CoCoEdit：通过区域正则化的强化学习实现内容一致的图像编辑
CoCoEdit: Content-Consistent Image Editing via Region Regularized Reinforcement Learning
AI总结：
本文提出CoCoEdit框架，通过区域正则化强化学习提升图像编辑内容一致性，利用改进的指令和掩码生成高质量训练集，并引入像素相似度奖励和区域正则化器，提升编辑质量和一致性。
摘要：
图像编辑随着大尺度生成模型的发展取得了显著成果，但现有模型常导致未意图像区域的意外变化。本文提出CoCoEdit框架，通过区域正则化的强化学习实现内容一致的图像编辑。首先通过改进的指令和掩码扩充现有编辑数据集，从中挑选出40K高质量样本作为训练集。然后引入像素级相似度奖励以补充基于大规模语言模型（MLLM）的奖励，使模型在编辑过程中确保编辑质量和内容一致性。为克服奖励的空域无关性，提出基于区域的正则化器，旨在保留非编辑区域以提高奖励样本的质量，同时鼓励低奖励样本的编辑效果。评估时对GEdit-Bench和ImgEdit-Bench标注编辑掩码，引入像素级相似度指标衡量内容一致性和编辑质量。将CoCoEdit应用于Qwen-Image-Edit和FLUX-Kontext，不仅在编辑评分上与最先进的模型竞争，还在PSNR/SSIM指标和人为主观评分上实现了显著更好的内容一致性。
英文摘要：
Image editing has achieved impressive results with the development of large-scale generative models. However, existing models mainly focus on the editing effects of intended objects and regions, often leading to unwanted changes in unintended regions. We present a post-training framework for Content-Consistent Editing (CoCoEdit) via region regularized reinforcement learning. We first augment existing editing datasets with refined instructions and masks, from which 40K diverse and high quality samples are curated as training set. We then introduce a pixel-level similarity reward to complement MLLM-based rewards, enabling models to ensure both editing quality and content consistency during the editing process. To overcome the spatial-agnostic nature of the rewards, we propose a region-based regularizer, aiming to preserve non-edited regions for high-reward samples while encouraging editing effects for low-reward samples. For evaluation, we annotate editing masks for GEdit-Bench and ImgEdit-Bench, introducing pixel-level similarity metrics to measure content consistency and editing quality. Applying CoCoEdit to Qwen-Image-Edit and FLUX-Kontext, we achieve not only competitive editing scores with state-of-the-art models, but also significantly better content consistency, measured by PSNR/SSIM metrics and human subjective ratings.
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。
