Update app.py

app.py

@@ -34,6 +34,9 @@ os.makedirs(HR_Ref_path_T)
 os.makedirs('result')
 os.system("wget https://www.dropbox.com/s/vqekqdz80d85gi4/UW.png -O LR.png")
 os.system("wget https://www.dropbox.com/s/lsopmquhpm87v83/W.png -O Ref.png")
+os.system("wget https://www.dropbox.com/s/xv6inxwy0so4ni0/LR.png -O HR_LR.png")
+os.system("wget https://www.dropbox.com/s/abydd1oczs1163l/Ref.png -O HR_Ref.png")
+
 
 ## resize if necessary (not used)
 def resize(img):
@@ -74,10 +77,10 @@ def inference(LR, Ref):
                 --is_gradio")               
     return "result/0000.png"
 
-title="RefVSR (under construction)"
+title="RefVSR | Fast inference with a low-res (430x270) frame"
 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 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>"
+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 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')
@@ -87,4 +90,47 @@ article = "<p style='text-align: center'><b>To check the full capability of the
 examples=[['LR.png', 'Ref.png']]
 
 ## interface
-gr.Interface(inference,[gr.inputs.Image(type="pil"), gr.inputs.Image(type="pil")],gr.outputs.Image(type="file"),title=title,description=description,article=article,theme ="peach",examples=examples).launch(enable_queue=True)
+gr.Interface(inference,[gr.inputs.Image(type="pil"), gr.inputs.Image(type="pil")],gr.outputs.Image(type="file"),title=title,description=description,article=article,theme ="peach",examples=examples).launch(enable_queue=True)
+
+#################### 8K ##################
+## inference  
+def inference_8K(LR, Ref):
+    ## resize for user selected input (not used)
+    #LR = resize(LR)
+    #Ref = resize(Ref)
+    
+    ## Input setup (creates folders and places inputs corresponding to the original RefVSR code)
+    LR.save(os.path.join(LR_path, '0000.png'))
+    Ref.save(os.path.join(Ref_path, '0000.png'))
+    Ref.save(os.path.join(Ref_path_T, '0000.png'))
+    LR.save(os.path.join(HR_LR_path, '0000.png'))
+    Ref.save(os.path.join(HR_Ref_path, '0000.png'))
+    Ref.save(os.path.join(HR_Ref_path_T, '0000.png'))
+    
+    ## Run RefVSR model
+    os.system("python -B run.py \
+                --mode amp_RefVSR_small_MFID_8K \
+                --config config_RefVSR_small_MFID_8K \
+                --data RealMCVSR \
+                --ckpt_abs_name ckpt/RefVSR_small_MFID_8K.pytorch \
+                --data_offset ./test \
+                --output_offset ./result \
+                --qualitative_only \
+                --cpu \
+                --is_gradio")               
+    return "result/0000.png"
+    
+title="RefVSR | Slow inference with a real-world HD (1920x1080) frame"
+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 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>"
+
+## resize for sample (not used)
+#LR = resize(Image.open('LR.png')).save('LR.png')
+#Ref = resize(Image.open('Ref.png')).save('Ref.png')
+
+## input
+examples=[['HR_LR.png', 'HR_Ref.png']]
+
+## interface
+gr.Interface(inference_8K,[gr.inputs.Image(type="pil"), gr.inputs.Image(type="pil")],gr.outputs.Image(type="file"),title=title,description=description,article=article,theme ="peach",examples=examples).launch(enable_queue=True)