init

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.task filter=lfs diff=lfs merge=lfs -text
+*.mat filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,36 @@
+
+## Introduction
+
+This repo provides the inference Gradio demo for **Hybrid (Trajectory + Landmark)** Control of MOFA-Video.
+
+## Environment Setup
+
+```
+cd MOFA-Hybrid
+conda create -n mofa python==3.10
+conda activate mofa
+pip install -r requirements.txt
+pip install opencv-python-headless
+pip install "git+https://github.com/facebookresearch/pytorch3d.git"
+```
+
+**IMPORTANT:** Gradio Version of **4.5.0** should be used since other versions may cause errors.
+
+
+## Checkpoints Download
+1. Download the checkpoint of CMP from [here](https://huggingface.co/MyNiuuu/MOFA-Video-Hybrid/blob/main/models/cmp/experiments/semiauto_annot/resnet50_vip%2Bmpii_liteflow/checkpoints/ckpt_iter_42000.pth.tar) and put it into `./models/cmp/experiments/semiauto_annot/resnet50_vip+mpii_liteflow/checkpoints`.
+
+2. Downloading the necessary pretrained checkpoints from [huggingface](https://huggingface.co/MyNiuuu/MOFA-Video-Hybrid). It is recommended to directly using git lfs to clone the [huggingface repo](https://huggingface.co/MyNiuuu/MOFA-Video-Hybrid). The checkpoints should be orgnized as `./ckpt_tree.md` (they will be automatically organized if you use git lfs to clone the [huggingface repo](https://huggingface.co/MyNiuuu/MOFA-Video-Hybrid)).
+
+
+## Run Gradio Demo
+
+### Using audio to animate the facial part
+
+`python run_gradio_audio_driven.py`
+
+### Using refernce video to animate the facial part
+
+`python run_gradio_audio_driven.py`
+
+**IMPORTANT:** Please refer to the instructions on the gradio interface during the inference process.

aniportrait/audio2ldmk.py

@@ -0,0 +1,310 @@
+import argparse
+import os
+# import ffmpeg
+import random
+import numpy as np
+import cv2
+import torch
+import torchvision
+from omegaconf import OmegaConf
+from PIL import Image
+
+from src.audio_models.model import Audio2MeshModel
+from src.audio_models.pose_model import Audio2PoseModel
+from src.utils.audio_util import prepare_audio_feature
+from src.utils.mp_utils  import LMKExtractor
+from src.utils.pose_util import project_points, smooth_pose_seq
+
+
+PARTS = [
+    ('FACE', [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], (10, 200, 10)),
+    ('LEFT_EYE', [43, 44, 45, 46, 47, 48, 43], (180, 200, 10)),
+    ('LEFT_EYEBROW', [23, 24, 25, 26, 27], (180, 220, 10)),
+    ('RIGHT_EYE', [37, 38, 39, 40, 41, 42, 37], (10, 200, 180)),
+    ('RIGHT_EYEBROW', [18, 19, 20, 21, 22], (10, 220, 180)),
+    ('NOSE_UP', [28, 29, 30, 31], (10, 200, 250)),
+    ('NOSE_DOWN', [32, 33, 34, 35, 36], (250, 200, 10)),
+    ('LIPS_OUTER_BOTTOM_LEFT', [55, 56, 57, 58], (10, 180, 20)),
+    ('LIPS_OUTER_BOTTOM_RIGHT', [49, 60, 59, 58], (20, 10, 180)),
+    ('LIPS_INNER_BOTTOM_LEFT', [65, 66, 67], (100, 100, 30)),
+    ('LIPS_INNER_BOTTOM_RIGHT', [61, 68, 67], (100, 150, 50)),
+    ('LIPS_OUTER_TOP_LEFT', [52, 53, 54, 55], (20, 80, 100)),
+    ('LIPS_OUTER_TOP_RIGHT', [52, 51, 50, 49], (80, 100, 20)),
+    ('LIPS_INNER_TOP_LEFT', [63, 64, 65], (120, 100, 200)),
+    ('LIPS_INNER_TOP_RIGHT', [63, 62, 61], (150, 120, 100)),
+]
+
+
+def draw_landmarks(keypoints, h, w):
+        
+    image = np.zeros((h, w, 3))
+
+    for name, indices, color in PARTS:
+        # 选择当前部分的关键点
+        indices = np.array(indices) - 1
+        current_part_keypoints = keypoints[indices]
+
+        # 绘制关键点
+        # for point in current_part_keypoints:
+        #     x, y = point
+        #     image[y, x, :] = color
+
+        # 绘制连接线
+        for i in range(len(indices) - 1):
+            x1, y1 = current_part_keypoints[i]
+            x2, y2 = current_part_keypoints[i + 1]
+            cv2.line(image, (int(x1), int(y1)), (int(x2), int(y2)), color, thickness=2)
+        
+    return image
+
+
+
+def convert_ldmk_to_68(mediapipe_ldmk):
+    return np.stack([
+    # face coutour
+    mediapipe_ldmk[:, 234], 
+    mediapipe_ldmk[:, 93], 
+    mediapipe_ldmk[:, 132], 
+    mediapipe_ldmk[:, 58], 
+    mediapipe_ldmk[:, 172], 
+    mediapipe_ldmk[:, 136], 
+    mediapipe_ldmk[:, 150], 
+    mediapipe_ldmk[:, 176], 
+    mediapipe_ldmk[:, 152], 
+    mediapipe_ldmk[:, 400], 
+    mediapipe_ldmk[:, 379], 
+    mediapipe_ldmk[:, 365], 
+    mediapipe_ldmk[:, 397], 
+    mediapipe_ldmk[:, 288], 
+    mediapipe_ldmk[:, 361], 
+    mediapipe_ldmk[:, 323], 
+    mediapipe_ldmk[:, 454], 
+    # right eyebrow
+    mediapipe_ldmk[:, 70], 
+    mediapipe_ldmk[:, 63], 
+    mediapipe_ldmk[:, 105], 
+    mediapipe_ldmk[:, 66], 
+    mediapipe_ldmk[:, 107], 
+    # left eyebrow
+    mediapipe_ldmk[:, 336], 
+    mediapipe_ldmk[:, 296], 
+    mediapipe_ldmk[:, 334], 
+    mediapipe_ldmk[:, 293], 
+    mediapipe_ldmk[:, 300], 
+    # nose
+    mediapipe_ldmk[:, 168], 
+    mediapipe_ldmk[:, 6], 
+    mediapipe_ldmk[:, 195], 
+    mediapipe_ldmk[:, 4], 
+    # nose down 
+    mediapipe_ldmk[:, 239], 
+    mediapipe_ldmk[:, 241], 
+    mediapipe_ldmk[:, 19], 
+    mediapipe_ldmk[:, 461], 
+    mediapipe_ldmk[:, 459], 
+    # right eye
+    mediapipe_ldmk[:, 33], 
+    mediapipe_ldmk[:, 160], 
+    mediapipe_ldmk[:, 158], 
+    mediapipe_ldmk[:, 133], 
+    mediapipe_ldmk[:, 153], 
+    mediapipe_ldmk[:, 144], 
+    # left eye
+    mediapipe_ldmk[:, 362], 
+    mediapipe_ldmk[:, 385], 
+    mediapipe_ldmk[:, 387], 
+    mediapipe_ldmk[:, 263], 
+    mediapipe_ldmk[:, 373], 
+    mediapipe_ldmk[:, 380],
+    # outer lips
+    mediapipe_ldmk[:, 61], 
+    mediapipe_ldmk[:, 40], 
+    mediapipe_ldmk[:, 37], 
+    mediapipe_ldmk[:, 0], 
+    mediapipe_ldmk[:, 267], 
+    mediapipe_ldmk[:, 270], 
+    mediapipe_ldmk[:, 291], 
+    mediapipe_ldmk[:, 321], 
+    mediapipe_ldmk[:, 314], 
+    mediapipe_ldmk[:, 17], 
+    mediapipe_ldmk[:, 84], 
+    mediapipe_ldmk[:, 91], 
+    # inner lips
+    mediapipe_ldmk[:, 78], 
+    mediapipe_ldmk[:, 81], 
+    mediapipe_ldmk[:, 13], 
+    mediapipe_ldmk[:, 311], 
+    mediapipe_ldmk[:, 308], 
+    mediapipe_ldmk[:, 402], 
+    mediapipe_ldmk[:, 14], 
+    mediapipe_ldmk[:, 178],
+], axis=1)
+
+
+
+# def parse_args():
+#     parser = argparse.ArgumentParser()
+#     parser.add_argument("--config", type=str, default='./configs/prompts/animation_audio.yaml')
+#     parser.add_argument("-W", type=int, default=512)
+#     parser.add_argument("-H", type=int, default=512)
+#     parser.add_argument("-L", type=int)
+#     parser.add_argument("--seed", type=int, default=42)
+#     parser.add_argument("--cfg", type=float, default=3.5)
+#     parser.add_argument("--steps", type=int, default=25)
+#     parser.add_argument("--fps", type=int, default=30)
+#     parser.add_argument("-acc", "--accelerate", action='store_true')
+#     parser.add_argument("--fi_step", type=int, default=3)
+#     args = parser.parse_args()
+
+#     return args
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--ref_image_path", type=str, required=True)
+    parser.add_argument("--audio_path", type=str, required=True)
+    parser.add_argument("--save_dir", type=str, required=True)
+    parser.add_argument("--fps", type=int, default=25)
+    parser.add_argument("--sr", type=int, default=16000)
+    args = parser.parse_args()
+
+    return args
+
+
+def set_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+        torch.backends.cudnn.deterministic = True
+
+
+def main():
+    args = parse_args()
+
+    config = OmegaConf.load('aniportrait/configs/config.yaml')
+
+    set_seed(42)
+
+    # if config.weight_dtype == "fp16":
+    #     weight_dtype = torch.float16
+    # else:
+    #     weight_dtype = torch.float32
+        
+    audio_infer_config = OmegaConf.load(config.audio_inference_config)
+    # prepare model
+    a2m_model = Audio2MeshModel(audio_infer_config['a2m_model'])
+    a2m_model.load_state_dict(torch.load(audio_infer_config['pretrained_model']['a2m_ckpt']), strict=False)
+    a2m_model.cuda().eval()
+
+    a2p_model = Audio2PoseModel(audio_infer_config['a2p_model'])
+    a2p_model.load_state_dict(torch.load(audio_infer_config['pretrained_model']['a2p_ckpt']), strict=False)
+    a2p_model.cuda().eval()
+
+    lmk_extractor = LMKExtractor()
+
+    ref_image_path = args.ref_image_path
+    audio_path = args.audio_path
+    save_dir = args.save_dir
+
+    ref_image_pil = Image.open(ref_image_path).convert("RGB")
+    ref_image_np = cv2.cvtColor(np.array(ref_image_pil), cv2.COLOR_RGB2BGR)
+    height, width, _ = ref_image_np.shape
+    
+    face_result = lmk_extractor(ref_image_np)
+    assert face_result is not None, "No face detected."
+    lmks = face_result['lmks'].astype(np.float32)
+    lmks[:, 0] *= width
+    lmks[:, 1] *= height
+
+    # print(lmks.shape)
+
+    # assert False
+    
+    sample = prepare_audio_feature(audio_path, fps=args.fps, wav2vec_model_path=audio_infer_config['a2m_model']['model_path'])
+    sample['audio_feature'] = torch.from_numpy(sample['audio_feature']).float().cuda()
+    sample['audio_feature'] = sample['audio_feature'].unsqueeze(0)
+
+    # print(sample['audio_feature'].shape)
+
+    # inference
+    pred = a2m_model.infer(sample['audio_feature'], sample['seq_len'])
+    pred = pred.squeeze().detach().cpu().numpy()
+    pred = pred.reshape(pred.shape[0], -1, 3)
+
+    pred = pred + face_result['lmks3d']
+
+    # print(pred.shape)
+    
+    # assert False
+
+    id_seed = 42
+    id_seed = torch.LongTensor([id_seed]).cuda()
+
+    # Currently, only inference up to a maximum length of 10 seconds is supported.
+    chunk_duration = 5  # 5 seconds
+    chunk_size = args.sr * chunk_duration 
+
+
+    audio_chunks = list(sample['audio_feature'].split(chunk_size, dim=1))
+    seq_len_list = [chunk_duration*args.fps] * (len(audio_chunks) - 1) + [sample['seq_len'] % (chunk_duration*args.fps)]
+    audio_chunks[-2] = torch.cat((audio_chunks[-2], audio_chunks[-1]), dim=1)
+    seq_len_list[-2] = seq_len_list[-2] + seq_len_list[-1]
+    del audio_chunks[-1]
+    del seq_len_list[-1]
+
+    # assert False
+
+    pose_seq = []
+    for audio, seq_len in zip(audio_chunks, seq_len_list):
+        pose_seq_chunk = a2p_model.infer(audio, seq_len, id_seed)
+        pose_seq_chunk = pose_seq_chunk.squeeze().detach().cpu().numpy()
+        pose_seq_chunk[:, :3] *= 0.5
+        pose_seq.append(pose_seq_chunk)
+    
+    pose_seq = np.concatenate(pose_seq, 0)
+    pose_seq = smooth_pose_seq(pose_seq, 7)
+
+    # project 3D mesh to 2D landmark
+    projected_vertices = project_points(pred, face_result['trans_mat'], pose_seq, [height, width])
+    projected_vertices = np.concatenate([lmks[:468, :2][None, :], projected_vertices], axis=0)
+    projected_vertices = convert_ldmk_to_68(projected_vertices)
+
+    # print(projected_vertices.shape)
+
+    pose_images = []
+    for i in range(projected_vertices.shape[0]):
+        pose_img = draw_landmarks(projected_vertices[i], height, width)
+        pose_images.append(pose_img)
+    pose_images = np.array(pose_images)
+
+    # print(pose_images.shape)
+
+    ref_image_np = cv2.cvtColor(ref_image_np, cv2.COLOR_BGR2RGB)
+    ref_imgs = np.stack([ref_image_np]*(pose_images.shape[0]), axis=0)
+
+    all_np = np.concatenate([ref_imgs, pose_images], axis=2)
+
+    # print(projected_vertices.shape)
+
+    os.makedirs(save_dir, exist_ok=True)
+
+    np.save(os.path.join(save_dir, 'landmarks.npy'), projected_vertices)
+
+    torchvision.io.write_video(os.path.join(save_dir, 'landmarks.mp4'), all_np, fps=args.fps, video_codec='h264', options={'crf': '10'})
+
+    # stream = ffmpeg.input(os.path.join(save_dir, 'landmarks.mp4'))
+    # audio = ffmpeg.input(args.audio_path)
+    # ffmpeg.output(stream.video, audio.audio, os.path.join(save_dir, 'landmarks_audio.mp4'), vcodec='copy', acodec='aac').run()
+
+
+
+
+
+            
+
+if __name__ == "__main__":
+    main()
+    

