def get_argument_list():
""" Put the arguments in a list so that they are accessible from both
argparse and gui """
argument_list = list()
argument_list.append({"opts": ("-A", "--input-A"),
"action": DirFullPaths,
"dest": "input_a",
"default": "input_a",
"help": "Input directory. A directory "
"containing training images for face A. "
"Defaults to 'input'"})
argument_list.append({"opts": ("-B", "--input-B"),
"action": DirFullPaths,
"dest": "input_b",
"default": "input_b",
"help": "Input directory. A directory "
"containing training images for face B. "
"Defaults to 'input'"})
argument_list.append({"opts": ("-ala", "--alignments-A"),
"action": FileFullPaths,
"filetypes": 'alignments',
"type": str,
"dest": "alignments_path_a",
"default": None,
"help": "Path to alignments file for training set A. Only required "
"if you are using a masked model or warp-to-landmarks is "
"enabled. Defaults to <input-A>/alignments.json if not "
"provided."})
argument_list.append({"opts": ("-alb", "--alignments-B"),
"action": FileFullPaths,
"filetypes": 'alignments',
"type": str,
"dest": "alignments_path_b",
"default": None,
"help": "Path to alignments file for training set B. Only required "
"if you are using a masked model or warp-to-landmarks is "
"enabled. Defaults to <input-B>/alignments.json if not "
"provided."})
argument_list.append({"opts": ("-m", "--model-dir"),
"action": DirFullPaths,
"dest": "model_dir",
"default": "models",
"help": "Model directory. This is where the "
"training data will be stored. "
"Defaults to 'model'"})
argument_list.append({"opts": ("-t", "--trainer"),
"type": str.lower,
"choices": PluginLoader.get_available_models(),
"default": PluginLoader.get_default_model(),
"help": "Select which trainer to use, Use "
"LowMem for cards with less than 2GB of "
"VRAM"})
argument_list.append({"opts": ("-s", "--save-interval"),
"type": int,
"action": Slider,
"min_max": (10, 1000),
"rounding": 10,
"dest": "save_interval",
"default": 100,
"help": "Sets the number of iterations before saving the model"})
argument_list.append({"opts": ("-bs", "--batch-size"),
"type": int,
"action": Slider,
"min_max": (2, 256),
"rounding": 2,
"dest": "batch_size",
"default": 64,
"help": "Batch size, as a power of 2 (64, 128, 256, etc)"})
argument_list.append({"opts": ("-it", "--iterations"),
"type": int,
"action": Slider,
"min_max": (0, 5000000),
"rounding": 20000,
"default": 1000000,
"help": "Length of training in iterations."})
argument_list.append({"opts": ("-g", "--gpus"),
"type": int,
"action": Slider,
"min_max": (1, 10),
"rounding": 1,
"default": 1,
"help": "Number of GPUs to use for training"})
argument_list.append({"opts": ("-ps", "--preview-scale"),
"type": int,
"action": Slider,
"dest": "preview_scale",
"min_max": (25, 200),
"rounding": 25,
"default": 100,
"help": "Percentage amount to scale the preview by."})
argument_list.append({"opts": ("-p", "--preview"),
"action": "store_true",
"dest": "preview",
"default": False,
"help": "Show preview output. If not specified, "
"write progress to file"})
argument_list.append({"opts": ("-w", "--write-image"),
"action": "store_true",
"dest": "write_image",
"default": False,
"help": "Writes the training result to a file "
"even on preview mode"})
argument_list.append({"opts": ("-ag", "--allow-growth"),
"action": "store_true",
"dest": "allow_growth",
"default": False,
"help": "Sets allow_growth option of Tensorflow "
"to spare memory on some configs"})
argument_list.append({"opts": ("-nl", "--no-logs"),
"action": "store_true",
"dest": "no_logs",
"default": False,
"help": "Disables TensorBoard logging. NB: Disabling logs means "
"that you will not be able to use the graph or analysis "
"for this session in the GUI."})
