The stylistic emulation of historical building facades presents significant challenges for artificial intelligence (AI), particularly for complex and data-scarce styles like Krakow’s Eclecticism. This study aims to develop a methodological framework for a “typological transcoding” of style that moves beyond mere visual mimicry, which is crucial for heritage preservation and urban renewal. The proposed methodology integrates architectural typology with Low-Rank Adaptation (LoRA) for fine-tuning a Stable Diffusion (SD) model. This process involves a typology-guided preparation of a curated dataset (150 images) and precise control of training parameters. The resulting typologically guided LoRA-tuned model demonstrates significant performance improvements over baseline models. Quantitative analysis shows a 24.6% improvement in Fréchet Inception Distance (FID) and a 7.0% improvement in Learned Perceptual Image Patch Similarity (LPIPS). Furthermore, qualitative evaluations by 68 experts confirm superior realism and stylistic accuracy. The findings indicate that this synergy enables data-efficient, typology-grounded stylistic emulation, highlighting AI’s potential as a creative partner for nuanced reinterpretation. However, achieving deeper semantic understanding and robust 3D inference remains an ongoing challenge.

Stylistic emulation of historical building facades, especially for complex and data - scarce styles such as Krakow's Eclecticism, poses significant challenges for AI. This study develops a methodological framework for a deeper, typologically - informed "transcoding" of style beyond mere visual mimicry, which is crucial for heritage preservation, urban renewal, and digital heritage. We integrate architectural typology with Low - Rank Adaptation (LoRA) fine - tuning of a Stable Diffusion model, specifically the FLUX architecture. The process includes typology - guided image dataset preparation (selection and hierarchical labeling) and precise control of LoRA training parameters like learning rate and loss value. The typologically - guided LoRA - tuned model significantly outperforms baseline models in quantitative metrics—FID, LPIPS, and PSNR—and in expert qualitative evaluations regarding realism and stylistic accuracy. LoRA's loss value and application weights effectively balance creative variation and faithful style emulation. This research validates the synergy between architectural typology and AI for sophisticated stylistic emulation, positioning AI as an intelligent creative partner for nuanced reinterpretation. However, challenges in achieving deeper semantic understanding and 3D modeling persist.