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）个性化目标是将扩散模型定制化适配到用户提供的参考概念，生成与目标提示对齐的概念多样化图像。传统方法使用独特文本嵌入表示参考概念，往往难以准确模仿参考外观。为此，一种解决方案是将参考图像显式条件化融入目标去噪过程（即键值替换）。然而，由于干扰预训练T2I模型的结构路径，现有工作仅局限于局部编辑。为克服这一局限，我们提出新型插件方法DreamMatcher，将T2I个性化重新定义为语义匹配。具体而言，DreamMatcher将目标值替换为经语义匹配对齐的参考值，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。我们还引入语义一致掩码策略，将个性化概念与目标提示引入的不相关区域隔离。DreamMatcher与现有T2I模型兼容，在复杂场景中展现出显著改进。深入分析验证了本方法的有效性。