aniportrait/configs/config.yaml

@@ -0,0 +1,12 @@
+pretrained_base_model_path: 'ckpts/aniportrait/stable-diffusion-v1-5'
+pretrained_vae_path: 'ckpts/aniportrait/sd-vae-ft-mse'
+image_encoder_path: 'ckpts/aniportrait/image_encoder'
+
+denoising_unet_path: "ckpts/aniportrait/denoising_unet.pth"
+reference_unet_path: "ckpts/aniportrait/reference_unet.pth"
+pose_guider_path: "ckpts/aniportrait/pose_guider.pth"
+motion_module_path: "ckpts/aniportrait/motion_module.pth"
+
+audio_inference_config: "aniportrait/configs/inference_audio.yaml"
+inference_config: "aniportrait/configs/inference_v2.yaml"
+weight_dtype: 'fp16'

aniportrait/configs/inference_audio.yaml

@@ -0,0 +1,17 @@
+a2m_model:
+  out_dim: 1404
+  latent_dim: 512
+  model_path: ckpts/aniportrait/wav2vec2-base-960h
+  only_last_fetures: True
+  from_pretrained: True
+
+a2p_model:
+  out_dim: 6 
+  latent_dim: 512
+  model_path: ckpts/aniportrait/wav2vec2-base-960h
+  only_last_fetures: True
+  from_pretrained: True
+
+pretrained_model:
+  a2m_ckpt: ckpts/aniportrait/audio2mesh.pt
+  a2p_ckpt: ckpts/aniportrait/audio2pose.pt

aniportrait/configs/inference_v2.yaml

@@ -0,0 +1,35 @@
+unet_additional_kwargs:
+  use_inflated_groupnorm: true
+  unet_use_cross_frame_attention: false 
+  unet_use_temporal_attention: false
+  use_motion_module: true
+  motion_module_resolutions:
+  - 1
+  - 2
+  - 4
+  - 8
+  motion_module_mid_block: true 
+  motion_module_decoder_only: false
+  motion_module_type: Vanilla
+  motion_module_kwargs:
+    num_attention_heads: 8
+    num_transformer_block: 1
+    attention_block_types:
+    - Temporal_Self
+    - Temporal_Self
+    temporal_position_encoding: true
+    temporal_position_encoding_max_len: 32
+    temporal_attention_dim_div: 1
+
+noise_scheduler_kwargs:
+  beta_start: 0.00085
+  beta_end: 0.012
+  beta_schedule: "linear"
+  clip_sample: false
+  steps_offset: 1
+  ### Zero-SNR params
+  prediction_type: "v_prediction"
+  rescale_betas_zero_snr: True
+  timestep_spacing: "trailing"
+
+sampler: DDIM 

aniportrait/src/audio_models/mish.py

@@ -0,0 +1,51 @@
+"""
+Applies the mish function element-wise:
+mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x)))
+"""
+
+# import pytorch
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+@torch.jit.script
+def mish(input):
+    """
+    Applies the mish function element-wise:
+    mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x)))
+    See additional documentation for mish class.
+    """
+    return input * torch.tanh(F.softplus(input))
+
+class Mish(nn.Module):
+    """
+    Applies the mish function element-wise:
+    mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x)))
+
+    Shape:
+        - Input: (N, *) where * means, any number of additional
+          dimensions
+        - Output: (N, *), same shape as the input
+
+    Examples:
+        >>> m = Mish()
+        >>> input = torch.randn(2)
+        >>> output = m(input)
+
+    Reference: https://pytorch.org/docs/stable/generated/torch.nn.Mish.html
+    """
+
+    def __init__(self):
+        """
+        Init method.
+        """
+        super().__init__()
+
+    def forward(self, input):
+        """
+        Forward pass of the function.
+        """
+        if torch.__version__ >= "1.9":
+            return F.mish(input)
+        else:
+            return mish(input)

aniportrait/src/audio_models/model.py

@@ -0,0 +1,71 @@
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import Wav2Vec2Config
+
+from .torch_utils import get_mask_from_lengths
+from .wav2vec2 import Wav2Vec2Model
+
+
+class Audio2MeshModel(nn.Module):
+    def __init__(
+        self,
+        config
+    ):  
+        super().__init__()
+        out_dim = config['out_dim']
+        latent_dim = config['latent_dim']
+        model_path = config['model_path']
+        only_last_fetures = config['only_last_fetures']
+        from_pretrained = config['from_pretrained']
+
+        self._only_last_features = only_last_fetures
+
+        self.audio_encoder_config = Wav2Vec2Config.from_pretrained(model_path, local_files_only=True)
+        if from_pretrained:
+            self.audio_encoder = Wav2Vec2Model.from_pretrained(model_path, local_files_only=True)
+        else:
+            self.audio_encoder = Wav2Vec2Model(self.audio_encoder_config)
+        self.audio_encoder.feature_extractor._freeze_parameters()
+
+        hidden_size = self.audio_encoder_config.hidden_size
+
+        self.in_fn = nn.Linear(hidden_size, latent_dim)
+       
+        self.out_fn = nn.Linear(latent_dim, out_dim)
+        nn.init.constant_(self.out_fn.weight, 0)
+        nn.init.constant_(self.out_fn.bias, 0)
+
+    def forward(self, audio, label, audio_len=None):
+        attention_mask = ~get_mask_from_lengths(audio_len) if audio_len else None
+        
+        seq_len = label.shape[1]
+
+        embeddings = self.audio_encoder(audio, seq_len=seq_len, output_hidden_states=True,
+                                        attention_mask=attention_mask)
+
+        if self._only_last_features:
+            hidden_states = embeddings.last_hidden_state
+        else:
+            hidden_states = sum(embeddings.hidden_states) / len(embeddings.hidden_states)
+
+        layer_in = self.in_fn(hidden_states)
+        out = self.out_fn(layer_in)
+
+        return out, None
+
+    def infer(self, input_value, seq_len):
+        embeddings = self.audio_encoder(input_value, seq_len=seq_len, output_hidden_states=True)
+
+        if self._only_last_features:
+            hidden_states = embeddings.last_hidden_state
+        else:
+            hidden_states = sum(embeddings.hidden_states) / len(embeddings.hidden_states)
+
+        layer_in = self.in_fn(hidden_states)
+        out = self.out_fn(layer_in)
+
+        return out
+    
+

aniportrait/src/audio_models/pose_model.py

@@ -0,0 +1,125 @@
+import os
+import math
+import torch
+import torch.nn as nn
+from transformers import Wav2Vec2Config
+
+from .torch_utils import get_mask_from_lengths
+from .wav2vec2 import Wav2Vec2Model
+
+
+def init_biased_mask(n_head, max_seq_len, period):
+    def get_slopes(n):
+        def get_slopes_power_of_2(n):
+            start = (2**(-2**-(math.log2(n)-3)))
+            ratio = start
+            return [start*ratio**i for i in range(n)]
+        if math.log2(n).is_integer():
+            return get_slopes_power_of_2(n)                   
+        else:                                                 
+            closest_power_of_2 = 2**math.floor(math.log2(n)) 
+            return get_slopes_power_of_2(closest_power_of_2) + get_slopes(2*closest_power_of_2)[0::2][:n-closest_power_of_2]
+    slopes = torch.Tensor(get_slopes(n_head))
+    bias = torch.arange(start=0, end=max_seq_len, step=period).unsqueeze(1).repeat(1,period).view(-1)//(period)
+    bias = - torch.flip(bias,dims=[0])
+    alibi = torch.zeros(max_seq_len, max_seq_len)
+    for i in range(max_seq_len):
+        alibi[i, :i+1] = bias[-(i+1):]
+    alibi = slopes.unsqueeze(1).unsqueeze(1) * alibi.unsqueeze(0)
+    mask = (torch.triu(torch.ones(max_seq_len, max_seq_len)) == 1).transpose(0, 1)
+    mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
+    mask = mask.unsqueeze(0) + alibi
+    return mask
+
+
+def enc_dec_mask(device, T, S):
+    mask = torch.ones(T, S)
+    for i in range(T):
+        mask[i, i] = 0
+    return (mask==1).to(device=device)
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=600):
+        super(PositionalEncoding, self).__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len).unsqueeze(1).float()
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:, :x.size(1)]
+        return x
+
+
+class Audio2PoseModel(nn.Module):
+    def __init__(
+        self,
+        config
+    ):  
+        
+        super().__init__()
+        
+        latent_dim = config['latent_dim']
+        model_path = config['model_path']
+        only_last_fetures = config['only_last_fetures']
+        from_pretrained = config['from_pretrained']
+        out_dim = config['out_dim']
+
+        self.out_dim = out_dim
+
+        self._only_last_features = only_last_fetures
+
+        self.audio_encoder_config = Wav2Vec2Config.from_pretrained(model_path, local_files_only=True)
+        if from_pretrained:
+            self.audio_encoder = Wav2Vec2Model.from_pretrained(model_path, local_files_only=True)
+        else:
+            self.audio_encoder = Wav2Vec2Model(self.audio_encoder_config)
+        self.audio_encoder.feature_extractor._freeze_parameters()
+
+        hidden_size = self.audio_encoder_config.hidden_size
+
+        self.pose_map = nn.Linear(out_dim, latent_dim)
+        self.in_fn = nn.Linear(hidden_size, latent_dim)
+
+        self.PPE = PositionalEncoding(latent_dim)
+        self.biased_mask = init_biased_mask(n_head = 8, max_seq_len = 600, period=1)
+        decoder_layer = nn.TransformerDecoderLayer(d_model=latent_dim, nhead=8, dim_feedforward=2*latent_dim, batch_first=True)        
+        self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=8)
+        self.pose_map_r = nn.Linear(latent_dim, out_dim)
+
+        self.id_embed = nn.Embedding(100, latent_dim) # 100 ids
+
+
+    def infer(self, input_value, seq_len, id_seed=None):
+        embeddings = self.audio_encoder(input_value, seq_len=seq_len, output_hidden_states=True)
+
+        if self._only_last_features:
+            hidden_states = embeddings.last_hidden_state
+        else:
+            hidden_states = sum(embeddings.hidden_states) / len(embeddings.hidden_states)
+
+        hidden_states = self.in_fn(hidden_states)
+
+        id_embedding = self.id_embed(id_seed).unsqueeze(1)
+        
+        init_pose = torch.zeros([hidden_states.shape[0], 1, self.out_dim]).to(hidden_states.device)
+        for i in range(seq_len):
+            if i==0:
+                pose_emb = self.pose_map(init_pose)
+                pose_input = self.PPE(pose_emb)
+            else:
+                pose_input = self.PPE(pose_emb)
+
+            pose_input = pose_input + id_embedding
+            tgt_mask = self.biased_mask[:, :pose_input.shape[1], :pose_input.shape[1]].clone().detach().to(hidden_states.device)
+            memory_mask = enc_dec_mask(hidden_states.device,  pose_input.shape[1], hidden_states.shape[1])
+            pose_out = self.transformer_decoder(pose_input, hidden_states, tgt_mask=tgt_mask, memory_mask=memory_mask)
+            pose_out = self.pose_map_r(pose_out)
+            new_output = self.pose_map(pose_out[:,-1,:]).unsqueeze(1)
+            pose_emb = torch.cat((pose_emb, new_output), 1)
+        return pose_out
+    

aniportrait/src/audio_models/torch_utils.py

@@ -0,0 +1,25 @@
+import torch
+import torch.nn.functional as F
+
+
+def get_mask_from_lengths(lengths, max_len=None):
+    lengths = lengths.to(torch.long)
+    if max_len is None:
+        max_len = torch.max(lengths).item()
+
+    ids = torch.arange(0, max_len).unsqueeze(0).expand(lengths.shape[0], -1).to(lengths.device)
+    mask = ids < lengths.unsqueeze(1).expand(-1, max_len)
+
+    return mask
+
+
+def linear_interpolation(features, seq_len):
+    features = features.transpose(1, 2)
+    output_features = F.interpolate(features, size=seq_len, align_corners=True, mode='linear')
+    return output_features.transpose(1, 2)
+
+
+if __name__ == "__main__":
+    import numpy as np
+    mask = ~get_mask_from_lengths(torch.from_numpy(np.array([4,6])))
+    import pdb; pdb.set_trace()

