Generative AI Models across Design & Creative Domains

Abstract

ABSTRACT Generative Artificial Intelligence (AI) has rapidly transformed creative and design-oriented industries in recent years. From visual arts and architecture to music composition and product prototyping, generative models are reshaping how creative outputs are conceptualized and produced. Technologies such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models like DALL·E and ChatGPT have demonstrated remarkable ability in synthesizing images, text, music, and even 3D models. This paper reviews major generative AI models and explores their applications across design and creative domains. It discusses opportunities, ethical concerns, authorship questions, and economic impacts. While generative AI enables unprecedented efficiency and ideation support, it also raises debates regarding originality, ownership, bias, and employment displacement. The study concludes that generative AI is not replacing creativity but augmenting it, though regulatory frameworks and ethical standards must evolve alongside the technology.

KEYWORDS: Generative AI, Creative Design, GANs, Diffusion Models, Computational Creativity, Digital Art, Architectural Design, AI Ethics, Creative Automation, Transformer Models