Generative Models Beyond GANs: Innovations in Image and Text Synthesis

An evolution within generative models has taken place, going beyond Generative Adversarial Networks (GANs) into different structured approaches for both image and text generation. With the rise of advanced non-GAN-based generative models, the aim of this study is to analyse and ultimately compare a brief history and the effectiveness of different models such as VAEs, Diffusion Models, and architectures based on Transformers like GPT and DALL·E to find their strengths in creating high-quality, coherent, and diverse outputs across both visual and text domains. The results reveal the superior stability of diffusion models, the interpretability of VAEs, and the improved contextual understanding gained by transformer-based architectures, allowing us to better understand these models, their advantages and disadvantages, and the potential impact on design for future applications. Additionally, we examine common issues like mode collapse, training instability, and high computational resources requirements, proposing novel solutions to mitigate these challenges. Also, experimental results show that state-of-the-art (SoTA) performance can be achieved not just through GAN architectures but also through non-GAN models, which lend themselves more to diverse uses cases from creative content generation to domain-specific tasks (medical imagery, personalized content creation) much more. Finally, this paper addresses the ethics behind generative models and provides some insights on future development of generative models. The chapter concludes with a discussion of its implications for bridging theoretical and practical aspects in generative modelling, marking a new era in the field.