aniportrait/src/audio_models/wav2vec2.py

@@ -0,0 +1,125 @@
+from transformers import Wav2Vec2Config, Wav2Vec2Model
+from transformers.modeling_outputs import BaseModelOutput
+
+from .torch_utils import linear_interpolation
+
+# the implementation of Wav2Vec2Model is borrowed from
+# https://github.com/huggingface/transformers/blob/HEAD/src/transformers/models/wav2vec2/modeling_wav2vec2.py
+# initialize our encoder with the pre-trained wav2vec 2.0 weights.
+class Wav2Vec2Model(Wav2Vec2Model):
+    def __init__(self, config: Wav2Vec2Config):
+        super().__init__(config)
+
+    def forward(
+        self,
+        input_values,
+        seq_len,
+        attention_mask=None,
+        mask_time_indices=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        self.config.output_attentions = True
+
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        extract_features = self.feature_extractor(input_values)
+        extract_features = extract_features.transpose(1, 2)
+        extract_features = linear_interpolation(extract_features, seq_len=seq_len)
+
+        if attention_mask is not None:
+            # compute reduced attention_mask corresponding to feature vectors
+            attention_mask = self._get_feature_vector_attention_mask(
+                extract_features.shape[1], attention_mask, add_adapter=False
+            )
+
+        hidden_states, extract_features = self.feature_projection(extract_features)
+        hidden_states = self._mask_hidden_states(
+            hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
+        )
+
+        encoder_outputs = self.encoder(
+            hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = encoder_outputs[0]
+
+        if self.adapter is not None:
+            hidden_states = self.adapter(hidden_states)
+
+        if not return_dict:
+            return (hidden_states, ) + encoder_outputs[1:]
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+    def feature_extract(
+        self,
+        input_values,
+        seq_len,
+    ):
+        extract_features = self.feature_extractor(input_values)
+        extract_features = extract_features.transpose(1, 2)
+        extract_features = linear_interpolation(extract_features, seq_len=seq_len)
+
+        return extract_features
+
+    def encode(
+        self,
+        extract_features,
+        attention_mask=None,
+        mask_time_indices=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        self.config.output_attentions = True
+
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if attention_mask is not None:
+            # compute reduced attention_mask corresponding to feature vectors
+            attention_mask = self._get_feature_vector_attention_mask(
+                extract_features.shape[1], attention_mask, add_adapter=False
+            )
+            
+
+        hidden_states, extract_features = self.feature_projection(extract_features)
+        hidden_states = self._mask_hidden_states(
+            hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
+        )
+
+        encoder_outputs = self.encoder(
+            hidden_states,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = encoder_outputs[0]
+
+        if self.adapter is not None:
+            hidden_states = self.adapter(hidden_states)
+
+        if not return_dict:
+            return (hidden_states, ) + encoder_outputs[1:]
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

aniportrait/src/utils/audio_util.py

@@ -0,0 +1,30 @@
+import os
+import math
+
+import librosa
+import numpy as np
+from transformers import Wav2Vec2FeatureExtractor
+
+
+class DataProcessor:
+    def __init__(self, sampling_rate, wav2vec_model_path):
+        self._processor = Wav2Vec2FeatureExtractor.from_pretrained(wav2vec_model_path, local_files_only=True)
+        self._sampling_rate = sampling_rate
+
+    def extract_feature(self, audio_path):
+        speech_array, sampling_rate = librosa.load(audio_path, sr=self._sampling_rate)
+        input_value = np.squeeze(self._processor(speech_array, sampling_rate=sampling_rate).input_values)
+        return input_value
+
+
+def prepare_audio_feature(wav_file, fps=25, sampling_rate=16000, wav2vec_model_path=None):
+    data_preprocessor = DataProcessor(sampling_rate, wav2vec_model_path)
+
+    input_value = data_preprocessor.extract_feature(wav_file)
+    seq_len = math.ceil(len(input_value)/sampling_rate*fps)
+    return {
+        "audio_feature": input_value,
+        "seq_len": seq_len
+    }
+
+

aniportrait/src/utils/draw_util.py

@@ -0,0 +1,149 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+from mediapipe.framework.formats import landmark_pb2
+
+class FaceMeshVisualizer:
+    def __init__(self, forehead_edge=False):
+        self.mp_drawing = mp.solutions.drawing_utils
+        mp_face_mesh = mp.solutions.face_mesh
+        self.mp_face_mesh = mp_face_mesh
+        self.forehead_edge = forehead_edge
+
+        DrawingSpec = mp.solutions.drawing_styles.DrawingSpec
+        f_thick = 2
+        f_rad = 1
+        right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad)
+        right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad)
+        right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad)
+        left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad)
+        left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad)
+        left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad)
+        head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad)
+        
+        mouth_draw_obl = DrawingSpec(color=(10, 180, 20), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_obr = DrawingSpec(color=(20, 10, 180), thickness=f_thick, circle_radius=f_rad)
+        
+        mouth_draw_ibl = DrawingSpec(color=(100, 100, 30), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_ibr = DrawingSpec(color=(100, 150, 50), thickness=f_thick, circle_radius=f_rad)
+        
+        mouth_draw_otl = DrawingSpec(color=(20, 80, 100), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_otr = DrawingSpec(color=(80, 100, 20), thickness=f_thick, circle_radius=f_rad)
+        
+        mouth_draw_itl = DrawingSpec(color=(120, 100, 200), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_itr = DrawingSpec(color=(150 ,120, 100), thickness=f_thick, circle_radius=f_rad)
+        
+        FACEMESH_LIPS_OUTER_BOTTOM_LEFT = [(61,146),(146,91),(91,181),(181,84),(84,17)]
+        FACEMESH_LIPS_OUTER_BOTTOM_RIGHT = [(17,314),(314,405),(405,321),(321,375),(375,291)]
+        
+        FACEMESH_LIPS_INNER_BOTTOM_LEFT = [(78,95),(95,88),(88,178),(178,87),(87,14)]
+        FACEMESH_LIPS_INNER_BOTTOM_RIGHT = [(14,317),(317,402),(402,318),(318,324),(324,308)]
+        
+        FACEMESH_LIPS_OUTER_TOP_LEFT = [(61,185),(185,40),(40,39),(39,37),(37,0)]
+        FACEMESH_LIPS_OUTER_TOP_RIGHT = [(0,267),(267,269),(269,270),(270,409),(409,291)]
+        
+        FACEMESH_LIPS_INNER_TOP_LEFT = [(78,191),(191,80),(80,81),(81,82),(82,13)]
+        FACEMESH_LIPS_INNER_TOP_RIGHT = [(13,312),(312,311),(311,310),(310,415),(415,308)]
+        
+        FACEMESH_CUSTOM_FACE_OVAL = [(176, 149), (150, 136), (356, 454), (58, 132), (152, 148), (361, 288), (251, 389), (132, 93), (389, 356), (400, 377), (136, 172), (377, 152), (323, 361), (172, 58), (454, 323), (365, 379), (379, 378), (148, 176), (93, 234), (397, 365), (149, 150), (288, 397), (234, 127), (378, 400), (127, 162), (162, 21)]
+
+        # mp_face_mesh.FACEMESH_CONTOURS has all the items we care about.
+        face_connection_spec = {}
+        if self.forehead_edge:
+            for edge in mp_face_mesh.FACEMESH_FACE_OVAL:
+                face_connection_spec[edge] = head_draw
+        else:
+            for edge in FACEMESH_CUSTOM_FACE_OVAL:
+                face_connection_spec[edge] = head_draw
+        for edge in mp_face_mesh.FACEMESH_LEFT_EYE:
+            face_connection_spec[edge] = left_eye_draw
+        for edge in mp_face_mesh.FACEMESH_LEFT_EYEBROW:
+            face_connection_spec[edge] = left_eyebrow_draw
+        # for edge in mp_face_mesh.FACEMESH_LEFT_IRIS:
+        #    face_connection_spec[edge] = left_iris_draw
+        for edge in mp_face_mesh.FACEMESH_RIGHT_EYE:
+            face_connection_spec[edge] = right_eye_draw
+        for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW:
+            face_connection_spec[edge] = right_eyebrow_draw
+        # for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS:
+        #    face_connection_spec[edge] = right_iris_draw
+        # for edge in mp_face_mesh.FACEMESH_LIPS:
+        #     face_connection_spec[edge] = mouth_draw
+        
+        for edge in FACEMESH_LIPS_OUTER_BOTTOM_LEFT:
+            face_connection_spec[edge] = mouth_draw_obl
+        for edge in FACEMESH_LIPS_OUTER_BOTTOM_RIGHT:
+            face_connection_spec[edge] = mouth_draw_obr
+        for edge in FACEMESH_LIPS_INNER_BOTTOM_LEFT:
+            face_connection_spec[edge] = mouth_draw_ibl
+        for edge in FACEMESH_LIPS_INNER_BOTTOM_RIGHT:
+            face_connection_spec[edge] = mouth_draw_ibr
+        for edge in FACEMESH_LIPS_OUTER_TOP_LEFT:
+            face_connection_spec[edge] = mouth_draw_otl
+        for edge in FACEMESH_LIPS_OUTER_TOP_RIGHT:
+            face_connection_spec[edge] = mouth_draw_otr
+        for edge in FACEMESH_LIPS_INNER_TOP_LEFT:
+            face_connection_spec[edge] = mouth_draw_itl
+        for edge in FACEMESH_LIPS_INNER_TOP_RIGHT:
+            face_connection_spec[edge] = mouth_draw_itr
+
+        
+        iris_landmark_spec = {468: right_iris_draw, 473: left_iris_draw}
+        
+        self.face_connection_spec = face_connection_spec
+    def draw_pupils(self, image, landmark_list, drawing_spec, halfwidth: int = 2):
+        """We have a custom function to draw the pupils because the mp.draw_landmarks method requires a parameter for all
+        landmarks.  Until our PR is merged into mediapipe, we need this separate method."""
+        if len(image.shape) != 3:
+            raise ValueError("Input image must be H,W,C.")
+        image_rows, image_cols, image_channels = image.shape
+        if image_channels != 3:  # BGR channels
+            raise ValueError('Input image must contain three channel bgr data.')
+        for idx, landmark in enumerate(landmark_list.landmark):
+            if (
+                    (landmark.HasField('visibility') and landmark.visibility < 0.9) or
+                    (landmark.HasField('presence') and landmark.presence < 0.5)
+            ):
+                continue
+            if landmark.x >= 1.0 or landmark.x < 0 or landmark.y >= 1.0 or landmark.y < 0:
+                continue
+            image_x = int(image_cols*landmark.x)
+            image_y = int(image_rows*landmark.y)
+            draw_color = None
+            if isinstance(drawing_spec, Mapping):
+                if drawing_spec.get(idx) is None:
+                    continue
+                else:
+                    draw_color = drawing_spec[idx].color
+            elif isinstance(drawing_spec, DrawingSpec):
+                draw_color = drawing_spec.color
+            image[image_y-halfwidth:image_y+halfwidth, image_x-halfwidth:image_x+halfwidth, :] = draw_color
+        
+
+
+    def draw_landmarks(self, image_size, keypoints, normed=False):
+        ini_size = [512, 512]
+        image = np.zeros([ini_size[1], ini_size[0], 3], dtype=np.uint8)
+        new_landmarks = landmark_pb2.NormalizedLandmarkList()
+        for i in range(keypoints.shape[0]):
+            landmark = new_landmarks.landmark.add()
+            if normed:
+                landmark.x = keypoints[i, 0]
+                landmark.y = keypoints[i, 1]
+            else:
+                landmark.x = keypoints[i, 0] / image_size[0]
+                landmark.y = keypoints[i, 1] / image_size[1]
+            landmark.z = 1.0
+
+        self.mp_drawing.draw_landmarks(
+            image=image,
+            landmark_list=new_landmarks,
+            connections=self.face_connection_spec.keys(),
+            landmark_drawing_spec=None,
+            connection_drawing_spec=self.face_connection_spec
+        )
+        # draw_pupils(image, face_landmarks, iris_landmark_spec, 2)
+        image = cv2.resize(image, (image_size[0], image_size[1]))
+        
+        return image
+

