This paper presents an overview of the theoretical research on generative diffusion models. It starts by briefly explaining existing generative models and discussing the need for diffusion models. The core studies on diffusion models are then examined in a systematic perspective, highlighting their relationships and missing points.
The theoretical research on diffusion models is categorized into two main approaches: training-based and sampling-based. Under these categories, the research is further classified based on the specific subjects they have focused on.
The training-based approaches cover areas such as diffusion planning, noise distribution and schedule, training procedure, space projection, optimal transport, and handling different data structures. These studies aim to improve the traditional training scheme and address key factors that affect the learning patterns and performance of diffusion models.
The sampling-based approaches focus on developing efficient sampling algorithms without modifying the training process. This includes methods like predictor-corrector samplers, reverse diffusion samplers, and techniques to accelerate the sampling process.
The paper also explains the evaluation metrics used for diffusion models and provides benchmark results on commonly used datasets. Finally, it discusses the current status of the diffusion model literature and suggests future research directions.
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