Intelligent generation of home design images based on AIGC technology

To improve the efficiency of the home design process and reduce time consumption, the experiment proposes an intelligent generation method for home design images based on an improved Generative Adversarial Network (GAN). First, the basic principles and structure of GAN are introduced, and then the concept of MSSF is introduced to improve the detailed performance and overall consistency of the generated images. The results show that experiments were conducted on the 7 Scenes data set and the LaMAR data set. When the running time is 0.845 s and 0.812 s, the AIGC-GAN algorithm proposed in the experiment has the smallest RMSE value, and the corresponding values are 4.32 and 0.874. In addition, the average PSNR value obtained when running on the 7 Scenes data set shows that when the system running time is 0.312 s, the average PSNR value of the AIGC-GAN algorithm is as high as 63.89 dB. The above results all show that the AIGC-GAN algorithm can effectively assist home design and contribute new ideas to the development of image generation technology.