new model

app.py

@@ -9,7 +9,7 @@ import kornia.color as color
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 model = Generator()
-model_weights = torch.load('model.pth', map_location=device, weights_only=True)
+model_weights = torch.load('gen6.pth', map_location=device, weights_only=True)
 model.load_state_dict(model_weights)
 model = model.to(device)
 model.eval()
@@ -28,7 +28,6 @@ def preprocess(image):
     L = image[[0], ...]
     L, _ = normalize_lab(L, 0)
 
-    print(L.shape)
     return L.unsqueeze(0)
 
 def crop_to_original_size(image, original_size):

model.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:893644c8e4b35d8dde82b867753c33e364c76d51b10fffeeb1ddf600220f13e4
+oid sha256:f5b66fedbc088544c4d2dab70c531b5fc54e33745f25f68fc74d07ab5995fd61
 size 217659569

model.py

@@ -2,6 +2,7 @@ import torch.nn as nn
 import torch
 import torch.nn.functional as F
 
+
 # Dropout layer that works even in the evaluation mode
 class DropoutAlways(nn.Dropout2d):
     def forward(self, x):

utils.py

@@ -23,8 +23,8 @@ def decide_size(image):
     height = image.size[1]
     width = image.size[0]
 
-    new_height = 2
-    new_width = 2
+    new_height = 256
+    new_width = 256
 
     while new_height < height:
         new_height *= 2