aniportrait/src/utils/face_landmark.py

@@ -0,0 +1,3305 @@
+# Copyright 2023 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MediaPipe face landmarker task."""
+
+import dataclasses
+import enum
+from typing import Callable, Mapping, Optional, List
+
+import numpy as np
+
+from mediapipe.framework.formats import classification_pb2
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.framework.formats import matrix_data_pb2
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import packet as packet_module
+# pylint: disable=unused-import
+from mediapipe.tasks.cc.vision.face_geometry.proto import face_geometry_pb2
+# pylint: enable=unused-import
+from mediapipe.tasks.cc.vision.face_landmarker.proto import face_landmarker_graph_options_pb2
+from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.components.containers import landmark as landmark_module
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_BaseOptions = base_options_module.BaseOptions
+_FaceLandmarkerGraphOptionsProto = (
+    face_landmarker_graph_options_pb2.FaceLandmarkerGraphOptions
+)
+_LayoutEnum = matrix_data_pb2.MatrixData.Layout
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_NORM_RECT_STREAM_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_NORM_LANDMARKS_STREAM_NAME = 'norm_landmarks'
+_NORM_LANDMARKS_TAG = 'NORM_LANDMARKS'
+_BLENDSHAPES_STREAM_NAME = 'blendshapes'
+_BLENDSHAPES_TAG = 'BLENDSHAPES'
+_FACE_GEOMETRY_STREAM_NAME = 'face_geometry'
+_FACE_GEOMETRY_TAG = 'FACE_GEOMETRY'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+class Blendshapes(enum.IntEnum):
+  """The 52 blendshape coefficients."""
+
+  NEUTRAL = 0
+  BROW_DOWN_LEFT = 1
+  BROW_DOWN_RIGHT = 2
+  BROW_INNER_UP = 3
+  BROW_OUTER_UP_LEFT = 4
+  BROW_OUTER_UP_RIGHT = 5
+  CHEEK_PUFF = 6
+  CHEEK_SQUINT_LEFT = 7
+  CHEEK_SQUINT_RIGHT = 8
+  EYE_BLINK_LEFT = 9
+  EYE_BLINK_RIGHT = 10
+  EYE_LOOK_DOWN_LEFT = 11
+  EYE_LOOK_DOWN_RIGHT = 12
+  EYE_LOOK_IN_LEFT = 13
+  EYE_LOOK_IN_RIGHT = 14
+  EYE_LOOK_OUT_LEFT = 15
+  EYE_LOOK_OUT_RIGHT = 16
+  EYE_LOOK_UP_LEFT = 17
+  EYE_LOOK_UP_RIGHT = 18
+  EYE_SQUINT_LEFT = 19
+  EYE_SQUINT_RIGHT = 20
+  EYE_WIDE_LEFT = 21
+  EYE_WIDE_RIGHT = 22
+  JAW_FORWARD = 23
+  JAW_LEFT = 24
+  JAW_OPEN = 25
+  JAW_RIGHT = 26
+  MOUTH_CLOSE = 27
+  MOUTH_DIMPLE_LEFT = 28
+  MOUTH_DIMPLE_RIGHT = 29
+  MOUTH_FROWN_LEFT = 30
+  MOUTH_FROWN_RIGHT = 31
+  MOUTH_FUNNEL = 32
+  MOUTH_LEFT = 33
+  MOUTH_LOWER_DOWN_LEFT = 34
+  MOUTH_LOWER_DOWN_RIGHT = 35
+  MOUTH_PRESS_LEFT = 36
+  MOUTH_PRESS_RIGHT = 37
+  MOUTH_PUCKER = 38
+  MOUTH_RIGHT = 39
+  MOUTH_ROLL_LOWER = 40
+  MOUTH_ROLL_UPPER = 41
+  MOUTH_SHRUG_LOWER = 42
+  MOUTH_SHRUG_UPPER = 43
+  MOUTH_SMILE_LEFT = 44
+  MOUTH_SMILE_RIGHT = 45
+  MOUTH_STRETCH_LEFT = 46
+  MOUTH_STRETCH_RIGHT = 47
+  MOUTH_UPPER_UP_LEFT = 48
+  MOUTH_UPPER_UP_RIGHT = 49
+  NOSE_SNEER_LEFT = 50
+  NOSE_SNEER_RIGHT = 51
+
+
+class FaceLandmarksConnections:
+  """The connections between face landmarks."""
+
+  @dataclasses.dataclass
+  class Connection:
+    """The connection class for face landmarks."""
+
+    start: int
+    end: int
+
+  FACE_LANDMARKS_LIPS: List[Connection] = [
+      Connection(61, 146),
+      Connection(146, 91),
+      Connection(91, 181),
+      Connection(181, 84),
+      Connection(84, 17),
+      Connection(17, 314),
+      Connection(314, 405),
+      Connection(405, 321),
+      Connection(321, 375),
+      Connection(375, 291),
+      Connection(61, 185),
+      Connection(185, 40),
+      Connection(40, 39),
+      Connection(39, 37),
+      Connection(37, 0),
+      Connection(0, 267),
+      Connection(267, 269),
+      Connection(269, 270),
+      Connection(270, 409),
+      Connection(409, 291),
+      Connection(78, 95),
+      Connection(95, 88),
+      Connection(88, 178),
+      Connection(178, 87),
+      Connection(87, 14),
+      Connection(14, 317),
+      Connection(317, 402),
+      Connection(402, 318),
+      Connection(318, 324),
+      Connection(324, 308),
+      Connection(78, 191),
+      Connection(191, 80),
+      Connection(80, 81),
+      Connection(81, 82),
+      Connection(82, 13),
+      Connection(13, 312),
+      Connection(312, 311),
+      Connection(311, 310),
+      Connection(310, 415),
+      Connection(415, 308),
+  ]
+
+  FACE_LANDMARKS_LEFT_EYE: List[Connection] = [
+      Connection(263, 249),
+      Connection(249, 390),
+      Connection(390, 373),
+      Connection(373, 374),
+      Connection(374, 380),
+      Connection(380, 381),
+      Connection(381, 382),
+      Connection(382, 362),
+      Connection(263, 466),
+      Connection(466, 388),
+      Connection(388, 387),
+      Connection(387, 386),
+      Connection(386, 385),
+      Connection(385, 384),
+      Connection(384, 398),
+      Connection(398, 362),
+  ]
+
+  FACE_LANDMARKS_LEFT_EYEBROW: List[Connection] = [
+      Connection(276, 283),
+      Connection(283, 282),
+      Connection(282, 295),
+      Connection(295, 285),
+      Connection(300, 293),
+      Connection(293, 334),
+      Connection(334, 296),
+      Connection(296, 336),
+  ]
+
+  FACE_LANDMARKS_LEFT_IRIS: List[Connection] = [
+      Connection(474, 475),
+      Connection(475, 476),
+      Connection(476, 477),
+      Connection(477, 474),
+  ]
+
+  FACE_LANDMARKS_RIGHT_EYE: List[Connection] = [
+      Connection(33, 7),
+      Connection(7, 163),
+      Connection(163, 144),
+      Connection(144, 145),
+      Connection(145, 153),
+      Connection(153, 154),
+      Connection(154, 155),
+      Connection(155, 133),
+      Connection(33, 246),
+      Connection(246, 161),
+      Connection(161, 160),
+      Connection(160, 159),
+      Connection(159, 158),
+      Connection(158, 157),
+      Connection(157, 173),
+      Connection(173, 133),
+  ]
+
+  FACE_LANDMARKS_RIGHT_EYEBROW: List[Connection] = [
+      Connection(46, 53),
+      Connection(53, 52),
+      Connection(52, 65),
+      Connection(65, 55),
+      Connection(70, 63),
+      Connection(63, 105),
+      Connection(105, 66),
+      Connection(66, 107),
+  ]
+
+  FACE_LANDMARKS_RIGHT_IRIS: List[Connection] = [
+      Connection(469, 470),
+      Connection(470, 471),
+      Connection(471, 472),
+      Connection(472, 469),
+  ]
+
+  FACE_LANDMARKS_FACE_OVAL: List[Connection] = [
+      Connection(10, 338),
+      Connection(338, 297),
+      Connection(297, 332),
+      Connection(332, 284),
+      Connection(284, 251),
+      Connection(251, 389),
+      Connection(389, 356),
+      Connection(356, 454),
+      Connection(454, 323),
+      Connection(323, 361),
+      Connection(361, 288),
+      Connection(288, 397),
+      Connection(397, 365),
+      Connection(365, 379),
+      Connection(379, 378),
+      Connection(378, 400),
+      Connection(400, 377),
+      Connection(377, 152),
+      Connection(152, 148),
+      Connection(148, 176),
+      Connection(176, 149),
+      Connection(149, 150),
+      Connection(150, 136),
+      Connection(136, 172),
+      Connection(172, 58),
+      Connection(58, 132),
+      Connection(132, 93),
+      Connection(93, 234),
+      Connection(234, 127),
+      Connection(127, 162),
+      Connection(162, 21),
+      Connection(21, 54),
+      Connection(54, 103),
+      Connection(103, 67),
+      Connection(67, 109),
+      Connection(109, 10),
+  ]
+
+  FACE_LANDMARKS_CONTOURS: List[Connection] = (
+      FACE_LANDMARKS_LIPS
+      + FACE_LANDMARKS_LEFT_EYE
+      + FACE_LANDMARKS_LEFT_EYEBROW
+      + FACE_LANDMARKS_RIGHT_EYE
+      + FACE_LANDMARKS_RIGHT_EYEBROW
+      + FACE_LANDMARKS_FACE_OVAL
+  )
+
+  FACE_LANDMARKS_TESSELATION: List[Connection] = [
+      Connection(127, 34),
+      Connection(34, 139),
+      Connection(139, 127),
+      Connection(11, 0),
+      Connection(0, 37),
+      Connection(37, 11),
+      Connection(232, 231),
+      Connection(231, 120),
+      Connection(120, 232),
+      Connection(72, 37),
+      Connection(37, 39),
+      Connection(39, 72),
+      Connection(128, 121),
+      Connection(121, 47),
+      Connection(47, 128),
+      Connection(232, 121),
+      Connection(121, 128),
+      Connection(128, 232),
+      Connection(104, 69),
+      Connection(69, 67),
+      Connection(67, 104),
+      Connection(175, 171),
+      Connection(171, 148),
+      Connection(148, 175),
+      Connection(118, 50),
+      Connection(50, 101),
+      Connection(101, 118),
+      Connection(73, 39),
+      Connection(39, 40),
+      Connection(40, 73),
+      Connection(9, 151),
+      Connection(151, 108),
+      Connection(108, 9),
+      Connection(48, 115),
+      Connection(115, 131),
+      Connection(131, 48),
+      Connection(194, 204),
+      Connection(204, 211),
+      Connection(211, 194),
+      Connection(74, 40),
+      Connection(40, 185),
+      Connection(185, 74),
+      Connection(80, 42),
+      Connection(42, 183),
+      Connection(183, 80),
+      Connection(40, 92),
+      Connection(92, 186),
+      Connection(186, 40),
+      Connection(230, 229),
+      Connection(229, 118),
+      Connection(118, 230),
+      Connection(202, 212),
+      Connection(212, 214),
+      Connection(214, 202),
+      Connection(83, 18),
+      Connection(18, 17),
+      Connection(17, 83),
+      Connection(76, 61),
+      Connection(61, 146),
+      Connection(146, 76),
+      Connection(160, 29),
+      Connection(29, 30),
+      Connection(30, 160),
+      Connection(56, 157),
+      Connection(157, 173),
+      Connection(173, 56),
+      Connection(106, 204),
+      Connection(204, 194),
+      Connection(194, 106),
+      Connection(135, 214),
+      Connection(214, 192),
+      Connection(192, 135),
+      Connection(203, 165),
+      Connection(165, 98),
+      Connection(98, 203),
+      Connection(21, 71),
+      Connection(71, 68),
+      Connection(68, 21),
+      Connection(51, 45),
+      Connection(45, 4),
+      Connection(4, 51),
+      Connection(144, 24),
+      Connection(24, 23),
+      Connection(23, 144),
+      Connection(77, 146),
+      Connection(146, 91),
+      Connection(91, 77),
+      Connection(205, 50),
+      Connection(50, 187),
+      Connection(187, 205),
+      Connection(201, 200),
+      Connection(200, 18),
+      Connection(18, 201),
+      Connection(91, 106),
+      Connection(106, 182),
+      Connection(182, 91),
+      Connection(90, 91),
+      Connection(91, 181),
+      Connection(181, 90),
+      Connection(85, 84),
+      Connection(84, 17),
+      Connection(17, 85),
+      Connection(206, 203),
+      Connection(203, 36),
+      Connection(36, 206),
+      Connection(148, 171),
+      Connection(171, 140),
+      Connection(140, 148),
+      Connection(92, 40),
+      Connection(40, 39),
+      Connection(39, 92),
+      Connection(193, 189),
+      Connection(189, 244),
+      Connection(244, 193),
+      Connection(159, 158),
+      Connection(158, 28),
+      Connection(28, 159),
+      Connection(247, 246),
+      Connection(246, 161),
+      Connection(161, 247),
+      Connection(236, 3),
+      Connection(3, 196),
+      Connection(196, 236),
+      Connection(54, 68),
+      Connection(68, 104),
+      Connection(104, 54),
+      Connection(193, 168),
+      Connection(168, 8),
+      Connection(8, 193),
+      Connection(117, 228),
+      Connection(228, 31),
+      Connection(31, 117),
+      Connection(189, 193),
+      Connection(193, 55),
+      Connection(55, 189),
+      Connection(98, 97),
+      Connection(97, 99),
+      Connection(99, 98),
+      Connection(126, 47),
+      Connection(47, 100),
+      Connection(100, 126),
+      Connection(166, 79),
+      Connection(79, 218),
+      Connection(218, 166),
+      Connection(155, 154),
+      Connection(154, 26),
+      Connection(26, 155),
+      Connection(209, 49),
+      Connection(49, 131),
+      Connection(131, 209),
+      Connection(135, 136),
+      Connection(136, 150),
+      Connection(150, 135),
+      Connection(47, 126),
+      Connection(126, 217),
+      Connection(217, 47),
+      Connection(223, 52),
+      Connection(52, 53),
+      Connection(53, 223),
+      Connection(45, 51),
+      Connection(51, 134),
+      Connection(134, 45),
+      Connection(211, 170),
+      Connection(170, 140),
+      Connection(140, 211),
+      Connection(67, 69),
+      Connection(69, 108),
+      Connection(108, 67),
+      Connection(43, 106),
+      Connection(106, 91),
+      Connection(91, 43),
+      Connection(230, 119),
+      Connection(119, 120),
+      Connection(120, 230),
+      Connection(226, 130),
+      Connection(130, 247),
+      Connection(247, 226),
+      Connection(63, 53),
+      Connection(53, 52),
+      Connection(52, 63),
+      Connection(238, 20),
+      Connection(20, 242),
+      Connection(242, 238),
+      Connection(46, 70),
+      Connection(70, 156),
+      Connection(156, 46),
+      Connection(78, 62),
+      Connection(62, 96),
+      Connection(96, 78),
+      Connection(46, 53),
+      Connection(53, 63),
+      Connection(63, 46),
+      Connection(143, 34),
+      Connection(34, 227),
+      Connection(227, 143),
+      Connection(123, 117),
+      Connection(117, 111),
+      Connection(111, 123),
+      Connection(44, 125),
+      Connection(125, 19),
+      Connection(19, 44),
+      Connection(236, 134),
+      Connection(134, 51),
+      Connection(51, 236),
+      Connection(216, 206),
+      Connection(206, 205),
+      Connection(205, 216),
+      Connection(154, 153),
+      Connection(153, 22),
+      Connection(22, 154),
+      Connection(39, 37),
+      Connection(37, 167),
+      Connection(167, 39),
+      Connection(200, 201),
+      Connection(201, 208),
+      Connection(208, 200),
+      Connection(36, 142),
+      Connection(142, 100),
+      Connection(100, 36),
+      Connection(57, 212),
+      Connection(212, 202),
+      Connection(202, 57),
+      Connection(20, 60),
+      Connection(60, 99),
+      Connection(99, 20),
+      Connection(28, 158),
+      Connection(158, 157),
+      Connection(157, 28),
+      Connection(35, 226),
+      Connection(226, 113),
+      Connection(113, 35),
+      Connection(160, 159),
+      Connection(159, 27),
+      Connection(27, 160),
+      Connection(204, 202),
+      Connection(202, 210),
+      Connection(210, 204),
+      Connection(113, 225),
+      Connection(225, 46),
+      Connection(46, 113),
+      Connection(43, 202),
+      Connection(202, 204),
+      Connection(204, 43),
+      Connection(62, 76),
+      Connection(76, 77),
+      Connection(77, 62),
+      Connection(137, 123),
+      Connection(123, 116),
+      Connection(116, 137),
+      Connection(41, 38),
+      Connection(38, 72),
+      Connection(72, 41),
+      Connection(203, 129),
+      Connection(129, 142),
+      Connection(142, 203),
+      Connection(64, 98),
+      Connection(98, 240),
+      Connection(240, 64),
+      Connection(49, 102),
+      Connection(102, 64),
+      Connection(64, 49),
+      Connection(41, 73),
+      Connection(73, 74),
+      Connection(74, 41),
+      Connection(212, 216),
+      Connection(216, 207),
+      Connection(207, 212),
+      Connection(42, 74),
+      Connection(74, 184),
+      Connection(184, 42),
+      Connection(169, 170),
+      Connection(170, 211),
+      Connection(211, 169),
+      Connection(170, 149),
+      Connection(149, 176),
+      Connection(176, 170),
+      Connection(105, 66),
+      Connection(66, 69),
+      Connection(69, 105),
+      Connection(122, 6),
+      Connection(6, 168),
+      Connection(168, 122),
+      Connection(123, 147),
+      Connection(147, 187),
+      Connection(187, 123),
+      Connection(96, 77),
+      Connection(77, 90),
+      Connection(90, 96),
+      Connection(65, 55),
+      Connection(55, 107),
+      Connection(107, 65),
+      Connection(89, 90),
+      Connection(90, 180),
+      Connection(180, 89),
+      Connection(101, 100),
+      Connection(100, 120),
+      Connection(120, 101),
+      Connection(63, 105),
+      Connection(105, 104),
+      Connection(104, 63),
+      Connection(93, 137),
+      Connection(137, 227),
+      Connection(227, 93),
+      Connection(15, 86),
+      Connection(86, 85),
+      Connection(85, 15),
+      Connection(129, 102),
+      Connection(102, 49),
+      Connection(49, 129),
+      Connection(14, 87),
+      Connection(87, 86),
+      Connection(86, 14),
+      Connection(55, 8),
+      Connection(8, 9),
+      Connection(9, 55),
+      Connection(100, 47),
+      Connection(47, 121),
+      Connection(121, 100),
+      Connection(145, 23),
+      Connection(23, 22),
+      Connection(22, 145),
+      Connection(88, 89),
+      Connection(89, 179),
+      Connection(179, 88),
+      Connection(6, 122),
+      Connection(122, 196),
+      Connection(196, 6),
+      Connection(88, 95),
+      Connection(95, 96),
+      Connection(96, 88),
+      Connection(138, 172),
+      Connection(172, 136),
+      Connection(136, 138),
+      Connection(215, 58),
+      Connection(58, 172),
+      Connection(172, 215),
+      Connection(115, 48),
+      Connection(48, 219),
+      Connection(219, 115),
+      Connection(42, 80),
+      Connection(80, 81),
+      Connection(81, 42),
+      Connection(195, 3),
+      Connection(3, 51),
+      Connection(51, 195),
+      Connection(43, 146),
+      Connection(146, 61),
+      Connection(61, 43),
+      Connection(171, 175),
+      Connection(175, 199),
+      Connection(199, 171),
+      Connection(81, 82),
+      Connection(82, 38),
+      Connection(38, 81),
+      Connection(53, 46),
+      Connection(46, 225),
+      Connection(225, 53),
+      Connection(144, 163),
+      Connection(163, 110),
+      Connection(110, 144),
+      Connection(52, 65),
+      Connection(65, 66),
+      Connection(66, 52),
+      Connection(229, 228),
+      Connection(228, 117),
+      Connection(117, 229),
+      Connection(34, 127),
+      Connection(127, 234),
+      Connection(234, 34),
+      Connection(107, 108),
+      Connection(108, 69),
+      Connection(69, 107),
+      Connection(109, 108),
+      Connection(108, 151),
+      Connection(151, 109),
+      Connection(48, 64),
+      Connection(64, 235),
+      Connection(235, 48),
+      Connection(62, 78),
+      Connection(78, 191),
+      Connection(191, 62),
+      Connection(129, 209),
+      Connection(209, 126),
+      Connection(126, 129),
+      Connection(111, 35),
+      Connection(35, 143),
+      Connection(143, 111),
+      Connection(117, 123),
+      Connection(123, 50),
+      Connection(50, 117),
+      Connection(222, 65),
+      Connection(65, 52),
+      Connection(52, 222),
+      Connection(19, 125),
+      Connection(125, 141),
+      Connection(141, 19),
+      Connection(221, 55),
+      Connection(55, 65),
+      Connection(65, 221),
+      Connection(3, 195),
+      Connection(195, 197),
+      Connection(197, 3),
+      Connection(25, 7),
+      Connection(7, 33),
+      Connection(33, 25),
+      Connection(220, 237),
+      Connection(237, 44),
+      Connection(44, 220),
+      Connection(70, 71),
+      Connection(71, 139),
+      Connection(139, 70),
+      Connection(122, 193),
+      Connection(193, 245),
+      Connection(245, 122),
+      Connection(247, 130),
+      Connection(130, 33),
+      Connection(33, 247),
+      Connection(71, 21),
+      Connection(21, 162),
+      Connection(162, 71),
+      Connection(170, 169),
+      Connection(169, 150),
+      Connection(150, 170),
+      Connection(188, 174),
+      Connection(174, 196),
+      Connection(196, 188),
+      Connection(216, 186),
+      Connection(186, 92),
+      Connection(92, 216),
+      Connection(2, 97),
+      Connection(97, 167),
+      Connection(167, 2),
+      Connection(141, 125),
+      Connection(125, 241),
+      Connection(241, 141),
+      Connection(164, 167),
+      Connection(167, 37),
+      Connection(37, 164),
+      Connection(72, 38),
+      Connection(38, 12),
+      Connection(12, 72),
+      Connection(38, 82),
+      Connection(82, 13),
+      Connection(13, 38),
+      Connection(63, 68),
+      Connection(68, 71),
+      Connection(71, 63),
+      Connection(226, 35),
+      Connection(35, 111),
+      Connection(111, 226),
+      Connection(101, 50),
+      Connection(50, 205),
+      Connection(205, 101),
+      Connection(206, 92),
+      Connection(92, 165),
+      Connection(165, 206),
+      Connection(209, 198),
+      Connection(198, 217),
+      Connection(217, 209),
+      Connection(165, 167),
+      Connection(167, 97),
+      Connection(97, 165),
+      Connection(220, 115),
+      Connection(115, 218),
+      Connection(218, 220),
