Core Viewpoint - The article discusses the comprehensive guide on diffusion models, highlighting their transformative impact on generative AI across various domains such as images, audio, video, and 3D environments [2][4]. Summary by Sections Introduction to Diffusion Models - Diffusion models are presented as a method that views the generation process as a gradual transformation over time, contrasting with traditional generative models that directly learn mappings from noise to data [11]. - The article emphasizes the need for a systematic understanding of diffusion models, which the book aims to provide, making it a valuable resource for both researchers and beginners [6][9]. Core Principles of Diffusion Models - The book outlines the foundational principles of diffusion models, connecting three key perspectives: variational methods, score-based methods, and flow-based methods, which together form a unified theoretical framework [11][13]. - It discusses how these models achieve efficient sample generation and enhanced controllability during the generation process [12]. Detailed Exploration of Perspectives - The variational view relates to denoising diffusion probabilistic models (DDPMs), providing a basis for probabilistic inference and optimization [23]. - The score-based view focuses on learning score functions to guide the denoising process, linking diffusion modeling with classical differential equation theory [23][24]. - The flow-based view describes the generation process as a continuous flow transformation, allowing for broader applications beyond simple generation tasks [24]. Sampling Techniques and Efficiency - The article highlights the unique feature of diffusion models, which refine samples from coarse to fine through noise removal, and discusses the trade-off between performance and efficiency [27][28]. - It introduces methods for improving sampling performance without retraining models, such as classifier guidance and advanced numerical solvers to enhance generation quality and speed [29][30]. Learning Fast Generative Models - The book explores strategies for directly learning fast generative models that approximate the diffusion process, aiming to reduce reliance on multi-step inference [30][31]. - Distillation-based methods are discussed, where a student model mimics a slower teacher model to achieve faster sampling while maintaining quality [30]. Comprehensive Coverage of Diffusion Models - The book aims to establish a lasting theoretical framework for diffusion models, focusing on continuous time dynamical systems that connect simple prior distributions to data distributions [33]. - It emphasizes the importance of understanding the underlying principles and connections between different methods to design and improve next-generation generative models [36].