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-rw-r--r--models/attention/control.py216
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diff --git a/models/attention/control.py b/models/attention/control.py
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1import torch
2import abc
3
4
5class AttentionControl(abc.ABC):
6 def step_callback(self, x_t):
7 return x_t
8
9 def between_steps(self):
10 return
11
12 @property
13 def num_uncond_att_layers(self):
14 return self.num_att_layers if LOW_RESOURCE else 0
15
16 @abc.abstractmethod
17 def forward(self, attn, is_cross: bool, place_in_unet: str):
18 raise NotImplementedError
19
20 def __call__(self, attn, is_cross: bool, place_in_unet: str):
21 if self.cur_att_layer >= self.num_uncond_att_layers:
22 if LOW_RESOURCE:
23 attn = self.forward(attn, is_cross, place_in_unet)
24 else:
25 h = attn.shape[0]
26 attn[h // 2 :] = self.forward(attn[h // 2 :], is_cross, place_in_unet)
27 self.cur_att_layer += 1
28 if self.cur_att_layer == self.num_att_layers + self.num_uncond_att_layers:
29 self.cur_att_layer = 0
30 self.cur_step += 1
31 self.between_steps()
32 return attn
33
34 def reset(self):
35 self.cur_step = 0
36 self.cur_att_layer = 0
37
38 def __init__(self):
39 self.cur_step = 0
40 self.num_att_layers = -1
41 self.cur_att_layer = 0
42
43
44class EmptyControl(AttentionControl):
45 def forward(self, attn, is_cross: bool, place_in_unet: str):
46 return attn
47
48
49class AttentionStore(AttentionControl):
50 @staticmethod
51 def get_empty_store():
52 return {
53 "down_cross": [],
54 "mid_cross": [],
55 "up_cross": [],
56 "down_self": [],
57 "mid_self": [],
58 "up_self": [],
59 }
60
61 def forward(self, attn, is_cross: bool, place_in_unet: str):
62 key = f"{place_in_unet}_{'cross' if is_cross else 'self'}"
63 if attn.shape[1] <= 32**2: # avoid memory overhead
64 self.step_store[key].append(attn)
65 return attn
66
67 def between_steps(self):
68 if len(self.attention_store) == 0:
69 self.attention_store = self.step_store
70 else:
71 for key in self.attention_store:
72 for i in range(len(self.attention_store[key])):
73 self.attention_store[key][i] += self.step_store[key][i]
74 self.step_store = self.get_empty_store()
75
76 def get_average_attention(self):
77 average_attention = {
78 key: [item / self.cur_step for item in self.attention_store[key]]
79 for key in self.attention_store
80 }
81 return average_attention
82
83 def reset(self):
84 super(AttentionStore, self).reset()
85 self.step_store = self.get_empty_store()
86 self.attention_store = {}
87
88 def __init__(self):
89 super(AttentionStore, self).__init__()
90 self.step_store = self.get_empty_store()
91 self.attention_store = {}
92
93
94class AttentionControlEdit(AttentionStore, abc.ABC):
95 def step_callback(self, x_t):
96 if self.local_blend is not None:
97 x_t = self.local_blend(x_t, self.attention_store)
98 return x_t
99
100 def replace_self_attention(self, attn_base, att_replace):
101 if att_replace.shape[2] <= 16**2:
102 return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)
103 else:
104 return att_replace
105
106 @abc.abstractmethod
107 def replace_cross_attention(self, attn_base, att_replace):
108 raise NotImplementedError
109
110 def forward(self, attn, is_cross: bool, place_in_unet: str):
111 super(AttentionControlEdit, self).forward(attn, is_cross, place_in_unet)
112 if is_cross or (
113 self.num_self_replace[0] <= self.cur_step < self.num_self_replace[1]
114 ):
115 h = attn.shape[0] // (self.batch_size)
