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


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Requires Python 3.11 and CUDA =>11.8. Install latest Pytorch (=>2.1) 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.

Start training by running:

python train.py -opt options.yml

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

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.

Datasets made by the upscaling community. More info can be found in the Enhance Everything discord

• FaceUp: Curated version of FFHQ
• SSDIR: Curated version of LSDIR.
• kim's 8k Dataset V2: Video Game Dataset

• OpenModelDB
• chaiNNer
• Training Guide from @Sirosky
• Training Info from @Kim

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 contributors @Phhofm, @Sirosky, @Kim2091, @terrainer, @Corpsecreate and @umzi2 for helping with tests and bug reporting.