The machine learning community is increasingly recognizing the importance of fostering trust and safety in modern generative AI (GenAI) models. We posit machine unlearning (MU) as a crucial foundation for developing safe, secure, and trustworthy GenAI models. Traditional MU methods often rely on stringent assumptions and require access to real data. This paper introduces Score Forgetting Distillation (SFD), an innovative MU approach that promotes the forgetting of undesirable information in diffusion models by aligning the conditional scores of ``unsafe'' classes or concepts with those of ``safe'' ones. To eliminate the need for real data, our SFD framework incorporates a score-based MU loss into the score distillation objective of a pretrained diffusion model. This serves as a regularization term that preserves desired generation capabilities while enabling the production of synthetic data through a one-step generator. Our experiments on pretrained label-conditional and text-to-image diffusion models demonstrate that our method effectively accelerates the forgetting of target classes or concepts during generation, while preserving the quality of other classes or concepts. This unlearned and distilled diffusion not only pioneers a novel concept in MU but also accelerates the generation speed of diffusion models. Our experiments and studies on a range of diffusion models and datasets confirm that our approach is generalizable, effective, and advantageous for MU in diffusion models.

本研究解决了现代生成AI模型机器遗忘的挑战，提出了一种新的方法——得分遗忘蒸馏（SFD），旨在安全地遗忘不需要的信息。通过将“非安全”类的条件得分与“安全”类对齐，SFD能够在不依赖真实数据的情况下快速实现遗忘，同时保持生成质量，推动了扩散模型生成速度的提升，具有重要的应用价值。

得分遗忘蒸馏：一种快速无数据的扩散模型机器遗忘方法