Txt2city. From the Prompt to the City’s Image

In the urban sphere, AI is often associated with the concept of smart cities and thus the use of technology and the enormous computing power of machines to increase the quality of life for citizens, creating greater efficiency in resources and services, but there are further applications. The sprawl of information technologies is changing relationships between people and space, contributing to the mutation of iconographic production and the way content is conceived and communicated. Digital management and communication processes focus on images. The set of images constitutes a highly evocative language; it can be immediately comprehensible or require decoding that refers to specific contexts and cultures. Their importance is evident in human-targeted communication, but they also constitute a data transmission vehicle for empirical knowledge generation by algorithms like those used for artificial image creation from other images. Text-to-image AI generators are trained through the analysis of hundreds of millions of images and their related textual descriptions, which allows the system to learn the relationship between text and visual elements. Through this process, the network is also able to infer other information about reality. Images can be the representation of actual objects, as well as a subjective product of imagination or sensory processing. So too are the images created by Italo Calvino’s Invisible Cities, which bear witness to mental and non-geographical spaces. Cities that cannot be seen can be constructed from their poetical descriptions. What textual variables affect the realization of an image? How do the datasets that different text-to-image tools draw on affect image realization? Can these tools be trained from the user’s realization of an image? The paper collects the first outcomes of a research project comparing the results produced by the main image-generation tools from text descriptions extracted from Calvino’s book.