diff --git a/codes/models/archs/arch_util.py b/codes/models/archs/arch_util.py
index 7be2961f..4295e908 100644
--- a/codes/models/archs/arch_util.py
+++ b/codes/models/archs/arch_util.py
@@ -139,6 +139,30 @@ class ResidualBlock_noBN(nn.Module):
         return identity + out
 
 
+class ResidualBlockGN(nn.Module):
+    '''Residual block with GroupNorm
+    ---Conv-GN-ReLU-Conv-+-
+     |________________|
+    '''
+
+    def __init__(self, nf=64):
+        super(ResidualBlockGN, self).__init__()
+        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
+        self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.BN1 = nn.GroupNorm(8, nf)
+        self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
+        self.BN2 = nn.GroupNorm(8, nf)
+
+        # initialization
+        initialize_weights([self.conv1, self.conv2], 0.1)
+
+    def forward(self, x):
+        identity = x
+        out = self.lrelu(self.BN1(self.conv1(x)))
+        out = self.BN2(self.conv2(out))
+        return identity + out
+
+
 def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros'):
     """Warp an image or feature map with optical flow
     Args:
diff --git a/codes/models/archs/discriminator_vgg_arch.py b/codes/models/archs/discriminator_vgg_arch.py
index 3570523a..731f2ffc 100644
--- a/codes/models/archs/discriminator_vgg_arch.py
+++ b/codes/models/archs/discriminator_vgg_arch.py
@@ -2,7 +2,7 @@ import torch
 import torch.nn as nn
 
 from models.archs.RRDBNet_arch import RRDB, RRDBWithBypass
-from models.archs.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu
+from models.archs.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu, ResidualBlockGN
 import torch.nn.functional as F
 from models.archs.SwitchedResidualGenerator_arch import gather_2d
 from models.archs.pyramid_arch import Pyramid
@@ -666,10 +666,15 @@ class PyramidDiscriminator(nn.Module):
     def __init__(self, in_nc, nf, block=ConvGnLelu):
         super(PyramidDiscriminator, self).__init__()
         self.initial_conv = block(in_nc, nf, kernel_size=3, stride=2, bias=True, norm=False, activation=True)
-        self.top_proc = nn.Sequential(*[ConvGnLelu(nf, nf, kernel_size=3, stride=2, bias=False, norm=True, activation=True)])
+        self.top_proc = nn.Sequential(*[ResidualBlockGN(nf),
+                                        ResidualBlockGN(nf),
+                                        ResidualBlockGN(nf)])
         self.pyramid = Pyramid(nf, depth=3, processing_convs_per_layer=2, processing_at_point=2,
                                scale_per_level=1.5, norm=True, return_outlevels=False)
-        self.bottom_proc = nn.Sequential(*[
+        self.bottom_proc = nn.Sequential(*[ResidualBlockGN(nf),
+                                       ResidualBlockGN(nf),
+                                       ResidualBlockGN(nf),
+                                       ResidualBlockGN(nf),
                                        ConvGnLelu(nf, nf // 2, kernel_size=1, activation=True, norm=True, bias=True),
                                        ConvGnLelu(nf // 2, nf // 4, kernel_size=1, activation=True, norm=True, bias=True),
                                        ConvGnLelu(nf // 4, 1, activation=False, norm=False, bias=True)])