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.

翻译：摘要：基于大规模数据集训练的扩散模型在图像合成领域取得了显著进展。然而，由于扩散过程的随机性，这类模型在处理需要细节保留的多样化底层任务时往往存在困难。为克服这一局限，我们提出新型Diff-Plugin框架，使单个预训练扩散模型能够在多种底层任务中生成高保真结果。具体而言，我们首先提出轻量级双分支架构的Task-Plugin模块，通过提供任务先验知识引导扩散过程保留图像内容；其次提出Plugin-Selector模块，可根据文本指令自动选择不同Task-Plugin，支持用户通过自然语言指定多类底层任务进行图像编辑。我们在8项底层视觉任务上进行了广泛实验，结果表明Diff-Plugin在真实场景中显著优于现有方法。消融实验进一步验证了Diff-Plugin具有稳定性、可调度性，并能适应不同数据集规模的鲁棒训练。