README.md

metadata

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, code).

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 (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 repository (Apache-2.0), trimmed to the single-GPU inference path.