initial commit

.gitignore

@@ -0,0 +1,17 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+**/__pycache__/
+*.py[cod]
+**/*.py[cod]
+*$py.class
+
+# Model weights
+**/*.pth
+**/*.onnx
+
+# Ipython notebook
+*.ipynb
+
+# Temporary files or benchmark resources
+animations/*
+tmp/*

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Kuaishou Visual Generation and Interaction Center
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

__init__.py

@@ -0,0 +1,3 @@
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

assets/.gitattributes

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liveportrait/config/argument_config.py

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+# coding: utf-8
+
+"""
+config for user
+"""
+
+import os.path as osp
+from dataclasses import dataclass
+#import tyro
+from typing_extensions import Annotated
+from .base_config import PrintableConfig, make_abs_path
+
+
+@dataclass(repr=False)  # use repr from PrintableConfig
+class ArgumentConfig(PrintableConfig):
+    ########## input arguments ##########
+    #source_image: Annotated[str, tyro.conf.arg(aliases=["-s"])] = make_abs_path('../../assets/examples/source/s6.jpg')  # path to the reference portrait
+    #driving_info:  Annotated[str, tyro.conf.arg(aliases=["-d"])] = make_abs_path('../../assets/examples/driving/d0.mp4')  # path to driving video or template (.pkl format)
+    #output_dir: Annotated[str, tyro.conf.arg(aliases=["-o"])] = 'animations/'  # directory to save output video
+    #####################################
+
+    ########## inference arguments ##########
+    device_id: int = 0
+    flag_lip_zero : bool = True # whether let the lip to close state before animation, only take effect when flag_eye_retargeting and flag_lip_retargeting is False
+    flag_eye_retargeting: bool = False
+    flag_lip_retargeting: bool = False
+    flag_stitching: bool = True  # we recommend setting it to True!
+    flag_relative: bool = True  # whether to use relative pose
+    flag_pasteback: bool = True  # whether to paste-back/stitch the animated face cropping from the face-cropping space to the original image space
+    flag_do_crop: bool = True  # whether to crop the reference portrait to the face-cropping space
+    flag_do_rot: bool = True  # whether to conduct the rotation when flag_do_crop is True
+    #########################################
+
+    ########## crop arguments ##########
+    dsize: int = 512
+    scale: float = 2.3
+    vx_ratio: float = 0  # vx ratio
+    vy_ratio: float = -0.125  # vy ratio +up, -down
+    ####################################
+
+    ########## gradio arguments ##########
+    #server_port: Annotated[int, tyro.conf.arg(aliases=["-p"])]  = 8890
+    #share: bool = False
+    #server_name: str = "0.0.0.0"

liveportrait/config/base_config.py

@@ -0,0 +1,29 @@
+# coding: utf-8
+
+"""
+pretty printing class
+"""
+
+from __future__ import annotations
+import os.path as osp
+from typing import Tuple
+
+
+def make_abs_path(fn):
+    return osp.join(osp.dirname(osp.realpath(__file__)), fn)
+
+
+class PrintableConfig:  # pylint: disable=too-few-public-methods
+    """Printable Config defining str function"""
+
+    def __repr__(self):
+        lines = [self.__class__.__name__ + ":"]
+        for key, val in vars(self).items():
+            if isinstance(val, Tuple):
+                flattened_val = "["
+                for item in val:
+                    flattened_val += str(item) + "\n"
+                flattened_val = flattened_val.rstrip("\n")
+                val = flattened_val + "]"
+            lines += f"{key}: {str(val)}".split("\n")
+        return "\n    ".join(lines)

liveportrait/config/crop_config.py

@@ -0,0 +1,18 @@
+# coding: utf-8
+
+"""
+parameters used for crop faces
+"""
+
+import os.path as osp
+from dataclasses import dataclass
+from typing import Union, List
+from .base_config import PrintableConfig
+
+
+@dataclass(repr=False)  # use repr from PrintableConfig
+class CropConfig(PrintableConfig):
+    dsize: int = 512  # crop size
+    scale: float = 2.3  # scale factor
+    vx_ratio: float = 0  # vx ratio
+    vy_ratio: float = -0.125  # vy ratio +up, -down

liveportrait/config/inference_config.py

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+# coding: utf-8
+
+"""
+config dataclass used for inference
+"""
+
+import os.path as osp
+from dataclasses import dataclass
+from typing import Literal, Tuple
+from .base_config import PrintableConfig, make_abs_path
+
+
+@dataclass(repr=False)  # use repr from PrintableConfig
+class InferenceConfig(PrintableConfig):
+    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
+    checkpoint_M: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/motion_extractor.pth')  # path to checkpoint
+    checkpoint_G: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/spade_generator.pth')  # path to checkpoint
+    checkpoint_W: str = make_abs_path('../../pretrained_weights/liveportrait/base_models/warping_module.pth')  # path to checkpoint
+
+    checkpoint_S: str = make_abs_path('../../pretrained_weights/liveportrait/retargeting_models/stitching_retargeting_module.pth')  # path to checkpoint
+    flag_use_half_precision: bool = True  # whether to use half precision
+
+    flag_lip_zero: bool = True  # whether let the lip to close state before animation, only take effect when flag_eye_retargeting and flag_lip_retargeting is False
+    lip_zero_threshold: float = 0.03
+
+    flag_eye_retargeting: bool = False
+    flag_lip_retargeting: bool = False
+    flag_stitching: bool = True  # we recommend setting it to True!
+
+    flag_relative: bool = True  # whether to use relative pose
+    anchor_frame: int = 0  # set this value if find_best_frame is True
+
+    input_shape: Tuple[int, int] = (256, 256)  # input shape
+    output_format: Literal['mp4', 'gif'] = 'mp4'  # output video format
+    output_fps: int = 30  # fps for output video
+    crf: int = 15  # crf for output video
+
+    flag_write_result: bool = True  # whether to write output video
+    flag_pasteback: bool = True  # whether to paste-back/stitch the animated face cropping from the face-cropping space to the original image space
+    mask_crop = None
+    flag_write_gif: bool = False
+    size_gif: int = 256
+    ref_max_shape: int = 1280
+    ref_shape_n: int = 2
+
+    device_id: int = 0
+    flag_do_crop: bool = False  # whether to crop the reference portrait to the face-cropping space
+    flag_do_rot: bool = True  # whether to conduct the rotation when flag_do_crop is True

liveportrait/config/models.yaml

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+model_params:
+  appearance_feature_extractor_params: # the F in the paper
+    image_channel: 3
+    block_expansion: 64
+    num_down_blocks: 2
+    max_features: 512
+    reshape_channel: 32
+    reshape_depth: 16
+    num_resblocks: 6
+  motion_extractor_params: # the M in the paper
+    num_kp: 21
+    backbone: convnextv2_tiny
+  warping_module_params: # the W in the paper
+    num_kp: 21
+    block_expansion: 64
+    max_features: 512
+    num_down_blocks: 2
+    reshape_channel: 32
+    estimate_occlusion_map: True
+    dense_motion_params:
+      block_expansion: 32
+      max_features: 1024
+      num_blocks: 5
+      reshape_depth: 16
+      compress: 4
+  spade_generator_params: # the G in the paper
+    upscale: 2 # represents upsample factor 256x256 -> 512x512
+    block_expansion: 64
+    max_features: 512
+    num_down_blocks: 2
+  stitching_retargeting_module_params: # the S in the paper
+    stitching:
+      input_size: 126 # (21*3)*2
+      hidden_sizes: [128, 128, 64]
+      output_size: 65 # (21*3)+2(tx,ty)
+    lip:
+      input_size: 65 # (21*3)+2
+      hidden_sizes: [128, 128, 64]
+      output_size: 63 # (21*3)
+    eye:
+      input_size: 66 # (21*3)+3
+      hidden_sizes: [256, 256, 128, 128, 64]
+      output_size: 63 # (21*3)

liveportrait/live_portrait_pipeline.py

@@ -0,0 +1,207 @@
+# coding: utf-8
+
+"""
+Pipeline of LivePortrait
+"""
+
+# TODO:
+# 1. 当前假定所有的模板都是已经裁好的，需要修改下
+# 2. pick样例图 source + driving
+
+import cv2
+import numpy as np
+import pickle
+import os.path as osp
+from rich.progress import track
+
+from .config.argument_config import ArgumentConfig
+from .config.inference_config import InferenceConfig
+from .config.crop_config import CropConfig
+from .utils.cropper import Cropper
+from .utils.camera import get_rotation_matrix
+from .utils.video import images2video, concat_frames
+from .utils.crop import _transform_img
+from .utils.retargeting_utils import calc_lip_close_ratio
+from .utils.io import load_image_rgb, load_driving_info
+from .utils.helper import mkdir, basename, dct2cuda, is_video, is_template, resize_to_limit
+from .utils.rprint import rlog as log
+from .live_portrait_wrapper import LivePortraitWrapper
+
+import comfy.utils
+def make_abs_path(fn):
+    return osp.join(osp.dirname(osp.realpath(__file__)), fn)
+
+
+class LivePortraitPipeline(object):
+
+    def __init__(self, appearance_feature_extractor, motion_extractor, warping_module,
+                 spade_generator, stitching_retargeting_module, inference_cfg: InferenceConfig, crop_cfg: CropConfig):
+
+        self.live_portrait_wrapper: LivePortraitWrapper = LivePortraitWrapper(
+                appearance_feature_extractor, motion_extractor, warping_module,
+                spade_generator, stitching_retargeting_module, cfg=inference_cfg)
+
+        self.cropper = Cropper(crop_cfg=crop_cfg)
+
+    def execute(self, img_rgb, driving_images_np, args: ArgumentConfig):
+        inference_cfg = self.live_portrait_wrapper.cfg # for convenience
+        ######## process reference portrait ########
+        #img_rgb = load_image_rgb(args.source_image)
+        img_rgb = resize_to_limit(img_rgb, inference_cfg.ref_max_shape, inference_cfg.ref_shape_n)
+        #log(f"Load source image from {args.source_image}")
+        crop_info = self.cropper.crop_single_image(img_rgb)
+        source_lmk = crop_info['lmk_crop']
+        img_crop, img_crop_256x256 = crop_info['img_crop'], crop_info['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(img_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:
+            # let lip-open scalar to be 0 at first
+            c_d_lip_before_animation = [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)
+        ############################################
+
+        ######## process driving info ########
+        #if is_video(args.driving_info):
+        #log(f"Load from video file (mp4 mov avi etc...): {args.driving_info}")
+        # TODO: 这里track一下驱动视频 -> 构建模板
+        #driving_rgb_lst = load_driving_info(args.driving_info)
+
+        driving_rgb_lst = driving_images_np
+
+        driving_rgb_lst_256 = [cv2.resize(_, (256, 256)) for _ in driving_rgb_lst]
+        I_d_lst = self.live_portrait_wrapper.prepare_driving_videos(driving_rgb_lst_256)
+        n_frames = I_d_lst.shape[0]
+        if inference_cfg.flag_eye_retargeting or inference_cfg.flag_lip_retargeting:
+            driving_lmk_lst = self.cropper.get_retargeting_lmk_info(driving_rgb_lst)
+            input_eye_ratio_lst, input_lip_ratio_lst = self.live_portrait_wrapper.calc_retargeting_ratio(source_lmk, driving_lmk_lst)
+        # elif is_template(args.driving_info):
+        #     log(f"Load from video templates {args.driving_info}")
+        #     with open(args.driving_info, 'rb') as f:
+        #         template_lst, driving_lmk_lst = pickle.load(f)
+        #     n_frames = template_lst[0]['n_frames']
+        #     input_eye_ratio_lst, input_lip_ratio_lst = self.live_portrait_wrapper.calc_retargeting_ratio(source_lmk, driving_lmk_lst)
+        # else:
+        #     raise Exception("Unsupported driving types!")
+        #########################################
+
+        ######## prepare for pasteback ########
+        if inference_cfg.flag_pasteback:
+            if inference_cfg.mask_crop is None:
+                inference_cfg.mask_crop = cv2.imread(make_abs_path('./utils/resources/mask_template.png'), cv2.IMREAD_COLOR)
+            mask_ori = _transform_img(inference_cfg.mask_crop, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0]))
+            mask_ori = mask_ori.astype(np.float32) / 255.
+            I_p_paste_lst = []
+        #########################################
+
+        I_p_lst = []
+        R_d_0, x_d_0_info = None, None
+        pbar = comfy.utils.ProgressBar(n_frames)
+        for i in track(range(n_frames), description='Animating...', total=n_frames):
+            #if is_video(args.driving_info):
+            # extract kp info by M
+            I_d_i = I_d_lst[i]
+            x_d_i_info = self.live_portrait_wrapper.get_kp_info(I_d_i)
+            R_d_i = get_rotation_matrix(x_d_i_info['pitch'], x_d_i_info['yaw'], x_d_i_info['roll'])
+            # else:
+            #     # from template
+            #     x_d_i_info = template_lst[i]
+            #     x_d_i_info = dct2cuda(x_d_i_info, inference_cfg.device_id)
+            #     R_d_i = x_d_i_info['R_d']
+
+            if i == 0:
+                R_d_0 = R_d_i
+                x_d_0_info = x_d_i_info
+
+            if inference_cfg.flag_relative:
+                R_new = (R_d_i @ R_d_0.permute(0, 2, 1)) @ R_s
+                delta_new = x_s_info['exp'] + (x_d_i_info['exp'] - x_d_0_info['exp'])
+                scale_new = x_s_info['scale'] * (x_d_i_info['scale'] / x_d_0_info['scale'])
+                t_new = x_s_info['t'] + (x_d_i_info['t'] - x_d_0_info['t'])
+            else:
+                R_new = R_d_i
+                delta_new = x_d_i_info['exp']
+                scale_new = x_s_info['scale']
+                t_new = x_d_i_info['t']
+
+            t_new[..., 2].fill_(0) # zero tz
+            x_d_i_new = scale_new * (x_c_s @ R_new + delta_new) + t_new
+
+            # Algorithm 1:
+            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)
+                    # ∆_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)
+                    # ∆_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)
+            I_p_i = self.live_portrait_wrapper.parse_output(out['out'])[0]
+            I_p_lst.append(I_p_i)
+            pbar.update(1)
+
+            #if inference_cfg.flag_pasteback:
+            I_p_i_to_ori = _transform_img(I_p_i, crop_info['M_c2o'], dsize=(img_rgb.shape[1], img_rgb.shape[0]))
+            I_p_i_to_ori_blend = np.clip(mask_ori * I_p_i_to_ori + (1 - mask_ori) * img_rgb, 0, 255).astype(np.uint8)
+            out = np.hstack([I_p_i_to_ori, I_p_i_to_ori_blend])
+            I_p_paste_lst.append(I_p_i_to_ori_blend)
+
+        #mkdir(args.output_dir)
+        wfp_concat = None
+        #if is_video(args.driving_info):
+
+        #frames_concatenated = concat_frames(I_p_lst, driving_rgb_lst, img_crop_256x256)
+        return I_p_lst, I_p_paste_lst
+        # # save (driving frames, source image, drived frames) result
+        # wfp_concat = osp.join(args.output_dir, f'{basename(args.source_image)}--{basename(args.driving_info)}_concat.mp4')
+        # images2video(frames_concatenated, wfp=wfp_concat)
+
+        # # save drived result
+        # wfp = osp.join(args.output_dir, f'{basename(args.source_image)}--{basename(args.driving_info)}.mp4')
+        # if inference_cfg.flag_pasteback:
+        #     images2video(I_p_paste_lst, wfp=wfp)
+        # else:
+        #     images2video(I_p_lst, wfp=wfp)
+
+        # return wfp, wfp_concat

