Exploration and Improvement of the Stable Diffusion Model in the Field of Image Generation

Abstract

Image generation is an important research direction in the field of computer vision, which covers many tasks such as image synthesis, image conversion, image editing and other tasks. In recent years, the rapid development of deep learning technology has provided powerful tools for image generation, where the stable diffusion model has attracted much attention in the image generation field as a kind of image generation model. Stable diffusion model (Stable Diffusion Model) is a generative model based on the diffusion process. The basic principle is to gradually generate the target image by conducting the diffusion process on the noise image. Different from the traditional generation models such as generative adversarial networks (GANs) and variation auto-encoders (VAEs), the stable diffusion model can gradually control the details and quality of the image during the generation process, with good generation stability and sample quality. With the continuous exploration and application of stable diffusion model in the field of image generation, researchers have proposed many improvement methods, including the improvement of generation network structure, loss function and sam-ple optimization method, to further improve the generation effect and generation speed of stable diffusion model. This paper aims to explore and summarize the application status and improvement methods of stable diffusion model in the field of image generation. Through the study and improvement of stable diffusion model, it can provide new methods and ideas to achieve more realistic, diversified and controllable image generation effects.