SRFlow

Learning the Super-Resolution Space with Normalizing Flow
_{[Paper] ECCV 2020 Spotlight}

Setup the Environment and start the Demo

git clone https://github.com/andreas128/SRFlow.git && cd SRFlow && ./setup.sh

This oneliner will:

Clone SRFlow
Setup a python3 virtual env
Install the packages from requirements.txt
Download the pretrained models
Download the validation data
Run the Demo Jupyter Notebook

If you want to install it manually, read the setup.sh file. (Links to data/models, pip packages)

Start the Demo once everything is setup

./run_jupyter.sh

Reproduce the SRFlow Results

source myenv/bin/activate                      # Use the env you created using setup.sh
cd code
python test.py ./confs/SRFlow_DF2K_4X.yml      # Diverse Images 4X (Dataset Included)
python test.py ./confs/SRFlow_DF2K_8X.yml      # Diverse Images 8X (Dataset Included)
python test.py ./confs/SRFlow_CelebA_8X.yml    # Faces 8X

Our paper explains

How to train Conditional Normalizing Flow
We designed an architecture that archives state-of-the-art super-resolution quality.
How to train Normalizing Flow on a single GPU
We based our network on GLOW, which uses up to 40 GPUs to train for image generation. SRFlow only needs a single GPU for training conditional image generation.
How to use Normalizing Flow for image manipulation
How to exploit the latent space for Normalizing Flow for controlled image manipulations
See many Visual Results
Compare GAN vs Normalizing Flow yourself. We've included a lot of visuals results in our [Paper].

Why I stopped using GAN - Blog

Sampling: SRFlow outputs many different images for a single input.
Stable Training: SRFlow has much fewer hyperparameters than GAN approaches, and we did not encounter training stability issues.
Convergence: While GANs cannot converge, conditional Normalizing Flows converge monotonic and stable.
Higher Consistency: When downsampling the super-resolution, one obtains almost the exact input.

Get a quick introduction to Normalizing Flow in our [Blog].

Code

Due to legal hurdles, we are not yet able to release the training code. Hope we can do so soon!

We use many components of https://github.com/chaiyujin/glow-pytorch and https://github.com/xinntao/BasicSR. Thanks a lot!

Paper

[Paper] ECCV 2020 Spotlight

@inproceedings{lugmayr2020srflow,
  title={SRFlow: Learning the Super-Resolution Space with Normalizing Flow},
  author={Lugmayr, Andreas and Danelljan, Martin and Van Gool, Luc and Timofte, Radu},
  booktitle={ECCV},
  year={2020}
}

sremedios/SRFlow