Research Advanced in Image Generation Based on Diffusion Probability Model

Abstract

Image generation has been a popular research task in the computer vision community, which aims to learn a distribution from a specific dataset and generate realistic images obeying this distribution. Thanks to the rapid development of deep learning technology, image generation models based on convolutional neural networks, especially generative adversarial networks (GANs) and variational autoencoders (VAEs), have become mainstream frameworks for image generation. However, in recent years, with the gradual deepening of the research on the denoising diffusion probability model (DDPM), the image generation technology based on DDPM has made new breakthroughs in accuracy and speed. Around the Diffusion Model, this paper introduces its latest research progress in image generation and derivative tasks. Specifically, this paper reviews the key techniques and basic theories of the diffusion model in detail. Then, the main research work, improvement mechanism and characteristics of the DDPM-based image generation method are summarized and summarized. This paper focuses on the basic structure and related applications of diffusion models, and evaluates some basic functions. Finally, the current problems and future development directions of image generation technology based on diffusion model are analyzed and summarized.