mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-05 20:42:11 -07:00
7386a9d6fd
SAEHD: added new option GAN power 0.0 .. 10.0 Train the network in Generative Adversarial manner. Forces the neural network to learn small details of the face. You can enable/disable this option at any time, but better to enable it when the network is trained enough. Typical value is 1.0 GAN power with pretrain mode will not work. Example of enabling GAN on 81k iters +5k iters https://i.imgur.com/OdXHLhU.jpg https://i.imgur.com/CYAJmJx.jpg dfhd: default Decoder dimensions are now 48 the preview for 256 res is now correctly displayed fixed model naming/renaming/removing Improvements for those involved in post-processing in AfterEffects: Codec is reverted back to x264 in order to properly use in AfterEffects and video players. Merger now always outputs the mask to workspace\data_dst\merged_mask removed raw modes except raw-rgb raw-rgb mode now outputs selected face mask_mode (before square mask) 'export alpha mask' button is replaced by 'show alpha mask'. You can view the alpha mask without recompute the frames. 8) 'merged *.bat' now also output 'result_mask.' video file. 8) 'merged lossless' now uses x264 lossless codec (before PNG codec) result_mask video file is always lossless. Thus you can use result_mask video file as mask layer in the AfterEffects.
357 lines
24 KiB
Python
357 lines
24 KiB
Python
if __name__ == "__main__":
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# Fix for linux
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import multiprocessing
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multiprocessing.set_start_method("spawn")
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from core.leras import nn
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nn.initialize_main_env()
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import os
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import sys
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import time
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import argparse
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from core import pathex
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from core import osex
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from pathlib import Path
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from core.interact import interact as io
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if sys.version_info[0] < 3 or (sys.version_info[0] == 3 and sys.version_info[1] < 6):
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raise Exception("This program requires at least Python 3.6")
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class fixPathAction(argparse.Action):
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def __call__(self, parser, namespace, values, option_string=None):
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setattr(namespace, self.dest, os.path.abspath(os.path.expanduser(values)))
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parser = argparse.ArgumentParser()
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subparsers = parser.add_subparsers()
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def process_extract(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import Extractor
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Extractor.main( detector = arguments.detector,
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input_path = Path(arguments.input_dir),
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output_path = Path(arguments.output_dir),
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output_debug = arguments.output_debug,
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manual_fix = arguments.manual_fix,
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manual_output_debug_fix = arguments.manual_output_debug_fix,
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manual_window_size = arguments.manual_window_size,
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face_type = arguments.face_type,
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cpu_only = arguments.cpu_only,
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force_gpu_idxs = [ int(x) for x in arguments.force_gpu_idxs.split(',') ] if arguments.force_gpu_idxs is not None else None,
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)
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p = subparsers.add_parser( "extract", help="Extract the faces from a pictures.")
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p.add_argument('--detector', dest="detector", choices=['s3fd','manual'], default=None, help="Type of detector.")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
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p.add_argument('--output-dir', required=True, action=fixPathAction, dest="output_dir", help="Output directory. This is where the extracted files will be stored.")
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p.add_argument('--output-debug', action="store_true", dest="output_debug", default=None, help="Writes debug images to <output-dir>_debug\ directory.")
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p.add_argument('--no-output-debug', action="store_false", dest="output_debug", default=None, help="Don't writes debug images to <output-dir>_debug\ directory.")
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p.add_argument('--face-type', dest="face_type", choices=['half_face', 'full_face', 'head', 'full_face_no_align', 'mark_only'], default='full_face', help="Default 'full_face'. Don't change this option, currently all models uses 'full_face'")
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p.add_argument('--manual-fix', action="store_true", dest="manual_fix", default=False, help="Enables manual extract only frames where faces were not recognized.")
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p.add_argument('--manual-output-debug-fix', action="store_true", dest="manual_output_debug_fix", default=False, help="Performs manual reextract input-dir frames which were deleted from [output_dir]_debug\ dir.")
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p.add_argument('--manual-window-size', type=int, dest="manual_window_size", default=1368, help="Manual fix window size. Default: 1368.")
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p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Extract on CPU..")
