MACE: Mass Concept Erasure in Diffusion Models
Shilin Lu Zilan Wang Leyang Li Yanzhu Liu Adams Wai-Kin Kong
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). In contrast,
MACE differs by successfully scaling the erasure scope up to 100 concepts and
by achieving an effective balance between generality and specificity. This is
achieved by leveraging closed-form cross-attention refinement along with LoRA
finetuning, collectively eliminating the information of undesirable concepts.
Furthermore, MACE integrates multiple LoRAs without mutual interference. We
conduct extensive evaluations of MACE against prior methods across four
different tasks: object erasure, celebrity erasure, explicit content erasure,
and artistic style erasure. Our results reveal that MACE surpasses prior
methods in all evaluated tasks. Code is available at
https://github.com/Shilin-LU/MACE.