vllm-omni/docs/user_guide/examples/online_serving/text_to_video.md

Text-To-Video

Source https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/text_to_video.

This example demonstrates how to deploy the Wan2.2 text-to-video model for online video generation using vLLM-Omni.

Start Server

Basic Start

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

Start with Parameters

Or use the startup script:

bash run_server.sh

The script allows overriding:

• MODEL (default: Wan-AI/Wan2.2-T2V-A14B-Diffusers)
• PORT (default: 8091)
• BOUNDARY_RATIO (default: 0.875)
• FLOW_SHIFT (default: 5.0)
• CACHE_BACKEND (default: none)
• ENABLE_CACHE_DIT_SUMMARY (default: 0)

API Calls

Method 1: Using curl

# Basic text-to-video generation
bash run_curl_text_to_video.sh

# Or execute directly (OpenAI-style multipart)
curl -s http://localhost:8091/v1/videos \
  -H "Accept: application/json" \
  -F "prompt=Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
  -F "width=832" \
  -F "height=480" \
  -F "num_frames=33" \
  -F "negative_prompt=色调艳丽 ，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" \
  -F "fps=16" \
  -F "num_inference_steps=40" \
  -F "guidance_scale=4.0" \
  -F "guidance_scale_2=4.0" \
  -F "boundary_ratio=0.875" \
  -F "seed=42" | jq -r '.data[0].b64_json' | base64 -d > wan22_output.mp4

Request Format

Simple Text-to-Video Generation

curl -X POST http://localhost:8091/v1/videos \
  -F "prompt=A cinematic view of a futuristic city at sunset"

Generation with Parameters

curl -X POST http://localhost:8091/v1/videos \
  -F "prompt=A cinematic view of a futuristic city at sunset" \
  -F "width=832" \
  -F "height=480" \
  -F "num_frames=33" \
  -F "negative_prompt=low quality, blurry, static" \
  -F "fps=16" \
  -F "num_inference_steps=40" \
  -F "guidance_scale=4.0" \
  -F "guidance_scale_2=4.0" \
  -F "boundary_ratio=0.875" \
  -F "flow_shift=5.0" \
  -F "seed=42"

Generation Parameters

Parameter	Type	Default	Description
prompt	str	-	Text description of the desired video
negative_prompt	str	None	Negative prompt
n	int	1	Number of videos to generate
width	int	None	Video width in pixels
height	int	None	Video height in pixels
num_frames	int	None	Number of frames to generate
fps	int	None	Frames per second for output video
num_inference_steps	int	None	Number of denoising steps
guidance_scale	float	None	CFG guidance scale (low-noise stage)
guidance_scale_2	float	None	CFG guidance scale (high-noise stage, Wan2.2)
boundary_ratio	float	None	Boundary split ratio for low/high DiT (Wan2.2)
flow_shift	float	None	Scheduler flow shift (Wan2.2)
seed	int	None	Random seed (reproducible)
lora	object	None	LoRA configuration
extra_body	object	None	Model-specific extra parameters

Response Format

{
  "created": 1234567890,
  "data": [
    { "b64_json": "<base64-mp4>" }
  ]
}

Extract Video

# Extract base64 from response and decode to video
cat response.json | jq -r '.data[0].b64_json' | base64 -d > wan22_output.mp4

Example materials

??? abstract "run_curl_text_to_video.sh" sh --8<-- "examples/online_serving/text_to_video/run_curl_text_to_video.sh" ??? abstract "run_server.sh" sh --8<-- "examples/online_serving/text_to_video/run_server.sh"