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| license: apache-2.0 |
| pipeline_tag: text-to-video |
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| # ForcingBooster |
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| ForcingBooster is a general framework that dynamically allocates denoising steps to individual patches in autoregressive video diffusion models, reducing total computation by about 50% while maintaining visual quality comparable to the four-step baseline. |
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| - **Paper:** [ForcingBooster: Adaptive Patch-wise Denoising for Fast Autoregressive Video Diffusion](https://huggingface.co/papers/2610.12625) |
| - **Project page:** [https://igl-hkust.github.io/ForcingBooster](https://igl-hkust.github.io/ForcingBooster) |
| - **Code:** [https://github.com/IGL-HKUST/ForcingBooster](https://github.com/IGL-HKUST/ForcingBooster) |
| - **Checkpoints:** [https://huggingface.co/EXCAI/ForcingBooster](https://huggingface.co/EXCAI/ForcingBooster) |
