deepfuze
This commit is contained in:
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 numpy
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import onnx
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import onnxruntime
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from onnx import numpy_helper
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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 has_execution_provider, apply_execution_provider_options
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from deepfuze.face_analyser import get_one_face, get_average_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_region_mask, clear_face_occluder, clear_face_parser
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from deepfuze.face_helper import warp_face_by_face_landmark_5, paste_back
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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, Embedding, VisionFrame, UpdateProgress, ProcessMode, ModelSet, OptionsWithModel, QueuePayload
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from deepfuze.filesystem import is_file, is_image, has_image, is_video, filter_image_paths, resolve_relative_path
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from deepfuze.download import conditional_download, is_download_done
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from deepfuze.vision import read_image, read_static_image, read_static_images, write_image
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from deepfuze.processors.frame.typings import FaceSwapperInputs
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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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MODEL_INITIALIZER = None
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NAME = __name__.upper()
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MODELS : ModelSet =\
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{
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'blendswap_256':
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{
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'type': 'blendswap',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/blendswap_256.onnx',
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'path': resolve_relative_path('../../../models/deepfuze/blendswap_256.onnx'),
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'template': 'ffhq_512',
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'size': (256, 256),
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'mean': [ 0.0, 0.0, 0.0 ],
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'standard_deviation': [ 1.0, 1.0, 1.0 ]
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},
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'inswapper_128':
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{
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'type': 'inswapper',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx',
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'path': resolve_relative_path('../../../models/deepfuze/inswapper_128.onnx'),
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'template': 'arcface_128_v2',
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'size': (128, 128),
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'mean': [ 0.0, 0.0, 0.0 ],
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'standard_deviation': [ 1.0, 1.0, 1.0 ]
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},
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'inswapper_128_fp16':
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{
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'type': 'inswapper',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx',
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'path': resolve_relative_path('../../../models/deepfuze/inswapper_128_fp16.onnx'),
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'template': 'arcface_128_v2',
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'size': (128, 128),
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'mean': [ 0.0, 0.0, 0.0 ],
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'standard_deviation': [ 1.0, 1.0, 1.0 ]
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},
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'simswap_256':
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{
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'type': 'simswap',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_256.onnx',
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'path': resolve_relative_path('../../../models/deepfuze/simswap_256.onnx'),
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'template': 'arcface_112_v1',
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'size': (256, 256),
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'mean': [ 0.485, 0.456, 0.406 ],
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'standard_deviation': [ 0.229, 0.224, 0.225 ]
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},
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'simswap_512_unofficial':
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{
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'type': 'simswap',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_512_unofficial.onnx',
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'path': resolve_relative_path('../../../models/deepfuze/simswap_512_unofficial.onnx'),
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'template': 'arcface_112_v1',
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'size': (512, 512),
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'mean': [ 0.0, 0.0, 0.0 ],
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'standard_deviation': [ 1.0, 1.0, 1.0 ]
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},
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'uniface_256':
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{
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'type': 'uniface',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/uniface_256.onnx',
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'path': resolve_relative_path('../../../models/deepfuze/uniface_256.onnx'),
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'template': 'ffhq_512',
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'size': (256, 256),
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'mean': [ 0.0, 0.0, 0.0 ],
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'standard_deviation': [ 1.0, 1.0, 1.0 ]
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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_model_initializer() -> Any:
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global MODEL_INITIALIZER
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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 MODEL_INITIALIZER is None:
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model_path = get_options('model').get('path')
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model = onnx.load(model_path)
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MODEL_INITIALIZER = numpy_helper.to_array(model.graph.initializer[-1])
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return MODEL_INITIALIZER
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def clear_model_initializer() -> None:
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global MODEL_INITIALIZER
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MODEL_INITIALIZER = 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.face_swapper_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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if has_execution_provider('CoreMLExecutionProvider') or has_execution_provider('OpenVINOExecutionProvider'):
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face_swapper_model_fallback = 'inswapper_128'
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else:
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face_swapper_model_fallback = 'inswapper_128_fp16'
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program.add_argument('--face-swapper-model', help = wording.get('help.face_swapper_model'), default = config.get_str_value('frame_processors.face_swapper_model', face_swapper_model_fallback), choices = frame_processors_choices.face_swapper_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.face_swapper_model = args.face_swapper_model
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if args.face_swapper_model == 'blendswap_256':
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deepfuze.globals.face_recognizer_model = 'arcface_blendswap'
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if args.face_swapper_model == 'inswapper_128' or args.face_swapper_model == 'inswapper_128_fp16':
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deepfuze.globals.face_recognizer_model = 'arcface_inswapper'
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if args.face_swapper_model == 'simswap_256' or args.face_swapper_model == 'simswap_512_unofficial':
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deepfuze.globals.face_recognizer_model = 'arcface_simswap'
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if args.face_swapper_model == 'uniface_256':
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deepfuze.globals.face_recognizer_model = 'arcface_uniface'
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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_image(deepfuze.globals.source_paths):
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logger.error(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
