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Anindya Maiti 1 year ago
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README.md

@ -81,3 +81,29 @@ Some technical background:
![](images/cn-sd.png) ![](images/cn-sd.png)
## AI Upscaling
Since VRAM on GPUs is limited, use one that fits your need.
### Context-based Upscaling (Suitable on GPUs, Better Quality)
Most universal is [Real-ERSGAN](https://github.com/xinntao/Real-ESRGAN). A1111 already has this and several other upscalers.
### Generic AI Upscaling (Suitable on CPUs, Lower Quality)
Most universal is [EDSR](https://huggingface.co/eugenesiow/edsr-base). Sample python code to run on CPU:
```
from super_image import EdsrModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
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