Development of a Method for Commercial Style Transfer of Historical Architectural Facades Based on Stable Diffusion Models

1. Introduction

Historical architecture serves as a testament to history, encapsulating substantial cultural and artistic value, reflecting the historical context, societal customs, and aesthetic preferences of a region [1]. However, with rapid societal development and modernization, these structures face numerous challenges, including inadequate functionality for contemporary needs, structural safety concerns, and elevated maintenance costs [2]. Therefore, it is critical to transform the historical architecture to modern forms, in order to satisfy the contemporary needs while addressing the safety and maintenance issues. To achieve this goal, prior work has focused on balancing the preservation of historical architecture with contemporary demands. For example, Bullen & Love [3] proposed the modernization of historical architecture not only to protect heritage but also to enhance the functionality, safety, and environmental sustainability of cultural districts. This approach facilitates the practical use of historical sites while ensuring their preservation.

With the rise of experiential tourism, traditional historical districts have become crucial locales for tourist experiences and serve as conduits for innovative business models. These cultural districts vividly display culture and history, enrich cultural content, and contribute to the preservation and transmission of culture [4]. They can also transform into centers of social and economic vitality [5], thereby accommodating modernization needs. Commercial activities significantly impact the renovation of traditional buildings, enhancing the vibrancy of historical districts. Historically, residential buildings within these districts primarily served as living spaces. However, the increasing demands of tourism and commerce have shifted their function from residential to commercial [6]. This transformation has led to a divergence between the buildings' original residential purpose and their new commercial uses. As such, the adaptation of traditional residential architecture for commercial purposes is not only significant but necessary, catering to the evolving needs of historical cultural districts. This process requires a delicate balance between preserving historical values and integrating modern commercial functionalities, presenting a complex challenge in urban cultural conservation [7]. This transformation underscores the dynamic interplay between maintaining historical integrity and meeting contemporary economic demands.

Building on the discussion of the commercial transformation of traditional residential architecture, it is essential to consider broader principles of historical architecture restoration. The Athens Charter, for instance, stresses that new constructions in proximity to historical districts should harmonize with the traditional aesthetic, preserving the historical period's visual continuity [8]. This principle demands that architects adopt a macroscopic perspective, deeply understand and respect historical culture, and challenge their capability to manage buildings from different historical contexts and cultural districts [9]. In the field of architecture, while it is possible to categorize facade elements, precisely fitting elements of individual buildings or applying strict mathematical proportions remains a daunting task. This process is not only time-consuming and labor-intensive but also challenging to ensure the accuracy and objectivity of the assessments.

The complexity of China's traditional architectural system, shaped by regional variations, historical transitions, and diverse design philosophies, poses significant challenges in the preservation and modernization of heritage buildings. One major challenge is the often insufficient understanding of traditional construction techniques among architects, which can lead to renovations that deviate significantly from traditional aesthetics, thus disrupting the cohesive appearance of architectural complexes [10]. In recent years, the urgent need for the preservation of historical architecture has necessitated optimizing resource allocation and conserving human efforts. This shift has emphasized the importance of freeing up human resources from labor-intensive tasks such as data organization, allowing more focus on core preservation activities and innovative design. Additionally, the commercial transformation of traditional architectural facades introduces further complexities. These transformations need to accommodate modern functional requirements while preserving and promoting historical and cultural values. The aesthetic form of the facade, playing a decisive role in defining the architectural character, must integrate style, proportion, and modern commercial needs without compromising the traditional integrity. Together, these challenges underscore the delicate balance required between modernization demands and traditional values, demanding a deep understanding of historical contexts and innovative approaches in both design and construction.

