PC-Edit：提示对比区域发现与区域引导编辑
PC-Edit: Prompt-Contrastive Region Discovery and Region-Guided Editing
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中文总结 AI 辅助
PC-Edit针对无训练图像编辑中定位不准确和背景保留问题，提出提示对比框架，通过对比源和目标提示下的图像令牌注意力输出，识别关键区域，抑制源残余，自然形成目标对象，实验证明其在无用户指定编辑区域方法中表现最佳。
中文摘要 AI 辅助
用不同类别或形状的对象替换原对象，需要完全去除源对象、自然形成不受源轮廓约束的目标对象，并保留无关内容。现有无训练编辑器存在定位编辑不准确和无法有效保留无关内容的问题。为此提出PC-Edit，一个用于无训练MM-DiT编辑的提示对比框架。它对比源和目标提示下的图像令牌注意力输出，直接捕捉提示诱导的语义差异，识别源擦除区域和目标出现区域，其并集抑制源残余同时让目标对象自然形成。还通过估计当前编辑区域并注入缓存源K/V特征来保护无关内容。实验表明PC-Edit在无用户指定编辑区域方法中编辑质量和背景保留最佳。
英文摘要
Replacing an object with one that differs in category or shape requires complete source removal, natural target formation unconstrained by the source silhouette, and preservation of unrelated content. Existing training-free editors either localize edits from terminal predictions under source and target prompts or preserve unrelated content through spatially unselective source-feature reuse without explicit region discovery. Before reaching the terminal predictions, prompt-induced semantic differences undergo additional network transformations that may obscure their spatial localization, reducing localization precision. Spatially unselective feature reuse forces a trade-off between edit completeness and background preservation. Therefore, we propose PC-Edit, a prompt-contrastive framework for training-free MM-DiT editing. PC-Edit contrasts the image-token attention outputs under the source and target prompts, capturing prompt-induced semantic differences directly where text-conditioned information is delivered to image tokens. The same contrast identifies a source-erasure region during inversion and a target-emergence region during denoising. Their union suppresses source remnants while allowing the target object to form naturally. PC-Edit further couples region discovery and background preservation within each sampling step by estimating the current edit region from preceding attention blocks and immediately injecting cached source K/V features outside it in subsequent blocks, thereby protecting unrelated content before the latent update. Experiments on PIE-Bench and our EditRegion-Bench, with human-verified edit-region annotations for single- and multi-object addition and replacement, show that PC-Edit achieves the best editing quality and background preservation among methods without user-specified edit regions.
