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.

IDAdapter 是一种无需微调的方法，通过在生成过程中结合文本和视觉注入以及面部身份损失，从单个人脸图像中增强个性化图像生成的多样性和身份保留。通过将特定身份的多个参考图像的混合特征融入训练阶段，丰富相关身份的内容细节，指导模型生成具有比以往作品更多样的风格、表情和角度的图像。广泛的评估显示了我们方法的有效性，在生成的图像中实现了多样性和身份保真度。