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)

Generative Modeling with Optimal Transport Maps - ICLR 2022

ECCV 2024 SuperGaussian for generic 3D upsampling

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

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

Multiplicative Normalizing Flows in PyTorch.

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

[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)

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! 🌌✨

Official Implementation of Paper "Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling" (ICML 2023)

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