This repository provides the implementation of SLA (Sparse–Linear Attention), a trainable attention method that fuses sparse and linear attention to accelerate diffusion models.

SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse–Linear Attention
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
Paper: https://www.arxiv.org/pdf/2509.24006

git clone https://github.com/thu-ml/SLA.git
cd SLA
pip install -e .

import torch
from sparse_linear_attention import SparseLinearAttention

attn = SparseLinearAttention(
    head_dim=128,
    topk=0.2,                 # = 1 - sparsity
    feature_map="softmax",    # options: elu, relu, softmax
    BLKQ=64,
    BLKK=64,
).cuda()

B, H, L, D = 2, 4, 4096, 128
q = torch.randn((B, H, L, D), dtype=torch.bfloat16, device='cuda')
k = torch.randn((B, H, L, D), dtype=torch.bfloat16, device='cuda')
v = torch.randn((B, H, L, D), dtype=torch.bfloat16, device='cuda')

o = attn(q, k, v)

We plan to release SageSLA, a high-performance implementation of SLA that integrates SageAttention, after our paper is accepted.

If you find this work useful, please cite:

@article{zhang2025sla,
  title={SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention},
  author={Zhang, Jintao and Wang, Haoxu and Jiang, Kai and Yang, Shuo and Zheng, Kaiwen and Xi, Haocheng and Wang, Ziteng and Zhu, Hongzhou and Zhao, Min and Stoica, Ion and Gonzalez, Joseph E. and Zhu, Jun and Chen, Jianfei},
  journal={arXiv preprint arXiv:2509.24006},
  year={2025}
}