      def converter(self, swap):
          autoencoder = self.autoencoder_B if not swap else self.autoencoder_A
          return autoencoder.predict
      
      def conv(self, filters, kernel_size=4, strides=2):
          def block(x):
              x = Conv2D(filters, kernel_size=kernel_size, strides=strides, padding='same')(x)         
              x = LeakyReLU(0.1)(x)
              return x
          return block    
 
</s> remove             x = Conv2D(filters, kernel_size=kernel_size, strides=strides, padding='same')(x)         
 </s> add             x = Conv2D(filters, kernel_size=kernel_size, strides=strides, kernel_initializer=conv_init, padding='same', **kwargs)(x)          </s> remove             print('Failed loading existing training data.')
            print(e)
 </s> add             print('Failed loading existing training data.', e) </s> remove             x = SeparableConv2D(filters, kernel_size=4, strides=2, padding='same')(x)        
            x = LeakyReLU(0.1)(x)
            return x
        return block
    
    def conv_sep_v3(self, filters, kernel_size=4, strides=2):
        def block(x):
            x = SeparableConv2D(filters, kernel_size=kernel_size, strides=strides, padding="same")(x)
 </s> add             x = SeparableConv2D(filters, kernel_size=kernel_size, strides=strides, kernel_initializer=conv_init, padding='same', **kwargs)(x)        
            if use_instance_norm:
                x = inst_norm()(x) </s> remove             return x
        return block       
    
 </s> add             return x    
        return block </s> remove     def conv_sep(self, filters):
 </s> add     def conv_sep3(self, filters, kernel_size=3, strides=2, use_instance_norm=True, **kwargs):
    
    def conv(self, filters, kernel_size=5, strides=2, **kwargs):
      def converter(self, swap):
          autoencoder = self.autoencoder_B if not swap else self.autoencoder_A
          return autoencoder.predict
      
    
    def conv(self, filters, kernel_size=5, strides=2, **kwargs):
          def block(x):
              x = Conv2D(filters, kernel_size=kernel_size, strides=strides, padding='same')(x)         
              x = LeakyReLU(0.1)(x)
              return x
          return block    
keep keep keep keep replace keep keep keep keep keep
 model update (#417)

New, functional Original 128 model 
plugins/Model_OriginalHighRes/Model.py