Preserve and Personalize: Personalized Text-to-Image Diffusion Models without Distributional Drift
arXiv:2505.19519v3 Announce Type: replace
Abstract: Personalizing text-to-image diffusion models involves integrating novel visual concepts from a small set of reference images while retaining the model’s original generative capabilities. However, thi…