Training-free sparse attention based on cumulative energy filtering

• URL: http://arxiv.org/abs/2606.16317v1
• Date: Mon, 15 Jun 2026 07:26:57 GMT
• Title: Training-free sparse attention based on cumulative energy filtering
• Abstract summary: Sparse attention accelerates Diffusion Transformers (DiTs) for video generation by computing only the important tokens while skipping the rest.<n>We formulate the token filtering process as a dual-goal optimization problem: maximizing sparsity and minimizing accuracy degradation.<n>Our algorithm is deeply integrated with Flash Attention (FA), eliminating the need for any additional masking overhead.
• Score: 5.794925677954963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse attention accelerates Diffusion Transformers (DiTs) for video generation by computing only the important tokens while skipping the rest. The token selection strategy is key to balancing sparsity and accuracy. We formulate the token filtering process as a dual-goal optimization problem: maximizing sparsity and minimizing accuracy degradation. Existing algorithms cannot fulfill both objectives simultaneously. For example, Top-p only considers the accuracy constraint, while Top-k maintains a fixed computational budget but loosens the accuracy constraint. This paper demonstrates that maintaining a fixed recall rate is sufficient for ensuring accuracy, whereas a fixed threshold is suboptimal for reducing computational cost. Therefore, we propose a dynamic thresholding scheme to improve sparsity while maintaining the same level of accuracy. Furthermore, our algorithm is deeply integrated with Flash Attention (FA), eliminating the need for any additional masking computation overhead. Experimental results on Wan 2.2 validate that, compared to the BLASST algorithm which is also integrated with FA, our dynamic thresholding strategy enhances sparsity from 61.42\% to 82\% with a VBench metric drop of less than 5\%. This results in an approximate 15\% in attention computation and a $1.61\times$ increase in computational efficiency, which is 1.18x higher than that of BLASST.

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