-
[pdf]
[bibtex]@InProceedings{Liu_2024_CVPR, author = {Liu, Yuhao and Ke, Zhanghan and Liu, Fang and Zhao, Nanxuan and Lau, Rynson W.H.}, title = {Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {4197-4208} }
Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks
Abstract
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.
Related Material