Navigating these architectural challenges necessitates not only a deep appreciation of historical aesthetics but also the adoption of innovative tools. With advancements in artificial intelligence, particularly through the development of Generative Adversarial Networks (GANs) by Goodfellow et al. [11], and further innovations like CycleGAN by Zhu et al. [12], the architecture industry is witnessing a transformation in designing historical districts. These AI models enable the creation of high-resolution images and the transformation of existing images to match the aesthetic of historical districts, while preserving semantic content. For instance, CycleGAN has been specifically applied to generate facades that harmonize with traditional architectural styles, as demonstrated by Sun et al. [9]. These technologies assist architects by generating design schemes that incorporate historical features efficiently, reducing the time required for data collection and preliminary design processes. Moreover, they aid in complex data analysis, facilitating the integration of modern functionalities and energy efficiency into traditional forms. This synthesis of generative AI tools supports the preservation of cultural heritage and enhances the utility and sustainability of renovated structures, showcasing a significant leap in how architectural design can accommodate and respect historical contexts while embracing modern technology.

In transforming traditional architectural facades into multifunctional commercial styles, this study introduces novel generation and evaluation methods using AI-generated content. The study's key contributions are threefold: 1) We developed a general framework based on Stable Diffusion models for commercial style transfer of historical facades, emphasizing a systematic design process and broad applicability across various building styles. Integrating the Low-Rank adaptation (LoRA) and ControlNet models enhances image accuracy and stability, making the approach versatile for architectural modernization. 2) We proposed a combined qualitative and quantitative evaluation method using Contrastive Language-Image Pre-training (CLIP) and Frechet Inception Distance (FID) scores to assess the quality of generated facades comprehensively, providing insights for future model optimization. 3) A specialized dataset featuring nighttime shopfront images from the historical districts was created, offering high-quality data support that enhances image detail and style consistency. This dataset not only meets the study’s experimental needs but also serves as a valuable resource for future research in similar domains.

2. Related Works

With the advancement of artificial intelligence technologies, the emergence of deep generative models such as Generative Adversarial Networks (GANs) and Stable Diffusion models has introduced new possibilities in architectural design and style transfer. These technologies, by learning from vast datasets, can autonomously generate images in designated styles, providing designers with a wealth of inspiration and rapid iteration capabilities for their projects. This research reviews the literature related to GANs and Stable Diffusion, compares their advantages, and discusses the potential and applications of generative AI in the design of facades for historical Chinese architecture.

2.1. Generative Adversarial Networks in Design Transfer

GANs are a type of machine learning algorithm that generates realistic images through the adversarial competition between two neural networks, further developed by researchers in various directions. GANs have been widely applied in the field of image generation, demonstrating significant capabilities in producing high-quality, high-resolution images. They hold considerable potential in generating traditional architectural facades and urban redevelopment. In the realm of traditional architectural facades, Yu et al. [13] utilized GANs to generate Chinese traditional architectural facades by training the model on labeled data samples and elements of historical Chinese architecture. Additionally, GANs also play a supportive role in urban redevelopment design. Sun et al. [9] and Ali & Lee [14] have developed tools based on GANs for urban renewal, preserving unique facade designs in urban districts. Khan et al. [15] employed GANs to generate realistic Urban Mobility Networks (MoGAN), thus depicting the entirety of mobility flows within a city. Hence, GANs can, to a certain extent, enhance the efficiency of facade design during the initial phases of design projects. Employing GAN models can reduce the cultural knowledge requirements for designers, generating a diversity of images with characteristics of traditional architectural facades, thereby facilitating work in the early stages [16].

2.2. Research on Diffusion Models in Design Transfer

In comparison to GANs, diffusion models have achieved more favorable outcomes in terms of image generation quality [17,18]. In the realm of image generation, Ho & Salimans [19] modified diffusion models, introducing the Denoising Diffusion Implicit Models (DDIMs), which significantly accelerate the sample generation process—10 to 50 times faster than Denoising Diffusion Probabilistic Models (DDPMs). DDPMs generate data through a learned reverse diffusion process [20], iteratively denoising to produce clear, high-quality images. The VQ-Diffusion model, a text-to-image architecture, is capable of generating high-quality images under both conditioned and unconditioned scenarios, excelling at creating more complex scenes [20]. W. Wang et al. [18] initially tested and validated the efficacy of diffusion models for semantic image synthesis, enhancing the models performance and displaying superior visual quality of generated images. While the output of single-step models is not yet competitive with GANs, it significantly surpasses previous likelihood-based single-step models. Future efforts in this area may bridge the sampling speed gap between diffusion models and GANs without compromise [17]. Furthermore, Kim & Ye [21] introduced the Diffusion CLIP model, which not only speeds up computations but also achieves near-perfect inversion—a task that presents certain limitations with GANs. Thus, the gap in computational speed between diffusion models and GANs continues to narrow, and diffusion models are establishing a distinct edge in application scope.

