Research On Text Generated Images Based on GAN And Diffusion

Abstract

With the continuous development of deep learning, the generation of content by artificial intelligence has become a hot topic. Especially in the field of text to image generation, significant progress has been made. This article comprehensively compares text image generation methods based on Generative Adversarial Network and Diffsuion and their applications in text image generation tasks, demonstrating their respective advantages and limitations as well as possible solutions. Meanwhile, this article delves into the specific methods of diffusion models in improving image quality, optimizing model efficiency, and generating images based on multilingual text prompts. Through experimental analysis on the Microsoft Common Object Context Dataset (COCO), the zero sample generation ability of the diffusion model and the performance improvement of the GAN model were verified, highlighting the advantages of the GAN model in terms of lightweight and small parameter count. In addition, this article introduces the broad application prospects of diffusion models in fields such as text 3D generation and video generation. Finally, the challenges and future development trends faced by diffusion models in text to image generation tasks were summarized, with the hope of providing convenience for further research in this field.