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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

vllm serve Wan-AI/Wan2.2-T2V-A14B-Diffusers --omni --port 8091

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:

{
  "id": "video-123",
  "status": "queued",
  "created_at": 1701234567
}

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:

export VLLM_OMNI_SERVER_STORAGE__PATH=/var/tmp/vllm-omni-videos

VLLM_OMNI_STORAGE_PATH is deprecated and will be removed in a future release; use VLLM_OMNI_SERVER_STORAGE__PATH instead.

Model-Specific Examples

For complete text-to-video, image-to-video, and model-specific video-to-video walkthroughs, see: