File size: 17,678 Bytes
70ceb8b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 | import torch
from torch import nn
from torch.nn import functional as F
from einops import rearrange
import numpy as np
from typing import Tuple
from .unet_causal_3d_blocks import get_down_block3d, CausalConv3d
class ControlNetCausalConditioningEmbedding(nn.Module):
def __init__(self, conditioning_embedding_channels: int, conditioning_channels: int = 3, block_out_channels: Tuple[int, ...] = (16, 32, 96, 256)):
super().__init__()
self.conv_in = CausalConv3d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
self.blocks = nn.ModuleList([])
for i in range(len(block_out_channels) - 1):
channel_in = block_out_channels[i]
channel_out = block_out_channels[i + 1]
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
self.conv_out = nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
def forward(self, conditioning):
embedding = self.conv_in(conditioning)
embedding = F.silu(embedding)
for block in self.blocks:
embedding = block(embedding)
embedding = F.silu(embedding)
embedding = self.conv_out(embedding)
return embedding
class MiniHunyuanEncoder(nn.Module):
'''
a direct copy of hunyuan encoder
'''
def __init__(
self,
in_channels = 3,
out_channels = 3,
down_block_types = ['DownEncoderBlockCausal3D', 'DownEncoderBlockCausal3D', 'DownEncoderBlockCausal3D', 'DownEncoderBlockCausal3D'],
block_out_channels = [128, 256, 512, 512],
layers_per_block = 2,
norm_num_groups = 32,
act_fn: str = "silu",
time_compression_ratio: int = 4,
spatial_compression_ratio: int = 8,
):
super().__init__()
self.layers_per_block = layers_per_block
self.conv_in = CausalConv3d(
in_channels, block_out_channels[0], kernel_size=3, stride=1)
self.mid_block = None
self.down_blocks = nn.ModuleList([])
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
num_spatial_downsample_layers = int(
np.log2(spatial_compression_ratio))
num_time_downsample_layers = int(np.log2(time_compression_ratio))
if time_compression_ratio == 4:
add_spatial_downsample = bool(
i < num_spatial_downsample_layers)
add_time_downsample = bool(i >= (
len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block)
elif time_compression_ratio == 8:
add_spatial_downsample = bool(
i < num_spatial_downsample_layers)
add_time_downsample = bool(i < num_time_downsample_layers)
else:
raise ValueError(
f"Unsupported time_compression_ratio: {time_compression_ratio}")
downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1)
downsample_stride_T = (2, ) if add_time_downsample else (1, )
downsample_stride = tuple(
downsample_stride_T + downsample_stride_HW)
down_block = get_down_block3d(
down_block_type,
num_layers=self.layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
add_downsample=bool(
add_spatial_downsample or add_time_downsample),
downsample_stride=downsample_stride,
resnet_eps=1e-6,
downsample_padding=0,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
)
self.down_blocks.append(down_block)
self.conv_out = CausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
def forward(self, sample):
assert len(sample.shape) == 5, "The input tensor should have 5 dimensions"
sample = self.conv_in(sample)
# down
for down_block in self.down_blocks:
sample = down_block(sample)
sample = self.conv_out(sample)
return sample
class ControlNetConditioningEmbedding(nn.Module):
"""
Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
[11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
(activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
model) to encode image-space conditions ... into feature maps ..."
