【DFL学习日记#1】DeepfaceLab入口文件main.py整体注释
#!/usr/bin/env python3# -*- coding: utf-8 -*-
"""
DeepFaceLab 主入口文件
这是 DeepFaceLab 的命令行界面入口，提供了多个子命令用于人脸处理、模型训练、视频编辑等功能。
主要功能包括：
- 人脸提取 (extract)
- 人脸排序 (sort)
- 工具集 (util)
- 模型训练 (train)
- 模型导出 (exportdfm)
- 结果合并 (merge)
- 视频编辑 (videoed)
- 人脸集工具 (facesettool)
- XSeg 工具 (xseg)
"""
if __name__ == "__main__":
# 修复 Linux 系统上的多进程问题
import multiprocessing
multiprocessing.set_start_method("spawn")
# 初始化神经网络环境
from core.leras import nn
nn.initialize_main_env()
# 导入必要的系统模块
import os
import sys
import time
import argparse
# 导入项目核心模块
from core import pathex
from core import osex
from pathlib import Path
from core.interact import interact as io
# 检查 Python 版本，要求至少 3.6
if sys.version_info < 3 or (sys.version_info == 3 and sys.version_info < 6):
raise Exception("This program requires at least Python 3.6")
# 自定义的路径处理 Action 类，用于将路径转换为绝对路径
class fixPathAction(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
setattr(namespace, self.dest, os.path.abspath(os.path.expanduser(values)))
exit_code = 0
# 创建主命令行解析器
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers()
# ========== extract 子命令：从图片中提取人脸 ==========
def process_extract(arguments):
"""处理人脸提取命令"""
# 设置进程优先级为最低，避免影响系统性能
osex.set_process_lowest_prio()
# 导入提取器模块
from mainscripts import Extractor
# 调用提取器的主函数
Extractor.main( detector = arguments.detector,
input_path = Path(arguments.input_dir),
output_path = Path(arguments.output_dir),
output_debug = arguments.output_debug,
manual_fix = arguments.manual_fix,
manual_output_debug_fix = arguments.manual_output_debug_fix,
manual_window_size = arguments.manual_window_size,
face_type = arguments.face_type,
max_faces_from_image = arguments.max_faces_from_image,
image_size = arguments.image_size,
jpeg_quality = arguments.jpeg_quality,
cpu_only = arguments.cpu_only,
force_gpu_idxs = [ int(x) for x in arguments.force_gpu_idxs.split(',') ] if arguments.force_gpu_idxs is not None else None,
)
# 创建 extract 子命令解析器
p = subparsers.add_parser( "extract", help="Extract the faces from a pictures.")
# 添加各种命令行参数
p.add_argument('--detector', dest="detector", choices=['s3fd','manual'], default=None, help="Type of detector.")
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
p.add_argument('--output-dir', required=True, action=fixPathAction, dest="output_dir", help="Output directory. This is where the extracted files will be stored.")
p.add_argument('--output-debug', action="store_true", dest="output_debug", default=None, help="Writes debug images to <output-dir>_debug\ directory.")
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.")
p.add_argument('--face-type', dest="face_type", choices=['half_face', 'full_face', 'whole_face', 'head', 'mark_only'], default=None)
p.add_argument('--max-faces-from-image', type=int, dest="max_faces_from_image", default=None, help="Max faces from image.")
p.add_argument('--image-size', type=int, dest="image_size", default=None, help="Output image size.")
p.add_argument('--jpeg-quality', type=int, dest="jpeg_quality", default=None, help="Jpeg quality.")
p.add_argument('--manual-fix', action="store_true", dest="manual_fix", default=False, help="Enables manual extract only frames where faces were not recognized.")
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 _debug\ dir.")
p.add_argument('--manual-window-size', type=int, dest="manual_window_size", default=1368, help="Manual fix window size. Default: 1368.")
p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Extract on CPU..")
p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
# 设置默认的处理函数
p.set_defaults (func=process_extract)
# ========== sort 子命令：对人脸进行排序 ==========
def process_sort(arguments):
"""处理人脸排序命令"""
osex.set_process_lowest_prio()
from mainscripts import Sorter
Sorter.main (input_path=Path(arguments.input_dir), sort_by_method=arguments.sort_by_method)
p = subparsers.add_parser( "sort", help="Sort faces in a directory.")
