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| from argparse import ArgumentParser | |
| from functools import lru_cache | |
| 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, video_manager, voice_extractor, wording | |
| from facefusion.audio import create_empty_audio_frame, get_voice_frame, read_static_voice | |
| from facefusion.common_helper import create_float_metavar | |
| from facefusion.common_helper import get_first | |
| from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url | |
| 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_area_mask, create_box_mask, create_occlusion_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.types import LipSyncerInputs, LipSyncerWeight | |
| from facefusion.program_helper import find_argument_group | |
| from facefusion.thread_helper import conditional_thread_semaphore | |
| from facefusion.types import ApplyStateItem, Args, AudioFrame, BoundingBox, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, QueuePayload, UpdateProgress, VisionFrame | |
| from facefusion.vision import read_image, read_static_image, restrict_video_fps, write_image | |
| def create_static_model_set(download_scope : DownloadScope) -> ModelSet: | |
| return\ | |
| { | |
| 'edtalk_256': | |
| { | |
| 'hashes': | |
| { | |
| 'lip_syncer': | |
| { | |
| 'url': resolve_download_url('models-3.3.0', 'edtalk_256.hash'), | |
| 'path': resolve_relative_path('../.assets/models/edtalk_256.hash') | |
| } | |
| }, | |
| 'sources': | |
| { | |
| 'lip_syncer': | |
| { | |
| 'url': resolve_download_url('models-3.3.0', 'edtalk_256.onnx'), | |
| 'path': resolve_relative_path('../.assets/models/edtalk_256.onnx') | |
| } | |
| }, | |
| 'type': 'edtalk', | |
| 'size': (256, 256) | |
| }, | |
| 'wav2lip_96': | |
| { | |
| 'hashes': | |
| { | |
| 'lip_syncer': | |
| { | |
| 'url': resolve_download_url('models-3.0.0', 'wav2lip_96.hash'), | |
| 'path': resolve_relative_path('../.assets/models/wav2lip_96.hash') | |
| } | |
| }, | |
| 'sources': | |
| { | |
| 'lip_syncer': | |
| { | |
| 'url': resolve_download_url('models-3.0.0', 'wav2lip_96.onnx'), | |
| 'path': resolve_relative_path('../.assets/models/wav2lip_96.onnx') | |
| } | |
| }, | |
| 'type': 'wav2lip', | |
| 'size': (96, 96) | |
| }, | |
| 'wav2lip_gan_96': | |
| { | |
| 'hashes': | |
| { | |
| 'lip_syncer': | |
| { | |
| 'url': resolve_download_url('models-3.0.0', 'wav2lip_gan_96.hash'), | |
| 'path': resolve_relative_path('../.assets/models/wav2lip_gan_96.hash') | |
| } | |
| }, | |
| 'sources': | |
| { | |
| 'lip_syncer': | |
| { | |
| 'url': resolve_download_url('models-3.0.0', 'wav2lip_gan_96.onnx'), | |
| 'path': resolve_relative_path('../.assets/models/wav2lip_gan_96.onnx') | |
| } | |
| }, | |
| 'type': 'wav2lip', | |
| 'size': (96, 96) | |
| } | |
| } | |
| def get_inference_pool() -> InferencePool: | |
| model_names = [ state_manager.get_item('lip_syncer_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('lip_syncer_model') ] | |
| inference_manager.clear_inference_pool(__name__, model_names) | |
| def get_model_options() -> ModelOptions: | |
| model_name = state_manager.get_item('lip_syncer_model') | |
| return create_static_model_set('full').get(model_name) | |
| 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_96'), choices = processors_choices.lip_syncer_models) | |
| group_processors.add_argument('--lip-syncer-weight', help = wording.get('help.lip_syncer_weight'), type = float, default = config.get_float_value('processors', 'lip_syncer_weight', '0.5'), choices = processors_choices.lip_syncer_weight_range, metavar = create_float_metavar(processors_choices.lip_syncer_weight_range)) | |
| facefusion.jobs.job_store.register_step_keys([ 'lip_syncer_model', 'lip_syncer_weight' ]) | |
| def apply_args(args : Args, apply_state_item : ApplyStateItem) -> None: | |
| apply_state_item('lip_syncer_model', args.get('lip_syncer_model')) | |
| apply_state_item('lip_syncer_weight', args.get('lip_syncer_weight')) | |
| def pre_check() -> bool: | |
| model_hash_set = get_model_options().get('hashes') | |
| model_source_set = get_model_options().get('sources') | |
| return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set) | |
| 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__) | |
| 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__) | |
| 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() | |
| read_static_voice.cache_clear() | |
