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title: Wave Forcing
emoji: 🌊
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 6.31.0
app_file: app.py
pinned: false
python_version: "3.12"
startup_duration_timeout: 1h
short_description: Streaming text-to-video with Wave Forcing on Wan2.1-T2V-1.3B
license: apache-2.0
---
# Wave Forcing — Streaming Text-to-Video
Demo of **Wave Forcing: Towards Speed-of-Light Streaming Video Generation**
([paper](https://huggingface.co/papers/2611.00859), [code](https://github.com/SJTU-DENG-Lab/WaveForcing)).
Wave Forcing is a rolling-window distillation scheme that turns a block-causal video
diffusion model into a streaming generator: each 3-frame block is denoised in only 4 or 5
steps while overlapping windows keep future blocks in-flight, so the model emits
continuous video at high frame rates. This Space runs the authors' released
[WaveForcing-1.3B checkpoints](https://huggingface.co/SJTU-DENG-Lab/WaveForcing-1.3B)
(on the Wan2.1-T2V-1.3B backbone) through their vendored single-GPU reference
pipeline (`naive_rt`), with both the 4-step and 5-step distilled variants.
Enter a prompt, pick a distilled checkpoint and a length in latent frames
(each latent frame is 4 pixel frames at 832×480, 16 fps), and generate.
## Notes
- The paper's speed-of-light throughput numbers come from the authors' multi-GPU
WaveRT runtime; this Space is the faithful single-GPU reference for the same
weights, so latencies here are higher than the paper's benchmark numbers.
- Example prompts are taken from the paper repo (Apache-2.0 license).
- Code in `naive_rt/` is vendored from the
[SJTU-DENG-Lab/WaveForcing](https://github.com/SJTU-DENG-Lab/WaveForcing) repository
(Apache-2.0), trimmed to the single-GPU inference path.
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