GENERATIVE AI: ADVANCEMENTS IN GANS AND DIFFUSION MODELS FOR CREATIVITY AND CONTENT GENERATION

Abstract

Generative AI has revolutionized creativity and content generation by enabling machines to produce realistic and imaginative outputs. Among the leading techniques, Generative Adversarial Networks (GANs) and diffusion models have shown remarkable advancements. GANs, known for their two-player architecture of generator and discriminator, have achieved state-of-the-art performance in generating high-quality images, videos, and music. However, they face challenges like training instability and mode collapse. Recent innovations, including progressive training and style-based generators, address these limitations.

On the other hand, diffusion models, inspired by thermodynamic processes, have emerged as a powerful alternative. By iteratively denoising data, these models generate diverse and coherent outputs, excelling in scenarios requiring high-fidelity image synthesis. Diffusion models also demonstrate advantages in stability, scalability, and adaptability to multimodal tasks. This paper explores the convergence of GANs and diffusion models, their strengths, and how their hybridization or integration with other AI paradigms, such as transformers, enhances generative capabilities. Applications span from entertainment and design to education and healthcare, with implications for accessibility and personalization. Finally, ethical concerns, including biases in generated content and potential misuse, are discussed to guide the responsible deployment of generative AI technologies.