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