Urban renewal and transformation processes necessitate the preservation of the historical urban fabric, particularly in districts known for their architectural and historical significance. These regions, with their diverse architectural styles, have traditionally required extensive preliminary research, often leading to subjective results. However, the advent of machine learning models has opened up new avenues for generating building facade images. Despite this, creating high-quality images for historical district renovations remains challenging, due to the complexity and diversity inherent in such districts. In response to these challenges, our study introduces a new methodology for automatically generating images of historical arcade facades, utilizing Stable Diffusion models conditioned on textual descriptions. By classifying and tagging a variety of arcade styles, we have constructed several realistic arcade facade image datasets. We trained multiple low-rank adaptation (LoRA) models to control the stylistic aspects of the generated images, supplemented by ControlNet models for improved precision and authenticity. Our approach has demonstrated high levels of precision, authenticity, and diversity in the generated images, showing promising potential for real-world urban renewal projects. This new methodology offers a more efficient and accurate alternative to conventional design processes in urban renewal, bypassing issues of unconvincing image details, lack of precision, and limited stylistic variety. Future research could focus on integrating this two-dimensional image generation with three-dimensional modeling techniques, providing a more comprehensive solution for renovating architectural facades in historical districts.

翻译：城市更新与改造过程需要保护历史城市肌理，尤其在具有建筑与历史意义的区域。这些地区建筑风格多样，传统上需要大量前期研究，且常导致主观性结果。然而，机器学习模型的出现为生成建筑立面图像开辟了新途径。尽管如此，由于历史街区的复杂性和多样性，为其改造生成高质量图像仍具挑战。针对这些问题，本研究提出了一种新方法，利用以文本描述为条件的稳定扩散模型自动生成历史骑楼立面图像。通过对多种骑楼风格进行分类和标注，我们构建了多个逼真的骑楼立面图像数据集。我们训练了多个低秩自适应（LoRA）模型来控制生成图像的风格属性，并辅以ControlNet模型以提高精度和真实性。该方法生成的图像在精度、真实性和多样性方面均表现优异，显示出在实际城市更新项目中的巨大潜力。这种新方法为城市更新中的传统设计流程提供了更高效、更准确的替代方案，避免了图像细节不可信、缺乏精度及风格多样性有限等问题。未来研究可聚焦于将这种二维图像生成与三维建模技术相结合，为历史街区建筑立面修缮提供更全面的解决方案。