from typing import Any, List, Dict, Literal, Optional from argparse import ArgumentParser import cv2 import threading import numpy import onnxruntime import facefusion.globals import facefusion.processors.frame.core as frame_processors from facefusion import wording from facefusion.face_analyser import get_many_faces, clear_face_analyser from facefusion.face_helper import warp_face, paste_back from facefusion.content_analyser import clear_content_analyser from facefusion.typing import Face, Frame, Update_Process, ProcessMode, ModelValue, OptionsWithModel from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video, is_file, is_download_done, create_metavar, update_status from facefusion.vision import read_image, read_static_image, write_image from facefusion.processors.frame import globals as frame_processors_globals from facefusion.processors.frame import choices as frame_processors_choices FRAME_PROCESSOR = None THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore() THREAD_LOCK : threading.Lock = threading.Lock() NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_ENHANCER' MODELS : Dict[str, ModelValue] =\ { 'codeformer': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/codeformer.onnx', 'path': resolve_relative_path('../.assets/models/codeformer.onnx'), 'template': 'ffhq', 'size': (512, 512) }, 'gfpgan_1.2': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.2.onnx', 'path': resolve_relative_path('../.assets/models/gfpgan_1.2.onnx'), 'template': 'ffhq', 'size': (512, 512) }, 'gfpgan_1.3': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.3.onnx', 'path': resolve_relative_path('../.assets/models/gfpgan_1.3.onnx'), 'template': 'ffhq', 'size': (512, 512) }, 'gfpgan_1.4': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.4.onnx', 'path': resolve_relative_path('../.assets/models/gfpgan_1.4.onnx'), 'template': 'ffhq', 'size': (512, 512) }, 'gpen_bfr_256': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_256.onnx', 'path': resolve_relative_path('../.assets/models/gpen_bfr_256.onnx'), 'template': 'arcface_v2', 'size': (128, 256) }, 'gpen_bfr_512': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_512.onnx', 'path': resolve_relative_path('../.assets/models/gpen_bfr_512.onnx'), 'template': 'ffhq', 'size': (512, 512) }, 'restoreformer': { 'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/restoreformer.onnx', 'path': resolve_relative_path('../.assets/models/restoreformer.onnx'), 'template': 'ffhq', 'size': (512, 512) } } OPTIONS : Optional[OptionsWithModel] = None def get_frame_processor() -> Any: global FRAME_PROCESSOR with THREAD_LOCK: if FRAME_PROCESSOR is None: model_path = get_options('model').get('path') FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = [ 'CUDAExecutionProvider' ]) # FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers) model_path = get_options('model').get('path') FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = [ 'CPUExecutionProvider' ]) return FRAME_PROCESSOR def clear_frame_processor() -> None: global FRAME_PROCESSOR FRAME_PROCESSOR = None def get_options(key : Literal['model']) -> Any: global OPTIONS if OPTIONS is None: OPTIONS =\ { 'model': MODELS[frame_processors_globals.face_enhancer_model] } return OPTIONS.get(key) def set_options(key : Literal['model'], value : Any) -> None: global OPTIONS OPTIONS[key] = value def register_args(program : ArgumentParser) -> None: program.add_argument('--face-enhancer-model', help = wording.get('frame_processor_model_help'), dest = 'face_enhancer_model', default = 'gfpgan_1.4', choices = frame_processors_choices.face_enhancer_models) program.add_argument('--face-enhancer-blend', help = wording.get('frame_processor_blend_help'), dest = 'face_enhancer_blend', type = int, default = 80, choices = frame_processors_choices.face_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.face_enhancer_blend_range)) def apply_args(program : ArgumentParser) -> None: args = program.parse_args() frame_processors_globals.face_enhancer_model = args.face_enhancer_model frame_processors_globals.face_enhancer_blend = args.face_enhancer_blend def pre_check() -> bool: if not facefusion.globals.skip_download: download_directory_path = resolve_relative_path('../.assets/models') model_url = get_options('model').get('url') conditional_download(download_directory_path, [ model_url ]) return True def pre_process(mode : ProcessMode) -> bool: model_url = get_options('model').get('url') model_path = get_options('model').get('path') if not facefusion.globals.skip_download and not is_download_done(model_url, model_path): update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME) return False elif not is_file(model_path): update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME) return False if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path): update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME) return False if mode == 'output' and not facefusion.globals.output_path: update_status(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME) return False return True def post_process() -> None: clear_frame_processor() clear_face_analyser() clear_content_analyser() read_static_image.cache_clear() def enhance_face(target_face: Face, temp_frame: Frame) -> Frame: frame_processor = get_frame_processor() model_template = get_options('model').get('template') model_size = get_options('model').get('size') crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, model_template, model_size) crop_frame = prepare_crop_frame(crop_frame) frame_processor_inputs = {} for frame_processor_input in frame_processor.get_inputs(): if frame_processor_input.name == 'input': frame_processor_inputs[frame_processor_input.name] = crop_frame if frame_processor_input.name == 'weight': frame_processor_inputs[frame_processor_input.name] = numpy.array([ 1 ], dtype = numpy.double) with THREAD_SEMAPHORE: crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0] crop_frame = normalize_crop_frame(crop_frame) paste_frame = paste_back(temp_frame, crop_frame, affine_matrix, facefusion.globals.face_mask_blur, (0, 0, 0, 0)) temp_frame = blend_frame(temp_frame, paste_frame) return temp_frame def prepare_crop_frame(crop_frame : Frame) -> Frame: crop_frame = crop_frame[:, :, ::-1] / 255.0 crop_frame = (crop_frame - 0.5) / 0.5 crop_frame = numpy.expand_dims(crop_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32) return crop_frame def normalize_crop_frame(crop_frame : Frame) -> Frame: crop_frame = numpy.clip(crop_frame, -1, 1) crop_frame = (crop_frame + 1) / 2 crop_frame = crop_frame.transpose(1, 2, 0) crop_frame = (crop_frame * 255.0).round() crop_frame = crop_frame.astype(numpy.uint8)[:, :, ::-1] return crop_frame def blend_frame(temp_frame : Frame, paste_frame : Frame) -> Frame: face_enhancer_blend = 1 - (frame_processors_globals.face_enhancer_blend / 100) temp_frame = cv2.addWeighted(temp_frame, face_enhancer_blend, paste_frame, 1 - face_enhancer_blend, 0) return temp_frame def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame: many_faces = get_many_faces(temp_frame) if many_faces: for target_face in many_faces: temp_frame = enhance_face(target_face, temp_frame) return temp_frame def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None: for temp_frame_path in temp_frame_paths: temp_frame = read_image(temp_frame_path) result_frame = process_frame(None, None, temp_frame) write_image(temp_frame_path, result_frame) update_progress() def process_image(source_path : str, target_path : str, output_path : str) -> None: target_frame = read_static_image(target_path) result_frame = process_frame(None, None, target_frame) write_image(output_path, result_frame) def process_video(source_path : str, temp_frame_paths : List[str]) -> None: frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)