LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba
<|reference_start|>LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba: Recent Transformer-based diffusion models have shown remarkable performance, largely attributed to the ability of the self-attention mechanism to accurately capture both global and local contexts by computing all-pair interactions among input tokens. However, their quadratic complexity poses significant computational challenges for long-sequence inputs. Conversely, a recent state space model called Mamba offers linear complexity by compressing a filtered global context into a hidden state. Despite its efficiency, compression inevitably leads to information loss of fine-grained local dependencies among tokens, which are crucial for effective visual generative modeling. Motivated by these observations, we introduce Local Attentional Mamba (LaMamba) blocks that combine the strengths of self-attention and Mamba, capturing both global contexts and local details with linear complexity. Leveraging the efficient U-Net architecture, our model exhibits exceptional scalability and surpasses the performance of DiT across various model scales on ImageNet at 256x256 resolution, all while utilizing substantially fewer GFLOPs and a comparable number of parameters. Compared to state-of-the-art diffusion models on ImageNet 256x256 and 512x512, our largest model presents notable advantages, such as a reduction of up to 62% GFLOPs compared to DiT-XL/2, while achieving superior performance with comparable or fewer parameters. Our code is available at https://github.com/yunxiangfu2001/LaMamba-Diff.<|reference_end|>
@article{fu2024lamamba-diff:,
    title={LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local
  Attention and Mamba},
    author={Yunxiang Fu, Chaoqi Chen, Yizhou Yu},
    journal={arXiv preprint arXiv:2408.02615},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02615},
    primaryClass={cs.CV}
}