LoSA: Near-Lossless Sparse Attention for Training-Free Video Diffusion Acceleration
Video diffusion transformers are costly to sample: every denoising step applies self-attention over a long 3D token sequence, a quadratic cost that dominates as resolution and duration grow. Sparse attention reduces this cost without retraining, but existing methods pursue aggressive sparsity, where further speedup costs disproportionately more attention fidelity. We target the opposite end of this trade-off: fix near-lossless fidelity by construction, and remove as much computation as this constraint permits. Two observations make this regime practical: roughly 40% of block interactions can be removed while retaining 99% of the attention mass, and the high-mass support remains stable acr...
| Authors | Enhuai Liu, Yunke Wang, Yutong Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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