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.

In the evolving domain of built-up area surveillance, remote sensing technology emerges as an essential instrument for Change Detection (CD). The introduction of deep learning has notably augmented the precision and efficiency of CD. This study focuses on the integration of deep learning methodologies, specifically the diffusion model, into remote sensing CD tasks for built-up urban areas. The goal is to explore the potential of a pre-trained Text-to-Image Stable Diffusion model for CD tasks and propose a new model called the Difference Guided Diffusion Model (DGDM). DGDM incorporates multiple pre-training techniques for image feature extraction and introduces the Difference Attention Module (DAM) and an Image-to-Text (ITT) adapter to improve the correlation between image features and text semantics. Additionally, DGDM utilizes attention generated from pre-trained Denoise UNet to enhance CD predictions. The effectiveness of the proposed method is evaluated through comparative assessments on four datasets, demonstrating its superiority over previous deep learning methods and its ability to produce more precise and detailed CD results. This innovative approach offers a promising direction for future research in urban remote sensing, emphasizing the potential of diffusion models in enhancing urban CD precision and automation. Our implementation code is available at https://github.com/morty20200301/cd-diffusion .

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.