The objective of text-to-image (T2I) personalization is to customize a diffusion model to a user-provided reference concept, generating diverse images of the concept aligned with the target prompts. Conventional methods representing the reference concepts using unique text embeddings often fail to accurately mimic the appearance of the reference. To address this, one solution may be explicitly conditioning the reference images into the target denoising process, known as key-value replacement. However, prior works are constrained to local editing since they disrupt the structure path of the pre-trained T2I model. To overcome this, we propose a novel plug-in method, called DreamMatcher, which reformulates T2I personalization as semantic matching. Specifically, DreamMatcher replaces the target values with reference values aligned by semantic matching, while leaving the structure path unchanged to preserve the versatile capability of pre-trained T2I models for generating diverse structures. We also introduce a semantic-consistent masking strategy to isolate the personalized concept from irrelevant regions introduced by the target prompts. Compatible with existing T2I models, DreamMatcher shows significant improvements in complex scenarios. Intensive analyses demonstrate the effectiveness of our approach.

翻译：文本到图像（T2I）个性化的目标是将扩散模型定制为用户提供的参考概念，生成与该概念对齐且符合目标提示的多样化图像。传统方法使用独特文本嵌入来表示参考概念，往往难以精确模仿参考外观。针对此问题，一种解决方案是将参考图像显式地注入目标去噪过程，即键值替换。然而，由于先前方法破坏了预训练T2I模型的结构路径，其应用局限于局部编辑。为克服这一限制，我们提出了一种新颖的插件式方法——DreamMatcher，将T2I个性化重新定义为语义匹配。具体而言，DreamMatcher将目标值替换为通过语义匹配对齐后的参考值，同时保持结构路径不变，以保留预训练T2I模型生成多样化结构的通用能力。我们还引入了语义一致掩码策略，将个性化概念与目标提示引入的非相关区域隔离。兼容现有T2I模型的DreamMatcher在复杂场景中展现出显著改进。深入分析证实了本方法的有效性。