Creative AI: Prompting Portraits and Matching Datasets

Abstract

This paper aims to provide a brief exploration of two versions of Creative AI, namely the prompting of portraits by using AI text-to-image generators and the use of GAN, AICAN and Facer to create AI generated portraits. These two versions are in turn compared to corresponding debates in the field of art history, namely the image-text debate as positioned by the image scholar, WJT Mitchell, followed by the concept of schemata as proposed by the art historian EH Gombrich. First, Mitchell’s understanding of the nature of the image versus text is utilized to compare portraits prompted through text-to-image generators. Secondly, Gombrich’s schemata is compared with recent AI portraits generated by means of image datasets. The differences between the art historical and the Creative AI processes are explored to draw initial conclusions about the future of portraiture and creativity.