Core Insights - The article discusses the comprehensive guide on diffusion models, which have significantly reshaped the landscape of generative AI across various domains such as images, audio, video, and 3D environments [3][5][6] - It emphasizes the need for a structured understanding of diffusion models, as researchers often struggle to piece together concepts from numerous papers [4][10] Summary by Sections Introduction to Diffusion Models - Diffusion models are framed as a gradual transformation process over time, contrasting with traditional generative models that directly learn mappings from noise to data [12] - The development of diffusion models is explored through three main perspectives: variational methods, score-based methods, and flow-based methods, which provide complementary frameworks for understanding and implementing diffusion modeling [12][13] Fundamental Principles of Diffusion Models - The origins of diffusion models are traced back, linking them to foundational perspectives such as Variational Autoencoders (VAE), score-based methods, and normalizing flows [14][15] - The chapter illustrates how these methods can be unified under a continuous time framework, highlighting their mathematical equivalence [17] Core Perspectives on Diffusion Models - The article outlines the core perspectives on diffusion models, including the forward process of adding noise and the reverse process of denoising [22] - Each perspective is detailed: - Variational view focuses on learning denoising processes through variational objectives [23] - Score-based view emphasizes learning score functions to guide denoising [23] - Flow-based view describes the generation process as a continuous transformation from a simple prior distribution to the data distribution [23][24] Sampling from Diffusion Models - The sampling process in diffusion models is characterized by a unique refinement from coarse to fine details, which presents a trade-off between performance and efficiency [27][28] - Techniques for improving sampling efficiency and quality are discussed, including classifier guidance and numerical solvers [29] Learning Fast Generative Models - The article explores methods for directly learning fast generative models that approximate the diffusion process, enhancing speed and scalability [30] - Distillation-based methods are highlighted, where a student model mimics a slower teacher model to achieve faster sampling [30][31] Conclusion - 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]