Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion
  Models
Sanghyun Kim Seohyeon Jung Balhae Kim Moonseok Choi Jinwoo Shin Juho Lee
Large-scale image generation models, with impressive quality made possible by
the vast amount of data available on the Internet, raise social concerns that
these models may generate harmful or copyrighted content. The biases and
harmfulness arise throughout the entire training process and are hard to
completely remove, which have become significant hurdles to the safe deployment
of these models. In this paper, we propose a method called SDD to prevent
problematic content generation in text-to-image diffusion models. We
self-distill the diffusion model to guide the noise estimate conditioned on the
target removal concept to match the unconditional one. Compared to the previous
methods, our method eliminates a much greater proportion of harmful content
from the generated images without degrading the overall image quality.
Furthermore, our method allows the removal of multiple concepts at once,
whereas previous works are limited to removing a single concept at a time.