vllm-project--vllm-omni/docs/serving/videos_api.md

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
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	model default	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 "input_reference=@input.png" \
  -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 "input_reference=@input.mp4;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 plus transfer options such as control_path, control_guidance, control_guidance_interval, num_video_frames_per_chunk, num_conditional_frames, show_control_condition, and show_input; see the Cosmos3 recipe for complete examples.

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

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:

• 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