Shangran Lin

RAPID: Reusing Attention Sparsity with Inter-step Adaptation for Efficient Video Diffusion

The prohibitive cost of 3D attention hinders high-quality video generation with diffusion models. Existing sparse attention methods either lack content adaptivity (static) or incur excessive overhead from per-step recalculation (dynamic). Our work challenges the necessity of this trade-off, based on