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processors/modules/lip_syncer/choices.py

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+from typing import List, Sequence, get_args
+
+from facefusion.common_helper import create_float_range
+from facefusion.processors.modules.lip_syncer.types import LipSyncerModel
+
+lip_syncer_models : List[LipSyncerModel] = list(get_args(LipSyncerModel))
+
+lip_syncer_weight_range : Sequence[float] = create_float_range(0.0, 1.0, 0.05)

processors/modules/lip_syncer/core.py

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+from argparse import ArgumentParser
+from functools import lru_cache
+from types import ModuleType
+from typing import List
+
+import cv2
+import numpy
+
+import facefusion.jobs.job_manager
+import facefusion.jobs.job_store
+from facefusion import config, content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, inference_manager, logger, state_manager, translator, video_manager, voice_extractor
+from facefusion.audio import read_static_voice
+from facefusion.common_helper import create_float_metavar, get_middle
+from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
+from facefusion.face_creator import scale_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 select_faces
+from facefusion.filesystem import has_audio, resolve_relative_path
+from facefusion.processors.modules.lip_syncer import choices as lip_syncer_choices
+from facefusion.processors.modules.lip_syncer.types import LipSyncerInputs, LipSyncerWeight
+from facefusion.processors.types import ProcessorOutputs
+from facefusion.program_helper import find_argument_group
+from facefusion.thread_helper import conditional_thread_semaphore
+from facefusion.types import ApplyStateItem, Args, AudioFrame, DownloadScope, Face, InferencePool, ModelOptions, ModelSet, ProcessMode, VisionFrame
+from facefusion.vision import read_static_image, read_static_video_frame
+
+
+@lru_cache()
+def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
+	return\
+	{
+		'edtalk_256':
+		{
+			'__metadata__':
+			{
+				'vendor': 'tanshuai0219',
+				'license': 'Apache-2.0',
+				'year': 2024
+			},
+			'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':
+		{
+			'__metadata__':
+			{
+				'vendor': 'Rudrabha',
+				'license': 'Non-Commercial',
+				'year': 2020
+			},
+			'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':
+		{
+			'__metadata__':
+			{
+				'vendor': 'Rudrabha',
+				'license': 'Non-Commercial',
+				'year': 2020
+			},
+			'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 = translator.get('help.model', __package__), default = config.get_str_value('processors', 'lip_syncer_model', 'wav2lip_gan_96'), choices = lip_syncer_choices.lip_syncer_models)
+		group_processors.add_argument('--lip-syncer-weight', help = translator.get('help.weight', __package__), type = float, default = config.get_float_value('processors', 'lip_syncer_weight', '0.5'), choices = lip_syncer_choices.lip_syncer_weight_range, metavar = create_float_metavar(lip_syncer_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 get_common_modules() -> List[ModuleType]:
+	return [ content_analyser, face_classifier, face_detector, face_landmarker, face_masker, face_recognizer, voice_extractor ]
+
+
+def pre_check() -> bool:
+	model_hash_set = get_model_options().get('hashes')
+	model_source_set = get_model_options().get('sources')
+
+	for common_module in get_common_modules():
+		if not common_module.pre_check():
+			return False
+
+	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(translator.get('choose_audio_source') + translator.get('exclamation_mark'), __name__)
+		return False
+	return True
+
+
+def post_process() -> None:
+	read_static_image.cache_clear()
+	read_static_video_frame.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':
+		for common_module in get_common_modules():
+			common_module.clear_inference_pool()
+
+
+def sync_lip(target_face : Face, source_voice_frame : AudioFrame, temp_vision_frame : VisionFrame) -> VisionFrame:
+	model_type = get_model_options().get('type')
+	model_size = get_model_options().get('size')
+	source_voice_frame = prepare_audio_frame(source_voice_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(source_voice_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)
+		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(source_voice_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(numpy.float32) / 255.0
+
+	return crop_vision_frame
+
+
+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 process_frame(inputs : LipSyncerInputs) -> ProcessorOutputs:
+	reference_vision_frame = inputs.get('reference_vision_frame')
+	source_vision_frames = inputs.get('source_vision_frames')
+	source_voice_frame = inputs.get('source_voice_frame')
+	target_vision_frames = inputs.get('target_vision_frames')
+	temp_vision_frame = inputs.get('temp_vision_frame')
+	temp_vision_mask = inputs.get('temp_vision_mask')
+
+	target_vision_frame = get_middle(target_vision_frames)
+	target_faces = select_faces(reference_vision_frame, source_vision_frames, target_vision_frames)
+
+	if target_faces:
+		for target_face in target_faces:
+			target_face = scale_face(target_face, target_vision_frame, temp_vision_frame)
+			temp_vision_frame = sync_lip(target_face, source_voice_frame, temp_vision_frame)
+
+	return temp_vision_frame, temp_vision_mask

processors/modules/lip_syncer/locales.py

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+from facefusion.types import Locales
+
+LOCALES : Locales =\
+{
+	'en':
+	{
+		'help':
+		{
+			'model': 'choose the model responsible for syncing the lips',
+			'weight': 'specify the degree of weight applied to the lips'
+		},
+		'uis':
+		{
+			'model_dropdown': 'LIP SYNCER MODEL',
+			'weight_slider': 'LIP SYNCER WEIGHT'
+		}
+	}
+}

processors/modules/lip_syncer/types.py

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+from typing import Any, List, Literal, TypeAlias, TypedDict
+
+from numpy.typing import NDArray
+
+from facefusion.types import AudioFrame, Mask, VisionFrame
+
+LipSyncerInputs = TypedDict('LipSyncerInputs',
+{
+	'reference_vision_frame' : VisionFrame,
+	'source_vision_frames' : List[VisionFrame],
+	'source_voice_frame' : AudioFrame,
+	'target_vision_frames' : List[VisionFrame],
+	'temp_vision_frame' : VisionFrame,
+	'temp_vision_mask' : Mask
+})
+
+LipSyncerModel = Literal['edtalk_256', 'wav2lip_96', 'wav2lip_gan_96']
+
+LipSyncerWeight : TypeAlias = NDArray[Any]