| 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.<br>Telegram BOT: https://t.me/restoration_photo_bot" | |
| article = "<div style='text-align: center;'>Twitter <a href='https://twitter.com/DoEvent' target='_blank'>Max Skobeev</a> | <a href='https://huggingface.co/sberbank-ai/Real-ESRGAN' target='_blank'>Model card</a> <center><img src='https://visitor-badge.glitch.me/badge?page_id=max_skobeev_face_esrgan' alt='visitor badge'></center></div>" | |
| 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() | |