Work

Structure-aware stable diffusion for traditional architectural decoration design

Year: 2025

Type: article

Abstract: The intelligent generation of traditional architectural styles faces significant challenges in structural integrity and style consistency. While existing methods can generate numerous realistic images, they lack a deep understanding of structural elements in traditional architectural decorative design. This paper proposes a Structure-aware Stable Diffusion (SSD) model, which enhances the model's comprehension of architectural features through three key innovations. First, we design a structure-aware feature injection module that adaptively fuses extracted architectural structural information with original features during the U-net upsampling phase, enhancing the model's understanding of geometric structures. Second, we introduce a dual-path text enhancement strategy that combines structural descriptions with original descriptions to provide richer textual guidance signals for the generation process. Finally, we design a progressive injection strategy that dynamically controls the injection intensity of structural information through cosine scheduling, ultimately achieving effective internalization of structural knowledge. Experimental results show that compared to existing methods, our model effectively improves both the diversity of generated traditional architectural decorations and the rationality of their structures, thus providing an effective new technical approach for traditional architectural decorative design.

Source: Machine Learning with Applications

DOI: 10.1016/j.mlwa.2025.100735

Authors: Jianhong Yang, Guoyong Wang

Institution: Luoyang Institute of Science and Technology

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,340

Funder: Henan Office of Philosophy and Social Science