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 832 \
  --num-frames 33 \
  --guidance-scale 4.0 \
  --guidance-scale-high 3.0 \
  --flow-shift 12.0 \
  --num-inference-steps 40 \
  --fps 16 \
  --output t2v_out.mp4

Key arguments:

• --prompt: text description (string).
• --height/--width: output resolution (defaults 480x832, i.e. 480P). 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).
• --boundary-ratio: Boundary split ratio for low/high DiT. Default 0.875 uses both transformers for best quality. Set to 1.0 to load only the low-noise transformer (saves noticeable memory with good quality, recommended if memory is limited). Set to 0.0 loads only the high-noise transformer (not recommended, lower quality).
• --fps: frames per second for the saved MP4 (requires diffusers export_to_video).
• --output: path to save the generated video.
• --vae-use-slicing: enable VAE slicing for memory optimization.
• --vae-use-tiling: enable VAE tiling for memory optimization.
• --cfg-parallel-size: set it to 2 to enable CFG Parallel. See more examples in user_guide.
• --enable-cpu-offload: enable CPU offloading for diffusion models.

ℹ️ If you encounter OOM errors, try using --vae-use-slicing and --vae-use-tiling to reduce memory usage.

Example materials

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