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arXiv 2610.29441 · cs.CV · 2026-10-21

Sparsity Should Be ALLOTted by Output: Training-Free Allocation of Block-Sparse Attention for Video Diffusion Transformers

Tianze Xia, Xiaoling Zhou, Ruya Lan, Yiqiao Liao, Chengru Song, Shiming Xiang

KlingAI Research

In latest · #374 Added on 2026-10-25

One-line takeaway

KlingAI's training-free ALLOT allocates block-sparse attention in video DiTs by estimating each layer-step cell's contribution to final-video error, exploiting that the most sensitive layer-step pairs stay stable across inputs.

可灵团队的 ALLOT 无需训练,为视频扩散 Transformer 分配块稀疏注意力:发现不同层和去噪步对画质影响差异大且跨输入稳定,于是按对最终视频误差的贡献联合优化各层各步的稀疏度。

24HF upvotes
–GitHub stars
0Citations (Semantic Scholar)
34.3Buzz score

Read on arXiv PDF Hugging Face discussion Project page

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Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-26.

The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.

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