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