+      Connection(133, 112),
+      Connection(112, 243),
+      Connection(243, 133),
+      Connection(239, 238),
+      Connection(238, 241),
+      Connection(241, 239),
+      Connection(214, 135),
+      Connection(135, 169),
+      Connection(169, 214),
+      Connection(190, 173),
+      Connection(173, 133),
+      Connection(133, 190),
+      Connection(171, 208),
+      Connection(208, 32),
+      Connection(32, 171),
+      Connection(125, 44),
+      Connection(44, 237),
+      Connection(237, 125),
+      Connection(86, 87),
+      Connection(87, 178),
+      Connection(178, 86),
+      Connection(85, 86),
+      Connection(86, 179),
+      Connection(179, 85),
+      Connection(84, 85),
+      Connection(85, 180),
+      Connection(180, 84),
+      Connection(83, 84),
+      Connection(84, 181),
+      Connection(181, 83),
+      Connection(201, 83),
+      Connection(83, 182),
+      Connection(182, 201),
+      Connection(137, 93),
+      Connection(93, 132),
+      Connection(132, 137),
+      Connection(76, 62),
+      Connection(62, 183),
+      Connection(183, 76),
+      Connection(61, 76),
+      Connection(76, 184),
+      Connection(184, 61),
+      Connection(57, 61),
+      Connection(61, 185),
+      Connection(185, 57),
+      Connection(212, 57),
+      Connection(57, 186),
+      Connection(186, 212),
+      Connection(214, 207),
+      Connection(207, 187),
+      Connection(187, 214),
+      Connection(34, 143),
+      Connection(143, 156),
+      Connection(156, 34),
+      Connection(79, 239),
+      Connection(239, 237),
+      Connection(237, 79),
+      Connection(123, 137),
+      Connection(137, 177),
+      Connection(177, 123),
+      Connection(44, 1),
+      Connection(1, 4),
+      Connection(4, 44),
+      Connection(201, 194),
+      Connection(194, 32),
+      Connection(32, 201),
+      Connection(64, 102),
+      Connection(102, 129),
+      Connection(129, 64),
+      Connection(213, 215),
+      Connection(215, 138),
+      Connection(138, 213),
+      Connection(59, 166),
+      Connection(166, 219),
+      Connection(219, 59),
+      Connection(242, 99),
+      Connection(99, 97),
+      Connection(97, 242),
+      Connection(2, 94),
+      Connection(94, 141),
+      Connection(141, 2),
+      Connection(75, 59),
+      Connection(59, 235),
+      Connection(235, 75),
+      Connection(24, 110),
+      Connection(110, 228),
+      Connection(228, 24),
+      Connection(25, 130),
+      Connection(130, 226),
+      Connection(226, 25),
+      Connection(23, 24),
+      Connection(24, 229),
+      Connection(229, 23),
+      Connection(22, 23),
+      Connection(23, 230),
+      Connection(230, 22),
+      Connection(26, 22),
+      Connection(22, 231),
+      Connection(231, 26),
+      Connection(112, 26),
+      Connection(26, 232),
+      Connection(232, 112),
+      Connection(189, 190),
+      Connection(190, 243),
+      Connection(243, 189),
+      Connection(221, 56),
+      Connection(56, 190),
+      Connection(190, 221),
+      Connection(28, 56),
+      Connection(56, 221),
+      Connection(221, 28),
+      Connection(27, 28),
+      Connection(28, 222),
+      Connection(222, 27),
+      Connection(29, 27),
+      Connection(27, 223),
+      Connection(223, 29),
+      Connection(30, 29),
+      Connection(29, 224),
+      Connection(224, 30),
+      Connection(247, 30),
+      Connection(30, 225),
+      Connection(225, 247),
+      Connection(238, 79),
+      Connection(79, 20),
+      Connection(20, 238),
+      Connection(166, 59),
+      Connection(59, 75),
+      Connection(75, 166),
+      Connection(60, 75),
+      Connection(75, 240),
+      Connection(240, 60),
+      Connection(147, 177),
+      Connection(177, 215),
+      Connection(215, 147),
+      Connection(20, 79),
+      Connection(79, 166),
+      Connection(166, 20),
+      Connection(187, 147),
+      Connection(147, 213),
+      Connection(213, 187),
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+      Connection(460, 305),
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+      Connection(435, 401),
+      Connection(309, 250),
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+      Connection(411, 433),
+      Connection(433, 376),
+      Connection(453, 341),
+      Connection(341, 464),
+      Connection(464, 453),
+      Connection(357, 453),
+      Connection(453, 465),
+      Connection(465, 357),
+      Connection(343, 357),
+      Connection(357, 412),
+      Connection(412, 343),
+      Connection(437, 343),
+      Connection(343, 399),
+      Connection(399, 437),
+      Connection(344, 360),
+      Connection(360, 440),
+      Connection(440, 344),
+      Connection(420, 437),
+      Connection(437, 456),
+      Connection(456, 420),
+      Connection(360, 420),
+      Connection(420, 363),
+      Connection(363, 360),
+      Connection(361, 401),
+      Connection(401, 288),
+      Connection(288, 361),
+      Connection(265, 372),
+      Connection(372, 353),
+      Connection(353, 265),
+      Connection(390, 339),
+      Connection(339, 249),
+      Connection(249, 390),
+      Connection(339, 448),
+      Connection(448, 255),
+      Connection(255, 339),
+  ]
+
+
+@dataclasses.dataclass
+class FaceLandmarkerResult:
+  """The face landmarks detection result from FaceLandmarker, where each vector element represents a single face detected in the image.
+
+  Attributes:
+    face_landmarks: Detected face landmarks in normalized image coordinates.
+    face_blendshapes: Optional face blendshapes results.
+    facial_transformation_matrixes: Optional facial transformation matrix.
+  """
+
+  face_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  face_blendshapes: List[List[category_module.Category]]
+  facial_transformation_matrixes: List[np.ndarray]
+
+
+def _build_landmarker_result(
+    output_packets: Mapping[str, packet_module.Packet]
+) -> FaceLandmarkerResult:
+  """Constructs a `FaceLandmarkerResult` from output packets."""
+  face_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_NORM_LANDMARKS_STREAM_NAME]
+  )
+
+  face_landmarks_results = []
+  for proto in face_landmarks_proto_list:
+    face_landmarks = landmark_pb2.NormalizedLandmarkList()
+    face_landmarks.MergeFrom(proto)
+    face_landmarks_list = []
+    for face_landmark in face_landmarks.landmark:
+      face_landmarks_list.append(
+          landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
+      )
+    face_landmarks_results.append(face_landmarks_list)
+
+  face_blendshapes_results = []
+  if _BLENDSHAPES_STREAM_NAME in output_packets:
+    face_blendshapes_proto_list = packet_getter.get_proto_list(
+        output_packets[_BLENDSHAPES_STREAM_NAME]
+    )
+    for proto in face_blendshapes_proto_list:
+      face_blendshapes_categories = []
+      face_blendshapes_classifications = classification_pb2.ClassificationList()
+      face_blendshapes_classifications.MergeFrom(proto)
+      for face_blendshapes in face_blendshapes_classifications.classification:
+        face_blendshapes_categories.append(
+            category_module.Category(
+                index=face_blendshapes.index,
+                score=face_blendshapes.score,
+                display_name=face_blendshapes.display_name,
+                category_name=face_blendshapes.label,
+            )
+        )
+      face_blendshapes_results.append(face_blendshapes_categories)
+
+  facial_transformation_matrixes_results = []
+  if _FACE_GEOMETRY_STREAM_NAME in output_packets:
+    facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
+        output_packets[_FACE_GEOMETRY_STREAM_NAME]
+    )
+    for proto in facial_transformation_matrixes_proto_list:
+      if hasattr(proto, 'pose_transform_matrix'):
+        matrix_data = matrix_data_pb2.MatrixData()
+        matrix_data.MergeFrom(proto.pose_transform_matrix)
+        matrix = np.array(matrix_data.packed_data)
+        matrix = matrix.reshape((matrix_data.rows, matrix_data.cols))
+        matrix = (
+            matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T
+        )
+        facial_transformation_matrixes_results.append(matrix)
+
+  return FaceLandmarkerResult(
+      face_landmarks_results,
+      face_blendshapes_results,
+      facial_transformation_matrixes_results,
+  )
+
+def _build_landmarker_result2(
+    output_packets: Mapping[str, packet_module.Packet]
+) -> FaceLandmarkerResult:
+  """Constructs a `FaceLandmarkerResult` from output packets."""
+  face_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_NORM_LANDMARKS_STREAM_NAME]
+  )
+
+  face_landmarks_results = []
+  for proto in face_landmarks_proto_list:
+    face_landmarks = landmark_pb2.NormalizedLandmarkList()
+    face_landmarks.MergeFrom(proto)
+    face_landmarks_list = []
+    for face_landmark in face_landmarks.landmark:
+      face_landmarks_list.append(
+          landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
+      )
+    face_landmarks_results.append(face_landmarks_list)
+
+  face_blendshapes_results = []
+  if _BLENDSHAPES_STREAM_NAME in output_packets:
+    face_blendshapes_proto_list = packet_getter.get_proto_list(
+        output_packets[_BLENDSHAPES_STREAM_NAME]
+    )
+    for proto in face_blendshapes_proto_list:
+      face_blendshapes_categories = []
+      face_blendshapes_classifications = classification_pb2.ClassificationList()
+      face_blendshapes_classifications.MergeFrom(proto)
+      for face_blendshapes in face_blendshapes_classifications.classification:
+        face_blendshapes_categories.append(
+            category_module.Category(
+                index=face_blendshapes.index,
+                score=face_blendshapes.score,
+                display_name=face_blendshapes.display_name,
+                category_name=face_blendshapes.label,
+            )
+        )
+      face_blendshapes_results.append(face_blendshapes_categories)
+
+  facial_transformation_matrixes_results = []
+  if _FACE_GEOMETRY_STREAM_NAME in output_packets:
+    facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
+        output_packets[_FACE_GEOMETRY_STREAM_NAME]
+    )
+    for proto in facial_transformation_matrixes_proto_list:
+      if hasattr(proto, 'pose_transform_matrix'):
+        matrix_data = matrix_data_pb2.MatrixData()
+        matrix_data.MergeFrom(proto.pose_transform_matrix)
+        matrix = np.array(matrix_data.packed_data)
+        matrix = matrix.reshape((matrix_data.rows, matrix_data.cols))
+        matrix = (
+            matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T
+        )
+        facial_transformation_matrixes_results.append(matrix)
+
+  return FaceLandmarkerResult(
+      face_landmarks_results,
+      face_blendshapes_results,
+      facial_transformation_matrixes_results,
+  ), facial_transformation_matrixes_proto_list[0].mesh
+
+@dataclasses.dataclass
+class FaceLandmarkerOptions:
+  """Options for the face landmarker task.
+
+  Attributes:
+    base_options: Base options for the face landmarker task.
+    running_mode: The running mode of the task. Default to the image mode.
+      FaceLandmarker has three running modes: 1) The image mode for detecting
+      face landmarks on single image inputs. 2) The video mode for detecting
+      face landmarks on the decoded frames of a video. 3) The live stream mode
+      for detecting face landmarks on the live stream of input data, such as
+      from camera. In this mode, the "result_callback" below must be specified
+      to receive the detection results asynchronously.
+    num_faces: The maximum number of faces that can be detected by the
+      FaceLandmarker.
+    min_face_detection_confidence: The minimum confidence score for the face
+      detection to be considered successful.
+    min_face_presence_confidence: The minimum confidence score of face presence
+      score in the face landmark detection.
+    min_tracking_confidence: The minimum confidence score for the face tracking
+      to be considered successful.
+    output_face_blendshapes: Whether FaceLandmarker outputs face blendshapes
+      classification. Face blendshapes are used for rendering the 3D face model.
+    output_facial_transformation_matrixes: Whether FaceLandmarker outputs facial
+      transformation_matrix. Facial transformation matrix is used to transform
+      the face landmarks in canonical face to the detected face, so that users
+      can apply face effects on the detected landmarks.
+    result_callback: The user-defined result callback for processing live stream
+      data. The result callback should only be specified when the running mode
+      is set to the live stream mode.
+  """
+
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.IMAGE
+  num_faces: int = 1
+  min_face_detection_confidence: float = 0.5
+  min_face_presence_confidence: float = 0.5
+  min_tracking_confidence: float = 0.5
+  output_face_blendshapes: bool = False
+  output_facial_transformation_matrixes: bool = False
+  result_callback: Optional[
+      Callable[[FaceLandmarkerResult, image_module.Image, int], None]
+  ] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _FaceLandmarkerGraphOptionsProto:
+    """Generates an FaceLandmarkerGraphOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = (
+        False if self.running_mode == _RunningMode.IMAGE else True
+    )
+
+    # Initialize the face landmarker options from base options.
+    face_landmarker_options_proto = _FaceLandmarkerGraphOptionsProto(
+        base_options=base_options_proto
+    )
+
+    # Configure face detector options.
+    face_landmarker_options_proto.face_detector_graph_options.num_faces = (
+        self.num_faces
+    )
+    face_landmarker_options_proto.face_detector_graph_options.min_detection_confidence = (
+        self.min_face_detection_confidence
+    )
+
+    # Configure face landmark detector options.
+    face_landmarker_options_proto.min_tracking_confidence = (
+        self.min_tracking_confidence
+    )
+    face_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
+        self.min_face_detection_confidence
+    )
+    return face_landmarker_options_proto
+
+
+class FaceLandmarker(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs face landmarks detection on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'FaceLandmarker':
+    """Creates an `FaceLandmarker` object from a TensorFlow Lite model and the default `FaceLandmarkerOptions`.
+
+    Note that the created `FaceLandmarker` instance is in image mode, for
+    detecting face landmarks on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `FaceLandmarker` object that's created from the model file and the
+      default `FaceLandmarkerOptions`.
+
+    Raises:
+      ValueError: If failed to create `FaceLandmarker` object from the
+        provided file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = FaceLandmarkerOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE
+    )
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(
+      cls, options: FaceLandmarkerOptions
+  ) -> 'FaceLandmarker':
+    """Creates the `FaceLandmarker` object from face landmarker options.
+
+    Args:
+      options: Options for the face landmarker task.
+
+    Returns:
+      `FaceLandmarker` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `FaceLandmarker` object from
+        `FaceLandmarkerOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+        empty_packet = output_packets[_NORM_LANDMARKS_STREAM_NAME]
+        options.result_callback(
+            FaceLandmarkerResult([], [], []),
+            image,
+            empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
+        )
+        return
+
+      face_landmarks_result = _build_landmarker_result(output_packets)
+      timestamp = output_packets[_NORM_LANDMARKS_STREAM_NAME].timestamp
+      options.result_callback(
+          face_landmarks_result,
+          image,
+          timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
+      )
+
+    output_streams = [
+        ':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
+        ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
+    ]
+
+    if options.output_face_blendshapes:
+      output_streams.append(
+          ':'.join([_BLENDSHAPES_TAG, _BLENDSHAPES_STREAM_NAME])
+      )
+    if options.output_facial_transformation_matrixes:
+      output_streams.append(
+          ':'.join([_FACE_GEOMETRY_TAG, _FACE_GEOMETRY_STREAM_NAME])
+      )
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[
+            ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
+        ],
+        output_streams=output_streams,
+        task_options=options,
+    )
+    return cls(
+        task_info.generate_graph_config(
+            enable_flow_limiting=options.running_mode
+            == _RunningMode.LIVE_STREAM
+        ),
+        options.running_mode,
+        packets_callback if options.result_callback else None,
+    )
+
+  def detect(
+      self,
+      image: image_module.Image,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> FaceLandmarkerResult:
+    """Performs face landmarks detection on the given image.
+
+    Only use this method when the FaceLandmarker is created with the image
+    running mode.
+
+    The image can be of any size with format RGB or RGBA.
+    TODO: Describes how the input image will be preprocessed after the yuv
+    support is implemented.
+
+    Args:
+      image: MediaPipe Image.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The face landmarks detection results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face landmarker detection failed to run.
+    """
+
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, image, roi_allowed=False
+    )
+    output_packets = self._process_image_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ),
+    })
+
+    if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+      return FaceLandmarkerResult([], [], [])
+
+    return _build_landmarker_result2(output_packets)
+
+  def detect_for_video(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ):
+    """Performs face landmarks detection on the provided video frame.
+
+    Only use this method when the FaceLandmarker is created with the video
+    running mode.
+
+    Only use this method when the FaceLandmarker is created with the video
+    running mode. It's required to provide the video frame's timestamp (in
+    milliseconds) along with the video frame. The input timestamps should be
+    monotonically increasing for adjacent calls of this method.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input video frame in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The face landmarks detection results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face landmarker detection failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, image, roi_allowed=False
+    )
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
+        ),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+    })
+
+    if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+      return FaceLandmarkerResult([], [], [])
+
+    return _build_landmarker_result2(output_packets)
+
+  def detect_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> None:
+    """Sends live image data to perform face landmarks detection.
+
+    The results will be available via the "result_callback" provided in the
+    FaceLandmarkerOptions. Only use this method when the FaceLandmarker is
+    created with the live stream running mode.
+
+    Only use this method when the FaceLandmarker is created with the live
+    stream running mode. The input timestamps should be monotonically increasing
+    for adjacent calls of this method. This method will return immediately after
+    the input image is accepted. The results will be available via the
+    `result_callback` provided in the `FaceLandmarkerOptions`. The
+    `detect_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, face landmarker may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` provides:
+      - The face landmarks detection results.
+      - The input image that the face landmarker runs on.
+      - The input timestamp in milliseconds.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input image in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Raises:
+      ValueError: If the current input timestamp is smaller than what the
+      face landmarker has already processed.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, image, roi_allowed=False
+    )
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
+        ),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+    })

