from typing import Any, List, Dict, Literal, Optional from argparse import ArgumentParser import threading import numpy import onnx import onnxruntime from onnx import numpy_helper import facefusion.globals import facefusion.processors.frame.core as frame_processors from facefusion import wording from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser from facefusion.face_helper import warp_face, paste_back from facefusion.face_reference import get_face_reference from facefusion.content_analyser import clear_content_analyser from facefusion.typing import Face, Frame, Update_Process, ProcessMode, ModelValue, OptionsWithModel, Embedding from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video, is_file, is_download_done, update_status from facefusion.vision import read_image, read_static_image, write_image from facefusion.processors.frame import globals as frame_processors_globals from facefusion.processors.frame import choices as frame_processors_choices FRAME_PROCESSOR = None MODEL_MATRIX = None THREAD_LOCK : threading.Lock = threading.Lock() NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_SWAPPER' MODELS : Dict[str, ModelValue] =\ { 'blendface_256': { 'type': 'blendface', 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/blendface_256.onnx', 'path': resolve_relative_path('../.assets/models/blendface_256.onnx'), 'template': 'ffhq', 'size': (512, 256), 'mean': [ 0.0, 0.0, 0.0 ], 'standard_deviation': [ 1.0, 1.0, 1.0 ] }, 'inswapper_128': { 'type': 'inswapper', 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx', 'path': resolve_relative_path('../.assets/models/inswapper_128.onnx'), 'template': 'arcface_v2', 'size': (128, 128), 'mean': [ 0.0, 0.0, 0.0 ], 'standard_deviation': [ 1.0, 1.0, 1.0 ] }, 'inswapper_128_fp16': { 'type': 'inswapper', 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx', 'path': resolve_relative_path('../.assets/models/inswapper_128_fp16.onnx'), 'template': 'arcface_v2', 'size': (128, 128), 'mean': [ 0.0, 0.0, 0.0 ], 'standard_deviation': [ 1.0, 1.0, 1.0 ] }, 'simswap_256': { 'type': 'simswap', 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_256.onnx', 'path': resolve_relative_path('../.assets/models/simswap_256.onnx'), 'template': 'arcface_v1', 'size': (112, 256), 'mean': [ 0.485, 0.456, 0.406 ], 'standard_deviation': [ 0.229, 0.224, 0.225 ] }, 'simswap_512_unofficial': { 'type': 'simswap', 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_512_unofficial.onnx', 'path': resolve_relative_path('../.assets/models/simswap_512_unofficial.onnx'), 'template': 'arcface_v1', 'size': (112, 512), 'mean': [ 0.0, 0.0, 0.0 ], 'standard_deviation': [ 1.0, 1.0, 1.0 ] } } OPTIONS : Optional[OptionsWithModel] = None def get_frame_processor() -> Any: global FRAME_PROCESSOR with THREAD_LOCK: if FRAME_PROCESSOR is None: model_path = get_options('model').get('path') FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers) return FRAME_PROCESSOR def clear_frame_processor() -> None: global FRAME_PROCESSOR FRAME_PROCESSOR = None def get_model_matrix() -> Any: global MODEL_MATRIX with THREAD_LOCK: if MODEL_MATRIX is None: model_path = get_options('model').get('path') model = onnx.load(model_path) MODEL_MATRIX = numpy_helper.to_array(model.graph.initializer[-1]) return MODEL_MATRIX def clear_model_matrix() -> None: global MODEL_MATRIX MODEL_MATRIX = None def get_options(key : Literal['model']) -> Any: global OPTIONS if OPTIONS is None: OPTIONS =\ { 'model': MODELS[frame_processors_globals.face_swapper_model] } return OPTIONS.get(key) def set_options(key : Literal['model'], value : Any) -> None: global OPTIONS OPTIONS[key] = value def register_args(program : ArgumentParser) -> None: program.add_argument('--face-swapper-model', help = wording.get('frame_processor_model_help'), dest = 'face_swapper_model', default = 'inswapper_128', choices = frame_processors_choices.face_swapper_models) def apply_args(program : ArgumentParser) -> None: args = program.parse_args() frame_processors_globals.face_swapper_model = args.face_swapper_model if args.face_swapper_model == 'blendface_256': facefusion.globals.face_recognizer_model = 'arcface_blendface' if args.face_swapper_model == 'inswapper_128' or args.face_swapper_model == 'inswapper_128_fp16': facefusion.globals.face_recognizer_model = 'arcface_inswapper' if args.face_swapper_model == 'simswap_256' or args.face_swapper_model == 'simswap_512_unofficial': facefusion.globals.face_recognizer_model = 'arcface_simswap' def pre_check() -> bool: if not facefusion.globals.skip_download: download_directory_path = resolve_relative_path('../.assets/models') model_url = get_options('model').get('url') conditional_download(download_directory_path, [ model_url ]) return True def pre_process(mode : ProcessMode) -> bool: model_url = get_options('model').get('url') model_path = get_options('model').get('path') if not facefusion.globals.skip_download and not is_download_done(model_url, model_path): update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME) return False elif not is_file(model_path): update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME) return