AB-Attention: Accurate Block Attention for Accelerating Video Diffusion Transformers

Video diffusion transformers generate high-quality videos, but the quadratic cost of 3D full attention dominates their inference. Dynamic block-sparse attention reduces this cost by selecting key/value blocks from the current activations and computing exact attention only on the selected ones. However, its fidelity degrades under aggressive sparsity: blocks are scored from coarse summaries or formed by costly global clustering, pruned blocks vanish from the softmax, and whole tiles are kept or dropped even when only a few of their keys matter. We present Accurate Block Attention (AB-Attention), a training-free method that addresses each limitation. Its Intra-Block Clustering Selector (IBCS) estimates block importance faithfully yet cheaply by clustering queries only within each query block, without permuting tokens. Sampling with Inclusion-Probability Weighting (SIP) then samples blocks in proportion to these estimates and reweights them by inverse inclusion probability, so that one pass estimates dense attention rather than truncating it. Finally, its kernel gathers key/value blocks much smaller than a tile into full tensor-core tiles, refining selection at no extra tensor-core cost. On Wan2.1 and HunyuanVideo, AB-Attention skips 89–92% of attention, whereas the baselines compute 1.8–3.6$\times$ as much, yet it still improves PSNR by up to 4.4 dB over the strongest baseline and achieves up to 2.23$\times$ end-to-end speedup.