Version 1
: Received: 25 May 2024 / Approved: 27 May 2024 / Online: 27 May 2024 (07:32:39 CEST)
How to cite:
Zhang, J.; Huang, Y.; Li, Z.; Li, Y.; Yu, Z.; Li, M. Development of a Method for Commercial Style Transfer of Historical Architectural Facades Based on Stable Diffusion Models. Preprints2024, 2024051680. https://doi.org/10.20944/preprints202405.1680.v1
Zhang, J.; Huang, Y.; Li, Z.; Li, Y.; Yu, Z.; Li, M. Development of a Method for Commercial Style Transfer of Historical Architectural Facades Based on Stable Diffusion Models. Preprints 2024, 2024051680. https://doi.org/10.20944/preprints202405.1680.v1
Zhang, J.; Huang, Y.; Li, Z.; Li, Y.; Yu, Z.; Li, M. Development of a Method for Commercial Style Transfer of Historical Architectural Facades Based on Stable Diffusion Models. Preprints2024, 2024051680. https://doi.org/10.20944/preprints202405.1680.v1
APA Style
Zhang, J., Huang, Y., Li, Z., Li, Y., Yu, Z., & Li, M. (2024). Development of a Method for Commercial Style Transfer of Historical Architectural Facades Based on Stable Diffusion Models. Preprints. https://doi.org/10.20944/preprints202405.1680.v1
Chicago/Turabian Style
Zhang, J., Zhilin Yu and Mingfei Li. 2024 "Development of a Method for Commercial Style Transfer of Historical Architectural Facades Based on Stable Diffusion Models" Preprints. https://doi.org/10.20944/preprints202405.1680.v1
Abstract
In the sphere of urban renewal in historic districts, preserving and innovatively reinterpreting traditional architectural styles remains a primary research focus. However, the modernization and adaptive reuse of traditional buildings often necessitate changes in their functionality. To cater to the demands of tourism in historic districts, many traditional residential buildings require conversion to commercial use, resulting in a mismatch between their external form and internal function. This study explores an automated approach to transform traditional residences into commercially viable designs, offering an efficient and scalable solution for the modernization of historic architecture. We developed a methodology based on diffusion models, focusing on a dataset of nighttime shopfront facades. By training a Low-Rank adaptation (LoRA) model and integrating the ControlNet model, we enhanced the accuracy and stability of generated images. The methodology’s performance was validated through qualitative and quantitative assessments, optimizing batch size, repetition, and learning rate configurations. These evaluations confirmed the method’s effectiveness. Our findings significantly advance the modern commercial style transformation of historical architectural facades, providing a novel solution that maintains aesthetic and functional integrity, thereby fostering breakthroughs in traditional de-sign thinking and exploring new possibilities for the preservation and commercial adaptation of historical buildings.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
