mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-09 04:36:50 -04:00
236c35f11a
* Initial prediction pipeline and display * Patch faces from convert pipeline. Load configs * Add tkinter canvas for image display * Radio buttons to config gui. Standardise config gui. Remove dssim loss from realface * Add action frame * Live update cli options * Live update config changes * Quicker frame loading. Refresh faces button callback * remove debug code * Add saving and reloading config buttons * Reduce lag with threading * Busy indicator + geometry updates * Fix options canvas resizing * Add global config saving and resetting * tools to own tab * Rename tool to 'preview' * geometry tweak * pep8 fixes
245 lines
13 KiB
Python
245 lines
13 KiB
Python
#!/usr/bin/env python3
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""" Default configurations for models """
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import logging
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from lib.config import FaceswapConfig
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from lib.model.masks import get_available_masks
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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MASK_TYPES = get_available_masks()
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MASK_INFO = ("The mask to be used for training:"
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"\n\t none: Doesn't use any mask."
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"\n\t components: An improved face hull mask using a facehull of 8 facial parts"
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"\n\t dfl_full: An improved face hull mask using a facehull of 3 facial parts"
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"\n\t facehull: Face cutout based on landmarks")
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COVERAGE_INFO = ("How much of the extracted image to train on. Generally the model is optimized\n"
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"to the default value. Sensible values to use are:"
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"\n\t62.5%% spans from eyebrow to eyebrow."
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"\n\t75.0%% spans from temple to temple."
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"\n\t87.5%% spans from ear to ear."
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"\n\t100.0%% is a mugshot.")
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ADDITIONAL_INFO = ("\nNB: Unless specifically stated, values changed here will only take effect "
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"when creating a new model.")
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class Config(FaceswapConfig):
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""" Config File for Models """
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# pylint: disable=too-many-statements
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def set_defaults(self):
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""" Set the default values for config """
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logger.debug("Setting defaults")
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# << GLOBAL OPTIONS >> #
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section = "global"
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self.add_section(title=section,
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info="Options that apply to all models" + ADDITIONAL_INFO)
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self.add_item(
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section=section, title="icnr_init", datatype=bool, default=False,
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info="Use ICNR Kernel Initializer for upscaling.\nThis can help reduce the "
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"'checkerboard effect' when upscaling the image.")
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self.add_item(
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section=section, title="subpixel_upscaling", datatype=bool, default=False,
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info="Use subpixel upscaling rather than pixel shuffler.\n"
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"Might increase speed at cost of VRAM")
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self.add_item(
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section=section, title="reflect_padding", datatype=bool, default=False,
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info="Use reflect padding rather than zero padding. Only enable this option if the "
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"model you are training has a distinct line appearing around the edge of the "
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"swap area.")
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self.add_item(
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section=section, title="dssim_loss", datatype=bool, default=True,
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info="Use DSSIM for Loss rather than Mean Absolute Error\n"
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"May increase overall quality.")
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self.add_item(
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section=section, title="penalized_mask_loss", datatype=bool, default=True,
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info="If using a mask, This penalizes the loss for the masked area, to give higher "
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"priority to the face area. \nShould increase overall quality and speed up "
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"training. This should probably be left at True")
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# << DFAKER OPTIONS >> #
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section = "model.dfaker"
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self.add_section(title=section,
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info="Dfaker Model (Adapted from https://github.com/dfaker/df)" +
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ADDITIONAL_INFO)
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self.add_item(
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section=section, title="mask_type", datatype=str, default="facehull",
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choices=MASK_TYPES, gui_radio=True, info=MASK_INFO)
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self.add_item(
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section=section, title="coverage", datatype=float, default=100.0, rounding=1,
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min_max=(62.5, 100.0), info=COVERAGE_INFO)
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# << DFL MODEL OPTIONS >> #
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section = "model.dfl_h128"
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self.add_section(title=section,
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info="DFL H128 Model (Adapted from "
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"https://github.com/iperov/DeepFaceLab)" + ADDITIONAL_INFO)
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self.add_item(
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section=section, title="lowmem", datatype=bool, default=False,
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info="Lower memory mode. Set to 'True' if having issues with VRAM useage.\nNB: Models "
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"with a changed lowmem mode are not compatible with each other.")
