Synthetic Water Crystal Image Generation Using VAE-GANs and Diffusion Models

In recent years, remarkable progress has been made in generative models, particularly in the fields of computer vision and natural language processing. The ability of generative models to generate new and diverse samples has resulted in a wide range of applications, such as image and video synthesis, text generation, and music composition. This study investigates generative modeling advances made using Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models. Although VAEs and GANs have been widely used for generative modeling tasks in the past, diffusion models have recently emerged as state-of-the-art models. This study provides a detailed analysis of each model, including its strengths and limitations, as well as its applications in image synthesis and video generation. Furthermore, this paper discusses recent developments in diffusion models such as denoising. Finally, this paper implements these proposed models to generate water crystal images.