Diff-Plugin presents a new framework to empower pre-trained diffusion models in handling diverse low-level vision tasks. By incorporating task-specific priors and a Plugin-Selector, the framework demonstrates superior performance over existing methods in real-world scenarios. Extensive experiments validate the stability, scalability, and robustness of Diff-Plugin across different datasets and tasks.
The content discusses the challenges faced by diffusion models in preserving details for low-level vision tasks and introduces the innovative Diff-Plugin framework as a solution. Through Task-Plugins and a Plugin-Selector, users can achieve high-fidelity results across various tasks using natural language instructions. The ablation studies highlight the effectiveness of different Task-Plugin designs, emphasizing the importance of integrating task-specific priors and spatial features for optimal performance.
Key metrics such as FID and KID are used to evaluate the performance of Diff-Plugin against state-of-the-art methods in various low-level vision tasks. User studies confirm the preference for Diff-Plugin due to its ability to maintain content consistency and quality. Ablation studies further demonstrate the significance of each component within the framework, showcasing its robustness and versatility.
Overall, Diff-Plugin offers a comprehensive solution for enhancing pre-trained diffusion models in addressing low-level vision tasks effectively while maintaining high-quality results.
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by Yuhao Liu,Fa... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00644.pdfDeeper Inquiries