          if self.input_shape[0] > 128:
              in_conv_filters = 128 + (self.input_shape[0] - 128) // 4
          dense_shape = self.input_shape[0] // 16
  
          var_x = Conv2DBlock(in_conv_filters, res_block_follows=True, **kwargs)(input_)
          tmp_x = var_x
          res_cycles = 8 if self.config.get("lowmem", False) else 16
          for _ in range(res_cycles):
              nn_x = ResidualBlock(in_conv_filters, **kwargs)(var_x)
              var_x = nn_x
 
</s> remove         var_x = UpscaleBlock(512, res_block_follows=True, **kwargs)(var_x)
 </s> add         var_x = UpscaleBlock(512, activation=None, **kwargs)(var_x) </s> remove         var_x = Conv2DBlock(128, **kwargs)(var_x)
 </s> add         var_x = Conv2DBlock(128, activation="leakyrelu", **kwargs)(var_x) </s> remove         var_x = Conv2DBlock(encoder_complexity, use_instance_norm=True, **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 2, use_instance_norm=True, **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 4, **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 6, **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 8, **kwargs)(var_x)
 </s> add         var_x = Conv2DBlock(encoder_complexity,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 2,
                            normalization="instance",
                            activation="leakyrelu",
                            **kwargs)(var_x)
        var_x = Conv2DBlock(encoder_complexity * 4, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 6, **kwargs, activation="leakyrelu")(var_x)
        var_x = Conv2DBlock(encoder_complexity * 8, **kwargs, activation="leakyrelu")(var_x) </s> remove         var_x = UpscaleBlock(decoder_b_complexity // 2**(idx + 1))(var_x)
 </s> add         var_x = UpscaleBlock(decoder_b_complexity // 2**(idx + 1), activation="leakyrelu")(var_x) </s> remove         var_x = Conv2DBlock(512, **kwargs)(var_x)
 </s> add         var_x = Conv2DBlock(512, activation="leakyrelu", **kwargs)(var_x)
        var_x = Conv2DBlock(in_conv_filters, activation=None, **kwargs)(input_)
          if self.input_shape[0] > 128:
              in_conv_filters = 128 + (self.input_shape[0] - 128) // 4
          dense_shape = self.input_shape[0] // 16
  
        var_x = Conv2DBlock(in_conv_filters, activation=None, **kwargs)(input_)
          tmp_x = var_x
          res_cycles = 8 if self.config.get("lowmem", False) else 16
          for _ in range(res_cycles):
              nn_x = ResidualBlock(in_conv_filters, **kwargs)(var_x)
              var_x = nn_x
keep keep keep keep replace keep keep keep keep keep
 lib.model - Maintenance
  - Add Depthwise option to Conv Block
  - Add Swish Activation function
  - remove res_block_follows and add_instance_norm_args
  - Add explicit normalization and activation args
  - Add K.resize_images layer
plugins.train.model - Inference creation bugfix 
plugins/train/model/villain.py