70 lines
4.1 KiB
Python
70 lines
4.1 KiB
Python
# coding: utf-8
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"""
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config dataclass used for inference
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"""
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import cv2
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from numpy import ndarray
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import pickle as pkl
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from dataclasses import dataclass, field
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from typing import Literal, Tuple
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from .base_config import PrintableConfig, make_abs_path
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def load_lip_array():
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with open(make_abs_path('../utils/resources/lip_array.pkl'), 'rb') as f:
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return pkl.load(f)
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@dataclass(repr=False) # use repr from PrintableConfig
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class InferenceConfig(PrintableConfig):
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# HUMAN MODEL CONFIG, NOT EXPORTED PARAMS
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models_config: str = make_abs_path('./models.yaml') # portrait animation config
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checkpoint_F: str = make_abs_path('../../') # path to checkpoint of F
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checkpoint_M: str = make_abs_path('models/live/pretrained_weights/liveportrait/base_models/motion_extractor.pth') # path to checkpoint pf M
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checkpoint_G: str = make_abs_path('models/live/pretrained_weights/liveportrait/base_models/spade_generator.pth') # path to checkpoint of G
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checkpoint_W: str = make_abs_path('models/live/pretrained_weights/liveportrait/base_models/warping_module.pth') # path to checkpoint of W
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checkpoint_S: str = make_abs_path('models/live/pretrained_weights/liveportrait/retargeting_models/stitching_retargeting_module.pth') # path to checkpoint to S and R_eyes, R_lip
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# ANIMAL MODEL CONFIG, NOT EXPORTED PARAMS
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checkpoint_F_animal: str = make_abs_path('models/live/pretrained_weights/liveportrait_animals/base_models/appearance_feature_extractor.pth') # path to checkpoint of F
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checkpoint_M_animal: str = make_abs_path('models/live/pretrained_weights/liveportrait_animals/base_models/motion_extractor.pth') # path to checkpoint pf M
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checkpoint_G_animal: str = make_abs_path('models/live/pretrained_weights/liveportrait_animals/base_models/spade_generator.pth') # path to checkpoint of G
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checkpoint_W_animal: str = make_abs_path('models/live/pretrained_weights/liveportrait_animals/base_models/warping_module.pth') # path to checkpoint of W
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checkpoint_S_animal: str = make_abs_path('models/live/pretrained_weights/liveportrait/retargeting_models/stitching_retargeting_module.pth') # path to checkpoint to S and R_eyes, R_lip, NOTE: use human temporarily!
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# EXPORTED PARAMS
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flag_use_half_precision: bool = True
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flag_crop_driving_video: bool = False
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device_id: int = 0
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flag_normalize_lip: bool = True
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flag_source_video_eye_retargeting: bool = False
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flag_eye_retargeting: bool = False
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flag_lip_retargeting: bool = False
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flag_stitching: bool = True
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flag_relative_motion: bool = True
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flag_pasteback: bool = True
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flag_do_crop: bool = True
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flag_do_rot: bool = True
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flag_force_cpu: bool = False
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flag_do_torch_compile: bool = False
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driving_option: str = "pose-friendly" # "expression-friendly" or "pose-friendly"
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driving_multiplier: float = 1.0
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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
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source_max_dim: int = 1280 # the max dim of height and width of source image or video
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source_division: int = 2 # make sure the height and width of source image or video can be divided by this number
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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
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# NOT EXPORTED PARAMS
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lip_normalize_threshold: float = 0.03 # threshold for flag_normalize_lip
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source_video_eye_retargeting_threshold: float = 0.18 # threshold for eyes retargeting if the input is a source video
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anchor_frame: int = 0 # TO IMPLEMENT
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input_shape: Tuple[int, int] = (256, 256) # input shape
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output_format: Literal['mp4', 'gif'] = 'mp4' # output video format
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crf: int = 15 # crf for output video
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output_fps: int = 25 # default output fps
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mask_crop: ndarray = field(default_factory=lambda: cv2.imread(make_abs_path('../utils/resources/mask_template.png'), cv2.IMREAD_COLOR))
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lip_array: ndarray = field(default_factory=load_lip_array)
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size_gif: int = 256 # default gif size, TO IMPLEMENT
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