from argparse import ArgumentParser from functools import lru_cache from typing import List, Tuple import cv2 import numpy from cv2.typing import Size import facefusion.jobs.job_manager import facefusion.jobs.job_store import facefusion.processors.core as processors from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, process_manager, state_manager, wording from facefusion.common_helper import create_int_metavar from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url_by_provider from facefusion.face_analyser import get_many_faces, get_one_face from facefusion.face_helper import paste_back, warp_face_by_face_landmark_5 from facefusion.face_masker import create_occlusion_mask, create_region_mask, create_static_box_mask from facefusion.face_selector import find_similar_faces, sort_and_filter_faces from facefusion.face_store import get_reference_faces from facefusion.filesystem import get_file_name, in_directory, is_image, is_video, resolve_file_paths, resolve_relative_path, same_file_extension from facefusion.processors import choices as processors_choices from facefusion.processors.types import DeepSwapperInputs, DeepSwapperMorph from facefusion.program_helper import find_argument_group from facefusion.thread_helper import thread_semaphore from facefusion.types import ApplyStateItem, Args, DownloadScope, Face, InferencePool, Mask, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame from facefusion.vision import conditional_match_frame_color, read_image, read_static_image, write_image @lru_cache(maxsize = None) def create_static_model_set(download_scope : DownloadScope) -> ModelSet: model_config = [] if download_scope == 'full': model_config.extend( [ ('druuzil', 'adrianne_palicki_384'), ('druuzil', 'agnetha_falskog_224'), ('druuzil', 'alan_ritchson_320'), ('druuzil', 'alicia_vikander_320'), ('druuzil', 'amber_midthunder_320'), ('druuzil', 'andras_arato_384'), ('druuzil', 'andrew_tate_320'), ('druuzil', 'anne_hathaway_320'), ('druuzil', 'anya_chalotra_320'), ('druuzil', 'arnold_schwarzenegger_320'), ('druuzil', 'benjamin_affleck_320'), ('druuzil', 'benjamin_stiller_384'), ('druuzil', 'bradley_pitt_224'), ('druuzil', 'brie_larson_384'), ('druuzil', 'bryan_cranston_320'), ('druuzil', 'catherine_blanchett_352'), ('druuzil', 'christian_bale_320'), ('druuzil', 'christopher_hemsworth_320'), ('druuzil', 'christoph_waltz_384'), ('druuzil', 'cillian_murphy_320'), ('druuzil', 'cobie_smulders_256'), ('druuzil', 'dwayne_johnson_384'), ('druuzil', 'edward_norton_320'), ('druuzil', 'elisabeth_shue_320'), ('druuzil', 'elizabeth_olsen_384'), ('druuzil', 'elon_musk_320'), ('druuzil', 'emily_blunt_320'), ('druuzil', 'emma_stone_384'), ('druuzil', 'emma_watson_320'), ('druuzil', 'erin_moriarty_384'), ('druuzil', 'eva_green_320'), ('druuzil', 'ewan_mcgregor_320'), ('druuzil', 'florence_pugh_320'), ('druuzil', 'freya_allan_320'), ('druuzil', 'gary_cole_224'), ('druuzil', 'gigi_hadid_224'), ('druuzil', 'harrison_ford_384'), ('druuzil', 'hayden_christensen_320'), ('druuzil', 'heath_ledger_320'), ('druuzil', 'henry_cavill_448'), ('druuzil', 'hugh_jackman_384'), ('druuzil', 'idris_elba_320'), ('druuzil', 'jack_nicholson_320'), ('druuzil', 'james_mcavoy_320'), ('druuzil', 'james_varney_320'), ('druuzil', 'jason_momoa_320'), ('druuzil', 'jason_statham_320'), ('druuzil', 'jennifer_connelly_384'), ('druuzil', 'jimmy_donaldson_320'), ('druuzil', 'jordan_peterson_384'), ('druuzil', 'karl_urban_224'), ('druuzil', 'kate_beckinsale_384'), ('druuzil', 'laurence_fishburne_384'), ('druuzil', 'lili_reinhart_320'), ('druuzil', 'mads_mikkelsen_384'), ('druuzil', 'mary_winstead_320'), ('druuzil', 'margaret_qualley_384'), ('druuzil', 'melina_juergens_320'), ('druuzil', 'michael_fassbender_320'), ('druuzil', 'michael_fox_320'), ('druuzil', 'millie_bobby_brown_320'), ('druuzil', 'morgan_freeman_320'), ('druuzil', 'patrick_stewart_320'), ('druuzil', 'rebecca_ferguson_320'), ('druuzil', 'scarlett_johansson_320'), ('druuzil', 'seth_macfarlane_384'), ('druuzil', 'thomas_cruise_320'), ('druuzil', 'thomas_hanks_384'), ('druuzil', 'william_murray_384'), ('edel', 'emma_roberts_224'), ('edel', 'ivanka_trump_224'), ('edel', 