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arXiv · 2403.00644

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

Yuhao Liu·Zhanghan Ke·Fang Liu·Nanxuan Zhao·Rynson W. H. Lau
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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.

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Yuhao Liu, Zhanghan Ke, Fang Liu, Nanxuan Zhao, Rynson W. H. Lau. 2024-03-01. Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks. https://arxiv.org/abs/2403.00644

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