Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks

1City University of Hong Kong     2Adobe Research

Figure 1. Real-world applications of Diff-Plugin visualized across distinct single-type and one multi-type low-level vision tasks. DiffPlugin allows users to selectively conduct interested low-level vision tasks via natural languages and can generate high-fidelity results.


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.

More Visual Results

Video Demo

YouTube link.


Please consider to cite Diff-Plugin if it helps your research.
  title={Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks},
  author={Liu, Yuhao and Ke, Zhanghan and Liu, Fang and Zhao, Nanxuan and Rynson W.H. Lau},