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p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
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p.set_defaults (func=process_extract)
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def process_dev_extract_vggface2_dataset(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import dev_misc
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dev_misc.extract_vggface2_dataset( arguments.input_dir,
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device_args={'cpu_only' : arguments.cpu_only,
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'multi_gpu' : arguments.multi_gpu,
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}
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)
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p = subparsers.add_parser( "dev_extract_vggface2_dataset", help="")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
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p.add_argument('--multi-gpu', action="store_true", dest="multi_gpu", default=False, help="Enables multi GPU.")
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p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Extract on CPU.")
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p.set_defaults (func=process_dev_extract_vggface2_dataset)
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def process_dev_extract_umd_csv(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import dev_misc
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dev_misc.extract_umd_csv( arguments.input_csv_file,
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device_args={'cpu_only' : arguments.cpu_only,
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'multi_gpu' : arguments.multi_gpu,
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}
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)
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p = subparsers.add_parser( "dev_extract_umd_csv", help="")
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p.add_argument('--input-csv-file', required=True, action=fixPathAction, dest="input_csv_file", help="input_csv_file")
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p.add_argument('--multi-gpu', action="store_true", dest="multi_gpu", default=False, help="Enables multi GPU.")
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p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Extract on CPU.")
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p.set_defaults (func=process_dev_extract_umd_csv)
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def process_dev_apply_celebamaskhq(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import dev_misc
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dev_misc.apply_celebamaskhq( arguments.input_dir )
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p = subparsers.add_parser( "dev_apply_celebamaskhq", help="")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
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p.set_defaults (func=process_dev_apply_celebamaskhq)
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def process_dev_test(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import dev_misc
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dev_misc.dev_test( arguments.input_dir )
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p = subparsers.add_parser( "dev_test", help="")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
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p.set_defaults (func=process_dev_test)
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def process_sort(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import Sorter
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Sorter.main (input_path=Path(arguments.input_dir), sort_by_method=arguments.sort_by_method)
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p = subparsers.add_parser( "sort", help="Sort faces in a directory.")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
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p.add_argument('--by', dest="sort_by_method", default=None, choices=("blur", "face-yaw", "face-pitch", "hist", "hist-dissim", "brightness", "hue", "black", "origname", "oneface", "final", "absdiff"), help="Method of sorting. 'origname' sort by original filename to recover original sequence." )
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p.set_defaults (func=process_sort)
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def process_util(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import Util
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if arguments.convert_png_to_jpg:
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Util.convert_png_to_jpg_folder (input_path=arguments.input_dir)
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if arguments.add_landmarks_debug_images:
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Util.add_landmarks_debug_images (input_path=arguments.input_dir)
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if arguments.recover_original_aligned_filename:
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Util.recover_original_aligned_filename (input_path=arguments.input_dir)
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#if arguments.remove_fanseg:
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# Util.remove_fanseg_folder (input_path=arguments.input_dir)
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if arguments.remove_ie_polys:
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Util.remove_ie_polys_folder (input_path=arguments.input_dir)
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if arguments.save_faceset_metadata:
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Util.save_faceset_metadata_folder (input_path=arguments.input_dir)
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if arguments.restore_faceset_metadata:
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Util.restore_faceset_metadata_folder (input_path=arguments.input_dir)
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if arguments.pack_faceset:
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io.log_info ("Performing faceset packing...\r\n")
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from samplelib import PackedFaceset
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PackedFaceset.pack( Path(arguments.input_dir) )
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if arguments.unpack_faceset:
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io.log_info ("Performing faceset unpacking...\r\n")
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from samplelib import PackedFaceset
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PackedFaceset.unpack( Path(arguments.input_dir) )
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p = subparsers.add_parser( "util", help="Utilities.")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
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p.add_argument('--convert-png-to-jpg', action="store_true", dest="convert_png_to_jpg", default=False, help="Convert DeepFaceLAB PNG files to JPEG.")
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p.add_argument('--add-landmarks-debug-images', action="store_true", dest="add_landmarks_debug_images", default=False, help="Add landmarks debug image for aligned faces.")
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p.add_argument('--recover-original-aligned-filename', action="store_true", dest="recover_original_aligned_filename", default=False, help="Recover original aligned filename.")
