arXiv: cs.AI preprintsInternational6 November 2026
HyperVAttention: Efficient Sparse Attention with Spatio-Temporal Clustering for Video Diffusion
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arXiv:2607.03012v2 Announce Type: replace-cross Abstract: Video Diffusion Transformers (VDiTs) have demonstrated significant capabilities in high-fidelity video generation. However, their ability to produce long-duration videos is fundamentally constrained by the quadratic complexity of the self-attention mechanism. Recent clustering-based sparse attention methods improve the quality-speed trade-off by grouping semantically similar tokens, but their practical efficiency remains limited by two bottlenecks: substantial clustering overhead and low CTA utilization caused by irregular cluster-induc
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