'lize_dzjabrailova_224'), ('edel', 'sidney_sweeney_224'), ('edel', 'winona_ryder_224') ]) if download_scope in [ 'lite', 'full' ]: model_config.extend( [ ('iperov', 'alexandra_daddario_224'), ('iperov', 'alexei_navalny_224'), ('iperov', 'amber_heard_224'), ('iperov', 'dilraba_dilmurat_224'), ('iperov', 'elon_musk_224'), ('iperov', 'emilia_clarke_224'), ('iperov', 'emma_watson_224'), ('iperov', 'erin_moriarty_224'), ('iperov', 'jackie_chan_224'), ('iperov', 'james_carrey_224'), ('iperov', 'jason_statham_320'), ('iperov', 'keanu_reeves_320'), ('iperov', 'margot_robbie_224'), ('iperov', 'natalie_dormer_224'), ('iperov', 'nicolas_coppola_224'), ('iperov', 'robert_downey_224'), ('iperov', 'rowan_atkinson_224'), ('iperov', 'ryan_reynolds_224'), ('iperov', 'scarlett_johansson_224'), ('iperov', 'sylvester_stallone_224'), ('iperov', 'thomas_cruise_224'), ('iperov', 'thomas_holland_224'), ('iperov', 'vin_diesel_224'), ('iperov', 'vladimir_putin_224') ]) if download_scope == 'full': model_config.extend( [ ('jen', 'angelica_trae_288'), ('jen', 'ella_freya_224'), ('jen', 'emma_myers_320'), ('jen', 'evie_pickerill_224'), ('jen', 'kang_hyewon_320'), ('jen', 'maddie_mead_224'), ('jen', 'nicole_turnbull_288'), ('mats', 'alica_schmidt_320'), ('mats', 'ashley_alexiss_224'), ('mats', 'billie_eilish_224'), ('mats', 'brie_larson_224'), ('mats', 'cara_delevingne_224'), ('mats', 'carolin_kebekus_224'), ('mats', 'chelsea_clinton_224'), ('mats', 'claire_boucher_224'), ('mats', 'corinna_kopf_224'), ('mats', 'florence_pugh_224'), ('mats', 'hillary_clinton_224'), ('mats', 'jenna_fischer_224'), ('mats', 'kim_jisoo_320'), ('mats', 'mica_suarez_320'), ('mats', 'shailene_woodley_224'), ('mats', 'shraddha_kapoor_320'), ('mats', 'yu_jimin_352'), ('rumateus', 'alison_brie_224'), ('rumateus', 'amber_heard_224'), ('rumateus', 'angelina_jolie_224'), ('rumateus', 'aubrey_plaza_224'), ('rumateus', 'bridget_regan_224'), ('rumateus', 'cobie_smulders_224'), ('rumateus', 'deborah_woll_224'), ('rumateus', 'dua_lipa_224'), ('rumateus', 'emma_stone_224'), ('rumateus', 'hailee_steinfeld_224'), ('rumateus', 'hilary_duff_224'), ('rumateus', 'jessica_alba_224'), ('rumateus', 'jessica_biel_224'), ('rumateus', 'john_cena_224'), ('rumateus', 'kim_kardashian_224'), ('rumateus', 'kristen_bell_224'), ('rumateus', 'lucy_liu_224'), ('rumateus', 'margot_robbie_224'), ('rumateus', 'megan_fox_224'), ('rumateus', 'meghan_markle_224'), ('rumateus', 'millie_bobby_brown_224'), ('rumateus', 'natalie_portman_224'), ('rumateus', 'nicki_minaj_224'), ('rumateus', 'olivia_wilde_224'), ('rumateus', 'shay_mitchell_224'), ('rumateus', 'sophie_turner_224'), ('rumateus', 'taylor_swift_224') ]) model_set : ModelSet = {} for model_scope, model_name in model_config: model_id = '/'.join([ model_scope, model_name ]) model_set[model_id] =\ { 'hashes': { 'deep_swapper': { 'url': resolve_download_url_by_provider('huggingface', 'deepfacelive-models-' + model_scope, model_name + '.hash'), 'path': resolve_relative_path('../.assets/models/' + model_scope + '/' + model_name + '.hash') } }, 'sources': { 'deep_swapper': { 'url': resolve_download_url_by_provider('huggingface', 'deepfacelive-models-' + model_scope, model_name + '.dfm'), 'path': resolve_relative_path('../.assets/models/' + model_scope + '/' + model_name + '.dfm') } }, 'template': 'dfl_whole_face' } custom_model_file_paths = resolve_file_paths(resolve_relative_path('../.assets/models/custom')) if custom_model_file_paths: for model_file_path in custom_model_file_paths: model_id = '/'.join([ 'custom', get_file_name(model_file_path) ]) model_set[model_id] =\ { 'sources': { 'deep_swapper': { 'path': resolve_relative_path(model_file_path) } }, 'template': 'dfl_whole_face' } return model_set def get_inference_pool() -> InferencePool: model_names = [ state_manager.get_item('deep_swapper_model') ] model_source_set = get_model_options().get('sources') return inference_manager.get_inference_pool(__name__, model_names, model_source_set) def clear_inference_pool() -> None: model_names = [ state_manager.get_item('deep_swapper_model') ] inference_manager.clear_inference_pool(__name__, model_names) def