ACE: Concept Editing in Diffusion Models without Performance Degradation
This addresses societal and ethical concerns for users of text-to-image models by enabling safer content generation without performance degradation, though it is an incremental improvement over existing concept editing methods.
The paper tackles the problem of unsafe content generation in diffusion-based text-to-image models by proposing ACE, a concept editing method that improves semantic consistency by 24.56% and image generation quality by 34.82% on average while reducing time cost to 1%.
Diffusion-based text-to-image models have demonstrated remarkable capabilities in generating realistic images, but they raise societal and ethical concerns, such as the creation of unsafe content. While concept editing is proposed to address these issues, they often struggle to balance the removal of unsafe concept with maintaining the model's general genera-tive capabilities. In this work, we propose ACE, a new editing method that enhances concept editing in diffusion models. ACE introduces a novel cross null-space projection approach to precisely erase unsafe concept while maintaining the model's ability to generate high-quality, semantically consistent images. Extensive experiments demonstrate that ACE significantly outperforms the advancing baselines,improving semantic consistency by 24.56% and image generation quality by 34.82% on average with only 1% of the time cost. These results highlight the practical utility of concept editing by mitigating its potential risks, paving the way for broader applications in the field. Code is avaliable at https://github.com/littlelittlenine/ACE-zero.git