PyTorch implementation of normalizing flow models

Official SRFlow training code: Super-Resolution using Normalizing Flow in PyTorch

A Julia framework for invertible neural networks

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

Real NVP PyTorch a Minimal Working Example | Normalizing Flow

Official implementation of GLARE, which is accpeted by ECCV 2024.

This is project page for the paper "RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior". Paper link: https://arxiv.org/abs/2010.00029

Pytorch source code for arXiv paper Neural Network Renormalization Group, a generative model using variational renormalization group and normalizing flow.

Official code base of "Perception-Oriented Video Frame Interpolation via Asymmetric Blending" (CVPR 2024), also denoted as ''PerVFI''.

Deep Probabilistic Imaging (DPI): Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging

PyTorch implementation of the Masked Autoregressive Flow

Code for "Style-Structure Disentangled Features and Normalizing Flows for Diverse Icon Colorization", CVPR 2022.

A pytorch implementation for FACE: A Normalizing Flow based Cardinality Estimator

Pytorch implementation of Planar Flow

Unsplash2K dataset: 2K resolution high quality images

A minimal working example of Free-Form Jacobian of Reversible Dynamics

(Conditional) Normalizing Flows in PyTorch. Offers a wide range of (conditional) invertible neural networks.

Modern normalizing flows in Python. Simple to use and easily extensible.

"FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow" (CVPRW 2022)

TensorFlow implementation of Normalizing Flow