SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention

• URL: http://arxiv.org/abs/2509.24006v1
• Date: Sun, 28 Sep 2025 17:58:59 GMT
• Title: SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention
• Authors: Jintao Zhang, Haoxu Wang, Kai Jiang, Shuo Yang, Kaiwen Zheng, Haocheng Xi, Ziteng Wang, Hongzhou Zhu, Min Zhao, Ion Stoica, Joseph E. Gonzalez, Jun Zhu, Jianfei Chen,
• Abstract summary: In Diffusion Transformer (DiT) models, particularly for video generation, attention latency is a major bottleneck.<n>We find that attention weights can be separated into two parts: a small fraction of large weights with high rank and the remaining weights with very low rank.<n>We propose SLA, a trainable attention method that fuses sparse and linear attention to accelerate diffusion models.
• Score: 88.47701139980636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Diffusion Transformer (DiT) models, particularly for video generation, attention latency is a major bottleneck due to the long sequence length and the quadratic complexity. We find that attention weights can be separated into two parts: a small fraction of large weights with high rank and the remaining weights with very low rank. This naturally suggests applying sparse acceleration to the first part and low-rank acceleration to the second. Based on this finding, we propose SLA (Sparse-Linear Attention), a trainable attention method that fuses sparse and linear attention to accelerate diffusion models. SLA classifies attention weights into critical, marginal, and negligible categories, applying O(N^2) attention to critical weights, O(N) attention to marginal weights, and skipping negligible ones. SLA combines these computations into a single GPU kernel and supports both forward and backward passes. With only a few fine-tuning steps using SLA, DiT models achieve a 20x reduction in attention computation, resulting in significant acceleration without loss of generation quality. Experiments show that SLA reduces attention computation by 95% without degrading end-to-end generation quality, outperforming baseline methods. In addition, we implement an efficient GPU kernel for SLA, which yields a 13.7x speedup in attention computation and a 2.2x end-to-end speedup in video generation on Wan2.1-1.3B.

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