          model.load(swapped=False)
  
          images_A = get_image_paths(self.arguments.input_A)
          images_B = get_image_paths(self.arguments.input_B)
          trainer = PluginLoader.get_trainer(self.arguments.trainer)(model,
                                                                     images_A,
                                                                     images_B,
                                                                     batch_size=self.arguments.batch_size)
  
          try:
 
</s> remove         model = PluginLoader.get_model(self.arguments.trainer)(self.arguments.model_dir)
 </s> add         # this is so that you can enter case insensitive values for trainer
        trainer = self.arguments.trainer
        trainer = trainer if trainer != "Lowmem" else "LowMem"
        model = PluginLoader.get_model(trainer)(self.arguments.model_dir) </s> remove             print('Processing %s' % (filename))
 </s> add  </s> remove         self.blurSize = 2
        self.seamlessClone = False
        self.maskType = 'Rect' # Choose in 'FaceHullAndRect','FaceHull','Rect'
 </s> add         if erosion_kernel_size is not None:
            self.erosion_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(erosion_kernel_size,erosion_kernel_size))

        self.blur_size = blur_size
        self.seamless_clone = seamless_clone
        self.mask_type = mask_type.lower() # Choose in 'FaceHullAndRect','FaceHull','Rect'
 </s> remove         # if args.erosionKernelSize > 0:
        #     self.erosion_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(args.erosionKernelSize,args.erosionKernelSize))
        # else:
        #     self.erosion_kernel = None

 </s> add  </s> remove         if self.blurSize!=0:
            image_mask = cv2.blur(image_mask,(self.blurSize,self.blurSize))
 </s> add         if self.blur_size!=0:
            image_mask = cv2.blur(image_mask,(self.blur_size,self.blur_size))

        trainer = PluginLoader.get_trainer(trainer)(model,
          model.load(swapped=False)
  
          images_A = get_image_paths(self.arguments.input_A)
          images_B = get_image_paths(self.arguments.input_B)
        trainer = PluginLoader.get_trainer(trainer)(model,
                                                                     images_A,
                                                                     images_B,
                                                                     batch_size=self.arguments.batch_size)
  
          try:
keep keep keep keep replace keep keep keep keep keep
 extended arguments for convert.py (#89)

* extended arguments for convert re https://github.com/deepfakes/faceswap/issues/85

* helptext for extended arguments.

* changed default mask type to facehullandrect 
scripts/train.py