  We introduce EraseDiff, an unlearning algorithm designed for diffusion models
to address concerns related to data memorization. Our approach formulates the
unlearning task as a constrained optimization problem, aiming to preserve the
utility of the diffusion model on retained data while removing the information
associated with the data to be forgotten. This is achieved by altering the
generative process to deviate away from the ground-truth denoising procedure.
To manage the computational complexity inherent in the diffusion process, we
develop a first-order method for solving the optimization problem, which has
shown empirical benefits. Extensive experiments and thorough comparisons with
state-of-the-art algorithms demonstrate that EraseDiff effectively preserves
the model's utility, efficacy, and efficiency.
