Score-Based Generative Modeling through Stochastic Differential Equations
score_sde_pytorch
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral).
This repo contains a PyTorch implementation for the paper Score-Based Generative Modeling through Stochastic Differential Equations
by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole
What does this code do?
Aside from the NCSN++ and DDPM++ models in our paper, this codebase also re-implements many previous score-based models in one place, including NCSN from Generative Modeling by Estimating Gradients of the Data Distribution, NCSNv2 from Improved Techniques for Training Score-Based Generative Models, and DDPM from Denoising Diffusion Probabilistic Models.
It supports training new models, evaluating the sample quality and likelihoods of existing models. We carefully designed the code to be modular and