argument_list.append({"opts": ("-wl", "--warp-to-landmarks"),
"action": "store_true",
"dest": "warp_to_landmarks",
"default": False,
"help": "Warps training faces to closely matched Landmarks from the "
"opposite face-set rather than randomly warping the face. "
"This is the 'dfaker' way of doing warping. Alignments "
"files for both sets of faces must be provided if using "
"this option."})
argument_list.append({"opts": ("-nf", "--no-flip"),
"action": "store_true",
"dest": "no_flip",
"default": False,
"help": "To effectively learn, a random set of images are flipped "
"horizontally. Sometimes it is desirable for this not to "
"occur. Generally this should be left off except for "
"during 'fit training'."})
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
def get_optional_arguments():
""" Put the arguments in a list so that they are accessible from both
argparse and gui """
argument_list = []
argument_list.append({"opts": ("-m", "--model-dir"),
"action": DirFullPaths,
"dest": "model_dir",
"default": "models",
"help": "Model directory. A directory "
"containing the trained model you wish "
"to process. Defaults to 'models'"})
argument_list.append({"opts": ("-a", "--input-aligned-dir"),
"action": DirFullPaths,
"dest": "input_aligned_dir",
"default": None,
"help": "Input \"aligned directory\". A "
"directory that should contain the "
"aligned faces extracted from the input "
"files. If you delete faces from this "
"folder, they'll be skipped during "
"conversion. If no aligned dir is "
"specified, all faces will be "
"converted"})
argument_list.append({"opts": ("-t", "--trainer"),
"type": str.lower,
"choices": PluginLoader.get_available_models(),
"default": PluginLoader.get_default_model(),
"help": "Select the trainer that was used to "
"create the model"})
argument_list.append({"opts": ("-c", "--converter"),
"type": str.lower,
"choices": PluginLoader.get_available_converters(),
"default": "masked",
"help": "Converter to use"})
argument_list.append({
"opts": ("-M", "--mask-type"),
"type": str.lower,
"dest": "mask_type",
"choices": ["ellipse",
"facehull",
"dfl",
# "cnn", Removed until implemented
"none"],
"default": "facehull",
"help": "R|Mask to use to replace faces."
"\nellipse: Oval around face."
"\nfacehull: Face cutout based on landmarks."
"\ndfl: A Face Hull mask from DeepFaceLabs."
# "\ncnn: Not yet implemented" Removed until implemented
"\nnone: No mask. Can still use blur and erode on the edges of the swap box."})
argument_list.append({"opts": ("-b", "--blur-size"),
"type": float,
"action": Slider,
"min_max": (0.0, 100.0),
"rounding": 2,
"default": 5.0,
"help": "Blur kernel size as a percentage of the swap area. Smooths "
"the transition between the swapped face and the background "
"image."})
argument_list.append({"opts": ("-e", "--erosion-size"),
"dest": "erosion_size",
"type": float,
"action": Slider,
"min_max": (-100.0, 100.0),
"rounding": 2,
"default": 0.0,
"help": "Erosion kernel size as a percentage of the mask radius "
"area. Positive values apply erosion which reduces the size "
"of the swapped area. Negative values apply dilation which "
"increases the swapped area"})
argument_list.append({"opts": ("-g", "--gpus"),
"type": int,
"action": Slider,
"min_max": (1, 10),
"rounding": 1,
"default": 1,
"help": "Number of GPUs to use for conversion"})
argument_list.append({"opts": ("-sh", "--sharpen"),
"type": str.lower,
"dest": "sharpen_image",
"choices": ["box_filter", "gaussian_filter", "none"],
"default": "none",
"help": "Sharpen the masked facial region of "
"the converted images. Choice of filter "
"to use in sharpening process -- box"
"filter or gaussian filter."})
argument_list.append({"opts": ("-fr", "--frame-ranges"),
"nargs": "+",