"""
def __init__(
self,
conditioning_embedding_channels: int,
conditioning_channels: int = 3,
block_out_channels: Tuple[int, ...] = (16, 32, 96, 256),
):
super().__init__()
self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
self.blocks = nn.ModuleList([])
for i in range(len(block_out_channels) - 1):
channel_in = block_out_channels[i]
channel_out = block_out_channels[i + 1]
self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
self.conv_out = nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
def forward(self, conditioning):
embedding = self.conv_in(conditioning)
embedding = F.silu(embedding)
for block in self.blocks:
embedding = block(embedding)
embedding = F.silu(embedding)
embedding = self.conv_out(embedding)
return embedding
class InflatedGroupNorm(nn.GroupNorm):
def forward(self, x):
video_length = x.shape[2]
x = rearrange(x, "b c f h w -> (b f) c h w")
x = super().forward(x)
x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
return x
class InflatedConv3d(nn.Conv2d):
def forward(self, x):
video_length = x.shape[2]
x = rearrange(x, "b c f h w -> (b f) c h w")
x = super().forward(x)
x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
return x
class ResnetBlockInflated(nn.Module):
def __init__(self, *, in_channels, out_channels=None, dropout=0.0, groups=32, groups_out=None, pre_norm=True, eps=1e-6, non_linearity="swish", output_scale_factor=1.0):
super().__init__()
self.pre_norm = pre_norm
self.pre_norm = True
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.output_scale_factor = output_scale_factor
if groups_out is None:
groups_out = groups
self.norm1 = InflatedGroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.norm2 = InflatedGroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
if non_linearity == "swish":
self.nonlinearity = lambda x: F.silu(x)
elif non_linearity == "silu":
self.nonlinearity = nn.SiLU()
def forward(self, input_tensor, temb):
if temb is not None:
print("Warning: temb is None in ResnetBlockInflated")
hidden_states = input_tensor
hidden_states = self.norm1(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.conv1(hidden_states)
if temb is not None:
hidden_states = hidden_states + temb
hidden_states = self.norm2(hidden_states)
hidden_states = self.nonlinearity(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.conv2(hidden_states)
output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
return output_tensor
class DownEncoderBlockInflated(nn.Module):
def __init__(self, *, num_layers: int, in_channels: int, out_channels: int, add_downsample: bool, downsample_stride: tuple = (1, 2, 2),
resnet_eps: float = 1e-6, resnet_act_fn: str = "silu", resnet_groups: int = 32):
super().__init__()
self.resnets = nn.ModuleList([ResnetBlockInflated(
in_channels=in_channels if i == 0 else out_channels,
out_channels=out_channels,
eps=resnet_eps,
non_linearity=resnet_act_fn,
groups=resnet_groups,
) for i in range(num_layers)])
self.downsamplers = nn.ModuleList()
if add_downsample:
self.downsamplers.append(
InflatedConv3d(
out_channels,
out_channels,
kernel_size=3,
stride=2,
padding=1,
)
)
self.down_stride = downsample_stride
else:
self.down_stride = (1, 1, 1)
def forward(self, x, temb=None):
for resnet in self.resnets:
x = resnet(x, temb)
for down in self.downsamplers:
x = down(x)
return x
class SFT(nn.Module): # 2D SFT
def __init__(
self, in_channels, out_channels, intermediate_channels=128, groups=32, eps=1e-6):
super().__init__()
self.out_channels = out_channels
self.norm = InflatedGroupNorm(groups, out_channels, eps, affine=True)
self.mlp_shared = nn.Sequential(InflatedConv3d(in_channels, intermediate_channels, kernel_size=3, stride=1, padding=1), nn.SiLU())
self.mlp_gamma = InflatedConv3d(intermediate_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.mlp_beta = InflatedConv3d(intermediate_channels, out_channels, kernel_size=3, stride=1, padding=1)
def forward(self, hidden_state, condition):
"""
hidden_state : (B, Cout, T, H, W)
condition : (B, Cin, 1, H, W)
"""
hidden_state = self.norm(hidden_state) #2D SFT 2D Norm
actv = self.mlp_shared(condition)
gamma = self.mlp_gamma(actv)
beta = self.mlp_beta(actv)
return torch.addcmul(beta, hidden_state, 1 + gamma)
class MiniEncoder2D(nn.Module):
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
