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 tech- nologies is changing relationships between people and space, contributing to the mutation of iconographic production and the way content is conceived and commu- nicated. Digital management and communication processes focus on images. The set of images constitutes a highly evocative language; it can be immediately comprehen- sible or require decoding that refers to specific contexts and cultures. Their impor- tance is evident in human-targeted communication, but they constitute also 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 with the analysis of hundreds of millions of images and their related textual description, which allows the system to learn the relationship between text and visual element. Through this process, the network is also able to infer other information about reality. Images can be the representation of actual objects, such as a subjective product of the imagination or a sensory processing. So 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 description. What textual variables affect the realization of an image? How do the datasets that the 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 first outcomes of a research conducted that compares the outcomes of the main image generation tools from text descriptions extracted from Calvino’s book.