diff --git a/codes/models/archs/discriminator_vgg_arch.py b/codes/models/archs/discriminator_vgg_arch.py
index 2124aab5..b1abc9d0 100644
--- a/codes/models/archs/discriminator_vgg_arch.py
+++ b/codes/models/archs/discriminator_vgg_arch.py
@@ -239,7 +239,7 @@ class Discriminator_UNet(nn.Module):
 
 
 class Discriminator_UNet_FeaOut(nn.Module):
-    def __init__(self, in_nc, nf):
+    def __init__(self, in_nc, nf, feature_mode=False):
         super(Discriminator_UNet_FeaOut, self).__init__()
         # [64, 128, 128]
         self.conv0_0 = ConvGnLelu(in_nc, nf, kernel_size=3, bias=True, activation=False)
@@ -269,6 +269,8 @@ class Discriminator_UNet_FeaOut(nn.Module):
         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):
         fea0 = self.conv0_0(x)
         fea0 = self.conv0_1(fea0)
@@ -294,15 +296,21 @@ class Discriminator_UNet_FeaOut(nn.Module):
         loss3 = self.collapse3(self.proc3(u3))
         res = loss3.shape[2:]
 
-        # 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)
+        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)
         if output_feature_vector:
             return combined_losses.view(-1, 1), feat
         else:
             return combined_losses.view(-1, 1)
 
     def pixgan_parameters(self):
-        return 3, 4
\ No newline at end of file
+        if self.feature_mode:
+            return 1, 4
+        else:
+            return 3, 4
\ No newline at end of file
diff --git a/codes/models/networks.py b/codes/models/networks.py
index 1bca11e3..84243f7e 100644
--- a/codes/models/networks.py
+++ b/codes/models/networks.py
@@ -123,7 +123,7 @@ def define_D(opt):
     elif which_model == "discriminator_unet":
         netD = SRGAN_arch.Discriminator_UNet(in_nc=opt_net['in_nc'], nf=opt_net['nf'])
     elif which_model == "discriminator_unet_fea":
-        netD = SRGAN_arch.Discriminator_UNet_FeaOut(in_nc=opt_net['in_nc'], nf=opt_net['nf'])
+        netD = SRGAN_arch.Discriminator_UNet_FeaOut(in_nc=opt_net['in_nc'], nf=opt_net['nf'], feature_mode=opt_net['feature_mode'])
     else:
         raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
     return netD