File size: 19,777 Bytes
cb6bd3a f0ff580 cb6bd3a f0ff580 cb6bd3a f0ff580 cb6bd3a f0ff580 cb6bd3a f0ff580 cb6bd3a f0ff580 cb6bd3a f0ff580 cb6bd3a f0ff580 cb6bd3a f0ff580 cb6bd3a f0ff580 92078be f0ff580 cb6bd3a 92078be cb6bd3a f0ff580 92078be cb6bd3a f0ff580 cb6bd3a f0ff580 1c92417 92078be 1c92417 f0ff580 1c92417 c2857b5 1c92417 f0ff580 1c92417 f0ff580 788a6e1 f0ff580 aee1300 f0ff580 aee1300 8d85e1f f0ff580 aee1300 f0ff580 95b2cf2 f0ff580 95b2cf2 f0ff580 95b2cf2 f0ff580 95b2cf2 f0ff580 95b2cf2 f0ff580 95b2cf2 c2857b5 95b2cf2 c2857b5 f0ff580 95b2cf2 f0ff580 95b2cf2 c2857b5 f0ff580 c2857b5 f0ff580 6801d5a f0ff580 c2857b5 f0ff580 c2857b5 f0ff580 c2857b5 f0ff580 c2857b5 f0ff580 c2857b5 f0ff580 c2857b5 f0ff580 c2857b5 f0ff580 38d054a f0ff580 38d054a f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 de5e356 f0ff580 6801d5a f0ff580 70a401a 6801d5a f0ff580 6801d5a 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 6801d5a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a f0ff580 70a401a 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 7377e9c f0ff580 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 | import torch
import torch.nn as nn
def get_time_embedding(time_steps, temb_dim):
r"""
Convert time steps tensor into an embedding using the
sinusoidal time embedding formula
:param time_steps: 1D tensor of length batch size
:param temb_dim: Dimension of the embedding
:return: BxD embedding representation of B time steps
"""
assert temb_dim % 2 == 0, "time embedding dimension must be divisible by 2"
# factor = 10000^(2i/d_model)
factor = 10000 ** ((torch.arange(
start=0, end=temb_dim // 2, dtype=torch.float32, device=time_steps.device) / (temb_dim // 2))
)
# pos / factor
# timesteps B -> B, 1 -> B, temb_dim
t_emb = time_steps[:, None].repeat(1, temb_dim // 2) / factor
t_emb = torch.cat([torch.sin(t_emb), torch.cos(t_emb)], dim=-1)
return t_emb
class DownBlock(nn.Module):
r"""
Down conv block with attention.
Sequence of following block
1. Resnet block with time embedding
2. Attention block
3. Downsample
"""
def __init__(self, in_channels, out_channels, t_emb_dim,
down_sample, num_heads, num_layers, attn, norm_channels, cross_attn=False, context_dim=None):
super().__init__()
self.num_layers = num_layers
self.down_sample = down_sample
self.attn = attn
self.context_dim = context_dim
self.cross_attn = cross_attn
self.t_emb_dim = t_emb_dim
self.resnet_conv_first = nn.ModuleList(
[
nn.Sequential(
nn.GroupNorm(norm_channels, in_channels if i == 0 else out_channels),
nn.SiLU(),
nn.Conv2d(in_channels if i == 0 else out_channels, out_channels,
kernel_size=3, stride=1, padding=1),
)
for i in range(num_layers)
]
)
if self.t_emb_dim is not None:
self.t_emb_layers = nn.ModuleList([
nn.Sequential(
nn.SiLU(),
nn.Linear(self.t_emb_dim, out_channels)
)
for _ in range(num_layers)
])
self.resnet_conv_second = nn.ModuleList(
[
nn.Sequential(
nn.GroupNorm(num_groups=norm_channels, num_channels=out_channels),
nn.SiLU(),
nn.Conv2d(out_channels, out_channels,
kernel_size=3, stride=1, padding=1),
)
for _ in range(num_layers)
]
)
if self.attn:
self.attention_norms = nn.ModuleList(
[nn.GroupNorm(num_groups=norm_channels, num_channels=out_channels)
for _ in range(num_layers)]
)
self.attentions = nn.ModuleList(
[nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
for _ in range(num_layers)]
)
if self.cross_attn:
assert context_dim is not None, "Context Dimension must be passed for cross attention"
self.cross_attention_norms = nn.ModuleList(
[nn.GroupNorm(norm_channels, out_channels)
for _ in range(num_layers)]
)
self.cross_attentions = nn.ModuleList(
[nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
for _ in range(num_layers)]
)
self.context_proj = nn.ModuleList(
