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* FaceSwap 3 (#1515) * Update extract pipeline * Update requirements + setup for nvidia * Remove allow-growth option * tf.keras to keras updates * lib.model.losses - Port + fix all loss functions for Keras3 * lib.model - port initializers, layers. normalization to Keras3 * lib.model.autoclip to Keras 3 * Update mixed precision layer storage * model file to .keras format * Restructure nn_blocks to initialize layers in __init__ * Tensorboard - Trainer: Add Torch compatible Tensorboard callbacks - GUI event reader remove TF dependency * Loss logging - Flush TB logs on save - Replace TB live iterator for GUI * Backup models on total loss drop rather than per side * Update all models to Keras3 Compat * Remove lib.model.session * Update clip ViT to Keras 3 * plugins.extract.mask.unet-dfl - Fix for Keras3/Torch backend * Port AdaBelief to Keras 3 * setup.py: - Add --dev flag for dev tool install * Fix Keras 3 syntax * Fix LR Finder for Keras 3 * Fix mixed precision switching for Keras 3 * Add more optimizers + open up config setting * train: Remove updating FS1 weights to FS2 models * Alignments: Remove support for legacy .json files * tools.model: - Remove TF Saved Format saving - Fix Backup/Restore + Nan-Scan * Fix inference model creation for Keras 3 * Preview tool: Fix for Keras3 * setup.py: Configure keras backend * train: Migration of FS2 models to FS3 * Training: Default coverage to 100% * Remove DirectML backend * Update setup for MacOS * GUI: Force line reading to UTF-8 * Remove redundant Tensorflow references * Remove redundant code * Legacy model loading: Fix TFLamdaOp scalar ops and DepthwiseConv2D * Add vertical offset option for training * Github actions: Add more python versions * Add python version to workflow names * Github workflow: Exclude Python 3.12 for macOS * Implement custom training loop * Fs3 - Add RTX5xxx and ROCm 6.1-6.4 support (#1511) * setup.py: Add Cuda/ROCm version select options * bump minimum python version to 3.11 * Switch from setup.cgf to pyproject.toml * Documentation: Update all docs to use automodapi * Allow sysinfo to run with missing packages + correctly install tk under Linux * Bugfix: dot naming convention in clip models * lib.config: Centralise globally rather than passing as object - Add torch DataParallel for multi-gpu training - GUI: Group switches together when generating cli args - CLI: Remove deprecated multi-character argparse args - Refactor: - Centralise tensorboard reading/writing + unit tests - Create trainer plugin interfaces + add original + distributed * Update installers
325 lines
15 KiB
Python
325 lines
15 KiB
Python
#!/usr/bin/env python3
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""" The Command Line Argument options for training with faceswap.py """
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import gettext
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import typing as T
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from lib.utils import get_module_objects
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from plugins.plugin_loader import PluginLoader
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from .actions import DirFullPaths, FileFullPaths, Radio, Slider
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from .args import FaceSwapArgs
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# LOCALES
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_LANG = gettext.translation("lib.cli.args_train", localedir="locales", fallback=True)
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_ = _LANG.gettext
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class TrainArgs(FaceSwapArgs):
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""" Creates the command line arguments for training. """
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@staticmethod
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def get_info() -> str:
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""" The information text for the Train command.
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Returns
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-------
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str
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The information text for the Train command.
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"""
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return _("Train a model on extracted original (A) and swap (B) faces.\n"
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"Training models can take a long time. Anything from 24hrs to over a week\n"
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"Model plugins can be configured in the 'Settings' Menu")
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@staticmethod
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def get_argument_list() -> list[dict[str, T.Any]]:
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""" Returns the argument list for Train arguments.
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Returns
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-------
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list
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The list of command line options for training
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"""
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argument_list: list[dict[str, T.Any]] = []
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argument_list.append({
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"opts": ("-A", "--input-A"),
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"action": DirFullPaths,
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"dest": "input_a",
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"required": True,
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"group": _("faces"),
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"help": _(
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"Input directory. A directory containing training images for face A. This is the "
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"original face, i.e. the face that you want to remove and replace with face B.")})
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argument_list.append({
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"opts": ("-B", "--input-B"),
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"action": DirFullPaths,
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"dest": "input_b",
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"required": True,
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"group": _("faces"),
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"help": _(
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"Input directory. A directory containing training images for face B. This is the "
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"swap face, i.e. the face that you want to place onto the head of person A.")})
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argument_list.append({
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"opts": ("-m", "--model-dir"),
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"action": DirFullPaths,
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"dest": "model_dir",
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"required": True,
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"group": _("model"),
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"help": _(
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"Model directory. This is where the training data will be stored. You should "
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"always specify a new folder for new models. If starting a new model, select "
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"either an empty folder, or a folder which does not exist (which will be "
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"created). If continuing to train an existing model, specify the location of the "
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"existing model.")})
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argument_list.append({
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"opts": ("-l", "--load-weights"),
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"action": FileFullPaths,
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"filetypes": "model",
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"dest": "load_weights",
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"required": False,
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"group": _("model"),
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"help": _(
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"R|Load the weights from a pre-existing model into a newly created model. For "
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"most models this will load weights from the Encoder of the given model into the "
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"encoder of the newly created model. Some plugins may have specific configuration "
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"options allowing you to load weights from other layers. Weights will only be "
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"loaded when creating a new model. This option will be ignored if you are "
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"resuming an existing model. Generally you will also want to 'freeze-weights' "
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"whilst the rest of your model catches up with your Encoder.\n"
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"NB: Weights can only be loaded from models of the same plugin as you intend to "
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"train.")})
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argument_list.append({
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"opts": ("-t", "--trainer"),
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"action": Radio,
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"type": str.lower,
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"default": PluginLoader.get_default_model(),
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"choices": PluginLoader.get_available_models(),
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"group": _("model"),
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"help": _(
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"R|Select which trainer to use. Trainers can be configured from the Settings menu "
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"or the config folder."
