Architectural facade design with style and structural features using stable diffusion model

With advancements in digital technology, the field of architectural design has increasingly embraced data and algorithms to enhance design efficiency and quality. Recent advancements in text-to-image (T2I) generation models have enabled the creation of images that correspond to textual descriptions. However, textual descriptions struggle to capture essential style characteristics in style images. In this study, we proposed a method for architectural facade design based on the stable diffusion model (SDM) that combined stylistic images or keywords as input with the structural conditions of content images to generate images with both stylistic and architectural features. By employing the constrastive language-image pre-training (CLIP) image encoder to convert the style image into its initial image embedding and feature extraction from multilayer cross-attention and training optimization to obtain a pretrained image embedding, the proposed method extracts stylistic features from style images and converts them into corresponding embeddings. This process enables the generated images to embody stylistic features and artistic semantic information. Furthermore, the T2I adapter model is employed to use the architectural structure of content images as conditional guidance, thereby ensuring that the generated images exhibit the corresponding structural features. By leveraging these two aspects, the proposed method can decorate architecture with stylistic features from stylistic images while preserving the architectural structure features of content images, resulting in images that reflect the content images after style transformation. Our method is mainly used in architectural design applications. It was capable of generating facade images from flat design drawings, three-dimensional (3D) architectural models, and hand-drawn sketches and has achieved commendable results.