Design and Development of StyleGAN and Recurrent Networks in Visual Art Creation

The application of artificial intelligence (AI) in visual art creation has unlocked new dimensions in creativity, automation, and personalized artistic expression. Conventional methods of generating art—typically based on rule-based systems or fixed templates—fail to engage the complexity, emotional depth, and stylistic variety that typify contemporary works of art. These existing drawbacks commonly lead to stiff and dull outputs that never manage to express rich artistic intent. To solve this aforementioned drawback, the current research investigates a hybrid AI strategy that exploits the strengths of StyleGAN and Long Short-Term Memory (LSTM) networks. In the suggested framework StyleGAN, is utilized to produce high-resolution and visually appealing visual artworks through training with a curated collection of classical and modern works. The latent vectors from the output are further analyzed by LSTM networks to add sequential and thematic aspects, adding contextual and emotional richness to the created work of art. The system was strictly tested using both quantitative and qualitative techniques. The system attained an FID score of 8.2 and a user satisfaction rate of 96% and has proven effective in creating visually engaging and emotionally appealing AI-generated pieces of art.