#!/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 torch import onnxruntime import pathlib import argparse from time import time import roop.globals import roop.metadata import roop.utilities as util import roop.util_ffmpeg as ffmpeg import ui.main as main from settings import Settings from roop.face_util import extract_face_images from roop.ProcessEntry import ProcessEntry from roop.ProcessMgr import ProcessMgr from roop.ProcessOptions import ProcessOptions from roop.capturer import get_video_frame_total, release_video clip_text = None call_display_ui = None process_mgr = 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()) roop.globals.headless = False program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100)) program.add_argument('--server_share', help='Public server', dest='server_share', action='store_true', default=False) program.add_argument('--cuda_device_id', help='Index of the cuda gpu to use', dest='cuda_device_id', type=int, default=0) roop.globals.startup_args = program.parse_args() # Always enable all processors when using GUI roop.globals.frame_processors = ['face_swapper', 'face_enhancer'] 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]: list_providers = [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)] try: for i in range(len(list_providers)): if list_providers[i] == 'CUDAExecutionProvider': list_providers[i] = ('CUDAExecutionProvider', {'device_id': roop.globals.cuda_device_id}) torch.cuda.set_device(roop.globals.cuda_device_id) break except: pass return list_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: # 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 # type: ignore[attr-defined] 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: import gc global process_mgr if process_mgr is not None: process_mgr.release_resources() process_mgr = None gc.collect() # if 'CUDAExecutionProvider' in roop.globals.execution_providers and torch.cuda.is_available(): # with torch.cuda.device('cuda'): # torch.cuda.empty_cache() # torch.cuda.ipc_collect() 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.onnx']) util.conditional_download(download_directory_path, ['https://github.com/csxmli2016/DMDNet/releases/download/v1/DMDNet.pth']) util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/GPEN-BFR-512.onnx']) util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/restoreformer_plus_plus.onnx']) util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/xseg.onnx']) 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://huggingface.co/countfloyd/deepfake/resolve/main/CodeFormerv0.1.onnx']) download_directory_path = util.resolve_relative_path('../models/Frame') util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/deoldify_artistic.onnx']) util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/deoldify_stable.onnx']) util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/isnet-general-use.onnx']) util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/real_esrgan_x4.onnx']) util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/real_esrgan_x2.onnx']) util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/lsdir_x4.onnx']) if not shutil.which('ffmpeg'): update_status('ffmpeg is not installed.') return True def set_display_ui(function): global call_display_ui call_display_ui = function def update_status(message: str) -> None: global call_display_ui print(message) if call_display_ui is not None: call_display_ui(message) def start() -> None: if roop.globals.headless: print('Headless mode currently unsupported - starting UI!') # faces = extract_face_images(roop.globals.source_path, (False, 0)) # roop.globals.INPUT_FACES.append(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.TARGET_FACES.append(faces[roop.globals.target_face_index]) # if 'face_enhancer' in roop.globals.frame_processors: # roop.globals.selected_enhancer = 'GFPGAN' batch_process_regular(None, False, None) def get_processing_plugins(masking_engine): processors = { "faceswap": {}} if masking_engine is not None: processors.update({masking_engine: {}}) if roop.globals.selected_enhancer == 'GFPGAN': processors.update({"gfpgan": {}}) elif roop.globals.selected_enhancer == 'Codeformer': processors.update({"codeformer": {}}) elif roop.globals.selected_enhancer == 'DMDNet': processors.update({"dmdnet": {}}) elif roop.globals.selected_enhancer == 'GPEN': processors.update({"gpen": {}}) elif roop.globals.selected_enhancer == 'Restoreformer++': processors.update({"restoreformer++": {}}) return processors def live_swap(frame, options): global process_mgr if frame is None: return frame if process_mgr is None: process_mgr = ProcessMgr(None) # if len(roop.globals.INPUT_FACESETS) <= selected_index: # selected_index = 0 process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options) newframe = process_mgr.process_frame(frame) if newframe is None: return frame return newframe def batch_process_regular(output_method, files:list[ProcessEntry], masking_engine:str, new_clip_text:str, use_new_method, imagemask, restore_original_mouth, num_swap_steps, progress, selected_index = 0) -> None: global clip_text, process_mgr release_resources() limit_resources() if process_mgr is None: process_mgr = ProcessMgr(progress) mask = imagemask["layers"][0] if imagemask is not None else None if len(roop.globals.INPUT_FACESETS) <= selected_index: selected_index = 0 options = ProcessOptions(get_processing_plugins(masking_engine), roop.globals.distance_threshold, roop.globals.blend_ratio, roop.globals.face_swap_mode, selected_index, new_clip_text, mask, num_swap_steps, roop.globals.subsample_size, False, restore_original_mouth) process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options) batch_process(output_method, files, use_new_method) return