deepfuze
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vnyash
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from typing import Any, List, Literal, Optional
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from argparse import ArgumentParser
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from time import sleep
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import cv2
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import numpy
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import onnxruntime
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import deepfuze.globals
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import deepfuze.processors.frame.core as frame_processors
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from deepfuze import config, process_manager, logger, wording
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from deepfuze.execution import apply_execution_provider_options
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from deepfuze.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser
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from deepfuze.face_masker import create_static_box_mask, create_occlusion_mask, create_mouth_mask, clear_face_occluder, clear_face_parser
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from deepfuze.face_helper import warp_face_by_face_landmark_5, warp_face_by_bounding_box, paste_back, create_bounding_box_from_face_landmark_68
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from deepfuze.face_store import get_reference_faces
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from deepfuze.content_analyser import clear_content_analyser
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from deepfuze.normalizer import normalize_output_path
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from deepfuze.thread_helper import thread_lock, conditional_thread_semaphore
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from deepfuze.typing import Face, VisionFrame, UpdateProgress, ProcessMode, ModelSet, OptionsWithModel, AudioFrame, QueuePayload
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from deepfuze.filesystem import is_file, has_audio, resolve_relative_path
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from deepfuze.download import conditional_download, is_download_done
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from deepfuze.audio import read_static_voice, get_voice_frame, create_empty_audio_frame
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from deepfuze.filesystem import is_image, is_video, filter_audio_paths
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from deepfuze.common_helper import get_first
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from deepfuze.vision import read_image, read_static_image, write_image, restrict_video_fps
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from deepfuze.processors.frame.typings import LipSyncerInputs
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from deepfuze.voice_extractor import clear_voice_extractor
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from deepfuze.processors.frame import globals as frame_processors_globals
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from deepfuze.processors.frame import choices as frame_processors_choices
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FRAME_PROCESSOR = None
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NAME = __name__.upper()
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MODELS : ModelSet =\
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{
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'wav2lip_gan':
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{
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/wav2lip_gan.onnx',
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'path': resolve_relative_path('../../../models/deepfuze/wav2lip_gan.onnx')
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}
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}
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OPTIONS : Optional[OptionsWithModel] = None
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def get_frame_processor() -> Any:
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global FRAME_PROCESSOR
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with thread_lock():
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while process_manager.is_checking():
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sleep(0.5)
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if FRAME_PROCESSOR is None:
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model_path = get_options('model').get('path')
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FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = apply_execution_provider_options(deepfuze.globals.execution_device_id, deepfuze.globals.execution_providers))
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return FRAME_PROCESSOR
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def clear_frame_processor() -> None:
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global FRAME_PROCESSOR
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FRAME_PROCESSOR = None
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def get_options(key : Literal['model']) -> Any:
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global OPTIONS
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if OPTIONS is None:
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OPTIONS =\
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{
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'model': MODELS[frame_processors_globals.lip_syncer_model]
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}
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return OPTIONS.get(key)
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def set_options(key : Literal['model'], value : Any) -> None:
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global OPTIONS
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OPTIONS[key] = value
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def register_args(program : ArgumentParser) -> None:
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program.add_argument('--lip-syncer-model', help = wording.get('help.lip_syncer_model'), default = config.get_str_value('frame_processors.lip_syncer_model', 'wav2lip_gan'), choices = frame_processors_choices.lip_syncer_models)
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def apply_args(program : ArgumentParser) -> None:
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args = program.parse_args()
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frame_processors_globals.lip_syncer_model = args.lip_syncer_model
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def pre_check() -> bool:
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download_directory_path = resolve_relative_path('../../../models/deepfuze')
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model_url = get_options('model').get('url')
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model_path = get_options('model').get('path')
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if not deepfuze.globals.skip_download:
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process_manager.check()
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conditional_download(download_directory_path, [ model_url ])
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process_manager.end()
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return is_file(model_path)
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def post_check() -> bool:
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model_url = get_options('model').get('url')
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model_path = get_options('model').get('path')
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if not deepfuze.globals.skip_download and not is_download_done(model_url, model_path):
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logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
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return False
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if not is_file(model_path):
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logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
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return False
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return True
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def pre_process(mode : ProcessMode) -> bool:
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if not has_audio(deepfuze.globals.source_paths):
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logger.error(wording.get('select_audio_source') + wording.get('exclamation_mark'), NAME)
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return False
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if mode in [ 'output', 'preview' ] and not is_image(deepfuze.globals.target_path) and not is_video(deepfuze.globals.target_path):
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logger.error(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
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return False
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if mode == 'output' and not normalize_output_path(deepfuze.globals.target_path, deepfuze.globals.output_path):
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logger.error(wording.get('select_file_or_directory_output') + 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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read_static_voice.cache_clear()
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if deepfuze.globals.video_memory_strategy == 'strict' or deepfuze.globals.video_memory_strategy == 'moderate':
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clear_frame_processor()
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if deepfuze.globals.video_memory_strategy == 'strict':
