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 continual personalization 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, to achieve continual personalization. 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.

本研究解决了用户在连续学习环境中个性化文本到图像扩散模型的挑战，特别是处理多个概念时因存储和隐私问题无法访问以前数据的情况。提出的创新方法利用扩散分类器分数进行正则化，实现了在保留旧概念的同时有效获得新概念的持续个性化。研究结果表明，该方法在多种评估设置下优于当前的最先进方法，实现零存储和参数开销。

挖掘自己的秘密：扩散分类器分数用于文本到图像扩散模型的持续个性化