          for idx in range(self.upscalers_no-2):
              var_x = self.blocks.upscale(var_x, decoder_a_complexity // 2**idx)
          var_x = self.blocks.upscale(var_x, decoder_a_complexity // 2**(idx + 1))
  
          var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
  
          outputs = [var_x]
  
          if self.config.get("mask_type", None) is not None:
              var_y = var_xy
 
</s> remove         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
 </s> add         var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out") </s> remove         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
 </s> add         var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out") </s> remove         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
 </s> add         var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out") </s> remove         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
 </s> add         var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out") </s> remove         var_x = self.blocks.conv2d(var_x, 3, kernel_size=5, padding="same", activation="sigmoid")
 </s> add         var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out")
        var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out")
          for idx in range(self.upscalers_no-2):
              var_x = self.blocks.upscale(var_x, decoder_a_complexity // 2**idx)
          var_x = self.blocks.upscale(var_x, decoder_a_complexity // 2**(idx + 1))
  
        var_x = self.blocks.conv2d(var_x, 3,
                                   kernel_size=5,
                                   padding="same",
                                   activation="sigmoid",
                                   name="face_out")
  
          outputs = [var_x]
  
          if self.config.get("mask_type", None) is not None:
              var_y = var_xy
keep keep keep keep replace keep keep keep keep keep
 Model Structure Changes

Standardize inputs for AutoEncoders
Name outputs from AutoEncoders
Name layers in nn_blocks.py 
plugins/train/model/realface.py