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self.add_item(
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section=section, title="mask_type", datatype=str, default="dfl_full",
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choices=MASK_TYPES, gui_radio=True, info=MASK_INFO)
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self.add_item(
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section=section, title="coverage", datatype=float, default=62.5, rounding=1,
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min_max=(62.5, 100.0), info=COVERAGE_INFO)
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# << IAE MODEL OPTIONS >> #
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section = "model.iae"
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self.add_section(title=section,
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info="Intermediate Auto Encoder. Based on Original Model, uses "
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"intermediate layers to try to better get details" + ADDITIONAL_INFO)
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self.add_item(
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section=section, title="mask_type", datatype=str, default="none",
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choices=MASK_TYPES, gui_radio=True, info=MASK_INFO)
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self.add_item(
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section=section, title="coverage", datatype=float, default=62.5, rounding=1,
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min_max=(62.5, 100.0), info=COVERAGE_INFO)
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# << LIGHTWEIGHT MODEL OPTIONS >> #
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section = "model.lightweight"
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self.add_section(title=section,
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info="A lightweight version of the Original Faceswap Model, designed to "
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"run on lower end GPUs (~2GB).\nDon't expect great results, but it "
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"allows users with lower end cards to play with the "
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"software." + ADDITIONAL_INFO)
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self.add_item(
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section=section, title="mask_type", datatype=str, default="none",
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choices=MASK_TYPES, gui_radio=True, info=MASK_INFO)
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self.add_item(
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section=section, title="coverage", datatype=float, default=62.5, rounding=1,
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min_max=(62.5, 100.0), info=COVERAGE_INFO)
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# << ORIGINAL MODEL OPTIONS >> #
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section = "model.original"
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self.add_section(title=section,
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info="Original Faceswap Model" + ADDITIONAL_INFO)
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self.add_item(
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section=section, title="lowmem", datatype=bool, default=False,
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info="Lower memory mode. Set to 'True' if having issues with VRAM useage.\nNB: Models "
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"with a changed lowmem mode are not compatible with each other.")
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self.add_item(
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section=section, title="mask_type", datatype=str, default="none",
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choices=MASK_TYPES, gui_radio=True, info=MASK_INFO)
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self.add_item(
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section=section, title="coverage", datatype=float, default=62.5, rounding=1,
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min_max=(62.5, 100.0), info=COVERAGE_INFO)
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# << UNBALANCED MODEL OPTIONS >> #
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section = "model.unbalanced"
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self.add_section(title=section,
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info="An unbalanced model with adjustable input size options.\nThis is "
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"an unbalanced model so b>a swaps may not work "
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"well" + ADDITIONAL_INFO)
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self.add_item(
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section=section, title="lowmem", datatype=bool, default=False,
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info="Lower memory mode. Set to 'True' if having issues with VRAM useage.\nNB: Models "
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"with a changed lowmem mode are not compatible with each other. NB: lowmem will "
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"override cutom nodes and complexity settings.")
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self.add_item(
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section=section, title="clipnorm", datatype=bool, default=True,
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info="Controls gradient clipping of the optimizer. Can prevent model corruption at "
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"the expense of VRAM")
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self.add_item(
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section=section, title="mask_type", datatype=str, default="none",
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choices=MASK_TYPES, gui_radio=True, info=MASK_INFO)
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self.add_item(
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section=section, title="nodes", datatype=int, default=1024, rounding=64,
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min_max=(512, 4096),
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info="Number of nodes for decoder. Don't change this unless you "
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"know what you are doing!")
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self.add_item(
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section=section, title="complexity_encoder", datatype=int, default=128,
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rounding=16, min_max=(64, 1024),
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info="Encoder Convolution Layer Complexity. sensible ranges: "
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"128 to 160")
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self.add_item(
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section=section, title="complexity_decoder_a", datatype=int, default=384,
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rounding=16, min_max=(64, 1024),
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info="Decoder A Complexity.")
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self.add_item(
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section=section, title="complexity_decoder_b", datatype=int, default=512,
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rounding=16, min_max=(64, 1024),
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info="Decoder B Complexity.")
