Analysis of Text-to-Image Artificial Intelligence Systems in Terms of Contribution to Interior Coloring

Abstract

In this article, based on its potential contribution to architectural design processes, research has been made on the “text-to-image” systems of artificial intelligence. In the research, the four most common systems Craiyon, Dall-E, Midjourney, and Stable Diffusion were selected, and these systems were tested for coloring a pre-school education space. First of all, the “kindergarten” text was presented to the systems and according to this text, four alternative images were produced from each system. Afterward, the dominant color coding of the images was analyzed in the computer environment. The 3D model of preschool space was colored with the obtained color codes. The 16 images that emerged because of coloring were presented to 62 expert participants, consisting of preschool teaching and architecture/interior architecture department members, accompanied by a survey. In the survey, the experts were asked to evaluate the colored images in “entertainment” and “academic” contexts. As a result of the statistical analysis of the survey data showed that the Craiyon system used colors more successfully than other systems in terms of coloring a preschool education space. This study measured the ability of artificial intelligence systems from text-to-image to interpret the text in terms of the production of color codes suitable for the type of space. However, it has been tried to articulate such systems to architectural design areas and to open the door from a unique perspective.