Face2Diffusion for Fast and Editable Face Personalization

Understand

Face personalization aims to insert specific faces, taken from images, into pretrained text-to-image diffusion models.

• However, it is still challenging for previous methods to preserve both the identity similarity and editability due to overfitting to training samples.
• In this paper, we propose Face2Diffusion (F2D) for high-editability face personalization.
• The core idea behind F2D is that removing identity-irrelevant information from the training pipeline prevents the overfitting problem and improves editability of encoded faces.