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return False
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source_image_paths = filter_image_paths(deepfuze.globals.source_paths)
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source_frames = read_static_images(source_image_paths)
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for source_frame in source_frames:
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if not get_one_face(source_frame):
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logger.error(wording.get('no_source_face_detected') + 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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if deepfuze.globals.video_memory_strategy == 'strict' or deepfuze.globals.video_memory_strategy == 'moderate':
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clear_model_initializer()
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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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def swap_face(source_face : Face, target_face : Face, temp_vision_frame : VisionFrame) -> VisionFrame:
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model_template = get_options('model').get('template')
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model_size = get_options('model').get('size')
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crop_vision_frame, affine_matrix = warp_face_by_face_landmark_5(temp_vision_frame, target_face.landmarks.get('5/68'), model_template, model_size)
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crop_mask_list = []
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if 'box' in deepfuze.globals.face_mask_types:
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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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crop_vision_frame = prepare_crop_frame(crop_vision_frame)
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crop_vision_frame = apply_swap(source_face, crop_vision_frame)
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crop_vision_frame = normalize_crop_frame(crop_vision_frame)
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if 'region' in deepfuze.globals.face_mask_types:
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region_mask = create_region_mask(crop_vision_frame, deepfuze.globals.face_mask_regions)
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crop_mask_list.append(region_mask)
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crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
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temp_vision_frame = paste_back(temp_vision_frame, crop_vision_frame, crop_mask, affine_matrix)
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return temp_vision_frame
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def apply_swap(source_face : Face, crop_vision_frame : VisionFrame) -> VisionFrame:
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frame_processor = get_frame_processor()
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model_type = get_options('model').get('type')
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frame_processor_inputs = {}
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for frame_processor_input in frame_processor.get_inputs():
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if frame_processor_input.name == 'source':
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if model_type == 'blendswap' or model_type == 'uniface':
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frame_processor_inputs[frame_processor_input.name] = prepare_source_frame(source_face)
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else:
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frame_processor_inputs[frame_processor_input.name] = prepare_source_embedding(source_face)
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if frame_processor_input.name == 'target':
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frame_processor_inputs[frame_processor_input.name] = crop_vision_frame
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with conditional_thread_semaphore(deepfuze.globals.execution_providers):
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crop_vision_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
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return crop_vision_frame
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def prepare_source_frame(source_face : Face) -> VisionFrame:
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model_type = get_options('model').get('type')
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source_vision_frame = read_static_image(deepfuze.globals.source_paths[0])
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if model_type == 'blendswap':
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source_vision_frame, _ = warp_face_by_face_landmark_5(source_vision_frame, source_face.landmarks.get('5/68'), 'arcface_112_v2', (112, 112))
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if model_type == 'uniface':
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source_vision_frame, _ = warp_face_by_face_landmark_5(source_vision_frame, source_face.landmarks.get('5/68'), 'ffhq_512', (256, 256))
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source_vision_frame = source_vision_frame[:, :, ::-1] / 255.0
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source_vision_frame = source_vision_frame.transpose(2, 0, 1)
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source_vision_frame = numpy.expand_dims(source_vision_frame, axis = 0).astype(numpy.float32)
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return source_vision_frame
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def prepare_source_embedding(source_face : Face) -> Embedding:
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model_type = get_options('model').get('type')
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if model_type == 'inswapper':
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model_initializer = get_model_initializer()
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source_embedding = source_face.embedding.reshape((1, -1))
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source_embedding = numpy.dot(source_embedding, model_initializer) / numpy.linalg.norm(source_embedding)
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else:
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source_embedding = source_face.normed_embedding.reshape(1, -1)
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return source_embedding
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def prepare_crop_frame(crop_vision_frame : VisionFrame) -> VisionFrame:
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model_mean = get_options('model').get('mean')
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model_standard_deviation = get_options('model').get('standard_deviation')
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crop_vision_frame = crop_vision_frame[:, :, ::-1] / 255.0
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crop_vision_frame = (crop_vision_frame - model_mean) / model_standard_deviation
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crop_vision_frame = crop_vision_frame.transpose(2, 0, 1)
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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.transpose(1, 2, 0)
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crop_vision_frame = (crop_vision_frame * 255.0).round()
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crop_vision_frame = crop_vision_frame[:, :, ::-1]
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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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return swap_face(source_face, target_face, temp_vision_frame)
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def process_frame(inputs : FaceSwapperInputs) -> VisionFrame:
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reference_faces = inputs.get('reference_faces')
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source_face = inputs.get('source_face')
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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 = swap_face(source_face, target_face, 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 = swap_face(source_face, target_face, 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 = swap_face(source_face, similar_face, 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_frames = read_static_images(source_paths)
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source_face = get_average_face(source_frames)
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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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'source_face': source_face,
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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_frames = read_static_images(source_paths)
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source_face = get_average_face(source_frames)
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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_face': source_face,
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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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frame_processors.multi_process_frames(source_paths, temp_frame_paths, process_frames)
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