down_block_types: list = (
"DownEncoderBlockInflated",
"DownEncoderBlockInflated",
"DownEncoderBlockInflated",
"DownEncoderBlockInflated",
),
block_out_channels: list = (128, 256, 512, 512),
layers_per_block: int = 2,
norm_num_groups: int = 32,
act_fn: str = "silu",
spatial_compression_ratio: int = 8,
):
super().__init__()
# -------------------------------------------------------------------
# conv in
# -------------------------------------------------------------------
self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1)
self.down_blocks = nn.ModuleList()
output_channel = block_out_channels[0]
num_spatial_down_layers = int(np.log2(spatial_compression_ratio))
for i, block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
# is_final_block = i == len(block_out_channels) - 1
add_spatial_downsample = bool(i < num_spatial_down_layers)
downsample_stride = (1, 2, 2) if add_spatial_downsample else (1, 1, 1)
down_block = DownEncoderBlockInflated(
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
add_downsample=add_spatial_downsample,
downsample_stride=downsample_stride,
resnet_eps=1e-6,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
)
self.down_blocks.append(down_block)
self.conv_out = InflatedConv3d(output_channel, out_channels, kernel_size=3, stride=1, padding=1)
def forward(self, x):
# (B,C,1,H,W)
x = self.conv_in(x)
for block in self.down_blocks:
x = block(x)
return self.conv_out(x)
class Driven_Ref_PoseEncoder(nn.Module):
def __init__(
self, in_channels = 3, out_channels = 3,
down_block_types = ['DownEncoderBlockCausal3D', 'DownEncoderBlockCausal3D', 'DownEncoderBlockCausal3D', 'DownEncoderBlockCausal3D'],
block_out_channels = [128, 256, 512, 512], layers_per_block = 2, norm_num_groups = 32,
act_fn: str = "silu", time_compression_ratio: int = 4, spatial_compression_ratio: int = 8,
):
super().__init__()
self.layers_per_block = layers_per_block
self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1)
self.mid_block = None
self.down_blocks = nn.ModuleList([])
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
num_spatial_downsample_layers = int(
np.log2(spatial_compression_ratio))
num_time_downsample_layers = int(np.log2(time_compression_ratio))
if time_compression_ratio == 4:
add_spatial_downsample = bool(
i < num_spatial_downsample_layers)
add_time_downsample = bool(i >= (
len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block)
elif time_compression_ratio == 8:
add_spatial_downsample = bool(
i < num_spatial_downsample_layers)
add_time_downsample = bool(i < num_time_downsample_layers)
else:
raise ValueError(
f"Unsupported time_compression_ratio: {time_compression_ratio}")
downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1)
downsample_stride_T = (2, ) if add_time_downsample else (1, )
downsample_stride = tuple(
downsample_stride_T + downsample_stride_HW)
down_block = get_down_block3d(
down_block_type,
num_layers=self.layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
add_downsample=bool(
add_spatial_downsample or add_time_downsample),
downsample_stride=downsample_stride,
resnet_eps=1e-6,
downsample_padding=0,
resnet_act_fn=act_fn,
resnet_groups=norm_num_groups,
attention_head_dim=output_channel,
)
self.down_blocks.append(down_block)
self.conv_out = CausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
self.ref_pose_encoder = MiniEncoder2D(
in_channels = in_channels,
out_channels = out_channels,
block_out_channels = block_out_channels,
norm_num_groups = norm_num_groups,
layers_per_block = layers_per_block,
spatial_compression_ratio = spatial_compression_ratio,
)
self.sft_layers = nn.ModuleList()
for i, ch in enumerate(block_out_channels):
if i == 0: # 0 层 (H/2,W/2) 不做 SFT
self.sft_layers.append(None)
else: # H/4、H/8、H/16 做 SFT
self.sft_layers.append(
SFT(
in_channels=ch,
out_channels=ch,
intermediate_channels=max(8, ch // 2),
groups=norm_num_groups,
)
)
def forward(self, driven_pose, ref_pose):
# driven_pose b c t h w
# ref_pose b c 1 h w
ref_pose_cond, ref_feats = self.ref_pose_encoder(ref_pose)
x = self.conv_in(driven_pose)
for i, down_block in enumerate(self.down_blocks):
x = down_block(x)
if self.sft_layers[i] is not None:
cond_feat = ref_feats[i]
x = self.sft_layers[i](x, cond_feat)
driven_pose_cond = self.conv_out(x)
return driven_pose_cond, ref_pose_cond
|