ForcingBooster：用于快速自回归视频扩散的自适应分块去噪
ForcingBooster: Adaptive Patch-wise Denoising for Fast Autoregressive Video Diffusion
AI总结：
针对自回归视频扩散模型均匀分配计算导致冗余或细化不足的问题，提出ForcingBooster框架动态分配去噪步骤，减少约50%去噪步骤且保持视觉质量，兼容现有少步模型。
摘要：
自回归视频扩散模型已成为实时交互式视频生成和视频世界建模的主流方法。然而，现有方法在生成下一个视频块时通常会在空间上均匀分配计算资源，采用相同的去噪调度和函数评估次数更新所有分块。这忽略了视频内容的空间异质性：静态、简单或重复的区域通常更容易去噪，而其他区域则需要进一步细化或额外上下文。因此，固定预算会导致对简单分块的冗余计算，而减少固定步数则会使困难分块的细化不足。我们提出ForcingBooster，这是一个将去噪步骤动态分配给各个分块的通用框架：简单分块提前停止以提高效率，困难分块则获得更多细化步骤。该设计在几乎不降低质量的情况下加速了生成过程。我们的方法与大多数现有的少步自回归扩散模型兼容，仅需一个轻量级路由模块，训练量适中。实验表明，ForcingBooster将应用于视频分块的总去噪步骤减少了约50%，同时保持了与四步基线相当的视觉质量。
英文摘要：
Autoregressive video diffusion models have become a leading approach to real-time interactive video generation and video world modeling. However, existing methods typically allocate computation uniformly across space when generating the next video chunk, updating all patches with the same denoising schedule and number of function evaluations. This ignores the spatial heterogeneity of video content: static, simple, or recurring regions are often easier to denoise, while other regions require further refinement or additional context. A uniform budget thus leads to redundant computation on easy patches, whereas reducing the fixed step count can leave difficult patches insufficiently refined. We introduce \methodname{}, a general framework that dynamically allocates denoising steps to individual patches: easy patches stop early to improve efficiency, while difficult patches receive more refinement steps. This design accelerates generation with negligible quality degradation. Our method is compatible with most existing few-step autoregressive diffusion models, requiring only a lightweight routing module with modest training. Experiments demonstrate that \methodname{} reduces the total number of denoising steps applied across video patches by about 50\% while maintaining visual quality comparable to the four-step baseline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
