ACE: Anti-Editing Concept Erasure in Text-to-Image Models
Journal:
arXiv
Published Date:
Jan 3, 2025
Abstract
Recent advance in text-to-image diffusion models have significantly
facilitated the generation of high-quality images, but also raising concerns
about the illegal creation of harmful content, such as copyrighted images.
Existing concept erasure methods achieve superior results in preventing the
production of erased concept from prompts, but typically perform poorly in
preventing undesired editing. To address this issue, we propose an Anti-Editing
Concept Erasure (ACE) method, which not only erases the target concept during
generation but also filters out it during editing. Specifically, we propose to
inject the erasure guidance into both conditional and the unconditional noise
prediction, enabling the model to effectively prevent the creation of erasure
concepts during both editing and generation. Furthermore, a stochastic
correction guidance is introduced during training to address the erosion of
unrelated concepts. We conducted erasure editing experiments with
representative editing methods (i.e., LEDITS++ and MasaCtrl) to erase IP
characters, and the results indicate that our ACE effectively filters out
target concepts in both types of edits. Additional experiments on erasing
explicit concepts and artistic styles further demonstrate that our ACE performs
favorably against state-of-the-art methods. Our code will be publicly available
at https://github.com/120L020904/ACE.
