Beyond the Line: Between Stylistic Learning and Controlled Generation with Stable Diffusion

The article presents an experiment on the use of generative artificial intelligence for stylistic transposition in architectural drawing, investigating the potential of diffusion-based models in learning authorial graphic languages. Through a protocol based on the fine-tuning of Stable Diffusion 1.5 via DreamBooth, three distinct models were trained on visual datasets dedicated to Paolo Portoghesi, Ludwig Mies van der Rohe, and Le Corbusier. The selected drawings allowed the model to internalize a pure stylistic code, based on strokes, compositional syntax, and graphic geometry. Controlled generation was carried out through a nodal pipeline in ComfyUI, with the integration of ControlNet for structural control. The qualitative analysis of the outputs highlighted coherent yet non-imitative generative behaviors: decorative amplification for Portoghesi, geometric rarefaction for Mies, plastic-chromatic variation for Le Corbusier. The results demonstrate that AI can operate not only as an executive tool but as a critical and interpretative agent capable of revealing the latent structures of architectural style. The research opens methodological and applicative perspectives for the construction of digital repertoires, educational tools, and unprecedented forms of computational representation.