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Diffusion3 articles

Diffusion

Articles

  • 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

    1 min

  • Score-Based Generative Modeling via Stochastic Differential Equations: How Continuous SDEs and Score Matching Unify Diffusion Models

    Diffusion and score-based models represent one of the foundational paradigms of modern generative artificial intelligence, underpinning systems across image synthesis, video generation, audio modeling, and continuous multimodal representations. For years, generative diffusion was approached from two distinct perspectives: discrete-step denoising diffusion probabilistic models (DDPM) pioneered by Sohl-Dickstein et al. and Ho et al., and score matching with Langevin dynamics (SMLD / NCSN) introduc

    1 min

  • Diffusion Transformers (DiT): How Patchification and adaLN-Zero Replaced U-Nets in Generative AI

    Generative visual models relied for years on convolutional U-Net architectures to execute iterative denoising. From Denoising Diffusion Probabilistic Models (DDPM) and Ablated Diffusion Models (ADM) to Latent Diffusion Models (LDMs) behind Stable Diffusion, convolutional backbones served as the default engine for image synthesis. While convolutional inductive biases provided translation equivariance and local spatial hierarchies, they imposed architectural rigidities that resisted compute scalin

    1 min