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
OpenReview:Hyg74h05tX

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

Usage

Environment Setup

1. Make sure you have Anaconda or Miniconda installed.
2. Clone repo with git clone https://github.com/chrischute/flowplusplus.git 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++.

Train

1. Make sure you've created and activated the conda environment as described above.
2. Run python train.py -h to see options.
3. Run python train.py [FLAGS] to train. E.g., run python train.py for the default configuration, or run python train.py --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.