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"\nL|original: The original model created by /u/deepfakes."
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"\nL|dfaker: 64px in/128px out model from dfaker. Enable 'warp-to-landmarks' for "
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"full dfaker method."
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"\nL|dfl-h128: 128px in/out model from deepfacelab"
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"\nL|dfl-sae: Adaptable model from deepfacelab"
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"\nL|dlight: A lightweight, high resolution DFaker variant."
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"\nL|iae: A model that uses intermediate layers to try to get better details"
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"\nL|lightweight: A lightweight model for low-end cards. Don't expect great "
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"results. Can train as low as 1.6GB with batch size 8."
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"\nL|realface: A high detail, dual density model based on DFaker, with "
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"customizable in/out resolution. The autoencoders are unbalanced so B>A swaps "
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"won't work so well. By andenixa et al. Very configurable."
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"\nL|unbalanced: 128px in/out model from andenixa. The autoencoders are "
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"unbalanced so B>A swaps won't work so well. Very configurable."
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"\nL|villain: 128px in/out model from villainguy. Very resource hungry (You will "
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"require a GPU with a fair amount of VRAM). Good for details, but more "
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"susceptible to color differences.")})
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argument_list.append({
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"opts": ("-u", "--summary"),
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"action": "store_true",
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"dest": "summary",
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"default": False,
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"group": _("model"),
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"help": _(
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"Output a summary of the model and exit. If a model folder is provided then a "
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"summary of the saved model is displayed. Otherwise a summary of the model that "
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"would be created by the chosen plugin and configuration settings is displayed.")})
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argument_list.append({
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"opts": ("-f", "--freeze-weights"),
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"action": "store_true",
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"dest": "freeze_weights",
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"default": False,
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"group": _("model"),
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"help": _(
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"Freeze the weights of the model. Freezing weights means that some of the "
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"parameters in the model will no longer continue to learn, but those that are not "
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"frozen will continue to learn. For most models, this will freeze the encoder, "
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"but some models may have configuration options for freezing other layers.")})
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argument_list.append({
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"opts": ("-b", "--batch-size"),
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"action": Slider,
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"min_max": (1, 256),
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"rounding": 1,
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"type": int,
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"dest": "batch_size",
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"default": 16,
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"group": _("training"),
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"help": _(
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"Batch size. This is the number of images processed through the model for each "
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"side per iteration. NB: As the model is fed 2 sides at a time, the actual number "
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"of images within the model at any one time is double the number that you set "
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"here. Larger batches require more GPU RAM.")})
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argument_list.append({
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"opts": ("-i", "--iterations"),
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"action": Slider,
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"min_max": (0, 5000000),
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"rounding": 20000,
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"type": int,
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"default": 1000000,
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"group": _("training"),
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"help": _(
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"Length of training in iterations. This is only really used for automation. There "
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"is no 'correct' number of iterations a model should be trained for. You should "
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"stop training when you are happy with the previews. However, if you want the "
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"model to stop automatically at a set number of iterations, you can set that "
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"value here.")})
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argument_list.append({
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"opts": ("-a", "--warmup"),
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"action": Slider,
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"min_max": (0, 5000),
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"rounding": 100,
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"type": int,
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"default": 0,
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"group": _("training"),
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"help": _(
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"Learning rate warmup. Linearly increase the learning rate from 0 to the chosen "
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"target rate over the number of iterations given here. 0 to disable.")})
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argument_list.append({
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"opts": ("-d", "--distributed"),
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"dest": "distributed",
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"action": "store_true",
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"default": False,
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"backend": ("nvidia", "rocm"),
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"group": _("training"),
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"help": _("Use distibuted training on multi-gpu setups.")})
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argument_list.append({
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"opts": ("-n", "--no-logs"),
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"action": "store_true",
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"dest": "no_logs",
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"default": False,
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"group": _("training"),
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"help": _(
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"Disables TensorBoard logging. NB: Disabling logs means that you will not be able "
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"to use the graph or analysis for this session in the GUI.")})
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argument_list.append({
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"opts": ("-r", "--use-lr-finder"),
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"action": "store_true",
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"dest": "use_lr_finder",
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"default": False,
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"group": _("training"),
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"help": _(
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"Use the Learning Rate Finder to discover the optimal learning rate for training. "
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"For new models, this will calculate the optimal learning rate for the model. For "
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"existing models this will use the optimal learning rate that was discovered when "
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"initializing the model. Setting this option will ignore the manually configured "
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"learning rate (configurable in train settings).")})
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argument_list.append({
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"opts": ("-s", "--save-interval"),
