diff --git a/codes/models/archs/SRG1_arch.py b/codes/models/archs/SRG1_arch.py
index d91eb09a..6ae5f8a4 100644
--- a/codes/models/archs/SRG1_arch.py
+++ b/codes/models/archs/SRG1_arch.py
@@ -22,6 +22,15 @@ class ConvBnLelu(nn.Module):
         else:
             self.lelu = None
 
+        # Init params.
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, a=.1, mode='fan_out',
+                                        nonlinearity='leaky_relu' if self.lelu else 'linear')
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
     def forward(self, x):
         x = self.conv(x)
         if self.bn:
@@ -32,16 +41,15 @@ class ConvBnLelu(nn.Module):
             return x
 
 
-class ResidualBranch(nn.Module):
-    def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, bn=False):
+class MultiConvBlock(nn.Module):
+    def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, scale_init=1, bn=False):
         assert depth >= 2
-        super(ResidualBranch, self).__init__()
+        super(MultiConvBlock, self).__init__()
         self.noise_scale = nn.Parameter(torch.full((1,), fill_value=.01))
         self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, bn=bn, bias=False)] +
                                      [ConvBnLelu(filters_mid, filters_mid, kernel_size, bn=bn, bias=False) for i in range(depth-2)] +
                                      [ConvBnLelu(filters_mid, filters_out, kernel_size, lelu=False, bn=False, bias=False)])
-
-        self.scale = nn.Parameter(torch.ones(1))
+        self.scale = nn.Parameter(torch.full((1,), fill_value=scale_init))
         self.bias = nn.Parameter(torch.zeros(1))
 
     def forward(self, x, noise=None):
@@ -139,7 +147,7 @@ class ConfigurableSwitchedResidualGenerator(nn.Module):
         super(ConfigurableSwitchedResidualGenerator, self).__init__()
         switches = []
         for filters, growth, sw_reduce, sw_proc, trans_count, kernel, layers, mid_filters in zip(switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers, trans_filters_mid):
-            switches.append(SwitchComputer(3, filters, growth, functools.partial(ResidualBranch, 3, mid_filters, 3, kernel_size=kernel, depth=layers), trans_count, sw_reduce, sw_proc, initial_temp, enable_negative_transforms=enable_negative_transforms, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
+            switches.append(SwitchComputer(3, filters, growth, functools.partial(MultiConvBlock, 3, mid_filters, 3, kernel_size=kernel, depth=layers), trans_count, sw_reduce, sw_proc, initial_temp, enable_negative_transforms=enable_negative_transforms, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
         initialize_weights(switches, 1)
         # Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
         initialize_weights([s.transforms for s in switches], .2 / len(switches))