In response to data protection regulations and the ``right to be forgotten'', in this work, we introduce an unlearning algorithm for diffusion models. Our algorithm equips a diffusion model with a mechanism to mitigate the concerns related to data memorization. To achieve this, we formulate the unlearning problem as a bi-level optimization problem, wherein the outer objective is to preserve the utility of the diffusion model on the remaining data. The inner objective aims to scrub the information associated with forgetting data by deviating the learnable generative process from the ground-truth denoising procedure. To solve the resulting bi-level problem, we adopt a first-order method, having superior practical performance while being vigilant about the diffusion process and solving a bi-level problem therein. Empirically, we demonstrate that our algorithm can preserve the model utility, effectiveness, and efficiency while removing across two widely-used diffusion models and in both conditional and unconditional image generation scenarios. In our experiments, we demonstrate the unlearning of classes, attributes, and even a race from face and object datasets such as UTKFace, CelebA, CelebA-HQ, and CIFAR10.

翻译：为应对数据保护法规及“被遗忘权”，本文提出了一种针对扩散模型的遗忘学习算法。该算法为扩散模型配备了一种机制，以缓解与数据记忆化相关的担忧。为此，我们将遗忘学习问题构建为一个双层优化问题：外层目标是在剩余数据上保持扩散模型的效用，内层目标则旨在通过使可学习的生成过程偏离真实去噪流程，来清除与遗忘数据相关的信息。为解决由此产生的双层问题，我们采用了一阶方法，该方法在关注扩散过程及其内部双层问题求解的同时，具有卓越的实际性能。实验表明，我们的算法能在两种广泛使用的扩散模型上，以及条件与无条件图像生成场景中，在移除数据的同时保持模型效用、有效性与效率。我们的实验展示了从UTKFace、CelebA、CelebA-HQ和CIFAR10等人脸及物体数据集中遗忘类别、属性甚至种族的能力。