liveportrait/live_portrait_wrapper.py

@@ -0,0 +1,341 @@
+# coding: utf-8
+
+"""
+Wrapper for LivePortrait core functions
+"""
+
+import os.path as osp
+import numpy as np
+import cv2
+import torch
+import yaml
+
+from .utils.timer import Timer
+from .utils.helper import load_model, concat_feat
+from .utils.retargeting_utils import compute_eye_delta, compute_lip_delta
+from .utils.camera import headpose_pred_to_degree, get_rotation_matrix
+from .utils.retargeting_utils import calc_eye_close_ratio, calc_lip_close_ratio
+from .config.inference_config import InferenceConfig
+from .utils.rprint import rlog as log
+
+
+class LivePortraitWrapper(object):
+
+    def __init__(self, appearance_feature_extractor, motion_extractor, warping_module,
+                 spade_generator, stitching_retargeting_module, cfg: InferenceConfig):
+
+        # model_config = yaml.load(open(cfg.models_config, 'r'), Loader=yaml.SafeLoader)
+
+        # # init F
+        # self.appearance_feature_extractor = load_model(cfg.checkpoint_F, model_config, cfg.device_id, 'appearance_feature_extractor')
+        # log(f'Load appearance_feature_extractor done.')
+        # # init M
+        # self.motion_extractor = load_model(cfg.checkpoint_M, model_config, cfg.device_id, 'motion_extractor')
+        # log(f'Load motion_extractor done.')
+        # # init W
+        # self.warping_module = load_model(cfg.checkpoint_W, model_config, cfg.device_id, 'warping_module')
+        # log(f'Load warping_module done.')
+        # # init G
+        # self.spade_generator = load_model(cfg.checkpoint_G, model_config, cfg.device_id, 'spade_generator')
+        # log(f'Load spade_generator done.')
+        # # init S and R
+        # if cfg.checkpoint_S is not None and osp.exists(cfg.checkpoint_S):
+        #     self.stitching_retargeting_module = load_model(cfg.checkpoint_S, model_config, cfg.device_id, 'stitching_retargeting_module')
+        #     log(f'Load stitching_retargeting_module done.')
+        # else:
+        #     self.stitching_retargeting_module = None
+        self.appearance_feature_extractor = appearance_feature_extractor
+        self.motion_extractor = motion_extractor
+        self.warping_module = warping_module
+        self.spade_generator = spade_generator
+        self.stitching_retargeting_module = stitching_retargeting_module
+
+        self.cfg = cfg
+        self.device_id = cfg.device_id
+        self.timer = Timer()
+
+    def update_config(self, user_args):
+        for k, v in user_args.items():
+            if hasattr(self.cfg, k):
+                setattr(self.cfg, k, v)
+
+    def prepare_source(self, img: np.ndarray) -> torch.Tensor:
+        """ construct the input as standard
+        img: HxWx3, uint8, 256x256
+        """
+        h, w = img.shape[:2]
+        if h != self.cfg.input_shape[0] or w != self.cfg.input_shape[1]:
+            x = cv2.resize(img, (self.cfg.input_shape[0], self.cfg.input_shape[1]))
+        else:
+            x = img.copy()
+
+        if x.ndim == 3:
+            x = x[np.newaxis].astype(np.float32) / 255.  # HxWx3 -> 1xHxWx3, normalized to 0~1
+        elif x.ndim == 4:
+            x = x.astype(np.float32) / 255.  # BxHxWx3, normalized to 0~1
+        else:
+            raise ValueError(f'img ndim should be 3 or 4: {x.ndim}')
+        x = np.clip(x, 0, 1)  # clip to 0~1
+        x = torch.from_numpy(x).permute(0, 3, 1, 2)  # 1xHxWx3 -> 1x3xHxW
+        x = x.cuda(self.device_id)
+        return x
+
+    def prepare_driving_videos(self, imgs) -> torch.Tensor:
+        """ construct the input as standard
+        imgs: NxBxHxWx3, uint8
+        """
+        if isinstance(imgs, list):
+            _imgs = np.array(imgs)[..., np.newaxis]  # TxHxWx3x1
+        elif isinstance(imgs, np.ndarray):
+            _imgs = imgs
+        else:
+            raise ValueError(f'imgs type error: {type(imgs)}')
+
+        y = _imgs.astype(np.float32) / 255.
+        y = np.clip(y, 0, 1)  # clip to 0~1
+        y = torch.from_numpy(y).permute(0, 4, 3, 1, 2)  # TxHxWx3x1 -> Tx1x3xHxW
+        y = y.cuda(self.device_id)
+
+        return y
+
+    def extract_feature_3d(self, x: torch.Tensor) -> torch.Tensor:
+        """ get the appearance feature of the image by F
+        x: Bx3xHxW, normalized to 0~1
+        """
+        with torch.no_grad():
+            with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=self.cfg.flag_use_half_precision):
+                feature_3d = self.appearance_feature_extractor(x)
+
+        return feature_3d.float()
+
+    def get_kp_info(self, x: torch.Tensor, **kwargs) -> dict:
+        """ get the implicit keypoint information
+        x: Bx3xHxW, normalized to 0~1
+        flag_refine_info: whether to trandform the pose to degrees and the dimention of the reshape
+        return: A dict contains keys: 'pitch', 'yaw', 'roll', 't', 'exp', 'scale', 'kp'
+        """
+        with torch.no_grad():
+            with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=self.cfg.flag_use_half_precision):
+                kp_info = self.motion_extractor(x)
+
+            if self.cfg.flag_use_half_precision:
+                # float the dict
+                for k, v in kp_info.items():
+                    if isinstance(v, torch.Tensor):
+                        kp_info[k] = v.float()
+
+        flag_refine_info: bool = kwargs.get('flag_refine_info', True)
+        if flag_refine_info:
+            bs = kp_info['kp'].shape[0]
+            kp_info['pitch'] = headpose_pred_to_degree(kp_info['pitch'])[:, None]  # Bx1
+            kp_info['yaw'] = headpose_pred_to_degree(kp_info['yaw'])[:, None]  # Bx1
+            kp_info['roll'] = headpose_pred_to_degree(kp_info['roll'])[:, None]  # Bx1
+            kp_info['kp'] = kp_info['kp'].reshape(bs, -1, 3)  # BxNx3
+            kp_info['exp'] = kp_info['exp'].reshape(bs, -1, 3)  # BxNx3
+
+        return kp_info
+
+    def get_pose_dct(self, kp_info: dict) -> dict:
+        pose_dct = dict(
+            pitch=headpose_pred_to_degree(kp_info['pitch']).item(),
+            yaw=headpose_pred_to_degree(kp_info['yaw']).item(),
+            roll=headpose_pred_to_degree(kp_info['roll']).item(),
+        )
+        return pose_dct
+
+    def get_fs_and_kp_info(self, source_prepared, driving_first_frame):
+
+        # get the canonical keypoints of source image by M
+        source_kp_info = self.get_kp_info(source_prepared, flag_refine_info=True)
+        source_rotation = get_rotation_matrix(source_kp_info['pitch'], source_kp_info['yaw'], source_kp_info['roll'])
+
+        # get the canonical keypoints of first driving frame by M
+        driving_first_frame_kp_info = self.get_kp_info(driving_first_frame, flag_refine_info=True)
+        driving_first_frame_rotation = get_rotation_matrix(
+            driving_first_frame_kp_info['pitch'],
+            driving_first_frame_kp_info['yaw'],
+            driving_first_frame_kp_info['roll']
+        )
+
+        # get feature volume by F
+        source_feature_3d = self.extract_feature_3d(source_prepared)
+
+        return source_kp_info, source_rotation, source_feature_3d, driving_first_frame_kp_info, driving_first_frame_rotation
+
+    def transform_keypoint(self, kp_info: dict):
+        """
+        transform the implicit keypoints with the pose, shift, and expression deformation
+        kp: BxNx3
+        """
+        kp = kp_info['kp']    # (bs, k, 3)
+        pitch, yaw, roll = kp_info['pitch'], kp_info['yaw'], kp_info['roll']
+
+        t, exp = kp_info['t'], kp_info['exp']
+        scale = kp_info['scale']
+
+        pitch = headpose_pred_to_degree(pitch)
+        yaw = headpose_pred_to_degree(yaw)
+        roll = headpose_pred_to_degree(roll)
+
+        bs = kp.shape[0]
+        if kp.ndim == 2:
+            num_kp = kp.shape[1] // 3  # Bx(num_kpx3)
+        else:
+            num_kp = kp.shape[1]  # Bxnum_kpx3
+
+        rot_mat = get_rotation_matrix(pitch, yaw, roll)    # (bs, 3, 3)
+
+        # Eqn.2: s * (R * x_c,s + exp) + t
+        kp_transformed = kp.view(bs, num_kp, 3) @ rot_mat + exp.view(bs, num_kp, 3)
+        kp_transformed *= scale[..., None]  # (bs, k, 3) * (bs, 1, 1) = (bs, k, 3)
+        kp_transformed[:, :, 0:2] += t[:, None, 0:2]  # remove z, only apply tx ty
+
+        return kp_transformed
+
+    def retarget_eye(self, kp_source: torch.Tensor, eye_close_ratio: torch.Tensor) -> torch.Tensor:
+        """
+        kp_source: BxNx3
+        eye_close_ratio: Bx3
+        Return: Bx(3*num_kp+2)
+        """
+        feat_eye = concat_feat(kp_source, eye_close_ratio)
+
+        with torch.no_grad():
+            delta = self.stitching_retargeting_module['eye'](feat_eye)
+
+        return delta
+
+    def retarget_lip(self, kp_source: torch.Tensor, lip_close_ratio: torch.Tensor) -> torch.Tensor:
+        """
+        kp_source: BxNx3
+        lip_close_ratio: Bx2
+        """
+        feat_lip = concat_feat(kp_source, lip_close_ratio)
+
+        with torch.no_grad():
+            delta = self.stitching_retargeting_module['lip'](feat_lip)
+
+        return delta
+
+    def retarget_keypoints(self, frame_idx, num_keypoints, input_eye_ratios, input_lip_ratios, source_landmarks, portrait_wrapper, kp_source, driving_transformed_kp):
+        # TODO: GPT style, refactor it...
+        if self.cfg.flag_eye_retargeting:
+            # ∆_eyes,i = R_eyes(x_s; c_s,eyes, c_d,eyes,i)
+            eye_delta = compute_eye_delta(frame_idx, input_eye_ratios, source_landmarks, portrait_wrapper, kp_source)
+        else:
+            # α_eyes = 0
+            eye_delta = None
+
+        if self.cfg.flag_lip_retargeting:
+            # ∆_lip,i = R_lip(x_s; c_s,lip, c_d,lip,i)
+            lip_delta = compute_lip_delta(frame_idx, input_lip_ratios, source_landmarks, portrait_wrapper, kp_source)
+        else:
+            # α_lip = 0
+            lip_delta = None
+
+        if self.cfg.flag_relative:  # use x_s
+            new_driving_kp = kp_source + \
+                (eye_delta.reshape(-1, num_keypoints, 3) if eye_delta is not None else 0) + \
+                (lip_delta.reshape(-1, num_keypoints, 3) if lip_delta is not None else 0)
+        else:  # use x_d,i
+            new_driving_kp = driving_transformed_kp + \
+                (eye_delta.reshape(-1, num_keypoints, 3) if eye_delta is not None else 0) + \
+                (lip_delta.reshape(-1, num_keypoints, 3) if lip_delta is not None else 0)
+
+        return new_driving_kp
+
+    def stitch(self, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
+        """
+        kp_source: BxNx3
+        kp_driving: BxNx3
+        Return: Bx(3*num_kp+2)
+        """
+        feat_stiching = concat_feat(kp_source, kp_driving)
+
+        with torch.no_grad():
+            delta = self.stitching_retargeting_module['stitching'](feat_stiching)
+
+        return delta
+
+    def stitching(self, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
+        """ conduct the stitching
+        kp_source: Bxnum_kpx3
+        kp_driving: Bxnum_kpx3
+        """
+
+        if self.stitching_retargeting_module is not None:
+
+            bs, num_kp = kp_source.shape[:2]
+
+            kp_driving_new = kp_driving.clone()
+            delta = self.stitch(kp_source, kp_driving_new)
+
+            delta_exp = delta[..., :3*num_kp].reshape(bs, num_kp, 3)  # 1x20x3
+            delta_tx_ty = delta[..., 3*num_kp:3*num_kp+2].reshape(bs, 1, 2)  # 1x1x2
+
+            kp_driving_new += delta_exp
+            kp_driving_new[..., :2] += delta_tx_ty
+
+            return kp_driving_new
+
+        return kp_driving
+
+    def warp_decode(self, feature_3d: torch.Tensor, kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
+        """ get the image after the warping of the implicit keypoints
+        feature_3d: Bx32x16x64x64, feature volume
+        kp_source: BxNx3
+        kp_driving: BxNx3
+        """
+        # The line 18 in Algorithm 1: D(W(f_s; x_s, x′_d,i)）
+        with torch.no_grad():
+            with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=self.cfg.flag_use_half_precision):
+                # get decoder input
+                ret_dct = self.warping_module(feature_3d, kp_source=kp_source, kp_driving=kp_driving)
+                # decode
+                ret_dct['out'] = self.spade_generator(feature=ret_dct['out'])
+
+            # float the dict
+            if self.cfg.flag_use_half_precision:
+                for k, v in ret_dct.items():
+                    if isinstance(v, torch.Tensor):
+                        ret_dct[k] = v.float()
+
+        return ret_dct
+
+    def parse_output(self, out: torch.Tensor) -> np.ndarray:
+        """ construct the output as standard
+        return: 1xHxWx3, uint8
+        """
+        out = np.transpose(out.data.cpu().numpy(), [0, 2, 3, 1])  # 1x3xHxW -> 1xHxWx3
+        out = np.clip(out, 0, 1)  # clip to 0~1
+        out = np.clip(out * 255, 0, 255).astype(np.uint8)  # 0~1 -> 0~255
+
+        return out
+
+    def calc_retargeting_ratio(self, source_lmk, driving_lmk_lst):
+        input_eye_ratio_lst = []
+        input_lip_ratio_lst = []
+        for lmk in driving_lmk_lst:
+            # for eyes retargeting
+            input_eye_ratio_lst.append(calc_eye_close_ratio(lmk[None]))
+            # for lip retargeting
+            input_lip_ratio_lst.append(calc_lip_close_ratio(lmk[None]))
+        return input_eye_ratio_lst, input_lip_ratio_lst
+
+    def calc_combined_eye_ratio(self, input_eye_ratio, source_lmk):
+        eye_close_ratio = calc_eye_close_ratio(source_lmk[None])
+        eye_close_ratio_tensor = torch.from_numpy(eye_close_ratio).float().cuda(self.device_id)
+        input_eye_ratio_tensor = torch.Tensor([input_eye_ratio[0][0]]).reshape(1, 1).cuda(self.device_id)
+        # [c_s,eyes, c_d,eyes,i]
+        combined_eye_ratio_tensor = torch.cat([eye_close_ratio_tensor, input_eye_ratio_tensor], dim=1)
+        return combined_eye_ratio_tensor
+
+    def calc_combined_lip_ratio(self, input_lip_ratio, source_lmk):
+        lip_close_ratio = calc_lip_close_ratio(source_lmk[None])
+        lip_close_ratio_tensor = torch.from_numpy(lip_close_ratio).float().cuda(self.device_id)
+        # [c_s,lip, c_d,lip,i]
+        input_lip_ratio_tensor = torch.Tensor([input_lip_ratio[0]]).cuda(self.device_id)
+        if input_lip_ratio_tensor.shape != [1, 1]:
+            input_lip_ratio_tensor = input_lip_ratio_tensor.reshape(1, 1)
+        combined_lip_ratio_tensor = torch.cat([lip_close_ratio_tensor, input_lip_ratio_tensor], dim=1)
+        return combined_lip_ratio_tensor

