Research on Intelligent Design Method for Vernacular Architecture Image Translation Based on Diffusion Models
Abstract
Image translation of vernacular architecture plays a significant role in intelligent design. Diffusion models, characterized by their progressive denoising mechanism for generating high-quality images, offer a novel solution for architectural style transformation. This study constructs a high-quality dataset tailored to vernacular architecture and adopts an architectural feature-aware training strategy to optimize diffusion models. Additionally, controllable conditional generation is introduced to enable precise regulation of style conversion. To address the high computational demands of diffusion models, we propose inference acceleration strategies and lightweight optimization schemes to improve conversion efficiency. Experimental results demonstrate that the proposed method outperforms existing approaches in terms of translation quality, controllability, and computational efficiency, providing a new technological pathway for intelligent design in vernacular architecture.
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