Where Compute Matters: Heterogeneous Attention for Efficient Video Diffusion
Efficient video generation requires reducing the quadratic cost of self-attention over long spatio-temporal token sequences. Existing efficient-attention methods typically apply the same computation pattern to every token, even though denoising difficulty varies substantially across video regions and evolves throughout the generation process. We introduce HetA-DiT, a heterogeneous attention mechanism that adaptively allocates computation according to token difficulty. A lightweight uncertainty branch predicts a token-wise estimate of denoising difficulty, which is used to route uncertain tokens through dense global attention while processing more reliable tokens with efficient local atten...
| Authors | Olga Zatsarynna, Denis Korzhenkov, Juergen Gall et al. |
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| Primary category | cs.CV |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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