https://github.com/openai/glow

Python
Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"

Glow

Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"

Requirements

• Tensorflow (tested with v1.8.0)
• Horovod (tested with v0.13.4) and (Open)MPI

Download datasets

The datasets are in the Google Cloud locations https://storage.googleapis.com/glow-demo/data/{dataset_name}-tfr.tar. The dataset_names are below, we mention the exact preprocessing / downsampling method for a correct comparison of likelihood.

Quantitative results

• imagenet-oord - Unconditional ImageNet 32x32 and 64x64, as described in PixelRNN/RealNVP papers (we downloaded this processed version).
• lsun_realnvp - LSUN 96x96. Random 64x64 crops taken at processing time, as described in RealNVP.

Qualitative results

• celeba - CelebA-HQ 1024x1024 dataset, as described in Progressive growing of GAN's.
• imagenet - ImageNet 32x32 and 64x64 with class labels. Centre cropped, area downsampled.
• lsun - LSUN 256x256. Centre cropped, area downsampled.

To download and extract celeb for example, run

curl https://storage.googleapis.com/glow-demo/data/celeba-tfr.tar
tar -xvf celeb-tfr.tar

Change hps.data_dir in train.py file to point to the above folder (or use the --data_dir flag when you run train.py)

Train with multiple GPUs using MPI and Horovod

Run default training script with 8 GPUs:

mpiexec -n 8 python train.py

Ablation experiments

mpiexec -n 8 python train.py --problem cifar10 --image_size 32 --n_level 3 --depth 32 --flow_permutation [0/1/2] --flow_coupling [0/1] --seed [0/1/2] --learntop --lr 0.001

CIFAR-10 Quantitative result

mpiexec -n 8 python train.py --problem cifar10 --image_size 32 --n_level 3 --depth 32 --flow_permutation 2 --flow_coupling 1 --seed 0 --learntop --lr 0.001 --n_bits_x 8

ImageNet 32x32 Quantitative result

mpiexec -n 8 python train.py --problem imagenet-oord --image_size 32 --n_level 3 --depth 48 --flow_permutation 2 --flow_coupling 1 --seed 0 --learntop --lr 0.001 --n_bits_x 8

ImageNet 64x64 Quantitative result

mpiexec -n 8 python train.py --problem imagenet-oord --image_size 64 --n_level 4 --depth 48 --flow_permutation 2 --flow_coupling 1 --seed 0 --learntop --lr 0.001 --n_bits_x 8

LSUN 64x64 Quantitative result

mpiexec -n 8 python train.py --problem lsun_realnvp --category [bedroom/church_outdoor/tower] --image_size 64 --n_level 3 --depth 48 --flow_permutation 2 --flow_coupling 1 --seed 0 --learntop --lr 0.001 --n_bits_x 8

CelebA-HQ 256x256 Qualitative result

mpiexec -n 40 python train.py --problem celeba --image_size 256 --n_level 6 --depth 32 --flow_permutation 2 --flow_coupling 0 --seed 0 --learntop --lr 0.001 --n_bits_x 5

LSUN 96x96 and 128x128 Qualitative result

mpiexec -n 40 python train.py --problem lsun --category [bedroom/church_outdoor/tower] --image_size [96/128] --n_level 5 --depth 64 --flow_permutation 2 --flow_coupling 0 --seed 0 --learntop --lr 0.001 --n_bits_x 5

Conditional CIFAR-10 Qualitative result

mpiexec -n 8 python train.py --problem cifar10 --image_size 32 --n_level 3 --depth 32 --flow_permutation 2 --flow_coupling 0 --seed 0 --learntop --lr 0.001 --n_bits_x 5 --ycond --weight_y=0.01

Conditional ImageNet 32x32 Qualitative result

mpiexec -n 8 python train.py --problem imagenet --image_size 32 --n_level 3 --depth 48 --flow_permutation 2 --flow_coupling 0 --seed 0 --learntop --lr 0.001 --n_bits_x 5 --ycond --weight_y=0.01