This version is fork of the original Xinntao Real-ESRGAN with multi-channel support (including alpha channel support) and some other optimizations and features. Only differences from the original version will be written here. You can read the description and features of the original version on it's page

👀Demos | 🚩Updates | ⚡Usage | 🏰Model Zoo | 🔧Install | 💻Train | ❓FAQ | 🎨Contribution

🔥 AnimeVideo-v3 model (动漫视频小模型). Please see [anime video models] and [comparisons]
🔥 RealESRGAN_x4plus_anime_6B for anime images (动漫插图模型). Please see [anime_model]

Online demo for Multichannel-Real-ESRGAN: StableDraw

Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data.

📖 Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

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🚩 Updates

• ✅ Add the realesr-general-x4v3 model - a tiny small model for general scenes. It also supports the -dn option to balance the noise (avoiding over-smooth results). -dn is short for denoising strength.
• ✅ Update the RealESRGAN AnimeVideo-v3 model. Please see anime video models and comparisons for more details.
• ✅ Add small models for anime videos. More details are in anime video models.
• ✅ Add the ncnn implementation Real-ESRGAN-ncnn-vulkan.
• ✅ Add RealESRGAN_x4plus_anime_6B.pth, which is optimized for anime images with much smaller model size. More details and comparisons with waifu2x are in anime_model.md
• ✅ Support finetuning on your own data or paired data (i.e., finetuning ESRGAN). See here
• ✅ Integrate GFPGAN to support face enhancement.
• ✅ Integrated to Huggingface Spaces with Gradio. See Gradio Web Demo. Thanks @AK391
• ✅ Support arbitrary scale with --outscale (It actually further resizes outputs with LANCZOS4). Add RealESRGAN_x2plus.pth model.
• ✅ The inference code supports: 1) tile options; 2) images with alpha channel; 3) gray images; 4) 16-bit images.
• ✅ The training codes have been released. A detailed guide can be found in Training.md.

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🔧 Dependencies and Installation

• Python >= 3.7 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.7

Installation

1. Clone repo

   git clone https://github.com/Robolightning/Multichannel-Real-ESRGAN
   cd Multichannel-Real-ESRGAN

2. Install dependent packages

   # We use BasicSR for both training and inference
   # facexlib and gfpgan are for face enhancement
   pip install facexlib
   pip install gfpgan
   pip install -r requirements.txt
   python setup.py develop

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⚡ Quick Inference

There are usually three ways to inference Real-ESRGAN.

1. From console
2. From Python script

Python script

Description is coming soon

Console usage

Usage of python script in console

1. You can use X4 model for arbitrary output size with the argument outscale. The program will further perform cheap resize operation after the Real-ESRGAN output.

Usage: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile -o outfile [options]...

A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile --outscale 3.5 --face_enhance

  -h                   show this help
  -i --input           Input image or folder. Default: inputs
  -o --output          Output folder. Default: results
  -n --model_name      Model name. Default: RealESRGAN_x4plus
  -s, --outscale       The final upsampling scale of the image. Default: 4
  --suffix             Suffix of the restored image. Default: out
  -t, --tile           Tile size, 0 for no tile during testing. Default: 0
  --face_enhance       Whether to use GFPGAN to enhance face. Default: False
  --fp32               Use fp32 precision during inference. Default: fp16 (half precision).
  --ext                Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto

Inference general images

4-channeled models is comming soon

Download pre-trained models: RealESRGAN_x4plus.pth

wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P weights

Inference!

python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs --face_enhance

Results are in the results folder

Inference anime images

Pre-trained models: RealESRGAN_x4plus_anime_6B
More details and comparisons with waifu2x are in anime_model.md

# download model
wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P weights
# inference
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i inputs

Results are in the results folder

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📧 Contact

If you have any question, please email [email protected] or to author of the original project [email protected] or [email protected].

🧩 Projects that use Multichannel-Real-ESRGAN

If you develop/use Multichannel-Real-ESRGAN in your projects, welcome to let me know.

• Minecraft mod: [MC-textures-upscaler-mod](coming soon) by Robolightning

GUI

• Web graphic editor with neural networks: StableDraw by Robolightning