aniportrait/src/utils/frame_interpolation.py

@@ -0,0 +1,69 @@
+# Adapted from https://github.com/dajes/frame-interpolation-pytorch
+import os
+import cv2
+import numpy as np
+import torch
+import bisect
+import shutil
+import pdb
+from tqdm import tqdm
+
+def init_frame_interpolation_model():
+    print("Initializing frame interpolation model")
+    checkpoint_name = os.path.join("./pretrained_model/film_net_fp16.pt")
+
+    model = torch.jit.load(checkpoint_name, map_location='cpu')
+    model.eval()
+    model = model.half()
+    model = model.to(device="cuda")
+    return model
+
+
+def batch_images_interpolation_tool(input_tensor, model, inter_frames=1):
+
+    video_tensor = []
+    frame_num = input_tensor.shape[2]  # bs, channel, frame, height, width
+    
+    for idx in tqdm(range(frame_num-1)):
+        image1 = input_tensor[:,:,idx]
+        image2 = input_tensor[:,:,idx+1]
+
+        results = [image1, image2]
+
+        inter_frames = int(inter_frames)
+        idxes = [0, inter_frames + 1]
+        remains = list(range(1, inter_frames + 1))
+
+        splits = torch.linspace(0, 1, inter_frames + 2)
+
+        for _ in range(len(remains)):
+            starts = splits[idxes[:-1]]
+            ends = splits[idxes[1:]]
+            distances = ((splits[None, remains] - starts[:, None]) / (ends[:, None] - starts[:, None]) - .5).abs()
+            matrix = torch.argmin(distances).item()
+            start_i, step = np.unravel_index(matrix, distances.shape)
+            end_i = start_i + 1
+
+            x0 = results[start_i]
+            x1 = results[end_i]
+
+            x0 = x0.half()
+            x1 = x1.half()
+            x0 = x0.cuda()
+            x1 = x1.cuda()
+
+            dt = x0.new_full((1, 1), (splits[remains[step]] - splits[idxes[start_i]])) / (splits[idxes[end_i]] - splits[idxes[start_i]])
+
+            with torch.no_grad():
+                prediction = model(x0, x1, dt)
+            insert_position = bisect.bisect_left(idxes, remains[step])
+            idxes.insert(insert_position, remains[step])
+            results.insert(insert_position, prediction.clamp(0, 1).cpu().float())
+            del remains[step]
+
+        for sub_idx in range(len(results)-1):
+            video_tensor.append(results[sub_idx].unsqueeze(2))
+
+    video_tensor.append(input_tensor[:,:,-1].unsqueeze(2))
+    video_tensor = torch.cat(video_tensor, dim=2)
+    return video_tensor

