vllm-omni/docs/user_guide/examples/offline_inference/image_to_video.md

Image-To-Video

Source https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_video.

This example demonstrates how to generate videos from images using Wan2.2 Image-to-Video models with vLLM-Omni's offline inference API.

Local CLI Usage

Wan2.2-I2V-A14B-Diffusers (MoE)

python image_to_video.py \
  --model Wan-AI/Wan2.2-I2V-A14B-Diffusers \
  --image input.png \
  --prompt "A cat playing with yarn, smooth motion" \
  --negative_prompt "<optional quality filter>" \
  --height 480 \
  --width 832 \
  --num_frames 48 \
  --guidance_scale 5.0 \
  --guidance_scale_high 6.0 \
  --num_inference_steps 40 \
  --boundary_ratio 0.875 \
  --flow_shift 12.0 \
  --fps 16 \
  --output i2v_output.mp4

Wan2.2-TI2V-5B-Diffusers (Unified)

python image_to_video.py \
  --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
  --image input.png \
  --prompt "A cat playing with yarn, smooth motion" \
  --negative_prompt "<optional quality filter>" \
  --height 480 \
  --width 832 \
  --num_frames 48 \
  --guidance_scale 4.0 \
  --num_inference_steps 40 \
  --flow_shift 12.0 \
  --fps 16 \
  --output i2v_output.mp4

Key arguments:

• --model: Model ID (I2V-A14B for MoE, TI2V-5B for unified T2V+I2V).
• --image: Path to input image (required).
• --prompt: Text description of desired motion/animation.
• --height/--width: Output resolution (auto-calculated from image if not set). Dimensions should be multiples of 16.
• --num_frames: Number of frames (default 81).
• --guidance_scale and --guidance_scale_high: CFG scale (applied to low/high-noise stages for MoE).
• --negative_prompt: Optional list of artifacts to suppress.
• --boundary_ratio: Boundary split ratio for two-stage MoE models.
• --flow_shift: Scheduler flow shift (5.0 for 720p, 12.0 for 480p).
• --num_inference_steps: Number of denoising steps (default 50).
• --fps: Frames per second for the saved MP4 (requires diffusers export_to_video).
• --output: Path to save the generated video.

Example materials

??? abstract "image_to_video.py" py --8<-- "examples/offline_inference/image_to_video/image_to_video.py"