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
p.add_argument('--by', dest="sort_by_method", default=None, choices=("blur", "motion-blur", "face-yaw", "face-pitch", "face-source-rect-size", "hist", "hist-dissim", "brightness", "hue", "black", "origname", "oneface", "final-by-blur", "final-by-size", "absdiff"), help="Method of sorting. 'origname' sort by original filename to recover original sequence." )
p.set_defaults (func=process_sort)
# ========== util 子命令：实用工具集合 ==========
def process_util(arguments):
"""处理工具命令"""
osex.set_process_lowest_prio()
from mainscripts import Util
if arguments.add_landmarks_debug_images:
Util.add_landmarks_debug_images (input_path=arguments.input_dir)
if arguments.recover_original_aligned_filename:
Util.recover_original_aligned_filename (input_path=arguments.input_dir)
if arguments.save_faceset_metadata:
Util.save_faceset_metadata_folder (input_path=arguments.input_dir)
if arguments.restore_faceset_metadata:
Util.restore_faceset_metadata_folder (input_path=arguments.input_dir)
if arguments.pack_faceset:
io.log_info ("Performing faceset packing...\r\n")
from samplelib import PackedFaceset
PackedFaceset.pack( Path(arguments.input_dir) )
if arguments.unpack_faceset:
io.log_info ("Performing faceset unpacking...\r\n")
from samplelib import PackedFaceset
PackedFaceset.unpack( Path(arguments.input_dir) )
if arguments.export_faceset_mask:
io.log_info ("Exporting faceset mask..\r\n")
Util.export_faceset_mask( Path(arguments.input_dir) )
p = subparsers.add_parser( "util", help="Utilities.")
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
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.")
p.add_argument('--recover-original-aligned-filename', action="store_true", dest="recover_original_aligned_filename", default=False, help="Recover original aligned filename.")
p.add_argument('--save-faceset-metadata', action="store_true", dest="save_faceset_metadata", default=False, help="Save faceset metadata to file.")
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.")
p.add_argument('--pack-faceset', action="store_true", dest="pack_faceset", default=False, help="Pack faceset into a binary format.")
p.add_argument('--unpack-faceset', action="store_true", dest="unpack_faceset", default=False, help="Unpack faceset from binary format.")
p.add_argument('--export-faceset-mask', action="store_true", dest="export_faceset_mask", default=False, help="Export faceset masks.")
p.set_defaults (func=process_util)
# ========== train 子命令：训练模型 ==========
def process_train(arguments):
"""处理模型训练命令"""
osex.set_process_lowest_prio()
# 准备训练参数
kwargs = {'model_class_name' : arguments.model_name,
'saved_models_path' : Path(arguments.model_dir),
'training_data_src_path' : Path(arguments.training_data_src_dir),
'training_data_dst_path' : Path(arguments.training_data_dst_dir),
'pretraining_data_path' : Path(arguments.pretraining_data_dir) if arguments.pretraining_data_dir is not None else None,
'pretrained_model_path' : Path(arguments.pretrained_model_dir) if arguments.pretrained_model_dir is not None else None,
'no_preview' : arguments.no_preview,
'force_model_name' : arguments.force_model_name,
'force_gpu_idxs' : [ int(x) for x in arguments.force_gpu_idxs.split(',') ] if arguments.force_gpu_idxs is not None else None,
'cpu_only' : arguments.cpu_only,
'silent_start' : arguments.silent_start,
'execute_programs' : [ ), x ] for x in arguments.execute_program ],
'debug' : arguments.debug,
}
from mainscripts import Trainer
Trainer.main(**kwargs)
p = subparsers.add_parser( "train", help="Trainer")
p.add_argument('--training-data-src-dir', required=True, action=fixPathAction, dest="training_data_src_dir", help="Dir of extracted SRC faceset.")
p.add_argument('--training-data-dst-dir', required=True, action=fixPathAction, dest="training_data_dst_dir", help="Dir of extracted DST faceset.")
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.")
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).")
p.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir", help="Saved models dir.")