liveportrait/modules/appearance_feature_extractor.py

@@ -0,0 +1,48 @@
+# coding: utf-8
+
+"""
+Appearance extractor(F) defined in paper, which maps the source image s to a 3D appearance feature volume.
+"""
+
+import torch
+from torch import nn
+from .util import SameBlock2d, DownBlock2d, ResBlock3d
+
+
+class AppearanceFeatureExtractor(nn.Module):
+
+    def __init__(self, image_channel, block_expansion, num_down_blocks, max_features, reshape_channel, reshape_depth, num_resblocks):
+        super(AppearanceFeatureExtractor, self).__init__()
+        self.image_channel = image_channel
+        self.block_expansion = block_expansion
+        self.num_down_blocks = num_down_blocks
+        self.max_features = max_features
+        self.reshape_channel = reshape_channel
+        self.reshape_depth = reshape_depth
+
+        self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(3, 3), padding=(1, 1))
+
+        down_blocks = []
+        for i in range(num_down_blocks):
+            in_features = min(max_features, block_expansion * (2 ** i))
+            out_features = min(max_features, block_expansion * (2 ** (i + 1)))
+            down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
+        self.down_blocks = nn.ModuleList(down_blocks)
+
+        self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1)
+
+        self.resblocks_3d = torch.nn.Sequential()
+        for i in range(num_resblocks):
+            self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1))
+
+    def forward(self, source_image):
+        out = self.first(source_image)  # Bx3x256x256 -> Bx64x256x256
+
+        for i in range(len(self.down_blocks)):
+            out = self.down_blocks[i](out)
+        out = self.second(out)
+        bs, c, h, w = out.shape  # ->Bx512x64x64
+
+        f_s = out.view(bs, self.reshape_channel, self.reshape_depth, h, w)  # ->Bx32x16x64x64
+        f_s = self.resblocks_3d(f_s)  # ->Bx32x16x64x64
+        return f_s

liveportrait/modules/convnextv2.py

@@ -0,0 +1,149 @@
+# coding: utf-8
+
+"""
+This moudle is adapted to the ConvNeXtV2 version for the extraction of implicit keypoints, poses, and expression deformation.
+"""
+
+import torch
+import torch.nn as nn
+# from timm.models.layers import trunc_normal_, DropPath
+from .util import LayerNorm, DropPath, trunc_normal_, GRN
+
+__all__ = ['convnextv2_tiny']
+
+
+class Block(nn.Module):
+    """ ConvNeXtV2 Block.
+
+    Args:
+        dim (int): Number of input channels.
+        drop_path (float): Stochastic depth rate. Default: 0.0
+    """
+
+    def __init__(self, dim, drop_path=0.):
+        super().__init__()
+        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim)  # depthwise conv
+        self.norm = LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, 4 * dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.grn = GRN(4 * dim)
+        self.pwconv2 = nn.Linear(4 * dim, dim)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+    def forward(self, x):
+        input = x
+        x = self.dwconv(x)
+        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.grn(x)
+        x = self.pwconv2(x)
+        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)
+
+        x = input + self.drop_path(x)
+        return x
+
+
+class ConvNeXtV2(nn.Module):
+    """ ConvNeXt V2
+
+    Args:
+        in_chans (int): Number of input image channels. Default: 3
+        num_classes (int): Number of classes for classification head. Default: 1000
+        depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
+        dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
+        drop_path_rate (float): Stochastic depth rate. Default: 0.
+        head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
+    """
+
+    def __init__(
+        self,
+        in_chans=3,
+        depths=[3, 3, 9, 3],
+        dims=[96, 192, 384, 768],
+        drop_path_rate=0.,
+        **kwargs
+    ):
+        super().__init__()
+        self.depths = depths
+        self.downsample_layers = nn.ModuleList()  # stem and 3 intermediate downsampling conv layers
+        stem = nn.Sequential(
+            nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
+            LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
+        )
+        self.downsample_layers.append(stem)
+        for i in range(3):
+            downsample_layer = nn.Sequential(
+                LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
+                nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
+            )
+            self.downsample_layers.append(downsample_layer)
+
+        self.stages = nn.ModuleList()  # 4 feature resolution stages, each consisting of multiple residual blocks
+        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+        cur = 0
+        for i in range(4):
+            stage = nn.Sequential(
+                *[Block(dim=dims[i], drop_path=dp_rates[cur + j]) for j in range(depths[i])]
+            )
+            self.stages.append(stage)
+            cur += depths[i]
+
+        self.norm = nn.LayerNorm(dims[-1], eps=1e-6)  # final norm layer
+
+        # NOTE: the output semantic items
+        num_bins = kwargs.get('num_bins', 66)
+        num_kp = kwargs.get('num_kp', 24)  # the number of implicit keypoints
+        self.fc_kp = nn.Linear(dims[-1], 3 * num_kp)  # implicit keypoints
+
+        # print('dims[-1]: ', dims[-1])
+        self.fc_scale = nn.Linear(dims[-1], 1)  # scale
+        self.fc_pitch = nn.Linear(dims[-1], num_bins)  # pitch bins
+        self.fc_yaw = nn.Linear(dims[-1], num_bins)  # yaw bins
+        self.fc_roll = nn.Linear(dims[-1], num_bins)  # roll bins
+        self.fc_t = nn.Linear(dims[-1], 3)  # translation
+        self.fc_exp = nn.Linear(dims[-1], 3 * num_kp)  # expression / delta
+
+    def _init_weights(self, m):
+        if isinstance(m, (nn.Conv2d, nn.Linear)):
+            trunc_normal_(m.weight, std=.02)
+            nn.init.constant_(m.bias, 0)
+
+    def forward_features(self, x):
+        for i in range(4):
+            x = self.downsample_layers[i](x)
+            x = self.stages[i](x)
+        return self.norm(x.mean([-2, -1]))  # global average pooling, (N, C, H, W) -> (N, C)
+
+    def forward(self, x):
+        x = self.forward_features(x)
+
+        # implicit keypoints
+        kp = self.fc_kp(x)
+
+        # pose and expression deformation
+        pitch = self.fc_pitch(x)
+        yaw = self.fc_yaw(x)
+        roll = self.fc_roll(x)
+        t = self.fc_t(x)
+        exp = self.fc_exp(x)
+        scale = self.fc_scale(x)
+
+        ret_dct = {
+            'pitch': pitch,
+            'yaw': yaw,
+            'roll': roll,
+            't': t,
+            'exp': exp,
+            'scale': scale,
+
+            'kp': kp,  # canonical keypoint
+        }
+
+        return ret_dct
+
+
+def convnextv2_tiny(**kwargs):
+    model = ConvNeXtV2(depths=[3, 3, 9, 3], dims=[96, 192, 384, 768], **kwargs)
+    return model

liveportrait/modules/dense_motion.py

@@ -0,0 +1,104 @@
+# coding: utf-8
+
+"""
+The module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving
+"""
+
+from torch import nn
+import torch.nn.functional as F
+import torch
+from .util import Hourglass, make_coordinate_grid, kp2gaussian
+
+
+class DenseMotionNetwork(nn.Module):
+    def __init__(self, block_expansion, num_blocks, max_features, num_kp, feature_channel, reshape_depth, compress, estimate_occlusion_map=True):
+        super(DenseMotionNetwork, self).__init__()
+        self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(compress+1), max_features=max_features, num_blocks=num_blocks)  # ~60+G
+
+        self.mask = nn.Conv3d(self.hourglass.out_filters, num_kp + 1, kernel_size=7, padding=3)  # 65G! NOTE: computation cost is large
+        self.compress = nn.Conv3d(feature_channel, compress, kernel_size=1)  # 0.8G
+        self.norm = nn.BatchNorm3d(compress, affine=True)
+        self.num_kp = num_kp
+        self.flag_estimate_occlusion_map = estimate_occlusion_map
+
+        if self.flag_estimate_occlusion_map:
+            self.occlusion = nn.Conv2d(self.hourglass.out_filters*reshape_depth, 1, kernel_size=7, padding=3)
+        else:
+            self.occlusion = None
+
+    def create_sparse_motions(self, feature, kp_driving, kp_source):
+        bs, _, d, h, w = feature.shape  # (bs, 4, 16, 64, 64)
+        identity_grid = make_coordinate_grid((d, h, w), ref=kp_source)  # (16, 64, 64, 3)
+        identity_grid = identity_grid.view(1, 1, d, h, w, 3)  # (1, 1, d=16, h=64, w=64, 3)
+        coordinate_grid = identity_grid - kp_driving.view(bs, self.num_kp, 1, 1, 1, 3)
+
+        k = coordinate_grid.shape[1]
+
+        # NOTE: there lacks an one-order flow
+        driving_to_source = coordinate_grid + kp_source.view(bs, self.num_kp, 1, 1, 1, 3)    # (bs, num_kp, d, h, w, 3)
+
+        # adding background feature
+        identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1, 1)
+        sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1)  # (bs, 1+num_kp, d, h, w, 3)
+        return sparse_motions
+
+    def create_deformed_feature(self, feature, sparse_motions):
+        bs, _, d, h, w = feature.shape
+        feature_repeat = feature.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp+1, 1, 1, 1, 1, 1)      # (bs, num_kp+1, 1, c, d, h, w)
+        feature_repeat = feature_repeat.view(bs * (self.num_kp+1), -1, d, h, w)                         # (bs*(num_kp+1), c, d, h, w)
+        sparse_motions = sparse_motions.view((bs * (self.num_kp+1), d, h, w, -1))                       # (bs*(num_kp+1), d, h, w, 3)
+        sparse_deformed = F.grid_sample(feature_repeat, sparse_motions, align_corners=False)
+        sparse_deformed = sparse_deformed.view((bs, self.num_kp+1, -1, d, h, w))                        # (bs, num_kp+1, c, d, h, w)
+
+        return sparse_deformed
+
+    def create_heatmap_representations(self, feature, kp_driving, kp_source):
+        spatial_size = feature.shape[3:]  # (d=16, h=64, w=64)
+        gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=0.01)  # (bs, num_kp, d, h, w)
+        gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=0.01)  # (bs, num_kp, d, h, w)
+        heatmap = gaussian_driving - gaussian_source  # (bs, num_kp, d, h, w)
+
+        # adding background feature
+        zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1], spatial_size[2]).type(heatmap.type()).to(heatmap.device)
+        heatmap = torch.cat([zeros, heatmap], dim=1)
+        heatmap = heatmap.unsqueeze(2)         # (bs, 1+num_kp, 1, d, h, w)
+        return heatmap
+
+    def forward(self, feature, kp_driving, kp_source):
+        bs, _, d, h, w = feature.shape  # (bs, 32, 16, 64, 64)
+
+        feature = self.compress(feature)  # (bs, 4, 16, 64, 64)
+        feature = self.norm(feature)  # (bs, 4, 16, 64, 64)
+        feature = F.relu(feature)  # (bs, 4, 16, 64, 64)
+
+        out_dict = dict()
+
+        # 1. deform 3d feature
+        sparse_motion = self.create_sparse_motions(feature, kp_driving, kp_source)  # (bs, 1+num_kp, d, h, w, 3)
+        deformed_feature = self.create_deformed_feature(feature, sparse_motion)  # (bs, 1+num_kp, c=4, d=16, h=64, w=64)
+
+        # 2. (bs, 1+num_kp, d, h, w)
+        heatmap = self.create_heatmap_representations(deformed_feature, kp_driving, kp_source)  # (bs, 1+num_kp, 1, d, h, w)
+
+        input = torch.cat([heatmap, deformed_feature], dim=2)  # (bs, 1+num_kp, c=5, d=16, h=64, w=64)
+        input = input.view(bs, -1, d, h, w)  # (bs, (1+num_kp)*c=105, d=16, h=64, w=64)
+
+        prediction = self.hourglass(input)
+
+        mask = self.mask(prediction)
+        mask = F.softmax(mask, dim=1)  # (bs, 1+num_kp, d=16, h=64, w=64)
+        out_dict['mask'] = mask
+        mask = mask.unsqueeze(2)                                   # (bs, num_kp+1, 1, d, h, w)
+        sparse_motion = sparse_motion.permute(0, 1, 5, 2, 3, 4)    # (bs, num_kp+1, 3, d, h, w)
+        deformation = (sparse_motion * mask).sum(dim=1)            # (bs, 3, d, h, w)  mask take effect in this place
+        deformation = deformation.permute(0, 2, 3, 4, 1)           # (bs, d, h, w, 3)
+
+        out_dict['deformation'] = deformation
+
+        if self.flag_estimate_occlusion_map:
+            bs, _, d, h, w = prediction.shape
+            prediction_reshape = prediction.view(bs, -1, h, w)
+            occlusion_map = torch.sigmoid(self.occlusion(prediction_reshape))  # Bx1x64x64
+            out_dict['occlusion_map'] = occlusion_map
+
+        return out_dict

liveportrait/modules/motion_extractor.py

@@ -0,0 +1,35 @@
+# coding: utf-8
+
+"""
+Motion extractor(M), which directly predicts the canonical keypoints, head pose and expression deformation of the input image
+"""
+
+from torch import nn
+import torch
+
+from .convnextv2 import convnextv2_tiny
+from .util import filter_state_dict
+
+model_dict = {
+    'convnextv2_tiny': convnextv2_tiny,
+}
+
+
+class MotionExtractor(nn.Module):
+    def __init__(self, **kwargs):
+        super(MotionExtractor, self).__init__()
+
+        # default is convnextv2_base
+        backbone = kwargs.get('backbone', 'convnextv2_tiny')
+        self.detector = model_dict.get(backbone)(**kwargs)
+
+    def load_pretrained(self, init_path: str):
+        if init_path not in (None, ''):
+            state_dict = torch.load(init_path, map_location=lambda storage, loc: storage)['model']
+            state_dict = filter_state_dict(state_dict, remove_name='head')
+            ret = self.detector.load_state_dict(state_dict, strict=False)
+            print(f'Load pretrained model from {init_path}, ret: {ret}')
+
+    def forward(self, x):
+        out = self.detector(x)
+        return out

liveportrait/modules/spade_generator.py

@@ -0,0 +1,59 @@
+# coding: utf-8
+
+"""
+Spade decoder(G) defined in the paper, which input the warped feature to generate the animated image.
+"""
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from .util import SPADEResnetBlock
+
+
+class SPADEDecoder(nn.Module):
+    def __init__(self, upscale=1, max_features=256, block_expansion=64, out_channels=64, num_down_blocks=2):
+        for i in range(num_down_blocks):
+            input_channels = min(max_features, block_expansion * (2 ** (i + 1)))
+        self.upscale = upscale
+        super().__init__()
+        norm_G = 'spadespectralinstance'
+        label_num_channels = input_channels  # 256
+
+        self.fc = nn.Conv2d(input_channels, 2 * input_channels, 3, padding=1)
+        self.G_middle_0 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.G_middle_1 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.G_middle_2 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.G_middle_3 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.G_middle_4 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.G_middle_5 = SPADEResnetBlock(2 * input_channels, 2 * input_channels, norm_G, label_num_channels)
+        self.up_0 = SPADEResnetBlock(2 * input_channels, input_channels, norm_G, label_num_channels)
+        self.up_1 = SPADEResnetBlock(input_channels, out_channels, norm_G, label_num_channels)
+        self.up = nn.Upsample(scale_factor=2)
+
+        if self.upscale is None or self.upscale <= 1:
+            self.conv_img = nn.Conv2d(out_channels, 3, 3, padding=1)
+        else:
+            self.conv_img = nn.Sequential(
+                nn.Conv2d(out_channels, 3 * (2 * 2), kernel_size=3, padding=1),
+                nn.PixelShuffle(upscale_factor=2)
+            )
+
+    def forward(self, feature):
+        seg = feature  # Bx256x64x64
+        x = self.fc(feature)  # Bx512x64x64
+        x = self.G_middle_0(x, seg)
+        x = self.G_middle_1(x, seg)
+        x = self.G_middle_2(x, seg)
+        x = self.G_middle_3(x, seg)
+        x = self.G_middle_4(x, seg)
+        x = self.G_middle_5(x, seg)
+
+        x = self.up(x)  # Bx512x64x64 -> Bx512x128x128
+        x = self.up_0(x, seg)  # Bx512x128x128 -> Bx256x128x128
+        x = self.up(x)  # Bx256x128x128 -> Bx256x256x256
+        x = self.up_1(x, seg)  # Bx256x256x256 -> Bx64x256x256
+
+        x = self.conv_img(F.leaky_relu(x, 2e-1))  # Bx64x256x256 -> Bx3xHxW
+        x = torch.sigmoid(x)  # Bx3xHxW
+
+        return x

