 A study on the path of Lingnan architectural style in China based on AI generation technologyWeidi Zhang (), Lei Wen () and Ruslana Bezuhla () International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 2, 908-921 Abstract: This paper explores the application of AI-generated content in architectural design, focusing on the Lingnan architectural style. Using Stable Diffusion and LoRA fine-tuning models, this study simulates and generates typical design features of Lingnan architecture by training on architectural images. Conditional control methods, such as ControlNet, are incorporated to enhance spatial structure recognition and architectural detail, ensuring precise outputs. Additionally, the study examines a hybrid generation approach, blending traditional Lingnan and modern architectural styles to evaluate potential style transitions and innovations. Findings suggest that AI generation technology effectively captures Lingnan architectural details while fostering style integration and evolution. This research provides a valuable technical and theoretical foundation for the digital preservation of Lingnan architecture and contemporary design. Keywords: AI generation techniques; Lingnan architecture; LoRA; Stable Diffusion. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:2:p:908-921:id:5390 Access Statistics for this article International Journal of Innovative Research and Scientific Studies is currently edited by Natalie Jean More articles in International Journal of Innovative Research and Scientific Studies from Innovative Research Publishing |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.