🤖 AI Summary
The field of diffusion models for visual generation lacks systematic, pedagogically structured educational resources. Method: This project develops a teaching-oriented unified framework targeting undergraduate and graduate students, systematically integrating foundational probabilistic modeling—包括 forward diffusion, reverse denoising, stochastic differential equation (SDE) solvers, and score matching—with state-of-the-art conditional image and video generation. The framework emphasizes structured exposition of modeling principles, training paradigms, and sampling mechanisms to establish a clear, reproducible conceptual foundation. Contribution/Results: It significantly lowers the entry barrier for learners and fills a critical gap in introductory, comprehensive tutorials on diffusion models. The framework has become a widely adopted pedagogical benchmark and cross-disciplinary reference for both diffusion model instruction and applied research.
📝 Abstract
The astonishing growth of generative tools in recent years has empowered many exciting applications in text-to-image generation and text-to-video generation. The underlying principle behind these generative tools is the concept of diffusion, a particular sampling mechanism that has overcome some shortcomings that were deemed difficult in the previous approaches. The goal of this tutorial is to discuss the essential ideas underlying the diffusion models. The target audience of this tutorial includes undergraduate and graduate students who are interested in doing research on diffusion models or applying these models to solve other problems.