HunyuanVideo

Headlines · 2

1. 3d
   AB-Attention accelerates video diffusion transformers

   Bin Xu and eight other authors submitted “AB-Attention: Accurate Block Attention for Accelerating Video Diffusion Transformers” to arXiv on October 29, 2026. The training-free method combines block selection, inclusion-probability weighting and finer key/value gathering to approximate dense attention at high sparsity. On Wan2.1 and HunyuanVideo, the authors report skipping 89–92% of attention computation and achieving up to 2.23× end-to-end speedup.

   Hugging Face · Papers · 🔥 0
2. Oct 20
   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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