Denoising Diffusion Probabilistic Models and DDIM: Mathematical Foundations, Variance Schedules, and Fast Deterministic Sampling
Generative modeling in continuous state spaces underwent a fundamental transformation with the formulation of Denoising Diffusion Probabilistic Models (DDPM) by Ho et al. (2020), building on the non-equilibrium thermodynamics foundations established by Sohl-Dickstein et al. (2015). Prior to diffusion models, deep generative synthesis was dominated by Generative Adversarial Networks (GANs), which suffered from training instability and mode collapse, and Variational Autoencoders (VAEs), which ofte
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