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| # coding: utf-8 | |
| """ | |
| config dataclass used for inference | |
| """ | |
| import cv2 | |
| from numpy import ndarray | |
| import pickle as pkl | |
| from dataclasses import dataclass, field | |
| from typing import Literal, Tuple | |
| from .base_config import PrintableConfig, make_abs_path | |
| def load_lip_array(): | |
| with open(make_abs_path('../utils/resources/lip_array.pkl'), 'rb') as f: | |
| return pkl.load(f) | |
| # use repr from PrintableConfig | |
| class InferenceConfig(PrintableConfig): | |
| # HUMAN MODEL CONFIG, NOT EXPORTED PARAMS | |
| models_config: str = make_abs_path('./models.yaml') # portrait animation config | |
| checkpoint_F: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/appearance_feature_extractor.pth') # path to checkpoint of F | |
| checkpoint_M: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/motion_extractor.pth') # path to checkpoint pf M | |
| checkpoint_G: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/spade_generator.pth') # path to checkpoint of G | |
| checkpoint_W: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/warping_module.pth') # path to checkpoint of W | |
| checkpoint_S: str = make_abs_path('../../pretrained_weights/liveportrait/retargeting_models/stitching_retargeting_module.pth') # path to checkpoint to S and R_eyes, R_lip | |
| # ANIMAL MODEL CONFIG, NOT EXPORTED PARAMS | |
| # version_animals = "" # old version | |
| version_animals = "_v1.1" # new (v1.1) version | |
| checkpoint_F_animal: str = make_abs_path(f'../../pretrained_weights/liveportrait_animals/base_models{version_animals}/appearance_feature_extractor.pth') # path to checkpoint of F | |
| checkpoint_M_animal: str = make_abs_path(f'../../pretrained_weights/liveportrait_animals/base_models{version_animals}/motion_extractor.pth') # path to checkpoint pf M | |
| checkpoint_G_animal: str = make_abs_path(f'../../pretrained_weights/liveportrait_animals/base_models{version_animals}/spade_generator.pth') # path to checkpoint of G | |
| checkpoint_W_animal: str = make_abs_path(f'../../pretrained_weights/liveportrait_animals/base_models{version_animals}/warping_module.pth') # path to checkpoint of W | |
| checkpoint_S_animal: str = make_abs_path('../../pretrained_weights/liveportrait/retargeting_models/stitching_retargeting_module.pth') # path to checkpoint to S and R_eyes, R_lip, NOTE: use human temporarily! | |
| # EXPORTED PARAMS | |
| flag_use_half_precision: bool = True | |
| flag_crop_driving_video: bool = False | |
| device_id: int = 0 | |
| flag_normalize_lip: bool = True | |
| flag_source_video_eye_retargeting: bool = False | |
| flag_eye_retargeting: bool = False | |
| flag_lip_retargeting: bool = False | |
| flag_stitching: bool = True | |
| flag_relative_motion: bool = True | |
| flag_pasteback: bool = True | |
| flag_do_crop: bool = True | |
| flag_do_rot: bool = True | |
| flag_force_cpu: bool = False | |
| flag_do_torch_compile: bool = False | |
| driving_option: str = "pose-friendly" # "expression-friendly" or "pose-friendly" | |
| driving_multiplier: float = 1.0 | |
| driving_smooth_observation_variance: float = 3e-7 # smooth strength scalar for the animated video when the input is a source video, the larger the number, the smoother the animated video; too much smoothness would result in loss of motion accuracy | |
| source_max_dim: int = 1280 # the max dim of height and width of source image or video | |
| source_division: int = 2 # make sure the height and width of source image or video can be divided by this number | |
| animation_region: Literal["exp", "pose", "lip", "eyes", "all"] = "all" # the region where the animation was performed, "exp" means the expression, "pose" means the head pose | |
| # NOT EXPORTED PARAMS | |
| lip_normalize_threshold: float = 0.03 # threshold for flag_normalize_lip | |
| source_video_eye_retargeting_threshold: float = 0.18 # threshold for eyes retargeting if the input is a source video | |
| anchor_frame: int = 0 # TO IMPLEMENT | |
| input_shape: Tuple[int, int] = (256, 256) # input shape | |
| output_format: Literal['mp4', 'gif'] = 'mp4' # output video format | |
| crf: int = 15 # crf for output video | |
| output_fps: int = 25 # default output fps | |
| mask_crop: ndarray = field(default_factory=lambda: cv2.imread(make_abs_path('../utils/resources/mask_template.png'), cv2.IMREAD_COLOR)) | |
| lip_array: ndarray = field(default_factory=load_lip_array) | |
| size_gif: int = 256 # default gif size, TO IMPLEMENT | |