Diffusion Models: Unlocking the “4 secrets” of High-quality Image Generation
Abstract
Abstract Diffusion Model (DM) is a hot topic in deep generative models, and it is widely applied in the image generation fields. In diffusion models, there are 4 main “secrets” that affect the generation high-quality image generation: constructing diffusion model, improving the sampling speed, designing diffusion process, and guiding diffusion models. However, how to construct the diffusion model? How to improve the sampling speed? How to design the diffusion process? How to guide diffusion models? These are critical to enhancing the performance of diffusion models. However, so far, most of the review papers are summarized from the application aspect, and the 4 key technologies of diffusion model are few. In response to the above issues, this paper summarizes 4 key technologies and 6 applications. The main innovative works are as following: Firstly, how to construct diffusion models? The basic principle of the diffusion model are summarized from 3 aspects: denoising diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Secondly, how to improve the sampling speed? Some key techniques for improving the sampling speed are summarized from 3 aspects: non-Markovian sampling, knowledge distillation, and discrete optimization. Thirdly, how to design the diffusion process? This paper summarizes how to design diffusion process from 3 aspects: Latent Space, diffusion process based on transformer and non-Euclidean space. Fourthly, how…
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