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SRMNet_real_world_denoising

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52Hz commited on Jan 14, 2022

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00449e7

1 Parent(s): a29a552

Update app.py

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@@ -23,7 +23,7 @@ title = "Selective Residual M-Net (SRMNet)"
 description = "Gradio demo for SwinIR. SwinIR achieves state-of-the-art performance on six tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. See the paper and project page for detailed results below. Here, we provide a demo for real-world image SR.To use it, simply upload your image, or click one of the examples to load them."
 article = "<p style='text-align: center'><a href='https://' target='_blank'>Selective Residual M-Net</a> | <a href='https://github.com/FanChiMao/SRMNet' target='_blank'>Github Repo</a></p>"
-examples = [['Noise.png']['Noise.png']]
 gr.Interface(
     inference,
     [gr.inputs.Image(type="pil", label="Input")],

 description = "Gradio demo for SwinIR. SwinIR achieves state-of-the-art performance on six tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. See the paper and project page for detailed results below. Here, we provide a demo for real-world image SR.To use it, simply upload your image, or click one of the examples to load them."
 article = "<p style='text-align: center'><a href='https://' target='_blank'>Selective Residual M-Net</a> | <a href='https://github.com/FanChiMao/SRMNet' target='_blank'>Github Repo</a></p>"
+examples = [['Noise.png']]
 gr.Interface(
     inference,
     [gr.inputs.Image(type="pil", label="Input")],