Diff-Plugin: Revitalizing Details for Diffusion-Based Low-Level Tasks
Yuhao Liu, Zhanghan Ke, Fang Liu, Nanxuan Zhao, Rynson W. H. Lau
2024Year
26Top-tier citations
Abstract
Please help me enhance the lighting of this photo." " I need to remove the snow in this photo." "… clear haze …" "remove snow and haze" Figure 1. Real-world applications of Diff-Plugin visualized across distinct single-type and one multi-type low-level vision tasks. Diff-Plugin allows users to selectively conduct interested low-level vision tasks via natural languages and can generate high-fidelity results.
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Install the CLIlune papers fulltext 0fdb2aac-ff0e-429c-8e1b-1d227bdab1a2Cited by top-tier papers26
- PromptFix: You Prompt and We Fix the PhotoYongsheng Yu, Ziyun Zeng, Hang Hua, Jianlong Fu et al.NeurIPS 2024 · 55 citations
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- Exploiting Diffusion Prior for Real-World Image Dehazing with Unpaired TrainingYunwei Lan, Zhigao Cui, Chang Liu, Jialun Peng et al.AAAI 2025 · 39 citations
- AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image EnhancementYunlong Lin, Tian Ye, Sixiang Chen, Zhenqi Fu et al.AAAI 2025 · 28 citations
- AWRaCLe: All-Weather Image Restoration Using Visual In-Context LearningSudarshan Rajagopalan, Vishal M. PatelAAAI 2025 · 20 citations
Builds on56
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- A Simple Framework for Contrastive Learning of Visual RepresentationsTing Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E. HintonICML 2020 · 24,064 citations
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 13,211 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
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