UniCP：一种用于高效视频生成的统一缓存与剪枝框架
UniCP: A Unified Caching and Pruning Framework for Efficient Video Generation
AI总结：
UniCP提出统一缓存与剪枝框架，通过误差感知动态缓存窗口、PCA切片和动态权重转移优化视频生成中的注意力计算，在提升效率的同时保持细节保真度。
摘要：
扩散Transformer（DiT）在视频生成中表现出色，但由于注意力的二次复杂度而面临显著的计算挑战。值得注意的是，相邻扩散步骤之间的注意力差异遵循U形模式。当前方法通过缓存注意力块来利用这一特性，然而它们仍然难以应对突发的误差尖峰和较大的差异。为了解决这些问题，我们提出了UniCP，一个用于高效视频生成的统一缓存与剪枝框架。UniCP通过以下方式同时优化时间和空间维度：误差感知动态缓存窗口（EDCW）：在不同时间步为不同块动态调整缓存窗口大小，以适应突发的误差变化。基于PCA的切片（PCAS）和动态权重转移（DWS）：PCAS剪枝冗余的注意力分量，DWS通过在剪枝输出和缓存输出之间实现动态切换来整合缓存与剪枝。通过调整缓存窗口和剪枝冗余分量，UniCP提高了计算效率并保持了视频细节保真度。实验结果表明，UniCP在性能和效率方面均优于现有方法。
英文摘要：
Diffusion Transformers (DiT) excel in video generation but encounter significant computational challenges due to the quadratic complexity of attention. Notably, attention differences between adjacent diffusion steps follow a U-shaped pattern. Current methods leverage this property by caching attention blocks, however, they still struggle with sudden error spikes and large discrepancies. To address these issues, we propose UniCP a unified caching and pruning framework for efficient video generation. UniCP optimizes both temporal and spatial dimensions through. Error Aware Dynamic Cache Window (EDCW): Dynamically adjusts cache window sizes for different blocks at various timesteps, adapting to abrupt error changes. PCA based Slicing (PCAS) and Dynamic Weight Shift (DWS): PCAS prunes redundant attention components, and DWS integrates caching and pruning by enabling dynamic switching between pruned and cached outputs. By adjusting cache windows and pruning redundant components, UniCP enhances computational efficiency and maintains video detail fidelity. Experimental results show that UniCP outperforms existing methods in both performance and efficiency.
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。
