UnGuide: Learning to Forget with LoRA-Guided Diffusion Models

Large-scale text-to-image diffusion models pose safety and compliance risks due to their ability to generate harmful or undesirable content. Machine unlearning seeks to remove specific concepts from pretrained models while preserving overall generative quality. Low-Rank Adaptation ({LoRA}) enables parameter-efficient targeted forgetting, but naive {LoRA}-based unlearning often induces a distributional shift, degrading fidelity and destabilizing sampling. In […]