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from abc import ABC, abstractmethod
from typing import Optional, Union, Tuple, List, Dict
import torch
import torch.nn.functional as F
from .ptp_utils import (get_word_inds, get_time_words_attention_alpha)
from .seq_aligner import (get_replacement_mapper, get_refinement_mapper)
class AttentionControl(ABC):
def __init__(self):
self.cur_step = 0
self.num_att_layers = -1
self.cur_att_layer = 0
def step_callback(self, x_t):
return x_t
def between_steps(self):
return
@property
def num_uncond_att_layers(self):
return 0
@abstractmethod
def forward(self, attn, is_cross: bool, place_in_unet: str):
raise NotImplementedError
def __call__(self, attn, is_cross: bool, place_in_unet: str):
if self.cur_att_layer >= self.num_uncond_att_layers:
h = attn.shape[0]
attn[h // 2:] = self.forward(attn[h // 2:], is_cross, place_in_unet)
self.cur_att_layer += 1
if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
self.cur_att_layer = 0
self.cur_step += 1
self.between_steps()
return attn
def reset(self):
self.cur_step = 0
self.cur_att_layer = 0
class EmptyControl(AttentionControl):
def forward(self, attn, is_cross: bool, place_in_unet: str):
return attn
class AttentionStore(AttentionControl):
def __init__(self):
super(AttentionStore, self).__init__()
self.step_store = self.get_empty_store()
self.attention_store = {}
@staticmethod
def get_empty_store():
return {"down_cross": [], "mid_cross": [], "up_cross": [],
"down_self": [], "mid_self": [], "up_self": []}
def forward(self, attn, is_cross: bool, place_in_unet: str):
key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
if attn.shape[1] <= 32 ** 2: # avoid memory overhead
self.step_store[key].append(attn)
return attn
def between_steps(self):
if len(self.attention_store) == 0:
self.attention_store = self.step_store
else:
for key in self.attention_store:
for i in range(len(self.attention_store[key])):
self.attention_store[key][i] += self.step_store[key][i]
self.step_store = self.get_empty_store()
def get_average_attention(self):
average_attention = {
key: [item / self.cur_step for item in self.attention_store[key]]
for key in self.attention_store
}
return average_attention
def reset(self):
super(AttentionStore, self).reset()
self.step_store = self.get_empty_store()
self.attention_store = {}
class LocalBlend:
def __init__(self,
prompts: List[str],
words: [List[List[str]]],
tokenizer,
device,
threshold=.3,
max_num_words=77):
self.max_num_words = max_num_words
alpha_layers1 = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words)
alpha_layers2 = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words)
for i, (prompt, words_) in enumerate(zip(prompts, words)):
assert type(words_) is list and len(words_) == 2
ind1 = get_word_inds(prompt, words_[0], tokenizer)
alpha_layers1[i, :, :, :, :, ind1] = 1
ind2 = get_word_inds(prompt, words_[1], tokenizer)
alpha_layers2[i, :, :, :, :, ind2] = 1
self.alpha_layers1 = alpha_layers1.to(device)
self.alpha_layers2 = alpha_layers2.to(device)
self.threshold = threshold
def __call__(self, x_t, attention_store):
k = 1
maps = attention_store["down_cross"][2:4] + attention_store["up_cross"][:3]
maps = [item.reshape(self.alpha_layers1.shape[0], -1, 1, 16, 16, self.max_num_words) for item in maps]
maps = torch.cat(maps, dim=1)
maps1 = (maps * self.alpha_layers1).sum(-1).mean(1)
mask1 = F.max_pool2d(maps1, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
mask1 = F.interpolate(mask1, size=(x_t.shape[2:]))
mask1 = mask1 / mask1.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
mask1 = mask1.gt(self.threshold)
maps2 = (maps * self.alpha_layers2).sum(-1).mean(1)
mask2 = F.max_pool2d(maps2, (k * 2 + 1, k * 2 + 1), (1, 1), padding=(k, k))
mask2 = F.interpolate(mask2, size=(x_t.shape[2:]))
mask2 = mask2 / mask2.max(2, keepdims=True)[0].max(3, keepdims=True)[0]
mask2 = mask2.gt(self.threshold)
mask = (mask1 + mask2).float()
prev_x_t = torch.cat([x_t[:1], x_t[:-1]], dim=0)
x_t = (1 - mask) * prev_x_t + mask * x_t
return x_t
class AttentionControlEdit(AttentionStore, ABC):
def __init__(self,
prompts,
num_steps: int,
cross_replace_steps: Union[float, Tuple[float, float], Dict[str, Tuple[float, float]]],
self_replace_steps: Union[float, Tuple[float, float]],
local_blend: Optional[LocalBlend],
tokenizer,
device):
super(AttentionControlEdit, self).__init__()
self.tokenizer = tokenizer
self.device = device
self.batch_size = len(prompts)
self.cross_replace_alpha = get_time_words_attention_alpha(prompts, num_steps, cross_replace_steps,
self.tokenizer).to(self.device)
if type(self_replace_steps) is float:
self_replace_steps = 0, self_replace_steps
self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
self.local_blend = local_blend # define outside
def step_callback(self, x_t):
if self.local_blend is not None:
x_t = self.local_blend(x_t, self.attention_store)
return x_t
def replace_self_attention(self, attn_base, att_replace):
if att_replace.shape[2] <= 16 ** 2:
return attn_base.clone()
