Research on Image-to-Image Generation and Optimization Methods Based on Diffusion Model Compared with Traditional Methods: Taking Façade as the Optimization Object

Abstract

AbstractThe intersection of technology and culture has become a topic of great interest worldwide, with China's development embracing this integration as an essential direction. One critical area where these two fields converge is in the inheritance, translation, and creative design of cultural heritage. In line with this trend, our study explores the potential of stable diffusion to produce highly detailed and visually stunning building façades. We start by providing an overall survey and algorithm fundamentals of the generative deep learning models used so far, namely, GAN and Diffusion models. Then, we present our methodology for using Diffusion Model to generate architecture façades. We then demonstrate how the fine-tuning is done for Stable Diffusion is done to yield optimal performance and then evaluate four different training methods of SD. We also compare existing GAN based façade generation method with our Diffusion based method. Our results show that our Diffusion-based approach outperforms existing methods in terms of detail and quality, highlighting the potential of stable diffusion in generating visually appealing building façades. This research contributes to the growing body of work on the integration of technology and culture in architecture and offers insights into the potential of stable diffusion for creative design applications.