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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.
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