The Stable Diffusion model, an evolution of the diffusion model technology from text-to-image, combines large-scale training data and advanced deep learning techniques to create a more stable and adaptable conditional image generator. A key advantage of Stable Diffusion model is its time and energy efficiency [22]. Known for its user-friendly interface, Stable Diffusion allows users to adjust the interface according to their needs, making it easy for even those inexperienced with AI to generate high-quality images [22]. Jo et al. [23] elucidates an approach that utilizes Stable Diffusion models to develop alternative architectural design options based on local identity. Users simply input a textual description of the desired image to rapidly generate corresponding visuals. This not only saves time but also enables designers to quickly and conveniently experiment with different ideas and concepts. Moreover, it offers good stability and controllability in style transfer, providing designers with greater creative freedom and encouraging widespread use. Consequently, the Stable Diffusion model is being rapidly adopted by designers, producing high-quality images efficiently.

2.3. Lateral Comparison and Limitations

Due to design choices in network structures, target functions, and optimization algorithms, GANs encounter challenges such as mode collapse and instability during training [24]. Training a GAN model requires not only extensive sample data and neural architecture engineering but also specific "tricks" [25]. However, diffusion models also exhibit limitations in generation stability and content consistency [26]. Specifically, when a new concept is added, diffusion models tend to forget the earliest text-to-image models, thereby reducing their ability to generate past high-quality images. Recent studies indicate that the LoRA model holds potential in enhancing the accuracy of stable diffusion-generated images. L. Sun et al. [26] proposed a content-consistent super-resolution approach to improve the training of diffusion models, aiming to enhance the stability of graphic generation. Smith et al. [27] introduced the C-LORA model to address catastrophic past issues in continuous diffusion models. Additionally, Luo et al. [28] presented Latent Consistency Models (LCM) - LoRA, which functions as an independent and efficient neural-network-based solver module, enabling rapid inference with minimal steps across various fine-tuned Stable Diffusion models and LoRA models. Hence, the LoRA model can generate images with stable styles and reduced memory usage, allowing for fine-tuning that further meets designers needs in architectural transformations.

However, textual descriptions often fail to generate accurate final results and struggle to understand complex text. Thus, additional control conditions are needed alongside textual descriptions to increase the accuracy of image generation. ControlNet is a neural network architecture designed to add spatial conditional control to large pre-trained text-to-image diffusion models like Stable Diffusion models [29]. It enables precise control of the generated images using conditions such as edges, depth, and human poses. Zhao et al. [30] proposed Uni-ControlNet, which allows the use of different local and global controls within the same model, further reducing the costs and size of model fine-tuning while enhancing the controllability and combinability of text-to-image transformations. Therefore, by integrating ControlNet into our model and controlling architectural outlines through line art extraction, we reduce excessive transformations of historical architecture, exploring the efficiency of models combining line art control conditions with ControlNet [31].

This study aims to explore the technique of commercial style transformation of residential buildings based on the Stable Diffusion model, proposing the LoRA model for transforming facades of traditional Chinese residences. Through this model, designers can easily transform the facades of traditional residences into commercially attractive design proposals while preserving the architectural historical characteristics and cultural value. Additionally, we emulate Sun's data collection method by manually capturing photographs from streets to create a specialized training dataset needed for this model. We also focus more on commercial signage design, incorporating finer architectural details to create a platform that autonomously generates commercialized traditional residences, serving both natural heritage conservation and urban cultural tradition preservation [9].

