Diffusion Models Live Event

The Annotated Diffusion Model

A Mathematical Introduction to Diffusion Models

arXiv:2607. 01693v1 Announce Type: new Abstract: These notes give a proof-oriented introduction to diffusion models from the viewpoint of sampling, tracing a single arc from classical sampling dynamics to modern diffusion samplers, their error analysis, and inference-time control.

Japanese Stable Diffusion

Information Spreading in Diffusion Models from Effective Field Theory

arXiv:2608. 14308v1 Announce Type: cross Abstract: We study score-matching diffusion models with a convolutional architecture.

VQ-Diffusion

Simplifying, stabilizing, and scaling continuous-time consistency models

We’ve simplified, stabilized, and scaled continuous-time consistency models, achieving comparable sample quality to leading diffusion models, while using only two sampling steps.

Simulation-free and finite-time diffusion model

arXiv:2608. 03117v1 Announce Type: new Abstract: The performance of generative diffusion models is determined by the choice of the reference diffusion process connecting the empirical and prior distributions.

Accelerating Stable Diffusion Inference on Intel CPUs

Stable Diffusion with 🧨 Diffusers

Fine-tuning Stable Diffusion models on Intel CPUs

Finetune Stable Diffusion Models with DDPO via TRL

Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction

The article "Foundations of Diffusion Models in General State Spaces: A Self-Contained Introduction" presents a unified primer on diffusion models that applies to both continuous Euclidean data and discrete categorical structures. It develops discrete-time forward noising via Markov kernels and learned reverse dynamics, and connects these to continuous-time limits such as stochastic differential equations in ρ^d and continuous-time Markov chains on finite alphabets, deriving the corresponding Fokker–Planck and master equations. The work also shows how different forward corruption choices—Gaussian processes for continuous spaces and structured categorical transition kernels for discrete spaces—affect reverse dynamics and the evidence lower bound used in training, offering a layered exposition for newcomers, practitioners, and experts alike.