AI Text-To-Image Procedure for the Visualization of Figurative and Literary Tòpoi

The paper proposes a workflow for translating literary and figurative textual descriptions into AI-generated images through text-to-image neural networks. The research combines linguistic analysis, lexical semantics, prompt engineering, and Stable Diffusion-based image generation to investigate the relationship between verbal and visual representation. Drawing on theories of visual culture, ekphrasis, and Aby Warburg’s Mnemosyne Atlas, the study develops a methodological framework for guiding neural networks through semantic keywords, syntactic structures, contextual references, and prompt modulation. The workflow is tested on literary and architectural texts from different historical periods, including utopian cities, nineteenth-century urban descriptions, and imaginary urban narratives. The research demonstrates how AI image generation can support the visualization of literary spatial imaginaries while also revealing the ambiguities, arbitrariness, and interpretative challenges inherent in translating text into visual form.