Title: LightCache: Memory-Efficient, Training-Free Acceleration for Video Generation
Primary area: optimization
Keywords: Cache, Training-free

Abstract: Training-free acceleration has emerged as an advanced research area in video generation. The redundancy of latents in diffusion model inference provides a natural entry point for acceleration. 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 tune the hyper-parameters to 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://anonymous.4open.science/r/LightCache-CD86.