LightCache: Memory-Efficient, Training-Free Acceleration for Video
  Generation
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 .
68e5caa3975ac4c405ef20f3
Yang Xiao
claimed_verified
2025-10-09T06:51:46.948Z
67c79e81245d968dfbb08a70
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/8BNwpnJBUV9wD7opv9Xny.png
Yang Xiao
user
AIYangXiao
68e5caa3975ac4c405ef20f4
Gen Li
68e5caa3975ac4c405ef20f5
Kaiyuan Deng
68e5caa3975ac4c405ef20f6
Yushu Wu
68e5caa3975ac4c405ef20f7
Zheng Zhan
68e5caa3975ac4c405ef20f8
Yanzhi Wang
68e5caa3975ac4c405ef20f9
Xiaolong Ma
68e5caa3975ac4c405ef20fa
Bo Hui
LightCache: Memory-Efficient, Training-Free Acceleration for Video
  Generation
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 .
The paper proposes stage-specific strategies to accelerate diffusion model inference in video generation, reducing memory usage and maintaining quality.
diffusion models inference process encoding denoising decoding cache-based acceleration memory surges asynchronous cache swapping feature chunk slicing latents