"type": str,
"help": "frame ranges to apply transfer to e.g. "
"For frames 10 to 50 and 90 to 100 use "
"--frame-ranges 10-50 90-100. Files "
"must have the frame-number as the last "
"number in the name!"})
argument_list.append({"opts": ("-d", "--discard-frames"),
"action": "store_true",
"dest": "discard_frames",
"default": False,
"help": "When used with --frame-ranges discards "
"frames that are not processed instead "
"of writing them out unchanged"})
argument_list.append({"opts": ("-s", "--swap-model"),
"action": "store_true",
"dest": "swap_model",
"default": False,
"help": "Swap the model. Instead of A -> B, "
"swap B -> A"})
argument_list.append({"opts": ("-S", "--seamless"),
"action": "store_true",
"dest": "seamless_clone",
"default": False,
"help": "Use cv2's seamless clone function to "
"remove extreme gradients at the mask "
"seam by smoothing colors."})
argument_list.append({"opts": ("-mh", "--match-histogram"),
"action": "store_true",
"dest": "match_histogram",
"default": False,
"help": "Adjust the histogram of each color "
"channel in the swapped reconstruction "
"to equal the histogram of the masked "
"area in the orginal image"})
argument_list.append({"opts": ("-aca", "--avg-color-adjust"),
"action": "store_true",
"dest": "avg_color_adjust",
"default": False,
"help": "Adjust the mean of each color channel "
" in the swapped reconstruction to "
"equal the mean of the masked area in "
"the orginal image"})
argument_list.append({"opts": ("-sb", "--smooth-box"),
"action": "store_true",
"dest": "smooth_box",
"default": False,
"help": "Perform a Gaussian blur on the edges of the face box "
"received from the model. Helps reduce pronounced edges "
"of the swap area"})
argument_list.append({"opts": ("-dt", "--draw-transparent"),
"action": "store_true",
"dest": "draw_transparent",
"default": False,
"help": "Place the swapped face on a "
"transparent layer rather than the "
"original frame."})
return argument_list
def get_optional_arguments():
""" Put the arguments in a list so that they are accessible from both
argparse and gui """
argument_list = []
argument_list.append({
"opts": ("-m", "--model-dir"),
"action":
DirFullPaths,
"dest":
"model_dir",
"default":
"models",
"help":
"Model directory. A directory "
"containing the trained model you wish "
"to process. Defaults to 'models'"
})
argument_list.append({
"opts": ("-a", "--input-aligned-dir"),
"action":
DirFullPaths,
"dest":
"input_aligned_dir",
"default":
None,
"help":
"Input \"aligned directory\". A "
"directory that should contain the "
"aligned faces extracted from the input "
"files. If you delete faces from this "
"folder, they'll be skipped during "
"conversion. If no aligned dir is "
"specified, all faces will be "
"converted"
})
argument_list.append({
"opts": ("-t", "--trainer"),
"type":
str,
# case sensitive because this is used to
# load a plug-in.
"choices":
PluginLoader.get_available_models(),
"default":
PluginLoader.get_default_model(),
"help":
"Select the trainer that was used to "
"create the model"
})
argument_list.append({
"opts": ("-c", "--converter"),
"type": str,
# case sensitive because this is used
# to load a plugin.
"choices": ("Masked", "Adjust"),
"default": "Masked",
"help": "Converter to use"
})
argument_list.append({
"opts": ("-b", "--blur-size"),
"type": int,
"default": 2,
"help": "Blur size. (Masked converter only)"
})
argument_list.append({
"opts": ("-e", "--erosion-kernel-size"),
"dest":
"erosion_kernel_size",
"type":
int,
"default":
None,
"help":
"Erosion kernel size. Positive values "
"apply erosion which reduces the edge "
"of the swapped face. Negative values "
"apply dilation which allows the "
"swapped face to cover more space. "
"(Masked converter only)"
})