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.")
p.add_argument('--debug', action="store_true", dest="debug", default=False, help="Debug samples.")
p.add_argument('--no-preview', action="store_true", dest="no_preview", default=False, help="Disable preview window.")
p.add_argument('--force-model-name', dest="force_model_name", default=None, help="Forcing to choose model name from model/ folder.")
p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Train on CPU.")
p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
p.add_argument('--silent-start', action="store_true", dest="silent_start", default=False, help="Silent start. Automatically chooses Best GPU and last used model.")
p.add_argument('--execute-program', dest="execute_program", default=[], action='append', nargs='+')
p.set_defaults (func=process_train)
# ========== exportdfm 子命令：导出模型用于 DeepFaceLive ==========
def process_exportdfm(arguments):
"""处理模型导出命令"""
osex.set_process_lowest_prio()
from mainscripts import ExportDFM
ExportDFM.main(model_class_name = arguments.model_name, saved_models_path = Path(arguments.model_dir))
p = subparsers.add_parser( "exportdfm", help="Export model to use in DeepFaceLive.")
p.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir", help="Saved models dir.")
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.")
p.set_defaults (func=process_exportdfm)
# ========== merge 子命令：合并结果 ==========
def process_merge(arguments):
"""处理结果合并命令"""
osex.set_process_lowest_prio()
from mainscripts import Merger
Merger.main ( model_class_name = arguments.model_name,
saved_models_path = Path(arguments.model_dir),
force_model_name = arguments.force_model_name,
input_path = Path(arguments.input_dir),
output_path = Path(arguments.output_dir),
output_mask_path = Path(arguments.output_mask_dir),
aligned_path = Path(arguments.aligned_dir) if arguments.aligned_dir is not None else None,
force_gpu_idxs = arguments.force_gpu_idxs,
cpu_only = arguments.cpu_only)
p = subparsers.add_parser( "merge", help="Merger")
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
p.add_argument('--output-dir', required=True, action=fixPathAction, dest="output_dir", help="Output directory. This is where the merged files will be stored.")
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.")
p.add_argument('--aligned-dir', action=fixPathAction, dest="aligned_dir", default=None, help="Aligned directory. This is where the extracted of dst faces stored.")
p.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir", help="Model dir.")
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.")
p.add_argument('--force-model-name', dest="force_model_name", default=None, help="Forcing to choose model name from model/ folder.")
p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Merge on CPU.")
p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
p.set_defaults(func=process_merge)
# ========== videoed 子命令：视频编辑工具 ==========
videoed_parser = subparsers.add_parser( "videoed", help="Video processing.").add_subparsers()
# videoed extract-video 子命令：从视频中提取帧
def process_videoed_extract_video(arguments):
"""处理视频帧提取命令"""
osex.set_process_lowest_prio()
from mainscripts import VideoEd
VideoEd.extract_video (arguments.input_file, arguments.output_dir, arguments.output_ext, arguments.fps)
p = videoed_parser.add_parser( "extract-video", help="Extract images from video file.")
p.add_argument('--input-file', required=True, action=fixPathAction, dest="input_file", help="Input file to be processed. Specify .*-extension to find first file.")
p.add_argument('--output-dir', required=True, action=fixPathAction, dest="output_dir", help="Output directory. This is where the extracted images will be stored.")
p.add_argument('--output-ext', dest="output_ext", default=None, help="Image format (extension) of output files.")
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.")
p.set_defaults(func=process_videoed_extract_video)
# videoed cut-video 子命令：裁剪视频
def process_videoed_cut_video(arguments):
"""处理视频裁剪命令"""
osex.set_process_lowest_prio()
from mainscripts import VideoEd
VideoEd.cut_video (arguments.input_file,
arguments.from_time,
arguments.to_time,
arguments.audio_track_id,
arguments.bitrate)
p = videoed_parser.add_parser( "cut-video", help="Cut video file.")
p.add_argument('--input-file', required=True, action=fixPathAction, dest="input_file", help="Input file to be processed. Specify .*-extension to find first file.")
p.add_argument('--from-time', dest="from_time", default=None, help="From time, for example 00:00:00.000")
p.add_argument('--to-time', dest="to_time", default=None, help="To time, for example 00:00:00.000")
p.add_argument('--audio-track-id', type=int, dest="audio_track_id", default=None, help="Specify audio track id.")
p.add_argument('--bitrate', type=int, dest="bitrate", default=None, help="Bitrate of output file in Megabits.")
p.set_defaults(func=process_videoed_cut_video)
# videoed denoise-image-sequence 子命令：降噪图像序列
def process_videoed_denoise_image_sequence(arguments):
"""处理图像序列降噪命令"""
osex.set_process_lowest_prio()
from mainscripts import VideoEd
VideoEd.denoise_image_sequence (arguments.input_dir, arguments.factor)
p = videoed_parser.add_parser( "denoise-image-sequence", help="Denoise sequence of images, keeping sharp edges. Helps to remove pixel shake from the predicted face.")
