Text to High Quality Image Generation Using Diffusion Model and Visual Transformer

In the realm of computer vision, the pursuit of generating high-quality images has long been challenged by issues such as fidelity, prompt translation, and model performance. This paper addresses these concerns by investigating the application of diffusion models for image creation—a novel approach hinging on the diffusion process. Unlike conventional methods, this technique progressively introduces controlled noise to images until the desired complexity level is attained. However, the practicality of training such models on training photos while achieving efficient sampling has been a roadblock. In response, this study introduces an innovative approach termed Denoising Diffusion Implicit Models (DDIMs) that substantially expedites the noise sampling process. Furthermore, the paper presents a breakthrough in translating natural language image prompts into actionable directives, devoid of additional training or external input. A visual transformer is incorporated to enhance the quality of generated images. The primary contribution of this research lies in two key aspects. Firstly, it demonstrates the capability of diffusion models to generate images while offering an accelerated training methodology. Secondly, it showcases superior performance of Diffusion Models over the commonly used Generative Adversarial Networks (GANs) when it comes to image generation from text-prompts. Through a meticulous evaluation process, the study substantiates that Diffusion Models hold immense potential in generating high-quality images across diverse applications such as data augmentation, image manipulation, and virtual reality. In conclusion, this paper rigorously addresses the prevailing challenges in image generation by innovatively applying diffusion models. By offering a streamlined process for both training and sampling, it presents a significant leap in performance over GANs, specifically in the realm of text-prompts. The study underscores the versatility of Diffusion Models to revolutionize various image-related domains.