ALLOT
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- ALLOT: training-free block-sparse attention allocation for video DiTs
A new arXiv preprint by Tianze Xia and five co-authors proposes ALLOT (Allocating Layer-step Latency by Output Tests), a training-free method for allocating block-sparse attention in video Diffusion Transformers. The authors observe that sparsifying attention in different layers and denoising steps affects final-video fidelity to very different degrees, while the most sensitive layer-step pairs stay nearly the same across inputs. ALLOT probes each layer-step cell and calibrates along the denoising trajectory, then jointly optimizes cell densities within a measured latency budget; the paper reports an 11.8 dB PSNR gain over XAttention at equal or shorter wall-clock time on HunyuanVideo, and wins or ties the default in all nine settings across Wan2.1, Wan2.2 and HunyuanVideo.
Hugging Face · Papers · 🔥 0
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