Diffusion Transformers (DiTs) incur quadratic self-attention cost over spatiotemporal tokens. Existing training-free sparse attention methods often construct sparse masks from block-level or cluster-level proxy scores, which can obscure fine-grained differences among keys and miss high contribution keys under aggressive sparsity.
arXiv:2609.38978v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have become a dominant architecture for video generation, but their efficiency is limited by the quadratic complexity of...
arXiv:2607. 03012v1 Announce Type: cross Abstract: Video Diffusion Transformers (VDiTs) have demonstrated significant capabilities in high-fidelity video generation.
arXiv:2607. 20214v1 Announce Type: cross Abstract: The quadratic $N\times N$ attention score matrix remains a central obstacle to extending Transformers to longer input lengths.
arXiv:2608. 12032v1 Announce Type: cross Abstract: Video diffusion transformers are costly to sample: every denoising step applies self-attention over a long 3D token sequence, a quadratic cost that dominates as resolution and duration grow.
arXiv:2511. 10696v3 Announce Type: replace-cross Abstract: Sparse attention is crucial in long-context Transformers, which restricts each token to a limited neighborhood and thereby reduces the quadratic cost of full self-attention.
arXiv:2609.06712v2 Announce Type: replace Abstract: Diffusion Transformers (DiTs) achieve strong video generation quality, but their dense spatiotemporal self-attention scales quadratically with sequ...
arXiv:2608.23921v1 Announce Type: new Abstract: Recent Vision-Language Models encode high-resolution images into long visual token sequences, incurring prohibitive prefill costs. To compress them, ex...
arXiv:2609.37001v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) enable high-quality video generation but suffer from substantial inference latency, primarily attributable to the compu...
The paper introduces SparsePR, a training‑free block‑sparse attention method for video transformers that partitions query‑key responses and reconstructs the residual via probe‑fitted affine corrections. By pairing sampled‑query key responses into K/V groups and using centroids to guide shared routing, SparsePR reduces attention‑reconstruction error across diverse video generation and world‑model tasks. Experiments show consistent error reductions, with probe fitting contributing most of the improvement, while maintaining generation quality at 22.0–26.0% executed‑pair density and delivering 1.48×–2.61× speedups.
ClusterAttention is a training‑free technique that speeds up bidirectional attention by recursively clustering keys and queries into fixed‑size, power‑of‑two blocks, enabling block‑sparse attention to match dense attention latency on GPUs. The method derives error bounds for sparse attention, showing tighter clusters can reduce error when compensated via centroids, and demonstrates significant speedups—up to six‑fold on large tabular data and 1.8× on video generation—while preserving over 99% of dense accuracy.
arXiv:2609.13141v1 Announce Type: new Abstract: Post-training attention sparsification reduces the quadratic cumulative attention cost of pretrained Transformers by selecting a small set of context u...
arXiv:2605.12491v2 Announce Type: replace Abstract: Vision Transformers (ViTs) learn rich visual-semantic representations through all-to-all self-attention among patch tokens. However, this design im...