TeaCache proposes a method to speed up video diffusion model inference by caching and reusing timestep-dependent features. It targets multiple video generation frameworks including CogVideoX, Open-Sora, and Latte. The approach analyzes timestep embeddings to determine when to reuse cached computations without sacrificing output quality, achieving significant speedups in text-to-video generation.