A study of enhancement strategies for artificial intelligence-based automated design tools in complex design tasks

With the important achievements of AI team in the application of diffusion model, automated generation demonstrates its stability, realism and accuracy in text-guided image generation design.In this paper, the automated design generation based on the diffusion model is divided into two processes: forward diffusion and reverse generation.The stable diffusion model is constructed from three parts: self-encoder, U-Net network structure, and text encoder, and the ControlNet control architecture is used to realize the control of the diffusion model to learn a specific task.The model is pre-trained using a combination of three functions: perceptual loss, adversarial loss, and cyclic consistency loss.The LoRA algorithm is added to the U-Net layer of the Stable diffusion model to realize the design task function enhancement of the stable diffusion model.The application effect of the improved Stable diffusion model is analyzed through comparative experiments.The experimental results show that the CLIP Score interval of this paper's model is between 20 and 32.5, while the LPIPS is between 0.1 and 0.6, and the kernel density centroid is (24.85,0.4), which means that the proposed method in this paper meets the design requirements with higher fidelity while accomplishing the image design task.In the design images generated by automation, in terms of visual logic rationality, the structural proportions, line contours, light and shadow and picture hierarchical relationships of the generated images perform well, with the evaluation results of 3.258, 3.564, 3.058, 3.615, respectively, which indicates that the design images generated by automation using the model have a strong richness.