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory to be processed.")
p.add_argument('--factor', type=int, dest="factor", default=None, help="Denoise factor (1-20).")
p.set_defaults(func=process_videoed_denoise_image_sequence)
# videoed video-from-sequence 子命令：从图像序列创建视频
def process_videoed_video_from_sequence(arguments):
"""处理从图像序列创建视频命令"""
osex.set_process_lowest_prio()
from mainscripts import VideoEd
VideoEd.video_from_sequence (input_dir = arguments.input_dir,
output_file = arguments.output_file,
reference_file = arguments.reference_file,
ext = arguments.ext,
fps = arguments.fps,
bitrate= arguments.bitrate,
include_audio = arguments.include_audio,
lossless = arguments.lossless)
p = videoed_parser.add_parser( "video-from-sequence", help="Make video from image sequence.")
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input file to be processed. Specify .*-extension to find first file.")
p.add_argument('--output-file', required=True, action=fixPathAction, dest="output_file", help="Input file to be processed. Specify .*-extension to find first file.")
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.")
p.add_argument('--ext', dest="ext", default='png', help="Image format (extension) of input files.")
p.add_argument('--fps', type=int, dest="fps", default=None, help="FPS of output file. Overwritten by reference-file.")
p.add_argument('--bitrate', type=int, dest="bitrate", default=None, help="Bitrate of output file in Megabits.")
p.add_argument('--include-audio', action="store_true", dest="include_audio", default=False, help="Include audio from reference file.")
p.add_argument('--lossless', action="store_true", dest="lossless", default=False, help="PNG codec.")
p.set_defaults(func=process_videoed_video_from_sequence)
# ========== facesettool 子命令：人脸集工具 ==========
facesettool_parser = subparsers.add_parser( "facesettool", help="Faceset tools.").add_subparsers()
# facesettool enhance 子命令：增强人脸集细节
def process_faceset_enhancer(arguments):
"""处理人脸集增强命令"""
osex.set_process_lowest_prio()
from mainscripts import FacesetEnhancer
FacesetEnhancer.process_folder ( Path(arguments.input_dir),
cpu_only=arguments.cpu_only,
force_gpu_idxs=arguments.force_gpu_idxs
)
p = facesettool_parser.add_parser ("enhance", help="Enhance details in DFL faceset.")
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory of aligned faces.")
p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Process on CPU.")
p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
p.set_defaults(func=process_faceset_enhancer)
p = facesettool_parser.add_parser ("resize", help="Resize DFL faceset.")
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory of aligned faces.")
def process_faceset_resizer(arguments):
"""处理人脸集调整大小命令"""
osex.set_process_lowest_prio()
from mainscripts import FacesetResizer
FacesetResizer.process_folder ( Path(arguments.input_dir) )
p.set_defaults(func=process_faceset_resizer)
# ========== dev_test 子命令：开发测试用 ==========
def process_dev_test(arguments):
"""处理开发测试命令"""
osex.set_process_lowest_prio()
from mainscripts import dev_misc
dev_misc.dev_gen_mask_files( arguments.input_dir )
p = subparsers.add_parser( "dev_test", help="Development test command.")
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
p.set_defaults (func=process_dev_test)
# ========== XSeg 子命令：XSeg 工具 ==========
xseg_parser = subparsers.add_parser( "xseg", help="XSeg tools.").add_subparsers()
# xseg editor 子命令：XSeg 编辑器
p = xseg_parser.add_parser( "editor", help="XSeg editor.")
def process_xsegeditor(arguments):
"""处理 XSeg 编辑器命令"""
osex.set_process_lowest_prio()
from XSegEditor import XSegEditor
global exit_code
exit_code = XSegEditor.start (Path(arguments.input_dir))
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
p.set_defaults (func=process_xsegeditor)
# xseg apply 子命令：应用训练好的 XSeg 模型到提取的人脸
p = xseg_parser.add_parser( "apply", help="Apply trained XSeg model to the extracted faces.")
