File size: 10,450 Bytes
c32f190 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 |
import cv2
import math
import numpy as np
from PIL import Image
import torch
from torchvision.transforms import InterpolationMode
from torchvision.transforms.functional import normalize, resize
from transformers import T5EncoderModel, T5Tokenizer
from typing import List, Optional, Tuple, Union
from diffusers.models.embeddings import get_3d_rotary_pos_embed
from diffusers.pipelines.cogvideo.pipeline_cogvideox import get_resize_crop_region_for_grid
def tensor_to_pil(src_img_tensor):
img = src_img_tensor.clone().detach()
if img.dtype == torch.bfloat16:
img = img.to(torch.float32)
img = img.cpu().numpy()
img = np.transpose(img, (1, 2, 0))
img = img.astype(np.uint8)
pil_image = Image.fromarray(img)
return pil_image
def _get_t5_prompt_embeds(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if tokenizer is not None:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
else:
if text_input_ids is None:
raise ValueError("`text_input_ids` must be provided when the tokenizer is not specified.")
prompt_embeds = text_encoder(text_input_ids.to(device))[0]
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
return prompt_embeds
def encode_prompt(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt_embeds = _get_t5_prompt_embeds(
tokenizer,
text_encoder,
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
text_input_ids=text_input_ids,
)
return prompt_embeds
def compute_prompt_embeddings(
tokenizer, text_encoder, prompt, max_sequence_length, device, dtype, requires_grad: bool = False
):
if requires_grad:
prompt_embeds = encode_prompt(
tokenizer,
text_encoder,
prompt,
num_videos_per_prompt=1,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
else:
with torch.no_grad():
prompt_embeds = encode_prompt(
tokenizer,
text_encoder,
prompt,
num_videos_per_prompt=1,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
return prompt_embeds
def prepare_rotary_positional_embeddings(
height: int,
width: int,
num_frames: int,
vae_scale_factor_spatial: int = 8,
patch_size: int = 2,
attention_head_dim: int = 64,
device: Optional[torch.device] = None,
base_height: int = 480,
base_width: int = 720,
) -> Tuple[torch.Tensor, torch.Tensor]:
grid_height = height // (vae_scale_factor_spatial * patch_size)
grid_width = width // (vae_scale_factor_spatial * patch_size)
base_size_width = base_width // (vae_scale_factor_spatial * patch_size)
base_size_height = base_height // (vae_scale_factor_spatial * patch_size)
grid_crops_coords = get_resize_crop_region_for_grid((grid_height, grid_width), base_size_width, base_size_height)
freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
embed_dim=attention_head_dim,
crops_coords=grid_crops_coords,
grid_size=(grid_height, grid_width),
temporal_size=num_frames,
)
freqs_cos = freqs_cos.to(device=device)
freqs_sin = freqs_sin.to(device=device)
return freqs_cos, freqs_sin
def img2tensor(imgs, bgr2rgb=True, float32=True):
"""Numpy array to tensor.
Args:
imgs (list[ndarray] | ndarray): Input images.
bgr2rgb (bool): Whether to change bgr to rgb.
float32 (bool): Whether to change to float32.
Returns:
list[tensor] | tensor: Tensor images. If returned results only have
one element, just return tensor.
