MasterWeaver: Taming Editability and Face Identity for Personalized Text-to-Image Generation
This work addresses the challenge of disentangling identity from irrelevant information in personalized image generation for users of text-to-image models, representing an incremental improvement over existing tuning-free methods.
The paper tackles the problem of overfitting and poor text controllability in personalized text-to-image generation by introducing MasterWeaver, a tuning-free method that achieves faithful identity fidelity and flexible editability, as demonstrated through extensive experiments showing superiority in text controllability.
Text-to-image (T2I) diffusion models have shown significant success in personalized text-to-image generation, which aims to generate novel images with human identities indicated by the reference images. Despite promising identity fidelity has been achieved by several tuning-free methods, they usually suffer from overfitting issues. The learned identity tends to entangle with irrelevant information, resulting in unsatisfied text controllability, especially on faces. In this work, we present MasterWeaver, a test-time tuning-free method designed to generate personalized images with both faithful identity fidelity and flexible editability. Specifically, MasterWeaver adopts an encoder to extract identity features and steers the image generation through additional introduced cross attention. To improve editability while maintaining identity fidelity, we propose an editing direction loss for training, which aligns the editing directions of our MasterWeaver with those of the original T2I model. Additionally, a face-augmented dataset is constructed to facilitate disentangled identity learning, and further improve the editability. Extensive experiments demonstrate that our MasterWeaver can not only generate personalized images with faithful identity, but also exhibit superiority in text controllability. Our code can be found at https://github.com/csyxwei/MasterWeaver.