Mining your own secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models
Saurav Jha Shiqi Yang Masato Ishii Mengjie Zhao christian simon Muhammad Jehanzeb Mirza Dong Gong Lina Yao Shusuke Takahashi Yuki Mitsufuji
Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However, in the real world, a user may wish to personalize a model on multiple concepts but one at a time, with no access to the data from previous concepts due to storage/privacy concerns. When faced with this continual learning (CL) setup, most personalization methods fail to find a balance between acquiring new concepts and retaining previous ones -- a challenge that *continual personalization* (CP) aims to solve. 
Inspired by the successful CL methods that rely on class-specific information for regularization, we resort to the  inherent class-conditioned density estimates, also known as *diffusion classifier* (DC) scores, for CP of text-to-image diffusion models. 
Namely, we propose using DC scores for regularizing the parameter-space and function-space of text-to-image diffusion models.
Using several diverse evaluation setups, datasets, and  metrics, we show that our proposed regularization-based CP methods outperform the state-of-the-art C-LoRA, and other baselines. Finally, by operating in the replay-free CL setup and on low-rank adapters, our method incurs zero storage and parameter overhead, respectively, over the state-of-the-art.
Text-to-image diffusion model, Continual Learning, Personalization
To continually acquire new user-defined concepts using pretrained text-to-image diffusion models, we propose leveraging their conditional density estimates, aka, Diffusion Classifier scores as regularization information.