ReTrack: Data Unlearning in Diffusion Models through Redirecting the Denoising Trajectory

Diffusion models excel at generating high-quality, diverse images but also suffer from undesirable training data memorization, raising critical privacy and safety concerns. Data unlearning has emerged to mitigate this issue by removing the influence of specific data through fine-tuning rather than retraining from scratch. We propose ReTrack, a fast and effective data unlearning method for diffusion models. ReTrack employs importance sampling to construct a more efficient unbiased fine-tuning loss. This loss is further approximated by retaining only the dominant terms, thereby reducing computational cost. This yields an interpretable objective that redirects denoising trajectories toward the $k$-nearest neighbors, enabling efficient unlearning while preserving generative quality. Experiments on MNIST T-Shirt, CelebA-HQ, CIFAR-10, and Stable Diffusion show that ReTrack achieves state-of-the-art performance, striking the best trade-off between unlearning strength and generation quality preservation.