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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-10 23:33:30 -07:00
added new model U-net Face Morpher.
removed AVATAR - useless model was just for demo removed MIAEF128 - use UFM insted removed LIAEF128YAW - use model option sort by yaw on start for any model All models now ask some options on start. Session options (such as target epoch, batch_size, write_preview_history etc) can be overrided by special command arg. Converter now always ask options and no more support to define options via command line. fix bug when ConverterMasked always used not predicted mask. SampleGenerator now always generate samples with replicated border, exclude mask samples. refactorings
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iperov
parent
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29 changed files with 673 additions and 1013 deletions
122
main.py
122
main.py
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@ -4,7 +4,6 @@ import argparse
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from utils import Path_utils
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from utils import os_utils
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from pathlib import Path
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import numpy as np
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if sys.version_info[0] < 3 or (sys.version_info[0] == 3 and sys.version_info[1] < 2):
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raise Exception("This program requires at least Python 3.2")
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@ -68,7 +67,7 @@ if __name__ == "__main__":
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def process_train(arguments):
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if 'DFL_TARGET_EPOCH' in os.environ.keys():
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arguments.target_epoch = int ( os.environ['DFL_TARGET_EPOCH'] )
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arguments.session_target_epoch = int ( os.environ['DFL_TARGET_EPOCH'] )
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if 'DFL_BATCH_SIZE' in os.environ.keys():
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arguments.batch_size = int ( os.environ['DFL_BATCH_SIZE'] )
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@ -79,11 +78,12 @@ if __name__ == "__main__":
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training_data_dst_dir=arguments.training_data_dst_dir,
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model_path=arguments.model_dir,
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model_name=arguments.model_name,
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ask_for_session_options = arguments.ask_for_session_options,
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debug = arguments.debug,
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#**options
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batch_size = arguments.batch_size,
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write_preview_history = arguments.write_preview_history,
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target_epoch = arguments.target_epoch,
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session_write_preview_history = arguments.session_write_preview_history,
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session_target_epoch = arguments.session_target_epoch,
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session_batch_size = arguments.session_batch_size,
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save_interval_min = arguments.save_interval_min,
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choose_worst_gpu = arguments.choose_worst_gpu,
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force_best_gpu_idx = arguments.force_best_gpu_idx,
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@ -97,94 +97,21 @@ if __name__ == "__main__":
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train_parser.add_argument('--training-data-dst-dir', required=True, action=fixPathAction, dest="training_data_dst_dir", help="Dir of dst-set.")
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train_parser.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir", help="Model dir.")
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train_parser.add_argument('--model', required=True, dest="model_name", choices=Path_utils.get_all_dir_names_startswith ( Path(__file__).parent / 'models' , 'Model_'), help="Type of model")
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train_parser.add_argument('--write-preview-history', action="store_true", dest="write_preview_history", default=False, help="Enable write preview history.")
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train_parser.add_argument('--debug', action="store_true", dest="debug", default=False, help="Debug training.")
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train_parser.add_argument('--batch-size', type=int, dest="batch_size", default=0, help="Model batch size. Default - auto. Environment variable: ODFS_BATCH_SIZE.")
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train_parser.add_argument('--target-epoch', type=int, dest="target_epoch", default=0, help="Train until target epoch. Default - unlimited. Environment variable: ODFS_TARGET_EPOCH.")
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train_parser.add_argument('--debug', action="store_true", dest="debug", default=False, help="Debug samples.")
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train_parser.add_argument('--ask-for-session-options', action="store_true", dest="ask_for_session_options", default=False, help="Ask to override session options.")
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train_parser.add_argument('--session-write-preview-history', action="store_true", dest="session_write_preview_history", default=None, help="Enable write preview history for this session.")
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train_parser.add_argument('--session-target-epoch', type=int, dest="session_target_epoch", default=0, help="Train until target epoch for this session. Default - unlimited. Environment variable to override: DFL_TARGET_EPOCH.")
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train_parser.add_argument('--session-batch-size', type=int, dest="session_batch_size", default=0, help="Model batch size for this session. Default - auto. Environment variable to override: DFL_BATCH_SIZE.")
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train_parser.add_argument('--save-interval-min', type=int, dest="save_interval_min", default=10, help="Save interval in minutes. Default 10.")
