from argparse import ArgumentParser from typing import List import cv2 import numpy 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, voice_extractor, wording from facefusion.audio import create_empty_audio_frame, get_voice_frame, read_static_voice from facefusion.common_helper import get_first from facefusion.download import conditional_download_hashes, conditional_download_sources from facefusion.face_analyser import get_many_faces, get_one_face from facefusion.face_helper import create_bounding_box, paste_back, warp_face_by_bounding_box, warp_face_by_face_landmark_5 from facefusion.face_masker import create_mouth_mask, create_occlusion_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 filter_audio_paths, has_audio, in_directory, is_image, is_video, resolve_relative_path, same_file_extension from facefusion.processors import choices as processors_choices from facefusion.processors.typing import LipSyncerInputs from facefusion.program_helper import find_argument_group from facefusion.thread_helper import conditional_thread_semaphore from facefusion.typing import Args, AudioFrame, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame from facefusion.vision import read_image, read_static_image, restrict_video_fps, write_image MODEL_SET : ModelSet =\ { 'wav2lip': { 'hashes': { 'lip_syncer': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/wav2lip.hash', 'path': resolve_relative_path('../.assets/models/wav2lip.hash') } }, 'sources': { 'lip_syncer': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/wav2lip.onnx', 'path': resolve_relative_path('../.assets/models/wav2lip.onnx') } } }, 'wav2lip_gan': { 'hashes': { 'lip_syncer': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/wav2lip_gan.hash', 'path': resolve_relative_path('../.assets/models/wav2lip_gan.hash') } }, 'sources': { 'lip_syncer': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/wav2lip_gan.onnx', 'path': resolve_relative_path('../.assets/models/wav2lip_gan.onnx') } } } } def get_inference_pool() -> InferencePool: model_sources = get_model_options().get('sources') return inference_manager.get_inference_pool(__name__, model_sources) def clear_inference_pool() -> None: inference_manager.clear_inference_pool(__name__) def get_model_options() -> ModelOptions: return MODEL_SET[state_manager.get_item('lip_syncer_model')] def register_args(program : ArgumentParser) -> None: group_processors = find_argument_group(program, 'processors') if group_processors: group_processors.add_argument('--lip-syncer-model', help = wording.get('help.lip_syncer_model'), default = config.get_str_value('processors.lip_syncer_model', 'wav2lip_gan'), choices = processors_choices.lip_syncer_models) facefusion.jobs.job_store.register_step_keys([ 'lip_syncer_model' ]) def apply_args(args : Args) -> None: state_manager.init_item('lip_syncer_model', args.get('lip_syncer_model')) def pre_check() -> bool: download_directory_path = resolve_relative_path('../.assets/models') model_hashes = get_model_options().get('hashes') model_sources = get_model_options().get('sources') return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources) def pre_process(mode : ProcessMode) -> bool: if not has_audio(state_manager.get_item('source_paths')): logger.error(wording.get('choose_audio_source') + wording.get('exclamation_mark'), __name__.upper()) return False 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__.upper()) 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__.upper()) 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__.upper()) return False return True def post_process() -> None: read_static_image.cache_clear() read_static_voice.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() voice_extractor.clear_inference_pool() def sync_lip(target_face : Face, temp_audio_frame : AudioFrame, temp_vision_frame : VisionFrame) -> VisionFrame: lip_syncer = get_inference_pool().get('lip_syncer') temp_audio_frame = prepare_audio_frame(temp_audio_frame) crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmark_set.get('5/68'), 'ffhq_512', (512, 512)) face_landmark_68 = cv2.transform(target_face.landmark_set.get('68').reshape(1, -1, 2), affine_matrix).reshape(-1, 2) bounding_box = create_bounding_box(face_landmark_68) bounding_box[1] -= numpy.abs(bounding_box[3] - bounding_box[1]) * 0.125 mouth_mask = create_mouth_mask(face_landmark_68) 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 =\ [ mouth_mask, 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) close_vision_frame, close_matrix = warp_face_by_bounding_box(crop_vision_frame, bounding_box, (96, 96)) close_vision_frame = prepare_crop_frame(close_vision_frame) with conditional_thread_semaphore(): close_vision_frame = lip_syncer.run(None, { 'source': temp_audio_frame, 'target': close_vision_frame })[0] crop_vision_frame = normalize_crop_frame(close_vision_frame) crop_vision_frame = cv2.warpAffine(crop_vision_frame, cv2.invertAffineTransform(close_matrix), (512, 512), borderMode = cv2.BORDER_REPLICATE) crop_mask = numpy.minimum.reduce(crop_masks) paste_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix) return paste_vision_frame def prepare_audio_frame(temp_audio_frame : AudioFrame) -> AudioFrame: temp_audio_frame = numpy.maximum(numpy.exp(-5 * numpy.log(10)), temp_audio_frame) temp_audio_frame = numpy.log10(temp_audio_frame) * 1.6 + 3.2 temp_audio_frame = temp_audio_frame.clip(-4, 4).astype(numpy.float32) temp_audio_frame = numpy.expand_dims(temp_audio_frame, axis = (0, 1)) return temp_audio_frame def prepare_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame: crop_vision_frame = numpy.expand_dims(crop_vision_frame, axis = 0) prepare_vision_frame = crop_vision_frame.copy() prepare_vision_frame[:, 48:] = 0 crop_vision_frame = numpy.concatenate((prepare_vision_frame, crop_vision_frame), axis = 3) crop_vision_frame = crop_vision_frame.transpose(0, 3, 1, 2).astype('float32') / 255.0 return crop_vision_frame def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame: crop_vision_frame = crop_vision_frame[0].transpose(1, 2, 0) crop_vision_frame = crop_vision_frame.clip(0, 1) * 255 crop_vision_frame = crop_vision_frame.astype(numpy.uint8) return crop_vision_frame def get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame: pass def process_frame(inputs : LipSyncerInputs) -> VisionFrame: reference_faces = inputs.get('reference_faces') source_audio_frame = inputs.get('source_audio_frame') 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 = sync_lip(target_face, source_audio_frame, 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 = sync_lip(target_face, source_audio_frame, 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 = sync_lip(similar_face, source_audio_frame, target_vision_frame) return target_vision_frame def process_frames(source_paths : 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 source_audio_path = get_first(filter_audio_paths(source_paths)) temp_video_fps = restrict_video_fps(state_manager.get_item('target_path'), state_manager.get_item('output_video_fps')) for queue_payload in process_manager.manage(queue_payloads): frame_number = queue_payload.get('frame_number') target_vision_path = queue_payload.get('frame_path') source_audio_frame = get_voice_frame(source_audio_path, temp_video_fps, frame_number) if not numpy.any(source_audio_frame): source_audio_frame = create_empty_audio_frame() target_vision_frame = read_image(target_vision_path) output_vision_frame = process_frame( { 'reference_faces': reference_faces, 'source_audio_frame': source_audio_frame, 'target_vision_frame': target_vision_frame }) write_image(target_vision_path, output_vision_frame) update_progress(1) def process_image(source_paths : List[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 source_audio_frame = create_empty_audio_frame() target_vision_frame = read_static_image(target_path) output_vision_frame = process_frame( { 'reference_faces': reference_faces, 'source_audio_frame': source_audio_frame, '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: source_audio_paths = filter_audio_paths(state_manager.get_item('source_paths')) temp_video_fps = restrict_video_fps(state_manager.get_item('target_path'), state_manager.get_item('output_video_fps')) for source_audio_path in source_audio_paths: read_static_voice(source_audio_path, temp_video_fps) processors.multi_process_frames(source_paths, temp_frame_paths, process_frames)