SRFlow

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

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 introductrion to Normalizing Flow in our [Blog].

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}
}

Code

• Due to our funding agreement we have to go through a legal process to publish the code.
• SRFlow is based on GLOW and and trained on a single GPU.