Accelerating Diffusion Models via Equal-Risk Caching

Diffusion models achieve strong image and video generation quality but remain slow at inference due to multi-step denoising. Caching accelerates inference by reusing intermediate features across timesteps, yet its effectiveness depends critically on refresh scheduling (when to perform full recomputation). Existing schedules fall into three paradigms: fixed-interval, online heuristic, and global optimization. The first two may miss temporal sensitivity structure or accumulated segment-level degradation, while the last incurs substantial offline cost. We propose Equal-Risk Caching (ERC), which builds a one-dimensional temporal risk profile from a single fixed-length probing cache curve and uses cumulative risk mass as a global surrogate for segment sensitivity. ERC places refresh boundaries by equal-mass partitioning on the cumulative risk axis, automatically allocating denser recomputation to high-risk regions and longer reuse to low-risk regions. This reduces the offline profiling cost of schedule construction from O(T ·L) to O(T). Experiments on multiple diffusion models demonstrate an overall competitive quality– speed trade-off.