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.

翻译：随着大规模生成模型的发展，图像编辑已取得令人瞩目的成果。然而，现有模型主要关注目标对象和区域的编辑效果，往往导致非目标区域产生不必要的改变。本文提出一种基于区域正则化强化学习的后训练框架，用于实现内容一致性编辑（CoCoEdit）。我们首先通过精炼的指令和掩码对现有编辑数据集进行增强，从中筛选出40K个多样化的高质量样本作为训练集。随后，我们引入像素级相似度奖励来补充基于MLLM的奖励机制，使模型能够在编辑过程中同时保证编辑质量和内容一致性。为克服奖励机制的空间无关性，我们提出一种基于区域的正则化器，旨在对高奖励样本保留非编辑区域，同时对低奖励样本增强编辑效果。为进行评估，我们为GEdit-Bench和ImgEdit-Bench数据集标注了编辑掩码，并引入像素级相似度指标来衡量内容一致性与编辑质量。将CoCoEdit应用于Qwen-Image-Edit和FLUX-Kontext模型后，不仅在编辑评分上与最先进模型保持竞争力，还通过PSNR/SSIM指标和人类主观评分验证了显著提升的内容一致性。