Leveraging Stable Diffusion for the generation of personalized portraits has emerged as a powerful and noteworthy tool, enabling users to create high-fidelity, custom character avatars based on their specific prompts. However, existing personalization methods face challenges, including test-time fine-tuning, the requirement of multiple input images, low preservation of identity, and limited diversity in generated outcomes. To overcome these challenges, we introduce IDAdapter, a tuning-free approach that enhances the diversity and identity preservation in personalized image generation from a single face image. IDAdapter integrates a personalized concept into the generation process through a combination of textual and visual injections and a face identity loss. During the training phase, we incorporate mixed features from multiple reference images of a specific identity to enrich identity-related content details, guiding the model to generate images with more diverse styles, expressions, and angles compared to previous works. Extensive evaluations demonstrate the effectiveness of our method, achieving both diversity and identity fidelity in generated images.

翻译：利用Stable Diffusion生成个性化人像已成为一种强大且重要的工具，用户可根据特定提示创建高保真、定制化的角色头像。然而，现有个性化方法面临测试时微调、需多张输入图像、身份保留度低及生成结果多样性有限等挑战。为克服这些难题，我们提出IDAdapter——一种免调参方法，通过单张人脸图像增强个性化图像生成中的多样性与身份保留能力。IDAdapter通过文本与视觉注入技术及人脸身份损失函数，将个性化概念融入生成过程。在训练阶段，我们融合特定身份的多张参考图像的混合特征以丰富身份相关细节信息，引导模型生成较此前工作更具多样性风格、表情与角度的图像。大量评估表明，本方法在生成图像的多样性与身份保真度方面均取得显著成效。