Diffusion Models have rapidly emerged as a groundbreaking paradigm in generative AI, demonstrating unprecedented capabilities in synthesizing high-quality, diverse, and realistic visual content. This tutorial provides a comprehensive introduction to the foundational principles and practical applications of diffusion models, specifically tailored for researchers, practitioners, and students interested in visual content generation. We will begin by demystifying the core mathematical and probabilistic concepts underlying diffusion processes. The tutorial will delve into key architectural components alongside various sampling strategies. Furthermore, we will explore advanced topics including conditional generation, classifier-free guidance, and the role of latent diffusion models in scaling these powerful techniques to high-resolution imagery.
Objective of the Tutorial : Through a blend of theoretical exposition, intuitive explanations and a hands-on session, participants will gain a solid understanding of how diffusion models learn complex data distributions and effectively generate visual content. This tutorial will enable them to confidently explore and contribute to this exciting field.
Expected background of the audience : The tutorial is suitable for senior undergrad and masters students, researchers & practitioners who wants to understand the fundamentals of visual content generation. The only prerequisite if the prior familiarity with deep learning fundamentals (CNNs, transformers etc) and basic probability concepts.