liveportrait/modules/stitching_retargeting_network.py

@@ -0,0 +1,38 @@
+# coding: utf-8
+
+"""
+Stitching module(S) and two retargeting modules(R) defined in the paper.
+
+- The stitching module pastes the animated portrait back into the original image space without pixel misalignment, such as in
+the stitching region.
+
+- The eyes retargeting module is designed to address the issue of incomplete eye closure during cross-id reenactment, especially
+when a person with small eyes drives a person with larger eyes.
+
+- The lip retargeting module is designed similarly to the eye retargeting module, and can also normalize the input by ensuring that
+the lips are in a closed state, which facilitates better animation driving.
+"""
+from torch import nn
+
+
+class StitchingRetargetingNetwork(nn.Module):
+    def __init__(self, input_size, hidden_sizes, output_size):
+        super(StitchingRetargetingNetwork, self).__init__()
+        layers = []
+        for i in range(len(hidden_sizes)):
+            if i == 0:
+                layers.append(nn.Linear(input_size, hidden_sizes[i]))
+            else:
+                layers.append(nn.Linear(hidden_sizes[i - 1], hidden_sizes[i]))
+            layers.append(nn.ReLU(inplace=True))
+        layers.append(nn.Linear(hidden_sizes[-1], output_size))
+        self.mlp = nn.Sequential(*layers)
+
+    def initialize_weights_to_zero(self):
+        for m in self.modules():
+            if isinstance(m, nn.Linear):
+                nn.init.zeros_(m.weight)
+                nn.init.zeros_(m.bias)
+
+    def forward(self, x):
+        return self.mlp(x)

liveportrait/modules/util.py

@@ -0,0 +1,441 @@
+# coding: utf-8
+
+"""
+This file defines various neural network modules and utility functions, including convolutional and residual blocks,
+normalizations, and functions for spatial transformation and tensor manipulation.
+"""
+
+from torch import nn
+import torch.nn.functional as F
+import torch
+import torch.nn.utils.spectral_norm as spectral_norm
+import math
+import warnings
+
+
+def kp2gaussian(kp, spatial_size, kp_variance):
+    """
+    Transform a keypoint into gaussian like representation
+    """
+    mean = kp
+
+    coordinate_grid = make_coordinate_grid(spatial_size, mean)
+    number_of_leading_dimensions = len(mean.shape) - 1
+    shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape
+    coordinate_grid = coordinate_grid.view(*shape)
+    repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 1)
+    coordinate_grid = coordinate_grid.repeat(*repeats)
+
+    # Preprocess kp shape
+    shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 3)
+    mean = mean.view(*shape)
+
+    mean_sub = (coordinate_grid - mean)
+
+    out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)
+
+    return out
+
+
+def make_coordinate_grid(spatial_size, ref, **kwargs):
+    d, h, w = spatial_size
+    x = torch.arange(w).type(ref.dtype).to(ref.device)
+    y = torch.arange(h).type(ref.dtype).to(ref.device)
+    z = torch.arange(d).type(ref.dtype).to(ref.device)
+
+    # NOTE: must be right-down-in
+    x = (2 * (x / (w - 1)) - 1)  # the x axis faces to the right
+    y = (2 * (y / (h - 1)) - 1)  # the y axis faces to the bottom
+    z = (2 * (z / (d - 1)) - 1)  # the z axis faces to the inner
+
+    yy = y.view(1, -1, 1).repeat(d, 1, w)
+    xx = x.view(1, 1, -1).repeat(d, h, 1)
+    zz = z.view(-1, 1, 1).repeat(1, h, w)
+
+    meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3), zz.unsqueeze_(3)], 3)
+
+    return meshed
+
+
+class ConvT2d(nn.Module):
+    """
+    Upsampling block for use in decoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, stride=2, padding=1, output_padding=1):
+        super(ConvT2d, self).__init__()
+
+        self.convT = nn.ConvTranspose2d(in_features, out_features, kernel_size=kernel_size, stride=stride,
+                                        padding=padding, output_padding=output_padding)
+        self.norm = nn.InstanceNorm2d(out_features)
+
+    def forward(self, x):
+        out = self.convT(x)
+        out = self.norm(out)
+        out = F.leaky_relu(out)
+        return out
+
+
+class ResBlock3d(nn.Module):
+    """
+    Res block, preserve spatial resolution.
+    """
+
+    def __init__(self, in_features, kernel_size, padding):
+        super(ResBlock3d, self).__init__()
+        self.conv1 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding)
+        self.conv2 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding)
+        self.norm1 = nn.BatchNorm3d(in_features, affine=True)
+        self.norm2 = nn.BatchNorm3d(in_features, affine=True)
+
+    def forward(self, x):
+        out = self.norm1(x)
+        out = F.relu(out)
+        out = self.conv1(out)
+        out = self.norm2(out)
+        out = F.relu(out)
+        out = self.conv2(out)
+        out += x
+        return out
+
+
+class UpBlock3d(nn.Module):
+    """
+    Upsampling block for use in decoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(UpBlock3d, self).__init__()
+
+        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups)
+        self.norm = nn.BatchNorm3d(out_features, affine=True)
+
+    def forward(self, x):
+        out = F.interpolate(x, scale_factor=(1, 2, 2))
+        out = self.conv(out)
+        out = self.norm(out)
+        out = F.relu(out)
+        return out
+
+
+class DownBlock2d(nn.Module):
+    """
+    Downsampling block for use in encoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(DownBlock2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups)
+        self.norm = nn.BatchNorm2d(out_features, affine=True)
+        self.pool = nn.AvgPool2d(kernel_size=(2, 2))
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        out = F.relu(out)
+        out = self.pool(out)
+        return out
+
+
+class DownBlock3d(nn.Module):
+    """
+    Downsampling block for use in encoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(DownBlock3d, self).__init__()
+        '''
+        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                                padding=padding, groups=groups, stride=(1, 2, 2))
+        '''
+        self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups)
+        self.norm = nn.BatchNorm3d(out_features, affine=True)
+        self.pool = nn.AvgPool3d(kernel_size=(1, 2, 2))
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        out = F.relu(out)
+        out = self.pool(out)
+        return out
+
+
+class SameBlock2d(nn.Module):
+    """
+    Simple block, preserve spatial resolution.
+    """
+
+    def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1, lrelu=False):
+        super(SameBlock2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups)
+        self.norm = nn.BatchNorm2d(out_features, affine=True)
+        if lrelu:
+            self.ac = nn.LeakyReLU()
+        else:
+            self.ac = nn.ReLU()
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        out = self.ac(out)
+        return out
+
+
+class Encoder(nn.Module):
+    """
+    Hourglass Encoder
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Encoder, self).__init__()
+
+        down_blocks = []
+        for i in range(num_blocks):
+            down_blocks.append(DownBlock3d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)), min(max_features, block_expansion * (2 ** (i + 1))), kernel_size=3, padding=1))
+        self.down_blocks = nn.ModuleList(down_blocks)
+
+    def forward(self, x):
+        outs = [x]
+        for down_block in self.down_blocks:
+            outs.append(down_block(outs[-1]))
+        return outs
+
+
+class Decoder(nn.Module):
+    """
+    Hourglass Decoder
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Decoder, self).__init__()
+
+        up_blocks = []
+
+        for i in range(num_blocks)[::-1]:
+            in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1)))
+            out_filters = min(max_features, block_expansion * (2 ** i))
+            up_blocks.append(UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1))
+
+        self.up_blocks = nn.ModuleList(up_blocks)
+        self.out_filters = block_expansion + in_features
+
+        self.conv = nn.Conv3d(in_channels=self.out_filters, out_channels=self.out_filters, kernel_size=3, padding=1)
+        self.norm = nn.BatchNorm3d(self.out_filters, affine=True)
+
+    def forward(self, x):
+        out = x.pop()
+        for up_block in self.up_blocks:
+            out = up_block(out)
+            skip = x.pop()
+            out = torch.cat([out, skip], dim=1)
+        out = self.conv(out)
+        out = self.norm(out)
+        out = F.relu(out)
+        return out
+
+
+class Hourglass(nn.Module):
+    """
+    Hourglass architecture.
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Hourglass, self).__init__()
+        self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features)
+        self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features)
+        self.out_filters = self.decoder.out_filters
+
+    def forward(self, x):
+        return self.decoder(self.encoder(x))
+
+
+class SPADE(nn.Module):
+    def __init__(self, norm_nc, label_nc):
+        super().__init__()
+
+        self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
+        nhidden = 128
+
+        self.mlp_shared = nn.Sequential(
+            nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1),
+            nn.ReLU())
+        self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1)
+        self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1)
+
+    def forward(self, x, segmap):
+        normalized = self.param_free_norm(x)
+        segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest')
+        actv = self.mlp_shared(segmap)
+        gamma = self.mlp_gamma(actv)
+        beta = self.mlp_beta(actv)
+        out = normalized * (1 + gamma) + beta
+        return out
+
+
+class SPADEResnetBlock(nn.Module):
+    def __init__(self, fin, fout, norm_G, label_nc, use_se=False, dilation=1):
+        super().__init__()
+        # Attributes
+        self.learned_shortcut = (fin != fout)
+        fmiddle = min(fin, fout)
+        self.use_se = use_se
+        # create conv layers
+        self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=dilation, dilation=dilation)
+        self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=dilation, dilation=dilation)
+        if self.learned_shortcut:
+            self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False)
+        # apply spectral norm if specified
+        if 'spectral' in norm_G:
+            self.conv_0 = spectral_norm(self.conv_0)
+            self.conv_1 = spectral_norm(self.conv_1)
+            if self.learned_shortcut:
+                self.conv_s = spectral_norm(self.conv_s)
+        # define normalization layers
+        self.norm_0 = SPADE(fin, label_nc)
+        self.norm_1 = SPADE(fmiddle, label_nc)
+        if self.learned_shortcut:
+            self.norm_s = SPADE(fin, label_nc)
+
+    def forward(self, x, seg1):
+        x_s = self.shortcut(x, seg1)
+        dx = self.conv_0(self.actvn(self.norm_0(x, seg1)))
+        dx = self.conv_1(self.actvn(self.norm_1(dx, seg1)))
+        out = x_s + dx
+        return out
+
+    def shortcut(self, x, seg1):
+        if self.learned_shortcut:
+            x_s = self.conv_s(self.norm_s(x, seg1))
+        else:
+            x_s = x
+        return x_s
+
+    def actvn(self, x):
+        return F.leaky_relu(x, 2e-1)
+
+
+def filter_state_dict(state_dict, remove_name='fc'):
+    new_state_dict = {}
+    for key in state_dict:
+        if remove_name in key:
+            continue
+        new_state_dict[key] = state_dict[key]
+    return new_state_dict
+
+
+class GRN(nn.Module):
+    """ GRN (Global Response Normalization) layer
+    """
+
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
+        self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x
+
+
+class LayerNorm(nn.Module):
+    r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
+    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
+    shape (batch_size, height, width, channels) while channels_first corresponds to inputs
+    with shape (batch_size, channels, height, width).
+    """
+
+    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(normalized_shape))
+        self.bias = nn.Parameter(torch.zeros(normalized_shape))
+        self.eps = eps
+        self.data_format = data_format
+        if self.data_format not in ["channels_last", "channels_first"]:
+            raise NotImplementedError
+        self.normalized_shape = (normalized_shape, )
+
+    def forward(self, x):
+        if self.data_format == "channels_last":
+            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+        elif self.data_format == "channels_first":
+            u = x.mean(1, keepdim=True)
+            s = (x - u).pow(2).mean(1, keepdim=True)
+            x = (x - u) / torch.sqrt(s + self.eps)
+            x = self.weight[:, None, None] * x + self.bias[:, None, None]
+            return x
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+                      "The distribution of values may be incorrect.",
+                      stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def drop_path(x, drop_prob=0., training=False, scale_by_keep=True):
+    """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+class DropPath(nn.Module):
+    """ Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    def __init__(self, drop_prob=None, scale_by_keep=True):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+        self.scale_by_keep = scale_by_keep
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)

liveportrait/modules/warping_network.py

@@ -0,0 +1,77 @@
+# coding: utf-8
+
+"""
+Warping field estimator(W) defined in the paper, which generates a warping field using the implicit
+keypoint representations x_s and x_d, and employs this flow field to warp the source feature volume f_s.
+"""
+
+from torch import nn
+import torch.nn.functional as F
+from .util import SameBlock2d
+from .dense_motion import DenseMotionNetwork
+
+
+class WarpingNetwork(nn.Module):
+    def __init__(
+        self,
+        num_kp,
+        block_expansion,
+        max_features,
+        num_down_blocks,
+        reshape_channel,
+        estimate_occlusion_map=False,
+        dense_motion_params=None,
+        **kwargs
+    ):
+        super(WarpingNetwork, self).__init__()
+
+        self.upscale = kwargs.get('upscale', 1)
+        self.flag_use_occlusion_map = kwargs.get('flag_use_occlusion_map', True)
+
+        if dense_motion_params is not None:
+            self.dense_motion_network = DenseMotionNetwork(
+                num_kp=num_kp,
+                feature_channel=reshape_channel,
+                estimate_occlusion_map=estimate_occlusion_map,
+                **dense_motion_params
+            )
+        else:
+            self.dense_motion_network = None
+
+        self.third = SameBlock2d(max_features, block_expansion * (2 ** num_down_blocks), kernel_size=(3, 3), padding=(1, 1), lrelu=True)
+        self.fourth = nn.Conv2d(in_channels=block_expansion * (2 ** num_down_blocks), out_channels=block_expansion * (2 ** num_down_blocks), kernel_size=1, stride=1)
+
+        self.estimate_occlusion_map = estimate_occlusion_map
+
+    def deform_input(self, inp, deformation):
+        return F.grid_sample(inp, deformation, align_corners=False)
+
+    def forward(self, feature_3d, kp_driving, kp_source):
+        if self.dense_motion_network is not None:
+            # Feature warper, Transforming feature representation according to deformation and occlusion
+            dense_motion = self.dense_motion_network(
+                feature=feature_3d, kp_driving=kp_driving, kp_source=kp_source
+            )
+            if 'occlusion_map' in dense_motion:
+                occlusion_map = dense_motion['occlusion_map']  # Bx1x64x64
+            else:
+                occlusion_map = None
+
+            deformation = dense_motion['deformation']  # Bx16x64x64x3
+            out = self.deform_input(feature_3d, deformation)  # Bx32x16x64x64
+
+            bs, c, d, h, w = out.shape  # Bx32x16x64x64
+            out = out.view(bs, c * d, h, w)  # -> Bx512x64x64
+            out = self.third(out)  # -> Bx256x64x64
+            out = self.fourth(out)  # -> Bx256x64x64
+
+            if self.flag_use_occlusion_map and (occlusion_map is not None):
+                out = out * occlusion_map
+
+        ret_dct = {
+            'occlusion_map': occlusion_map,
+            'deformation': deformation,
+            'out': out,
+        }
+
+        return ret_dct

liveportrait/template_maker.py

@@ -0,0 +1,65 @@
+# coding: utf-8
+
+"""
+Make video template
+"""
+
+import os
+import cv2
+import numpy as np
+import pickle
+from rich.progress import track
+from .utils.cropper import Cropper
+
+from .utils.io import load_driving_info
+from .utils.camera import get_rotation_matrix
+from .utils.helper import mkdir, basename
+from .utils.rprint import rlog as log
+from .config.crop_config import CropConfig
+from .config.inference_config import InferenceConfig
+from .live_portrait_wrapper import LivePortraitWrapper
+
+class TemplateMaker:
+
+    def __init__(self, inference_cfg: InferenceConfig, crop_cfg: CropConfig):
+        self.live_portrait_wrapper: LivePortraitWrapper = LivePortraitWrapper(cfg=inference_cfg)
+        self.cropper = Cropper(crop_cfg=crop_cfg)
+
+    def make_motion_template(self, video_fp: str, output_path: str, **kwargs):
+        """ make video template (.pkl format)
+        video_fp: driving video file path
+        output_path: where to save the pickle file
+        """
+
+        driving_rgb_lst = load_driving_info(video_fp)
+        driving_rgb_lst = [cv2.resize(_, (256, 256)) for _ in driving_rgb_lst]
+        driving_lmk_lst = self.cropper.get_retargeting_lmk_info(driving_rgb_lst)
+        I_d_lst = self.live_portrait_wrapper.prepare_driving_videos(driving_rgb_lst)
+
+        n_frames = I_d_lst.shape[0]
+
+        templates = []
+
+
+        for i in track(range(n_frames), description='Making templates...', total=n_frames):
+            I_d_i = I_d_lst[i]
+            x_d_i_info = self.live_portrait_wrapper.get_kp_info(I_d_i)
+            R_d_i = get_rotation_matrix(x_d_i_info['pitch'], x_d_i_info['yaw'], x_d_i_info['roll'])
+            # collect s_d, R_d, δ_d and t_d for inference
+            template_dct = {
+                'n_frames': n_frames,
+                'frames_index': i,
+            }
+            template_dct['scale'] = x_d_i_info['scale'].cpu().numpy().astype(np.float32)
+            template_dct['R_d'] = R_d_i.cpu().numpy().astype(np.float32)
+            template_dct['exp'] = x_d_i_info['exp'].cpu().numpy().astype(np.float32)
+            template_dct['t'] = x_d_i_info['t'].cpu().numpy().astype(np.float32)
+
+            templates.append(template_dct)
+
+        mkdir(output_path)
+        # Save the dictionary as a pickle file
+        pickle_fp = os.path.join(output_path, f'{basename(video_fp)}.pkl')
+        with open(pickle_fp, 'wb') as f:
+            pickle.dump([templates, driving_lmk_lst], f)
+        log(f"Template saved at {pickle_fp}")

