基于扩散模型的图像编辑：综述
Diffusion Model-Based Image Editing: A Survey
AI总结：
本文综述了基于扩散模型的图像编辑方法，从学习策略、输入条件和编辑任务等角度系统分类现有工作，并提出基准EditEval及指标LMM Score以评估文本引导编辑性能。
摘要：
去噪扩散模型已成为各种图像生成与编辑任务的强大工具，能够以无条件或输入条件方式促进视觉内容的合成。其核心思想是学习逆转逐步向图像添加噪声的过程，从而使模型能够从复杂分布中生成高质量样本。在本综述中，我们对使用扩散模型进行图像编辑的现有方法进行了详尽概述，涵盖该领域的理论与实践两个方面。我们从多个视角对这些工作进行了深入分析与分类，包括学习策略、用户输入条件以及可完成的各类具体编辑任务。此外，我们特别关注图像修复与图像外扩，并探讨了早期传统上下文驱动方法和当前多模态条件方法，对其方法论进行了全面分析。为了进一步评估文本引导图像编辑算法的性能，我们提出了一个系统性基准EditEval，其中包含创新性指标LMM Score。最后，我们讨论了当前的局限性，并展望了未来研究的一些潜在方向。相关代码库发布于https://github.com/SiatMMLab/Awesome-Diffusion-Model-Based-Image-Editing-Methods。
英文摘要：
Denoising diffusion models have emerged as a powerful tool for various image generation and editing tasks, facilitating the synthesis of visual content in an unconditional or input-conditional manner. The core idea behind them is learning to reverse the process of gradually adding noise to images, allowing them to generate high-quality samples from a complex distribution. In this survey, we provide an exhaustive overview of existing methods using diffusion models for image editing, covering both theoretical and practical aspects in the field. We delve into a thorough analysis and categorization of these works from multiple perspectives, including learning strategies, user-input conditions, and the array of specific editing tasks that can be accomplished. In addition, we pay special attention to image inpainting and outpainting, and explore both earlier traditional context-driven and current multimodal conditional methods, offering a comprehensive analysis of their methodologies. To further evaluate the performance of text-guided image editing algorithms, we propose a systematic benchmark, EditEval, featuring an innovative metric, LMM Score. Finally, we address current limitations and envision some potential directions for future research. The accompanying repository is released at https://github.com/SiatMMLab/Awesome-Diffusion-Model-Based-Image-Editing-Methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