argument_list.append({
"opts": ("-M", "--mask-type"),
# lowercase this, because it's just a
# string later on.
"type":
str.lower,
"dest":
"mask_type",
"choices": ["rect", "facehull", "facehullandrect"],
"default":
"facehullandrect",
"help":
"Mask to use to replace faces. "
"(Masked converter only)"
})
argument_list.append({
"opts": ("-sh", "--sharpen"),
"type":
str.lower,
"dest":
"sharpen_image",
"choices": ["bsharpen", "gsharpen"],
"default":
None,
"help":
"Use Sharpen Image. bsharpen for Box "
"Blur, gsharpen for Gaussian Blur "
"(Masked converter only)"
})
argument_list.append({
"opts": ("-g", "--gpus"),
"type": int,
"default": 1,
"help": "Number of GPUs to use for conversion"
})
argument_list.append({
"opts": ("-fr", "--frame-ranges"),
"nargs":
"+",
"type":
str,
"help":
"frame ranges to apply transfer to e.g. "
"For frames 10 to 50 and 90 to 100 use "
"--frame-ranges 10-50 90-100. Files "
"must have the frame-number as the last "
"number in the name!"
})
argument_list.append({
"opts": ("-d", "--discard-frames"),
"action":
"store_true",
"dest":
"discard_frames",
"default":
False,
"help":
"When used with --frame-ranges discards "
"frames that are not processed instead "
"of writing them out unchanged"
})
argument_list.append({
"opts": ("-s", "--swap-model"),
"action":
"store_true",
"dest":
"swap_model",
"default":
False,
"help":
"Swap the model. Instead of A -> B, "
"swap B -> A"
})
argument_list.append({
"opts": ("-S", "--seamless"),
"action":
"store_true",
"dest":
"seamless_clone",
"default":
False,
"help":
"Use cv2's seamless clone. "
"(Masked converter only)"
})
argument_list.append({
"opts": ("-mh", "--match-histogram"),
"action":
"store_true",
"dest":
"match_histogram",
"default":
False,
"help":
"Use histogram matching. "
"(Masked converter only)"
})
argument_list.append({
"opts": ("-sm", "--smooth-mask"),
"action": "store_true",
"dest": "smooth_mask",
"default": False,
"help": "Smooth mask (Adjust converter only)"
})
argument_list.append({
"opts": ("-aca", "--avg-color-adjust"),
"action":
"store_true",
"dest":
"avg_color_adjust",
"default":
False,
"help":
"Average color adjust. "
"(Adjust converter only)"
})
argument_list.append({
"opts": ("-dt", "--draw-transparent"),
"action":
"store_true",
"dest":
"draw_transparent",
"default":
False,
"help":
"Place the swapped face on a "
"transparent layer rather than the "
"original frame."
})
return argument_list
def get_argument_list():
""" Put the arguments in a list so that they are accessible from both
argparse and gui """
argument_list = list()
argument_list.append({
"opts": ("-A", "--input-A"),
"action":
DirFullPaths,
"dest":
"input_A",
"default":
"input_A",
"help":
"Input directory. A directory "
"containing training images for face A. "
"Defaults to 'input'"
})
argument_list.append({
"opts": ("-B", "--input-B"),
"action":
DirFullPaths,
"dest":
"input_B",
"default":
"input_B",
"help":
"Input directory. A directory "
"containing training images for face B. "
"Defaults to 'input'"
})
argument_list.append({
"opts": ("-m", "--model-dir"),
"action":
DirFullPaths,
"dest":
"model_dir",
"default":
"models",
"help":
"Model directory. This is where the "
"training data will be stored. "
"Defaults to 'model'"
})
argument_list.append({
"opts": ("-s", "--save-interval"),
"type":
int,
"dest":
"save_interval",
"default":
100,
"help":
"Sets the number of iterations before "
"saving the model"
})
argument_list.append({
"opts": ("-t", "--trainer"),
"type":
str,
"choices":
PluginLoader.get_available_models(),
"default":
PluginLoader.get_default_model(),
"help":
"Select which trainer to use, Use "
"LowMem for cards with less than 2GB of "
"VRAM"
})
argument_list.append({
"opts": ("-bs", "--batch-size"),
"type":
int,
"default":
64,
"help":
"Batch size, as a power of 2 "
"(64, 128, 256, etc)"
})
argument_list.append({