get_model_options() -> ModelOptions: deep_swapper_model = state_manager.get_item('deep_swapper_model') return create_static_model_set('full').get(deep_swapper_model) def get_model_size() -> Size: deep_swapper = get_inference_pool().get('deep_swapper') for deep_swapper_input in deep_swapper.get_inputs(): if deep_swapper_input.name == 'in_face:0': return deep_swapper_input.shape[1:3] return 0, 0 def register_args(program : ArgumentParser) -> None: group_processors = find_argument_group(program, 'processors') if group_processors: group_processors.add_argument('--deep-swapper-model', help = wording.get('help.deep_swapper_model'), default = config.get_str_value('processors', 'deep_swapper_model', 'iperov/elon_musk_224'), choices = processors_choices.deep_swapper_models) group_processors.add_argument('--deep-swapper-morph', help = wording.get('help.deep_swapper_morph'), type = int, default = config.get_int_value('processors', 'deep_swapper_morph', '100'), choices = processors_choices.deep_swapper_morph_range, metavar = create_int_metavar(processors_choices.deep_swapper_morph_range)) facefusion.jobs.job_store.register_step_keys([ 'deep_swapper_model', 'deep_swapper_morph' ]) def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None: apply_state_item('deep_swapper_model', args.get('deep_swapper_model')) apply_state_item('deep_swapper_morph', args.get('deep_swapper_morph')) def pre_check() -> bool: model_hash_set = get_model_options().get('hashes') model_source_set = get_model_options().get('sources') if model_hash_set and model_source_set: return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set) return True def pre_process(mode : ProcessMode) -> bool: if mode in [ 'output', 'preview' ] and not is_image(state_manager.get_item('target_path')) and not is_video(state_manager.get_item('target_path')): logger.error(wording.get('choose_image_or_video_target') + wording.get('exclamation_mark'), __name__) return False if mode == 'output' and not in_directory(state_manager.get_item('output_path')): logger.error(wording.get('specify_image_or_video_output') + wording.get('exclamation_mark'), __name__) return False if mode == 'output' and not same_file_extension(state_manager.get_item('target_path'), state_manager.get_item('output_path')): logger.error(wording.get('match_target_and_output_extension') + wording.get('exclamation_mark'), __name__) return False return True def post_process() -> None: read_static_image.cache_clear() if state_manager.get_item('video_memory_strategy') in [ 'strict', 'moderate' ]: clear_inference_pool() if state_manager.get_item('video_memory_strategy') == 'strict': content_analyser.clear_inference_pool() face_classifier.clear_inference_pool() face_detector.clear_inference_pool() face_landmarker.clear_inference_pool() face_masker.clear_inference_pool() face_recognizer.clear_inference_pool() def swap_face(target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: model_template = get_model_options().get('template') model_size = get_model_size() crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark_set.get('5/68'), model_template, model_size) crop_vision_frame_raw = crop_vision_frame.copy() box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], state_manager.get_item('face_mask_blur'), state_manager.get_item('face_mask_padding')) crop_masks =\ [ box_mask ] if 'occlusion' in state_manager.get_item('face_mask_types'): occlusion_mask = create_occlusion_mask(crop_vision_frame) crop_masks.append(occlusion_mask) crop_vision_frame = prepare_crop_frame(crop_vision_frame) deep_swapper_morph = numpy.array([ numpy.interp(state_manager.get_item('deep_swapper_morph'), [ 0, 100 ], [ 0, 1 ]) ]).astype(numpy.float32) crop_vision_frame, crop_source_mask, crop_target_mask = forward(crop_vision_frame, deep_swapper_morph) crop_vision_frame = normalize_crop_frame(crop_vision_frame) crop_vision_frame = conditional_match_frame_color(crop_vision_frame_raw, crop_vision_frame) crop_masks.append(prepare_crop_mask(crop_source_mask, crop_target_mask)) if 'region' in state_manager.get_item('face_mask_types'): region_mask = create_region_mask(crop_vision_frame, state_manager.get_item('face_mask_regions')) crop_masks.append(region_mask) crop_mask = numpy.minimum.reduce(crop_masks).clip(0, 1) paste_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix) return paste_vision_frame def forward(crop_vision_frame : VisionFrame, deep_swapper_morph : DeepSwapperMorph) -> Tuple[VisionFrame, Mask, Mask]: deep_swapper = get_inference_pool().get('deep_swapper') deep_swapper_inputs = {} for deep_swapper_input in deep_swapper.get_inputs(): if deep_swapper_input.name == 'in_face:0': deep_swapper_inputs[deep_swapper_input.name] = crop_vision_frame if deep_swapper_input.name == 'morph_value:0': deep_swapper_inputs[deep_swapper_input.name] = deep_swapper_morph with thread_semaphore(): crop_target_mask, crop_vision_frame, crop_source_mask = deep_swapper.run(None, deep_swapper_inputs) return crop_vision_frame[0], crop_source_mask[0], crop_target_mask[0] def has_morph_input() -> bool: deep_swapper = get_inference_pool().get('deep_swapper') for deep_swapper_input in deep_swapper.get_inputs(): if deep_swapper_input.name == 'morph_value:0': return True return False def prepare_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame: crop_vision_frame = cv2.addWeighted(crop_vision_frame, 1.75, cv2.GaussianBlur(crop_vision_frame, (0, 0), 2), -0.75, 0) crop_vision_frame = crop_vision_frame / 255.0 crop_vision_frame = numpy.expand_dims(crop_vision_frame, axis = 0).astype(numpy.float32) return crop_vision_frame def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame: crop_vision_frame = (crop_vision_frame * 255.0).clip(0, 255) crop_vision_frame = crop_vision_frame.astype(numpy.uint8) return crop_vision_frame def prepare_crop_mask(crop_source_mask : Mask, crop_target_mask : Mask) -> Mask: model_size = get_model_size() blur_size = 6.25 kernel_size = 3 crop_mask = numpy.minimum.reduce([ crop_source_mask, crop_target_mask ]) crop_mask = crop_mask.reshape(model_size).clip(0, 1) crop_mask = cv2.erode(crop_mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size)), iterations = 2) crop_mask = cv2.GaussianBlur(crop_mask, (0, 0), blur_size) return crop_mask def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: return swap_face(target_face, temp_vision_frame) def process_frame(inputs : DeepSwapperInputs) -> VisionFrame: reference_faces = inputs.get('reference_faces') target_vision_frame = inputs.get('target_vision_frame') many_faces = sort_and_filter_faces(get_many_faces([ target_vision_frame ])) if state_manager.get_item('face_selector_mode') == 'many': if many_faces: for target_face in many_faces: target_vision_frame = swap_face(target_face, target_vision_frame) if state_manager.get_item('face_selector_mode') == 'one': target_face = get_one_face(many_faces) if target_face: target_vision_frame = swap_face(target_face, target_vision_frame) if state_manager.get_item('face_selector_mode') == 'reference': similar_faces = find_similar_faces(many_faces, reference_faces, state_manager.get_item('reference_face_distance')) if similar_faces: for similar_face in similar_faces: target_vision_frame = swap_face(similar_face, target_vision_frame) return target_vision_frame def process_frames(source_path : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None: reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None for queue_payload in process_manager.manage(queue_payloads): target_vision_path = queue_payload['frame_path'] target_vision_frame = read_image(target_vision_path) output_vision_frame = process_frame( { 'reference_faces': reference_faces, 'target_vision_frame': target_vision_frame }) write_image(target_vision_path, output_vision_frame) update_progress(1) def process_image(source_path : str, target_path : str, output_path : str) -> None: reference_faces = get_reference_faces() if 'reference' in state_manager.get_item('face_selector_mode') else None target_vision_frame = read_static_image(target_path) output_vision_frame = process_frame( { 'reference_faces': reference_faces, 'target_vision_frame': target_vision_frame }) write_image(output_path, output_vision_frame) def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None: processors.multi_process_frames(None, temp_frame_paths, process_frames)