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train_parser.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Train on CPU.")
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train_parser.add_argument('--force-gpu-idxs', type=str, dest="force_gpu_idxs", default=None, help="Override final GPU idxs. Example: 0,1,2.")
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train_parser.add_argument('--multi-gpu', action="store_true", dest="multi_gpu", default=False, help="MultiGPU option (if model supports it). It will select only same best(worst) GPU models.")
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train_parser.add_argument('--choose-worst-gpu', action="store_true", dest="choose_worst_gpu", default=False, help="Choose worst GPU instead of best.")
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train_parser.add_argument('--force-best-gpu-idx', type=int, dest="force_best_gpu_idx", default=-1, help="Force to choose this GPU idx as best(worst).")
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train_parser.add_argument('--multi-gpu', action="store_true", dest="multi_gpu", default=False, help="MultiGPU option. It will select only same best(worst) GPU models.")
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train_parser.add_argument('--force-gpu-idxs', type=str, dest="force_gpu_idxs", default=None, help="Override final GPU idxs. Example: 0,1,2.")
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train_parser.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Train on CPU.")
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train_parser.set_defaults (func=process_train)
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def process_convert(arguments):
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if arguments.ask_for_params:
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try:
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mode = int ( input ("Choose mode: (1) hist match, (2) hist match bw, (3) seamless (default), (4) seamless hist match : ") )
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except:
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mode = 3
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if mode == 1:
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arguments.mode = 'hist-match'
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elif mode == 2:
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arguments.mode = 'hist-match-bw'
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elif mode == 3:
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arguments.mode = 'seamless'
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elif mode == 4:
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arguments.mode = 'seamless-hist-match'
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if arguments.mode == 'hist-match' or arguments.mode == 'hist-match-bw':
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try:
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arguments.masked_hist_match = bool ( {"1":True,"0":False}[input("Masked hist match? [0 or 1] (default 1) : ").lower()] )
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except:
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arguments.masked_hist_match = True
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if arguments.mode == 'hist-match' or arguments.mode == 'hist-match-bw' or arguments.mode == 'seamless-hist-match':
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try:
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hist_match_threshold = int ( input ("Hist match threshold. [0..255] (default - 255) : ") )
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arguments.hist_match_threshold = hist_match_threshold
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except:
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arguments.hist_match_threshold = 255
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try:
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arguments.use_predicted_mask = bool ( {"1":True,"0":False}[input("Use predicted mask? [0 or 1] (default 1) : ").lower()] )
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except:
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arguments.use_predicted_mask = False
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try:
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arguments.erode_mask_modifier = int ( input ("Choose erode mask modifier [-200..200] (default 0) : ") )
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except:
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arguments.erode_mask_modifier = 0
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try:
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arguments.blur_mask_modifier = int ( input ("Choose blur mask modifier [-200..200] (default 0) : ") )
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except:
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arguments.blur_mask_modifier = 0
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if arguments.mode == 'seamless' or arguments.mode == 'seamless-hist-match':
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try:
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arguments.seamless_erode_mask_modifier = int ( input ("Choose seamless erode mask modifier [-100..100] (default 0) : ") )
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except:
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arguments.seamless_erode_mask_modifier = 0
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try:
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arguments.output_face_scale_modifier = int ( input ("Choose output face scale modifier [-50..50] (default 0) : ") )
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except:
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arguments.output_face_scale_modifier = 0
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try:
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arguments.transfercolor = bool ( {"1":True,"0":False}[input("Transfer color from dst face to converted final face? [0 or 1] (default 0) : ").lower()] )
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except:
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arguments.transfercolor = False
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try:
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arguments.final_image_color_degrade_power = int ( input ("Degrade color power of final image [0..100] (default 0) : ") )
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except:
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arguments.final_image_color_degrade_power = 0
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try:
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arguments.alpha = bool ( {"1":True,"0":False}[input("Export png with alpha channel? [0..1] (default 0) : ").lower()] )