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#p.add_argument('--remove-fanseg', action="store_true", dest="remove_fanseg", default=False, help="Remove fanseg mask from aligned faces.")
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p.add_argument('--remove-ie-polys', action="store_true", dest="remove_ie_polys", default=False, help="Remove ie_polys from aligned faces.")
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p.add_argument('--save-faceset-metadata', action="store_true", dest="save_faceset_metadata", default=False, help="Save faceset metadata to file.")
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p.add_argument('--restore-faceset-metadata', action="store_true", dest="restore_faceset_metadata", default=False, help="Restore faceset metadata to file. Image filenames must be the same as used with save.")
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p.add_argument('--pack-faceset', action="store_true", dest="pack_faceset", default=False, help="")
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p.add_argument('--unpack-faceset', action="store_true", dest="unpack_faceset", default=False, help="")
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p.set_defaults (func=process_util)
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def process_train(arguments):
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osex.set_process_lowest_prio()
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kwargs = {'model_class_name' : arguments.model_name,
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'saved_models_path' : Path(arguments.model_dir),
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'training_data_src_path' : Path(arguments.training_data_src_dir),
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'training_data_dst_path' : Path(arguments.training_data_dst_dir),
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'pretraining_data_path' : Path(arguments.pretraining_data_dir) if arguments.pretraining_data_dir is not None else None,
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'pretrained_model_path' : Path(arguments.pretrained_model_dir) if arguments.pretrained_model_dir is not None else None,
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'no_preview' : arguments.no_preview,
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'force_model_name' : arguments.force_model_name,
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'force_gpu_idxs' : arguments.force_gpu_idxs,
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'cpu_only' : arguments.cpu_only,
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'execute_programs' : [ [int(x[0]), x[1] ] for x in arguments.execute_program ],
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'debug' : arguments.debug,
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}
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from mainscripts import Trainer
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Trainer.main(**kwargs)
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p = subparsers.add_parser( "train", help="Trainer")
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p.add_argument('--training-data-src-dir', required=True, action=fixPathAction, dest="training_data_src_dir", help="Dir of extracted SRC faceset.")
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p.add_argument('--training-data-dst-dir', required=True, action=fixPathAction, dest="training_data_dst_dir", help="Dir of extracted DST faceset.")
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p.add_argument('--pretraining-data-dir', action=fixPathAction, dest="pretraining_data_dir", default=None, help="Optional dir of extracted faceset that will be used in pretraining mode.")
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p.add_argument('--pretrained-model-dir', action=fixPathAction, dest="pretrained_model_dir", default=None, help="Optional dir of pretrain model files. (Currently only for Quick96).")
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p.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir", help="Saved models dir.")
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p.add_argument('--model', required=True, dest="model_name", choices=pathex.get_all_dir_names_startswith ( Path(__file__).parent / 'models' , 'Model_'), help="Model class name.")
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p.add_argument('--debug', action="store_true", dest="debug", default=False, help="Debug samples.")
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p.add_argument('--no-preview', action="store_true", dest="no_preview", default=False, help="Disable preview window.")
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p.add_argument('--force-model-name', dest="force_model_name", default=None, help="Forcing to choose model name from model/ folder.")
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p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Train on CPU.")
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p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
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p.add_argument('--execute-program', dest="execute_program", default=[], action='append', nargs='+')
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p.set_defaults (func=process_train)
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def process_merge(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import Merger
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Merger.main ( model_class_name = arguments.model_name,
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saved_models_path = Path(arguments.model_dir),
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training_data_src_path = Path(arguments.training_data_src_dir) if arguments.training_data_src_dir is not None else None,
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force_model_name = arguments.force_model_name,
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input_path = Path(arguments.input_dir),
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output_path = Path(arguments.output_dir),
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output_mask_path = Path(arguments.output_mask_dir),
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aligned_path = Path(arguments.aligned_dir) if arguments.aligned_dir is not None else None,
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force_gpu_idxs = arguments.force_gpu_idxs,
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cpu_only = arguments.cpu_only)
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p = subparsers.add_parser( "merge", help="Merger")
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p.add_argument('--training-data-src-dir', action=fixPathAction, dest="training_data_src_dir", default=None, help="(optional, may be required by some models) Dir of extracted SRC faceset.")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
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p.add_argument('--output-dir', required=True, action=fixPathAction, dest="output_dir", help="Output directory. This is where the merged files will be stored.")
