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import torch
from PIL import Image
import numpy as np
from realesrgan import RealESRGAN
import os
import gradio as gr
os.system("gdown https://drive.google.com/uc?id=1pG2S3sYvSaO0V0B8QPOl1RapPHpUGOaV -O RealESRGAN_x2.pth")
os.system("gdown https://drive.google.com/uc?id=1SGHdZAln4en65_NQeQY9UjchtkEF9f5F -O RealESRGAN_x4.pth")
os.system("gdown https://drive.google.com/uc?id=1mT9ewx86PSrc43b-ax47l1E2UzR7Ln4j -O RealESRGAN_x8.pth")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model2 = RealESRGAN(device, scale=2)
model2.load_weights('RealESRGAN_x2.pth')
model4 = RealESRGAN(device, scale=4)
model4.load_weights('RealESRGAN_x4.pth')
model8 = RealESRGAN(device, scale=8)
model8.load_weights('RealESRGAN_x8.pth')
def inference(image: Image, size: str) -> Image:
if size == '2x':
result = model2.predict(image.convert('RGB'))
elif size == '4x':
result = model4.predict(image.convert('RGB'))
else:
result = model8.predict(image.convert('RGB'))
return result
title = "Face Real ESRGAN: 2x 4x 8x"
description = "Increases the resolution of a photo. This model shows better results on faces compared to the original version."
article = ""
gr.Interface(inference,
[gr.inputs.Image(type="pil"),
gr.inputs.Radio(['2x', '4x', '8x'],
type="value",
default='2x',
label='Resolution model')],
gr.outputs.Image(type="pil", label="Output"),
title=title,
description=description,
article=article,
examples=[['groot.jpeg', "2x"]],
allow_flagging='never',
theme="default",
).launch(enable_queue=True)
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