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"action": Slider,
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"min_max": (10, 1000),
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"rounding": 10,
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"type": int,
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"dest": "save_interval",
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"default": 250,
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"group": _("Saving"),
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"help": _("Sets the number of iterations between each model save.")})
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argument_list.append({
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"opts": ("-I", "--snapshot-interval"),
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"action": Slider,
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"min_max": (0, 100000),
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"rounding": 5000,
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"type": int,
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"dest": "snapshot_interval",
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"default": 25000,
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"group": _("Saving"),
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"help": _(
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"Sets the number of iterations before saving a backup snapshot of the model in "
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"it's current state. Set to 0 for off.")})
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argument_list.append({
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"opts": ("-x", "--timelapse-input-A"),
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"action": DirFullPaths,
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"dest": "timelapse_input_a",
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"default": None,
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"group": _("timelapse"),
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"help": _(
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"Optional for creating a timelapse. Timelapse will save an image of your selected "
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"faces into the timelapse-output folder at every save iteration. This should be "
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"the input folder of 'A' faces that you would like to use for creating the "
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"timelapse. You must also supply a --timelapse-output and a --timelapse-input-B "
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"parameter.")})
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argument_list.append({
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"opts": ("-y", "--timelapse-input-B"),
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"action": DirFullPaths,
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"dest": "timelapse_input_b",
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"default": None,
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"group": _("timelapse"),
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"help": _(
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"Optional for creating a timelapse. Timelapse will save an image of your selected "
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"faces into the timelapse-output folder at every save iteration. This should be "
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"the input folder of 'B' faces that you would like to use for creating the "
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"timelapse. You must also supply a --timelapse-output and a --timelapse-input-A "
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"parameter.")})
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argument_list.append({
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"opts": ("-z", "--timelapse-output"),
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"action": DirFullPaths,
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"dest": "timelapse_output",
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"default": None,
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"group": _("timelapse"),
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"help": _(
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"Optional for creating a timelapse. Timelapse will save an image of your selected "
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"faces into the timelapse-output folder at every save iteration. If the input "
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"folders are supplied but no output folder, it will default to your model folder/"
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"timelapse/")})
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argument_list.append({
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"opts": ("-p", "--preview"),
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"action": "store_true",
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"dest": "preview",
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"default": False,
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"group": _("preview"),
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"help": _("Show training preview output. in a separate window.")})
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argument_list.append({
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"opts": ("-w", "--write-image"),
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"action": "store_true",
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"dest": "write_image",
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"default": False,
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"group": _("preview"),
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"help": _(
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"Writes the training result to a file. The image will be stored in the root of "
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"your FaceSwap folder.")})
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argument_list.append({
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"opts": ("-M", "--warp-to-landmarks"),
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"action": "store_true",
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"dest": "warp_to_landmarks",
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"default": False,
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"group": _("augmentation"),
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"help": _(
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"Warps training faces to closely matched Landmarks from the opposite face-set "
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"rather than randomly warping the face. This is the 'dfaker' way of doing "
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"warping.")})
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argument_list.append({
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"opts": ("-P", "--no-flip"),
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"action": "store_true",
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"dest": "no_flip",
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"default": False,
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"group": _("augmentation"),
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"help": _(
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"To effectively learn, a random set of images are flipped horizontally. Sometimes "
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"it is desirable for this not to occur. Generally this should be left off except "
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"for during 'fit training'.")})
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argument_list.append({
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"opts": ("-c", "--no-augment-color"),
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"action": "store_true",
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"dest": "no_augment_color",
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"default": False,
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"group": _("augmentation"),
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"help": _(
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"Color augmentation helps make the model less susceptible to color differences "
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"between the A and B sets, at an increased training time cost. Enable this option "
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"to disable color augmentation.")})
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argument_list.append({
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"opts": ("-W", "--no-warp"),
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"action": "store_true",
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"dest": "no_warp",
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"default": False,
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"group": _("augmentation"),
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"help": _(
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"Warping is integral to training the Neural Network. This option should only be "
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"enabled towards the very end of training to try to bring out more detail. Think "
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"of it as 'fine-tuning'. Enabling this option from the beginning is likely to "
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"kill a model and lead to terrible results.")})
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return argument_list
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__all__ = get_module_objects(__name__)
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