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p.add_argument('--output-mask-dir', required=True, action=fixPathAction, dest="output_mask_dir", help="Output mask directory. This is where the mask files will be stored.")
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p.add_argument('--aligned-dir', action=fixPathAction, dest="aligned_dir", default=None, help="Aligned directory. This is where the extracted of dst faces stored.")
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p.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir", help="Model dir.")
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p.add_argument('--model', required=True, dest="model_name", choices=pathex.get_all_dir_names_startswith ( Path(__file__).parent / 'models' , 'Model_'), help="Model class name.")
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p.add_argument('--force-model-name', dest="force_model_name", default=None, help="Forcing to choose model name from model/ folder.")
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p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Merge on CPU.")
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p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
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p.set_defaults(func=process_merge)
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videoed_parser = subparsers.add_parser( "videoed", help="Video processing.").add_subparsers()
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def process_videoed_extract_video(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import VideoEd
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VideoEd.extract_video (arguments.input_file, arguments.output_dir, arguments.output_ext, arguments.fps)
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p = videoed_parser.add_parser( "extract-video", help="Extract images from video file.")
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p.add_argument('--input-file', required=True, action=fixPathAction, dest="input_file", help="Input file to be processed. Specify .*-extension to find first file.")
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p.add_argument('--output-dir', required=True, action=fixPathAction, dest="output_dir", help="Output directory. This is where the extracted images will be stored.")
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p.add_argument('--output-ext', dest="output_ext", default=None, help="Image format (extension) of output files.")
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p.add_argument('--fps', type=int, dest="fps", default=None, help="How many frames of every second of the video will be extracted. 0 - full fps.")
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p.set_defaults(func=process_videoed_extract_video)
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def process_videoed_cut_video(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import VideoEd
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VideoEd.cut_video (arguments.input_file,
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arguments.from_time,
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arguments.to_time,
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arguments.audio_track_id,
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arguments.bitrate)
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p = videoed_parser.add_parser( "cut-video", help="Cut video file.")
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p.add_argument('--input-file', required=True, action=fixPathAction, dest="input_file", help="Input file to be processed. Specify .*-extension to find first file.")
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p.add_argument('--from-time', dest="from_time", default=None, help="From time, for example 00:00:00.000")
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p.add_argument('--to-time', dest="to_time", default=None, help="To time, for example 00:00:00.000")
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p.add_argument('--audio-track-id', type=int, dest="audio_track_id", default=None, help="Specify audio track id.")
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p.add_argument('--bitrate', type=int, dest="bitrate", default=None, help="Bitrate of output file in Megabits.")
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p.set_defaults(func=process_videoed_cut_video)
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def process_videoed_denoise_image_sequence(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import VideoEd
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VideoEd.denoise_image_sequence (arguments.input_dir, arguments.ext, arguments.factor)
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p = videoed_parser.add_parser( "denoise-image-sequence", help="Denoise sequence of images, keeping sharp edges. This allows you to make the final fake more believable, since the neural network is not able to make a detailed skin texture, but it makes the edges quite clear. Therefore, if the whole frame is more `blurred`, then a fake will seem more believable. Especially true for scenes of the film, which are usually very clear.")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input file to be processed. Specify .*-extension to find first file.")
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p.add_argument('--ext', dest="ext", default=None, help="Image format (extension) of input files.")
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p.add_argument('--factor', type=int, dest="factor", default=None, help="Denoise factor (1-20).")
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p.set_defaults(func=process_videoed_denoise_image_sequence)
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def process_videoed_video_from_sequence(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import VideoEd
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VideoEd.video_from_sequence (input_dir = arguments.input_dir,
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output_file = arguments.output_file,
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reference_file = arguments.reference_file,
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ext = arguments.ext,
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fps = arguments.fps,
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bitrate = arguments.bitrate,
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include_audio = arguments.include_audio,
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lossless = arguments.lossless)
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p = videoed_parser.add_parser( "video-from-sequence", help="Make video from image sequence.")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input file to be processed. Specify .*-extension to find first file.")