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self.add_item(
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section=section, title="input_size", datatype=int, default=128,
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rounding=64, min_max=(64, 512),
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info="Resolution (in pixels) of the image to train on.\n"
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"BE AWARE Larger resolution will dramatically increase"
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"VRAM requirements.\n"
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"Make sure your resolution is divisible by 64 (e.g. 64, 128, 256 etc.).\n"
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"NB: Your faceset must be at least 1.6x larger than your required input size.\n"
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" (e.g. 160 is the maximum input size for a 256x256 faceset)")
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self.add_item(
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section=section, title="coverage", datatype=float, default=62.5, rounding=1,
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min_max=(62.5, 100.0), info=COVERAGE_INFO)
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# << PEGASUS MODEL OPTIONS >> #
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section = "model.realface"
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self.add_section(title=section,
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info="An extra detailed variant of Original model.\n"
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"Incorporates ideas from Bryanlyon and inspiration from the Villain "
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"model.\n"
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"Requires about 6GB-8GB of VRAM (batchsize 8-16)." + ADDITIONAL_INFO)
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self.add_item(
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section=section, title="mask_type", datatype=str, default="components",
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choices=MASK_TYPES, gui_radio=True, info=MASK_INFO)
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self.add_item(
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section=section, title="coverage", datatype=float, default=62.5, rounding=1,
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min_max=(62.5, 100.0),
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info="{}\nThe model is essentially created for 60-80% coverage as it follows "
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"Original paradigm.\nYou may try higher values but good results are not "
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"guaranteed.".format(COVERAGE_INFO))
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self.add_item(
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section=section, title="input_size", datatype=int, default=64,
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rounding=16, min_max=(64, 128),
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info="Resolution (in pixels) of the input image to train on.\n"
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"BE AWARE Larger resolution will dramatically increase"
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"VRAM requirements.\n"
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"Higher resolutions may increase prediction accuracy, but does not effect the "
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"resulting output size.\n"
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"Must be between 64 and 128 and be divisible by 16.")
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self.add_item(
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section=section, title="output_size", datatype=int, default=128,
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rounding=16, min_max=(64, 256),
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info="Output image resolution (in pixels).\n"
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"Be aware that larger resolution will increase VRAM requirements.\n"
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"NB: Must be between 64 and 256 and be divisible by 16.")
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self.add_item(
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section=section, title="dense_nodes", datatype=int, default=1536, rounding=64,
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min_max=(768, 2048),
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info="Number of nodes for decoder. Might affect your model's ability to learn in "
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"general.\n"
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"Note that: Lower values will affect the ability to predict details.")
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self.add_item(
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section=section, title="complexity_encoder", datatype=int, default=128,
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min_max=(96, 160), rounding=4,
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info="Encoder Convolution Layer Complexity. sensible ranges: "
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"128 to 150")
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self.add_item(
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section=section, title="complexity_decoder", datatype=int, default=512,
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rounding=4, min_max=(512, 544),
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info="Decoder Complexity.")
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self.add_item(
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section=section, title="learning_rate", datatype=float, default=5e-5,
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min_max=(5e-6, 1e-4), rounding=6, fixed=False,
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info="Learning rate - how fast your network will learn.\n"
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"Note that: Higher values might result in RSoD failure.")
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# << VILLAIN MODEL OPTIONS >> #
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section = "model.villain"
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self.add_section(title=section,
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info="A Higher resolution version of the Original "
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"Model by VillainGuy.\nExtremely VRAM heavy. Full model requires "
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"9GB+ for batchsize 16" + ADDITIONAL_INFO)
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self.add_item(
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section=section, title="lowmem", datatype=bool, default=False,
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info="Lower memory mode. Set to 'True' if having issues with VRAM useage.\nNB: Models "
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"with a changed lowmem mode are not compatible with each other.")
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self.add_item(
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section=section, title="mask_type", datatype=str, default="none",
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choices=MASK_TYPES, gui_radio=True, info=MASK_INFO)
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self.add_item(
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section=section, title="coverage", datatype=float, default=62.5, rounding=1,
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min_max=(62.5, 100.0), info=COVERAGE_INFO)
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