Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers

Concept erasure in text-to-image diffusion models aims to disable pre-trained diffusion models from generating images related to a target concept. To perform reliable concept erasure, the properties of robustness and locality are desirable. The former refrains the model from producing images associated with the target concept for any paraphrased or learned prompts, while the latter preserves the model ability in generating images for non-target concepts. In this paper, we propose Reliable Concept Erasing via Lightweight Erasers (Receler), which learns a lightweight Eraser to perform concept erasing and enhances locality and robustness with the proposed concept-localized regularization and adversarial prompt learning, respectively. Comprehensive quantitative and qualitative experiments with various concept prompts verify the superiority of Receler over the previous erasing methods on the above two desirable properties.