LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba
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| Format: | Preprint |
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2024
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| _version_ | 1866929505696743424 |
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| author | Fu, Yunxiang Chen, Chaoqi Yu, Yizhou |
| author_facet | Fu, Yunxiang Chen, Chaoqi Yu, Yizhou |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_02615 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba Fu, Yunxiang Chen, Chaoqi Yu, Yizhou Computer Vision and Pattern Recognition 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. |
| title | LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.02615 |