Denoising Diffusion Implicit Models, Jiaming Song, Chenlin Meng, and Stefano Ermon, 2020International Conference on Learning Representations (ICLR 2021)DOI: 10.48550/arXiv.2010.02502 - Introduces the Denoising Diffusion Implicit Model (DDIM), presenting a non-Markovian generative process that significantly accelerates sampling by allowing larger steps and offering deterministic generation.
Denoising Diffusion Probabilistic Models, Jonathan Ho, Ajay Jain, and Pieter Abbeel, 2020Advances in Neural Information Processing Systems 33 (NeurIPS 2020)DOI: 10.48550/arXiv.2006.11239 - The foundational paper introducing Denoising Diffusion Probabilistic Models (DDPM), which DDIM builds upon, detailing the forward diffusion and reverse generative processes.
Lecture 12: Denoising Diffusion Models, Stefano Ermon, 2023 (Stanford University) - Provides detailed lecture notes from a leading university course on deep generative models, explaining the mathematical formulation and intuition behind DDIM.