116 attn = attn.reshape(self.batch_size, h, *attn.shape[1:])
117 attn_base, attn_repalce = attn[0], attn[1:]
118 if is_cross:
119 alpha_words = self.cross_replace_alpha[self.cur_step]
120 attn_repalce_new = (
121 self.replace_cross_attention(attn_base, attn_repalce) * alpha_words
122 + (1 - alpha_words) * attn_repalce
123 )
124 attn[1:] = attn_repalce_new
125 else:
126 attn[1:] = self.replace_self_attention(attn_base, attn_repalce)
127 attn = attn.reshape(self.batch_size * h, *attn.shape[2:])
128 return attn
129
130 def __init__(
131 self,
132 prompts,
133 num_steps: int,
134 cross_replace_steps: Union[
135 float, Tuple[float, float], Dict[str, Tuple[float, float]]
136 ],
137 self_replace_steps: Union[float, Tuple[float, float]],
138 local_blend: Optional[LocalBlend],
139 ):
140 super(AttentionControlEdit, self).__init__()
141 self.batch_size = len(prompts)
142 self.cross_replace_alpha = ptp_utils.get_time_words_attention_alpha(
143 prompts, num_steps, cross_replace_steps, tokenizer
144 ).to(device)
145 if type(self_replace_steps) is float:
146 self_replace_steps = 0, self_replace_steps
147 self.num_self_replace = int(num_steps * self_replace_steps[0]), int(
148 num_steps * self_replace_steps[1]
149 )
150 self.local_blend = local_blend
151
152
153class AttentionReplace(AttentionControlEdit):
154 def replace_cross_attention(self, attn_base, att_replace):
155 return torch.einsum("hpw,bwn->bhpn", attn_base, self.mapper)
156
157 def __init__(
158 self,
159 prompts,
160 num_steps: int,
161 cross_replace_steps: float,
162 self_replace_steps: float,
163 local_blend: Optional[LocalBlend] = None,
164 ):
165 super(AttentionReplace, self).__init__(
166 prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend
167 )
168 self.mapper = seq_aligner.get_replacement_mapper(prompts, tokenizer).to(device)
169
170
171class AttentionRefine(AttentionControlEdit):
172 def replace_cross_attention(self, attn_base, att_replace):
173 attn_base_replace = attn_base[:, :, self.mapper].permute(2, 0, 1, 3)
174 attn_replace = attn_base_replace * self.alphas + att_replace * (1 - self.alphas)
175 return attn_replace
176
177 def __init__(
178 self,
179 prompts,
180 num_steps: int,
181 cross_replace_steps: float,
182 self_replace_steps: float,
183 local_blend: Optional[LocalBlend] = None,
184 ):
185 super(AttentionRefine, self).__init__(
186 prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend
187 )
188 self.mapper, alphas = seq_aligner.get_refinement_mapper(prompts, tokenizer)
189 self.mapper, alphas = self.mapper.to(device), alphas.to(device)
190 self.alphas = alphas.reshape(alphas.shape[0], 1, 1, alphas.shape[1])
191
192
193class AttentionReweight(AttentionControlEdit):
194 def replace_cross_attention(self, attn_base, att_replace):
195 if self.prev_controller is not None:
196 attn_base = self.prev_controller.replace_cross_attention(
197 attn_base, att_replace
198 )
199 attn_replace = attn_base[None, :, :, :] * self.equalizer[:, None, None, :]
200 return attn_replace
201
202 def __init__(
203 self,
204 prompts,
205 num_steps: int,
206 cross_replace_steps: float,
207 self_replace_steps: float,
208 equalizer,
209 local_blend: Optional[LocalBlend] = None,
210 controller: Optional[AttentionControlEdit] = None,
211 ):
212 super(AttentionReweight, self).__init__(
213 prompts, num_steps, cross_replace_steps, self_replace_steps, local_blend
214 )
215 self.equalizer = equalizer.to(device)
216 self.prev_controller = controller
generated by cgit v1.2.3-70-g09d2 (git 2.50.0) at 2025-10-29 10:45:17 +0000