LocInv：面向文本引导图像编辑的定位感知反演
LocInv: Localization-aware Inversion for Text-Guided Image Editing
AI总结：
本文提出LocInv方法，利用分割图或边界框作为定位先验来细化扩散模型中的交叉注意力图，从而在文本引导图像编辑中实现对特定目标的精确编辑，避免对非目标区域的意外修改。
摘要：
大规模文本到图像（T2I）扩散模型展示了基于文本提示的显著生成能力。基于T2I扩散模型，文本引导图像编辑研究旨在使用户能够通过修改文本提示来操控生成的图像。然而，现有的图像编辑技术容易在超出预期目标区域的非有意区域上进行编辑，这主要是由于交叉注意力图的不准确性所致。为了解决这一问题，我们提出了定位感知反演（LocInv），该方法利用分割图或边界框作为额外的定位先验，在扩散过程的去噪阶段细化交叉注意力图。通过对文本输入中与名词词元对应的标记进行动态更新，我们迫使交叉注意力图与文本提示中正确的名词和形容词词元紧密对齐。基于该技术，我们实现了对特定对象的细粒度图像编辑，同时防止对其他区域产生不期望的更改。我们的方法LocInv基于公开可用的Stable Diffusion，在COCO数据集的一个子集上进行了广泛评估，并在定量和定性方面均持续获得了优越的结果。代码将在https://github.com/wangkai930418/DPL发布。
英文摘要：
Large-scale Text-to-Image (T2I) diffusion models demonstrate significant generation capabilities based on textual prompts. Based on the T2I diffusion models, text-guided image editing research aims to empower users to manipulate generated images by altering the text prompts. However, existing image editing techniques are prone to editing over unintentional regions that are beyond the intended target area, primarily due to inaccuracies in cross-attention maps. To address this problem, we propose Localization-aware Inversion (LocInv), which exploits segmentation maps or bounding boxes as extra localization priors to refine the cross-attention maps in the denoising phases of the diffusion process. Through the dynamic updating of tokens corresponding to noun words in the textual input, we are compelling the cross-attention maps to closely align with the correct noun and adjective words in the text prompt. Based on this technique, we achieve fine-grained image editing over particular objects while preventing undesired changes to other regions. Our method LocInv, based on the publicly available Stable Diffusion, is extensively evaluated on a subset of the COCO dataset, and consistently obtains superior results both quantitatively and qualitatively.The code will be released at https://github.com/wangkai930418/DPL
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。
