Advances in generative models have led to significant interest in image synthesis, demonstrating the ability to generate high-quality images for a diverse range of text prompts. Despite this progress, most studies ignore the presence of bias. In this paper, we examine several text-to-image models not only by qualitatively assessing their performance in generating accurate images of human faces, groups, and specified numbers of objects but also by presenting a social bias analysis. As expected, models with larger capacity generate higher-quality images. However, we also document the inherent gender or social biases these models possess, offering a more complete understanding of their impact and limitations.
@article{arxiv.2407.00138,
title = {Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models},
author = {Nila Masrourisaadat and Nazanin Sedaghatkish and Fatemeh Sarshartehrani and Edward A. Fox},
journal= {arXiv preprint arXiv:2407.00138},
year = {2024}
}