File size: 15,166 Bytes
88590fc |
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 |
# Adapted from https://github.com/magic-research/magic-animate/blob/main/magicanimate/models/mutual_self_attention.py
from typing import Any, Dict, Optional
import torch
from einops import rearrange
from .attention import BasicTransformerBlock
from .attention import TemporalBasicTransformerBlock
def torch_dfs(model: torch.nn.Module):
result = [model]
for child in model.children():
result += torch_dfs(child)
return result
class ReferenceAttentionControl:
def __init__(
self,
unet,
mode="write",
do_classifier_free_guidance=False,
attention_auto_machine_weight=float("inf"),
gn_auto_machine_weight=1.0,
style_fidelity=1.0,
reference_attn=True,
reference_adain=False,
fusion_blocks="midup",
batch_size=1,
reference_attention_weight=1.,
audio_attention_weight=1.,
) -> None:
# 10. Modify self attention and group norm
self.unet = unet
assert mode in ["read", "write"]
assert fusion_blocks in ["midup", "full"]
self.reference_attn = reference_attn
self.reference_adain = reference_adain
self.fusion_blocks = fusion_blocks
self.reference_attention_weight = reference_attention_weight
self.audio_attention_weight = audio_attention_weight
self.register_reference_hooks(
mode,
do_classifier_free_guidance,
attention_auto_machine_weight,
gn_auto_machine_weight,
style_fidelity,
reference_attn,
reference_adain,
fusion_blocks,
batch_size=batch_size,
)
def register_reference_hooks(
self,
mode,
do_classifier_free_guidance,
attention_auto_machine_weight,
gn_auto_machine_weight,
style_fidelity,
reference_attn,
reference_adain,
dtype=torch.float16,
batch_size=1,
num_images_per_prompt=1,
device=torch.device("cpu"),
fusion_blocks="midup",
):
MODE = mode
do_classifier_free_guidance = do_classifier_free_guidance
attention_auto_machine_weight = attention_auto_machine_weight
gn_auto_machine_weight = gn_auto_machine_weight
style_fidelity = style_fidelity
reference_attn = reference_attn
reference_adain = reference_adain
fusion_blocks = fusion_blocks
num_images_per_prompt = num_images_per_prompt
reference_attention_weight = self.reference_attention_weight
audio_attention_weight = self.audio_attention_weight
dtype = dtype
if do_classifier_free_guidance:
uc_mask = (
torch.Tensor(
[1] * batch_size * num_images_per_prompt * 16
+ [0] * batch_size * num_images_per_prompt * 16
)
.to(device)
.bool()
)
else:
uc_mask = (
torch.Tensor([0] * batch_size * num_images_per_prompt * 2)
.to(device)
.bool()
)
def hacked_basic_transformer_inner_forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
timestep: Optional[torch.LongTensor] = None,
cross_attention_kwargs: Dict[str, Any] = None,
class_labels: Optional[torch.LongTensor] = None,
video_length=None,
):
if self.use_ada_layer_norm: # False
norm_hidden_states = self.norm1(hidden_states, timestep)
elif self.use_ada_layer_norm_zero:
(
norm_hidden_states,
gate_msa,
shift_mlp,
scale_mlp,
gate_mlp,
) = self.norm1(
hidden_states,
timestep,
class_labels,
hidden_dtype=hidden_states.dtype,
)
else:
norm_hidden_states = self.norm1(hidden_states)
# 1. Self-Attention
# self.only_cross_attention = False
cross_attention_kwargs = (
cross_attention_kwargs if cross_attention_kwargs is not None else {}
)
if self.only_cross_attention:
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states
if self.only_cross_attention
else None,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
else:
if MODE == "write":
attn_output = self.attn1(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states
if self.only_cross_attention
else None,
attention_mask=attention_mask,
**cross_attention_kwargs,
)
if self.use_ada_layer_norm_zero:
attn_output = gate_msa.unsqueeze(1) * attn_output
hidden_states = attn_output + hidden_states
if self.attn2 is not None:
norm_hidden_states = (
self.norm2(hidden_states, timestep)
if self.use_ada_layer_norm
else self.norm2(hidden_states)
)
self.bank.append(norm_hidden_states.clone())
# 2. Cross-Attention
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
**cross_attention_kwargs,
)
hidden_states = attn_output + hidden_states
if MODE == "read":
hidden_states = (
self.attn1(
norm_hidden_states,
encoder_hidden_states=norm_hidden_states,
attention_mask=attention_mask,
)
+ hidden_states
)
if self.use_ada_layer_norm: # False
norm_hidden_states = self.norm1_5(hidden_states, timestep)
elif self.use_ada_layer_norm_zero:
(
norm_hidden_states,
gate_msa,
shift_mlp,
scale_mlp,
gate_mlp,
) = self.norm1_5(
hidden_states,
timestep,
class_labels,
hidden_dtype=hidden_states.dtype,
