MACE: Mass Concept Erasure in Diffusion Models

Understand

The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content.

• In this paper, we introduce MACE, a finetuning framework for the task of mass concept erasure.
• This task aims to prevent models from generating images that embody unwanted concepts when prompted.
• Existing concept erasure methods are typically restricted to handling fewer than five concepts simultaneously and struggle to find a balance between erasing concept synonyms (generality) and maintaining unrelated concepts (specificity).