[nn.Linear(context_dim, out_channels)
for _ in range(num_layers)]
)
self.residual_input_conv = nn.ModuleList(
[
nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=1)
for i in range(num_layers)
]
)
self.down_sample_conv = nn.Conv2d(out_channels, out_channels,
4, 2, 1) if self.down_sample else nn.Identity()
def forward(self, x, t_emb=None, context=None):
out = x
for i in range(self.num_layers):
# Resnet block of Unet
resnet_input = out
out = self.resnet_conv_first[i](out)
if self.t_emb_dim is not None:
out = out + self.t_emb_layers[i](t_emb)[:, :, None, None]
out = self.resnet_conv_second[i](out)
out = out + self.residual_input_conv[i](resnet_input)
if self.attn:
# Attention block of Unet
batch_size, channels, h, w = out.shape
in_attn = out.reshape(batch_size, channels, h * w)
in_attn = self.attention_norms[i](in_attn)
in_attn = in_attn.transpose(1, 2)
out_attn, _ = self.attentions[i](in_attn, in_attn, in_attn)
out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
out = out + out_attn
if self.cross_attn:
assert context is not None, "context cannot be None if cross attention layers are used"
batch_size, channels, h, w = out.shape
in_attn = out.reshape(batch_size, channels, h * w)
in_attn = self.cross_attention_norms[i](in_attn)
in_attn = in_attn.transpose(1, 2)
assert context.shape[0] == x.shape[0] and context.shape[-1] == self.context_dim
context_proj = self.context_proj[i](context)
out_attn, _ = self.cross_attentions[i](in_attn, context_proj, context_proj)
out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
out = out + out_attn
# Downsample
out = self.down_sample_conv(out)
return out
class MidBlock(nn.Module):
r"""
Mid conv block with attention.
Sequence of following blocks
1. Resnet block with time embedding
2. Attention block
3. Resnet block with time embedding
"""
def __init__(self, in_channels, out_channels, t_emb_dim, num_heads, num_layers, norm_channels, cross_attn=None, context_dim=None):
super().__init__()
self.num_layers = num_layers
self.t_emb_dim = t_emb_dim
self.context_dim = context_dim
self.cross_attn = cross_attn
self.resnet_conv_first = nn.ModuleList(
[
nn.Sequential(
nn.GroupNorm(norm_channels, in_channels if i == 0 else out_channels),
nn.SiLU(),
nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=3, stride=1,
padding=1),
)
for i in range(num_layers + 1)
]
)
if self.t_emb_dim is not None:
self.t_emb_layers = nn.ModuleList([
nn.Sequential(
nn.SiLU(),
nn.Linear(t_emb_dim, out_channels)
)
for _ in range(num_layers + 1)
])
self.resnet_conv_second = nn.ModuleList(
[
nn.Sequential(
nn.GroupNorm(norm_channels, out_channels),
nn.SiLU(),
nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
)
for _ in range(num_layers + 1)
]
)
self.attention_norms = nn.ModuleList(
[nn.GroupNorm(norm_channels, out_channels)
for _ in range(num_layers)]
)
self.attentions = nn.ModuleList(
[nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
for _ in range(num_layers)]
)
if self.cross_attn:
assert context_dim is not None, "Context Dimension must be passed for cross attention"
self.cross_attention_norms = nn.ModuleList(
[nn.GroupNorm(norm_channels, out_channels)
for _ in range(num_layers)]
)
self.cross_attentions = nn.ModuleList(
[nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
for _ in range(num_layers)]
)
self.context_proj = nn.ModuleList(
[nn.Linear(context_dim, out_channels)
for _ in range(num_layers)]
)
self.residual_input_conv = nn.ModuleList(
[
nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=1)
for i in range(num_layers + 1)
]
)
def forward(self, x, t_emb=None, context=None):
out = x
# First resnet block
resnet_input = out
out = self.resnet_conv_first[0](out)
if self.t_emb_dim is not None:
out = out + self.t_emb_layers[0](t_emb)[:, :, None, None]
