A Comparative Analysis Between GAN and Diffusion Models in Image Generation
Abstract
In the field of artificial intelligence, image-generation techniques have been a hotspot for research. Two generative models that have garnered a lot of attention are diffusion models and generative adversarial networks (GANs). This review paper aims to compare and analyze GAN and Diffusion Models in the field of picture generation, as well as to give a thorough discussion of their features, applications, benefits, and drawbacks. Firstly, the work related to the working principle of GAN and diffusion models are introduced, and then their applications and results in image generation are reviewed. By comparing the existing research results, the author find that GAN performs well in generating realistic images but suffers from problems such as pattern collapse and unstable training, while the diffusion model has better stability and controllability. Combining the advantages of the two methods, this paper explores the possible fusion methods and looks forward to the future development direction in the field of image generation. These research results provide important references and insights to further enhance the level and application scope of image generation technology.
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