https://github.com/openai/glow
Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"
https://github.com/openai/glow
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Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"
- Host: GitHub
- URL: https://github.com/openai/glow
- Owner: openai
- License: mit
- Created: 2018-06-19T20:03:00.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-07-23T14:56:11.000Z (11 months ago)
- Last Synced: 2025-04-11T10:05:32.823Z (about 2 months ago)
- Topics: paper
- Language: Python
- Homepage: https://arxiv.org/abs/1807.03039
- Size: 1.22 MB
- Stars: 3,141
- Watchers: 229
- Forks: 516
- Open Issues: 65
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
**Status:** Archive (code is provided as-is, no updates expected)
# Glow
Code for reproducing results in ["Glow: Generative Flow with Invertible 1x1 Convolutions"](https://d4mucfpksywv.cloudfront.net/research-covers/glow/paper/glow.pdf)
To use pretrained CelebA-HQ model, make your own manipulation vectors and run our interactive demo, check `demo` folder.
## Requirements
- 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](https://github.com/uber/horovod).
## Download datasets
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](http://image-net.org/small/download.php) 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), use `celeba-full-tfr.tar` while 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.
## Simple Train with 1 GPU
Run wtih small depth to test
```
CUDA_VISIBLE_DEVICES=0 python train.py --depth 1
```
## 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
```
Pretrained models, logs and samples
```
wget https://openaipublic.azureedge.net/glow-demo/logs/abl-[reverse/shuffle/1x1]-[add/aff].tar
```
##### 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
```
Pretrained models, logs and samples
```
wget https://openaipublic.azureedge.net/glow-demo/logs/lsun-rnvp-[bdr/crh/twr].tar
```
##### 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
```
Logs and samples
```
wget https://openaipublic.azureedge.net/glow-demo/logs/lsun-bdr-[96/128].tar
```
##### 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
```