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

Text-To-Video

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

The Wan-AI/Wan2.2-T2V-A14B-Diffusers pipeline generates short videos from text prompts.

Local CLI Usage

python text_to_video.py \
  --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
  --negative_prompt "<optional quality filter>" \
  --height 480 \
  --width 640 \
  --num_frames 32 \
  --guidance_scale 4.0 \
  --guidance_scale_high 3.0 \
  --num_inference_steps 40 \
  --fps 16 \
  --output t2v_out.mp4

Key arguments:

• --prompt: text description (string).
• --height/--width: output resolution (defaults 720x1280). Dimensions should align with Wan VAE downsampling (multiples of 8).
• --num_frames: Number of frames (Wan default is 81).
• --guidance_scale and --guidance_scale_high: CFG scale (applied to low/high)..
• --negative_prompt: optional list of artifacts to suppress (the PR demo used a long Chinese string).
• --cfg_parallel_size: the number of devices to run CFG Parallel. CFG Parallel is valid only if classifier-free guidance is enabled and cfg_parallel_size is set to 2.
• --boundary_ratio: Boundary split ratio for low/high DiT.
• --fps: frames per second for the saved MP4 (requires diffusers export_to_video).
• --output: path to save the generated video.

Example materials

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