERVER_STORAGE__PATHinstead.
Model-Specific Examples¶
For complete text-to-video, image-to-video, and model-specific video-to-video walkthroughs, see:
- Text-to-Video
- Image-to-Video
- Speech-to-Video for Wan2.2-S2V audio-driven lip-sync generation
- Cosmos3 recipes for model-specific video-to-video examples and conditioning controls