GPT for Treatise Image Creation. A critical overview

Text-to-image systems based on generative pre-trained transformers have become pervasive in recent years. This result is mainly due to the generation tools' ease of use, application availability, and increasingly refined graphical and algorithmic capabilities. On the other hand, the non-controllability given by a random process and the dependency on existing information databases highlight some limitations of these automatic image-generation methods. The rapid construction of increasingly realistic digital images draws new boundaries between real and unreal, highlighting a strict relation between text and image regarding semantics and logic descriptions. Therefore, a critical look into the use of these applications as tools for a reliable representation of architecture becomes cogent. We started from representation established by the treatises, as in the case of orders. Most of these drawings, proportions, and rules are derived from descriptive parts in Vitruvius' text. However, the graphic interpretations result from the architect's experience and culture, which has become the basic grammar of architecture. This research stems precisely from the connection between Vitruvius' text, the new text-to-image contents and the established representations of the treatise writers. The comparison will consider both the image reliability and the geometric rules, testing the current potential of GPT systems for image creation and reliability.