IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models
This work addresses the need for efficient and high-quality personalization in text-to-image models for users, though it is incremental as it builds on existing Stable Diffusion methods.
The paper tackled the problem of generating personalized portraits from a single face image without test-time fine-tuning, achieving high diversity and identity preservation in the generated images.
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.