diff --git a/codes/models/archs/RRDBNet_arch.py b/codes/models/archs/RRDBNet_arch.py
index 5a73c490..64873c4a 100644
--- a/codes/models/archs/RRDBNet_arch.py
+++ b/codes/models/archs/RRDBNet_arch.py
@@ -47,12 +47,12 @@ class RRDB(nn.Module):
 
 
 class RRDBNet(nn.Module):
-    def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2):
+    def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2, initial_stride=1):
         super(RRDBNet, self).__init__()
         RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
 
         self.scale = scale
-        self.conv_first = nn.Conv2d(in_nc, nf, 7, 1, padding=3, bias=True)
+        self.conv_first = nn.Conv2d(in_nc, nf, 7, initial_stride, padding=3, bias=True)
         self.RRDB_trunk = arch_util.make_layer(RRDB_block_f, nb)
         self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
         #### upsampling
@@ -87,10 +87,11 @@ class AssistedRRDBNet(nn.Module):
     # nb=number of additional blocks after the assistance layers.
     # gc=growth channel inside of residual blocks
     # scale=the number of times the output is doubled in size.
-    def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2):
+    # initial_stride=the stride on the first conv. can be used to downsample the image for processing.
+    def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2, initial_stride=1):
         super(AssistedRRDBNet, self).__init__()
         self.scale = scale
-        self.conv_first = nn.Conv2d(in_nc, nf, 7, 1, padding=3, bias=True)
+        self.conv_first = nn.Conv2d(in_nc, nf, 7, initial_stride, padding=3, bias=True)
 
         # Set-up the assist-net, which should do feature extraction for us.
         self.assistnet = torchvision.models.wide_resnet50_2(pretrained=True)
diff --git a/codes/models/networks.py b/codes/models/networks.py
index e6ec0f4a..137a3361 100644
--- a/codes/models/networks.py
+++ b/codes/models/networks.py
@@ -22,8 +22,12 @@ def define_G(opt, net_key='network_G'):
                                        nf=opt_net['nf'], nb=opt_net['nb'], upscale=opt_net['scale'])
     elif which_model == 'RRDBNet':
         # RRDB does scaling in two steps, so take the sqrt of the scale we actually want to achieve and feed it to RRDB.
+        initial_stride = 1 if 'initial_stride' not in opt_net else opt_net['initial_stride']
+        assert initial_stride == 1 or initial_stride == 2
+        # Need to adjust the scale the generator sees by the stride since the stride causes a down-sample.
+        gen_scale = scale * initial_stride
         netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
-                                    nf=opt_net['nf'], nb=opt_net['nb'], scale=scale)
+                                    nf=opt_net['nf'], nb=opt_net['nb'], scale=scale, initial_stride=initial_stride)
     elif which_model == 'AssistedRRDBNet':
         netG = RRDBNet_arch.AssistedRRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
                                     nf=opt_net['nf'], nb=opt_net['nb'], scale=scale)