# Ensuring Visual Commonsense Morality for Text-to-Image Generation

arXiv: https://arxiv.org/abs/2212.03507 · HTML: https://mlsanity.com/paper/2212.03507/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 12 of 12)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020)
- [NVAE: A Deep Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2007.03898/) (2020)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [On Fast Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2106.00132/) (2021)

## Built on by (most cited first, 2 of 2)

- [Vision + Language Applications: A Survey](https://mlsanity.com/paper/2305.14598/) (2023)
- [Visual moral inference and communication](https://mlsanity.com/paper/2504.11473/) (2025)

## Abstract (arXiv)

Text-to-image generation methods produce high-resolution and high-quality images, but these methods should not produce immoral images that may contain inappropriate content from the perspective of commonsense morality. In this paper, we aim to automatically judge the immorality of synthesized images and manipulate these images into morally acceptable alternatives. To this end, we build a model that has three main primitives: (1) recognition of the visual commonsense immorality in a given image, (2) localization or highlighting of immoral visual (and textual) attributes that contribute to the immorality of the image, and (3) manipulation of an immoral image to create a morally-qualifying alternative. We conduct experiments and human studies using the state-of-the-art Stable Diffusion text-to-image generation model, demonstrating the effectiveness of our ethical image manipulation approach.