Comparative Analysis of Image Generation Based on GAN, VAE, and Diffusion Models
TL;DR
A multidimensional, standardized comparison of three generative models: Generative Adversarial Networks, Variational Autoencoders and Diffusion Models is conducted, based on extensive literature review, combining theoretical analysis and experiments to provide a scientific basis for model selection and support decision optimization in practical applications.
Abstract
: The core task of image generation models is to generate visual content that meets specific requirements based on given inputs. The wide application of artificial intelligence has accelerated the development of generative technologies, and image generation has had a significant impact across various fields in real-world scenarios. However, with many generative models available, selecting the appropriate one becomes a key issue. Current research mostly focuses on optimizing single models and lacks a comprehensive, systematic comparative analysis of multiple models. To address this, this paper conducts a multidimensional, standardized comparison of three generative models: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and Diffusion Models, based on extensive literature review, combining theoretical analysis and experiments. The aim is to provide a scientific basis for model selection and support decision optimization in practical applications. Experimental results show that diffusion models produce better image quality but require higher hardware specifications. GANs and VAEs generate images of moderate quality with lower hardware requirements.