Faster Video Diffusion with Trainable Sparse Attention
Authors: name: Peiyuan Zhang, name: Haofeng Huang, name: Yongqi Chen, name: Will Lin, name: Zhengzhong Liu, name: Ion Stoica, name: Eric P. Xing, name: Hao Zhang

Scaling video diffusion transformers (DiTs) is limited by their quadratic 3D
attention, even though most of the attention mass concentrates on a small
subset of positions. We turn this observation into VSA, a trainable,
hardware-efficient sparse attention that replaces full attention at both
training and inference. In VSA, a lightweight coarse stage pools tokens into
tiles and identifies high-weight critical tokens; a fine stage computes
token-level attention only inside those tiles subjecting to block computing
layout to ensure hard efficiency. This leads to a single differentiable kernel
that trains end-to-end, requires no post-hoc profiling, and sustains 85\% of
FlashAttention3 MFU. We perform a large sweep of ablation studies and
scaling-law experiments by pretraining DiTs from 60M to 1.4B parameters. VSA
reaches a Pareto point that cuts training FLOPS by 2.53times with no drop in
diffusion loss. Retrofitting the open-source Wan-2.1 model speeds up attention
time by 6times and lowers end-to-end generation time from 31s to 18s with
comparable quality. These results establish trainable sparse attention as a
practical alternative to full attention and a key enabler for further scaling
of video diffusion models.