| 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(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 | |
| input_image = gr.Image(type='pil', label='Input Image') | |
| input_model_image = gr.Radio(['2x', '4x', '8x'], type="value", value="4x", label="Model Upscale/Enhance Type") | |
| submit_image_button = gr.Button('Submit') | |
| output_image = gr.Image(type="filepath", label="Output Image") | |
| tab_img = gr.Interface( | |
| fn=inference_image, | |
| inputs=[input_image, input_model_image], | |
| outputs=output_image, | |
| title="Real-ESRGAN Pytorch", | |
| description="Gradio UI for Real-ESRGAN Pytorch version. To use it, simply upload your image, or click one of examples and choose the model. Read more at the links below. Please click submit only once <br><p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/ai-forever/Real-ESRGAN'>Github Repo</a></p>" | |
| ) | |
| input_video = gr.Video(label='Input Video') | |
| input_model_video = gr.Radio(['2x', '4x', '8x'], type="value", value="4x", label="Model Upscale/Enhance Type") | |
| submit_video_button = gr.Button('Submit') | |
| output_video = gr.Video(label='Output Video') | |
| tab_vid = gr.Interface( | |
| fn=inference_image, | |
| inputs=[input_video, input_model_video], | |
| outputs=output_video, | |
| title="Real-ESRGAN Pytorch", | |
| description="Gradio UI for Real-ESRGAN Pytorch version. To use it, simply upload your video, or click one of examples and choose the model. Read more at the links below. Please click submit only once <br><p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/ai-forever/Real-ESRGAN'>Github Repo</a></p>" | |
| ) | |
| demo = gr.TabbedInterface([tab_img, tab_vid], ["Image", "Video"]) | |
| demo.launch(debug=True, show_error=True) |