Awesome resources on normalizing flows.

Rectified Flow Inversion (RF-Inversion) - ICLR 2025

Deep Learning sample programs using PyTorch in C++

Regression Transformer (2023; Nature Machine Intelligence)

Unofficial Implementation of "Denoising Diffusion Probabilistic Models" in PyTorch(Lightning)

ECCV 2024 SuperGaussian for generic 3D upsampling

Ying Nian Wu's UCLA Statistical Machine Learning Tutorial on generative modeling.

Generative Modeling with Optimal Transport Maps - ICLR 2022

Official repo of ICASSP 2024 paper - Generative De-Quantization for Neural Speech Codec via Latent Diffusion.

Code for the paper Iterated Denoising Energy Matching for Sampling from Boltzmann Densities.

Flow-based generative model for 3D point clouds.

Noise Contrastive Estimation (NCE) in PyTorch

The official repository for NeurIPS 2024 Oral <Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models>

DiffuLab is designed to provide a simple and flexible way to train diffusion models while allowing full customization of its core components.

Multiplicative Normalizing Flows in PyTorch.

[AISTATS2020] The official repository of "Invertible Generative Modling using Linear Rational Splines (LRS)".

Official code for Continuous-Time Functional Diffusion Processes (NeurIPS 2023).

PyTorch implementation of "Light Unbalanced Optimal Transport" (NeurIPS 2024)

[NeurIPS 2024] Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation

Unlock the potential of latent diffusion models with MNIST! 🚀 Dive into reconstructing and generating digits using cutting-edge techniques like Autoencoders with Channel Attention Blocks and DDPMs. Perfect for enthusiasts of computer vision, deep learning, and generative modeling! 🌌✨