Diffusion2 articles

Diffusion

Articles

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
August 22, 20261 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
August 20, 20261 min