codeslake commited on
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1 Parent(s): 456ec06

Update app.py

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  1. app.py +2 -2
app.py CHANGED
@@ -85,7 +85,7 @@ def inference(LR, Ref):
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  title="RefVSR | 4xVSR"
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  description="Demo application for Reference-based Video Super-Resolution (RefVSR). Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively. The demo runs on CPUs and takes about 150s."
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- article = "<p style='text-align: center'><b>To check the full capability of the module, we recommend to clone Github repository and run RefVSR models on videos using GPUs.</b></p><p style='text-align: center'>This demo runs on CPUs and only supports RefVSR for a single LR and Ref frame due to computational complexity. Hence, the model will not take advantage of temporal LR and Ref frames.</p><p style='text-align: center'>The model is trained by the proposed pre-training strategy only. The sample frames are in 480x270 resolution and saved in the PNG format</p><p style='text-align: center'>For user given frames, the size will be adjusted for the longer side to have 480 pixels.</p><p style='text-align: center'><a href='https://junyonglee.me/projects/RefVSR' target='_blank'>Project</a> | <a href='https://arxiv.org/abs/2203.14537' target='_blank'>arXiv</a> | <a href='https://github.com/codeslake/RefVSR' target='_blank'>Github</a></p>"
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  ## resize for sample
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  LR = resize(Image.open('LR.png')).save('LR.png')
@@ -128,7 +128,7 @@ def inference_8K(LR, Ref):
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  title="RefVSR"
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  description="Demo application for Reference-based Video Super-Resolution (RefVSR). Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively. The demo runs on CPUs and takes about 120s."
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- article = "<p style='text-align: center'><b>To check the full capability of the module, we recommend to clone Github repository and run RefVSR models on videos using GPUs.</b></p><p style='text-align: center'>This demo runs on CPUs and only supports RefVSR for a single LR and Ref frame due to computational complexity. Hence, the model will not take advantage of temporal LR and Ref frames.</p><p style='text-align: center'>The model is trained by the proposed two-stage training strategy. The sample frames are in HD resolution (1920x1080) and saved in the PNG format. </p><p style='text-align: center'><a href='https://junyonglee.me/projects/RefVSR' target='_blank'>Project</a> | <a href='https://arxiv.org/abs/2203.14537' target='_blank'>arXiv</a> | <a href='https://github.com/codeslake/RefVSR' target='_blank'>Github</a></p>"
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  ## resize for sample (not used)
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  #LR = resize(Image.open('LR.png')).save('LR.png')
 
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  title="RefVSR | 4xVSR"
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  description="Demo application for Reference-based Video Super-Resolution (RefVSR). Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively. The demo runs on CPUs and takes about 150s."
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+ article = "<p style='text-align: center'><b>To check the full capability of the module, we recommend to clone Github repository and run RefVSR models on videos using GPUs.</b></p><p style='text-align: center'><div>This demo runs on CPUs and only supports RefVSR for a single LR and Ref frame due to computational complexity.</div><div>Hence, the model <b>will not take advantage</b> of temporal LR and Ref frames.</div></p><p style='text-align: center'>Moreover, the model is trained <b>only with the proposed pre-training strategy</b>. The sample frames are in 480x270 resolution and saved in the PNG format</p><p style='text-align: center'>For user given frames, the size will be adjusted for the longer side to have 480 pixels.</p><p style='text-align: center'><a href='https://junyonglee.me/projects/RefVSR' target='_blank'>Project</a> | <a href='https://arxiv.org/abs/2203.14537' target='_blank'>arXiv</a> | <a href='https://github.com/codeslake/RefVSR' target='_blank'>Github</a></p>"
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  ## resize for sample
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  LR = resize(Image.open('LR.png')).save('LR.png')
 
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  title="RefVSR"
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  description="Demo application for Reference-based Video Super-Resolution (RefVSR). Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively. The demo runs on CPUs and takes about 120s."
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+ article = "<p style='text-align: center'><b>To check the full capability of the module, we recommend to clone Github repository and run RefVSR models on videos using GPUs.</b></p><p style='text-align: center'><div>This demo runs on CPUs and only supports RefVSR for a single LR and Ref frame due to computational complexity.</div><div>Hence, the model will not take advantage of temporal LR and Ref frames.</div></p><p style='text-align: center'>The model is trained by the proposed two-stage training strategy. The sample frames are in HD resolution (1920x1080) and saved in the PNG format. </p><p style='text-align: center'><a href='https://junyonglee.me/projects/RefVSR' target='_blank'>Project</a> | <a href='https://arxiv.org/abs/2203.14537' target='_blank'>arXiv</a> | <a href='https://github.com/codeslake/RefVSR' target='_blank'>Github</a></p>"
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  ## resize for sample (not used)
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  #LR = resize(Image.open('LR.png')).save('LR.png')