| video_manager.clear_video_pool() | |
| 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: | |
| model_type = get_model_options().get('type') | |
| model_size = get_model_options().get('size') | |
| 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)) | |
| crop_masks = [] | |
| if 'occlusion' in state_manager.get_item('face_mask_types'): | |
| occlusion_mask = create_occlusion_mask(crop_vision_frame) | |
| crop_masks.append(occlusion_mask) | |
| if model_type == 'edtalk': | |
| lip_syncer_weight = numpy.array([ state_manager.get_item('lip_syncer_weight') ]).astype(numpy.float32) | |
| box_mask = create_box_mask(crop_vision_frame, state_manager.get_item('face_mask_blur'), state_manager.get_item('face_mask_padding')) | |
| crop_masks.append(box_mask) | |
| crop_vision_frame = prepare_crop_frame(crop_vision_frame) | |
| crop_vision_frame = forward_edtalk(temp_audio_frame, crop_vision_frame, lip_syncer_weight) | |
| crop_vision_frame = normalize_crop_frame(crop_vision_frame) | |
| if model_type == 'wav2lip': | |
| face_landmark_68 = cv2.transform(target_face.landmark_set.get('68').reshape(1, -1, 2), affine_matrix).reshape(-1, 2) | |
| area_mask = create_area_mask(crop_vision_frame, face_landmark_68, [ 'lower-face' ]) | |
| crop_masks.append(area_mask) | |
| bounding_box = create_bounding_box(face_landmark_68) | |
| bounding_box = resize_bounding_box(bounding_box, 1 / 8) | |
| area_vision_frame, area_matrix = warp_face_by_bounding_box(crop_vision_frame, bounding_box, model_size) | |
| area_vision_frame = prepare_crop_frame(area_vision_frame) | |
| area_vision_frame = forward_wav2lip(temp_audio_frame, area_vision_frame) | |
| area_vision_frame = normalize_crop_frame(area_vision_frame) | |
| crop_vision_frame = cv2.warpAffine(area_vision_frame, cv2.invertAffineTransform(area_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 forward_edtalk(temp_audio_frame : AudioFrame, crop_vision_frame : VisionFrame, lip_syncer_weight : LipSyncerWeight) -> VisionFrame: | |
| lip_syncer = get_inference_pool().get('lip_syncer') | |
| with conditional_thread_semaphore(): | |
| crop_vision_frame = lip_syncer.run(None, | |
| { | |
| 'source': temp_audio_frame, | |
| 'target': crop_vision_frame, | |
| 'weight': lip_syncer_weight | |
| })[0] | |
| return crop_vision_frame | |
| def forward_wav2lip(temp_audio_frame : AudioFrame, area_vision_frame : VisionFrame) -> VisionFrame: | |
| lip_syncer = get_inference_pool().get('lip_syncer') | |
| with conditional_thread_semaphore(): | |
| area_vision_frame = lip_syncer.run(None, | |
| { | |
| 'source': temp_audio_frame, | |
| 'target': area_vision_frame | |
| })[0] | |
| return area_vision_frame | |
| def prepare_audio_frame(temp_audio_frame : AudioFrame) -> AudioFrame: | |
| model_type = get_model_options().get('type') | |
| 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) | |
| if model_type == 'wav2lip': | |
| temp_audio_frame = temp_audio_frame * state_manager.get_item('lip_syncer_weight') * 2.0 | |
| 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: | |
| model_type = get_model_options().get('type') | |
| model_size = get_model_options().get('size') | |
| if model_type == 'edtalk': | |
| crop_vision_frame = cv2.resize(crop_vision_frame, model_size, interpolation = cv2.INTER_AREA) | |
| crop_vision_frame = crop_vision_frame[:, :, ::-1] / 255.0 | |
| crop_vision_frame = numpy.expand_dims(crop_vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32) | |
| if model_type == 'wav2lip': | |
| crop_vision_frame = numpy.expand_dims(crop_vision_frame, axis = 0) | |
| prepare_vision_frame = crop_vision_frame.copy() | |
| prepare_vision_frame[:, model_size[0] // 2:] = 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 resize_bounding_box(bounding_box : BoundingBox, aspect_ratio : float) -> BoundingBox: | |
| x1, y1, x2, y2 = bounding_box | |
| y1 -= numpy.abs(y2 - y1) * aspect_ratio | |
| bounding_box[1] = max(y1, 0) | |
| return bounding_box | |
| def normalize_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame: | |
| model_type = get_model_options().get('type') | |
| 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) | |
| if model_type == 'edtalk': | |
| crop_vision_frame = crop_vision_frame[:, :, ::-1] | |
| crop_vision_frame = cv2.resize(crop_vision_frame, (512, 512), interpolation = cv2.INTER_CUBIC) | |
| 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) | |