Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks
<|reference_start|>Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks: 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.<|reference_end|>
@article{liu2024diff-plugin:,
    title={Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks},
    author={Yuhao Liu, Zhanghan Ke, Fang Liu, Nanxuan Zhao, Rynson W.H. Lau},
    journal={arXiv preprint arXiv:2403.00644},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00644},
    primaryClass={cs.CV}
}