to 1080p

app.py

@@ -1,9 +1,8 @@
-import numpy as np
+import gradio as gr
 import torch
 import torch.nn as nn
-import gradio as gr
-from PIL import Image
 import torchvision.transforms as transforms
+from PIL import Image
 
 norm_layer = nn.InstanceNorm2d
 
@@ -93,7 +92,7 @@ model2.eval()
 
 def predict(input_img, ver):
     input_img = Image.open(input_img)
-    transform = transforms.Compose([transforms.Resize(512, Image.BICUBIC), transforms.ToTensor()])
+    transform = transforms.Compose([transforms.Resize(1080, Image.BICUBIC), transforms.ToTensor()])
     input_img = transform(input_img)
     input_img = torch.unsqueeze(input_img, 0)
 
@@ -103,7 +102,7 @@ def predict(input_img, ver):
             drawing = model2(input_img)[0].detach()
         else:
             drawing = model1(input_img)[0].detach()
-    
+
     drawing = transforms.ToPILImage()(drawing)
     return drawing
 
@@ -123,4 +122,4 @@ iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'),
     gr.inputs.Radio(['Complex Lines','Simple Lines'], type="value", default='Simple Lines', label='version')],
     gr.outputs.Image(type="pil"), title=title,examples=examples)
 
-iface.launch()
+iface.launch()