mirror of
https://github.com/zebrajr/LivePortrait.git
synced 2026-01-15 12:15:12 +00:00
73ddb69d38
* feat: edit-pose * feat: edit-pose * feat: edit pose (#207) * feat: edit-pose * chore: i2v cropper * chore: i2v cropper * chore: update gradio * chore: update gradio * chore: i2v cropper * chore: update * chore: update * doc: update readme * doc: update readme --------- Co-authored-by: zhangdingyun <zhangdingyun@kuaishou.com> Co-authored-by: ZhizhouZhong <1819489045@qq.com>
56 lines
2.9 KiB
Python
56 lines
2.9 KiB
Python
# coding: utf-8
|
|
|
|
"""
|
|
config dataclass used for inference
|
|
"""
|
|
|
|
import cv2
|
|
from numpy import ndarray
|
|
from dataclasses import dataclass, field
|
|
from typing import Literal, Tuple
|
|
from .base_config import PrintableConfig, make_abs_path
|
|
|
|
|
|
@dataclass(repr=False) # use repr from PrintableConfig
|
|
class InferenceConfig(PrintableConfig):
|
|
# 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
|
|
|
|
# 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_video_editing_head_rotation: 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
|
|
|
|
# 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
|
|
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
|
|
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))
|
|
size_gif: int = 256 # default gif size, TO IMPLEMENT
|
|
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
|