3 papers
cs.CV2025
Accelerating Controllable Generation via Hybrid-grained Cache
Lin Liu, Huixia Ben, Shuo Wang +4
Controllable generative models have been widely used to improve the realism of synthetic visual content. However, such models must handle control conditions and content generation…
cs.CV2025
Accelerating Diffusion Transformer via Gradient-Optimized Cache
Junxiang Qiu, Lin Liu, Shuo Wang +3
Feature caching has emerged as an effective strategy to accelerate diffusion transformer (DiT) sampling through temporal feature reuse. It is a challenging problem since (1) Progre…
cs.CV2025
Accelerating Diffusion Transformer via Error-Optimized Cache
Junxiang Qiu, Shuo Wang, Jinda Lu +4
Diffusion Transformer (DiT) is a crucial method for content generation. However, it needs a lot of time to sample. Many studies have attempted to use caching to reduce the time con…