Application and optimization of adaptive image generation algorithm in AI art creation

The adaptive image generation algorithm, as an important application of AI in the field of art creation, provides an efficient, convenient, and creative new approach for the creation of AI art works. This article explores in depth the application of adaptive image generation algorithms in AI art creation, including scenarios such as style transfer, creative image generation, and art restoration, and constructs corresponding algorithm models based on the Generative Adversarial Network (GAN) framework. By improving the network structure, optimizing the loss function, adopting progressive training and adaptive learning rate scheduling, the quality and diversity of generated images are significantly improved. The experimental results show that the optimized model performs well in FRé chet Incidence Distance (FID), Learned Perceptual Image Patch Similarity (LPIPS), style similarity and other indicators, and shows excellent performance in art restoration tasks, which proves its advantages in image fidelity, structural similarity and artistry. The research in this paper provides effective technical support and theoretical basis for promoting the development of AI art works.