out = self.resnet_conv_second[0](out)
out = out + self.residual_input_conv[0](resnet_input)
for i in range(self.num_layers):
# Attention Block
batch_size, channels, h, w = out.shape
in_attn = out.reshape(batch_size, channels, h * w)
in_attn = self.attention_norms[i](in_attn)
in_attn = in_attn.transpose(1, 2)
out_attn, _ = self.attentions[i](in_attn, in_attn, in_attn)
out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
out = out + out_attn
if self.cross_attn:
assert context is not None, "context cannot be None if cross attention layers are used"
batch_size, channels, h, w = out.shape
in_attn = out.reshape(batch_size, channels, h * w)
in_attn = self.cross_attention_norms[i](in_attn)
in_attn = in_attn.transpose(1, 2)
assert context.shape[0] == x.shape[0] and context.shape[-1] == self.context_dim
context_proj = self.context_proj[i](context)
out_attn, _ = self.cross_attentions[i](in_attn, context_proj, context_proj)
out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
out = out + out_attn
# Resnet Block
resnet_input = out
out = self.resnet_conv_first[i + 1](out)
if self.t_emb_dim is not None:
out = out + self.t_emb_layers[i + 1](t_emb)[:, :, None, None]
out = self.resnet_conv_second[i + 1](out)
out = out + self.residual_input_conv[i + 1](resnet_input)
return out
class UpBlock(nn.Module):
r"""
Up conv block with attention.
Sequence of following blocks
1. Upsample
1. Concatenate Down block output
2. Resnet block with time embedding
3. Attention Block
"""
def __init__(self, in_channels, out_channels, t_emb_dim,
up_sample, num_heads, num_layers, attn, norm_channels):
super().__init__()
self.num_layers = num_layers
self.up_sample = up_sample
self.t_emb_dim = t_emb_dim
self.attn = attn
self.resnet_conv_first = nn.ModuleList(
[
nn.Sequential(
nn.GroupNorm(norm_channels, in_channels if i == 0 else out_channels),
nn.SiLU(),
nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=3, stride=1,
padding=1),
)
for i in range(num_layers)
]
)
if self.t_emb_dim is not None:
self.t_emb_layers = nn.ModuleList([
nn.Sequential(
nn.SiLU(),
nn.Linear(t_emb_dim, out_channels)
)
for _ in range(num_layers)
])
self.resnet_conv_second = nn.ModuleList(
[
nn.Sequential(
nn.GroupNorm(norm_channels, out_channels),
nn.SiLU(),
nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
)
for _ in range(num_layers)
]
)
if self.attn:
self.attention_norms = nn.ModuleList(
[
nn.GroupNorm(norm_channels, out_channels)
for _ in range(num_layers)
]
)
self.attentions = nn.ModuleList(
[
nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
for _ in range(num_layers)
]
)
self.residual_input_conv = nn.ModuleList(
[
nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=1)
for i in range(num_layers)
]
)
self.up_sample_conv = nn.ConvTranspose2d(in_channels, in_channels,
4, 2, 1) \
if self.up_sample else nn.Identity()
def forward(self, x, out_down=None, t_emb=None):
# Upsample
x = self.up_sample_conv(x)
# Concat with Downblock output
if out_down is not None:
x = torch.cat([x, out_down], dim=1)
out = x
for i in range(self.num_layers):
# Resnet Block
resnet_input = out
out = self.resnet_conv_first[i](out)
if self.t_emb_dim is not None:
out = out + self.t_emb_layers[i](t_emb)[:, :, None, None]
out = self.resnet_conv_second[i](out)
out = out + self.residual_input_conv[i](resnet_input)
# Self Attention
if self.attn:
batch_size, channels, h, w = out.shape
in_attn = out.reshape(batch_size, channels, h * w)
in_attn = self.attention_norms[i](in_attn)
in_attn = in_attn.transpose(1, 2)
out_attn, _ = self.attentions[i](in_attn, in_attn, in_attn)
out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
out = out + out_attn
return out
class UpBlockUnet(nn.Module):
r"""
Up conv block with attention.
