AI-driven artistic design: enhancing image generation with complex GANs, style transfer, and advanced color mapping

In recent years, the application of deep learning techniques in the field of art image generation has received a lot of attention, especially in the context of the growing demand for automation and creative tools in the field of art design and digital art. Generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs) have been remarkably successful in generating high-quality images. However, existing models often have challenges in generating diversity and unique patterns, and are prone to pattern collapse where local features are similar across different images. To this end, this research aims to improve these models, enhance the quality and structural diversity of the generated images, and provide artists and designers with more powerful tools so that they can better control the generation process and create diverse, high-quality artistic images. A complex generation model is developed, which integrates GAN-based generator model, style transfer technique and advanced color mapping method. Although some progress has been made in improving the uniqueness of patterns, future research is needed to further explore multimodal generation and super-resolution enhancement techniques to further improve the diversity and detail quality of the generated images.