import os import sys # single thread doubles cuda performance - needs to be set before torch import if any(arg.startswith('--execution-provider') for arg in sys.argv): os.environ['OMP_NUM_THREADS'] = '1' # reduce tensorflow log level os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Force TensorFlow to use Metal os.environ['TENSORFLOW_METAL'] = '1' import warnings from typing import List import platform import signal import shutil import argparse import torch import onnxruntime import tensorflow import modules.globals import modules.metadata import modules.ui as ui from modules.processors.frame.core import get_frame_processors_modules 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 if 'ROCMExecutionProvider' in modules.globals.execution_providers: del torch warnings.filterwarnings('ignore', category=FutureWarning, module='insightface') warnings.filterwarnings('ignore', category=UserWarning, module='torchvision') def get_system_memory() -> int: """ Get the total system memory in GB. Returns: int: Total system memory in GB. """ if platform.system().lower() == 'darwin': try: import psutil return psutil.virtual_memory().total // (1024 ** 3) except ImportError: # If psutil is not available, return a default value return 16 # Assuming 16GB as a default for macOS else: # For other systems, we can use psutil if available, or implement system-specific methods try: import psutil return psutil.virtual_memory().total // (1024 ** 3) except ImportError: # If psutil is not available, return a default value return 8 # Assuming 8GB as a default for other systems def suggest_max_memory() -> int: """ Suggest the maximum memory to use based on the system's total memory. Returns: int: Suggested maximum memory in GB. """ total_memory = get_system_memory() # Suggest using 70% of total memory, but not more than 64GB suggested_memory = min(int(total_memory * 0.7), 64) return max(suggested_memory, 4) # Ensure at least 4GB is suggested def parse_args() -> None: signal.signal(signal.SIGINT, lambda signal_number, frame: destroy()) program = argparse.ArgumentParser() program.add_argument('-s', '--source', help='select an source image', dest='source_path') program.add_argument('-t', '--target', help='select an target image or video', dest='target_path') program.add_argument('-o', '--output', help='select output file or directory', dest='output_path') program.add_argument('--frame-processor', help='pipeline of frame processors', dest='frame_processor', default=['face_swapper'], choices=['face_swapper', 'face_enhancer'], nargs='+') program.add_argument('--keep-fps', help='keep original fps', dest='keep_fps', action='store_true', default=True) program.add_argument('--keep-audio', help='keep original audio', dest='keep_audio', action='store_true', default=True) program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true', default=True) program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true', default=False) program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx265', choices=['libx264', 'libx265', 'libvpx-vp9']) program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=1, choices=range(52), metavar='[0-51]') program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory()) program.add_argument('--execution-provider', help='execution provider', dest='execution_provider', default=['coreml'], choices=suggest_execution_providers(), nargs='+') program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads()) program.add_argument('--video-processor', help='video processor to use', dest='video_processor', default='cv2', choices=['cv2', 'ffmpeg']) program.add_argument('--model', help='model to use for face swapping', dest='model', default='inswapper_128v2.fp16.onnx') program.add_argument('-v', '--version', action='version', version=f'{modules.metadata.name} {modules.metadata.version}') args = program.parse_args() modules.globals.source_path = args.source_path modules.globals.target_path = args.target_path modules.globals.output_path = normalize_output_path(modules.globals.source_path, modules.globals.target_path, args.output_path) modules.globals.frame_processors = args.frame_processor modules.globals.headless = args.source_path or args.target_path or args.output_path modules.globals.keep_fps = args.keep_fps modules.globals.keep_audio = args.keep_audio modules.globals.keep_frames = args.keep_frames modules.globals.many_faces = args.many_faces modules.globals.video_encoder = args.video_encoder modules.globals.video_quality = args.video_quality modules.globals.max_memory = args.max_memory modules.globals.execution_providers = ['CoreMLExecutionProvider'] # Force CoreML modules.globals.execution_threads = args.execution_threads modules.globals.video_processor = args.video_processor modules.globals.model = args.model if 'face_enhancer' in args.frame_processor: modules.globals.fp_ui['face_enhancer'] = True else: modules.globals.fp_ui['face_enhancer'] = False modules.globals.nsfw = False def suggest_max_memory() -> int: if platform.system().lower() == 'darwin': return 6 return 4 def suggest_execution_providers() -> List[str]: return ['coreml'] # Only suggest CoreML def suggest_execution_threads() -> int: if platform.system().lower() == 'darwin': return 12 return 4 def limit_resources() -> None: if modules.globals.max_memory: memory = modules.globals.max_memory * 1024 ** 6 import resource resource.setrlimit(resource.RLIMIT_DATA, (memory, memory)) def release_resources() -> None: pass # No need to release CUDA resources def pre_check() -> bool: if sys.version_info < (3, 9): update_status('Python version is not supported - please upgrade to 3.9 or higher.') return False if not