Sequence of following blocks
1. Upsample
1. Concatenate Down block output
2. Resnet block with time embedding
3. Attention Block
"""
def __init__(self, in_channels, out_channels, t_emb_dim, up_sample,
num_heads, num_layers, norm_channels, cross_attn=False, context_dim=None):
super().__init__()
self.num_layers = num_layers
self.up_sample = up_sample
self.t_emb_dim = t_emb_dim
self.cross_attn = cross_attn
self.context_dim = context_dim
self.resnet_conv_first = nn.ModuleList(
[
nn.Sequential(
nn.GroupNorm(norm_channels, in_channels if i == 0 else out_channels),
nn.SiLU(),
nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=3, stride=1,
padding=1),
)
for i in range(num_layers)
]
)
if self.t_emb_dim is not None:
self.t_emb_layers = nn.ModuleList([
nn.Sequential(
nn.SiLU(),
nn.Linear(t_emb_dim, out_channels)
)
for _ in range(num_layers)
])
self.resnet_conv_second = nn.ModuleList(
[
nn.Sequential(
nn.GroupNorm(norm_channels, out_channels),
nn.SiLU(),
nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
)
for _ in range(num_layers)
]
)
self.attention_norms = nn.ModuleList(
[
nn.GroupNorm(norm_channels, out_channels)
for _ in range(num_layers)
]
)
self.attentions = nn.ModuleList(
[
nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
for _ in range(num_layers)
]
)
if self.cross_attn:
assert context_dim is not None, "Context Dimension must be passed for cross attention"
self.cross_attention_norms = nn.ModuleList(
[nn.GroupNorm(norm_channels, out_channels)
for _ in range(num_layers)]
)
self.cross_attentions = nn.ModuleList(
[nn.MultiheadAttention(out_channels, num_heads, batch_first=True)
for _ in range(num_layers)]
)
self.context_proj = nn.ModuleList(
[nn.Linear(context_dim, out_channels)
for _ in range(num_layers)]
)
self.residual_input_conv = nn.ModuleList(
[
nn.Conv2d(in_channels if i == 0 else out_channels, out_channels, kernel_size=1)
for i in range(num_layers)
]
)
self.up_sample_conv = nn.ConvTranspose2d(in_channels // 2, in_channels // 2,
4, 2, 1) \
if self.up_sample else nn.Identity()
def forward(self, x, out_down=None, t_emb=None, context=None):
x = self.up_sample_conv(x)
if out_down is not None:
x = torch.cat([x, out_down], dim=1)
out = x
for i in range(self.num_layers):
# Resnet
resnet_input = out
out = self.resnet_conv_first[i](out)
if self.t_emb_dim is not None:
out = out + self.t_emb_layers[i](t_emb)[:, :, None, None]
out = self.resnet_conv_second[i](out)
out = out + self.residual_input_conv[i](resnet_input)
# Self Attention
batch_size, channels, h, w = out.shape
in_attn = out.reshape(batch_size, channels, h * w)
in_attn = self.attention_norms[i](in_attn)
in_attn = in_attn.transpose(1, 2)
out_attn, _ = self.attentions[i](in_attn, in_attn, in_attn)
out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
out = out + out_attn
# Cross Attention
if self.cross_attn:
assert context is not None, "context cannot be None if cross attention layers are used"
batch_size, channels, h, w = out.shape
in_attn = out.reshape(batch_size, channels, h * w)
in_attn = self.cross_attention_norms[i](in_attn)
in_attn = in_attn.transpose(1, 2)
assert len(context.shape) == 3, \
"Context shape does not match B,_,CONTEXT_DIM"
assert context.shape[0] == x.shape[0] and context.shape[-1] == self.context_dim,\
"Context shape does not match B,_,CONTEXT_DIM"
context_proj = self.context_proj[i](context)
out_attn, _ = self.cross_attentions[i](in_attn, context_proj, context_proj)
out_attn = out_attn.transpose(1, 2).reshape(batch_size, channels, h, w)
out = out + out_attn
return out
|