diff --git a/codes/models/archs/DiscriminatorResnet_arch_passthrough.py b/codes/models/archs/DiscriminatorResnet_arch_passthrough.py
index 521f9c8b..462261f7 100644
--- a/codes/models/archs/DiscriminatorResnet_arch_passthrough.py
+++ b/codes/models/archs/DiscriminatorResnet_arch_passthrough.py
@@ -156,8 +156,12 @@ class FixupResNet(nn.Module):
         return nn.Sequential(*layers)
 
     def forward(self, x):
-        # This class expects a medium skip (half-res) and low skip (quarter-res) provided as a tuple in the input.
-        x, med_skip, lo_skip = x
+        if len(x) == 3:
+            # This class can take a medium skip (half-res) and low skip (quarter-res) provided as a tuple in the input.
+            x, med_skip, lo_skip = x
+        else:
+            # Or just a tuple with only the high res input (this assumes number_skips was set right).
+            x = x[0]
 
         x = self.layer0(x)
         if self.number_skips > 0:
diff --git a/codes/models/archs/RRDBNet_arch.py b/codes/models/archs/RRDBNet_arch.py
index 2ec07ff1..d11170d9 100644
--- a/codes/models/archs/RRDBNet_arch.py
+++ b/codes/models/archs/RRDBNet_arch.py
@@ -46,10 +46,11 @@ class RRDB(nn.Module):
 
 
 class RRDBNet(nn.Module):
-    def __init__(self, in_nc, out_nc, nf, nb, gc=32, interpolation_scale_factor=2):
+    def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2):
         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.RRDB_trunk = arch_util.make_layer(RRDB_block_f, nb)
         self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
@@ -61,15 +62,17 @@ class RRDBNet(nn.Module):
 
         self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
 
-        self.interpolation_scale_factor = interpolation_scale_factor
-
     def forward(self, x):
         fea = self.conv_first(x)
         trunk = self.trunk_conv(self.RRDB_trunk(fea))
         fea = fea + trunk
 
-        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=self.interpolation_scale_factor, mode='nearest')))
-        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=self.interpolation_scale_factor, mode='nearest')))
+        if self.scale >= 2:
+            fea = F.interpolate(fea, scale_factor=2, mode='nearest')
+        fea = self.lrelu(self.upconv1(fea))
+        if self.scale >= 4:
+            fea = F.interpolate(fea, scale_factor=2, mode='nearest')
+        fea = self.lrelu(self.upconv2(fea))
         out = self.conv_last(self.lrelu(self.HRconv(fea)))
 
-        return out
+        return (out,)
diff --git a/codes/models/networks.py b/codes/models/networks.py
index efe9ed1c..446322bc 100644
--- a/codes/models/networks.py
+++ b/codes/models/networks.py
@@ -25,9 +25,8 @@ 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.
-        scale_per_step = math.sqrt(scale)
         netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
-                                    nf=opt_net['nf'], nb=opt_net['nb'], interpolation_scale_factor=scale_per_step)
+                                    nf=opt_net['nf'], nb=opt_net['nb'], scale=scale)
     elif which_model == 'RRDBNetXL':
         scale_per_step = math.sqrt(scale)
         netG = RRDBNetXL_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
diff --git a/codes/train.py b/codes/train.py
index f81a3484..9a19a558 100644
--- a/codes/train.py
+++ b/codes/train.py
@@ -30,7 +30,7 @@ def init_dist(backend='nccl', **kwargs):
 def main():
     #### options
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vix_resgenv2.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vix_rrdb_v2.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
                         help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
@@ -147,7 +147,7 @@ def main():
         current_step = resume_state['iter']
         model.resume_training(resume_state)  # handle optimizers and schedulers
     else:
-        current_step = 0
+        current_step = -1
         start_epoch = 0
 
     #### training