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.

通过使用轻量级橡皮擦实现可靠的概念消除，本研究提出了Receler；通过提出的概念本地化正则化和对抗性提示学习，增强了局部性和鲁棒性；与之前的擦除方法相比，通过各种概念提示进行了全面的定量和定性实验证实了Receler的优越性。

Receler: 轻量级擦除工具下文图异质扩散模型的可靠概念擦除