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 = "This is an unofficial demo for Real-ESRGAN. Scales the resolution of a photo. This model shows better results on faces compared to the original version.
Telegram BOT: https://t.me/restoration_photo_bot" article = "
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" 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", cache_examples=False, ).queue()launch()