279 lines
11 KiB
Python
279 lines
11 KiB
Python
# coding: utf-8
|
|
|
|
"""
|
|
Pipeline of LivePortrait
|
|
"""
|
|
|
|
import cv2
|
|
import comfy.utils
|
|
from tqdm import tqdm
|
|
import numpy as np
|
|
from .config.inference_config import InferenceConfig
|
|
|
|
from .utils.camera import get_rotation_matrix
|
|
from .utils.crop import _transform_img
|
|
from .live_portrait_wrapper import LivePortraitWrapper
|
|
|
|
import os
|
|
script_directory = os.path.dirname(os.path.abspath(__file__))
|
|
|
|
class LivePortraitPipeline(object):
|
|
def __init__(
|
|
self,
|
|
appearance_feature_extractor,
|
|
motion_extractor,
|
|
warping_module,
|
|
spade_generator,
|
|
stitching_retargeting_module,
|
|
inference_cfg: InferenceConfig,
|
|
):
|
|
self.live_portrait_wrapper: LivePortraitWrapper = LivePortraitWrapper(
|
|
appearance_feature_extractor,
|
|
motion_extractor,
|
|
warping_module,
|
|
spade_generator,
|
|
stitching_retargeting_module,
|
|
cfg=inference_cfg,
|
|
)
|
|
|
|
def _get_source_frame(self, source_np, idx, total_frames, method):
|
|
if source_np.shape[0] == 1:
|
|
return source_np[0]
|
|
|
|
if method == "constant":
|
|
return source_np[min(idx, source_np.shape[0] - 1)]
|
|
elif method == "cycle":
|
|
return source_np[idx % source_np.shape[0]]
|
|
elif method == "mirror":
|
|
cycle_length = 2 * source_np.shape[0] - 2
|
|
mirror_idx = idx % cycle_length
|
|
if mirror_idx >= source_np.shape[0]:
|
|
mirror_idx = cycle_length - mirror_idx
|
|
return source_np[mirror_idx]
|
|
elif method == "nearest":
|
|
ratio = idx / (total_frames - 1)
|
|
return source_np[
|
|
min(int(ratio * (source_np.shape[0] - 1)), source_np.shape[0] - 1)
|
|
]
|
|
|
|
def execute(
|
|
self, source_np, driving_images_np, crop_info, mismatch_method="constant", reference_frame=0
|
|
):
|
|
inference_cfg = self.live_portrait_wrapper.cfg
|
|
|
|
cropped_image_list = []
|
|
composited_image_list = []
|
|
driving_landmark_list = []
|
|
out_mask_list = []
|
|
R_d_0, x_d_0_info = None, None
|
|
|
|
total_frames = driving_images_np.shape[0]
|
|
|
|
pbar = comfy.utils.ProgressBar(total_frames)
|
|
|
|
if inference_cfg.flag_eye_retargeting or inference_cfg.flag_lip_retargeting:
|
|
if 'driving_landmark_list' in crop_info.keys():
|
|
driving_landmark_list = crop_info["driving_landmark_list"]
|
|
else:
|
|
raise ValueError("Missing driving_landmark_list in crop_info, get it from opt_driving_images in the Cropper node")
|
|
|
|
for i in tqdm(range(total_frames), desc='Animating...', total=total_frames):
|
|
source_frame_rgb = self._get_source_frame(
|
|
source_np, i, total_frames, mismatch_method
|
|
)
|
|
driving_frame = driving_images_np[i]
|
|
|
|
safe_index = min(i, len(crop_info["crop_info_list"]) - 1)
|
|
source_lmk = crop_info["crop_info_list"][safe_index]["lmk_crop"]
|
|
img_crop_256x256 = crop_info["crop_info_list"][safe_index]["img_crop_256x256"]
|
|
|
|
if inference_cfg.flag_do_crop:
|
|
