  Motivated by recent advancements in text-to-image diffusion, we study erasure
of specific concepts from the model's weights. While Stable Diffusion has shown
promise in producing explicit or realistic artwork, it has raised concerns
regarding its potential for misuse. We propose a fine-tuning method that can
erase a visual concept from a pre-trained diffusion model, given only the name
of the style and using negative guidance as a teacher. We benchmark our method
against previous approaches that remove sexually explicit content and
demonstrate its effectiveness, performing on par with Safe Latent Diffusion and
censored training. To evaluate artistic style removal, we conduct experiments
erasing five modern artists from the network and conduct a user study to assess
the human perception of the removed styles. Unlike previous methods, our
approach can remove concepts from a diffusion model permanently rather than
modifying the output at the inference time, so it cannot be circumvented even
if a user has access to model weights. Our code, data, and results are
available at https://erasing.baulab.info/
