diff --git a/nnlib/nnlib.py b/nnlib/nnlib.py
index 711edb8..61c7aef 100644
--- a/nnlib/nnlib.py
+++ b/nnlib/nnlib.py
@@ -59,6 +59,7 @@ Input = KL.Input
 
 Dense = KL.Dense
 Conv2D = KL.Conv2D
+WScaleConv2DLayer = nnlib.WScaleConv2DLayer
 Conv2DTranspose = KL.Conv2DTranspose
 EqualConv2D = nnlib.EqualConv2D
 SeparableConv2D = KL.SeparableConv2D
@@ -788,12 +789,9 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
                 base = K.tile(pixels_batch, (1, output_h * output_w ) )
                 base = K.flatten(base)
 
-                # base_y0 = base + (y0 * width)
-                base_y0 = y0 * width
-                base_y0 = base + base_y0
-                # base_y1 = base + (y1 * width)
-                base_y1 = y1 * width
-                base_y1 = base_y1 + base
+                base_y0 = base + y0 * width
+  
+                base_y1 = base + y1 * width
 
                 indices_a = base_y0 + x0
                 indices_b = base_y1 + x0
@@ -854,10 +852,26 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
      
         nnlib.BilinearInterpolation = BilinearInterpolation
 
-        
-        
-        
+        class WScaleConv2DLayer(KL.Conv2D):
+            def __init__(self, *args, **kwargs):               
+                kwargs['kernel_initializer'] = keras.initializers.random_normal()
+                
+                super(WScaleConv2DLayer,self).__init__(*args,**kwargs)
 
+            def build(self, input_shape):
+                super().build(input_shape)
+                kernel_shape = K.int_shape(self.kernel)
+                std = np.sqrt(2) / np.sqrt( np.prod(kernel_shape[:-1]) )
+                self.wscale = K.constant(std, dtype=K.floatx() )
+
+            def call(self, input, **kwargs):
+                k = self.kernel                        
+                self.kernel = self.kernel*self.wscale
+                x = super().call(input,**kwargs)
+                self.kernel = k                        
+                return x
+        nnlib.WScaleConv2DLayer = WScaleConv2DLayer
+        
         class SelfAttention(KL.Layer):
             def __init__(self, nc, squeeze_factor=8, **kwargs):
                 assert nc//squeeze_factor > 0, f"Input channels must be >= {squeeze_factor}, recieved nc={nc}"