diff --git a/src/Restorer/Cond_NAF.py b/src/Restorer/Cond_NAF.py
index 1125db8..b87700a 100644
--- a/src/Restorer/Cond_NAF.py
+++ b/src/Restorer/Cond_NAF.py
@@ -65,30 +65,48 @@ def forward(self, x, conditioning):
 
         return ca
     
+# class CondFuser(nn.Module):
+#     def __init__(self, chan, cond_chan=1):
+#         super().__init__()
+#         self.cca = ConditionedChannelAttention(chan * 2, cond_chan)
+#         # self.spa = nn.Conv2d(
+#         #     in_channels=chan * 2,
+#         #     out_channels=1,
+#         #     kernel_size=3,
+#         #     padding=1,
+#         #     stride=1,
+#         #     groups=1,
+#         #     bias=True,
+#         # )
+
+#     def forward(self, x1, x2, cond):
+#         x = torch.cat([x1, x2], dim=1)
+#         x = self.cca(x, cond) * x
+#         # spa = torch.sigmoid(self.spa(x))
+
+#         x1, x2 = x.chunk(2, dim=1)
+#         # return x1 * spa + x2 * (1 - spa)
+#         return x1 + x2
+
+
 class CondFuser(nn.Module):
     def __init__(self, chan, cond_chan=1):
         super().__init__()
         self.cca = ConditionedChannelAttention(chan * 2, cond_chan)
-        # self.spa = nn.Conv2d(
-        #     in_channels=chan * 2,
-        #     out_channels=1,
-        #     kernel_size=3,
-        #     padding=1,
-        #     stride=1,
-        #     groups=1,
-        #     bias=True,
-        # )
+        self.sig = nn.Sigmoid()
 
+        self.sa = nn.Sequential(
+            nn.Conv2d(in_channels = 2 * chan, out_channels=chan, kernel_size=3, 
+                      padding=1, stride=1,
+                      groups=1, bias=True),
+            nn.Sigmoid()
+        )
+        
     def forward(self, x1, x2, cond):
         x = torch.cat([x1, x2], dim=1)
-        x = self.cca(x, cond) * x
-        # spa = torch.sigmoid(self.spa(x))
-
-        x1, x2 = x.chunk(2, dim=1)
-        # return x1 * spa + x2 * (1 - spa)
+        x2 = 1 * self.sig(self.cca(x)) * self.sa(x) * x2
         return x1 + x2
 
-
 class NKA(nn.Module):
     def __init__(self, dim, channel_reduction = 8):
         super().__init__()