shutil.which('ffmpeg'): update_status('ffmpeg is not installed.') return False return True def update_status(message: str, scope: str = 'DLC.CORE') -> None: print(f'[{scope}] {message}') if not modules.globals.headless: ui.update_status(message) def start() -> None: for frame_processor in get_frame_processors_modules(modules.globals.frame_processors): if not frame_processor.pre_start(): return if has_image_extension(modules.globals.target_path): process_image() else: process_video() def process_image(): if modules.globals.nsfw == False: from modules.predicter import predict_image if predict_image(modules.globals.target_path): destroy() shutil.copy2(modules.globals.target_path, modules.globals.output_path) for frame_processor in get_frame_processors_modules(modules.globals.frame_processors): update_status('Progressing...', frame_processor.NAME) frame_processor.process_image(modules.globals.source_path, modules.globals.output_path, modules.globals.output_path) if is_image(modules.globals.target_path): update_status('Processing to image succeed!') else: update_status('Processing to image failed!') def process_video(): if modules.globals.nsfw == False: from modules.predicter import predict_video if predict_video(modules.globals.target_path): destroy() update_status('Creating temp resources...') create_temp(modules.globals.target_path) update_status('Extracting frames...') if modules.globals.video_processor == 'cv2': extract_frames_cv2(modules.globals.target_path) else: extract_frames_ffmpeg(modules.globals.target_path) temp_frame_paths = get_temp_frame_paths(modules.globals.target_path) for frame_processor in get_frame_processors_modules(modules.globals.frame_processors): update_status('Progressing...', frame_processor.NAME) frame_processor.process_video(modules.globals.source_path, temp_frame_paths) if modules.globals.keep_fps: update_status('Detecting fps...') fps = detect_fps(modules.globals.target_path) update_status(f'Creating video with {fps} fps...') create_video(modules.globals.target_path, fps) else: update_status('Creating video with 30.0 fps...') create_video(modules.globals.target_path) if modules.globals.keep_audio: if modules.globals.keep_fps: update_status('Restoring audio...') else: update_status('Restoring audio might cause issues as fps are not kept...') restore_audio(modules.globals.target_path, modules.globals.output_path) else: move_temp(modules.globals.target_path, modules.globals.output_path) clean_temp(modules.globals.target_path) if is_video(modules.globals.target_path): update_status('Processing to video succeed!') else: update_status('Processing to video failed!') def extract_frames_cv2(target_path: str) -> None: import cv2 capture = cv2.VideoCapture(target_path) frame_num = 0 while True: success, frame = capture.read() if not success: break cv2.imwrite(f'{get_temp_frame_paths(target_path)}/%04d.png' % frame_num, frame) frame_num += 1 capture.release() def extract_frames_ffmpeg(target_path: str) -> None: import ffmpeg ( ffmpeg .input(target_path) .output(f'{get_temp_frame_paths(target_path)}/%04d.png', start_number=0) .overwrite_output() .run(capture_stdout=True, capture_stderr=True) ) def destroy() -> None: if modules.globals.target_path: clean_temp(modules.globals.target_path) quit() def run() -> None: parse_args() if not pre_check(): return for frame_processor in get_frame_processors_modules(modules.globals.frame_processors): if not frame_processor.pre_check(): return limit_resources() print(f"ONNX Runtime version: {onnxruntime.__version__}") print(f"Available execution providers: {onnxruntime.get_available_providers()}") print(f"Selected execution provider: CoreMLExecutionProvider") # Configure ONNX Runtime to use only CoreML onnxruntime.set_default_logger_severity(3) # Set to WARNING level options = onnxruntime.SessionOptions() options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL # Test CoreML with a dummy model try: import numpy as np from onnx import helper, TensorProto # Create a simple ONNX model X = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1, 3, 224, 224]) Y = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 3, 224, 224]) node = helper.make_node('Identity', ['input'], ['output']) graph = helper.make_graph([node], 'test_model', [X], [Y]) model = helper.make_model(graph) # Save the model model_path = 'test_model.onnx' with open(model_path, 'wb') as f: f.write(model.SerializeToString()) # Create a CoreML session session = onnxruntime.InferenceSession(model_path, options, providers=['CoreMLExecutionProvider']) # Run inference input_data = np.random.rand(1, 3, 224, 224).astype(np.float32) output = session.run(None, {'input': input_data}) print("CoreML init successful and being used") print(f"Input shape: {input_data.shape}, Output shape: {output[0].shape}") # Clean up os.remove(model_path) except Exception as e: print(f"Error testing CoreML: {str(e)}") print("The application may not be able to use GPU acceleration") # Configure TensorFlow to use Metal try: tf_devices = tensorflow.config.list_physical_devices() print("TensorFlow devices:", tf_devices) if any('GPU' in device.name for device in tf_devices): print("TensorFlow is using GPU (Metal)") else: print("TensorFlow is not using GPU") except Exception as e: print(f"Error configuring TensorFlow: {str(e)}") # Configure PyTorch to use MPS (Metal Performance Shaders) try: if torch.backends.mps.is_available(): print("PyTorch is using MPS (Metal Performance Shaders)") torch.set_default_device('mps') else: print("PyTorch MPS is not available") except Exception as e: print(f"Error configuring PyTorch: {str(e)}") if modules.globals.headless: start() else: window = ui.init(start, destroy) window.mainloop()