Scholarlink

<p>Preserving the architectural heritage of traditional historical districts is a crucial aspect of urban renewal. Traditional design workflows are time-consuming and subjective. Current data-driven design methods generate specific style images but are labor-intensive. Recent research highlights Stable Diffusion models’ potential in generating high-fidelity images based on prompts. However, research applying these models to historical districts is scarce, with challenges in creating effective prompts and training parameters. This study proposes a framework combining Stable Diffusion models with expert system-based techniques to generate architectural facades from professional prompts. We constructed a dataset of traditional arcade facades, trained Low-Rank Adaptation (LoRA) models, and integrated ControlNet models to enhance controllability. Experimental results showed our models excelled in precision, realism, and diversity. Both qualitative and quantitative evaluations, along with practical application tests, confirmed our approach aids designers and prompts engineers, contributing to the preservation of architectural heritage and the renewal of urban historical districts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Preserving architectural heritage in urban renewal: a stable diffusion model framework for automated historical facade generation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月10日

查看全文
  • Zheyuan Kuang,
  • Jiaxin Zhang,
  • Yunqin Li,
  • Tomohiro Fukuda

摘要

Preserving the architectural heritage of traditional historical districts is a crucial aspect of urban renewal. Traditional design workflows are time-consuming and subjective. Current data-driven design methods generate specific style images but are labor-intensive. Recent research highlights Stable Diffusion models’ potential in generating high-fidelity images based on prompts. However, research applying these models to historical districts is scarce, with challenges in creating effective prompts and training parameters. This study proposes a framework combining Stable Diffusion models with expert system-based techniques to generate architectural facades from professional prompts. We constructed a dataset of traditional arcade facades, trained Low-Rank Adaptation (LoRA) models, and integrated ControlNet models to enhance controllability. Experimental results showed our models excelled in precision, realism, and diversity. Both qualitative and quantitative evaluations, along with practical application tests, confirmed our approach aids designers and prompts engineers, contributing to the preservation of architectural heritage and the renewal of urban historical districts.