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faceswap/plugins/train/_config.py

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#!/usr/bin/env python3
""" Default configurations for models """
import logging
from lib.config import FaceswapConfig
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
MASK_TYPES = ["none", "dfaker", "dfl_full"]
MASK_INFO = "The mask to be used for training. Select none to not use a mask"
COVERAGE_INFO = ("How much of the extracted image to train on. Generally the model is optimized\n"
"to the default value. Sensible values to use are:"
"\n\t62.5%% spans from eyebrow to eyebrow."
"\n\t75.0%% spans from temple to temple."
"\n\t87.5%% spans from ear to ear."
"\n\t100.0%% is a mugshot.")
ADDITIONAL_INFO = "\nNB: Values changed here will only take effect when creating a new model."
class Config(FaceswapConfig):
""" Config File for Models """
def set_defaults(self):
""" Set the default values for config """
logger.debug("Setting defaults")
# << GLOBAL OPTIONS >> #
section = "global"
self.add_section(title=section,
info="Options that apply to all models" + ADDITIONAL_INFO)
self.add_item(
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="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.")
self.add_item(
section=section, title="dssim_mask_loss", datatype=bool, default=True,
info="If using a mask, Use DSSIM loss for Mask training rather than Mean Absolute "
"Error\nMay increase overall quality.")
self.add_item(
section=section, title="penalized_mask_loss", datatype=bool, default=True,
info="If using a mask, Use Penalized loss for Mask training. Can stack with DSSIM.\n"
"May increase overall quality.")
# << DFAKER OPTIONS >> #
section = "model.dfaker"
self.add_section(title=section,
info="Dfaker Model (Adapted from https://github.com/dfaker/df)" +
ADDITIONAL_INFO)
self.add_item(
section=section, title="mask_type", datatype=str, default="dfaker",
choices=MASK_TYPES, info=MASK_INFO)
self.add_item(
section=section, title="coverage", datatype=float, default=100.0, rounding=1,
min_max=(62.5, 100.0), info=COVERAGE_INFO)
# << DFL MODEL OPTIONS >> #
section = "model.dfl_h128"
self.add_section(title=section,
info="DFL H128 Model (Adapted from "
"https://github.com/iperov/DeepFaceLab)" + ADDITIONAL_INFO)
self.add_item(
section=section, title="lowmem", datatype=bool, default=False,
info="Lower memory mode. Set to 'True' if having issues with VRAM useage.\nNB: Models "
"with a changed lowmem mode are not compatible with each other.")
self.add_item(
section=section, title="mask_type", datatype=str, default="dfl_full",
choices=MASK_TYPES, info=MASK_INFO)
self.add_item(
section=section, title="coverage", datatype=float, default=62.5, rounding=1,
min_max=(62.5, 100.0), info=COVERAGE_INFO)
# << IAE MODEL OPTIONS >> #
section = "model.iae"
self.add_section(title=section,
info="Intermediate Auto Encoder. Based on Original Model, uses "
"intermediate layers to try to better get details" + ADDITIONAL_INFO)
self.add_item(
section=section, title="dssim_loss", datatype=bool, default=False,
info="Use DSSIM for Loss rather than Mean Absolute Error\n"
"May increase overall quality.")
self.add_item(
section=section, title="mask_type", datatype=str, default="none",
choices=MASK_TYPES, info=MASK_INFO)
self.add_item(
section=section, title="coverage", datatype=float, default=62.5, rounding=1,
min_max=(62.5, 100.0), info=COVERAGE_INFO)
# << ORIGINAL MODEL OPTIONS >> #
section = "model.original"
self.add_section(title=section,
info="Original Faceswap Model" + ADDITIONAL_INFO)
self.add_item(
section=section, title="lowmem", datatype=bool, default=False,
info="Lower memory mode. Set to 'True' if having issues with VRAM useage.\nNB: Models "
"with a changed lowmem mode are not compatible with each other.")