def batch_process_with_options(files:list[ProcessEntry], options, progress): global clip_text, process_mgr release_resources() limit_resources() if process_mgr is None: process_mgr = ProcessMgr(progress) process_mgr.initialize(roop.globals.INPUT_FACESETS, roop.globals.TARGET_FACES, options) roop.globals.keep_frames = False roop.globals.wait_after_extraction = False roop.globals.skip_audio = False batch_process("Files", files, True) def batch_process(output_method, files:list[ProcessEntry], use_new_method) -> None: global clip_text, process_mgr roop.globals.processing = True # limit threads for some providers max_threads = suggest_execution_threads() if max_threads == 1: roop.globals.execution_threads = 1 imagefiles:list[ProcessEntry] = [] videofiles:list[ProcessEntry] = [] update_status('Sorting videos/images') for index, f in enumerate(files): fullname = f.filename if util.has_image_extension(fullname): destination = util.get_destfilename_from_path(fullname, roop.globals.output_path, f'.{roop.globals.CFG.output_image_format}') destination = util.replace_template(destination, index=index) pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True) f.finalname = destination imagefiles.append(f) elif util.is_video(fullname) or util.has_extension(fullname, ['gif']): destination = util.get_destfilename_from_path(fullname, roop.globals.output_path, f'__temp.{roop.globals.CFG.output_video_format}') f.finalname = destination videofiles.append(f) if(len(imagefiles) > 0): update_status('Processing image(s)') origimages = [] fakeimages = [] for f in imagefiles: origimages.append(f.filename) fakeimages.append(f.finalname) process_mgr.run_batch(origimages, fakeimages, roop.globals.execution_threads) origimages.clear() fakeimages.clear() if(len(videofiles) > 0): for index,v in enumerate(videofiles): if not roop.globals.processing: end_processing('Processing stopped!') return fps = v.fps if v.fps > 0 else util.detect_fps(v.filename) if v.endframe == 0: v.endframe = get_video_frame_total(v.filename) is_streaming_only = output_method == "Virtual Camera" if is_streaming_only == False: update_status(f'Creating {os.path.basename(v.finalname)} with {fps} FPS...') start_processing = time() if is_streaming_only == False and roop.globals.keep_frames or not use_new_method: util.create_temp(v.filename) update_status('Extracting frames...') ffmpeg.extract_frames(v.filename,v.startframe,v.endframe, fps) if not roop.globals.processing: end_processing('Processing stopped!') return temp_frame_paths = util.get_temp_frame_paths(v.filename) process_mgr.run_batch(temp_frame_paths, temp_frame_paths, roop.globals.execution_threads) if not roop.globals.processing: end_processing('Processing stopped!') return if roop.globals.wait_after_extraction: extract_path = os.path.dirname(temp_frame_paths[0]) util.open_folder(extract_path) input("Press any key to continue...") print("Resorting frames to create video") util.sort_rename_frames(extract_path) ffmpeg.create_video(v.filename, v.finalname, fps) if not roop.globals.keep_frames: util.delete_temp_frames(temp_frame_paths[0]) else: if util.has_extension(v.filename, ['gif']): skip_audio = True else: skip_audio = roop.globals.skip_audio process_mgr.run_batch_inmem(output_method, v.filename, v.finalname, v.startframe, v.endframe, fps,roop.globals.execution_threads) if not roop.globals.processing: end_processing('Processing stopped!') return video_file_name = v.finalname if os.path.isfile(video_file_name): destination = '' if util.has_extension(v.filename, ['gif']): gifname = util.get_destfilename_from_path(v.filename, roop.globals.output_path, '.gif') destination = util.replace_template(gifname, index=index) pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True) update_status('Creating final GIF') ffmpeg.create_gif_from_video(video_file_name, destination) if os.path.isfile(destination): os.remove(video_file_name) else: skip_audio = roop.globals.skip_audio destination = util.replace_template(video_file_name, index=index) pathlib.Path(os.path.dirname(destination)).mkdir(parents=True, exist_ok=True) if not skip_audio: ffmpeg.restore_audio(video_file_name, v.filename, v.startframe, v.endframe, destination) if os.path.isfile(destination): os.remove(video_file_name) else: shutil.move(video_file_name, destination) elif is_streaming_only == False: update_status(f'Failed processing {os.path.basename(v.finalname)}!') elapsed_time = time() - start_processing average_fps = (v.endframe - v.startframe) / elapsed_time update_status(f'\nProcessing {os.path.basename(destination)} took {elapsed_time:.2f} secs, {average_fps:.2f} frames/s') end_processing('Finished') def end_processing(msg:str): update_status(msg) roop.globals.target_folder_path = None release_resources() def destroy() -> None: if roop.globals.target_path: util.clean_temp(roop.globals.target_path) release_resources() sys.exit() def run() -> None: parse_args() if not pre_check(): return roop.globals.CFG = Settings('config.yaml') roop.globals.cuda_device_id = roop.globals.startup_args.cuda_device_id roop.globals.execution_threads = roop.globals.CFG.max_threads roop.globals.video_encoder = roop.globals.CFG.output_video_codec roop.globals.video_quality = roop.globals.CFG.video_quality roop.globals.max_memory = roop.globals.CFG.memory_limit if roop.globals.CFG.memory_limit > 0 else None if roop.globals.startup_args.server_share: roop.globals.CFG.server_share = True main.run()