A knowledge-augmented diffusion framework for generating regionally distinctive architectural facades: a case study of Huizhou dwellings

Abstract

Generating heritage facade images with recognizable regional style is increasingly feasible with diffusion models, but prompt engineering or standard fine-tuning alone rarely encodes the construction logic, spatial hierarchy, and symbolic rules that make such heritage architecture intelligible. This gap is critical for Huizhou traditional dwellings (HzTD), a distinctive type of vernacular architecture in southern Anhui Province, China, whose facade forms depend on typological relations among courtyard layouts, timber-frame bay rhythms, materials and decorative motifs. To address this gap, this study proposes a Knowledge-Augmented Stable Diffusion Model (KA-SDM) for semantic representation and controlled facade generation of HzTD. The framework first organizes construction knowledge into a CRM-inspired property graph schema implemented in Neo4j, where posts, spaces, ratios, wall plans, elements, and details are represented as queryable entities and relations. Rule-based graph inference then derives facade composition attributes, opening configurations, and detail semantics, which are converted by a Prompt-Conditioned Semantic Controller (PCSC) into structured prompt fragments and ControlNet masks. Experiments on Huizhou dwelling facade images from Xidi Village compare KA-SDM with a Stable Diffusion (SD) baseline and ablation settings based on LoRA and ControlNet. The results indicate that knowledge-guided semantic control improves typological consistency, structural plausibility, and expert-rated semantic alignment while preserving visual diversity. This research demonstrates a practical pathway for coupling architectural heritage knowledge representation with generative AI, supporting cultural documentation, design assistance, and virtual reconstruction.