Diffusion Probabilistic Models

Reference implementation for Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Features

Diffusion Foundations - Deep unsupervised learning using nonequilibrium thermodynamics.

ermongroup/ncsn

This repo contains the official implementation for the NeurIPS 2019 paper Generative Modeling by Estimating Gradients of the Data Distribution,

hojonathanho/diffusion

5,053View on GitHub

This project is a diffusion model training framework and image synthesis pipeline. It provides the tools necessary to train generative models to learn image data distributions through an iterative denoising process. The framework includes a generative model evaluation tool consisting of automated scripts used to measure the quality and accuracy of produced samples. The system covers model training pipelines and performance evaluation for generative diffusion models.

ermongroup/ddim

1,833View on GitHub

Jiaming Song, Chenlin Meng and Stefano Ermon, Stanford

AlexiaJM/AdversarialConsistentScoreMatching

125View on GitHub

Code for paper "Adversarial score matching and improved sampling for image generation"