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生成个性化肖像已成为一种强大且引人注目的工具，使用户能够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诸多挑战，包括测试时微调、需要多张输入图像、身份保持度低以及生成结果多样性有限。为克服这些挑战，我们提出IDAdapter——一种无需微调的方法，可从单张人脸图像增强个性化图像生成中的多样性与身份保持性。IDAdapter通过文本与视觉注入的组合以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，我们整合特定身份的多张参考图像的混合特征，以丰富身份相关的内容细节，引导模型生成比以往工作具有更多样化风格、表情和角度的图像。大量评估证明了我们方法的有效性，在生成图像中同时实现了多样性与身份保真度。