The renovation of traditional architecture contributes to the inheritance of cultural heritage and promotes the development of social civilization. However, executing renovation plans that simultaneously align with the demands of residents, heritage conservation personnel, and architectural experts poses a significant challenge. In this paper, we introduce an Artificial Intelligence (AI) agent, Architectural GPT (ArchGPT), designed for comprehensively and accurately understanding needs and tackling architectural renovation tasks, accelerating and assisting the renovation process. To address users’ requirements, ArchGPT utilizes the reasoning capabilities of large language models (LLMs) for task planning. Operating under the use of tools, task-specific models, and professional architectural guidelines, it resolves issues within the architectural domain through sensible planning, combination, and invocation. Ultimately, ArchGPT achieves satisfactory results in terms of response and overall satisfaction rates for customized tasks related to the conservation and restoration of traditional architecture.

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.

The preservation of historical traditional architectural ensembles faces multifaceted challenges, and the need for facade renovation and updates has become increasingly prominent. In conventional architectural updating and renovation processes, assessing design schemes and the redesigning component are often time-consuming and labor-intensive. The knowledge-driven method utilizes a wide range of knowledge resources, such as historical documents, architectural drawings, and photographs, commonly used to guide and optimize the conservation, restoration, and management of architectural heritage. Recently, the emergence of artificial intelligence-generated content (AIGC) technologies has provided new solutions for creating architectural facades, introducing a new research paradigm to the renovation plans for historic districts with their variety of options and high efficiency. In this study, we propose a workflow combining Grasshopper with Stable Diffusion: starting with Grasshopper to generate concise line drawings, then using the ControlNet and low-rank adaptation (LoRA) models to produce images of traditional Minnan architectural facades, allowing designers to quickly preview and modify the facade designs during the renovation of traditional architectural clusters. Our research results demonstrate Stable Diffusion’s precise understanding and execution ability concerning architectural facade elements, capable of generating regional traditional architectural facades that meet architects’ requirements for style, size, and form based on existing images and prompt descriptions, revealing the immense potential for application in the renovation of traditional architectural groups and historic districts. It should be noted that the correlation between specific architectural images and proprietary term prompts still requires further addition due to the limitations of the database. Although the model generally performs well when trained on traditional Chinese ancient buildings, the accuracy and clarity of more complex decorative parts still need enhancement, necessitating further exploration of solutions for handling facade details in the future.

In the sphere of urban renewal of 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 their internal function. This study explored 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 the generated images. The methodology’s performance was validated through qualitative and quantitative assessments, optimizing the 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 the aesthetic and functional integrity, thereby fostering breakthroughs in traditional design thinking and exploring new possibilities for the preservation and commercial adaptation of historical buildings.