liveportrait/utils/camera.py

@@ -0,0 +1,75 @@
+# coding: utf-8
+
+"""
+functions for processing and transforming 3D facial keypoints
+"""
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+PI = np.pi
+
+
+def headpose_pred_to_degree(pred):
+    """
+    pred: (bs, 66) or (bs, 1) or others
+    """
+    if pred.ndim > 1 and pred.shape[1] == 66:
+        # NOTE: note that the average is modified to 97.5
+        device = pred.device
+        idx_tensor = [idx for idx in range(0, 66)]
+        idx_tensor = torch.FloatTensor(idx_tensor).to(device)
+        pred = F.softmax(pred, dim=1)
+        degree = torch.sum(pred*idx_tensor, axis=1) * 3 - 97.5
+
+        return degree
+
+    return pred
+
+
+def get_rotation_matrix(pitch_, yaw_, roll_):
+    """ the input is in degree
+    """
+    # calculate the rotation matrix: vps @ rot
+
+    # transform to radian
+    pitch = pitch_ / 180 * PI
+    yaw = yaw_ / 180 * PI
+    roll = roll_ / 180 * PI
+
+    device = pitch.device
+
+    if pitch.ndim == 1:
+        pitch = pitch.unsqueeze(1)
+    if yaw.ndim == 1:
+        yaw = yaw.unsqueeze(1)
+    if roll.ndim == 1:
+        roll = roll.unsqueeze(1)
+
+    # calculate the euler matrix
+    bs = pitch.shape[0]
+    ones = torch.ones([bs, 1]).to(device)
+    zeros = torch.zeros([bs, 1]).to(device)
+    x, y, z = pitch, yaw, roll
+
+    rot_x = torch.cat([
+        ones, zeros, zeros,
+        zeros, torch.cos(x), -torch.sin(x),
+        zeros, torch.sin(x), torch.cos(x)
+    ], dim=1).reshape([bs, 3, 3])
+
+    rot_y = torch.cat([
+        torch.cos(y), zeros, torch.sin(y),
+        zeros, ones, zeros,
+        -torch.sin(y), zeros, torch.cos(y)
+    ], dim=1).reshape([bs, 3, 3])
+
+    rot_z = torch.cat([
+        torch.cos(z), -torch.sin(z), zeros,
+        torch.sin(z), torch.cos(z), zeros,
+        zeros, zeros, ones
+    ], dim=1).reshape([bs, 3, 3])
+
+    rot = rot_z @ rot_y @ rot_x
+    return rot.permute(0, 2, 1)  # transpose

liveportrait/utils/crop.py

@@ -0,0 +1,393 @@
+# coding: utf-8
+
+"""
+cropping function and the related preprocess functions for cropping
+"""
+
+import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False) # NOTE: enforce single thread
+import numpy as np
+from .rprint import rprint as print
+from math import sin, cos, acos, degrees
+
+DTYPE = np.float32
+CV2_INTERP = cv2.INTER_LINEAR
+
+
+def _transform_img(img, M, dsize, flags=CV2_INTERP, borderMode=None):
+    """ conduct similarity or affine transformation to the image, do not do border operation!
+    img:
+    M: 2x3 matrix or 3x3 matrix
+    dsize: target shape (width, height)
+    """
+    if isinstance(dsize, tuple) or isinstance(dsize, list):
+        _dsize = tuple(dsize)
+    else:
+        _dsize = (dsize, dsize)
+
+    if borderMode is not None:
+        return cv2.warpAffine(img, M[:2, :], dsize=_dsize, flags=flags, borderMode=borderMode, borderValue=(0, 0, 0))
+    else:
+        return cv2.warpAffine(img, M[:2, :], dsize=_dsize, flags=flags)
+
+
+def _transform_pts(pts, M):
+    """ conduct similarity or affine transformation to the pts
+    pts: Nx2 ndarray
+    M: 2x3 matrix or 3x3 matrix
+    return: Nx2
+    """
+    return pts @ M[:2, :2].T + M[:2, 2]
+
+
+def parse_pt2_from_pt101(pt101, use_lip=True):
+    """
+    parsing the 2 points according to the 101 points, which cancels the roll
+    """
+    # the former version use the eye center, but it is not robust, now use interpolation
+    pt_left_eye = np.mean(pt101[[39, 42, 45, 48]], axis=0)  # left eye center
+    pt_right_eye = np.mean(pt101[[51, 54, 57, 60]], axis=0)  # right eye center
+
+    if use_lip:
+        # use lip
+        pt_center_eye = (pt_left_eye + pt_right_eye) / 2
+        pt_center_lip = (pt101[75] + pt101[81]) / 2
+        pt2 = np.stack([pt_center_eye, pt_center_lip], axis=0)
+    else:
+        pt2 = np.stack([pt_left_eye, pt_right_eye], axis=0)
+    return pt2
+
+
+def parse_pt2_from_pt106(pt106, use_lip=True):
+    """
+    parsing the 2 points according to the 106 points, which cancels the roll
+    """
+    pt_left_eye = np.mean(pt106[[33, 35, 40, 39]], axis=0)  # left eye center
+    pt_right_eye = np.mean(pt106[[87, 89, 94, 93]], axis=0)  # right eye center
+
+    if use_lip:
+        # use lip
+        pt_center_eye = (pt_left_eye + pt_right_eye) / 2
+        pt_center_lip = (pt106[52] + pt106[61]) / 2
+        pt2 = np.stack([pt_center_eye, pt_center_lip], axis=0)
+    else:
+        pt2 = np.stack([pt_left_eye, pt_right_eye], axis=0)
+    return pt2
+
+
+def parse_pt2_from_pt203(pt203, use_lip=True):
+    """
+    parsing the 2 points according to the 203 points, which cancels the roll
+    """
+    pt_left_eye = np.mean(pt203[[0, 6, 12, 18]], axis=0)  # left eye center
+    pt_right_eye = np.mean(pt203[[24, 30, 36, 42]], axis=0)  # right eye center
+    if use_lip:
+        # use lip
+        pt_center_eye = (pt_left_eye + pt_right_eye) / 2
+        pt_center_lip = (pt203[48] + pt203[66]) / 2
+        pt2 = np.stack([pt_center_eye, pt_center_lip], axis=0)
+    else:
+        pt2 = np.stack([pt_left_eye, pt_right_eye], axis=0)
+    return pt2
+
+
+def parse_pt2_from_pt68(pt68, use_lip=True):
+    """
+    parsing the 2 points according to the 68 points, which cancels the roll
+    """
+    lm_idx = np.array([31, 37, 40, 43, 46, 49, 55], dtype=np.int32) - 1
+    if use_lip:
+        pt5 = np.stack([
+            np.mean(pt68[lm_idx[[1, 2]], :], 0),  # left eye
+            np.mean(pt68[lm_idx[[3, 4]], :], 0),  # right eye
+            pt68[lm_idx[0], :],  # nose
+            pt68[lm_idx[5], :],  # lip
+            pt68[lm_idx[6], :]   # lip
+        ], axis=0)
+
+        pt2 = np.stack([
+            (pt5[0] + pt5[1]) / 2,
+            (pt5[3] + pt5[4]) / 2
+        ], axis=0)
+    else:
+        pt2 = np.stack([
+            np.mean(pt68[lm_idx[[1, 2]], :], 0),  # left eye
+            np.mean(pt68[lm_idx[[3, 4]], :], 0),  # right eye
+        ], axis=0)
+
+    return pt2
+
+
+def parse_pt2_from_pt5(pt5, use_lip=True):
+    """
+    parsing the 2 points according to the 5 points, which cancels the roll
+    """
+    if use_lip:
+        pt2 = np.stack([
+            (pt5[0] + pt5[1]) / 2,
+            (pt5[3] + pt5[4]) / 2
+        ], axis=0)
+    else:
+        pt2 = np.stack([
+            pt5[0],
+            pt5[1]
+        ], axis=0)
+    return pt2
+
+
+def parse_pt2_from_pt_x(pts, use_lip=True):
+    if pts.shape[0] == 101:
+        pt2 = parse_pt2_from_pt101(pts, use_lip=use_lip)
+    elif pts.shape[0] == 106:
+        pt2 = parse_pt2_from_pt106(pts, use_lip=use_lip)
+    elif pts.shape[0] == 68:
+        pt2 = parse_pt2_from_pt68(pts, use_lip=use_lip)
+    elif pts.shape[0] == 5:
+        pt2 = parse_pt2_from_pt5(pts, use_lip=use_lip)
+    elif pts.shape[0] == 203:
+        pt2 = parse_pt2_from_pt203(pts, use_lip=use_lip)
+    elif pts.shape[0] > 101:
+        # take the first 101 points
+        pt2 = parse_pt2_from_pt101(pts[:101], use_lip=use_lip)
+    else:
+        raise Exception(f'Unknow shape: {pts.shape}')
+
+    if not use_lip:
+        # NOTE: to compile with the latter code, need to rotate the pt2 90 degrees clockwise manually
+        v = pt2[1] - pt2[0]
+        pt2[1, 0] = pt2[0, 0] - v[1]
+        pt2[1, 1] = pt2[0, 1] + v[0]
+
+    return pt2
+
+
+def parse_rect_from_landmark(
+    pts,
+    scale=1.5,
+    need_square=True,
+    vx_ratio=0,
+    vy_ratio=0,
+    use_deg_flag=False,
+    **kwargs
+):
+    """parsing center, size, angle from 101/68/5/x landmarks
+    vx_ratio: the offset ratio along the pupil axis x-axis, multiplied by size
+    vy_ratio: the offset ratio along the pupil axis y-axis, multiplied by size, which is used to contain more forehead area
+
+    judge with pts.shape
+    """
+    pt2 = parse_pt2_from_pt_x(pts, use_lip=kwargs.get('use_lip', True))
+
+    uy = pt2[1] - pt2[0]
+    l = np.linalg.norm(uy)
+    if l <= 1e-3:
+        uy = np.array([0, 1], dtype=DTYPE)
+    else:
+        uy /= l
+    ux = np.array((uy[1], -uy[0]), dtype=DTYPE)
+
+    # the rotation degree of the x-axis, the clockwise is positive, the counterclockwise is negative (image coordinate system)
+    # print(uy)
+    # print(ux)
+    angle = acos(ux[0])
+    if ux[1] < 0:
+        angle = -angle
+
+    # rotation matrix
+    M = np.array([ux, uy])
+
+    # calculate the size which contains the angle degree of the bbox, and the center
+    center0 = np.mean(pts, axis=0)
+    rpts = (pts - center0) @ M.T  # (M @ P.T).T = P @ M.T
+    lt_pt = np.min(rpts, axis=0)
+    rb_pt = np.max(rpts, axis=0)
+    center1 = (lt_pt + rb_pt) / 2
+
+    size = rb_pt - lt_pt
+    if need_square:
+        m = max(size[0], size[1])
+        size[0] = m
+        size[1] = m
+
+    size *= scale  # scale size
+    center = center0 + ux * center1[0] + uy * center1[1]  # counterclockwise rotation, equivalent to M.T @ center1.T
+    center = center + ux * (vx_ratio * size) + uy * \
+        (vy_ratio * size)  # considering the offset in vx and vy direction
+
+    if use_deg_flag:
+        angle = degrees(angle)
+
+    return center, size, angle
+
+
+def parse_bbox_from_landmark(pts, **kwargs):
+    center, size, angle = parse_rect_from_landmark(pts, **kwargs)
+    cx, cy = center
+    w, h = size
+
+    # calculate the vertex positions before rotation
+    bbox = np.array([
+        [cx-w/2, cy-h/2],  # left, top
+        [cx+w/2, cy-h/2],
+        [cx+w/2, cy+h/2],  # right, bottom
+        [cx-w/2, cy+h/2]
+    ], dtype=DTYPE)
+
+    # construct rotation matrix
+    bbox_rot = bbox.copy()
+    R = np.array([
+        [np.cos(angle), -np.sin(angle)],
+        [np.sin(angle),  np.cos(angle)]
+    ], dtype=DTYPE)
+
+    # calculate the relative position of each vertex from the rotation center, then rotate these positions, and finally add the coordinates of the rotation center
+    bbox_rot = (bbox_rot - center) @ R.T + center
+
+    return {
+        'center': center,  # 2x1
+        'size': size,  # scalar
+        'angle': angle,  # rad, counterclockwise
+        'bbox': bbox,  # 4x2
+        'bbox_rot': bbox_rot,  # 4x2
+    }
+
+
+def crop_image_by_bbox(img, bbox, lmk=None, dsize=512, angle=None, flag_rot=False, **kwargs):
+    left, top, right, bot = bbox
+    if int(right - left) != int(bot - top):
+        print(f'right-left {right-left} != bot-top {bot-top}')
+    size = right - left
+
+    src_center = np.array([(left + right) / 2, (top + bot) / 2], dtype=DTYPE)
+    tgt_center = np.array([dsize / 2, dsize / 2], dtype=DTYPE)
+
+    s = dsize / size  # scale
+    if flag_rot and angle is not None:
+        costheta, sintheta = cos(angle), sin(angle)
+        cx, cy = src_center[0], src_center[1]  # ori center
+        tcx, tcy = tgt_center[0], tgt_center[1]  # target center
+        # need to infer
+        M_o2c = np.array(
+            [[s * costheta, s * sintheta, tcx - s * (costheta * cx + sintheta * cy)],
+             [-s * sintheta, s * costheta, tcy - s * (-sintheta * cx + costheta * cy)]],
+            dtype=DTYPE
+        )
+    else:
+        M_o2c = np.array(
+            [[s, 0, tgt_center[0] - s * src_center[0]],
+             [0, s, tgt_center[1] - s * src_center[1]]],
+            dtype=DTYPE
+        )
+
+    if flag_rot and angle is None:
+        print('angle is None, but flag_rotate is True', style="bold yellow")
+
+    img_crop = _transform_img(img, M_o2c, dsize=dsize, borderMode=kwargs.get('borderMode', None))
+
+    lmk_crop = _transform_pts(lmk, M_o2c) if lmk is not None else None
+
+    M_o2c = np.vstack([M_o2c, np.array([0, 0, 1], dtype=DTYPE)])
+    M_c2o = np.linalg.inv(M_o2c)
+
+    # cv2.imwrite('crop.jpg', img_crop)
+
+    return {
+        'img_crop': img_crop,
+        'lmk_crop': lmk_crop,
+        'M_o2c': M_o2c,
+        'M_c2o': M_c2o,
+    }
+
+
+def _estimate_similar_transform_from_pts(
+    pts,
+    dsize,
+    scale=1.5,
+    vx_ratio=0,
+    vy_ratio=-0.1,
+    flag_do_rot=True,
+    **kwargs
+):
+    """ calculate the affine matrix of the cropped image from sparse points, the original image to the cropped image, the inverse is the cropped image to the original image
+    pts: landmark, 101 or 68 points or other points, Nx2
+    scale: the larger scale factor, the smaller face ratio
+    vx_ratio: x shift
+    vy_ratio: y shift, the smaller the y shift, the lower the face region
+    rot_flag: if it is true, conduct correction
+    """
+    center, size, angle = parse_rect_from_landmark(
+        pts, scale=scale, vx_ratio=vx_ratio, vy_ratio=vy_ratio,
+        use_lip=kwargs.get('use_lip', True)
+    )
+
+    s = dsize / size[0]  # scale
+    tgt_center = np.array([dsize / 2, dsize / 2], dtype=DTYPE)  # center of dsize
+
+    if flag_do_rot:
+        costheta, sintheta = cos(angle), sin(angle)
+        cx, cy = center[0], center[1]  # ori center
+        tcx, tcy = tgt_center[0], tgt_center[1]  # target center
+        # need to infer
+        M_INV = np.array(
+            [[s * costheta, s * sintheta, tcx - s * (costheta * cx + sintheta * cy)],
+             [-s * sintheta, s * costheta, tcy - s * (-sintheta * cx + costheta * cy)]],
+            dtype=DTYPE
+        )
+    else:
+        M_INV = np.array(
+            [[s, 0, tgt_center[0] - s * center[0]],
+             [0, s, tgt_center[1] - s * center[1]]],
+            dtype=DTYPE
+        )
+
+    M_INV_H = np.vstack([M_INV, np.array([0, 0, 1])])
+    M = np.linalg.inv(M_INV_H)
+
+    # M_INV is from the original image to the cropped image, M is from the cropped image to the original image
+    return M_INV, M[:2, ...]
+
+
+def crop_image(img, pts: np.ndarray, **kwargs):
+    dsize = kwargs.get('dsize', 224)
+    scale = kwargs.get('scale', 1.5)  # 1.5 | 1.6
+    vy_ratio = kwargs.get('vy_ratio', -0.1)  # -0.0625 | -0.1
+
+    M_INV, _ = _estimate_similar_transform_from_pts(
+        pts,
+        dsize=dsize,
+        scale=scale,
+        vy_ratio=vy_ratio,
+        flag_do_rot=kwargs.get('flag_do_rot', True),
+    )
+
+    if img is None:
+        M_INV_H = np.vstack([M_INV, np.array([0, 0, 1], dtype=DTYPE)])
+        M = np.linalg.inv(M_INV_H)
+        ret_dct = {
+            'M': M[:2, ...],  # from the original image to the cropped image
+            'M_o2c': M[:2, ...],  # from the cropped image to the original image
+            'img_crop': None,
+            'pt_crop': None,
+        }
+        return ret_dct
+
+    img_crop = _transform_img(img, M_INV, dsize)  # origin to crop
+    pt_crop = _transform_pts(pts, M_INV)
+
+    M_o2c = np.vstack([M_INV, np.array([0, 0, 1], dtype=DTYPE)])
+    M_c2o = np.linalg.inv(M_o2c)
+
+    ret_dct = {
+        'M_o2c': M_o2c,  # from the original image to the cropped image 3x3
+        'M_c2o': M_c2o,  # from the cropped image to the original image 3x3
+        'img_crop': img_crop,  # the cropped image
+        'pt_crop': pt_crop,  # the landmarks of the cropped image
+    }
+
+    return ret_dct
+
+def average_bbox_lst(bbox_lst):
+    if len(bbox_lst) == 0:
+        return None
+    bbox_arr = np.array(bbox_lst)
+    return np.mean(bbox_arr, axis=0).tolist()
+

