neosr

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neosr is an open-source framework for training super-resolution models. It provides a comprehensive and reproducible environment for achieving state-of-the-art image restoration results, making it suitable for both the enthusiastic community, professionals and machine learning academic researchers. It serves as a versatile platform and aims to bridge the gap between practical application and academic research in the field.

• Accessible: implements a wide range of the latest advancements in single-image super-resolution networks, losses, optimizers and augmentations. Users can easily explore, adapt and experiment with various configurations for their specific needs, even without coding skills.

• Efficient: optimized for faster training iterations, quicker convergence and low GPU requirements, making it the most efficient choice for both research and practical use cases.

• Practical: focuses on the real-world use of super-resolution to realistically restore degraded images in various domains, including photos, anime/cartoons, illustrations and more. It's also suitable for critical applications like medical imaging, forensics, geospatial and others (although caution should be taken in those cases).

• Reproducible: this framework emphasizes the importance of reproducible research. It provides deterministic training environments that can create bit-exact reproducible models (on the same platform), ensuring predictable and reliable results, which are essential for maintaining consistency in academic validation.

• Simple: features are easy to implement or modify. Code is written in readable Python, no fancy styling. All code is managed, validated and formatted by uv, ruff, mypy and torchfix.

For more information see our wiki.

🤝 support the project

[!TIP] Consider supporting the project on KoFi ☕ or Patreon

💻 installation

Requires CUDA >=12.4

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://raw.githubusercontent.com/neosr-project/neosr/refs/heads/master/install_windows.ps1 | iex"

Linux:

curl -LsSf https://raw.githubusercontent.com/neosr-project/neosr/refs/heads/master/install_linux.sh | sh

Note: neosr will be installed on your terminal current path.

To update neosr, run:

neosr-update

For manual installation details, see our Installation Instructions wiki.

⏩ quick start

Start training by running:

neosr-train config.toml

Where config.toml is a configuration file. Templates can be found in options.

[!TIP] Please read the wiki Configuration Walkthrough for an explanation of each option.

✨ features

supported archs:

[!NOTE] For all arch-specific parameters, read the wiki.

under testing

supported discriminators:

supported optimizers:

supported losses:

supported metrics

supported augmentations:

supported models:

supported dataloaders:

📸 datasets

As part of neosr, we have released a series of datasets. The purpose of these datasets is to distill only the best images from the academic and community datasets. A total of 14+ datasets were manually reviewed and processed, including: Adobe-MIT-5k, RAISE, LSDIR, LIU4k-v2, KONIQ-10k, Nikon LL RAW, DIV8k, FFHQ, Flickr2k, ModernAnimation1080_v2, Rawsamples, SignatureEdits, Hasselblad raw samples and Unsplash.

• BHI series from @Phhofm. Read more about the selection process here. Full version is recommended for large networks, while small version is recommended for lightweight SISR:

• Nomos-v2: contains 6000 images, multipurpose.
• nomos_uni: contains 2989 images, multipurpose. Meant to be used on lightweight networks (<800k parameters).

community datasets

Datasets made by the upscaling community. More info can be found in author's repository.

• digital_art_v3: Digital art dataset from @umzi2.
• ModernAnimation_v3: High-quality modern anime dataset from Zarxrax.
• ClassicAnimation: High-quality analog (cel) anime dataset from Zarxrax.
• DF2k-BHI: a curated version of the classic DF2k dataset, made by @Phhofm.
• 4xNomosRealWeb Dataset: realistically degraded LQ's for Nomos-v2 dataset (from @Phhofm).
• FaceUp: Curated version of FFHQ.
• SSDIR: Curated version of LSDIR.
• ArtFaces: Curated version of MetFaces.
• Nature Dataset: Curated version of iNaturalist.

📖 resources

• Training Guide from @Sirosky
• Philip's youtube channel
• Dataset processing tools: WTP Dataset Destroyer and SR dataset creator
• OpenModelDB
• chaiNNer

📄 license and acknowledgements

Released under the Apache license. All licenses listed on license/readme. This code was originally based on BasicSR.

Thanks to victorca25/traiNNer, styler00dollar/Colab-traiNNer and timm for providing helpful insights into some problems.

Thanks to active contributors @Phhofm, @Sirosky, and @umzi2 for helping with tests and bug reporting.