Spaces:
Sleeping
Sleeping
File size: 19,558 Bytes
19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 e262715 19fe404 |
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 |
"""Modified from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/motion_module.py
"""
import math
import diffusers
import pkg_resources
import torch
installed_version = diffusers.__version__
if pkg_resources.parse_version(installed_version) >= pkg_resources.parse_version("0.28.2"):
from diffusers.models.attention_processor import (Attention,
AttnProcessor2_0,
HunyuanAttnProcessor2_0)
else:
from diffusers.models.attention_processor import Attention, AttnProcessor2_0
from diffusers.models.attention import FeedForward
from diffusers.utils.import_utils import is_xformers_available
from einops import rearrange, repeat
from torch import nn
from .norm import FP32LayerNorm
if is_xformers_available():
import xformers
import xformers.ops
else:
xformers = None
def zero_module(module):
# Zero out the parameters of a module and return it.
for p in module.parameters():
p.detach().zero_()
return module
def get_motion_module(
in_channels,
motion_module_type: str,
motion_module_kwargs: dict,
):
if motion_module_type == "Vanilla":
return VanillaTemporalModule(in_channels=in_channels, **motion_module_kwargs,)
elif motion_module_type == "VanillaGrid":
return VanillaTemporalModule(in_channels=in_channels, grid=True, **motion_module_kwargs,)
else:
raise ValueError
class VanillaTemporalModule(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads = 8,
num_transformer_block = 2,
attention_block_types =( "Temporal_Self", "Temporal_Self" ),
cross_frame_attention_mode = None,
temporal_position_encoding = False,
temporal_position_encoding_max_len = 4096,
temporal_attention_dim_div = 1,
zero_initialize = True,
block_size = 1,
grid = False,
remove_time_embedding_in_photo = False,
global_num_attention_heads = 16,
global_attention = False,
qk_norm = False,
):
super().__init__()
self.temporal_transformer = TemporalTransformer3DModel(
in_channels=in_channels,
num_attention_heads=num_attention_heads,
attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div,
num_layers=num_transformer_block,
attention_block_types=attention_block_types,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
grid=grid,
block_size=block_size,
remove_time_embedding_in_photo=remove_time_embedding_in_photo,
qk_norm=qk_norm,
)
self.global_transformer = GlobalTransformer3DModel(
in_channels=in_channels,
num_attention_heads=global_num_attention_heads,
attention_head_dim=in_channels // global_num_attention_heads // temporal_attention_dim_div,
qk_norm=qk_norm,
) if global_attention else None
if zero_initialize:
self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out)
if global_attention:
self.global_transformer.proj_out = zero_module(self.global_transformer.proj_out)
def forward(self, input_tensor, encoder_hidden_states=None, attention_mask=None, anchor_frame_idx=None):
hidden_states = input_tensor
hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask)
if self.global_transformer is not None:
hidden_states = self.global_transformer(hidden_states)
output = hidden_states
return output
class GlobalTransformer3DModel(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads,
attention_head_dim,
dropout = 0.0,
attention_bias = False,
upcast_attention = False,
qk_norm = False,
):
super().__init__()
inner_dim = num_attention_heads * attention_head_dim
self.norm1 = FP32LayerNorm(inner_dim)
self.proj_in = nn.Linear(in_channels, inner_dim)
self.norm2 = FP32LayerNorm(inner_dim)
if pkg_resources.parse_version(installed_version) >= pkg_resources.parse_version("0.28.2"):
self.attention = Attention(
query_dim=inner_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
qk_norm="layer_norm" if qk_norm else None,
processor=HunyuanAttnProcessor2_0() if qk_norm else AttnProcessor2_0(),
)
else:
self.attention = Attention(
query_dim=inner_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
)
self.proj_out = nn.Linear(inner_dim, in_channels)
def forward(self, hidden_states):
assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