aniportrait/src/utils/mp_models/blaze_face_short_range.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4578f35940bf5a1a655214a1cce5cab13eba73c1297cd78e1a04c2380b0152f
+size 229746

aniportrait/src/utils/mp_models/face_landmarker_v2_with_blendshapes.task

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64184e229b263107bc2b804c6625db1341ff2bb731874b0bcc2fe6544e0bc9ff
+size 3758596

aniportrait/src/utils/mp_models/pose_landmarker_heavy.task

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64437af838a65d18e5ba7a0d39b465540069bc8aae8308de3e318aad31fcbc7b
+size 30664242

aniportrait/src/utils/mp_utils.py

@@ -0,0 +1,95 @@
+import os
+import numpy as np
+import cv2
+import time
+from tqdm import tqdm
+import multiprocessing
+import glob
+
+import mediapipe as mp
+from mediapipe import solutions
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.tasks import python
+from mediapipe.tasks.python import vision
+from . import face_landmark
+
+CUR_DIR = os.path.dirname(__file__)
+
+
+class LMKExtractor():
+    def __init__(self, FPS=25):
+        # Create an FaceLandmarker object.
+        self.mode = mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE
+        base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/face_landmarker_v2_with_blendshapes.task'))
+        base_options.delegate = mp.tasks.BaseOptions.Delegate.CPU
+        options = vision.FaceLandmarkerOptions(base_options=base_options,
+                                            running_mode=self.mode,
+                                            output_face_blendshapes=True,
+                                            output_facial_transformation_matrixes=True,
+                                            num_faces=1)
+        self.detector = face_landmark.FaceLandmarker.create_from_options(options)
+        self.last_ts = 0
+        self.frame_ms = int(1000 / FPS)
+
+        det_base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/blaze_face_short_range.tflite'))
+        det_options = vision.FaceDetectorOptions(base_options=det_base_options)
+        self.det_detector = vision.FaceDetector.create_from_options(det_options)
+                
+
+    def __call__(self, img):
+        frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
+        t0 = time.time()
+        if self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.VIDEO:
+            det_result = self.det_detector.detect(image)
+            if len(det_result.detections) != 1:
+                return None
+            self.last_ts += self.frame_ms
+            try:
+                detection_result, mesh3d = self.detector.detect_for_video(image, timestamp_ms=self.last_ts)
+            except:
+                return None
+        elif self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE:
+            # det_result = self.det_detector.detect(image)
+
+            # if len(det_result.detections) != 1:
+            #     return None
+            try:
+                detection_result, mesh3d = self.detector.detect(image)
+            except:
+                return None
+            
+        
+        bs_list = detection_result.face_blendshapes
+        if len(bs_list) == 1:
+            bs = bs_list[0]
+            bs_values = []
+            for index in range(len(bs)):
+                bs_values.append(bs[index].score)
+            bs_values = bs_values[1:] # remove neutral
+            trans_mat = detection_result.facial_transformation_matrixes[0]
+            face_landmarks_list = detection_result.face_landmarks
+            face_landmarks = face_landmarks_list[0]
+            lmks = []
+            for index in range(len(face_landmarks)):
+                x = face_landmarks[index].x
+                y = face_landmarks[index].y
+                z = face_landmarks[index].z
+                lmks.append([x, y, z])
+            lmks = np.array(lmks)
+            
+            lmks3d = np.array(mesh3d.vertex_buffer)
+            lmks3d = lmks3d.reshape(-1, 5)[:, :3]
+            mp_tris = np.array(mesh3d.index_buffer).reshape(-1, 3) + 1
+
+            return {
+                "lmks": lmks,
+                'lmks3d': lmks3d,
+                "trans_mat": trans_mat,
+                'faces': mp_tris,
+                "bs": bs_values
+            }
+        else:
+            # print('multiple faces in the image: {}'.format(img_path))
+            return None
+        

aniportrait/src/utils/pose_util.py

@@ -0,0 +1,89 @@
+import math
+
+import numpy as np
+from scipy.spatial.transform import Rotation as R
+
+
+def create_perspective_matrix(aspect_ratio):
+    kDegreesToRadians = np.pi / 180.
+    near = 1
+    far = 10000
+    perspective_matrix = np.zeros(16, dtype=np.float32)
+
+    # Standard perspective projection matrix calculations.
+    f = 1.0 / np.tan(kDegreesToRadians * 63 / 2.)
+
+    denom = 1.0 / (near - far)
+    perspective_matrix[0] = f / aspect_ratio
+    perspective_matrix[5] = f
+    perspective_matrix[10] = (near + far) * denom
+    perspective_matrix[11] = -1.
+    perspective_matrix[14] = 1. * far * near * denom
+
+    # If the environment's origin point location is in the top left corner,
+    # then skip additional flip along Y-axis is required to render correctly.
+
+    perspective_matrix[5] *= -1.
+    return perspective_matrix
+
+
+def project_points(points_3d, transformation_matrix, pose_vectors, image_shape):
+    P = create_perspective_matrix(image_shape[1] / image_shape[0]).reshape(4, 4).T
+    L, N, _ = points_3d.shape
+    projected_points = np.zeros((L, N, 2))
+    for i in range(L):
+        points_3d_frame = points_3d[i]
+        ones = np.ones((points_3d_frame.shape[0], 1))
+        points_3d_homogeneous = np.hstack([points_3d_frame, ones])  
+        transformed_points = points_3d_homogeneous @ (transformation_matrix @ euler_and_translation_to_matrix(pose_vectors[i][:3], pose_vectors[i][3:])).T @ P
+        projected_points_frame = transformed_points[:, :2] / transformed_points[:, 3, np.newaxis] # -1 ~ 1
+        projected_points_frame[:, 0] = (projected_points_frame[:, 0] + 1) * 0.5 * image_shape[1] 
+        projected_points_frame[:, 1]  = (projected_points_frame[:, 1] + 1) * 0.5 * image_shape[0]
+        projected_points[i] = projected_points_frame
+    return projected_points
+
+
+def project_points_with_trans(points_3d, transformation_matrix, image_shape):
+    P = create_perspective_matrix(image_shape[1] / image_shape[0]).reshape(4, 4).T
+    L, N, _ = points_3d.shape
+    projected_points = np.zeros((L, N, 2))
+    for i in range(L):
+        points_3d_frame = points_3d[i]
+        ones = np.ones((points_3d_frame.shape[0], 1))
+        points_3d_homogeneous = np.hstack([points_3d_frame, ones])  
+        transformed_points = points_3d_homogeneous @ transformation_matrix[i].T @ P
+        projected_points_frame = transformed_points[:, :2] / transformed_points[:, 3, np.newaxis] # -1 ~ 1
+        projected_points_frame[:, 0] = (projected_points_frame[:, 0] + 1) * 0.5 * image_shape[1] 
+        projected_points_frame[:, 1]  = (projected_points_frame[:, 1] + 1) * 0.5 * image_shape[0]
+        projected_points[i] = projected_points_frame
+    return projected_points
+
+
+def euler_and_translation_to_matrix(euler_angles, translation_vector):
+    rotation = R.from_euler('xyz', euler_angles, degrees=True)
+    rotation_matrix = rotation.as_matrix()
+    
+    matrix = np.eye(4)
+    matrix[:3, :3] = rotation_matrix
+    matrix[:3, 3] = translation_vector
+    
+    return matrix
+
+
+def matrix_to_euler_and_translation(matrix):
+    rotation_matrix = matrix[:3, :3]
+    translation_vector = matrix[:3, 3]
+    rotation = R.from_matrix(rotation_matrix)
+    euler_angles = rotation.as_euler('xyz', degrees=True)
+    return euler_angles, translation_vector
+
+
+def smooth_pose_seq(pose_seq, window_size=5):
+    smoothed_pose_seq = np.zeros_like(pose_seq)
+
+    for i in range(len(pose_seq)):
+        start = max(0, i - window_size // 2)
+        end = min(len(pose_seq), i + window_size // 2 + 1)
+        smoothed_pose_seq[i] = np.mean(pose_seq[start:end], axis=0)
+
+    return smoothed_pose_seq

aniportrait/src/utils/util.py

@@ -0,0 +1,181 @@
+import importlib
+import os
+import os.path as osp
+import shutil
+import sys
+import cv2
+from pathlib import Path
+
+import av
+import numpy as np
+import torch
+import torchvision
+from einops import rearrange
+from PIL import Image
+
+
+def seed_everything(seed):
+    import random
+
+    import numpy as np
+
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed % (2**32))
+    random.seed(seed)
+
+
+def import_filename(filename):
+    spec = importlib.util.spec_from_file_location("mymodule", filename)
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+def delete_additional_ckpt(base_path, num_keep):
+    dirs = []
+    for d in os.listdir(base_path):
+        if d.startswith("checkpoint-"):
+            dirs.append(d)
+    num_tot = len(dirs)
+    if num_tot <= num_keep:
+        return
+    # ensure ckpt is sorted and delete the ealier!
+    del_dirs = sorted(dirs, key=lambda x: int(x.split("-")[-1]))[: num_tot - num_keep]
+    for d in del_dirs:
+        path_to_dir = osp.join(base_path, d)
+        if osp.exists(path_to_dir):
+            shutil.rmtree(path_to_dir)
+
+
+def save_videos_from_pil(pil_images, path, fps=8):
+    import av
+
+    save_fmt = Path(path).suffix
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    width, height = pil_images[0].size
+
+    if save_fmt == ".mp4":
+        codec = "libx264"
+        container = av.open(path, "w")
+        stream = container.add_stream(codec, rate=fps)
+
+        stream.width = width
+        stream.height = height
+
+        for pil_image in pil_images:
+            # pil_image = Image.fromarray(image_arr).convert("RGB")
+            av_frame = av.VideoFrame.from_image(pil_image)
+            container.mux(stream.encode(av_frame))
+        container.mux(stream.encode())
+        container.close()
+
+    elif save_fmt == ".gif":
+        pil_images[0].save(
+            fp=path,
+            format="GIF",
+            append_images=pil_images[1:],
+            save_all=True,
+            duration=(1 / fps * 1000),
+            loop=0,
+        )
+    else:
+        raise ValueError("Unsupported file type. Use .mp4 or .gif.")
+
+
+def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8):
+    videos = rearrange(videos, "b c t h w -> t b c h w")
+    height, width = videos.shape[-2:]
+    outputs = []
+
+    for x in videos:
+        x = torchvision.utils.make_grid(x, nrow=n_rows)  # (c h w)
+        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)  # (h w c)
+        if rescale:
+            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
+        x = (x * 255).numpy().astype(np.uint8)
+        x = Image.fromarray(x)
+
+        outputs.append(x)
+
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+
+    save_videos_from_pil(outputs, path, fps)
+
+
+def read_frames(video_path):
+    container = av.open(video_path)
+
+    video_stream = next(s for s in container.streams if s.type == "video")
+    frames = []
+    for packet in container.demux(video_stream):
+        for frame in packet.decode():
+            image = Image.frombytes(
+                "RGB",
+                (frame.width, frame.height),
+                frame.to_rgb().to_ndarray(),
+            )
+            frames.append(image)
+
+    return frames
+
+
+def get_fps(video_path):
+    container = av.open(video_path)
+    video_stream = next(s for s in container.streams if s.type == "video")
+    fps = video_stream.average_rate
+    container.close()
+    return fps
+
+def crop_face(img, lmk_extractor, expand=1.5):
+    result = lmk_extractor(img)  # cv2 BGR
+
+    if result is None:
+        return None
+    
+    H, W, _ = img.shape
+    lmks = result['lmks']
+    lmks[:, 0] *= W
+    lmks[:, 1] *= H
+
+    x_min = np.min(lmks[:, 0])
+    x_max = np.max(lmks[:, 0])
+    y_min = np.min(lmks[:, 1])
+    y_max = np.max(lmks[:, 1])
+
+    width = x_max - x_min
+    height = y_max - y_min
+    
+    if width*height >= W*H*0.15:
+        if W == H:
+            return img
+        size = min(H, W)
+        offset = int((max(H, W) - size)/2)
+        if size == H:
+            return img[:, offset:-offset]
+        else:
+            return img[offset:-offset, :]
+    else:
+        center_x = x_min + width / 2
+        center_y = y_min + height / 2
+
+        width *= expand
+        height *= expand
+
+        size = max(width, height)
+
+        x_min = int(center_x - size / 2)
+        x_max = int(center_x + size / 2)
+        y_min = int(center_y - size / 2)
+        y_max = int(center_y + size / 2)
+
+        top = max(0, -y_min)
+        bottom = max(0, y_max - img.shape[0])
+        left = max(0, -x_min)
+        right = max(0, x_max - img.shape[1])
+        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=0)
+
+        cropped_img = img[y_min + top:y_max + top, x_min + left:x_max + left]
+
+    return cropped_img

