Implementation of Flow++ in PyTorch
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.

Flow++ in PyTorch

Implementation of Flow++ in PyTorch. Based on the paper:

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel

Training script and hyperparameters designed to match the CIFAR-10 experiments described in the paper.


Environment Setup

  1. Make sure you have Anaconda or Miniconda installed.
  2. Clone repo with git clone flowplusplus.
  3. Go into the cloned repo: cd flowplusplus.
  4. Create the environment: conda env create -f environment.yml.
  5. Activate the environment: source activate f++.


  1. Make sure you've created and activated the conda environment as described above.
  2. Run python -h to see options.
  3. Run python [FLAGS] to train. E.g., run python for the default configuration, or run python --gpu_ids=0,1 to run on 2 GPUs instead of the default of 1 GPU. This will also double the batch size.
  4. At the end of each epoch, samples from the model will be saved to samples/epoch_N.png, where N is the epoch number.