Python Diffusion-probabilistic Resources

FOPPL: A First-Order Probabilistic Programming Language This is an implementation of FOPPL, an S-expression based probabilistic programming language d

Active Learning for Deep Object Detection via Probabilistic Modeling This repository is the official PyTorch implementation of Active Learning for Dee

Uncertainty Baselines The goal of Uncertainty Baselines is to provide a template for researchers to build on. The baselines can be a starting point fo

Diffusion Normalizing Flow (DiffFlow) Reproduce setup environment The repo heavily depends on jam, a personal toolbox developed by Qsh.zh. The API may

A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

QSIprep: Preprocessing and analysis of q-space images Full documentation at https://qsiprep.readthedocs.io About qsiprep configures pipelines for proc

Code for Multinomial Diffusion Abstract Generative flows and diffusion models have been predominantly trained on ordinal data, for example natural ima

Active Learning for Deep Object Detection via Probabilistic Modeling This repository is the official PyTorch implementation of Active Learning for Dee

Abstract DeeProb-kit is a Python library that implements deep probabilistic models such as various kinds of Sum-Product Networks, Normalizing Flows an

CLIP-Guided-Diffusion Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab. Original colab notebooks by Ka

ILVR + ADM This is the implementation of ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models (ICCV 2021 Oral). This repository is h

Codebase for Diffusion Models Beat GANS on Image Synthesis.

Image Super-Resolution via Iterative Refinement Paper | Project Brief This is a unoffical implementation about Image Super-Resolution via Iterative Re

ProHMR - Probabilistic Modeling for Human Mesh Recovery Code repository for the paper: Probabilistic Modeling for Human Mesh Recovery Nikos Kolotouros

CLIP Guided Diffusion From RiversHaveWings. Generate vibrant and detailed images using only text. See captions and more generations in the Gallery See

Sum-Product Probabilistic Language SPPL is a probabilistic programming language that delivers exact solutions to a broad range of probabilistic infere

Table of Contents Table of Contents Local Planner Visualization Project (LPVP) Features Installation/Usage Local Planners Probabilistic Roadmap (PRM)

GradTTS Unofficial Pytorch implementation of "Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech" (arxiv) About this repo This is an unoffic

NU-Wave — Official PyTorch Implementation NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling Junhyeok Lee, Seungu Han @ MINDsLab Inc

Denoising Diffusion Probabilistic Models This repo contains code for DDPM training. Based on Denoising Diffusion Probabilistic Models, Improved Denois

DeepProbLog DeepProbLog is an extension of ProbLog that integrates Probabilistic Logic Programming with deep learning by introducing the neural predic

Probabilistic Cross-Modal Embedding (PCME) CVPR 2021 Official Pytorch implementation of PCME | Paper Sanghyuk Chun1 Seong Joon Oh1 Rafael Sampaio de R

Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking (CVPR 2021) Pytorch implementation of the ArTIST motion model. In this repo

wechat-wow-analysis Understanding WeChat User Preferences and “Wow” Diffusion. Fanjin Zhang, Jie Tang, Xueyi Liu, Zhenyu Hou, Yuxiao Dong, Jing Zhang,

We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.

Python code for "Machine learning: a probabilistic perspective" (2nd edition)

Denoising Diffusion Probabilistic Model for Proteins Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to gen

Official PyTorch implementation for "On Fast Sampling of Diffusion Probabilistic Models". FastDPM generation on CIFAR-10, CelebA, and LSUN datasets. S

PGBM Probabilistic Gradient Boosting Machines (PGBM) is a probabilistic gradient boosting framework in Python based on PyTorch/Numba, developed by Air

Attack-Probabilistic-Models This is the source code for Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. This repository contai

SYMPAIS: Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis Overview | Installation | Documentation | Examples | Notebo

DiffSinger - PyTorch Implementation PyTorch implementation of DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis (TTS Extension). Status

RLLReg This repository contains a Pytorch implementation of the point set registration method RLLReg. Details about the method can be found in the 3DV

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling For Official repo of NU-Wave: A Diffusion Probabilistic Model for Neural Audio Up

pyprobables is a pure-python library for probabilistic data structures. The goal is to provide the developer with a pure-python implementation of common probabilistic data-structures to use in their work.

Deep Probabilistic Programming Course @ DIKU

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

RecSim NG, a probabilistic platform for multi-agent recommender systems simulation. RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for agent-behavior specification;

Learning Energy-Based Models by Diffusion Recovery Likelihood Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma Paper: https://arxiv.o

PClean: A Domain-Specific Probabilistic Programming Language for Bayesian Data Cleaning Warning: This is a rapidly evolving research prototype.

ZhuSuan is a Python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and

Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilis

Disclaimer This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to chang

Funsor Funsor is a tensor-like library for functions and distributions. See Functional tensors for probabilistic programming for a system description.

pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

TensorFlow Probability TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFl

GluonTS - Probabilistic Time Series Modeling in Python GluonTS is a Python toolkit for probabilistic time series modeling, built around Apache MXNet (

MXFusion | | | | Tutorials | Documentation | Contribution Guide MXFusion is a modular deep probabilistic programming library. With MXFusion Modules yo

emcee The Python ensemble sampling toolkit for affine-invariant MCMC emcee is a stable, well tested Python implementation of the affine-invariant ense

PtStat Probabilistic Programming and Statistical Inference in PyTorch. Introduction This project is being developed during my time at Cogent Labs. The

A supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data

InferPy: Deep Probabilistic Modeling Made Easy InferPy is a high-level API for probabilistic modeling written in Python and capable of running on top

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

Time series analysis today is an important cornerstone of quantitative science in many disciplines, including natural and life sciences as well as eco

Navier-Stokes-numerical-solution-using-Python- Python script for Linear, Non-Linear Convection, Burger’s & Poisson Equation in 1D & 2D, 1D D

improved-diffusion This is the codebase for Improved Denoising Diffusion Probabilistic Models. Usage This section of the README walks through how to t

Source code for "Diffusion is All You Need for Learning on Surfaces", by Nicholas Sharp Souhaib Attaiki Keenan Crane Maks Ovsjanikov NOTE: the linked

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

emcee The Python ensemble sampling toolkit for affine-invariant MCMC emcee is a stable, well tested Python implementation of the affine-invariant ense

Lightwood is like Legos for Machine Learning. A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glu

pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Bayesian Methods for Hackers Using Python and PyMC The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chap