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.
Telegram BOT: https://t.me/restoration_photo_bot" article = "
Twitter Max Skobeev | Model card
" 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=False).launch(show_error=True, show_api=False)