3. Method and Material

3.1. Methodology

This paper describes a methodology utilizing diffusion models for the automated generation of images, designed to facilitate the commercial style transformation of traditional residences. This process not only ensures the quality of the transformation but also extends its application to the aesthetic design of various building types. The refined workflow, depicted in Figure 1, involves several key steps. Initially, commercial facade images of southern residences are collected and curated to establish a high-quality dataset of commercialized traditional residences. This dataset, which is crucial as it guides the process toward the desired outcome, contains vital information on traditional architectural styles. While several open-source facade datasets are available for network training, a proprietary dataset representing the traditional style of a specific district is necessary. Subsequently, each facade image is labeled with textual tags to form the training dataset for the LoRA model, enabling the generation of high-quality, stylized images by the Stable Diffusion model system. The images generated undergo both qualitative and quantitative assessments. Finally, the ControlNet model is introduced to enhance the accuracy and stability of the outputs, with the workflow efficacy validated against existing cases of traditional residences.

3.2. Data Collection and Processing

For this study, Wanshou Palace in Jiangxi Province was selected as the subject. Located in Nanchang City, Jiangxi Province, China (Figure 2), this street is a large, comprehensive urban district. It spans a significant area of 5.46 hectares and preserves valuable wooden residential buildings, representing one of the few urban areas that retain their historical structure and scale.

The commercial sections of the Wanshou Palace Historical and Cultural District feature a variety of traditional architectural facades that have been adapted for commercial use. This district exemplifies how historical architecture can be integrated with modern commercial activities to create a vibrant and historically rich commercial area, making it an outstanding case study for the commercial style transformation of historical architecture. The variety in shop signs within the Wanshou Palace district—differing in form, location, materials, and lighting—poses a design challenge in creating multiple styles quickly to meet client needs while ensuring cohesion within the district street scape.

Compared to traditional design phases, the diffusion model plays a crucial role in this study by learning the commercial style transformation of historical architecture. Additionally, the architectural style complexity of the street makes it an ideal target for testing the proposed method. All images were manually captured by the authors from the commercial streets, selecting only high-quality shopfront images to ensure the quality and reliability of the facade image dataset. The selection was based on three considerations: (1) the local government positive appraisal of the commercial value of the Wanshou Palace historical and cultural district, highlighting successful commercial transformations of historical architecture; (2) lighting and weather conditions, with night-time images better showcasing the effects of the facade shop signs; (3) the richness of architectural details, as the facades of historical architecture typically include complex decorations and details, making high-quality, high-resolution images particularly important.

A total of 71 photos were initially taken from the street, with 43 remaining after removing those with incorrect perspectives or severe obstructions by crowds. Each image underwent preprocessing, and textual tags were generated for each using DeepDanbooru. Figure 3 illustrates the preprocessing steps taken for each image, including correction, squarization, and uniform adjustment to a resolution of 512×512 pixels.

Based on the generated textual tags, specific textual labels were applied to each image according to the characteristic elements of commercial architectural facades. These specific textual labels included the location of the shop signs: entrance lintels, shop fronts along the street; the structure of the shop signs: signboards, awnings; and the arrangement of the shop signs: parallel or vertical. Figure 4 shows examples of traditional architectural shop sign facades paired with their corresponding textual labels. This rigorous tagging and preprocessing lay the foundation for the subsequent training of the LoRA model and the generation of stylized commercial facade images.

3.3. Model Training and LoRA Model Generation

The LoRA model achieves efficient adaptation to specific tasks by incorporating low-rank matrices within critical layers of the model. For this study, 43 images of nighttime traditional architectural shopfronts were used to define a specific image domain. The objective was for the model to learn and generate new images with similar styles and elements.

We selected v1-5-pruned.safetensorsas [32] the base and adapted self-attention layers or other critical layers in the model to accommodate the visual characteristics of nighttime historical architecture.

Suppose $W$ is the weight matrix of a layer in the pretrained model. We introduce two low-rank matrices $A$ and $B$ to modify this weight. The adjusted weights $W^{~}$ can be represented as: $W^{~}=W+A{B}^T$.The adjustment and training process involves modifying the original weights by the low-rank matrices to form new weight matrices $W$, while keeping most parameters of the pretrained model unchanged, only training these low-rank matrices [33]. The 43 images and their annotations are used to train these low-rank matrices, adapting the model for the task of generating images of traditional architectural shopfronts at night. These matrices are specifically designed to capture the visual effects of architectural elements and lighting conditions at night.

