Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913366424944640 |
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| author | Liu, Yuhao Ke, Zhanghan Liu, Fang Zhao, Nanxuan Lau, Rynson W. H. |
| author_facet | Liu, Yuhao Ke, Zhanghan Liu, Fang Zhao, Nanxuan Lau, Rynson W. H. |
| contents | Diffusion models trained on large-scale datasets have achieved remarkable progress in image synthesis. However, due to the randomness in the diffusion process, they often struggle with handling diverse low-level tasks that require details preservation. To overcome this limitation, we present a new Diff-Plugin framework to enable a single pre-trained diffusion model to generate high-fidelity results across a variety of low-level tasks. Specifically, we first propose a lightweight Task-Plugin module with a dual branch design to provide task-specific priors, guiding the diffusion process in preserving image content. We then propose a Plugin-Selector that can automatically select different Task-Plugins based on the text instruction, allowing users to edit images by indicating multiple low-level tasks with natural language. We conduct extensive experiments on 8 low-level vision tasks. The results demonstrate the superiority of Diff-Plugin over existing methods, particularly in real-world scenarios. Our ablations further validate that Diff-Plugin is stable, schedulable, and supports robust training across different dataset sizes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00644 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks Liu, Yuhao Ke, Zhanghan Liu, Fang Zhao, Nanxuan Lau, Rynson W. H. Computer Vision and Pattern Recognition Diffusion models trained on large-scale datasets have achieved remarkable progress in image synthesis. However, due to the randomness in the diffusion process, they often struggle with handling diverse low-level tasks that require details preservation. To overcome this limitation, we present a new Diff-Plugin framework to enable a single pre-trained diffusion model to generate high-fidelity results across a variety of low-level tasks. Specifically, we first propose a lightweight Task-Plugin module with a dual branch design to provide task-specific priors, guiding the diffusion process in preserving image content. We then propose a Plugin-Selector that can automatically select different Task-Plugins based on the text instruction, allowing users to edit images by indicating multiple low-level tasks with natural language. We conduct extensive experiments on 8 low-level vision tasks. The results demonstrate the superiority of Diff-Plugin over existing methods, particularly in real-world scenarios. Our ablations further validate that Diff-Plugin is stable, schedulable, and supports robust training across different dataset sizes. |
| title | Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.00644 |