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Image.fromarray(results[0]).save(unique_name)
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denoise_image = upscale_image(denoise_image, upscale, unit_resolution=32, min_size=min_size)
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if type(e).__name__ == 'gradio.exceptions.Error':
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"The image" + (" has" if len(results) == 1 else "s have") + " been generated in " + \
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print('Exception of type ' + str(type(e)))
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LQ = HWC3(np.array(Image.open(input_image)))
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parser.add_argument("--use_tile_vae", action='store_true', default=True)#False
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parser.add_argument("--use_tile_vae", action='store_true', default=False)#False
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SUPIR is for beauty and illustration only
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model.ae_dtype = convert_dtype(ae_dtype)
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result_slider = ImageSlider(label='Comparator', show_label=False, elem_id="slider1", show_download_button = False)
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If you don't get the image you wanted, add more details in the « Image description »
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Rename gradio_demo.py to app.py
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, ["9 min", 9], ["10 min", 10]
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Input (*.png, *.webp, *.jpeg, *.gif, *.bmp)
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denoise_image = gr.Image(label="Denoised image", show_label=True, type="numpy", sources=[], height=600, elem_id="image-s1")
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