Spaces:
Sleeping
Sleeping
File size: 14,390 Bytes
81d8e7c |
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 |
# Adapted from https://github.com/magic-research/magic-animate/blob/main/magicanimate/models/mutual_self_attention.py
import torch
import torch.nn.functional as F
import random
from einops import rearrange
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from diffusers.models.attention import BasicTransformerBlock
from .attention import BasicTransformerBlock as _BasicTransformerBlock
def torch_dfs(model: torch.nn.Module):
result = [model]
for child in model.children():
result += torch_dfs(child)
return result
def calc_mean_std(feat, eps: float = 1e-5):
feat_std = (feat.var(dim=-2, keepdims=True) + eps).sqrt()
feat_mean = feat.mean(dim=-2, keepdims=True)
return feat_mean, feat_std
class ReferenceNetAttention():
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,
fusion_blocks="full",
batch_size=1,
is_image=False,
) -> 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.fusion_blocks = fusion_blocks
self.register_reference_hooks(
mode,
do_classifier_free_guidance,
attention_auto_machine_weight,
gn_auto_machine_weight,
style_fidelity,
reference_attn,
fusion_blocks,
batch_size=batch_size,
is_image=is_image,
)
def register_reference_hooks(
self,
mode,
do_classifier_free_guidance,
attention_auto_machine_weight,
gn_auto_machine_weight,
style_fidelity,
reference_attn,
# dtype=torch.float16,
dtype=torch.float32,
batch_size=1,
num_images_per_prompt=1,
device=torch.device("cpu"),
fusion_blocks='midup',
is_image=False,
):
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
fusion_blocks = fusion_blocks
num_images_per_prompt = num_images_per_prompt
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:
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
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":
self.bank.append(norm_hidden_states.clone())
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 MODE == "read":
if not is_image:
self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), "b t l c -> (b t) l c")[:hidden_states.shape[0]] for d in self.bank]
modify_norm_hidden_states = torch.cat([norm_hidden_states] + self.bank, dim=1)
hidden_states_uc = self.attn1(modify_norm_hidden_states,
encoder_hidden_states=modify_norm_hidden_states,
attention_mask=attention_mask)[:,:hidden_states.shape[-2],:] #+ hidden_states
hidden_states_raw = self.attn1(norm_hidden_states,
encoder_hidden_states=norm_hidden_states,
attention_mask=attention_mask) #+ hidden_states
ratio = 0.5
hidden_states_uc = hidden_states_uc * ratio + hidden_states_raw * (1-ratio) + hidden_states
hidden_states_c = hidden_states_uc.clone()
_uc_mask = uc_mask.clone()
if do_classifier_free_guidance:
if hidden_states.shape[0] != _uc_mask.shape[0]:
_uc_mask = (
torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))
.to(device)
.bool()
)
hidden_states_c[_uc_mask] = self.attn1(
norm_hidden_states[_uc_mask],
encoder_hidden_states=norm_hidden_states[_uc_mask],
attention_mask=attention_mask,
) + hidden_states[_uc_mask]
# randomly drop the reference attention during training
else:
mask_index = [0 for _ in range(hidden_states_c.shape[0])]
for i in range( int(hidden_states_c.shape[0] * 0.25)):
mask_index[i] = 1
_uc_mask = (
torch.Tensor(mask_index)
.to(device)
.bool()
)
hidden_states_c[_uc_mask] = self.attn1(
norm_hidden_states[_uc_mask],
encoder_hidden_states=norm_hidden_states[_uc_mask],
attention_mask=attention_mask,
) + hidden_states[_uc_mask]
hidden_states = hidden_states_c.clone()
# 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)
)
hidden_states = (
self.attn2(
norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
)
+ hidden_states
)
# Feed-forward
hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
# Temporal-Attention
if not is_image:
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
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)
)
# 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
# 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, _BasicTransformerBlock)]
elif self.fusion_blocks == "full":
attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
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
module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
module.bank = []
module.attn_weight = float(i) / float(len(attn_modules))
# def update(self, writer, dtype=torch.float16):
def update(self, writer, dtype=torch.float32):
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, BasicTransformerBlock)]
writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock) or isinstance(module, BasicTransformerBlock)]
elif self.fusion_blocks == "full":
reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock) or isinstance(module, BasicTransformerBlock)]
writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, _BasicTransformerBlock) or 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])
if len(reader_attn_modules) == 0:
print('reader_attn_modules is null')
assert False
if len(writer_attn_modules) == 0:
print('writer_attn_modules is null')
assert False
for r, w in zip(reader_attn_modules, writer_attn_modules):
r.bank = [v.clone().to(dtype) for v in w.bank]
# w.bank.clear()
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, _BasicTransformerBlock)]
elif self.fusion_blocks == "full":
reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
for r in reader_attn_modules:
r.bank.clear()
|