neosr 0.1.0

neosr is a framework for training real-world single-image super-resolution networks. wiki.


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💻 installation

Requires Python 3.12 and CUDA =>11.8. Install latest Pytorch (>=2.4) and TorchVision (required).

Clone the repository:

git clone https://github.com/muslll/neosr
cd neosr

Then install other dependencies via pip:

pip install -e .

Alternatively, use poetry (recommended on linux):

poetry install
poetry add torch@latest torchvision@latest

Note: You must use poetry shell to enter the env after installation.

(optional) If you want to convert your models (convert.py), you need the following dependencies:

pip install onnx onnxruntime-gpu onnxconverter-common onnxsim

You can also install using poetry (recommended on linux):

poetry add onnx onnxruntime-gpu onnxconverter-common onnxsim

Please read the wiki tutorial for converting your models.

quick start

Start training by running:

python train.py -opt options.toml

Where options.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 Augmentations:

Supported Models:

Supported dataset loaders:

📸 datasets

As part of neosr, I have released a dataset series called Nomos. 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.

• Nomos-v2 (recommended): contains 6000 images, multipurpose. Data distribution:

pie
  title Nomos-v2 distribution
  "Animal / fur" : 439
  "Interiors" : 280
  "Exteriors / misc" : 696
  "Architecture / geometric" : 1470
  "Drawing / painting / anime" : 1076
  "Humans" : 598
  "Mountain / Rocks" : 317
  "Text" : 102
  "Textures" : 439
  "Vegetation" : 574

• nomos_uni (recommended for lightweight networks): contains 2989 images, multipurpose. Meant to be used on lightweight networks (<800k parameters).
• hfa2k: contains 2568 anime images.

community datasets

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

• 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.
• digital_art_v2: Digital art dataset from @umzi2.

resources

• Training Guide from @Sirosky
• Philip's youtube channel
• 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.