Improved Diffusion Model for Fast Image Generation
Abstract
This paper provides methods and implementations to increase the speed of image generation with less sacrifice of image generation quality. This study reviews the development of image generation techniques, including early convolutional neural networks, variational autoencoders (VAEs), and generative models such as generative adversarial networks (GANs). In recent years, denoising diffusion probabilistic models (DDPM) have become a new trend in image generation. This trend is mainly attributed to the superior performance of DDPMs in generating high-quality images, as well as their demonstration of higher stability and consistency when dealing with complex data distributions. The ability of DDPMs to provide more granular control and predict the quality of generation through a process of gradual noise reduction compared to traditional GANs has led to their widespread interest and use in academic and industrial applications. However, in order to reduce the time and computational cost required for DDPM to generate images, an improved version of DDPM, the Denoising Diffusion Implicit Model (DDIM), is used in this study. This model effectively speeds up the image generation by optimising the diffusion process while maintaining less loss in the quality of the generated images. Firstly, The DDIM model was experimented on four different datasets, including Swiss Volume dataset, MNIST dataset, CIFAR10 dataset, and Celeba dataset, in order to comprehensively evaluate the performance and…
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