291 lines
14 KiB
Python
291 lines
14 KiB
Python
import os
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import sys
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# single thread doubles cuda performance - needs to be set before torch import
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if any(arg.startswith('--execution-provider') for arg in sys.argv):
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os.environ['OMP_NUM_THREADS'] = '1'
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# reduce tensorflow log level
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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# Force TensorFlow to use Metal
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os.environ['TENSORFLOW_METAL'] = '1'
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import warnings
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from typing import List
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import platform
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import signal
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import shutil
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import argparse
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import torch
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import onnxruntime
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import tensorflow
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import modules.globals
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import modules.metadata
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import modules.ui as ui
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from modules.processors.frame.core import get_frame_processors_modules
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from modules.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
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if 'ROCMExecutionProvider' in modules.globals.execution_providers:
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del torch
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warnings.filterwarnings('ignore', category=FutureWarning, module='insightface')
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warnings.filterwarnings('ignore', category=UserWarning, module='torchvision')
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def parse_args() -> None:
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signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
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program = argparse.ArgumentParser()
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program.add_argument('-s', '--source', help='select an source image', dest='source_path')
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program.add_argument('-t', '--target', help='select an target image or video', dest='target_path')
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program.add_argument('-o', '--output', help='select output file or directory', dest='output_path')
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program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer'], nargs='+')
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program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=True)
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program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True)
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program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=True)
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program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False)
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program.add_argument('--nsfw-filter', help='filter the NSFW image or video', dest='nsfw_filter', action='store_true', default=False)
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program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9'])
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program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]')
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program.add_argument('--live-mirror', help='The live camera display as you see it in the front-facing camera frame', dest='live_mirror', action='store_true', default=False)
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program.add_argument('--live-resizable', help='The live camera frame is resizable', dest='live_resizable', action='store_true', default=False)
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program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory())
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program.add_argument('--execution-provider', help='execution provider', dest='execution_provider', default=['coreml'], choices=suggest_execution_providers(), nargs='+')
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program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads())
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program.add_argument('-v', '--version', action='version', version=f'{modules.metadata.name} {modules.metadata.version}')
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args = program.parse_args()
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modules.globals.source_path = args.source_path
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modules.globals.target_path = args.target_path
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modules.globals.output_path = normalize_output_path(modules.globals.source_path, modules.globals.target_path, args.output_path)
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modules.globals.frame_processors = args.frame_processor
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modules.globals.headless = args.source_path or args.target_path or args.output_path
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modules.globals.keep_fps = args.keep_fps
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modules.globals.keep_audio = args.keep_audio
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modules.globals.keep_frames = args.keep_frames
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modules.globals.many_faces = args.many_faces
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modules.globals.nsfw_filter = args.nsfw_filter
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modules.globals.video_encoder = args.video_encoder
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modules.globals.video_quality = args.video_quality
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modules.globals.live_mirror = args.live_mirror
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modules.globals.live_resizable = args.live_resizable
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modules.globals.max_memory = args.max_memory
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modules.globals.execution_providers = ['CoreMLExecutionProvider'] # Force CoreML
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modules.globals.execution_threads = args.execution_threads
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if 'face_enhancer' in args.frame_processor:
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modules.globals.fp_ui['face_enhancer'] = True
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else:
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modules.globals.fp_ui['face_enhancer'] = False
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# translate deprecated args
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if args.source_path_deprecated:
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print('\033[33mArgument -f and --face are deprecated. Use -s and --source instead.\033[0m')
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modules.globals.source_path = args.source_path_deprecated
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modules.globals.output_path = normalize_output_path(args.source_path_deprecated, modules.globals.target_path, args.output_path)
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if args.cpu_cores_deprecated:
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print('\033[33mArgument --cpu-cores is deprecated. Use --execution-threads instead.\033[0m')
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modules.globals.execution_threads = args.cpu_cores_deprecated
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if args.gpu_vendor_deprecated == 'apple':
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print('\033[33mArgument --gpu-vendor apple is deprecated. Use --execution-provider coreml instead.\033[0m')
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modules.globals.execution_providers = decode_execution_providers(['coreml'])
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if args.gpu_vendor_deprecated == 'nvidia':
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print('\033[33mArgument --gpu-vendor nvidia is deprecated. Use --execution-provider cuda instead.\033[0m')
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modules.globals.execution_providers = decode_execution_providers(['cuda'])
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if args.gpu_vendor_deprecated == 'amd':
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print('\033[33mArgument --gpu-vendor amd is deprecated. Use --execution-provider cuda instead.\033[0m')
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modules.globals.execution_providers = decode_execution_providers(['rocm'])
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if args.gpu_threads_deprecated:
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print('\033[33mArgument --gpu-threads is deprecated. Use --execution-threads instead.\033[0m')
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modules.globals.execution_threads = args.gpu_threads_deprecated
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def encode_execution_providers(execution_providers: List[str]) -> List[str]:
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return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers]
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def decode_execution_providers(execution_providers: List[str]) -> List[str]:
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return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers()))
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if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)]
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def suggest_max_memory() -> int:
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if platform.system().lower() == 'darwin':
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return 6
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return 4
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def suggest_execution_providers() -> List[str]:
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return ['coreml'] # Only suggest CoreML
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def suggest_execution_threads() -> int:
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if platform.system().lower() == 'darwin':
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return 12
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return 4
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def limit_resources() -> None:
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if modules.globals.max_memory:
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memory = modules.globals.max_memory * 1024 ** 6
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import resource
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resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
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def release_resources() -> None:
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pass # No need to release CUDA resources
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def pre_check() -> bool:
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if sys.version_info < (3, 9):
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update_status('Python version is not supported - please upgrade to 3.9 or higher.')