)
else:
norm_hidden_states = self.norm1_5(hidden_states)
bank_fea = []
for d in self.bank:
if len(d.shape) == 3:
d = d.unsqueeze(1).repeat(1, video_length, 1, 1)
bank_fea.append(rearrange(d, "b t l c -> (b t) l c"))
attn_hidden_states = self.attn1_5(
norm_hidden_states,
encoder_hidden_states=bank_fea[0],
attention_mask=attention_mask,
)
if reference_attention_weight != 1.:
attn_hidden_states *= reference_attention_weight
hidden_states = (attn_hidden_states + hidden_states)
# self.bank.clear()
if self.attn2 is not None:
# Cross-Attention
norm_hidden_states = (
self.norm2(hidden_states, timestep)
if self.use_ada_layer_norm
else self.norm2(hidden_states)
)
attn_hidden_states = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=attention_mask,
)
if audio_attention_weight != 1.:
attn_hidden_states *= audio_attention_weight
hidden_states = (attn_hidden_states + hidden_states)
# Feed-forward
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
# Temporal-Attention
if self.unet_use_temporal_attention:
d = hidden_states.shape[1]
hidden_states = rearrange(
hidden_states, "(b f) d c -> (b d) f c", f=video_length
)
norm_hidden_states = (
self.norm_temp(hidden_states, timestep)
if self.use_ada_layer_norm
else self.norm_temp(hidden_states)
)
hidden_states = (
self.attn_temp(norm_hidden_states) + hidden_states
)
hidden_states = rearrange(
hidden_states, "(b d) f c -> (b f) d c", d=d
)
return hidden_states
# 3. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
if self.use_ada_layer_norm_zero:
norm_hidden_states = (
norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
)
ff_output = self.ff(norm_hidden_states)
if self.use_ada_layer_norm_zero:
ff_output = gate_mlp.unsqueeze(1) * ff_output
hidden_states = ff_output + hidden_states
return hidden_states
if self.reference_attn:
if self.fusion_blocks == "midup":
attn_modules = [
module
for module in (
torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks)
)
if isinstance(module, BasicTransformerBlock)
or isinstance(module, TemporalBasicTransformerBlock)
]
elif self.fusion_blocks == "full":
attn_modules = [
module
for module in torch_dfs(self.unet)
if isinstance(module, BasicTransformerBlock)
or isinstance(module, TemporalBasicTransformerBlock)
]
attn_modules = sorted(
attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
)
for i, module in enumerate(attn_modules):
module._original_inner_forward = module.forward
if isinstance(module, BasicTransformerBlock):
module.forward = hacked_basic_transformer_inner_forward.__get__(
module, BasicTransformerBlock
)
if isinstance(module, TemporalBasicTransformerBlock):
module.forward = hacked_basic_transformer_inner_forward.__get__(
module, TemporalBasicTransformerBlock
)
module.bank = []
module.attn_weight = float(i) / float(len(attn_modules))
def update(
self,
writer,
do_classifier_free_guidance=True,
dtype=torch.float16,
):
if self.reference_attn:
if self.fusion_blocks == "midup":
reader_attn_modules = [
module
for module in (torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks))
if isinstance(module, TemporalBasicTransformerBlock)
]
writer_attn_modules = [
module
for module in (torch_dfs(writer.unet.mid_block) + torch_dfs(writer.unet.up_blocks))
if isinstance(module, BasicTransformerBlock)
]
elif self.fusion_blocks == "full":
reader_attn_modules = [
module
for module in torch_dfs(self.unet)
if isinstance(module, TemporalBasicTransformerBlock)
]
writer_attn_modules = [
module
for module in torch_dfs(writer.unet)
if isinstance(module, BasicTransformerBlock)
]
reader_attn_modules = sorted(
reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
)
writer_attn_modules = sorted(
writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
)
for r, w in zip(reader_attn_modules, writer_attn_modules):
if do_classifier_free_guidance:
r.bank = [torch.cat([torch.zeros_like(v), v]).to(dtype) for v in w.bank]
else:
r.bank = [v.clone().to(dtype) for v in w.bank]
def clear(self):
if self.reference_attn:
if self.fusion_blocks == "midup":
reader_attn_modules = [
module
for module in (
torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks)
)
if isinstance(module, BasicTransformerBlock)
or isinstance(module, TemporalBasicTransformerBlock)
]
elif self.fusion_blocks == "full":
reader_attn_modules = [
module
for module in torch_dfs(self.unet)
if isinstance(module, BasicTransformerBlock)
or isinstance(module, TemporalBasicTransformerBlock)
]
reader_attn_modules = sorted(
reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
)
for r in reader_attn_modules:
r.bank.clear()
|