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clear_face_analyser()
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clear_content_analyser()
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clear_face_occluder()
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clear_face_parser()
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clear_voice_extractor()
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def sync_lip(target_face : Face, temp_audio_frame : AudioFrame, temp_vision_frame : VisionFrame) -> VisionFrame:
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frame_processor = get_frame_processor()
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crop_mask_list = []
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temp_audio_frame = prepare_audio_frame(temp_audio_frame)
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crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmarks.get('5/68'), 'ffhq_512', (512, 512))
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face_landmark_68 = cv2.transform(target_face.landmarks.get('68').reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
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bounding_box = create_bounding_box_from_face_landmark_68(face_landmark_68)
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bounding_box[1] -= numpy.abs(bounding_box[3] - bounding_box[1]) * 0.125
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mouth_mask = create_mouth_mask(face_landmark_68)
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crop_mask_list.append(mouth_mask)
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box_mask = create_static_box_mask(crop_vision_frame.shape[:2][::-1], deepfuze.globals.face_mask_blur, deepfuze.globals.face_mask_padding)
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crop_mask_list.append(box_mask)
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if 'occlusion' in deepfuze.globals.face_mask_types:
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occlusion_mask = create_occlusion_mask(crop_vision_frame)
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crop_mask_list.append(occlusion_mask)
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close_vision_frame, close_matrix = warp_face_by_bounding_box(crop_vision_frame, bounding_box, (96, 96))
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close_vision_frame = prepare_crop_frame(close_vision_frame)
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with conditional_thread_semaphore(deepfuze.globals.execution_providers):
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close_vision_frame = frame_processor.run(None,
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{
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'source': temp_audio_frame,
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'target': close_vision_frame
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})[0]
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crop_vision_frame = normalize_crop_frame(close_vision_frame)
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crop_vision_frame = cv2.warpAffine(crop_vision_frame, cv2.invertAffineTransform(close_matrix), (512, 512), borderMode = cv2.BORDER_REPLICATE)
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crop_mask = numpy.minimum.reduce(crop_mask_list)
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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 prepare_audio_frame(temp_audio_frame : AudioFrame) -> AudioFrame:
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temp_audio_frame = numpy.maximum(numpy.exp(-5 * numpy.log(10)), temp_audio_frame)
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temp_audio_frame = numpy.log10(temp_audio_frame) * 1.6 + 3.2
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temp_audio_frame = temp_audio_frame.clip(-4, 4).astype(numpy.float32)
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temp_audio_frame = numpy.expand_dims(temp_audio_frame, axis = (0, 1))
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return temp_audio_frame
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def prepare_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
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crop_vision_frame = numpy.expand_dims(crop_vision_frame, axis = 0)
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prepare_vision_frame = crop_vision_frame.copy()
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prepare_vision_frame[:, 48:] = 0
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crop_vision_frame = numpy.concatenate((prepare_vision_frame, crop_vision_frame), axis = 3)
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crop_vision_frame = crop_vision_frame.transpose(0, 3, 1, 2).astype('float32') / 255.0
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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[0].transpose(1, 2, 0)
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crop_vision_frame = crop_vision_frame.clip(0, 1) * 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 get_reference_frame(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
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pass
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def process_frame(inputs : LipSyncerInputs) -> VisionFrame:
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reference_faces = inputs.get('reference_faces')
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source_audio_frame = inputs.get('source_audio_frame')
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target_vision_frame = inputs.get('target_vision_frame')
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if deepfuze.globals.face_selector_mode == 'many':
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many_faces = get_many_faces(target_vision_frame)
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if many_faces:
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for target_face in many_faces:
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target_vision_frame = sync_lip(target_face, source_audio_frame, target_vision_frame)
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if deepfuze.globals.face_selector_mode == 'one':
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target_face = get_one_face(target_vision_frame)
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if target_face:
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target_vision_frame = sync_lip(target_face, source_audio_frame, target_vision_frame)
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if deepfuze.globals.face_selector_mode == 'reference':
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similar_faces = find_similar_faces(reference_faces, target_vision_frame, deepfuze.globals.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 = sync_lip(similar_face, source_audio_frame, target_vision_frame)
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return target_vision_frame
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def process_frames(source_paths : List[str], queue_payloads : List[QueuePayload], update_progress : UpdateProgress) -> None:
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reference_faces = get_reference_faces() if 'reference' in deepfuze.globals.face_selector_mode else None
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source_audio_path = get_first(filter_audio_paths(source_paths))
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temp_video_fps = restrict_video_fps(deepfuze.globals.target_path, deepfuze.globals.output_video_fps)
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for queue_payload in process_manager.manage(queue_payloads):
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frame_number = queue_payload['frame_number']
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target_vision_path = queue_payload['frame_path']
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source_audio_frame = get_voice_frame(source_audio_path, temp_video_fps, frame_number)
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if not numpy.any(source_audio_frame):
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source_audio_frame = create_empty_audio_frame()
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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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'source_audio_frame': source_audio_frame,
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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_paths : List[str], target_path : str, output_path : str) -> None:
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reference_faces = get_reference_faces() if 'reference' in deepfuze.globals.face_selector_mode else None
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source_audio_frame = create_empty_audio_frame()
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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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{
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'reference_faces': reference_faces,
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'source_audio_frame': source_audio_frame,
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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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source_audio_paths = filter_audio_paths(deepfuze.globals.source_paths)
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temp_video_fps = restrict_video_fps(deepfuze.globals.target_path, deepfuze.globals.output_video_fps)
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for source_audio_path in source_audio_paths:
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read_static_voice(source_audio_path, temp_video_fps)
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frame_processors.multi_process_frames(source_paths, temp_frame_paths, process_frames)
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