                                "help": "Color augmentation helps make the model less susceptible "
                                        "to color differences between the A and B sets, at an "
                                        "increased training time cost. Enable this option to "
                                        "disable color augmentation."})
          argument_list.append({"opts": ("-tia", "--timelapse-input-A"),
                                "action": DirFullPaths,
                                "dest": "timelapse_input_a",
                                "default": None,
                                "help": "For if you want a timelapse: "
                                        "The input folder for the timelapse. "
                                        "This folder should contain faces of A "
                                        "which will be converted for the "
                                        "timelapse. You must supply a "
                                        "--timelapse-output and a "
                                        "--timelapse-input-B parameter."})
          argument_list.append({"opts": ("-tib", "--timelapse-input-B"),
                                "action": DirFullPaths,
                                "dest": "timelapse_input_b",
                                "default": None,
                                "help": "For if you want a timelapse: "
                                        "The input folder for the timelapse. "
                                        "This folder should contain faces of B "
                                        "which will be converted for the "
                                        "timelapse. You must supply a "
                                        "--timelapse-output and a "
                                        "--timelapse-input-A parameter."})
          argument_list.append({"opts": ("-to", "--timelapse-output"),
                                "action": DirFullPaths,
                                "dest": "timelapse_output",
                                "default": None,
                                "help": "The output folder for the timelapse. "
                                        "If the input folders are supplied but "
                                        "no output folder, it will default to "
                                        "your model folder /timelapse/"})
          return argument_list
  
  
  class GuiArgs(FaceSwapArgs):
      """ Class to parse the command line arguments for training """
 
</s> remove                               "dest": "model_dir",
                              "required": True,
                              "help": "Model directory. This is where the training data will be "
                                      "stored."})
 </s> add                               "dest": "timelapse_input_b",
                              "default": None,
                              "help": "For if you want a timelapse: "
                                      "The input folder for the timelapse. "
                                      "This folder should contain faces of B "
                                      "which will be converted for the "
                                      "timelapse. You must supply a "
                                      "--timelapse-output and a "
                                      "--timelapse-input-A parameter."}) </s> remove         argument_list.append({"opts": ("-m", "--model-dir"),
 </s> add         argument_list.append({"opts": ("-tib", "--timelapse-input-B"), </s> remove         argument_list.append({"opts": ("-B", "--input-B"),
                              "action": DirFullPaths,
                              "dest": "input_b",
                              "required": True,
                              "help": "Input directory. A directory containing training images "
                                      "for face B."})
 </s> add  </s> remove             section=section, title="icnr_init", datatype=bool, default=False,
            info="Use ICNR Kernel Initializer for upscaling.\nThis can help reduce the "
                 "'checkerboard effect' when upscaling the image.")
        self.add_item(
            section=section, title="conv_aware_init", datatype=bool, default=False,
            info="Use Convolution Aware Initialization for convolutional layers\nThis can help "
                 "eradicate the vanishing and exploding gradient problem as well as lead to "
                 "higher accuracy, lower loss and faster convergence."
                 "\nNB This can use more VRAM when creating a new model so you may want to lower "
                 "the batch size for the first run. The batch size can be raised again when "
                 "reloading the model."
                 "\nNB: Building the model will likely take several minutes as the caluclations "
                 "for this initialization technique are expensive.")
        self.add_item(
            section=section, title="subpixel_upscaling", datatype=bool, default=False,
            info="Use subpixel upscaling rather than pixel shuffler.\n"
                 "Might increase speed at cost of VRAM")
        self.add_item(
            section=section, title="reflect_padding", datatype=bool, default=False,
            info="Use reflect padding rather than zero padding. Only enable this option if the "
                 "model you are training has a distinct line appearing around the edge of the "
                 "swap area.")
 </s> add             section=section, title="learning_rate", datatype=float, default=5e-5,
            min_max=(1e-6, 1e-4), rounding=6, fixed=False,
            info="Learning rate - how fast your network will learn (how large are "
                 "the modifications to the model weights after one batch of training). "
                 "Values that are too large might result in model crashes and the "
                 "inability of the model to find the best solution. "
                 "Values that are too small might be unable to escape from dead-ends "
                 "and find the best global minimum.")
                                "help": "Color augmentation helps make the model less susceptible "
                                        "to color differences between the A and B sets, at an "
                                        "increased training time cost. Enable this option to "
                                        "disable color augmentation."})






























          return argument_list
  
  
  class GuiArgs(FaceSwapArgs):
      """ Class to parse the command line arguments for training """
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 documentation, pep8, style, clarity updates  - Prep for Segmentation (#812)

* documentation, pep8, style, clarity updates

* Update cli.py

* Update _config.py

remove extra mask and coverage

mask type as dropdown

* Update training_data.py

move coverage / LR to global
cut down on loss description

style change
losses working in PR

* simpler logging

* legacy update 
lib/cli.py