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return False
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if not shutil.which('ffmpeg'):
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update_status('ffmpeg is not installed.')
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return False
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return True
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def update_status(message: str, scope: str = 'DLC.CORE') -> None:
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print(f'[{scope}] {message}')
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if not modules.globals.headless:
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ui.update_status(message)
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def start() -> None:
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for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
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if not frame_processor.pre_start():
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return
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# process image to image
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if has_image_extension(modules.globals.target_path):
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if modules.globals.nsfw == False:
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from modules.predicter import predict_image
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if predict_image(modules.globals.target_path):
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destroy()
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shutil.copy2(modules.globals.target_path, modules.globals.output_path)
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for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
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update_status('Progressing...', frame_processor.NAME)
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frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path)
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release_resources()
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if is_image(modules.globals.target_path):
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update_status('Processing to image succeed!')
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else:
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update_status('Processing to image failed!')
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return
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# process image to videos
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if modules.globals.nsfw == False:
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from modules.predicter import predict_video
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if predict_video(modules.globals.target_path):
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destroy()
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update_status('Creating temp resources...')
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create_temp(modules.globals.target_path)
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update_status('Extracting frames...')
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extract_frames(modules.globals.target_path)
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temp_frame_paths = get_temp_frame_paths(modules.globals.target_path)
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for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
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update_status('Progressing...', frame_processor.NAME)
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frame_processor.process_video(modules.globals.source_path, temp_frame_paths)
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if modules.globals.keep_fps:
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update_status('Detecting fps...')
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fps = detect_fps(modules.globals.target_path)
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update_status(f'Creating video with {fps} fps...')
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create_video(modules.globals.target_path, fps)
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else:
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update_status('Creating video with 30.0 fps...')
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create_video(modules.globals.target_path)
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if modules.globals.keep_audio:
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if modules.globals.keep_fps:
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update_status('Restoring audio...')
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else:
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update_status('Restoring audio might cause issues as fps are not kept...')
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restore_audio(modules.globals.target_path, modules.globals.output_path)
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else:
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move_temp(modules.globals.target_path, modules.globals.output_path)
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clean_temp(modules.globals.target_path)
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if is_video(modules.globals.target_path):
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update_status('Processing to video succeed!')
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else:
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update_status('Processing to video failed!')
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def destroy(to_quit=True) -> None:
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if modules.globals.target_path:
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clean_temp(modules.globals.target_path)
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if to_quit: quit()
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def run() -> None:
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parse_args()
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if not pre_check():
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return
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for frame_processor in get_frame_processors_modules(modules.globals.frame_processors):
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if not frame_processor.pre_check():
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return
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limit_resources()
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print(f"ONNX Runtime version: {onnxruntime.__version__}")
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print(f"Available execution providers: {onnxruntime.get_available_providers()}")
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print(f"Selected execution provider: CoreMLExecutionProvider")
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# Configure ONNX Runtime to use only CoreML
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onnxruntime.set_default_logger_severity(3) # Set to WARNING level
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options = onnxruntime.SessionOptions()
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options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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# Test CoreML with a dummy model
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try:
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import numpy as np
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from onnx import helper, TensorProto
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# Create a simple ONNX model
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X = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1, 3, 224, 224])
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Y = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 3, 224, 224])
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node = helper.make_node('Identity', ['input'], ['output'])
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graph = helper.make_graph([node], 'test_model', [X], [Y])
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model = helper.make_model(graph)
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# Save the model
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model_path = 'test_model.onnx'
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with open(model_path, 'wb') as f:
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f.write(model.SerializeToString())
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# Create a CoreML session
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session = onnxruntime.InferenceSession(model_path, options, providers=['CoreMLExecutionProvider'])
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# Run inference
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input_data = np.random.rand(1, 3, 224, 224).astype(np.float32)
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output = session.run(None, {'input': input_data})
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print("CoreML init successful and being used")
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print(f"Input shape: {input_data.shape}, Output shape: {output[0].shape}")
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# Clean up
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os.remove(model_path)
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except Exception as e:
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print(f"Error testing CoreML: {str(e)}")
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print("The application may not be able to use GPU acceleration")
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# Configure TensorFlow to use Metal
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try:
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tf_devices = tensorflow.config.list_physical_devices()
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print("TensorFlow devices:", tf_devices)
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if any('GPU' in device.name for device in tf_devices):
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print("TensorFlow is using GPU (Metal)")
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else:
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print("TensorFlow is not using GPU")
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except Exception as e:
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print(f"Error configuring TensorFlow: {str(e)}")
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# Configure PyTorch to use MPS (Metal Performance Shaders)
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try:
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if torch.backends.mps.is_available():
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print("PyTorch is using MPS (Metal Performance Shaders)")
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torch.set_default_device('mps')
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else:
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print("PyTorch MPS is not available")
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except Exception as e:
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print(f"Error configuring PyTorch: {str(e)}")
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if modules.globals.headless:
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start()
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else:
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window = ui.init(start, destroy)
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window.mainloop()
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