#!/usr/bin/env python3 import os import sys import shutil # 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' import warnings from typing import List import platform import signal import argparse import torch import onnxruntime import roop.globals import roop.metadata import roop.utilities as util import roop.ui as ui from settings import Settings from roop.face_helper import extract_face_images from chain_img_processor import ChainImgProcessor, ChainVideoProcessor, ChainBatchImageProcessor clip_text = None if 'ROCMExecutionProvider' in roop.globals.execution_providers: del torch warnings.filterwarnings('ignore', category=FutureWarning, module='insightface') warnings.filterwarnings('ignore', category=UserWarning, module='torchvision') def parse_args() -> None: signal.signal(signal.SIGINT, lambda signal_number, frame: destroy()) program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100)) program.add_argument('-s', '--source', help='select a source image', dest='source_path') program.add_argument('-t', '--target', help='select a target image or video', dest='target_path') program.add_argument('-o', '--output', help='select output file or directory', dest='output_path') program.add_argument('-f', '--folder', help='select a target folder with images or videos to batch process', dest='target_folder_path') program.add_argument('--frame-processor', help='frame processors (choices: face_swapper, face_enhancer, ...)', dest='frame_processor', default=['face_swapper'], nargs='+') program.add_argument('--keep-fps', help='keep target fps', dest='keep_fps', action='store_true') program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true') program.add_argument('--skip-audio', help='skip target audio', dest='skip_audio', action='store_true') program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true') program.add_argument('--source-face_index', help='index position of source face in image', dest='source_face_index', type=int, default=0) program.add_argument('--target-face_index', help='index position of target face in image', dest='target_face_index', type=int, default=0) program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9']) program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, 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='available execution provider (choices: cpu, ...)', dest='execution_provider', default=['cpu'], 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('-v', '--version', action='version', version=f'{roop.metadata.name} {roop.metadata.version}') args = program.parse_args() roop.globals.source_path = args.source_path roop.globals.target_path = args.target_path roop.globals.output_path = util.normalize_output_path(roop.globals.source_path, roop.globals.target_path, args.output_path) roop.globals.target_folder_path = args.target_folder_path roop.globals.headless = args.source_path or args.target_path or args.output_path # Always enable all processors when using GUI if not roop.globals.headless: roop.globals.frame_processors = ['face_swapper', 'face_enhancer'] else: roop.globals.frame_processors = args.frame_processor roop.globals.keep_fps = args.keep_fps roop.globals.keep_frames = args.keep_frames roop.globals.skip_audio = args.skip_audio roop.globals.many_faces = args.many_faces roop.globals.source_face_index = args.source_face_index roop.globals.target_face_index = args.target_face_index roop.globals.video_encoder = args.video_encoder roop.globals.video_quality = args.video_quality roop.globals.max_memory = args.max_memory roop.globals.execution_providers = decode_execution_providers(args.execution_provider) roop.globals.execution_threads = args.execution_threads def encode_execution_providers(execution_providers: List[str]) -> List[str]: return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers] def decode_execution_providers(execution_providers: List[str]) -> List[str]: return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers())) if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)] def suggest_max_memory() -> int: if platform.system().lower() == 'darwin': return 4 return 16 def suggest_execution_providers() -> List[str]: return encode_execution_providers(onnxruntime.get_available_providers()) def suggest_execution_threads() -> int: if 'DmlExecutionProvider' in roop.globals.execution_providers: return 1 if 'ROCMExecutionProvider' in roop.globals.execution_providers: return 1 return 8 def limit_resources() -> None: # prevent tensorflow memory leak # gpus = tensorflow.config.experimental.list_physical_devices('GPU') # for gpu in gpus: # tensorflow.config.experimental.set_virtual_device_configuration(gpu, [ # tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit=1024) # ]) # limit memory usage if roop.globals.max_memory: memory = roop.globals.max_memory * 1024 ** 3 if platform.system().lower() == 'darwin': memory = roop.globals.max_memory * 1024 ** 6 if platform.system().lower() == 'windows': import ctypes kernel32 = ctypes.windll.kernel32 kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory)) else: import resource resource.setrlimit(resource.RLIMIT_DATA, (memory, memory)) def release_resources() -> None: if 'CUDAExecutionProvider' in roop.globals.execution_providers: torch.cuda.empty_cache() 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 download_directory_path = util.resolve_relative_path('../models') util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/inswapper_128.onnx']) util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/GFPGANv1.4.pth']) util.conditional_download(download_directory_path, ['https://github.com/csxmli2016/DMDNet/releases/download/v1/DMDNet.pth']) download_directory_path = util.resolve_relative_path('../models/CLIP') util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/rd64-uni-refined.pth']) download_directory_path = util.resolve_relative_path('../models/CodeFormer') util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth']) download_directory_path = util.resolve_relative_path('../models/CodeFormer/facelib') util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth']) util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth']) download_directory_path = util.resolve_relative_path('../models/CodeFormer/realesrgan') util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/RealESRGAN_x2plus.pth']) if not shutil.which('ffmpeg'): update_status('ffmpeg is not installed.') return True def update_status(message: str, scope: str = 'ROOP.CORE') -> None: print(f'[{scope}] {message}') # if not roop.globals.headless: # ui.update_status(message) def start() -> None: if roop.globals.headless: faces = extract_face_images(roop.globals.source_path, (False, 0)) roop.globals.SELECTED_FACE_DATA_INPUT = faces[roop.globals.source_face_index] faces = extract_face_images(roop.globals.target_path, (False, util.has_image_extension(roop.globals.target_path))) roop.globals.SELECTED_FACE_DATA_OUTPUT = faces[roop.globals.target_face_index] if 'face_enhancer' in roop.globals.frame_processors: roop.globals.selected_enhancer = 'GFPGAN' batch_process(None, False, None) def InitPlugins(): if not roop.globals.IMAGE_CHAIN_PROCESSOR: roop.globals.IMAGE_CHAIN_PROCESSOR = ChainImgProcessor() roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR = ChainBatchImageProcessor() roop.globals.VIDEO_CHAIN_PROCESSOR = ChainVideoProcessor() roop.globals.IMAGE_CHAIN_PROCESSOR.init_with_plugins() roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR.init_with_plugins() roop.globals.VIDEO_CHAIN_PROCESSOR.init_with_plugins() def get_processing_plugins(use_clip): processors = "faceswap" if use_clip: processors += ",txt2clip" if roop.globals.selected_enhancer == 'GFPGAN': processors += ",gfpgan" elif roop.globals.selected_enhancer == 'Codeformer': processors += ",codeformer" elif roop.globals.selected_enhancer == 'DMDNet': processors += ",dmdnet" return processors def live_swap(frame, swap_mode, use_clip, clip_text): if frame is None: return frame InitPlugins() processors = get_processing_plugins(use_clip) temp_frame, _ = roop.globals.IMAGE_CHAIN_PROCESSOR.run_chain(frame, {"swap_mode": swap_mode, "original_frame": frame, "blend_ratio": roop.globals.blend_ratio, "face_distance_threshold": roop.globals.distance_threshold, "input_face_datas": [roop.globals.SELECTED_FACE_DATA_INPUT], "target_face_datas": [roop.globals.SELECTED_FACE_DATA_OUTPUT], "clip_prompt": clip_text}, processors) return temp_frame def params_gen_func(proc, frame): global clip_text return {"original_frame": frame, "blend_ratio": roop.globals.blend_ratio, "swap_mode": roop.globals.face_swap_mode, "face_distance_threshold": roop.globals.distance_threshold, "input_face_datas": [roop.globals.SELECTED_FACE_DATA_INPUT], "target_face_datas": [roop.globals.SELECTED_FACE_DATA_OUTPUT], "clip_prompt": clip_text} def batch_process(files, use_clip, new_clip_text) -> None: global clip_text InitPlugins() processors = get_processing_plugins(use_clip) clip_text = new_clip_text imagefiles = [] imagefinalnames = [] videofiles = [] videofinalnames = [] need_join = False if files is None: need_join = True if roop.globals.target_folder_path is None: roop.globals.target_folder_path = os.path.dirname(roop.globals.target_path) files = [os.path.basename(roop.globals.target_path)] roop.globals.output_path = os.path.dirname(roop.globals.output_path) else: files = [f for f in os.listdir(roop.globals.target_folder_path) if os.path.isfile(os.path.join(roop.globals.target_folder_path, f))] update_status('Sorting videos/images') for f in files: if need_join: fullname = os.path.join(roop.globals.target_folder_path, f) else: fullname = f if util.has_image_extension(fullname): imagefiles.append(fullname) imagefinalnames.append(util.get_destfilename_from_path(fullname, roop.globals.output_path, f'_fake.{roop.globals.CFG.output_image_format}')) elif util.is_video(fullname) or util.has_extension(fullname, ['gif']): videofiles.append(fullname) videofinalnames.append(util.get_destfilename_from_path(fullname, roop.globals.output_path, f'_fake.{roop.globals.CFG.output_video_format}')) if(len(imagefiles) > 0): update_status('Processing image(s)') roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR.run_batch_chain(imagefiles, imagefinalnames, roop.globals.execution_threads, processors, params_gen_func) if(len(videofiles) > 0): for index,v in enumerate(videofiles): update_status(f'Processing video {v}') fps = util.detect_fps(v) if roop.globals.keep_frames: update_status('Creating temp resources...') util.create_temp(v) update_status('Extracting frames...') util.extract_frames(v) temp_frame_paths = util.get_temp_frame_paths(v) roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR.run_batch_chain(temp_frame_paths, temp_frame_paths, roop.globals.execution_threads, processors, params_gen_func) update_status(f'Creating video with {fps} FPS...') util.create_video(v, videofinalnames[index], fps) else: update_status(f'Creating video with {fps} FPS...') roop.globals.VIDEO_CHAIN_PROCESSOR.run_video_chain(v,videofinalnames[index], fps, roop.globals.execution_threads, processors, params_gen_func, roop.globals.target_path) if os.path.isfile(videofinalnames[index]): if util.has_extension(v, ['gif']): gifname = roop.utilities.get_destfilename_from_path(v, './output', '_fake.gif') update_status('Creating final GIF') util.create_gif_from_video(videofinalnames[index], gifname) elif not roop.globals.skip_audio: finalname = roop.utilities.get_destfilename_from_path(videofinalnames[index], roop.globals.output_path, f'_final.{roop.globals.CFG.output_video_format}') util.restore_audio(videofinalnames[index], v, finalname) if os.path.isfile(videofinalnames[index]): os.remove(videofinalnames[index]) else: update_status('Failed!') update_status('Finished') roop.globals.target_folder_path = None def destroy() -> None: if roop.globals.target_path: util.clean_temp(roop.globals.target_path) sys.exit() def run() -> None: parse_args() if not pre_check(): return limit_resources() roop.globals.CFG = Settings('config.yaml') if roop.globals.headless: start() else: ui.run()