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

Mitigating Inappropriateness in Image Generation: Can there be Value in Reflecting the World's Ugliness?