Recent advancements in personalized image generation using diffusion models have been noteworthy. However, existing methods suffer from inefficiencies due to the requirement for subject-specific fine-tuning. This computationally intensive process hinders efficient deployment, limiting practical usability. Moreover, these methods often grapple with identity distortion and limited expression diversity. In light of these challenges, we propose PortraitBooth, an innovative approach designed for high efficiency, robust identity preservation, and expression-editable text-to-image generation, without the need for fine-tuning. PortraitBooth leverages subject embeddings from a face recognition model for personalized image generation without fine-tuning. It eliminates computational overhead and mitigates identity distortion. The introduced dynamic identity preservation strategy further ensures close resemblance to the original image identity. Moreover, PortraitBooth incorporates emotion-aware cross-attention control for diverse facial expressions in generated images, supporting text-driven expression editing. Its scalability enables efficient and high-quality image creation, including multi-subject generation. Extensive results demonstrate superior performance over other state-of-the-art methods in both single and multiple image generation scenarios.

翻译：近年来，基于扩散模型的个性化图像生成技术取得了显著进展。然而，现有方法因需要针对特定主体进行微调而面临效率低下的问题。这种计算密集型过程阻碍了高效部署，限制了实际可用性。此外，这些方法常面临身份扭曲和表情多样性受限的挑战。针对上述问题，我们提出PortraitBooth——一种无需微调即可实现高效率、强身份保留及表情可编辑文本到图像生成的创新方法。PortraitBooth利用人脸识别模型中的主体嵌入来实现无需微调的个性化图像生成，消除了计算开销并缓解了身份扭曲。引入的动态身份保留策略进一步确保与原始图像身份的高度相似性。同时，PortraitBooth整合了情感感知交叉注意力控制机制，可生成具有多样化面部表情的图像，支持文本驱动的表情编辑。其可扩展性支持包括多主体生成在内的高效高质量图像创作。大量实验结果表明，该方法在单图像和多图像生成场景中均优于现有最先进方法。