  Text-to-image models suffer from various safety issues that may limit their
suitability for deployment. Previous methods have separately addressed
individual issues of bias, copyright, and offensive content in text-to-image
models. However, in the real world, all of these issues appear simultaneously
in the same model. We present a method that tackles all issues with a single
approach. Our method, Unified Concept Editing (UCE), edits the model without
training using a closed-form solution, and scales seamlessly to concurrent
edits on text-conditional diffusion models. We demonstrate scalable
simultaneous debiasing, style erasure, and content moderation by editing
text-to-image projections, and we present extensive experiments demonstrating
improved efficacy and scalability over prior work. Our code is available at
https://unified.baulab.info
