sb10

app.py

@@ -25,16 +25,15 @@ net_c=net_c.to('cpu')
 net_i.eval().to('cuda')
 net_c.eval().to('cuda')
 
-
-
-
-
-@spaces.GPU(duration=120)
 def align(x1):
     h, w = x1.shape[-2], x1.shape[-1]
     h, w = h // 32 * 32, w // 32 * 32
     x1 = torch.nn.functional.interpolate(x1, size=(h, w), mode='bilinear')
     return x1
+
+
+
+@spaces.GPU(duration=120)
 def predict(img):
     with torch.no_grad():
         image_tensor = torch.from_numpy(img).permute(2, 0, 1).float().unsqueeze(0)