diff --git a/codes/models/archs/ResGen_arch.py b/codes/models/archs/ResGen_arch.py
index 8783e252..ea597f64 100644
--- a/codes/models/archs/ResGen_arch.py
+++ b/codes/models/archs/ResGen_arch.py
@@ -59,10 +59,11 @@ class FixupBasicBlock(nn.Module):
 
 class FixupResNet(nn.Module):
 
-    def __init__(self, block, layers, num_filters=64):
+    def __init__(self, block, layers, upscale_applications=2, num_filters=64):
         super(FixupResNet, self).__init__()
         self.num_layers = sum(layers) + layers[-1]  # The last layer is applied twice to achieve 4x upsampling.
         self.inplanes = num_filters
+        self.upscale_applications = upscale_applications
         # Part 1 - Process raw input image. Most denoising should appear here and this should be the most complicated
         # part of the block.
         self.conv1 = nn.Conv2d(3, num_filters, kernel_size=5, stride=1, padding=2,
@@ -123,11 +124,14 @@ class FixupResNet(nn.Module):
         skip_lo = self.skip1(x) + self.skip1_bias
 
         x = self.lrelu(self.upsampler_conv(x) + self.uc_bias)
-        x = F.interpolate(x, scale_factor=2, mode='nearest')
+        x = F.interpolate(x, scale_factor=2.0, mode='nearest')
         x = self.layer2(x)
         skip_med = self.skip2(x) + self.skip2_bias
-        x = F.interpolate(x, scale_factor=2, mode='nearest')
-        x = self.layer2(x)
+
+        if self.upscale_applications > 1:
+            x = F.interpolate(x, scale_factor=2.0, mode='nearest')
+            x = self.layer2(x)
+
         x = self.final_defilter(x) + self.bias2
         return x, skip_med, skip_lo
 
diff --git a/codes/models/networks.py b/codes/models/networks.py
index f1f39640..2a75014d 100644
--- a/codes/models/networks.py
+++ b/codes/models/networks.py
@@ -35,7 +35,7 @@ def define_G(opt):
                                     interpolation_scale_factor=scale_per_step)
     elif which_model == 'ResGen':
         netG = ResGen_arch.fixup_resnet34(nb_denoiser=opt_net['nb_denoiser'], nb_upsampler=opt_net['nb_upsampler'],
-                                          num_filters=opt_net['nf'])
+                                          upscale_applications=opt_net['upscale_applications'], num_filters=opt_net['nf'])
 
     # image corruption
     elif which_model == 'HighToLowResNet':