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on
T4
Running
on
T4
| import torch | |
| from PIL import Image | |
| from RealESRGAN import RealESRGAN | |
| import gradio as gr | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| model2 = RealESRGAN(device, scale=2) | |
| model2.load_weights('weights/RealESRGAN_x2.pth', download=True) | |
| model4 = RealESRGAN(device, scale=4) | |
| model4.load_weights('weights/RealESRGAN_x4.pth', download=True) | |
| model8 = RealESRGAN(device, scale=8) | |
| model8.load_weights('weights/RealESRGAN_x8.pth', download=True) | |
| def inference(image, size): | |
| global model2 | |
| global model4 | |
| global model8 | |
| if image is None: | |
| raise gr.Error("Image not uploaded") | |
| width, height = image.size | |
| if width >= 5000 or height >= 5000: | |
| raise gr.Error("The image is too large.") | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| if size == '2x': | |
| try: | |
| result = model2.predict(image.convert('RGB')) | |
| except torch.cuda.OutOfMemoryError as e: | |
| print(e) | |
| model2 = RealESRGAN(device, scale=2) | |
| model2.load_weights('weights/RealESRGAN_x2.pth', download=False) | |
| result = model2.predict(image.convert('RGB')) | |
| elif size == '4x': | |
| try: | |
| result = model4.predict(image.convert('RGB')) | |
| except torch.cuda.OutOfMemoryError as e: | |
| print(e) | |
| model4 = RealESRGAN(device, scale=4) | |
| model4.load_weights('weights/RealESRGAN_x4.pth', download=False) | |
| result = model2.predict(image.convert('RGB')) | |
| else: | |
| try: | |
| result = model8.predict(image.convert('RGB')) | |
| except torch.cuda.OutOfMemoryError as e: | |
| print(e) | |
| model8 = RealESRGAN(device, scale=8) | |
| model8.load_weights('weights/RealESRGAN_x8.pth', download=False) | |
| result = model2.predict(image.convert('RGB')) | |
| print(f"Image size ({device}): {size} ... OK") | |
| return result | |
| title = "Face Real ESRGAN UpScale: 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><div>" | |
| gr.Interface(inference, | |
| [gr.Image(type="pil"), | |
| gr.Radio(['2x', '4x', '8x'], | |
| type="value", | |
| value='2x', | |
| label='Resolution model')], | |
| gr.Image(type="pil", label="Output"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[['groot.jpeg', "2x"]], | |
| allow_flagging='never', | |
| cache_examples=False, | |
| ).queue(api_open=True).launch(show_error=True, show_api=True) | |