ermongroup/ncsnv2
The official PyTorch implementation for NCSNv2 (NeurIPS 2020)
This project helps researchers and artists generate high-resolution, realistic images without needing adversarial training. It takes existing image datasets as input and produces new, diverse image samples that capture the characteristics of the original data. This is ideal for those working on image synthesis, creative AI, or advanced computer vision applications.
321 stars. No commits in the last 6 months.
Use this if you need to generate high-quality images from a dataset that are sharp and diverse, without the complexities often associated with Generative Adversarial Networks (GANs).
Not ideal if your primary goal is exact log-likelihood computation or even higher sample quality, as a subsequent extension (Score-Based Generative Modeling through Stochastic Differential Equations) offers further improvements.
Stars
321
Forks
62
Language
Python
License
MIT
Category
Last pushed
Jun 12, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/ermongroup/ncsnv2"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yang-song/score_sde_pytorch
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential...
yang-song/score_sde
Official code for Score-Based Generative Modeling through Stochastic Differential Equations...
amazon-science/unconditional-time-series-diffusion
Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict,...
AI4HealthUOL/SSSD-ECG
Repository for the paper: 'Diffusion-based Conditional ECG Generation with Structured State Space Models'
homerjed/sbgm
Score-based Diffusion models in JAX.