vllm-omni/examples/online_serving/image_to_video

Image-To-Video

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

Start Server

Basic Start

vllm serve Wan-AI/Wan2.2-I2V-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-I2V-A14B-Diffusers)
• PORT (default: 8091)
• BOUNDARY_RATIO (default: 0.875)
• FLOW_SHIFT (default: 12.0)
• CACHE_BACKEND (default: none)
• ENABLE_CACHE_DIT_SUMMARY (default: 0)

API Calls

Method 1: Using curl

# Basic image-to-video generation
bash run_curl_image_to_video.sh

# Or execute directly (OpenAI-style multipart)
curl -X POST http://localhost:8091/v1/videos \
  -H "Accept: application/json" \
  -F "prompt=A bear playing with yarn, smooth motion" \
  -F "negative_prompt=low quality, blurry, static" \
  -F "input_reference=@/path/to/qwen-bear.png" \
  -F "width=832" \
  -F "height=480" \
  -F "num_frames=33" \
  -F "fps=16" \
  -F "num_inference_steps=40" \
  -F "guidance_scale=1.0" \
  -F "guidance_scale_2=1.0" \
  -F "boundary_ratio=0.875" \
  -F "flow_shift=12.0" \
  -F "seed=42" | jq -r '.data[0].b64_json' | base64 -d > wan22_i2v_output.mp4

Request Format

Required Fields

curl -X POST http://localhost:8091/v1/videos \
  -F "prompt=A bear playing with yarn, smooth motion" \
  -F "negative_prompt=low quality, blurry, static" \
  -F "input_reference=@/path/to/qwen-bear.png"

Generation with Parameters

curl -X POST http://localhost:8091/v1/videos \
  -F "prompt=A bear playing with yarn, smooth motion" \
  -F "negative_prompt=low quality, blurry, static" \
  -F "input_reference=@/path/to/qwen-bear.png" \
  -F "width=832" \
  -F "height=480" \
  -F "num_frames=33" \
  -F "fps=16" \
  -F "num_inference_steps=40" \
  -F "guidance_scale=1.0" \
  -F "guidance_scale_2=1.0" \
  -F "boundary_ratio=0.875" \
  -F "flow_shift=12.0" \
  -F "seed=42"