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성과공개
성과공개
상세 정보
상세 정보
논문
LoRA와 ControlNet을 활용한 Stable Diffusion 기반 건축 입면 생성 방법과 평가에 관한 연구
연도
5차
분류
구성기술1
연구기관
경북대학교
Kyungpook National Uinversity
Kyungpook National Uinversity
구분2
일반학술지
논문명
LoRA와 ControlNet을 활용한 Stable Diffusion 기반 건축 입면 생성 방법과 평가에 관한 연구
A Generation Method and Evaluation of Architectural Facade Design Using Stable Diffusion with LoRA and ControlNet
A Generation Method and Evaluation of Architectural Facade Design Using Stable Diffusion with LoRA and ControlNet
학술지명
대한건축학회논문집
ISSN
2733-6239
학술지 볼륨번호
41(8)
게재일
2025.08
논문페이지
85-96
주저자명
박정민
Park, Jungmin
Park, Jungmin
교신저자명
추승연
Choo, Seungyeon
Choo, Seungyeon
공동저자명
홍순민
Hong, Soonmin
Hong, Soonmin
논문 초록
This study proposes a novel approach for generating architectural facade images by combining the Stable Diffusion model with Low-Rank
Adaptation (LoRA) and ControlNet. The standard Stable Diffusion model faces limitations in accurately reflecting architectural elements and
material characteristics, which are critical in the design process. To address these challenges, this research integrates domain-specific
fine-tuning using LoRA and precise shape control through ControlNet. LoRA allows the model to effectively learn architectural styles and
details, ensuring better representation of essential design elements such as windows, balconies, and facade materials. Meanwhile, ControlNet
utilizes Canny Edge and Depth Map information to enhance shape accuracy and spatial consistency, enabling more reliable image generation.
The generated images were evaluated through Contrastive Language-Image Pretraining (CLIP) scores for quantitative analysis and
GPT-4V-based qualitative evaluation, providing a more comprehensive understanding of architectural coherence and visual fidelity. The
GPT-4V assessment offered insights into spatial relationships, contextual relevance, and material expression that are not easily captured
through traditional metrics. This combined approach reduces the repetitive manual adjustments commonly required in text-prompt-based image
generation and facilitates a more intuitive and efficient design process during the early stages of architectural planning. By improving control
over detailed architectural features, the proposed method contributes to the automation of facade design, offering significant potential for
real-world applications in architectural design and visualization. Future research will focus on expanding the dataset to include diverse
architectural styles and validating its practical application in design and construction.
Adaptation (LoRA) and ControlNet. The standard Stable Diffusion model faces limitations in accurately reflecting architectural elements and
material characteristics, which are critical in the design process. To address these challenges, this research integrates domain-specific
fine-tuning using LoRA and precise shape control through ControlNet. LoRA allows the model to effectively learn architectural styles and
details, ensuring better representation of essential design elements such as windows, balconies, and facade materials. Meanwhile, ControlNet
utilizes Canny Edge and Depth Map information to enhance shape accuracy and spatial consistency, enabling more reliable image generation.
The generated images were evaluated through Contrastive Language-Image Pretraining (CLIP) scores for quantitative analysis and
GPT-4V-based qualitative evaluation, providing a more comprehensive understanding of architectural coherence and visual fidelity. The
GPT-4V assessment offered insights into spatial relationships, contextual relevance, and material expression that are not easily captured
through traditional metrics. This combined approach reduces the repetitive manual adjustments commonly required in text-prompt-based image
generation and facilitates a more intuitive and efficient design process during the early stages of architectural planning. By improving control
over detailed architectural features, the proposed method contributes to the automation of facade design, offering significant potential for
real-world applications in architectural design and visualization. Future research will focus on expanding the dataset to include diverse
architectural styles and validating its practical application in design and construction.