diff --git a/codes/models/archs/discriminator_vgg_arch.py b/codes/models/archs/discriminator_vgg_arch.py
index b1abc9d0..5dc8df23 100644
--- a/codes/models/archs/discriminator_vgg_arch.py
+++ b/codes/models/archs/discriminator_vgg_arch.py
@@ -259,16 +259,9 @@ class Discriminator_UNet_FeaOut(nn.Module):
 
         self.up1 = ExpansionBlock(nf * 8, nf * 8, block=ConvGnLelu)
         self.proc1 = ConvGnLelu(nf * 8, nf * 8, bias=False)
+        self.fea_proc = ConvGnLelu(nf * 8, nf * 8, bias=True, norm=False, activation=False)
         self.collapse1 = ConvGnLelu(nf * 8, 1, bias=True, norm=False, activation=False)
 
-        self.up2 = ExpansionBlock(nf * 8, nf * 4, block=ConvGnLelu)
-        self.proc2 = ConvGnLelu(nf * 4, nf * 4, bias=False)
-        self.collapse2 = ConvGnLelu(nf * 4, 1, bias=True, norm=False, activation=False)
-
-        self.up3 = ExpansionBlock(nf * 4, nf * 2, block=ConvGnLelu)
-        self.proc3 = ConvGnLelu(nf * 2, nf * 2, bias=False)
-        self.collapse3 = ConvGnLelu(nf * 2, 1, bias=True, norm=False, activation=False)
-
         self.feature_mode = feature_mode
 
     def forward(self, x, output_feature_vector=False):
@@ -284,33 +277,19 @@ class Discriminator_UNet_FeaOut(nn.Module):
         fea3 = self.conv3_0(fea2)
         fea3 = self.conv3_1(fea3)
 
-        feat = self.conv4_0(fea3)
-        fea4 = self.conv4_1(feat)
+        fea4 = self.conv4_0(fea3)
+        fea4 = self.conv4_1(fea4)
 
         # And the pyramid network!
         u1 = self.up1(fea4, fea3)
         loss1 = self.collapse1(self.proc1(u1))
-        u2 = self.up2(u1, fea2)
-        loss2 = self.collapse2(self.proc2(u2))
-        u3 = self.up3(u2, fea1)
-        loss3 = self.collapse3(self.proc3(u3))
-        res = loss3.shape[2:]
+        fea_out = self.fea_proc(u1)
 
-        if self.feature_mode:
-            combined_losses = F.interpolate(loss1, scale_factor=4)
-        else:
-            # Compress all of the loss values into the batch dimension. The actual loss attached to this output will
-            # then know how to handle them.
-            combined_losses = torch.cat([F.interpolate(loss1, scale_factor=4),
-                                         F.interpolate(loss2, scale_factor=2),
-                                         F.interpolate(loss3, scale_factor=1)], dim=1)
+        combined_losses = F.interpolate(loss1, scale_factor=4)
         if output_feature_vector:
-            return combined_losses.view(-1, 1), feat
+            return combined_losses.view(-1, 1), fea_out
         else:
             return combined_losses.view(-1, 1)
 
     def pixgan_parameters(self):
-        if self.feature_mode:
-            return 1, 4
-        else:
-            return 3, 4
\ No newline at end of file
+        return 1, 4
\ No newline at end of file