"opts": ("-it", "--iterations"),
"type": int,
"default": 1000000,
"help": "Length of training in iterations"
})
argument_list.append({
"opts": ("-g", "--gpus"),
"type": int,
"default": 1,
"help": "Number of GPUs to use for training"
})
argument_list.append({
"opts": ("-p", "--preview"),
"action":
"store_true",
"dest":
"preview",
"default":
False,
"help":
"Show preview output. If not specified, "
"write progress to file"
})
argument_list.append({
"opts": ("-w", "--write-image"),
"action":
"store_true",
"dest":
"write_image",
"default":
False,
"help":
"Writes the training result to a file "
"even on preview mode"
})
argument_list.append({
"opts": ("-pl", "--use-perceptual-loss"),
"action": "store_true",
"dest": "perceptual_loss",
"default": False,
"help": "Use perceptual loss while training"
})
argument_list.append({
"opts": ("-ag", "--allow-growth"),
"action":
"store_true",
"dest":
"allow_growth",
"default":
False,
"help":
"Sets allow_growth option of Tensorflow "
"to spare memory on some configs"
})
argument_list.append({
"opts": ("-v", "--verbose"),
"action": "store_true",
"dest": "verbose",
"default": False,
"help": "Show verbose output"
})
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/"
})
# This is a hidden argument to indicate that the GUI is being used,
# so the preview window should be redirected Accordingly
argument_list.append({
"opts": ("-gui", "--gui"),
"action": "store_true",
"dest": "redirect_gui",
"default": False,
"help": argparse.SUPPRESS
})
return argument_list
def get_argument_list():
""" Put the arguments in a list so that they are accessible from both
argparse and gui """
argument_list = list()
argument_list.append({
"opts": ("-A", "--input-A"),
"action":
DirFullPaths,
"dest":
"input_a",
"default":
"input_a",
"help":
"Input directory. A directory "
"containing training images for face A. "
"Defaults to 'input'"
})
argument_list.append({
"opts": ("-B", "--input-B"),
"action":
DirFullPaths,
"dest":
"input_b",
"default":
"input_b",
"help":
"Input directory. A directory "
"containing training images for face B. "
"Defaults to 'input'"
})
argument_list.append({
"opts": ("-ala", "--alignments-A"),
"action":
FileFullPaths,
"filetypes":
'alignments',
"type":
str,
"dest":
"alignments_path_a",
"default":
None,
"help":
"Path to alignments file for training set A. Only required "
"if you are using a masked model or warp-to-landmarks is "
"enabled. Defaults to <input-A>/alignments.json if not "
"provided."
})
argument_list.append({
"opts": ("-alb", "--alignments-B"),
"action":
FileFullPaths,
"filetypes":
'alignments',
"type":
str,
"dest":
"alignments_path_b",
"default":
None,
"help":
"Path to alignments file for training set B. Only required "
"if you are using a masked model or warp-to-landmarks is "
"enabled. Defaults to <input-B>/alignments.json if not "
"provided."
})
argument_list.append({
"opts": ("-m", "--model-dir"),
"action":
DirFullPaths,
"dest":
"model_dir",
"default":
"models",
"help":
"Model directory. This is where the "
"training data will be stored. "
"Defaults to 'model'"
})
argument_list.append({
"opts": ("-t", "--trainer"),
"action":
Radio,
"type":
str.lower,
"choices":
PluginLoader.get_available_models(),
"default":
PluginLoader.get_default_model(),
"help":
"R|Select which trainer to use. Trainers can be"
"\nconfigured from the edit menu or the config folder."
"\n'original': The original model created by /u/deepfakes."
"\n'dfaker': 64px in/128px out model from dfaker."
"\n\tEnable 'warp-to-landmarks' for full dfaker method."
"\n'dfl-h128'. 128px in/out model from deepfacelab"
"\n'iae': A model that uses intermediate layers to try to"
"\n\tget better details"
"\n'lightweight': A lightweight model for low-end cards."
"\n\tDon't expect great results. Can train as low as 1.6GB"
"\n\twith batch size 8."
