Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks
CVPR 2024(2024)
摘要
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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