  Text-conditioned image generation models have recently achieved astonishing
results in image quality and text alignment and are consequently employed in a
fast-growing number of applications. Since they are highly data-driven, relying
on billion-sized datasets randomly scraped from the web, they also reproduce
inappropriate human behavior. Specifically, we demonstrate inappropriate
degeneration on a large-scale for various generative text-to-image models, thus
motivating the need for monitoring and moderating them at deployment. To this
end, we evaluate mitigation strategies at inference to suppress the generation
of inappropriate content. Our findings show that we can use models'
representations of the world's ugliness to align them with human preferences.
