          self.model = model
          from timeit import default_timer as clock
          self._clock = clock
          
          generator = TrainingDataGenerator(self.random_transform_args, 160, 5, zoom=2)        
          
          self.images_A = generator.minibatchAB(fn_A, self.batch_size)
          self.images_B = generator.minibatchAB(fn_B, self.batch_size)
                  
          self.generator = generator        
 
</s> remove         x = self.conv_sep3(in_conv_filters+32, use_instance_norm=False)(x)
        x = self.conv_sep3(256)(x)        
        x = self.conv_sep3(512)(x)
        x = self.conv_sep3(1024)(x)        
 </s> add         x = self.conv_inst_norm(in_conv_filters+32, use_instance_norm=False)(x)
        x = self.conv_inst_norm(256)(x)        
        x = self.conv_inst_norm(512)(x)
        x = self.conv_inst_norm(1024)(x)         </s> remove         x = self.upscale_sep3(512)(inpt)
        x = self.upscale_sep3(256)(x)
        x = self.upscale_sep3(self.IMAGE_SHAPE[0])(x)
 </s> add         x = self.upscale_inst_norm(512)(inpt)
        x = self.upscale_inst_norm(256)(x)
        x = self.upscale_inst_norm(self.IMAGE_SHAPE[0])(x) </s> remove         x = Dense(self.ENCODER_DIM)(Flatten()(x))
        x = Dense(dense_shape * dense_shape * 768)(x)
 </s> add         x = Dense(self.ENCODER_DIM, kernel_initializer=_kern_init)(Flatten()(x))
        x = Dense(dense_shape * dense_shape * 768, kernel_initializer=_kern_init)(x) </s> remove         x = Dense(self.ENCODER_DIM, kernel_initializer=RandomNormal(0, 0.02))(Flatten()(x))
        x = Dense(dense_shape * dense_shape * 512, kernel_initializer=RandomNormal(0, 0.02))(x)
 </s> add         x = Dense(self.ENCODER_DIM, kernel_initializer=_kern_init)(Flatten()(x))
        x = Dense(dense_shape * dense_shape * 512, kernel_initializer=_kern_init)(x)
        generator = TrainingDataGenerator(self.random_transform_args, 160, 5, zoom=self.model.IMAGE_SHAPE[0]//64)        
          self.model = model
          from timeit import default_timer as clock
          self._clock = clock
          
        generator = TrainingDataGenerator(self.random_transform_args, 160, 5, zoom=self.model.IMAGE_SHAPE[0]//64)        
          
          self.images_A = generator.minibatchAB(fn_A, self.batch_size)
          self.images_B = generator.minibatchAB(fn_B, self.batch_size)
                  
          self.generator = generator        
keep keep keep keep replace keep keep keep keep keep
 Slight OriginaHightRes Model re-factoring (#464)

* Model re-factoring, might fix red screen of death on multi-gpu configurations

* Limited trainer support for various INPUT_SIZE 
plugins/Model_OriginalHighRes/Trainer.py