zuko and torchflows

These are competitors offering alternative implementations of normalizing flows in PyTorch, with Zuko being a mature, well-adopted library while torchflows is an early-stage project with similar goals but minimal adoption.

zuko
62
Established
torchflows
51
Established
Maintenance 10/25
Adoption 12/25
Maturity 25/25
Community 15/25
Maintenance 10/25
Adoption 5/25
Maturity 25/25
Community 11/25
Stars: 446
Forks: 35
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stars: 12
Forks: 2
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
No risk flags
No risk flags

About zuko

probabilists/zuko

Normalizing flows in PyTorch

This project helps machine learning engineers and researchers build advanced probabilistic models. It takes in structured data and outputs flexible, high-dimensional probability distributions that can be easily trained and sampled. It is ideal for those working on complex density estimation or generative modeling tasks.

probabilistic-modeling deep-learning-research generative-models density-estimation machine-learning-engineering

About torchflows

davidnabergoj/torchflows

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

This library helps machine learning researchers and practitioners train generative models and estimate data density using modern normalizing flows. You provide your dataset, and it outputs a model that can generate new, similar data points or calculate the likelihood of existing ones. It's designed for those working with advanced machine learning models who need flexible tools for generative tasks.

generative-modeling density-estimation machine-learning-research data-synthesis deep-learning-engineering

Related comparisons

zuko and normalizing-flows zuko and normalizing-flows zuko and normalizing_flows zuko and PyTorchDiscreteFlows zuko and normalizing-flows zuko and pytorch-flows

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