I_s = self.live_portrait_wrapper.prepare_source(img_crop_256x256)
|
|
else:
|
|
I_s = self.live_portrait_wrapper.prepare_source(source_frame_rgb)
|
|
|
|
x_s_info = self.live_portrait_wrapper.get_kp_info(I_s)
|
|
x_c_s = x_s_info["kp"]
|
|
R_s = get_rotation_matrix(
|
|
x_s_info["pitch"], x_s_info["yaw"], x_s_info["roll"]
|
|
)
|
|
f_s = self.live_portrait_wrapper.extract_feature_3d(I_s)
|
|
x_s = self.live_portrait_wrapper.transform_keypoint(x_s_info)
|
|
|
|
if inference_cfg.flag_lip_zero:
|
|
c_d_lip_before_animation = [0.0]
|
|
combined_lip_ratio_tensor_before_animation = (
|
|
self.live_portrait_wrapper.calc_combined_lip_ratio(
|
|
c_d_lip_before_animation, source_lmk
|
|
)
|
|
)
|
|
if (
|
|
combined_lip_ratio_tensor_before_animation[0][0]
|
|
< inference_cfg.lip_zero_threshold
|
|
):
|
|
inference_cfg.flag_lip_zero = False
|
|
else:
|
|
lip_delta_before_animation = (
|
|
self.live_portrait_wrapper.retarget_lip(
|
|
x_s, combined_lip_ratio_tensor_before_animation
|
|
)
|
|
)
|
|
|
|
driving_frame_256 = cv2.resize(driving_frame, (256, 256))
|
|
I_d = self.live_portrait_wrapper.prepare_driving_videos(
|
|
[driving_frame_256]
|
|
)[0]
|
|
|
|
if inference_cfg.flag_eye_retargeting or inference_cfg.flag_lip_retargeting:
|
|
input_eye_ratio_lst, input_lip_ratio_lst = (
|
|
self.live_portrait_wrapper.calc_retargeting_ratio(
|
|
source_lmk, driving_landmark_list
|
|
)
|
|
)
|
|
|
|
x_d_info = self.live_portrait_wrapper.get_kp_info(I_d)
|
|
R_d = get_rotation_matrix(
|
|
x_d_info["pitch"], x_d_info["yaw"], x_d_info["roll"]
|
|
)
|
|
|
|
if i == 0:
|
|
R_d_0 = R_d
|
|
x_d_0_info = x_d_info
|
|
|
|
if inference_cfg.flag_relative:
|
|
R_new = (R_d @ R_d_0.permute(0, 2, 1)) @ R_s
|
|
delta_new = x_s_info["exp"] + (x_d_info["exp"] - x_d_0_info["exp"])
|
|
scale_new = x_s_info["scale"] * (
|
|
x_d_info["scale"] / x_d_0_info["scale"]
|
|
)
|
|
t_new = x_s_info["t"] + (x_d_info["t"] - x_d_0_info["t"])
|
|
else:
|
|
R_new = R_d
|
|
delta_new = x_d_info["exp"]
|
|
scale_new = x_s_info["scale"]
|
|
t_new = x_d_info["t"]
|
|
|
|
t_new[..., 2].fill_(0) # zero tz
|
|
x_d_i_new = scale_new * (x_c_s @ R_new + delta_new) + t_new
|
|
if (
|
|
not inference_cfg.flag_stitching
|
|
and not inference_cfg.flag_eye_retargeting
|
|
and not inference_cfg.flag_lip_retargeting
|
|
):
|
|
# without stitching or retargeting
|
|
if inference_cfg.flag_lip_zero:
|
|
x_d_i_new += lip_delta_before_animation.reshape(-1, x_s.shape[1], 3)
|
|
else:
|
|
pass
|
|
elif (
|
|
inference_cfg.flag_stitching
|
|
and not inference_cfg.flag_eye_retargeting
|
|
and not inference_cfg.flag_lip_retargeting
|
|
):
|
|
# with stitching and without retargeting
|
|
if inference_cfg.flag_lip_zero:
|
|
x_d_i_new = self.live_portrait_wrapper.stitching(
|
|
x_s, x_d_i_new
|
|
) + lip_delta_before_animation.reshape(-1, x_s.shape[1], 3)
|
|
else:
|
|
x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new)
|
|
else:
|
|
eyes_delta, lip_delta = None, None
