Customized image generation is crucial for delivering personalized content based on user-provided image prompts, aligning large-scale text-to-image diffusion models with individual needs. However, existing models often overlook the relationships between customized objects in generated images. Instead, this work addresses that gap by focusing on relation-aware customized image generation, which aims to preserve the identities from image prompts while maintaining the predicate relations described in text prompts. Specifically, we introduce RelationBooth, a framework that disentangles identity and relation learning through a well-curated dataset. Our training data consists of relation-specific images, independent object images containing identity information, and text prompts to guide relation generation. Then, we propose two key modules to tackle the two main challenges: generating accurate and natural relations, especially when significant pose adjustments are required, and avoiding object confusion in cases of overlap. First, we introduce a keypoint matching loss that effectively guides the model in adjusting object poses closely tied to their relationships. Second, we incorporate local features from the image prompts to better distinguish between objects, preventing confusion in overlapping cases. Extensive results on three benchmarks demonstrate the superiority of RelationBooth in generating precise relations while preserving object identities across a diverse set of objects and relations. The source code and trained models will be made available to the public.

翻译：定制化图像生成对于基于用户提供的图像提示生成个性化内容至关重要，它使大规模文生图扩散模型能够与个体需求对齐。然而，现有模型在生成图像时常常忽略定制化物体之间的关系。相反，本工作通过聚焦于关系感知的定制化图像生成来填补这一空白，其目标是在保持文本提示所描述谓词关系的同时，保留图像提示中的物体身份。具体而言，我们提出了RelationBooth框架，该框架通过精心构建的数据集解耦了身份学习与关系学习。我们的训练数据包含关系特定图像、包含身份信息的独立物体图像以及用于指导关系生成的文本提示。随后，我们提出了两个关键模块来解决两个主要挑战：生成准确且自然的关系（尤其是在需要显著姿态调整时），以及在物体重叠情况下避免混淆。首先，我们引入了一种关键点匹配损失，它能有效指导模型调整与物体关系紧密相关的姿态。其次，我们整合了来自图像提示的局部特征，以更好地区分物体，防止在重叠情况下发生混淆。在三个基准测试上的大量结果表明，RelationBooth在生成精确关系的同时，能够在一系列多样化的物体和关系中保持物体身份，展现出优越性。源代码及训练模型将向公众开放。