torchflows and normalizing_flows
These are competitors offering overlapping implementations of normalizing flow algorithms, where the more established kamenbliznashki/normalizing_flows provides a broader set of architectures (BNAF, Glow, MAF, RealNVP, planar flows) while davidnabergoj/torchflows emphasizes ease of use and extensibility for similar density estimation tasks.
About torchflows
davidnabergoj/torchflows
Modern normalizing flows in Python. Simple to use and easily extensible.
About normalizing_flows
kamenbliznashki/normalizing_flows
Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows
Provides reference implementations for training normalizing flow models on both synthetic 2D datasets and real image/tabular data (CelebA, MNIST, UCI), with support for multi-GPU distributed training and gradient checkpointing for large models. Includes generative capabilities beyond density estimation, such as temperature-controlled sampling, latent space attribute manipulation, and conditional density estimation. Built entirely in PyTorch with modular architecture allowing interchange of flow components and straightforward integration into probabilistic inference pipelines.
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