Comparative Analysis of GANs and Diffusion Models in Image Generation

Abstract

Image generation has emerged as a rapidly evolving field with transformative applications in entertainment, medical imaging, virtual environments, and various other industries. This paper presents a thorough analysis of technological developments and current mainstream models in image generation, focusing on Generative Adversarial Networks (GANs) and diffusion models. It explores their theoretical underpinnings, advantages, limitations, and practical applications. The study highlights that GANs are particularly effective in producing diverse and high-quality images with notable speed but are often hindered by issues such as mode collapse and training instability. In contrast, diffusion models are praised for their ability to generate high-fidelity and detailed images, though they require extensive computational resources, making them less suitable for resource-constrained environments. The paper also discusses the current challenges faced by these models, such as biases and inefficiencies, and proposes potential solutions and future research directions. By addressing these issues and exploring ways to enhance model efficiency and multi-modal generation capabilities, this study aims to provide valuable insights that will drive further innovation and practical application in the field of image generation.