The generative network architecture of the LoRA model uses two downsampling convolution layers with a stride of 2, followed by five residual blocks to process image features, and then two upsampling convolution layers with a stride of 1/2 to generate the target images. All non-residual block convolution layers are followed by instance normalization to accelerate training and enhance the quality of generation [33].

During training, the LoRA model aims to minimize the difference between the generated images and the actual nighttime shopfront images. This is achieved through a combination of reconstruction loss and regularization loss of the low-rank matrices:

Reconstruction Loss ($L_{recon}$) measures the difference between the generated images and the target nighttime images. If $x$ is the input image, $y$ is the target image, and $G$ is the generator (including weight adjustments), then the reconstruction loss can be expressed as:

$$L_{recon}=\parallel G\left(x\right)-y{\parallel }_2^2,$$

(1)

Regularization Loss ($L_{reg}$)​ is to prevent overfitting and maintain the model generalization capability, a regularization loss is applied to the low-rank matrices $A$ and$B$:

$$L_{reg}=\lambda \left(\parallel A{\parallel }_F^2+\parallel B{\parallel }_F^2\right),$$

(2)

Here, $\Vert .\mathrm{}\Vert F$ denotes the Frobenius norm, and $\lambda$ is the weight of the regularization term, used to balance the impact of both losses.

The total loss $L_{total}$​ for the LoRA model is a combination of these two losses:

$$L_{total}=L_{recon}+L_{reg},$$

(3)

Using these two primary loss functions, during training, the values of low-rank matrices $A$ and $B$ are adjusted by minimizing the total loss $L_{total}$. This involves a loss based on the parameter updates of low-rank matrices $A$ and $B$ and an evaluation loss for the quality of generated images, which might include pixel-level or feature-level losses to ensure that the visual quality of generated images matches the original nighttime images.

4. Results

4.1. Commercial Style LoRA Model and Generated Outcomes

In our study, we successfully trained six LoRA models on a single NVIDIA GeForce RTX 3090 graphics card, utilizing the same dataset of traditional architectural shopfront images and the v1-5-pruned.safetensors base model. Table 1 meticulously details the training parameters for these six LoRA models. Throughout the training process, we finely tuned and optimized the models by adjusting critical parameters such as batch size, repeat frequency, and learning rate to enhance their performance. The loss values of the models were continuously monitored in real-time; this provided a crucial metric for assessing the models learning efficacy and for making strategic adjustments to the training regimen. Figure 5 displays the loss values across the six LoRA models.

We conducted a comprehensive and detailed comparative test of facade generation for commercial style transformation using the six trained LoRA models, to ensure that the model we selected exhibited the optimal performance. Figure 6 displays a comparison of traditional architectural facades generated using the LoRA models within the Stable Diffusion model framework; the horizontal axis represents different LoRA models, and the vertical axis shows the weight values of the LoRA models. We tested the performance of the LoRA models across a range of weight values from 0.4 to 1.0. The comparison reveals that different LoRA models produce varying results at different weight settings in terms of how effectively they trigger the elements specified in the prompts. Notably, Model 2 performs optimally at weight values between 0.7 and 0.9, where the generated facades accurately and robustly trigger the labels in the prompts, with overall clarity and brightness in the images.

Considering all factors, we ultimately selected the best-performing model for fine-tuning the larger model in Stable Diffusion models to generate images of traditional architectural shopfronts from prompts. Compared to actual images, it is evident that the generated shopfront facades accurately learn the necessary details of the commercial style transformation of historical architecture. This learning extends beyond mere replication of existing facade data, encompassing a comprehensive understanding and integration of the shop signs forms, positioning, and lighting colors. These results also indicate that our model can provide substantial technical support for the renewal and transformation of urban historical districts.

4.2. Quantitative Analysis

In this study, a series of experiments was designed to evaluate the performance of different versions of LoRA models under various weight configurations. The experiments employed two key performance metrics: the CLIP score and the FID score. The CLIP score assesses the model performance on language-image alignment tasks, while the FID score measures the quality of the generated images. By utilizing these metrics, we can obtain a comprehensive understanding of the model performance. Each weight setting of every LoRA model generated 200 images under randomly assigned seeds, from which 50 images were randomly selected to calculate both the FID and CLIP scores.