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p.add_argument('--output-file', required=True, action=fixPathAction, dest="output_file", help="Input file to be processed. Specify .*-extension to find first file.")
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p.add_argument('--reference-file', action=fixPathAction, dest="reference_file", help="Reference file used to determine proper FPS and transfer audio from it. Specify .*-extension to find first file.")
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p.add_argument('--ext', dest="ext", default='png', help="Image format (extension) of input files.")
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p.add_argument('--fps', type=int, dest="fps", default=None, help="FPS of output file. Overwritten by reference-file.")
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p.add_argument('--bitrate', type=int, dest="bitrate", default=None, help="Bitrate of output file in Megabits.")
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p.add_argument('--include-audio', action="store_true", dest="include_audio", default=False, help="Include audio from reference file.")
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p.add_argument('--lossless', action="store_true", dest="lossless", default=False, help="PNG codec.")
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p.set_defaults(func=process_videoed_video_from_sequence)
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def process_labelingtool_edit_mask(arguments):
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from mainscripts import MaskEditorTool
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MaskEditorTool.mask_editor_main (arguments.input_dir, arguments.confirmed_dir, arguments.skipped_dir, no_default_mask=arguments.no_default_mask)
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labeling_parser = subparsers.add_parser( "labelingtool", help="Labeling tool.").add_subparsers()
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p = labeling_parser.add_parser ( "edit_mask", help="")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory of aligned faces.")
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p.add_argument('--confirmed-dir', required=True, action=fixPathAction, dest="confirmed_dir", help="This is where the labeled faces will be stored.")
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p.add_argument('--skipped-dir', required=True, action=fixPathAction, dest="skipped_dir", help="This is where the labeled faces will be stored.")
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p.add_argument('--no-default-mask', action="store_true", dest="no_default_mask", default=False, help="Don't use default mask.")
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|
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p.set_defaults(func=process_labelingtool_edit_mask)
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|
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facesettool_parser = subparsers.add_parser( "facesettool", help="Faceset tools.").add_subparsers()
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|
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def process_faceset_enhancer(arguments):
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osex.set_process_lowest_prio()
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from mainscripts import FacesetEnhancer
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FacesetEnhancer.process_folder ( Path(arguments.input_dir),
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cpu_only=arguments.cpu_only,
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force_gpu_idxs=arguments.force_gpu_idxs
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|
)
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|
|
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p = facesettool_parser.add_parser ("enhance", help="Enhance details in DFL faceset.")
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p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory of aligned faces.")
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|
p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Process on CPU.")
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p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
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|
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p.set_defaults(func=process_faceset_enhancer)
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|
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"""
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def process_relight_faceset(arguments):
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osex.set_process_lowest_prio()
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|
from mainscripts import FacesetRelighter
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FacesetRelighter.relight (arguments.input_dir, arguments.lighten, arguments.random_one)
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|
|
|
def process_delete_relighted(arguments):
|
|
osex.set_process_lowest_prio()
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|
from mainscripts import FacesetRelighter
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|
FacesetRelighter.delete_relighted (arguments.input_dir)
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|
|
|
p = facesettool_parser.add_parser ("relight", help="Synthesize new faces from existing ones by relighting them. With the relighted faces neural network will better reproduce face shadows.")
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|
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory of aligned faces.")
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|
p.add_argument('--lighten', action="store_true", dest="lighten", default=None, help="Lighten the faces.")
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|
p.add_argument('--random-one', action="store_true", dest="random_one", default=None, help="Relight the faces only with one random direction, otherwise relight with all directions.")
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|
p.set_defaults(func=process_relight_faceset)
|
|
|
|
p = facesettool_parser.add_parser ("delete_relighted", help="Delete relighted faces.")
|
|
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory of aligned faces.")
|
|
p.set_defaults(func=process_delete_relighted)
|
|
"""
|
|
|
|
def bad_args(arguments):
|
|
parser.print_help()
|
|
exit(0)
|
|
parser.set_defaults(func=bad_args)
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|
|
|
arguments = parser.parse_args()
|
|
arguments.func(arguments)
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|
|
|
print ("Done.")
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|
|
|
'''
|
|
import code
|
|
code.interact(local=dict(globals(), **locals()))
|
|
'''
|