LightCache: Memory-Efficient, Training-Free Acceleration for Video
  Generation
Authors: name: Yang Xiao, name: Gen Li, name: Kaiyuan Deng, name: Yushu Wu, name: Zheng Zhan, name: Yanzhi Wang, name: Xiaolong Ma, name: Bo Hui

Training-free acceleration has emerged as an advanced research area in video
generation based on diffusion models. The redundancy of latents in diffusion
model inference provides a natural entry point for acceleration. In this paper,
we decompose the inference process into the encoding, denoising, and decoding
stages, and observe that cache-based acceleration methods often lead to
substantial memory surges in the latter two stages. To address this problem, we
analyze the characteristics of inference across different stages and propose
stage-specific strategies for reducing memory consumption: 1) Asynchronous
Cache Swapping. 2) Feature chunk. 3) Slicing latents to decode. At the same
time, we ensure that the time overhead introduced by these three strategies
remains lower than the acceleration gains themselves. Compared with the
baseline, our approach achieves faster inference speed and lower memory usage,
while maintaining quality degradation within an acceptable range. The Code is
available at https://github.com/NKUShaw/LightCache .