liveportrait/utils/cropper.py

@@ -0,0 +1,147 @@
+# coding: utf-8
+
+import numpy as np
+import os.path as osp
+from typing import List, Union, Tuple
+from dataclasses import dataclass, field
+import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
+
+from .landmark_runner import LandmarkRunner
+from .face_analysis_diy import FaceAnalysisDIY
+from .helper import prefix
+from .crop import crop_image, crop_image_by_bbox, parse_bbox_from_landmark, average_bbox_lst
+from .timer import Timer
+from .rprint import rlog as log
+from .io import load_image_rgb
+from .video import VideoWriter, get_fps, change_video_fps
+
+import folder_paths
+import os
+script_directory = os.path.dirname(os.path.abspath(__file__))
+
+def make_abs_path(fn):
+    return osp.join(osp.dirname(osp.realpath(__file__)), fn)
+
+
+@dataclass
+class Trajectory:
+    start: int = -1  # 起始帧 闭区间
+    end: int = -1  # 结束帧 闭区间
+    lmk_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list)  # lmk list
+    bbox_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list)  # bbox list
+    frame_rgb_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list)  # frame list
+    frame_rgb_crop_lst: Union[Tuple, List, np.ndarray] = field(default_factory=list)  # frame crop list
+
+
+class Cropper(object):
+    def __init__(self, **kwargs) -> None:
+        device_id = kwargs.get('device_id', 0)
+        self.landmark_runner = LandmarkRunner(
+            #ckpt_path=make_abs_path('../../pretrained_weights/liveportrait/landmark.onnx'),
+            ckpt_path=os.path.join(folder_paths.models_dir, 'liveportrait', 'landmark.onnx'),
+            onnx_provider='cuda',
+            device_id=device_id
+        )
+        self.landmark_runner.warmup()
+
+        self.face_analysis_wrapper = FaceAnalysisDIY(
+            name='buffalo_l',
+            root=os.path.join(folder_paths.models_dir, 'insightface'),
+            providers=["CUDAExecutionProvider"]
+        )
+        self.face_analysis_wrapper.prepare(ctx_id=device_id, det_size=(512, 512))
+        self.face_analysis_wrapper.warmup()
+
+        self.crop_cfg = kwargs.get('crop_cfg', None)
+
+    def update_config(self, user_args):
+        for k, v in user_args.items():
+            if hasattr(self.crop_cfg, k):
+                setattr(self.crop_cfg, k, v)
+
+    def crop_single_image(self, obj, **kwargs):
+        direction = kwargs.get('direction', 'large-small')
+
+        # crop and align a single image
+        if isinstance(obj, str):
+            img_rgb = load_image_rgb(obj)
+        elif isinstance(obj, np.ndarray):
+            img_rgb = obj
+
+        src_face = self.face_analysis_wrapper.get(
+            img_rgb,
+            flag_do_landmark_2d_106=True,
+            direction=direction
+        )
+
+        if len(src_face) == 0:
+            log('No face detected in the source image.')
+            raise Exception("No face detected in the source image!")
+        elif len(src_face) > 1:
+            log(f'More than one face detected in the image, only pick one face by rule {direction}.')
+
+        src_face = src_face[0]
+        pts = src_face.landmark_2d_106
+
+        # crop the face
+        ret_dct = crop_image(
+            img_rgb,  # ndarray
+            pts,  # 106x2 or Nx2
+            dsize=kwargs.get('dsize', 512),
+            scale=kwargs.get('scale', 2.3),
+            vy_ratio=kwargs.get('vy_ratio', -0.15),
+        )
+        # update a 256x256 version for network input or else
+        ret_dct['img_crop_256x256'] = cv2.resize(ret_dct['img_crop'], (256, 256), interpolation=cv2.INTER_AREA)
+        ret_dct['pt_crop_256x256'] = ret_dct['pt_crop'] * 256 / kwargs.get('dsize', 512)
+
+        recon_ret = self.landmark_runner.run(img_rgb, pts)
+        lmk = recon_ret['pts']
+        ret_dct['lmk_crop'] = lmk
+
+        return ret_dct
+
+    def get_retargeting_lmk_info(self, driving_rgb_lst):
+        # TODO: implement a tracking-based version
+        driving_lmk_lst = []
+        for driving_image in driving_rgb_lst:
+            ret_dct = self.crop_single_image(driving_image)
+            driving_lmk_lst.append(ret_dct['lmk_crop'])
+        return driving_lmk_lst
+
+    def make_video_clip(self, driving_rgb_lst, output_path, output_fps=30, **kwargs):
+        trajectory = Trajectory()
+        direction = kwargs.get('direction', 'large-small')
+        for idx, driving_image in enumerate(driving_rgb_lst):
+            if idx == 0 or trajectory.start == -1:
+                src_face = self.face_analysis_wrapper.get(
+                    driving_image,
+                    flag_do_landmark_2d_106=True,
+                    direction=direction
+                )
+                if len(src_face) == 0:
+                    # No face detected in the driving_image
+                    continue
+                elif len(src_face) > 1:
+                    log(f'More than one face detected in the driving frame_{idx}, only pick one face by rule {direction}.')
+                src_face = src_face[0]
+                pts = src_face.landmark_2d_106
+                lmk_203 = self.landmark_runner(driving_image, pts)['pts']
+                trajectory.start, trajectory.end = idx, idx
+            else:
+                lmk_203 = self.face_recon_wrapper(driving_image, trajectory.lmk_lst[-1])['pts']
+                trajectory.end = idx
+
+            trajectory.lmk_lst.append(lmk_203)
+            ret_bbox = parse_bbox_from_landmark(lmk_203, scale=self.crop_cfg.globalscale, vy_ratio=elf.crop_cfg.vy_ratio)['bbox']
+            bbox = [ret_bbox[0, 0], ret_bbox[0, 1], ret_bbox[2, 0], ret_bbox[2, 1]]  # 4,
+            trajectory.bbox_lst.append(bbox)  # bbox
+            trajectory.frame_rgb_lst.append(driving_image)
+
+        global_bbox = average_bbox_lst(trajectory.bbox_lst)
+        for idx, (frame_rgb, lmk) in enumerate(zip(trajectory.frame_rgb_lst, trajectory.lmk_lst)):
+            ret_dct = crop_image_by_bbox(
+                frame_rgb, global_bbox, lmk=lmk,
+                dsize=self.video_crop_cfg.dsize, flag_rot=self.video_crop_cfg.flag_rot, borderValue=self.video_crop_cfg.borderValue
+            )
+            frame_rgb_crop = ret_dct['img_crop']

liveportrait/utils/face_analysis_diy.py

@@ -0,0 +1,79 @@
+# coding: utf-8
+
+"""
+face detectoin and alignment using InsightFace
+"""
+
+import numpy as np
+from .rprint import rlog as log
+from insightface.app import FaceAnalysis
+from insightface.app.common import Face
+from .timer import Timer
+
+
+def sort_by_direction(faces, direction: str = 'large-small', face_center=None):
+    if len(faces) <= 0:
+        return faces
+
+    if direction == 'left-right':
+        return sorted(faces, key=lambda face: face['bbox'][0])
+    if direction == 'right-left':
+        return sorted(faces, key=lambda face: face['bbox'][0], reverse=True)
+    if direction == 'top-bottom':
+        return sorted(faces, key=lambda face: face['bbox'][1])
+    if direction == 'bottom-top':
+        return sorted(faces, key=lambda face: face['bbox'][1], reverse=True)
+    if direction == 'small-large':
+        return sorted(faces, key=lambda face: (face['bbox'][2] - face['bbox'][0]) * (face['bbox'][3] - face['bbox'][1]))
+    if direction == 'large-small':
+        return sorted(faces, key=lambda face: (face['bbox'][2] - face['bbox'][0]) * (face['bbox'][3] - face['bbox'][1]), reverse=True)
+    if direction == 'distance-from-retarget-face':
+        return sorted(faces, key=lambda face: (((face['bbox'][2]+face['bbox'][0])/2-face_center[0])**2+((face['bbox'][3]+face['bbox'][1])/2-face_center[1])**2)**0.5)
+    return faces
+
+
+class FaceAnalysisDIY(FaceAnalysis):
+    def __init__(self, name='buffalo_l', root='~/.insightface', allowed_modules=None, **kwargs):
+        super().__init__(name=name, root=root, allowed_modules=allowed_modules, **kwargs)
+
+        self.timer = Timer()
+
+    def get(self, img_bgr, **kwargs):
+        max_num = kwargs.get('max_num', 0)  # the number of the detected faces, 0 means no limit
+        flag_do_landmark_2d_106 = kwargs.get('flag_do_landmark_2d_106', True)  # whether to do 106-point detection
+        direction = kwargs.get('direction', 'large-small')  # sorting direction
+        face_center = None
+
+        bboxes, kpss = self.det_model.detect(img_bgr, max_num=max_num, metric='default')
+        if bboxes.shape[0] == 0:
+            return []
+        ret = []
+        for i in range(bboxes.shape[0]):
+            bbox = bboxes[i, 0:4]
+            det_score = bboxes[i, 4]
+            kps = None
+            if kpss is not None:
+                kps = kpss[i]
+            face = Face(bbox=bbox, kps=kps, det_score=det_score)
+            for taskname, model in self.models.items():
+                if taskname == 'detection':
+                    continue
+
+                if (not flag_do_landmark_2d_106) and taskname == 'landmark_2d_106':
+                    continue
+
+                # print(f'taskname: {taskname}')
+                model.get(img_bgr, face)
+            ret.append(face)
+
+        ret = sort_by_direction(ret, direction, face_center)
+        return ret
+
+    def warmup(self):
+        self.timer.tic()
+
+        img_bgr = np.zeros((512, 512, 3), dtype=np.uint8)
+        self.get(img_bgr)
+
+        elapse = self.timer.toc()
+        log(f'FaceAnalysisDIY warmup time: {elapse:.3f}s')

