PyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original version. It is also easier to integrate this model into your projects.
You can try it in google colab
- Paper: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
- Official github
git clone https://https://github.com/sberbank-ai/Real-ESRGAN cd Real-ESRGAN
pip install -r requirements.txt
Download pretrained weights and put them into
import torch from PIL import Image import numpy as np from realesrgan import RealESRGAN device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = RealESRGAN(device, scale=4) model.load_weights('weights/RealESRGAN_x4.pth') path_to_image = 'inputs/lr_image.png' image = Image.open(path_to_image).convert('RGB') sr_image = model.predict(image) sr_image.save('results/sr_image.png')