|
|
if inference_cfg.flag_eye_retargeting:
|
|
c_d_eyes_i = input_eye_ratio_lst[i]
|
|
combined_eye_ratio_tensor = (
|
|
self.live_portrait_wrapper.calc_combined_eye_ratio(
|
|
c_d_eyes_i, source_lmk
|
|
)
|
|
)
|
|
combined_eye_ratio_tensor = (
|
|
combined_eye_ratio_tensor
|
|
* inference_cfg.eyes_retargeting_multiplier
|
|
)
|
|
# ∆_eyes,i = R_eyes(x_s; c_s,eyes, c_d,eyes,i)
|
|
eyes_delta = self.live_portrait_wrapper.retarget_eye(
|
|
x_s, combined_eye_ratio_tensor
|
|
)
|
|
if inference_cfg.flag_lip_retargeting:
|
|
c_d_lip_i = input_lip_ratio_lst[i]
|
|
combined_lip_ratio_tensor = (
|
|
self.live_portrait_wrapper.calc_combined_lip_ratio(
|
|
c_d_lip_i, source_lmk
|
|
)
|
|
)
|
|
combined_lip_ratio_tensor = (
|
|
combined_lip_ratio_tensor
|
|
* inference_cfg.lip_retargeting_multiplier
|
|
)
|
|
# ∆_lip,i = R_lip(x_s; c_s,lip, c_d,lip,i)
|
|
lip_delta = self.live_portrait_wrapper.retarget_lip(
|
|
x_s, combined_lip_ratio_tensor
|
|
)
|
|
|
|
if inference_cfg.flag_relative: # use x_s
|
|
x_d_i_new = (
|
|
x_s
|
|
+ (
|
|
eyes_delta.reshape(-1, x_s.shape[1], 3)
|
|
if eyes_delta is not None
|
|
else 0
|
|
)
|
|
+ (
|
|
lip_delta.reshape(-1, x_s.shape[1], 3)
|
|
if lip_delta is not None
|
|
else 0
|
|
)
|
|
)
|
|
else: # use x_d,i
|
|
x_d_i_new = (
|
|
x_d_i_new
|
|
+ (
|
|
eyes_delta.reshape(-1, x_s.shape[1], 3)
|
|
if eyes_delta is not None
|
|
else 0
|
|
)
|
|
+ (
|
|
lip_delta.reshape(-1, x_s.shape[1], 3)
|
|
if lip_delta is not None
|
|
else 0
|
|
)
|
|
)
|
|
|
|
if inference_cfg.flag_stitching:
|
|
x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new)
|
|
|
|
out = self.live_portrait_wrapper.warp_decode(f_s, x_s, x_d_i_new)
|
|
|
|
if inference_cfg.flag_stitching:
|
|
x_d_i_new = self.live_portrait_wrapper.stitching(x_s, x_d_i_new)
|
|
|
|
out = self.live_portrait_wrapper.warp_decode(f_s, x_s, x_d_i_new)
|
|
I_p_i = self.live_portrait_wrapper.parse_output(out["out"])[0]
|
|
cropped_image_list.append(I_p_i)
|
|
|
|
# Transform and blend
|
|
I_p_i_to_ori = _transform_img(
|
|
I_p_i,
|
|
crop_info["crop_info_list"][safe_index]["M_c2o"],
|
|
dsize=(source_frame_rgb.shape[1], source_frame_rgb.shape[0]),
|
|
)
|
|
|
|
if inference_cfg.flag_pasteback:
|
|
if inference_cfg.mask_crop is None:
|
|
inference_cfg.mask_crop = cv2.imread(os.path.join(script_directory, "utils", "resources", "mask_template.png"), cv2.IMREAD_COLOR)
|
|
mask_ori = _transform_img(
|
|
inference_cfg.mask_crop,
|
|
crop_info["crop_info_list"][safe_index]["M_c2o"],
|
|
dsize=(source_frame_rgb.shape[1], source_frame_rgb.shape[0]),
|
|
)
|
|
mask_ori = mask_ori.astype(np.float32) / 255.0
|
|
I_p_i_to_ori_blend = np.clip(
|
|
mask_ori * I_p_i_to_ori + (1 - mask_ori) * source_frame_rgb, 0, 255
|
|
).astype(np.uint8)
|
|
|
|
composited_image_list.append(I_p_i_to_ori_blend)
|
|
out_mask_list.append(mask_ori)
|
|
pbar.update(1)
|
|
|
|
return cropped_image_list, composited_image_list, out_mask_list
|