liveportrait/utils/helper.py

@@ -0,0 +1,175 @@
+# coding: utf-8
+
+"""
+utility functions and classes to handle feature extraction and model loading
+"""
+
+import os
+import os.path as osp
+import cv2
+import torch
+from rich.console import Console
+from collections import OrderedDict
+
+from ..modules.spade_generator import SPADEDecoder
+from ..modules.warping_network import WarpingNetwork
+from ..modules.motion_extractor import MotionExtractor
+from ..modules.appearance_feature_extractor import AppearanceFeatureExtractor
+from ..modules.stitching_retargeting_network import StitchingRetargetingNetwork
+from .rprint import rlog as log
+
+
+def suffix(filename):
+    """a.jpg -> jpg"""
+    pos = filename.rfind(".")
+    if pos == -1:
+        return ""
+    return filename[pos + 1:]
+
+
+def prefix(filename):
+    """a.jpg -> a"""
+    pos = filename.rfind(".")
+    if pos == -1:
+        return filename
+    return filename[:pos]
+
+
+def basename(filename):
+    """a/b/c.jpg -> c"""
+    return prefix(osp.basename(filename))
+
+
+def is_video(file_path):
+    if file_path.lower().endswith((".mp4", ".mov", ".avi", ".webm")) or osp.isdir(file_path):
+        return True
+    return False
+
+def is_template(file_path):
+    if file_path.endswith(".pkl"):
+        return True
+    return False
+
+
+def mkdir(d, log=False):
+    # return self-assined `d`, for one line code
+    if not osp.exists(d):
+        os.makedirs(d, exist_ok=True)
+        if log:
+            print(f"Make dir: {d}")
+    return d
+
+
+def squeeze_tensor_to_numpy(tensor):
+    out = tensor.data.squeeze(0).cpu().numpy()
+    return out
+
+
+def dct2cuda(dct: dict, device_id: int):
+    for key in dct:
+        dct[key] = torch.tensor(dct[key]).cuda(device_id)
+    return dct
+
+
+def concat_feat(kp_source: torch.Tensor, kp_driving: torch.Tensor) -> torch.Tensor:
+    """
+    kp_source: (bs, k, 3)
+    kp_driving: (bs, k, 3)
+    Return: (bs, 2k*3)
+    """
+    bs_src = kp_source.shape[0]
+    bs_dri = kp_driving.shape[0]
+    assert bs_src == bs_dri, 'batch size must be equal'
+
+    feat = torch.cat([kp_source.view(bs_src, -1), kp_driving.view(bs_dri, -1)], dim=1)
+    return feat
+
+
+def remove_ddp_dumplicate_key(state_dict):
+    state_dict_new = OrderedDict()
+    for key in state_dict.keys():
+        state_dict_new[key.replace('module.', '')] = state_dict[key]
+    return state_dict_new
+
+
+def load_model(ckpt_path, model_config, device, model_type):
+    model_params = model_config['model_params'][f'{model_type}_params']
+
+    if model_type == 'appearance_feature_extractor':
+        model = AppearanceFeatureExtractor(**model_params).cuda(device)
+    elif model_type == 'motion_extractor':
+        model = MotionExtractor(**model_params).cuda(device)
+    elif model_type == 'warping_module':
+        model = WarpingNetwork(**model_params).cuda(device)
+    elif model_type == 'spade_generator':
+        model = SPADEDecoder(**model_params).cuda(device)
+    elif model_type == 'stitching_retargeting_module':
+        # Special handling for stitching and retargeting module
+        config = model_config['model_params']['stitching_retargeting_module_params']
+        checkpoint = torch.load(ckpt_path, map_location=lambda storage, loc: storage)
+
+        stitcher = StitchingRetargetingNetwork(**config.get('stitching'))
+        stitcher.load_state_dict(remove_ddp_dumplicate_key(checkpoint['retarget_shoulder']))
+        stitcher = stitcher.cuda(device)
+        stitcher.eval()
+
+        retargetor_lip = StitchingRetargetingNetwork(**config.get('lip'))
+        retargetor_lip.load_state_dict(remove_ddp_dumplicate_key(checkpoint['retarget_mouth']))
+        retargetor_lip = retargetor_lip.cuda(device)
+        retargetor_lip.eval()
+
+        retargetor_eye = StitchingRetargetingNetwork(**config.get('eye'))
+        retargetor_eye.load_state_dict(remove_ddp_dumplicate_key(checkpoint['retarget_eye']))
+        retargetor_eye = retargetor_eye.cuda(device)
+        retargetor_eye.eval()
+
+        return {
+            'stitching': stitcher,
+            'lip': retargetor_lip,
+            'eye': retargetor_eye
+        }
+    else:
+        raise ValueError(f"Unknown model type: {model_type}")
+
+    model.load_state_dict(torch.load(ckpt_path, map_location=lambda storage, loc: storage))
+    model.eval()
+    return model
+
+
+# get coefficients of Eqn. 7
+def calculate_transformation(config, s_kp_info, t_0_kp_info, t_i_kp_info, R_s, R_t_0, R_t_i):
+    if config.relative:
+        new_rotation = (R_t_i @ R_t_0.permute(0, 2, 1)) @ R_s
+        new_expression = s_kp_info['exp'] + (t_i_kp_info['exp'] - t_0_kp_info['exp'])
+    else:
+        new_rotation = R_t_i
+        new_expression = t_i_kp_info['exp']
+    new_translation = s_kp_info['t'] + (t_i_kp_info['t'] - t_0_kp_info['t'])
+    new_translation[..., 2].fill_(0)  # Keep the z-axis unchanged
+    new_scale = s_kp_info['scale'] * (t_i_kp_info['scale'] / t_0_kp_info['scale'])
+    return new_rotation, new_expression, new_translation, new_scale
+
+def load_description(fp):
+    with open(fp, 'r', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+
+def resize_to_limit(img, max_dim=1280, n=2):
+    h, w = img.shape[:2]
+    if max_dim > 0 and max(h, w) > max_dim:
+        if h > w:
+            new_h = max_dim
+            new_w = int(w * (max_dim / h))
+        else:
+            new_w = max_dim
+            new_h = int(h * (max_dim / w))
+        img = cv2.resize(img, (new_w, new_h))
+    n = max(n, 1)
+    new_h = img.shape[0] - (img.shape[0] % n)
+    new_w = img.shape[1] - (img.shape[1] % n)
+    if new_h == 0 or new_w == 0:
+        return img
+    if new_h != img.shape[0] or new_w != img.shape[1]:
+        img = img[:new_h, :new_w]
+    return img

liveportrait/utils/io.py

@@ -0,0 +1,97 @@
+# coding: utf-8
+
+import os
+from glob import glob
+import os.path as osp
+import imageio
+import numpy as np
+import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
+
+
+def load_image_rgb(image_path: str):
+    if not osp.exists(image_path):
+        raise FileNotFoundError(f"Image not found: {image_path}")
+    img = cv2.imread(image_path, cv2.IMREAD_COLOR)
+    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+
+def load_driving_info(driving_info):
+    driving_video_ori = []
+
+    def load_images_from_directory(directory):
+        image_paths = sorted(glob(osp.join(directory, '*.png')) + glob(osp.join(directory, '*.jpg')))
+        return [load_image_rgb(im_path) for im_path in image_paths]
+
+    def load_images_from_video(file_path):
+        reader = imageio.get_reader(file_path)
+        return [image for idx, image in enumerate(reader)]
+
+    if osp.isdir(driving_info):
+        driving_video_ori = load_images_from_directory(driving_info)
+    elif osp.isfile(driving_info):
+        driving_video_ori = load_images_from_video(driving_info)
+
+    return driving_video_ori
+
+
+def contiguous(obj):
+    if not obj.flags.c_contiguous:
+        obj = obj.copy(order="C")
+    return obj
+
+
+def _resize_to_limit(img: np.ndarray, max_dim=1920, n=2):
+    """
+    ajust the size of the image so that the maximum dimension does not exceed max_dim, and the width and the height of the image are multiples of n.
+    :param img: the image to be processed.
+    :param max_dim: the maximum dimension constraint.
+    :param n: the number that needs to be multiples of.
+    :return: the adjusted image.
+    """
+    h, w = img.shape[:2]
+
+    # ajust the size of the image according to the maximum dimension
+    if max_dim > 0 and max(h, w) > max_dim:
+        if h > w:
+            new_h = max_dim
+            new_w = int(w * (max_dim / h))
+        else:
+            new_w = max_dim
+            new_h = int(h * (max_dim / w))
+        img = cv2.resize(img, (new_w, new_h))
+
+    # ensure that the image dimensions are multiples of n
+    n = max(n, 1)
+    new_h = img.shape[0] - (img.shape[0] % n)
+    new_w = img.shape[1] - (img.shape[1] % n)
+
+    if new_h == 0 or new_w == 0:
+        # when the width or height is less than n, no need to process
+        return img
+
+    if new_h != img.shape[0] or new_w != img.shape[1]:
+        img = img[:new_h, :new_w]
+
+    return img
+
+
+def load_img_online(obj, mode="bgr", **kwargs):
+    max_dim = kwargs.get("max_dim", 1920)
+    n = kwargs.get("n", 2)
+    if isinstance(obj, str):
+        if mode.lower() == "gray":
+            img = cv2.imread(obj, cv2.IMREAD_GRAYSCALE)
+        else:
+            img = cv2.imread(obj, cv2.IMREAD_COLOR)
+    else:
+        img = obj
+
+    # Resize image to satisfy constraints
+    img = _resize_to_limit(img, max_dim=max_dim, n=n)
+
+    if mode.lower() == "bgr":
+        return contiguous(img)
+    elif mode.lower() == "rgb":
+        return contiguous(img[..., ::-1])
+    else:
+        raise Exception(f"Unknown mode {mode}")

liveportrait/utils/landmark_runner.py

@@ -0,0 +1,89 @@
+# coding: utf-8
+
+import os.path as osp
+import cv2; cv2.setNumThreads(0); cv2.ocl.setUseOpenCL(False)
+import torch
+import numpy as np
+import onnxruntime
+from .timer import Timer
+from .rprint import rlog
+from .crop import crop_image, _transform_pts
+
+
+def make_abs_path(fn):
+    return osp.join(osp.dirname(osp.realpath(__file__)), fn)
+
+
+def to_ndarray(obj):
+    if isinstance(obj, torch.Tensor):
+        return obj.cpu().numpy()
+    elif isinstance(obj, np.ndarray):
+        return obj
+    else:
+        return np.array(obj)
+
+
+class LandmarkRunner(object):
+    """landmark runner"""
+    def __init__(self, **kwargs):
+        ckpt_path = kwargs.get('ckpt_path')
+        onnx_provider = kwargs.get('onnx_provider', 'cuda')  # 默认用cuda
+        device_id = kwargs.get('device_id', 0)
+        self.dsize = kwargs.get('dsize', 224)
+        self.timer = Timer()
+
+        if onnx_provider.lower() == 'cuda':
+            self.session = onnxruntime.InferenceSession(
+                ckpt_path, providers=[
+                    ('CUDAExecutionProvider', {'device_id': device_id})
+                ]
+            )
+        else:
+            opts = onnxruntime.SessionOptions()
+            opts.intra_op_num_threads = 4  # 默认线程数为 4
+            self.session = onnxruntime.InferenceSession(
+                ckpt_path, providers=['CPUExecutionProvider'],
+                sess_options=opts
+            )
+
+    def _run(self, inp):
+        out = self.session.run(None, {'input': inp})
+        return out
+
+    def run(self, img_rgb: np.ndarray, lmk=None):
+        if lmk is not None:
+            crop_dct = crop_image(img_rgb, lmk, dsize=self.dsize, scale=1.5, vy_ratio=-0.1)
+            img_crop_rgb = crop_dct['img_crop']
+        else:
+            img_crop_rgb = cv2.resize(img_rgb, (self.dsize, self.dsize))
+            scale = max(img_rgb.shape[:2]) / self.dsize
+            crop_dct = {
+                'M_c2o': np.array([
+                    [scale, 0., 0.],
+                    [0., scale, 0.],
+                    [0., 0., 1.],
+                ], dtype=np.float32),
+            }
+
+        inp = (img_crop_rgb.astype(np.float32) / 255.).transpose(2, 0, 1)[None, ...]  # HxWx3 (BGR) -> 1x3xHxW (RGB!)
+
+        out_lst = self._run(inp)
+        out_pts = out_lst[2]
+
+        pts = to_ndarray(out_pts[0]).reshape(-1, 2) * self.dsize  # scale to 0-224
+        pts = _transform_pts(pts, M=crop_dct['M_c2o'])
+
+        return {
+            'pts': pts,  # 2d landmarks 203 points
+        }
+
+    def warmup(self):
+        # 构造dummy image进行warmup
+        self.timer.tic()
+
+        dummy_image = np.zeros((1, 3, self.dsize, self.dsize), dtype=np.float32)
+
+        _ = self._run(dummy_image)
+
+        elapse = self.timer.toc()
+        rlog(f'LandmarkRunner warmup time: {elapse:.3f}s')

liveportrait/utils/resources/.gitattributes

@@ -0,0 +1 @@
+mask_template.png filter=lfs diff=lfs merge=lfs -text

liveportrait/utils/resources/mask_template.png

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c53e64a07ae3056af2b38548ae5b6cceb04d5c3e6514c7a8d2c3aff9da1ee76
+size 3470

liveportrait/utils/retargeting_utils.py

@@ -0,0 +1,76 @@
+
+"""
+Functions to compute distance ratios between specific pairs of facial landmarks
+"""
+
+import numpy as np
+import torch
+
+
+def calculate_distance_ratio(lmk: np.ndarray, idx1: int, idx2: int, idx3: int, idx4: int, eps: float = 1e-6) -> np.ndarray:
+    """
+    Calculate the ratio of the distance between two pairs of landmarks.
+
+    Parameters:
+    lmk (np.ndarray): Landmarks array of shape (B, N, 2).
+    idx1, idx2, idx3, idx4 (int): Indices of the landmarks.
+    eps (float): Small value to avoid division by zero.
+
+    Returns:
+    np.ndarray: Calculated distance ratio.
+    """
+    return (np.linalg.norm(lmk[:, idx1] - lmk[:, idx2], axis=1, keepdims=True) /
+            (np.linalg.norm(lmk[:, idx3] - lmk[:, idx4], axis=1, keepdims=True) + eps))
+
+
+def calc_eye_close_ratio(lmk: np.ndarray, target_eye_ratio: np.ndarray = None) -> np.ndarray:
+    """
+    Calculate the eye-close ratio for left and right eyes.
+
+    Parameters:
+    lmk (np.ndarray): Landmarks array of shape (B, N, 2).
+    target_eye_ratio (np.ndarray, optional): Additional target eye ratio array to include.
+
+    Returns:
+    np.ndarray: Concatenated eye-close ratios.
+    """
+    lefteye_close_ratio = calculate_distance_ratio(lmk, 6, 18, 0, 12)
+    righteye_close_ratio = calculate_distance_ratio(lmk, 30, 42, 24, 36)
+    if target_eye_ratio is not None:
+        return np.concatenate([lefteye_close_ratio, righteye_close_ratio, target_eye_ratio], axis=1)
+    else:
+        return np.concatenate([lefteye_close_ratio, righteye_close_ratio], axis=1)
+
+
+def calc_lip_close_ratio(lmk: np.ndarray) -> np.ndarray:
+    """
+    Calculate the lip-close ratio.
+
+    Parameters:
+    lmk (np.ndarray): Landmarks array of shape (B, N, 2).
+
+    Returns:
+    np.ndarray: Calculated lip-close ratio.
+    """
+    return calculate_distance_ratio(lmk, 90, 102, 48, 66)
+
+
+def compute_eye_delta(frame_idx, input_eye_ratios, source_landmarks, portrait_wrapper, kp_source):
+    input_eye_ratio = input_eye_ratios[frame_idx][0][0]
+    eye_close_ratio = calc_eye_close_ratio(source_landmarks[None])
+    eye_close_ratio_tensor = torch.from_numpy(eye_close_ratio).float().cuda(portrait_wrapper.device_id)
+    input_eye_ratio_tensor = torch.Tensor([input_eye_ratio]).reshape(1, 1).cuda(portrait_wrapper.device_id)
+    combined_eye_ratio_tensor = torch.cat([eye_close_ratio_tensor, input_eye_ratio_tensor], dim=1)
+    # print(combined_eye_ratio_tensor.mean())
+    eye_delta = portrait_wrapper.retarget_eye(kp_source, combined_eye_ratio_tensor)
+    return eye_delta
+
+
+def compute_lip_delta(frame_idx, input_lip_ratios, source_landmarks, portrait_wrapper, kp_source):
+    input_lip_ratio = input_lip_ratios[frame_idx][0]
+    lip_close_ratio = calc_lip_close_ratio(source_landmarks[None])
+    lip_close_ratio_tensor = torch.from_numpy(lip_close_ratio).float().cuda(portrait_wrapper.device_id)
+    input_lip_ratio_tensor = torch.Tensor([input_lip_ratio]).cuda(portrait_wrapper.device_id)
+    combined_lip_ratio_tensor = torch.cat([lip_close_ratio_tensor, input_lip_ratio_tensor], dim=1)
+    lip_delta = portrait_wrapper.retarget_lip(kp_source, combined_lip_ratio_tensor)
+    return lip_delta

liveportrait/utils/rprint.py

@@ -0,0 +1,16 @@
+# coding: utf-8
+
+"""
+custom print and log functions 
+"""
+
+__all__ = ['rprint', 'rlog']
+
+try:
+    from rich.console import Console
+    console = Console()
+    rprint = console.print
+    rlog = console.log
+except:
+    rprint = print
+    rlog = print

liveportrait/utils/timer.py

@@ -0,0 +1,29 @@
+# coding: utf-8
+
+"""
+tools to measure elapsed time
+"""
+
+import time
+
+class Timer(object):
+    """A simple timer."""
+
+    def __init__(self):
+        self.total_time = 0.
+        self.calls = 0
+        self.start_time = 0.
+        self.diff = 0.
+
+    def tic(self):
+        # using time.time instead of time.clock because time time.clock
+        # does not normalize for multithreading
+        self.start_time = time.time()
+
+    def toc(self, average=True):
+        self.diff = time.time() - self.start_time
+        return self.diff
+
+    def clear(self):
+        self.start_time = 0.
+        self.diff = 0.

