FeatFix：通过局部精确特征校正重用已验证特征以实现更快的缓存扩散模型推理
FeatFix: Reuse What You Verify through Local Exact-Feature Correction for Faster Cached Diffusion Inference
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中文总结 AI 辅助
本文针对扩散模型推理计算密集的问题，提出FeatFix方法，通过重用已验证的局部精确特征校正草稿，实现最高6.70倍的生成加速且保持输出质量。
中文摘要 AI 辅助
扩散模型被广泛用于生成高质量图像和视频，但其迭代去噪过程仍存在计算密集的问题。一类无需训练的加速器通过重用缓存的中间特征或预测未来特征来降低该成本，为控制草稿漂移，这些方法有时会计算精确块特征用于验证，但所得精确特征通常仅用于测量差异或指导后续决策，随后被丢弃。本研究发现，该先前计算的特征可被重用用于校正，在验证位置转发它可重置局部草稿残差并减少下游特征误差。基于此，本文提出FeatFix，一种用于缓存扩散推理的局部精确特征校正方法，FeatFix在固定的稀疏层-时间步位置运行，在每个选定位置，它用从相同输入状态计算出的精确输出替换完整的草稿块输出，避免了token或通道级的部分替换以及全时间步的重新计算。在四种图像和视频主干上的实验表明，FeatFix可持续加速生成，相比Vanilla方法实现了最高6.70倍的加速，同时保持了有竞争力的输出质量。
英文摘要
Diffusion models are widely used to generate high-quality images and videos, but their iterative denoising process remains computationally intensive. A growing class of training-free accelerators reduces this cost by reusing cached intermediate features or forecasting future ones. To control draft drift, these methods sometimes compute an exact block feature for verification. Yet the resulting exact feature is typically used only to measure discrepancy or guide a later decision and is then discarded. We find that this previously computed feature can instead be reused for correction. Forwarding it at the verification site resets the local draft residual and reduces downstream feature error. Based on this observation, we introduce FeatFix, a local exact-feature correction method for cached diffusion inference. FeatFix operates at a fixed sparse set of layer--timestep sites. At each selected site, it replaces the complete draft block output with the exact output computed from the same incoming state, avoiding token- or channel-level partial replacement and full-timestep recomputation. Experiments across four image and video backbones show that FeatFix consistently accelerates generation, achieving a speedup of up to $6.70\times$ over Vanilla while maintaining competitive output quality.