video_length, height, width = hidden_states.shape[2], hidden_states.shape[3], hidden_states.shape[4]
hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c")
residual = hidden_states
hidden_states = self.norm1(hidden_states)
hidden_states = self.proj_in(hidden_states)
# Attention Blocks
hidden_states = self.norm2(hidden_states)
hidden_states = self.attention(hidden_states)
hidden_states = self.proj_out(hidden_states)
output = hidden_states + residual
output = rearrange(output, "b (f h w) c -> b c f h w", f=video_length, h=height, w=width)
return output
class TemporalTransformer3DModel(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads,
attention_head_dim,
num_layers,
attention_block_types = ( "Temporal_Self", "Temporal_Self", ),
dropout = 0.0,
norm_num_groups = 32,
cross_attention_dim = 768,
activation_fn = "geglu",
attention_bias = False,
upcast_attention = False,
cross_frame_attention_mode = None,
temporal_position_encoding = False,
temporal_position_encoding_max_len = 4096,
grid = False,
block_size = 1,
remove_time_embedding_in_photo = False,
qk_norm = False,
):
super().__init__()
inner_dim = num_attention_heads * attention_head_dim
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
self.proj_in = nn.Linear(in_channels, inner_dim)
self.block_size = block_size
self.transformer_blocks = nn.ModuleList(
[
TemporalTransformerBlock(
dim=inner_dim,
num_attention_heads=num_attention_heads,
attention_head_dim=attention_head_dim,
attention_block_types=attention_block_types,
dropout=dropout,
norm_num_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
attention_bias=attention_bias,
upcast_attention=upcast_attention,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
block_size=block_size,
grid=grid,
remove_time_embedding_in_photo=remove_time_embedding_in_photo,
qk_norm=qk_norm
)
for d in range(num_layers)
]
)
self.proj_out = nn.Linear(inner_dim, in_channels)
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
video_length = hidden_states.shape[2]
hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
batch, channel, height, weight = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
hidden_states = self.proj_in(hidden_states)
# Transformer Blocks
for block in self.transformer_blocks:
hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states, video_length=video_length, height=height, weight=weight)
# output
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
output = hidden_states + residual
output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
return output
class TemporalTransformerBlock(nn.Module):
def __init__(
self,
dim,
num_attention_heads,
attention_head_dim,
attention_block_types = ( "Temporal_Self", "Temporal_Self", ),
dropout = 0.0,
norm_num_groups = 32,
cross_attention_dim = 768,
activation_fn = "geglu",
attention_bias = False,
upcast_attention = False,
cross_frame_attention_mode = None,
temporal_position_encoding = False,
temporal_position_encoding_max_len = 4096,
block_size = 1,
grid = False,
remove_time_embedding_in_photo = False,
qk_norm = False,
):
super().__init__()
attention_blocks = []
norms = []
for block_name in attention_block_types:
attention_blocks.append(
VersatileAttention(
attention_mode=block_name.split("_")[0],
cross_attention_dim=cross_attention_dim if block_name.endswith("_Cross") else None,
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
block_size=block_size,
grid=grid,
remove_time_embedding_in_photo=remove_time_embedding_in_photo,
qk_norm="layer_norm" if qk_norm else None,
processor=HunyuanAttnProcessor2_0() if qk_norm else AttnProcessor2_0(),
) if pkg_resources.parse_version(installed_version) >= pkg_resources.parse_version("0.28.2") else \
VersatileAttention(
attention_mode=block_name.split("_")[0],
cross_attention_dim=cross_attention_dim if block_name.endswith("_Cross") else None,
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
block_size=block_size,
grid=grid,
remove_time_embedding_in_photo=remove_time_embedding_in_photo,
)
)
norms.append(FP32LayerNorm(dim))
self.attention_blocks = nn.ModuleList(attention_blocks)
self.norms = nn.ModuleList(norms)
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