def process_xsegapply(arguments):
"""处理应用 XSeg 模型命令"""
osex.set_process_lowest_prio()
from mainscripts import XSegUtil
XSegUtil.apply_xseg (Path(arguments.input_dir), Path(arguments.model_dir))
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
p.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir")
p.set_defaults (func=process_xsegapply)
# xseg remove 子命令：从提取的人脸中移除应用的 XSeg 掩码
p = xseg_parser.add_parser( "remove", help="Remove applied XSeg masks from the extracted faces.")
def process_xsegremove(arguments):
"""处理移除 XSeg 掩码命令"""
osex.set_process_lowest_prio()
from mainscripts import XSegUtil
XSegUtil.remove_xseg (Path(arguments.input_dir) )
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
p.set_defaults (func=process_xsegremove)
# xseg remove_labels 子命令：从提取的人脸中移除 XSeg 标签
p = xseg_parser.add_parser( "remove_labels", help="Remove XSeg labels from the extracted faces.")
def process_xsegremovelabels(arguments):
"""处理移除 XSeg 标签命令"""
osex.set_process_lowest_prio()
from mainscripts import XSegUtil
XSegUtil.remove_xseg_labels (Path(arguments.input_dir) )
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
p.set_defaults (func=process_xsegremovelabels)
# xseg fetch 子命令：复制包含 XSeg 多边形的人脸到 <input_dir>_xseg 目录
p = xseg_parser.add_parser( "fetch", help="Copies faces containing XSeg polygons in <input_dir>_xseg dir.")
def process_xsegfetch(arguments):
"""处理获取 XSeg 标记人脸命令"""
osex.set_process_lowest_prio()
from mainscripts import XSegUtil
XSegUtil.fetch_xseg (Path(arguments.input_dir) )
p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir")
p.set_defaults (func=process_xsegfetch)
# 默认命令：显示帮助信息
def bad_args(arguments):
parser.print_help()
exit(0)
parser.set_defaults(func=bad_args)
# 解析命令行参数并执行对应的功能
arguments = parser.parse_args()
arguments.func(arguments)
# 输出完成信息并退出
if exit_code == 0:
print ("Done.")
exit(exit_code)
# 调试代码（被注释掉）
'''import code
code.interact(local=dict(globals(), **locals()))'''
parser规则理解：
举例说明 ：
- 当用户运行 python main.py extract --input-dir images --output-dir faces
- parser.parse_args() 会解析出子命令是 "extract"，并获取 input-dir 和 output-dir 的值
- arguments 对象将包含 arguments.func = process_extract 、 arguments.input_dir = "images" 、 arguments.output_dir = "faces" 等属性
- arguments.func(arguments) 相当于 process_extract(arguments)
然后执行 process_extract(arguments) 函数，开始人脸提取过程
【进阶知识】
### subparsers 基本规则
1. 1.
创建方式 ：
- 首先创建主解析器： parser = argparse.ArgumentParser()
- 然后添加子解析器： subparsers = parser.add_subparsers()
2. 2.
子命令定义 ：
- 为每个功能创建子命令解析器： p = subparsers.add_parser("命令名", help="帮助文本")
- 为子命令添加参数： p.add_argument("--参数名", dest="变量名", ...)
3. 3.
绑定处理函数 ：
- 使用 p.set_defaults(func=处理函数名) 将子命令与相应的处理函数关联
- 这是关键，它决定了选择某个子命令时应该执行哪个函数
4. 4.
工作流程 ：
- 用户输入命令（如 python main.py extract --input-dir images ）
- parser.parse_args() 解析命令行参数
- 系统根据输入的子命令，自动设置 arguments.func 指向对应的处理函数
- 通过 arguments.func(arguments) 执行该处理函数，并传入解析后的参数
### 简单例子理解
想象 subparsers 就像一个菜单系统：
- 主程序是餐厅，提供多个菜品（子命令）
- 每个菜品（子命令）有自己的选项（参数）
- 当你点了某个菜品（如 extract），厨房就会执行对应的烹饪过程（process_extract 函数）
在 DeepFaceLab 中，这种设计使得程序可以灵活地支持多种功能，同时保持代码结构清晰，便于维护和扩展。 {:3_41:}{:3_41:}{:3_41:}{:3_41:} 真厉害啊真厉害....++
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