Update app.py

app.py

@@ -63,10 +63,10 @@ def inference(LR, Ref):
     
     ## Run RefVSR model
     os.system("python -B run.py \
-                --mode amp_RefVSR_small_MFID_8K \
+                --mode amp_RefVSR_small_MFID \
                 --config config_RefVSR_small_MFID \
                 --data RealMCVSR \
-                --ckpt_abs_name ckpt/RefVSR_small_MFID_8K.pytorch \
+                --ckpt_abs_name ckpt/RefVSR_small_MFID.pytorch \
                 --data_offset ./test \
                 --output_offset ./result \
                 --qualitative_only \
@@ -75,9 +75,9 @@ def inference(LR, Ref):
     return "result/0000.png"
 
 title="RefVSR (under construction)"
-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"
+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."
 
-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 proposed two-stage training strategy, and the sample frames are in 430x270 resolution 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>"
+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-stage training strategy only. The sample frames are in 430x270 resolution 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>"
 
 ## resize for sample (not used)
 #LR = resize(Image.open('LR.png')).save('LR.png')