Historical Building Information Modeling is a digital modeling technology applied to cultural heritage buildings. It has made remarkable progress in aspects such as data integration and management, digital protection of historical buildings, parametric and semantic modeling, multi-source data fusion, and interdisciplinary cooperation platforms. However, the sustainability of this technology has not been explored yet. This paper analyzes nearly a hundred related research achievements between 2010 and 2024 and finds that there is not only a lack of a review of the development and application of Historical Building Information Modeling (HBIM) technology in China, but also a serious shortage of discussions and explorations regarding its sustainability. Therefore, taking the development and application of Historical Building Information Modeling technology in China as the research scope, using relevant practical projects and research achievements in China, and combining a small number of the latest foreign achievements as cases, centering around the research question of the sustainability of Historical Building Information Modeling, this paper adopts the methods of review research and comparative research. It sorts out four development directions and the faced dilemmas in the development process of Historical Building Information Modeling in China and puts forward constructive suggestions for sustainable development, as well as a set of theoretical paths for the sustainability of HBIM technology based on Revit (version 23.0.1.318), Dynamo (version 2.17), Python (version 3.12.1) (Open 3D v0.18 and PointNet++), Network Attached Storage, and cloud-based collaboration platforms. The purpose is to provide a referable path for the sustainable development of Historical Building Information Modeling technology in China.

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.