Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearning

Data unlearning aims to remove the influence of specific training samples from a trained model.

In fine-tuning methods, data unlearning relies primarily on loss maximization over forget samples, which often leads to quality degradation or incomplete forgetting.

Existing methods perform unlearning uniformly across diffusion stages, ignoring diffusion dynamics from noise to data.

Our systematic study of diffusion phases shows that forgetting in diffusion models is uneven across time and frequency, with theoretical justification of distributive distortion and forgetting-utility trade-off.

By selectively forgetting time and frequency in diffusion models, we achieve both higher unlearning success rates and improved generation quality across diverse settings, including both conditional and unconditional scenarios.

We also introduce an improved SSCD metric that measures dissimilarity using a normalized perturbation distance.

Together, we provide practical insights for understanding and improving data unlearning in diffusion models.