self.ff_norm = FP32LayerNorm(dim)
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None, height=None, weight=None):
for attention_block, norm in zip(self.attention_blocks, self.norms):
norm_hidden_states = norm(hidden_states)
hidden_states = attention_block(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states if attention_block.is_cross_attention else None,
video_length=video_length,
height=height,
weight=weight,
) + hidden_states
hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
output = hidden_states
return output
class PositionalEncoding(nn.Module):
def __init__(
self,
d_model,
dropout = 0.,
max_len = 4096
):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(1, max_len, d_model)
pe[0, :, 0::2] = torch.sin(position * div_term)
pe[0, :, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:, :x.size(1)]
return self.dropout(x)
class VersatileAttention(Attention):
def __init__(
self,
attention_mode = None,
cross_frame_attention_mode = None,
temporal_position_encoding = False,
temporal_position_encoding_max_len = 4096,
grid = False,
block_size = 1,
remove_time_embedding_in_photo = False,
*args, **kwargs
):
super().__init__(*args, **kwargs)
assert attention_mode == "Temporal" or attention_mode == "Global"
self.attention_mode = attention_mode
self.is_cross_attention = kwargs["cross_attention_dim"] is not None
self.block_size = block_size
self.grid = grid
self.remove_time_embedding_in_photo = remove_time_embedding_in_photo
self.pos_encoder = PositionalEncoding(
kwargs["query_dim"],
dropout=0.,
max_len=temporal_position_encoding_max_len
) if (temporal_position_encoding and attention_mode == "Temporal") or (temporal_position_encoding and attention_mode == "Global") else None
def extra_repr(self):
return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None, height=None, weight=None):
batch_size, sequence_length, _ = hidden_states.shape
if self.attention_mode == "Temporal":
# for add pos_encoder
_, before_d, _c = hidden_states.size()
hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
if self.remove_time_embedding_in_photo:
if self.pos_encoder is not None and video_length > 1:
hidden_states = self.pos_encoder(hidden_states)
else:
if self.pos_encoder is not None:
hidden_states = self.pos_encoder(hidden_states)
if self.grid:
hidden_states = rearrange(hidden_states, "(b d) f c -> b f d c", f=video_length, d=before_d)
hidden_states = rearrange(hidden_states, "b f (h w) c -> b f h w c", h=height, w=weight)
hidden_states = rearrange(hidden_states, "b f (h n) (w m) c -> (b h w) (f n m) c", n=self.block_size, m=self.block_size)
d = before_d // self.block_size // self.block_size
else:
d = before_d
encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d) if encoder_hidden_states is not None else encoder_hidden_states
elif self.attention_mode == "Global":
# for add pos_encoder
_, d, _c = hidden_states.size()
hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
if self.pos_encoder is not None:
hidden_states = self.pos_encoder(hidden_states)
hidden_states = rearrange(hidden_states, "(b d) f c -> b (f d) c", f=video_length, d=d)
else:
raise NotImplementedError
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
bs = 512
new_hidden_states = []
for i in range(0, hidden_states.shape[0], bs):
__hidden_states = super().forward(
hidden_states[i : i + bs],
encoder_hidden_states=encoder_hidden_states[i : i + bs],
attention_mask=attention_mask
)
new_hidden_states.append(__hidden_states)
hidden_states = torch.cat(new_hidden_states, dim = 0)
if self.attention_mode == "Temporal":
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
if self.grid:
hidden_states = rearrange(hidden_states, "(b f n m) (h w) c -> (b f) h n w m c", f=video_length, n=self.block_size, m=self.block_size, h=height // self.block_size, w=weight // self.block_size)
hidden_states = rearrange(hidden_states, "b h n w m c -> b (h n) (w m) c")
hidden_states = rearrange(hidden_states, "b h w c -> b (h w) c")
elif self.attention_mode == "Global":
hidden_states = rearrange(hidden_states, "b (f d) c -> (b f) d c", f=video_length, d=d)
return hidden_states |