"""
def _totensor(img, bgr2rgb, float32):
if img.shape[2] == 3 and bgr2rgb:
if img.dtype == 'float64':
img = img.astype('float32')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = torch.from_numpy(img.transpose(2, 0, 1))
if float32:
img = img.float()
return img
if isinstance(imgs, list):
return [_totensor(img, bgr2rgb, float32) for img in imgs]
return _totensor(imgs, bgr2rgb, float32)
def to_gray(img):
x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3]
x = x.repeat(1, 3, 1, 1)
return x
def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
stickwidth = 4
limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
kps = np.array(kps)
w, h = image_pil.size
out_img = np.zeros([h, w, 3])
for i in range(len(limbSeq)):
index = limbSeq[i]
color = color_list[index[0]]
x = kps[index][:, 0]
y = kps[index][:, 1]
length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
out_img = (out_img * 0.6).astype(np.uint8)
for idx_kp, kp in enumerate(kps):
color = color_list[idx_kp]
x, y = kp
out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
out_img_pil = Image.fromarray(out_img.astype(np.uint8))
return out_img_pil
def process_face_embeddings(face_helper, clip_vision_model, handler_ante, eva_transform_mean, eva_transform_std, app, device, weight_dtype, image, original_id_image=None, is_align_face=True, cal_uncond=False):
"""
Args:
image: numpy rgb image, range [0, 255]
"""
face_helper.clean_all()
image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # (724, 502, 3)
# get antelopev2 embedding
face_info = app.get(image_bgr)
if len(face_info) > 0:
face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[
-1
] # only use the maximum face
id_ante_embedding = face_info['embedding'] # (512,)
face_kps = face_info['kps']
else:
id_ante_embedding = None
face_kps = None
# using facexlib to detect and align face
face_helper.read_image(image_bgr)
face_helper.get_face_landmarks_5(only_center_face=True)
if face_kps is None:
face_kps = face_helper.all_landmarks_5[0]
face_helper.align_warp_face()
if len(face_helper.cropped_faces) == 0:
raise RuntimeError('facexlib align face fail')
align_face = face_helper.cropped_faces[0] # (512, 512, 3) # RGB
# incase insightface didn't detect face
if id_ante_embedding is None:
print('fail to detect face using insightface, extract embedding on align face')
id_ante_embedding = handler_ante.get_feat(align_face)
id_ante_embedding = torch.from_numpy(id_ante_embedding).to(device, weight_dtype) # torch.Size([512])
if id_ante_embedding.ndim == 1:
id_ante_embedding = id_ante_embedding.unsqueeze(0) # torch.Size([1, 512])
# parsing
if is_align_face:
input = img2tensor(align_face, bgr2rgb=True).unsqueeze(0) / 255.0 # torch.Size([1, 3, 512, 512])
input = input.to(device)
parsing_out = face_helper.face_parse(normalize(input, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0]
parsing_out = parsing_out.argmax(dim=1, keepdim=True) # torch.Size([1, 1, 512, 512])
bg_label = [0, 16, 18, 7, 8, 9, 14, 15]
bg = sum(parsing_out == i for i in bg_label).bool()
white_image = torch.ones_like(input) # torch.Size([1, 3, 512, 512])
# only keep the face features
return_face_features_image = torch.where(bg, white_image, to_gray(input)) # torch.Size([1, 3, 512, 512])
return_face_features_image_2 = torch.where(bg, white_image, input) # torch.Size([1, 3, 512, 512])
else:
original_image_bgr = cv2.cvtColor(original_id_image, cv2.COLOR_RGB2BGR)
input = img2tensor(original_image_bgr, bgr2rgb=True).unsqueeze(0) / 255.0 # torch.Size([1, 3, 512, 512])
input = input.to(device)
return_face_features_image = return_face_features_image_2 = input
# transform img before sending to eva-clip-vit
face_features_image = resize(return_face_features_image, clip_vision_model.image_size,
InterpolationMode.BICUBIC) # torch.Size([1, 3, 336, 336])
face_features_image = normalize(face_features_image, eva_transform_mean, eva_transform_std)
id_cond_vit, id_vit_hidden = clip_vision_model(face_features_image.to(weight_dtype), return_all_features=False, return_hidden=True, shuffle=False) # torch.Size([1, 768]), list(torch.Size([1, 577, 1024]))
id_cond_vit_norm = torch.norm(id_cond_vit, 2, 1, True)
id_cond_vit = torch.div(id_cond_vit, id_cond_vit_norm)
id_cond = torch.cat([id_ante_embedding, id_cond_vit], dim=-1) # torch.Size([1, 512]), torch.Size([1, 768]) -> torch.Size([1, 1280])
return id_cond, id_vit_hidden, return_face_features_image_2, face_kps # torch.Size([1, 1280]), list(torch.Size([1, 577, 1024])) |