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arXiv · 2407.00138

Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models

Nila Masrourisaadat·Nazanin Sedaghatkish·Fatemeh Sarshartehrani·Edward A. Fox
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Abstract

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.

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Nila Masrourisaadat, Nazanin Sedaghatkish, Fatemeh Sarshartehrani, Edward A. Fox. 2024-06-28. Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models. https://arxiv.org/abs/2407.00138

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