Status: Archive (code is provided as-is, no updates expected)
Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"
To use pretrained CelebA-HQ model, make your own manipulation vectors and run our interactive demo, check demo folder.
- Tensorflow (tested with v1.8.0)
- Horovod (tested with v0.13.8) and (Open)MPI
Run
pip install -r requirements.txt
To setup (Open)MPI, check instructions on Horovod github page.
For small scale experiments, use MNIST/CIFAR-10 (directly downloaded by train.py using keras)
For larger scale experiments, the datasets used are in the Google Cloud locations https://openaipublic.azureedge.net/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- 20GB. Unconditional ImageNet 32x32 and 64x64, as described in PixelRNN/RealNVP papers (we downloaded this processed version).lsun_realnvp- 140GB. LSUN 96x96. Random 64x64 crops taken at processing time, as described in RealNVP.
Qualitative results
celeba- 4GB. CelebA-HQ 256x256 dataset, as described in Progressive growing of GAN's. For 1024x1024 version (120GB), useceleba-full-tfr.tarwhile downloading.imagenet- 20GB. ImageNet 32x32 and 64x64 with class labels. Centre cropped, area downsampled.lsun- 700GB. LSUN 256x256. Centre cropped, area downsampled.
To download and extract celeb for example, run
wget https://openaipublic.azureedge.net/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)
For lsun, since download can be quite big, you can instead follow the instructions in data_loaders/generate_tfr/lsun.py to generate the tfr file directly from LSUN images. church_outdoor will be the smallest category.
Run wtih small depth to test
CUDA_VISIBLE_DEVICES=0 python train.py --depth 1
Run default training script with 8 GPUs:
mpiexec -n 8 python train.py
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
Pretrained models, logs and samples
wget https://openaipublic.azureedge.net/glow-demo/logs/abl-[reverse/shuffle/1x1]-[add/aff].tar
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
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
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
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
Pretrained models, logs and samples
wget https://openaipublic.azureedge.net/glow-demo/logs/lsun-rnvp-[bdr/crh/twr].tar
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
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
Logs and samples
wget https://openaipublic.azureedge.net/glow-demo/logs/lsun-bdr-[96/128].tar
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
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