4.2.1. Analysis of FID Scores

During the training of the LoRA models, which are used to tune a larger model for image generation tasks—either by integration with an image generation model or by adjusting the model to handle image-related inputs and outputs—the oscillatory nature of the loss function makes it unreliable to directly assess model performance through loss curves. For this reason, the FID is employed as an evaluation metric [34]. The FID score operates by comparing the distribution of generated images to that of real images within a feature space. This feature space is extracted using a pre-trained deep learning model, typically the Inception v3 model [35]. FID calculates the mean and covariance of the features of both generated and real images, and then computes the Frechet distance (also known as Wasserstein-2 distance) between these distributions. A lower FID score indicates a closer distribution of generated images to real images, implying higher quality of the generated images and thus reflecting superior model performance. FID has become an essential tool for assessing performance in image generation tasks, especially where direct evaluation of model performance using loss curves is challenging. The stability of FID and its consistency with human visual perception make it a reliable metric for evaluating LoRA models.

Research indicates that the range of FID scores can vary depending on the dataset and model architecture. For instance, GAN models generating handwritten digits on the MNIST dataset exhibit FID scores ranging from 78.0 to 299.0 [36]; models trained on higher-resolution natural image datasets show FID scores between 6.9 and 27 [37]. Additionally, studies on high-resolution pre-labeled datasets have reported FID scores ranging from 22.6 to 104.7 [38]; Sun reported FID ranges from 85 to 145.9. Consequently, for the facade dataset used in this study, by applying the LoRA model across different facade datasets, a benchmark range of 140-190 [9] provides a reference standard, aiding in the understanding and evaluation of the LoRA model performance on specific datasets.

4.2.2. Analysis of FID Scores Across Different LoRA Model Versions

The FID scores for several LoRA models are displayed, indicating that image quality has improved with model updates (Figure 7). Significant variations in the distribution of FID scores are noted under different batch sizes, learning rates, and types of optimizers. Particularly, under a configuration with a batch size of 2, a learning rate of 0.0002, and using the AdamW8bit optimizer, the models produced the highest quality images with the lowest FID scores, notably Model 2 as previously mentioned.

The range of FID scores for all models is within a certain threshold (indicated by a green dashed line for the minimum FID and a red dashed line for the maximum FID). This suggests that all models perform within an acceptable range, yet no single model is optimal across all weight values. The variation in FID scores under different weight configurations indicates that model performance fluctuates with changes in weight (Figure 8). Most models appear to perform better at weights approximately between 0.7 and 0.8, where almost all versions of the models achieve their lowest FID scores, although the specific optimal weight may vary by model. As weights increase, most models show a fluctuating trend in FID scores, which may indicate that different weight settings inconsistently affect each model, potentially involving specific characteristics of the model structure or the training process.

4.2.3. Analysis of CLIP Scores Across Different LoRA Model Versions

Experimental results show significant fluctuations in CLIP scores across different versions of LoRA models as the models are iteratively updated (Figure 9). Each version was tested under varying weight parameters, and median CLIP scores for each version exhibited various degrees of increase or decrease as weights changed from 0.4 to 1.0, suggesting that weight adjustments significantly impact model performance. For example, LoRAModel_V3 performs best at a weight of 0.7, while LoRAModel_V2 reaches its peak performance at a weight of 1.0. This may be because increasing the weight to a certain level better captures the mapping relationships between language and images. Overall, the median CLIP score at a weight of 0.7 is usually higher than other weight configurations, indicating that this weight setting may be more suitable for language-image alignment tasks within the current model architecture.

Further experimental analysis examined the impact of hyperparameters such as batch size, repeat count, learning rate, and type of optimizer on CLIP scores through a violin plot analysis (Figure 10). Results show that a batch size of 2 yields a higher median and more stable CLIP scores compared to configurations with a batch size of 3, implying that smaller batch sizes contribute to enhanced model performance. Increasing the number of repetitions appears to have a positive effect on model stability, particularly at 8 repetitions, where the coefficient of variation of CLIP scores is minimal, resulting in a tighter score distribution. Regarding learning rates, a setting of 0.0001 results in a more compact distribution of CLIP scores, indicating that this rate is favorable for improving model performance. In terms of optimizer choice, the AdamW optimizer generally achieves higher CLIP scores than the Lion optimizer across most scenarios.