"\n'unbalanced': 128px in/out model from andenixa. The"
"\n\tautoencoders are unbalanced so B>A swaps won't work so"
"\n\twell. Very configurable,"
"\n'villain': 128px in/out model from villainguy. Very"
"\n\tresource hungry (11GB for batchsize 16). Good for"
"\n\tdetails, but more susceptible to color differences"
})
argument_list.append({
"opts": ("-s", "--save-interval"),
"type":
int,
"action":
Slider,
"min_max": (10, 1000),
"rounding":
10,
"dest":
"save_interval",
"default":
100,
"help":
"Sets the number of iterations before saving the model"
})
argument_list.append({
"opts": ("-bs", "--batch-size"),
"type":
int,
"action":
Slider,
"min_max": (2, 256),
"rounding":
2,
"dest":
"batch_size",
"default":
64,
"help":
"Batch size, as a power of 2 (64, 128, 256, etc)"
})
argument_list.append({
"opts": ("-it", "--iterations"),
"type": int,
"action": Slider,
"min_max": (0, 5000000),
"rounding": 20000,
"default": 1000000,
"help": "Length of training in iterations."
})
argument_list.append({
"opts": ("-g", "--gpus"),
"type": int,
"action": Slider,
"min_max": (1, 10),
"rounding": 1,
"default": 1,
"help": "Number of GPUs to use for training"
})
argument_list.append({
"opts": ("-ps", "--preview-scale"),
"type":
int,
"action":
Slider,
"dest":
"preview_scale",
"min_max": (25, 200),
"rounding":
25,
"default":
50,
"help":
"Percentage amount to scale the preview by."
})
argument_list.append({
"opts": ("-p", "--preview"),
"action":
"store_true",
"dest":
"preview",
"default":
False,
"help":
"Show preview output. If not specified, "
"write progress to file"
})
argument_list.append({
"opts": ("-w", "--write-image"),
"action":
"store_true",
"dest":
"write_image",
"default":
False,
"help":
"Writes the training result to a file "
"even on preview mode"
})
argument_list.append({
"opts": ("-ag", "--allow-growth"),
"action":
"store_true",
"dest":
"allow_growth",
"default":
False,
"help":
"Sets allow_growth option of Tensorflow "
"to spare memory on some configs"
})
argument_list.append({
"opts": ("-nl", "--no-logs"),
"action":
"store_true",
"dest":
"no_logs",
"default":
False,
"help":
"Disables TensorBoard logging. NB: Disabling logs means "
"that you will not be able to use the graph or analysis "
"for this session in the GUI."
})
argument_list.append({
"opts": ("-pp", "--ping-pong"),
"action":
"store_true",
"dest":
"pingpong",
"default":
False,
"help":
"Enable ping pong training. Trains one side at a time, "
"switching sides at each save iteration. Training will take "
"2 to 4 times longer, with about a 30%%-50%% reduction in "
"VRAM useage. NB: Preview won't show until both sides have "
"been trained once."
})
argument_list.append({
"opts": ("-msg", "--memory-saving-gradients"),
"action":
"store_true",
"dest":
"memory_saving_gradients",
"default":
False,
"help":
"Trades off VRAM useage against computation time. Can fit "
"larger models into memory at a cost of slower training "
"speed. 50%%-150%% batch size increase for 20%%-50%% longer "
"training time. NB: Launch time will be significantly "
"delayed. Switching sides using ping-pong training will "
"take longer."
})
argument_list.append({
"opts": ("-wl", "--warp-to-landmarks"),
"action":
"store_true",
"dest":
"warp_to_landmarks",
"default":
False,
"help":
"Warps training faces to closely matched Landmarks from the "
"opposite face-set rather than randomly warping the face. "
"This is the 'dfaker' way of doing warping. Alignments "
"files for both sets of faces must be provided if using "
"this option."
})
argument_list.append({
"opts": ("-nf", "--no-flip"),
"action":
"store_true",
"dest":
"no_flip",
"default":
False,
"help":
"To effectively learn, a random set of images are flipped "
"horizontally. Sometimes it is desirable for this not to "
"occur. Generally this should be left off except for "
"during 'fit training'."
})
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
