Preprint Open access
Enabling Preference-driven Unlearning in Few-step Distilled Text-to-Image Diffusion Models
Text-to-image diffusion models are increasingly distilled into few-step variants and being deployed to enable fast inference. However, their ability to generate harmful or undesired content poses significant safety risks. Data-driven unlearning methods suppress targeted generations by fine-tuning model weights using sp …