4.3. Qualitative Analysis

While FID and CLIP scores provide a preliminary assessment of image quality and text-image alignment, they are subject to potential errors. To mitigate these errors and enhance the precision and consistency of the results, we also conducted a qualitative analysis by visually comparing the generated images with actual scenes to assess their realism and consistency. According to Borji [39] analysis, this method is the most direct and widely used evaluation approach. Given that this study primarily focuses on whether models can transition traditional historical facades into a commercial style, our qualitative analysis concentrated on architects preferences and the visual effects of the model-generated images. To minimize the impact of personal biases and subjectivity, we invited 10 architectural researchers to rate the generated images across three dimensions: "image quality," "alignment with traditional architectural style," and "commercial effect of the facade." Ratings were quantified on a scale from 1 to 10, with intervals defined as follows: [0, 2) "very low," [2, 4) "low," [4, 6) "medium," [6, 8) "high," [8,10] "very high," to quantify the degree of fit for each criterion. The evaluation process was divided into two stages: initially, participants rated each model images under each weight setting; subsequently, they assessed images generated by the same model under different weights. By aggregating and averaging all researchers scores, we obtained a quantified qualitative analysis.

As shown in Figure 11, a weight analysis of the LoRA model series was conducted to evaluate its performance in the commercial transformation application of historical architecture. Results indicated that within the weight range of 0.7 to 0.9, LoRAModel_v2 and LoRAModel_v5 scored significantly higher than other models, demonstrating better adaptability, suggesting that these two models are more suited to meet the specific needs of traditional architectural transformation. Meanwhile, LoRAModel_v6 and LoRAModel_v3 showed a consistent scoring trend across the entire range of weights, with LoRAModel_v6 slightly outperforming in all scenarios. The results of this qualitative analysis not only affirm the LoRA models capability to effectively convert traditional residential facades into commercial style facades but also offer novel technical pathways for the commercial transformation of historical architecture. Additionally, these findings provide a basis for subsequent model selection and optimization, ensuring that the chosen models achieve the best style transfer effects under specific transformation requirements.

4.4. Applications in Different Scenarios

We have applied this innovative method to projects focused on the commercial style transformation of historical architecture, bringing more efficient and personalized solutions to the commercial renovation of historical architecture. Utilizing architectural sketches and provided onsite photos, we input this constraint information into ControlNet, serving as controlling conditions for style transition in historical architecture. The use of these conditions significantly enhanced the accuracy and relevance of the images generated by our model. Figure 12 displays the facade renovation results based on prompt descriptions and existing photographs. The results show that the shopfront facades generated after inputting the results preprocessed by ControlNet Multi-Level Straight Line Detection (MLSD) model as controlling conditions exhibit controlled changes contempered to before the transformation. These changes extend beyond the facades, potentially affecting the entire streetscape to align more closely with our design objectives. These controllable modifications include the style and positioning of shop signs, design and replacement of components like lighting, as well as significant improvements in overall harmony. These meticulous adjustments are automatically performed by our model, which greatly saves on design and correction time while also ensuring the quality of the design solutions.

In this study, the demonstrated image synthesis effects achieve remarkable levels in terms of realism, style consistency, and detail presentation. Regarding realism, the synthetic images visually approximate real scenes, featuring rich colors and textures, and the effects of lighting and shadows are vividly life-like, showcasing the model accurate capability in capturing lighting effects and material textures. In terms of style consistency, each generated image maintains a high degree of uniformity in style, reflecting the model accurate interpretation and reproduction capability of the commercial style inputs, such as universally recognizable commercial logos, prominent signs, and lighting elements, which are typical expressions of commercial architectural style. In detail handling, the model cleverly integrates commercial elements while preserving the original architectural structures, such as well-designed signs and window advertisements, which enrich the visual effect and enhance the commercial atmosphere, demonstrating the model profound understanding of interpreting and reproducing architectural functionality. Additionally, although all images adhere to the same commercial style, they exhibit a rich diversity in color coordination, sign design, and brand display, reflecting the traits of a high-quality synthesis model, that is, while being faithful to a set style, it still manages to create personalized and diverse images.