liveportrait/utils/video.py

@@ -0,0 +1,139 @@
+# coding: utf-8
+
+"""
+functions for processing video
+"""
+
+import os.path as osp
+import numpy as np
+import subprocess
+import imageio
+import cv2
+
+from rich.progress import track
+from .helper import prefix
+from .rprint import rprint as print
+
+
+def exec_cmd(cmd):
+    subprocess.run(cmd, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+
+
+def images2video(images, wfp, **kwargs):
+    fps = kwargs.get('fps', 30)
+    video_format = kwargs.get('format', 'mp4')  # default is mp4 format
+    codec = kwargs.get('codec', 'libx264')  # default is libx264 encoding
+    quality = kwargs.get('quality')  # video quality
+    pixelformat = kwargs.get('pixelformat', 'yuv420p')  # video pixel format
+    image_mode = kwargs.get('image_mode', 'rgb')
+    macro_block_size = kwargs.get('macro_block_size', 2)
+    ffmpeg_params = ['-crf', str(kwargs.get('crf', 18))]
+
+    writer = imageio.get_writer(
+        wfp, fps=fps, format=video_format,
+        codec=codec, quality=quality, ffmpeg_params=ffmpeg_params, pixelformat=pixelformat, macro_block_size=macro_block_size
+    )
+
+    n = len(images)
+    for i in track(range(n), description='writing', transient=True):
+        if image_mode.lower() == 'bgr':
+            writer.append_data(images[i][..., ::-1])
+        else:
+            writer.append_data(images[i])
+
+    writer.close()
+
+    # print(f':smiley: Dump to {wfp}\n', style="bold green")
+    print(f'Dump to {wfp}\n')
+
+
+def video2gif(video_fp, fps=30, size=256):
+    if osp.exists(video_fp):
+        d = osp.split(video_fp)[0]
+        fn = prefix(osp.basename(video_fp))
+        palette_wfp = osp.join(d, 'palette.png')
+        gif_wfp = osp.join(d, f'{fn}.gif')
+        # generate the palette
+        cmd = f'ffmpeg -i {video_fp} -vf "fps={fps},scale={size}:-1:flags=lanczos,palettegen" {palette_wfp} -y'
+        exec_cmd(cmd)
+        # use the palette to generate the gif
+        cmd = f'ffmpeg -i {video_fp} -i {palette_wfp} -filter_complex "fps={fps},scale={size}:-1:flags=lanczos[x];[x][1:v]paletteuse" {gif_wfp} -y'
+        exec_cmd(cmd)
+    else:
+        print(f'video_fp: {video_fp} not exists!')
+
+
+def merge_audio_video(video_fp, audio_fp, wfp):
+    if osp.exists(video_fp) and osp.exists(audio_fp):
+        cmd = f'ffmpeg -i {video_fp} -i {audio_fp} -c:v copy -c:a aac {wfp} -y'
+        exec_cmd(cmd)
+        print(f'merge {video_fp} and {audio_fp} to {wfp}')
+    else:
+        print(f'video_fp: {video_fp} or audio_fp: {audio_fp} not exists!')
+
+
+def blend(img: np.ndarray, mask: np.ndarray, background_color=(255, 255, 255)):
+    mask_float = mask.astype(np.float32) / 255.
+    background_color = np.array(background_color).reshape([1, 1, 3])
+    bg = np.ones_like(img) * background_color
+    img = np.clip(mask_float * img + (1 - mask_float) * bg, 0, 255).astype(np.uint8)
+    return img
+
+
+def concat_frames(I_p_lst, driving_rgb_lst, img_rgb):
+    # TODO: add more concat style, e.g., left-down corner driving
+    out_lst = []
+    for idx, _ in track(enumerate(I_p_lst), total=len(I_p_lst), description='Concatenating result...'):
+        source_image_drived = I_p_lst[idx]
+        image_drive = driving_rgb_lst[idx]
+
+        # resize images to match source_image_drived shape
+        h, w, _ = source_image_drived.shape
+        image_drive_resized = cv2.resize(image_drive, (w, h))
+        img_rgb_resized = cv2.resize(img_rgb, (w, h))
+
+        # concatenate images horizontally
+        frame = np.concatenate((image_drive_resized, img_rgb_resized, source_image_drived), axis=1)
+        out_lst.append(frame)
+    return out_lst
+
+
+class VideoWriter:
+    def __init__(self, **kwargs):
+        self.fps = kwargs.get('fps', 30)
+        self.wfp = kwargs.get('wfp', 'video.mp4')
+        self.video_format = kwargs.get('format', 'mp4')
+        self.codec = kwargs.get('codec', 'libx264')
+        self.quality = kwargs.get('quality')
+        self.pixelformat = kwargs.get('pixelformat', 'yuv420p')
+        self.image_mode = kwargs.get('image_mode', 'rgb')
+        self.ffmpeg_params = kwargs.get('ffmpeg_params')
+
+        self.writer = imageio.get_writer(
+            self.wfp, fps=self.fps, format=self.video_format,
+            codec=self.codec, quality=self.quality,
+            ffmpeg_params=self.ffmpeg_params, pixelformat=self.pixelformat
+        )
+
+    def write(self, image):
+        if self.image_mode.lower() == 'bgr':
+            self.writer.append_data(image[..., ::-1])
+        else:
+            self.writer.append_data(image)
+
+    def close(self):
+        if self.writer is not None:
+            self.writer.close()
+
+
+def change_video_fps(input_file, output_file, fps=20, codec='libx264', crf=5):
+    cmd = f"ffmpeg -i {input_file} -c:v {codec} -crf {crf} -r {fps} {output_file} -y"
+    exec_cmd(cmd)
+
+
+def get_fps(filepath):
+    import ffmpeg
+    probe = ffmpeg.probe(filepath)
+    video_stream = next((stream for stream in probe['streams'] if stream['codec_type'] == 'video'), None)
+    fps = eval(video_stream['avg_frame_rate'])
+    return fps

nodes.py

@@ -0,0 +1,268 @@
+import os
+import torch
+import yaml
+import folder_paths
+import comfy.model_management as mm
+import comfy.utils
+
+script_directory = os.path.dirname(os.path.abspath(__file__))
+
+from .liveportrait.config.argument_config import ArgumentConfig
+from .liveportrait.live_portrait_pipeline import LivePortraitPipeline
+
+from .liveportrait.modules.spade_generator import SPADEDecoder
+from .liveportrait.modules.warping_network import WarpingNetwork
+from .liveportrait.modules.motion_extractor import MotionExtractor
+from .liveportrait.modules.appearance_feature_extractor import AppearanceFeatureExtractor
+from .liveportrait.modules.stitching_retargeting_network import StitchingRetargetingNetwork
+
+class InferenceConfig:
+    def __init__(self,
+                    mask_crop = None,
+                    flag_use_half_precision=True,
+                    flag_lip_zero=True,
+                    lip_zero_threshold=0.03,
+                    flag_eye_retargeting=False,
+                    flag_lip_retargeting=False,
+                    flag_stitching=True,
+                    flag_relative=True,
+                    anchor_frame=0,
+                    input_shape=(256, 256),
+                    output_format='mp4',
+                    output_fps=30,
+                    crf=15,
+                    flag_write_result=True,
+                    flag_pasteback=True,
+                    flag_write_gif=False,
+                    size_gif=256,
+                    ref_max_shape=1280,
+                    ref_shape_n=2,
+                    device_id=0,
+                    flag_do_crop=True,
+                    flag_do_rot=True):
+        self.flag_use_half_precision = flag_use_half_precision
+        self.flag_lip_zero = flag_lip_zero
+        self.lip_zero_threshold = lip_zero_threshold
+        self.flag_eye_retargeting = flag_eye_retargeting
+        self.flag_lip_retargeting = flag_lip_retargeting
+        self.flag_stitching = flag_stitching
+        self.flag_relative = flag_relative
+        self.anchor_frame = anchor_frame
+        self.input_shape = input_shape
+        self.output_format = output_format
+        self.output_fps = output_fps
+        self.crf = crf
+        self.flag_write_result = flag_write_result
+        self.flag_pasteback = flag_pasteback
+        self.flag_write_gif = flag_write_gif
+        self.size_gif = size_gif
+        self.ref_max_shape = ref_max_shape
+        self.ref_shape_n = ref_shape_n
+        self.device_id = device_id
+        self.flag_do_crop = flag_do_crop
+        self.flag_do_rot = flag_do_rot
+        self.mask_crop=mask_crop
+
+class CropConfig:
+    def __init__(self, dsize=512, scale=2.3, vx_ratio=0, vy_ratio=-0.125):
+        self.dsize = dsize
+        self.scale = scale
+        self.vx_ratio = vx_ratio
+        self.vy_ratio = vy_ratio
+
+class ArgumentConfig:
+    def __init__(self,
+                    device_id=0,
+                    flag_lip_zero=True,
+                    flag_eye_retargeting=False,
+                    flag_lip_retargeting=False,
+                    flag_stitching=True,
+                    flag_relative=True,
+                    flag_pasteback=True,
+                    flag_do_crop=True,
+                    flag_do_rot=True,
+                    dsize=512,
+                    scale=2.3,
+                    vx_ratio=0,
+                    vy_ratio=-0.125,
+                    ):
+        self.device_id = device_id
+        self.flag_lip_zero = flag_lip_zero
+        self.flag_eye_retargeting = flag_eye_retargeting
+        self.flag_lip_retargeting = flag_lip_retargeting
+        self.flag_stitching = flag_stitching
+        self.flag_relative = flag_relative
+        self.flag_pasteback = flag_pasteback
+        self.flag_do_crop = flag_do_crop
+        self.flag_do_rot = flag_do_rot
+        self.dsize = dsize
+        self.scale = scale
+        self.vx_ratio = vx_ratio
+        self.vy_ratio = vy_ratio
+
+class DownloadAndLoadLivePortraitModels:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            },
+        }
+
+    RETURN_TYPES = ("LIVEPORTRAITPIPE",)
+    RETURN_NAMES = ("live_portrait_pipe",)
+    FUNCTION = "loadmodel"
+    CATEGORY = "LivePortrait"
+
+    def loadmodel(self):
+        device = mm.get_torch_device()
+        mm.soft_empty_cache()
+
+        pbar = comfy.utils.ProgressBar(3)
+
+        download_path = os.path.join(folder_paths.models_dir, "liveportrait")
+        model_path = os.path.join(download_path)
+
+        if not os.path.exists(model_path):
+            print(f"Downloading model to: {model_path}")
+            from huggingface_hub import snapshot_download
+            snapshot_download(repo_id="Kijai/LivePortrait_safetensors",
+                                local_dir=download_path,
+                                local_dir_use_symlinks=False)
+
+        model_config_path = os.path.join(script_directory, 'liveportrait', 'config', 'models.yaml')
+        with open(model_config_path, 'r') as file:
+            model_config = yaml.safe_load(file)
+        print(model_config)
+        feature_extractor_path = os.path.join(model_path, 'appearance_feature_extractor.safetensors')
+        motion_extractor_path = os.path.join(model_path, 'motion_extractor.safetensors')
+        warping_module_path = os.path.join(model_path, 'warping_module.safetensors')
+        spade_generator_path = os.path.join(model_path, 'spade_generator.safetensors')
+        stitching_retargeting_path = os.path.join(model_path, 'stitching_retargeting_module.safetensors')
+
+        # init F
+        model_params = model_config['model_params']['appearance_feature_extractor_params']
+        self.appearance_feature_extractor = AppearanceFeatureExtractor(**model_params).to(device)
+        self.appearance_feature_extractor.load_state_dict(comfy.utils.load_torch_file(feature_extractor_path))
+        self.appearance_feature_extractor.eval()
+        print('Load appearance_feature_extractor done.')
+        pbar.update(1)
+        # init M
+        model_params = model_config['model_params']['motion_extractor_params']
+        self.motion_extractor = MotionExtractor(**model_params).to(device)
+        self.motion_extractor.load_state_dict(comfy.utils.load_torch_file(motion_extractor_path))
+        self.motion_extractor.eval()
+        print('Load motion_extractor done.')
+        pbar.update(1)
+        # init W
+        model_params = model_config['model_params']['warping_module_params']
+        self.warping_module = WarpingNetwork(**model_params).to(device)
+        self.warping_module.load_state_dict(comfy.utils.load_torch_file(warping_module_path))
+        self.warping_module.eval()
+        print('Load warping_module done.')
+        pbar.update(1)
+        # init G
+        model_params = model_config['model_params']['spade_generator_params']
+        self.spade_generator = SPADEDecoder(**model_params).to(device)
+        self.spade_generator.load_state_dict(comfy.utils.load_torch_file(spade_generator_path))
+        self.spade_generator.eval()
+        print('Load spade_generator done.')
+        pbar.update(1)
+
+        def filter_checkpoint_for_model(checkpoint, prefix):
+            """Filter and adjust the checkpoint dictionary for a specific model based on the prefix."""
+            # Create a new dictionary where keys are adjusted by removing the prefix and the model name
+            filtered_checkpoint = {key.replace(prefix + "_module.", ""): value for key, value in checkpoint.items() if key.startswith(prefix)}
+            return filtered_checkpoint
+
+        config = model_config['model_params']['stitching_retargeting_module_params']
+        checkpoint = comfy.utils.load_torch_file(stitching_retargeting_path)
+
+        # Example usage for the stitcher model
+        stitcher_prefix = 'retarget_shoulder'
+        stitcher_checkpoint = filter_checkpoint_for_model(checkpoint, stitcher_prefix)
+        stitcher = StitchingRetargetingNetwork(**config.get('stitching'))
+        stitcher.load_state_dict(stitcher_checkpoint)
+        stitcher = stitcher.to(device)
+        stitcher.eval()
+
+        # Repeat for other models with their respective prefixes
+        lip_prefix = 'retarget_mouth'
+        lip_checkpoint = filter_checkpoint_for_model(checkpoint, lip_prefix)
+        retargetor_lip = StitchingRetargetingNetwork(**config.get('lip'))
+        retargetor_lip.load_state_dict(lip_checkpoint)
+        retargetor_lip = retargetor_lip.to(device)
+        retargetor_lip.eval()
+
+        eye_prefix = 'retarget_eye'
+        eye_checkpoint = filter_checkpoint_for_model(checkpoint, eye_prefix)
+        retargetor_eye = StitchingRetargetingNetwork(**config.get('eye'))
+        retargetor_eye.load_state_dict(eye_checkpoint)
+        retargetor_eye = retargetor_eye.to(device)
+        retargetor_eye.eval()
+        print('Load stitching_retargeting_module done.')
+
+        self.stich_retargeting_module = {
+            'stitching': stitcher,
+            'lip': retargetor_lip,
+            'eye': retargetor_eye
+        }
+
+        pipeline = LivePortraitPipeline(
+            self.appearance_feature_extractor,
+            self.motion_extractor,
+            self.warping_module,
+            self.spade_generator,
+            self.stich_retargeting_module,
+            InferenceConfig(),
+            CropConfig()
+        )
+
+        return (pipeline,)
+
+class LivePortraitProcess:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+
+            "pipeline": ("LIVEPORTRAITPIPE",),
+            "source_image": ("IMAGE",),
+            "driving_images": ("IMAGE",),
+
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "IMAGE",)
+    RETURN_NAMES = ("cropped_images", "full_images",)
+    FUNCTION = "process"
+    CATEGORY = "LivePortrait"
+
+    def process(self, source_image, driving_images, pipeline):
+        device = mm.get_torch_device()
+
+        source_image_np = (source_image.squeeze(0) * 255).byte().numpy()
+        driving_images_np = (driving_images * 255).byte().numpy()
+
+        args =  ArgumentConfig()
+
+        cropped_frames, full_frame = pipeline.execute(source_image_np, driving_images_np, args)
+
+        cropped_tensors = [torch.from_numpy(np_array) for np_array in cropped_frames]
+        cropped_tensors_out = torch.stack(cropped_tensors) / 255
+        cropped_tensors_out = cropped_tensors_out.cpu().float()
+
+        full_tensors = [torch.from_numpy(np_array) for np_array in full_frame]
+        full_tensors_out = torch.stack(full_tensors) / 255
+        full_tensors_out = full_tensors_out.cpu().float()
+
+        print(cropped_tensors_out.shape)
+        print(cropped_tensors_out.min(), cropped_tensors_out.max())
+
+        return (cropped_tensors_out, full_tensors_out)
+
+NODE_CLASS_MAPPINGS = {
+    "DownloadAndLoadLivePortraitModels": DownloadAndLoadLivePortraitModels,
+    "LivePortraitProcess": LivePortraitProcess,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DownloadAndLoadLivePortraitModels": "(Down)Load LivePortraitModels",
+    "LivePortraitProcess": "LivePortraitProcess",
+    }

readme.md

@@ -0,0 +1,9 @@
+# ComfyUI nodes to use LivePortrait
+
+I have converted all the pickle files to safetensors, and they
+
+are automatically downloaded from here to `ComfyUI/models/liveportrait`:
+
+https://huggingface.co/Kijai/LivePortrait_safetensors/tree/main
+
+Insightface is required, and it's models are loaded from the usual `ComfyUI/models/insightface`

requirements.txt

@@ -0,0 +1,3 @@
+yaml
+numpy
+opencv-python