False if not is_image(facefusion.globals.source_path): update_status(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME) return False elif not get_one_face(read_static_image(facefusion.globals.source_path)): update_status(wording.get('no_source_face_detected') + wording.get('exclamation_mark'), NAME) return False if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path): update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME) return False if mode == 'output' and not facefusion.globals.output_path: update_status(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME) return False return True def post_process() -> None: clear_frame_processor() clear_model_matrix() clear_face_analyser() clear_content_analyser() read_static_image.cache_clear() def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame: frame_processor = get_frame_processor() model_template = get_options('model').get('template') model_size = get_options('model').get('size') model_type = get_options('model').get('type') crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, model_template, model_size) crop_frame = prepare_crop_frame(crop_frame) frame_processor_inputs = {} for frame_processor_input in frame_processor.get_inputs(): if frame_processor_input.name == 'source': if model_type == 'blendface': frame_processor_inputs[frame_processor_input.name] = prepare_source_frame(source_face) else: frame_processor_inputs[frame_processor_input.name] = prepare_source_embedding(source_face) if frame_processor_input.name == 'target': frame_processor_inputs[frame_processor_input.name] = crop_frame crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0] crop_frame = normalize_crop_frame(crop_frame) temp_frame = paste_back(temp_frame, crop_frame, affine_matrix, facefusion.globals.face_mask_blur, facefusion.globals.face_mask_padding) return temp_frame def prepare_source_frame(source_face : Face) -> numpy.ndarray[Any, Any]: source_frame = read_static_image(facefusion.globals.source_path) source_frame, _ = warp_face(source_frame, source_face.kps, 'arcface_v2', (112, 112)) source_frame = source_frame[:, :, ::-1] / 255.0 source_frame = source_frame.transpose(2, 0, 1) source_frame = numpy.expand_dims(source_frame, axis = 0).astype(numpy.float32) return source_frame def prepare_source_embedding(source_face : Face) -> Embedding: model_type = get_options('model').get('type') if model_type == 'inswapper': model_matrix = get_model_matrix() source_embedding = source_face.embedding.reshape((1, -1)) source_embedding = numpy.dot(source_embedding, model_matrix) / numpy.linalg.norm(source_embedding) else: source_embedding = source_face.normed_embedding.reshape(1, -1) return source_embedding def prepare_crop_frame(crop_frame : Frame) -> Frame: model_mean = get_options('model').get('mean') model_standard_deviation = get_options('model').get('standard_deviation') crop_frame = crop_frame[:, :, ::-1] / 255.0 crop_frame = (crop_frame - model_mean) / model_standard_deviation crop_frame = crop_frame.transpose(2, 0, 1) crop_frame = numpy.expand_dims(crop_frame, axis = 0).astype(numpy.float32) return crop_frame def normalize_crop_frame(crop_frame : Frame) -> Frame: crop_frame = crop_frame.transpose(1, 2, 0) crop_frame = (crop_frame * 255.0).round() crop_frame = crop_frame[:, :, ::-1].astype(numpy.uint8) return crop_frame def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame: if 'reference' in facefusion.globals.face_selector_mode: similar_faces = find_similar_faces(temp_frame, reference_face, facefusion.globals.reference_face_distance) if similar_faces: for similar_face in similar_faces: temp_frame = swap_face(source_face, similar_face, temp_frame) if 'one' in facefusion.globals.face_selector_mode: target_face = get_one_face(temp_frame) if target_face: temp_frame = swap_face(source_face, target_face, temp_frame) if 'many' in facefusion.globals.face_selector_mode: many_faces = get_many_faces(temp_frame) if many_faces: for target_face in many_faces: temp_frame = swap_face(source_face, target_face, temp_frame) return temp_frame def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None: source_face = get_one_face(read_static_image(source_path)) reference_face = get_face_reference() if 'reference' in facefusion.globals.face_selector_mode else None for temp_frame_path in temp_frame_paths: temp_frame = read_image(temp_frame_path) result_frame = process_frame(source_face, reference_face, temp_frame) write_image(temp_frame_path, result_frame) update_progress() def process_image(source_path : str, target_path : str, output_path : str) -> None: source_face = get_one_face(read_static_image(source_path)) target_frame = read_static_image(target_path) reference_face = get_one_face(target_frame, facefusion.globals.reference_face_position) if 'reference' in facefusion.globals.face_selector_mode else None result_frame = process_frame(source_face, reference_face, target_frame) write_image(output_path, result_frame) def process_video(source_path : str, temp_frame_paths : List[str]) -> None: frame_processors.multi_process_frames(source_path, temp_frame_paths, process_frames)