AB-Attention: Accurate Block Attention for Accelerating Video Diffusion Transformers
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabAB-Attention is a training-free method for approximating dense attention in video diffusion transformers at high sparsity. It uses an Intra-Block Clustering Selector to estimate block importance by clustering queries within each query block, Sampling with Inclusion-Probability Weighting to sample and reweight blocks, and a kernel that gathers small key/value blocks into full tensor-core tiles. On Wan2.1 and HunyuanVideo, the paper reports skipping 89-92% of attention, improving PSNR by up to 4.4 dB over the strongest baseline, and achieving up to 2.23x end-to-end speedup.
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- Primary sourceHugging Face Daily Papers
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AB-Attention is a training-free method for approximating dense attention in video diffusion transformers at high sparsity. It uses an Intra-Block Clustering Selector to estimate block importance by clustering queries within each query block, Sampling with Inclusion-Probability Weighting to sample and reweight blocks, and a kernel that gathers small key/value blocks into full tensor-core tiles. On Wan2.1 and HunyuanVideo, the paper reports skipping 89-92% of attention, improving PSNR by up to 4.4 dB over the strongest baseline, and achieving up to 2.23x end-to-end speedup.
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Source claims not independently checked; results are reported on two video models and the paper's own benchmarks.
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Key findings from the stored brief
- AB-Attention is training-free and skips 89-92% of attention on Wan2.1 and HunyuanVideo.
- Baselines compute 1.8-3.6 times as much attention as AB-Attention, per the paper.
- It improves PSNR by up to 4.4 dB over the strongest baseline.
- It reports up to 2.23x end-to-end speedup.
Source claims not independently checked; results are reported on two video models and the paper's own benchmarks.
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Why this may matter
Attention cost dominates video diffusion transformer inference. A training-free method that keeps fidelity under high sparsity could make video generation cheaper to run without retraining models.
Limits and open questions
Source claims not independently checked; results are reported on two video models and the paper's own benchmarks.
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