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| import gradio as gr | |
| import torch | |
| from bsrgan import BSRGAN | |
| # Images | |
| torch.hub.download_url_to_file('https://raw.githubusercontent.com/kadirnar/bsrgan-pip/main/data/images/butterfly.png', 'butterfly.jpg') | |
| def bsrgan_inference( | |
| image: gr.inputs.Image = None, | |
| model_path: gr.inputs.Dropdown = 'kadirnar/bsrgan', | |
| ): | |
| """ | |
| BSRGAN inference function | |
| Args: | |
| image: Input image | |
| model_path: Path to the model | |
| Returns: | |
| Rendered image | |
| """ | |
| device = 'cuda:0' if torch.cuda.is_available() else 'cpu' | |
| model = BSRGAN(model_path, device=device, hf_model=True) | |
| pred = model.predict(img_path=image) | |
| return pred | |
| inputs = [ | |
| gr.inputs.Image(type="filepath", label="Input Image"), | |
| gr.inputs.Dropdown( | |
| label="Model", | |
| choices=[ | |
| "kadirnar/bsrgan", | |
| "kadirnar/BSRGANx2", | |
| "kadirnar/RRDB_PSNR_x4", | |
| "kadirnar/RRDB_ESRGAN_x4", | |
| "kadirnar/DF2K", | |
| "kadirnar/DPED", | |
| "kadirnar/DF2K_JPEG", | |
| ], | |
| default="kadirnar/bsrgan", | |
| ), | |
| ] | |
| outputs = gr.outputs.Image(type="filepath", label="Output Image") | |
| title = "BSRGAN: Designing a Practical Degradation Model for Deep Blind Image Super-Resolution." | |
| description = "BSRGAN for Deep Blind Image Super-Resolution model aims to design a practical degradation model for deep blind image super-resolution by considering the deterioration of image quality over time. It uses deep learning methods to predict the deterioration of image quality and to assist in the re-creation of images at higher resolution using these predictions." | |
| examples = [["butterfly.jpg", "kadirnar/bsrgan"]] | |
| demo_app = gr.Interface( | |
| fn=bsrgan_inference, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| cache_examples=True, | |
| live=True, | |
| theme='huggingface', | |
| ) | |
| demo_app.launch(debug=True, enable_queue=True) |