zuko and normalizing_flows
These are competitors offering overlapping implementations of normalizing flow architectures (both include RealNVP and MAF), but Zuko is more actively maintained and integrated into PyTorch workflows (evidenced by its substantial monthly downloads), while the other is a dormant research repository.
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.
About normalizing_flows
kamenbliznashki/normalizing_flows
Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
This project offers tools to model complex data distributions, useful for tasks like generating new images or understanding the underlying structure of datasets. It takes in existing data, such as images or numerical tables, and outputs models that can recreate similar data or allow for subtle modifications. Researchers and data scientists who work with generative models or need robust density estimation will find this valuable.
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