else:
return att_replace
@abstractmethod
def replace_cross_attention(self, attn_base, att_replace):
raise NotImplementedError
def forward(self, attn, is_cross: bool, place_in_unet: str):
super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
# FIXME not replace correctly
if is_cross or (self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]):
h = attn.shape[0] // (self.batch_size)
attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
attn_base, attn_repalce = attn[:-1], attn[1:]
if is_cross:
alpha_words = self.cross_replace_alpha[self.cur_step]
attn_repalce_new = self.replace_cross_attention(attn_base, attn_repalce) * alpha_words + (
1 - alpha_words) * attn_repalce
attn[1:] = attn_repalce_new
else:
attn[1:] = self.replace_self_attention(attn_base, attn_repalce)
attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
return attn
class AttentionReplace(AttentionControlEdit):
def __init__(self,
prompts,
num_steps: int,
cross_replace_steps: float,
self_replace_steps: float,
local_blend: Optional[LocalBlend] = None,
tokenizer=None,
device=None):
super(AttentionReplace, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps,
local_blend, tokenizer, device)
self.mapper = get_replacement_mapper(prompts, self.tokenizer).to(self.device)
def replace_cross_attention(self, attn_base, att_replace):
# attn_base/att_replace: (len(prompts)-1, 8, 4096, 77)
# self.mapper: (len(prompts)-1, 77, 77)
version = 'v2'
if version == 'v1':
return torch.einsum('bhpw,bwn->bhpn', attn_base, self.mapper)
else:
bsz = attn_base.size()[0]
attn_base_replace = []
for batch_i in range(bsz):
if batch_i == 0:
attn_base_i = attn_base[batch_i] # (8, 4096, 77)
else:
attn_base_i = attn_base_replace[-1]
mapper_i = self.mapper[batch_i:batch_i + 1, :, :] # (1, 77, 77)
attn_base_replace_i = torch.einsum('hpw,bwn->bhpn', attn_base_i, mapper_i) # (1, 8, 4096, 77)
attn_base_replace.append(attn_base_replace_i[0])
attn_base_replace = torch.stack(attn_base_replace, dim=0) # (len(prompts)-1, 8, 4096, 77)
return attn_base_replace
class AttentionRefine(AttentionControlEdit):
def __init__(self,
prompts,
num_steps: int,
cross_replace_steps: float,
self_replace_steps: float,
local_blend: Optional[LocalBlend] = None,
tokenizer=None,
device=None):
super(AttentionRefine, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps,
local_blend, tokenizer, device)
self.mapper, alphas = get_refinement_mapper(prompts, self.tokenizer)
self.mapper, alphas = self.mapper.to(self.device), alphas.to(self.device)
self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
def replace_cross_attention(self, attn_base, att_replace):
# attn_base/att_replace: (len(prompts)-1, 8, 4096, 77)
version = 'v2'
bsz = attn_base.size()[0]
attn_base_replace = []
for batch_i in range(bsz):
if version == 'v1':
attn_base_i = attn_base[batch_i] # (8, 4096, 77)
else:
if batch_i == 0:
attn_base_i = attn_base[batch_i]
else:
attn_base_i = attn_base_replace[-1]
mapper_i = self.mapper[batch_i:batch_i + 1, :] # (1, 77)
attn_base_replace_i = attn_base_i[:, :, mapper_i].permute(2, 0, 1, 3) # (1, 8, 4096, 77)
attn_base_replace.append(attn_base_replace_i[0])
attn_base_replace = torch.stack(attn_base_replace, dim=0) # (len(prompts)-1, 8, 4096, 77)
attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
return attn_replace
class AttentionReweight(AttentionControlEdit):
def __init__(self,
prompts,
num_steps: int,
cross_replace_steps: float,
self_replace_steps: float,
equalizer,
local_blend: Optional[LocalBlend] = None,
controller: Optional[AttentionControlEdit] = None,
tokenizer=None,
device=None):
super(AttentionReweight, self).__init__(prompts, num_steps, cross_replace_steps, self_replace_steps,
local_blend, tokenizer, device)
self.equalizer = equalizer.to(self.device)
self.prev_controller = controller
def replace_cross_attention(self, attn_base, att_replace):
# attn_base/att_replace: (len(prompts)-1, 8, 4096, 77)
# self.equalizer: (len(prompts)-1, 77)
if self.prev_controller is not None:
attn_base = self.prev_controller.replace_cross_attention(attn_base, att_replace)
version = 'v2'
if version == 'v1':
attn_replace = attn_base[:, :, :, :] * self.equalizer[:, None, None, :]
return attn_replace
else:
bsz = attn_base.size()[0]
attn_replace_rst_all = []
for bi in range(bsz):
if bi == 0:
attn_replace_rst = attn_base[bi, :, :, :] * self.equalizer[bi, None, None, :]
else:
attn_replace_rst = attn_replace_rst_all[-1] * self.equalizer[bi, None, None, :]
attn_replace_rst_all.append(attn_replace_rst)
attn_replace_rst_all = torch.stack(attn_replace_rst_all, dim=0)
return attn_replace_rst_all
def get_equalizer(tokenizer, texts: List[str],
word_select: List[str],
values: List[float]):
equalizer = torch.ones(len(values), 77)
values = torch.tensor(values, dtype=torch.float32)
for wi, word in enumerate(word_select):
text = texts[wi]
value = values[wi]
inds = get_word_inds(text, word, tokenizer)
equalizer[wi, inds] = value
return equalizer
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