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except:
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arguments.alpha = False
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arguments.erode_mask_modifier = np.clip ( int(arguments.erode_mask_modifier), -200, 200)
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arguments.blur_mask_modifier = np.clip ( int(arguments.blur_mask_modifier), -200, 200)
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arguments.seamless_erode_mask_modifier = np.clip ( int(arguments.seamless_erode_mask_modifier), -100, 100)
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arguments.output_face_scale_modifier = np.clip ( int(arguments.output_face_scale_modifier), -50, 50)
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from mainscripts import Converter
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Converter.main (
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input_dir=arguments.input_dir,
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@ -193,17 +120,6 @@ if __name__ == "__main__":
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model_dir=arguments.model_dir,
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model_name=arguments.model_name,
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debug = arguments.debug,
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mode = arguments.mode,
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masked_hist_match = arguments.masked_hist_match,
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hist_match_threshold = arguments.hist_match_threshold,
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use_predicted_mask = arguments.use_predicted_mask,
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erode_mask_modifier = arguments.erode_mask_modifier,
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blur_mask_modifier = arguments.blur_mask_modifier,
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seamless_erode_mask_modifier = arguments.seamless_erode_mask_modifier,
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output_face_scale_modifier = arguments.output_face_scale_modifier,
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final_image_color_degrade_power = arguments.final_image_color_degrade_power,
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transfercolor = arguments.transfercolor,
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alpha = arguments.alpha,
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force_best_gpu_idx = arguments.force_best_gpu_idx,
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cpu_only = arguments.cpu_only
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)
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@ -214,18 +130,6 @@ if __name__ == "__main__":
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convert_parser.add_argument('--aligned-dir', action=fixPathAction, dest="aligned_dir", help="Aligned directory. This is where the extracted of dst faces stored. Not used in AVATAR model.")
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convert_parser.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir", help="Model dir.")
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convert_parser.add_argument('--model', required=True, dest="model_name", choices=Path_utils.get_all_dir_names_startswith ( Path(__file__).parent / 'models' , 'Model_'), help="Type of model")
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convert_parser.add_argument('--ask-for-params', action="store_true", dest="ask_for_params", default=False, help="Ask for params.")
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convert_parser.add_argument('--mode', dest="mode", choices=['seamless','hist-match', 'hist-match-bw','seamless-hist-match'], default='seamless', help="Face overlaying mode. Seriously affects result.")
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convert_parser.add_argument('--masked-hist-match', type=str2bool, nargs='?', const=True, default=True, help="True or False. Excludes background for hist match. Default - True.")
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convert_parser.add_argument('--hist-match-threshold', type=int, dest="hist_match_threshold", default=255, help="Hist match threshold. Decrease to hide artifacts of hist match. Valid range [0..255]. Default 255")
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convert_parser.add_argument('--use-predicted-mask', action="store_true", dest="use_predicted_mask", default=True, help="Use predicted mask by model. Default - True.")
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convert_parser.add_argument('--erode-mask-modifier', type=int, dest="erode_mask_modifier", default=0, help="Automatic erode mask modifier. Valid range [-200..200].")
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convert_parser.add_argument('--blur-mask-modifier', type=int, dest="blur_mask_modifier", default=0, help="Automatic blur mask modifier. Valid range [-200..200].")
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convert_parser.add_argument('--seamless-erode-mask-modifier', type=int, dest="seamless_erode_mask_modifier", default=0, help="Automatic seamless erode mask modifier. Valid range [-200..200].")
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convert_parser.add_argument('--output-face-scale-modifier', type=int, dest="output_face_scale_modifier", default=0, help="Output face scale modifier. Valid range [-50..50].")
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convert_parser.add_argument('--final-image-color-degrade-power', type=int, dest="final_image_color_degrade_power", default=0, help="Degrades colors of final image to hide face problems. Valid range [0..100].")
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convert_parser.add_argument('--transfercolor', action="store_true", dest="transfercolor", default=False, help="Transfer color from dst face to converted final face.")
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convert_parser.add_argument('--alpha', action="store_true", dest="alpha", default=False, help="Embeds alpha channel of face mask to final PNG. Used in manual composing video by editors such as Sony Vegas or After Effects.")
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convert_parser.add_argument('--debug', action="store_true", dest="debug", default=False, help="Debug converter.")
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convert_parser.add_argument('--force-best-gpu-idx', type=int, dest="force_best_gpu_idx", default=-1, help="Force to choose this GPU idx as best.")
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convert_parser.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Convert on CPU.")
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