5. Discussion

This study aims to present a systematic method for automatically generating facades of traditional buildings in a commercial style and to explore the application effectiveness of this method in the design of traditional building shopfronts, thereby verifying its feasibility. A central discussion of this study is whether this method can enhance the efficiency and outcomes of traditional architectural preservation processes. However, the method requires further in-depth research and refinement.

Firstly, to enhance the diversity and realism of the learning samples, it is necessary to incorporate more case studies. Training the LoRA model with night-time shopfront photos from specific locations, such as the Wanshou Palace Historical and Cultural District in Nanchang in China, may show superior performance for specific applications, like recognizing or generating night-time facade images from specific locations. However, choosing night-time photos from specific locations might limit the diversity of the dataset. This lack of diversity could impact the model generalizability, potentially underperforming when processing a broader range of images. Additionally, since the photos are taken at night, the model might overly adapt to nighttime lighting conditions, color distributions, and shadow effects, leading to poorer performance under daytime or other lighting conditions. In some cases, it might be necessary to expand the dataset to include photos from different times of the day or similar scenes from other locations to enhance diversity.

In terms of experimental results, the FID scores reported in this paper are relatively high compared to other studies, possibly due to the dataset primarily containing specific night-time scenes. While FID scores are a useful metric for assessing the quality of natural images, the Inception model may not effectively capture the key features of images from specific domains, such as cartoons, abstract art, or im-ages with a specific style, leading to distorted FID scores. Nevertheless, FID scores should not be the sole metric for evaluating generative models. This study integrates the results of qualitative and quantitative evaluations to select the best-performing LoRA model for application research. In the preliminary phase of architectural de-sign, utilizing digital technology to aid design not only improves the efficiency of architects but also promotes efficient communication with clients. Thus, the evaluation methods proposed in this study offer valuable reference points for other re-search.

In modernizing historical architecture, particularly during commercial trans-formations, preserving its cultural and historical value is crucial. The LoRA model, through deep learning, can understand and reproduce the style and details of historical architecture, helping designers find a balance in blending the old with the new. This ensures that the renovation plans not only meet modern needs but also respect and preserve the original spirit and cultural significance of the architecture.

6. Conclusion

This paper presents a method leveraging diffusion models to convert traditional residences into commercial styles. We initially curated a dataset of commercial fa-cades from southern residences, crucial for transforming traditional architectural styles. Annotating image elements to train the LoRA model enabled the diffusion models to generate high-quality, stylized images. The image quality was validated through qualitative and quantitative assessments, and the incorporation of the ControlNet model enhanced accuracy and stability. The method’s application in existing traditional residences demonstrated its effectiveness and potential in architectural design.

Experimental results confirm this workflow's reliability in preserving traditional historical architecture. We efficiently generated high-quality images of traditional buildings with commercial style transformations, providing architects with creative ideas for modern transformations. This study challenges traditional design constraints and explores new possibilities for historical architecture conservation. Key contributions include: 1) Proposing a design process for transitioning traditional residences into commercial styles, allowing architects to quickly generate us-er-expected designs; 2) Establishing combined qualitative and quantitative evaluation methods for assessing commercial style images of traditional buildings, offering crucial feedback for generative model optimization; 3) Providing a pre-trained LoRA model for commercial style transformation, enhancing design efficiency and quality.

However, the method may be influenced by architects' experience and perception, potentially limiting comprehensive keyword provision during prompt input for image generation. In historical architecture renewal, architects should select the ap-propriate control network model based on specific application needs to ensure consistent high-quality outputs. This study focused on traditional residences' commercial transformation. Future research could refine the process for different stages, enhance workflow convenience and efficiency, and expand the dataset by creating diverse traditional architectural facade datasets. This would broaden the Stable Diffusion model’s application, produce diverse results, and consider the operational space of traditional cultural districts. Additionally, combining 2D image generation with 3D modeling to create visualization models of historical commercial street transformations merits further exploration.