ckpt_tree.md

@@ -0,0 +1,108 @@
+
+```
+|-- ckpts
+|   |-- aniportrait
+|   |   `-- motion_module.pth
+|   |   `-- audio2mesh.pt
+|   |   `-- film_net_fp16.pt
+|   |   |-- sd-vae-ft-mse
+|   |   |   `-- diffusion_pytorch_model.safetensors
+|   |   |   `-- config.json
+|   |   |   `-- diffusion_pytorch_model.bin
+|   |   `-- denoising_unet.pth
+|   |   `-- audio2pose.pt
+|   |   `-- pose_guider.pth
+|   |   |-- sd-image-variations-diffusers
+|   |   |   `-- v1-montage.jpg
+|   |   |   |-- scheduler
+|   |   |   |   `-- scheduler_config.json
+|   |   |   `-- README.md
+|   |   |   `-- model_index.json
+|   |   |   |-- unet
+|   |   |   |   `-- config.json
+|   |   |   |   `-- diffusion_pytorch_model.bin
+|   |   |   |-- feature_extractor
+|   |   |   |   `-- preprocessor_config.json
+|   |   |   `-- v2-montage.jpg
+|   |   |   |-- vae
+|   |   |   |   `-- config.json
+|   |   |   |   `-- diffusion_pytorch_model.bin
+|   |   |   `-- alias-montage.jpg
+|   |   |   `-- inputs.jpg
+|   |   |   |-- safety_checker
+|   |   |   |   `-- pytorch_model.bin
+|   |   |   |   `-- config.json
+|   |   |   `-- earring.jpg
+|   |   |   `-- default-montage.jpg
+|   |   |-- image_encoder
+|   |   |   `-- pytorch_model.bin
+|   |   |   `-- config.json
+|   |   |-- stable-diffusion-v1-5
+|   |   |   `-- model_index.json
+|   |   |   `-- v1-inference.yaml
+|   |   |   |-- unet
+|   |   |   |   `-- config.json
+|   |   |   |   `-- diffusion_pytorch_model.bin
+|   |   |   |-- feature_extractor
+|   |   |   |   `-- preprocessor_config.json
+|   |   `-- reference_unet.pth
+|   |   |-- wav2vec2-base-960h
+|   |   |   `-- pytorch_model.bin
+|   |   |   `-- README.md
+|   |   |   `-- vocab.json
+|   |   |   `-- config.json
+|   |   |   `-- tf_model.h5
+|   |   |   `-- tokenizer_config.json
+|   |   |   `-- model.safetensors
+|   |   |   `-- special_tokens_map.json
+|   |   |   `-- preprocessor_config.json
+|   |   |   `-- feature_extractor_config.json
+|   |-- mofa
+|   |   |-- traj_controlnet
+|   |   |   `-- diffusion_pytorch_model.safetensors
+|   |   |   `-- config.json
+|   |   |-- stable-video-diffusion-img2vid-xt-1-1
+|   |   |   |-- scheduler
+|   |   |   |   `-- scheduler_config.json
+|   |   |   `-- README.md
+|   |   |   `-- model_index.json
+|   |   |   |-- unet
+|   |   |   |   `-- diffusion_pytorch_model.fp16.safetensors
+|   |   |   |   `-- config.json
+|   |   |   |-- feature_extractor
+|   |   |   |   `-- preprocessor_config.json
+|   |   |   |-- vae
+|   |   |   |   `-- diffusion_pytorch_model.fp16.safetensors
+|   |   |   |   `-- config.json
+|   |   |   `-- LICENSE
+|   |   |   `-- svd11.webp
+|   |   |   |-- image_encoder
+|   |   |   |   `-- config.json
+|   |   |   |   `-- model.fp16.safetensors
+|   |   |-- ldmk_controlnet
+|   |   |   `-- diffusion_pytorch_model.safetensors
+|   |   |   `-- config.json
+|   |-- sad_talker
+|   |   `-- SadTalker_V0.0.2_256.safetensors
+|   |   |-- hub
+|   |   `-- mapping_00229-model.pth.tar
+|   |   |-- BFM_Fitting
+|   |   |   `-- select_vertex_id.mat
+|   |   |   `-- facemodel_info.mat
+|   |   |   `-- BFM_exp_idx.mat
+|   |   |   `-- BFM_model_front.mat
+|   |   |   `-- 01_MorphableModel.mat
+|   |   |   `-- similarity_Lm3D_all.mat
+|   |   |   `-- BFM_front_idx.mat
+|   |   |   `-- Exp_Pca.bin
+|   |   |   `-- std_exp.txt
+|   |   `-- SadTalker_V0.0.2_512.safetensors
+|   |   `-- similarity_Lm3D_all.mat
+|   |   `-- epoch_00190_iteration_000400000_checkpoint.pt
+|   |   `-- mapping_00109-model.pth.tar
+|   |-- gfpgan
+|   |   `-- alignment_WFLW_4HG.pth
+|   |   `-- parsing_parsenet.pth
+|   |   `-- detection_Resnet50_Final.pth
+
+```

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+    "CLIPVisionModelWithProjection"
+  ],
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+  "dropout": 0.0,
+  "hidden_act": "quick_gelu",
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+  "patch_size": 14,
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ckpts/aniportrait/sd-image-variations-diffusers/README.md

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+---
+thumbnail: "https://repository-images.githubusercontent.com/523487884/fdb03a69-8353-4387-b5fc-0d85f888a63f"
+datasets:
+- ChristophSchuhmann/improved_aesthetics_6plus
+license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- image-to-image
+---
+
+# Stable Diffusion Image Variations Model Card
+
+📣 V2 model released, and blurriness issues fixed! 📣
+
+🧨🎉 Image Variations is now natively supported in 🤗 Diffusers! 🎉🧨
+
+![](https://raw.githubusercontent.com/justinpinkney/stable-diffusion/main/assets/im-vars-thin.jpg)
+
+## Version 2
+
+This version of Stable Diffusion has been fine tuned from [CompVis/stable-diffusion-v1-4-original](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) to accept CLIP image embedding rather than text embeddings. This allows the creation of "image variations" similar to DALLE-2 using Stable Diffusion. This version of the weights has been ported to huggingface Diffusers, to use this with the Diffusers library requires the [Lambda Diffusers repo](https://github.com/LambdaLabsML/lambda-diffusers).
+
+This model was trained in two stages and longer than the original variations model and gives better image quality and better CLIP rated similarity compared to the original version
+
+See training details and v1 vs v2 comparison below.
+
+
+## Example
+
+Make sure you are using a version of Diffusers >=0.8.0 (for older version see the old instructions at the bottom of this model card)
+
+```python
+from diffusers import StableDiffusionImageVariationPipeline
+from PIL import Image
+
+device = "cuda:0"
+sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(
+  "lambdalabs/sd-image-variations-diffusers",
+  revision="v2.0",
+  )
+sd_pipe = sd_pipe.to(device)
+
+im = Image.open("path/to/image.jpg")
+tform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Resize(
+        (224, 224),
+        interpolation=transforms.InterpolationMode.BICUBIC,
+        antialias=False,
+        ),
+    transforms.Normalize(
+      [0.48145466, 0.4578275, 0.40821073],
+      [0.26862954, 0.26130258, 0.27577711]),
+])
+inp = tform(im).to(device).unsqueeze(0)
+
+out = sd_pipe(inp, guidance_scale=3)
+out["images"][0].save("result.jpg")
+```
+
+### The importance of resizing correctly... (or not)
+
+Note that due a bit of an oversight during training, the model expects resized images without anti-aliasing. This turns out to make a big difference and is important to do the resizing the same way during inference. When passing a PIL image to the Diffusers pipeline antialiasing will be applied during resize, so it's better to input a tensor which you have prepared manually according to the transfrom in the example above!
+
+Here are examples of images generated without (top) and with (bottom) anti-aliasing during resize. (Input is [this image](https://github.com/SHI-Labs/Versatile-Diffusion/blob/master/assets/ghibli.jpg))
+
+![](alias-montage.jpg)
+
+![](default-montage.jpg)
+
+### V1 vs V2
+
+Here's an example of V1 vs V2, version two was trained more carefully and for longer, see the details below. V2-top vs V1-bottom
+
+![](v2-montage.jpg)
+
+![](v1-montage.jpg)
+
+Input images:
+
+![](inputs.jpg)
+
+One important thing to note is that due to the longer training V2 appears to have memorised some common images from the training data, e.g. now the previous example of the Girl with a Pearl Earring almosts perfectly reproduce the original rather than creating variations. You can always use v1 by specifiying `revision="v1.0"`.
+
+v2 output for girl with a pearl earing as input (guidance scale=3)
+
+![](earring.jpg)
+
+# Training
+
+
+**Training Procedure**
+This model is fine tuned from Stable Diffusion v1-3 where the text encoder has been replaced with an image encoder. The training procedure is the same as for Stable Diffusion except for the fact that images are encoded through a ViT-L/14 image-encoder including the final projection layer to the CLIP shared embedding space. The model was trained on LAION improved aesthetics 6plus.
+
+- **Hardware:** 8 x A100-40GB GPUs (provided by [Lambda GPU Cloud](https://lambdalabs.com/service/gpu-cloud))
+- **Optimizer:** AdamW
+
+- **Stage 1** - Fine tune only CrossAttention layer weights from Stable Diffusion v1.4 model
+  - **Steps**: 46,000
+  - **Batch:** batch size=4, GPUs=8, Gradient Accumulations=4. Total batch size=128
+  - **Learning rate:** warmup to 1e-5 for 10,000 steps and then kept constant
+
+- **Stage 2** - Resume from Stage 1 training the whole unet
+  - **Steps**: 50,000
+  - **Batch:** batch size=4, GPUs=8, Gradient Accumulations=5. Total batch size=160
+  - **Learning rate:** warmup to 1e-5 for 5,000 steps and then kept constant
+
+
+Training was done using a [modified version of the original Stable Diffusion training code](https://github.com/justinpinkney/stable-diffusion).
+
+
+# Uses
+_The following section is adapted from the [Stable Diffusion model card](https://huggingface.co/CompVis/stable-diffusion-v1-4)_
+
+## Direct Use
+The model is intended for research purposes only. Possible research areas and
+tasks include
+
+- Safe deployment of models which have the potential to generate harmful content.
+- Probing and understanding the limitations and biases of generative models.
+- Generation of artworks and use in design and other artistic processes.
+- Applications in educational or creative tools.
+- Research on generative models.
+
+Excluded uses are described below.
+
+ ### Misuse, Malicious Use, and Out-of-Scope Use
+
+The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
+
+#### Out-of-Scope Use
+The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
+
+#### Misuse and Malicious Use
+Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
+
+- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
+- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
+- Impersonating individuals without their consent.
+- Sexual content without consent of the people who might see it.
+- Mis- and disinformation
+- Representations of egregious violence and gore
+- Sharing of copyrighted or licensed material in violation of its terms of use.
+- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
+
+## Limitations and Bias
+
+### Limitations
+
+- The model does not achieve perfect photorealism
+- The model cannot render legible text
+- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
+- Faces and people in general may not be generated properly.
+- The model was trained mainly with English captions and will not work as well in other languages.
+- The autoencoding part of the model is lossy
+- The model was trained on a large-scale dataset
+  [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
+  and is not fit for product use without additional safety mechanisms and
+  considerations.
+- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
+  The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
+
+### Bias
+
+While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
+Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
+which consists of images that are primarily limited to English descriptions.
+Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
+This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
+ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
+
+### Safety Module
+
+The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
+This checker works by checking model outputs against known hard-coded NSFW concepts.
+The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
+Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPModel` *after generation* of the images.
+The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
+
+
+## Old instructions
+
+If you are using a diffusers version <0.8.0 there is no `StableDiffusionImageVariationPipeline`,
+in this case you need to use an older revision (`2ddbd90b14bc5892c19925b15185e561bc8e5d0a`) in conjunction with the lambda-diffusers repo:
+
+
+First clone [Lambda Diffusers](https://github.com/LambdaLabsML/lambda-diffusers) and install any requirements (in a virtual environment in the example below):
+
+```bash
+git clone https://github.com/LambdaLabsML/lambda-diffusers.git
+cd lambda-diffusers
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+Then run the following python code:
+
+```python
+from pathlib import Path
+from lambda_diffusers import StableDiffusionImageEmbedPipeline
+from PIL import Image
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = StableDiffusionImageEmbedPipeline.from_pretrained(
+"lambdalabs/sd-image-variations-diffusers",
+revision="2ddbd90b14bc5892c19925b15185e561bc8e5d0a",
+)
+pipe = pipe.to(device)
+
+im = Image.open("your/input/image/here.jpg")
+num_samples = 4
+image = pipe(num_samples*[im], guidance_scale=3.0)
+image = image["sample"]
+
+base_path = Path("outputs/im2im")
+base_path.mkdir(exist_ok=True, parents=True)
+for idx, im in enumerate(image):
+    im.save(base_path/f"{idx:06}.jpg")
+```
+
+
+
+*This model card was written by: Justin Pinkney and is based on the [Stable Diffusion model card](https://huggingface.co/CompVis/stable-diffusion-v1-4).*

ckpts/aniportrait/sd-image-variations-diffusers/feature_extractor/preprocessor_config.json

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+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
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+    0.40821073
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+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
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ckpts/aniportrait/sd-image-variations-diffusers/model_index.json

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+  "_class_name": "StableDiffusionImageVariationPipeline",
+  "_diffusers_version": "0.9.0",
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "image_encoder": [
+    "transformers",
+    "CLIPVisionModelWithProjection"
+  ],
+  "requires_safety_checker": true,
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "PNDMScheduler"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

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