Navigating the Realm of Generative Models: GANs, Diffusion, Limitations, and Future Prospects—A Review

This review delves into the realm of generative models for image synthesis, with a specific focus on two prominent approaches: generative adversarial networks (GANs) and diffusion models. We conduct an in-depth analysis of their architectures, applications, and challenges, providing a comprehensive overview. While GANs are renowned for their ability to produce realistic images, they encounter issues such as non-convergence and mode collapse. On the other hand, diffusion models, rooted in principles of physics and mathematics, excel in generating high-quality images but grapple with complexity and interpretability. Applications of these models span various domains including image inpainting, high-fidelity generation, and text-to-image synthesis. However, both approaches face limitations such as computational complexity, difficulty in training, and challenges in evaluating the quality of generated images. We address common challenges such as bias concerns and interpretability, offering insights and solutions for their large-scale deployment. Through this exploration, we aim to provide a nuanced understanding of the capabilities and obstacles in the realm of photorealistic image generation.