Update app.py
Browse files
app.py
CHANGED
@@ -41,8 +41,8 @@ def resize(max_side,img):
|
|
41 |
return img
|
42 |
|
43 |
def inference(LR, Ref):
|
44 |
-
LR = resize(256,
|
45 |
-
Ref = resize(256,
|
46 |
|
47 |
LR.save(os.path.join(LR_path, '0000.png'))
|
48 |
Ref.save(os.path.join(Ref_path, '0000.png'))
|
@@ -66,7 +66,7 @@ title="RefVSR"
|
|
66 |
#description="Demo application for Reference-based Video Super-Resolution (RefVSR).\nInstruction: Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively.\nNote 1: This demo only supports RefVSR for a single LR and Ref frame due to computational complexity. Hence, the model might not take advantage of temporal frames. \nNote 2: The model is our small 8K model trained with the proposed two-stage training strategy. \nNote 3: The spatial size of input LR and Ref frames is 1920x1080 (HD), in the PNG format."
|
67 |
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."
|
68 |
|
69 |
-
article = "<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
|
70 |
|
71 |
LR = resize(256, Image.open('LR.png'))
|
72 |
Ref = resize(256, Image.open('Ref.png'))
|
|
|
41 |
return img
|
42 |
|
43 |
def inference(LR, Ref):
|
44 |
+
LR = resize(256, LR)
|
45 |
+
Ref = resize(256, Ref)
|
46 |
|
47 |
LR.save(os.path.join(LR_path, '0000.png'))
|
48 |
Ref.save(os.path.join(Ref_path, '0000.png'))
|
|
|
66 |
#description="Demo application for Reference-based Video Super-Resolution (RefVSR).\nInstruction: Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively.\nNote 1: This demo only supports RefVSR for a single LR and Ref frame due to computational complexity. Hence, the model might not take advantage of temporal frames. \nNote 2: The model is our small 8K model trained with the proposed two-stage training strategy. \nNote 3: The spatial size of input LR and Ref frames is 1920x1080 (HD), in the PNG format."
|
67 |
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."
|
68 |
|
69 |
+
article = "<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 the small-sized model trained with the proposed two-stage training strategy.</p><p style='text-align: center'>The frames will be resized so that the length of a longer side of the frames doesn't exceed 256 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>"
|
70 |
|
71 |
LR = resize(256, Image.open('LR.png'))
|
72 |
Ref = resize(256, Image.open('Ref.png'))
|