self.add_item(
section=section, title="dssim_loss", datatype=bool, default=False,
info="Use DSSIM for Loss rather than Mean Absolute Error\n"
"May increase overall quality.")
self.add_item(
section=section, title="mask_type", datatype=str, default="none",
choices=MASK_TYPES, info=MASK_INFO)
self.add_item(
section=section, title="coverage", datatype=float, default=62.5, rounding=1,
min_max=(62.5, 100.0), info=COVERAGE_INFO)
# << UNBALANCED MODEL OPTIONS >> #
section = "model.unbalanced"
self.add_section(title=section,
info="An unbalanced model with adjustable input size options.\nThis is "
"an unbalanced model so b>a swaps may not work "
"well" + ADDITIONAL_INFO)
self.add_item(
section=section, title="lowmem", datatype=bool, default=False,
info="Lower memory mode. Set to 'True' if having issues with VRAM useage.\nNB: Models "
"with a changed lowmem mode are not compatible with each other. NB: lowmem will "
"override cutom nodes and complexity settings.")
self.add_item(
section=section, title="dssim_loss", datatype=bool, default=False,
info="Use DSSIM for Loss rather than Mean Absolute Error\n"
"May increase overall quality.")
self.add_item(
section=section, title="clipnorm", datatype=bool, default=True,
info="Controls gradient clipping of the optimizer. Can prevent model corruption at "
"the expense of VRAM")
self.add_item(
section=section, title="mask_type", datatype=str, default="none",
choices=MASK_TYPES, info=MASK_INFO)
self.add_item(
section=section, title="nodes", datatype=int, default=1024, rounding=64,
min_max=(512, 4096),
info="Number of nodes for decoder. Don't change this unless you "
"know what you are doing!")
self.add_item(
section=section, title="complexity_encoder", datatype=int, default=128,
rounding=16, min_max=(64, 1024),
info="Encoder Convolution Layer Complexity. sensible ranges: "
"128 to 160")
self.add_item(
section=section, title="complexity_decoder_a", datatype=int, default=384,
rounding=16, min_max=(64, 1024),
info="Decoder A Complexity.")
self.add_item(
section=section, title="complexity_decoder_b", datatype=int, default=512,
rounding=16, min_max=(64, 1024),
info="Decoder B Complexity.")
self.add_item(
section=section, title="input_size", datatype=int, default=128,
rounding=64, min_max=(64, 512),
info="Resolution (in pixels) of the image to train on.\n"
"BE AWARE Larger resolution will dramatically increase"
"VRAM requirements.\n"
"Make sure your resolution is divisible by 64 (e.g. 64, 128, 256 etc.).\n"
"NB: Your faceset must be at least 1.6x larger than your required input size.\n"
" (e.g. 160 is the maximum input size for a 256x256 faceset)")
self.add_item(
section=section, title="coverage", datatype=float, default=62.5, rounding=1,
min_max=(62.5, 100.0), info=COVERAGE_INFO)
# << VILLAIN MODEL OPTIONS >> #
section = "model.villain"
self.add_section(title=section,
info="A Higher resolution version of the Original "
"Model by VillainGuy.\nExtremely VRAM heavy. Full model requires "
"9GB+ for batchsize 16" + ADDITIONAL_INFO)
self.add_item(
section=section, title="lowmem", datatype=bool, default=False,
info="Lower memory mode. Set to 'True' if having issues with VRAM useage.\nNB: Models "
"with a changed lowmem mode are not compatible with each other.")
self.add_item(
section=section, title="dssim_loss", datatype=bool, default=False,
info="Use DSSIM for Loss rather than Mean Absolute Error\n"
"May increase overall quality.")
self.add_item(
section=section, title="mask_type", datatype=str, default="none",
choices=["none", "dfaker", "dfl_full"],
info="The mask to be used for training. Select none to not use a mask")
self.add_item(
section=section, title="coverage", datatype=float, default=62.5, rounding=1,
min_max=(62.5, 100.0), info=COVERAGE_INFO)
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