Paper recorded by Signals 4 on 2026-10-15 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16
Category: cs.CV · 计算机视觉 · first seen 2026-10-16
Diffusion and flow models generate a sample over iterative steps, each a full pass through a large network, and that pass dominates generation cost. Training-free caching accelerates them by reusing activations from recent evaluations instead of recomputing them. Dynamic caching methods decide when to refresh by accumulating a cheap probe of the network input into a staleness signal and spending a