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-rw-r--r--models/attention/control.py216
-rw-r--r--models/attention/hook.py63
-rw-r--r--models/attention/structured.py145
-rw-r--r--models/convnext/discriminator.py34
4 files changed, 0 insertions, 458 deletions
diff --git a/models/attention/control.py b/models/attention/control.py
deleted file mode 100644
index ec378c4..0000000
--- a/models/attention/control.py
+++ /dev/null
@@ -1,216 +0,0 @@
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
diff --git a/models/attention/hook.py b/models/attention/hook.py
deleted file mode 100644
index 6b5fb68..0000000
--- a/models/attention/hook.py
+++ /dev/null
@@ -1,63 +0,0 @@
1import torch
2
3
4try:
5 import xformers.ops
6
7 xformers._is_functorch_available = True
8 MEM_EFFICIENT_ATTN = True
9except ImportError:
10 print("[!] Not using xformers memory efficient attention.")
11 MEM_EFFICIENT_ATTN = False
12
13
14def register_attention_control(model, controller):
15 def ca_forward(self, place_in_unet):
16 def forward(x, context=None, mask=None):
17 batch_size, sequence_length, dim = x.shape
18 h = self.heads
19 q = self.to_q(x)
20 is_cross = context is not None
21 context = context if is_cross else x
22 k = self.to_k(context)
23 v = self.to_v(context)
24 q = self.reshape_heads_to_batch_dim(q)
25 k = self.reshape_heads_to_batch_dim(k)
26 v = self.reshape_heads_to_batch_dim(v)
27
28 sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
29
30 if mask is not None:
31 mask = mask.reshape(batch_size, -1)
32 max_neg_value = -torch.finfo(sim.dtype).max
33 mask = mask[:, None, :].repeat(h, 1, 1)
34 sim.masked_fill_(~mask, max_neg_value)
35
36 # attention, what we cannot get enough of
37 attn = sim.softmax(dim=-1)
38 attn = controller(attn, is_cross, place_in_unet)
39 out = torch.einsum("b i j, b j d -> b i d", attn, v)
40 out = self.reshape_batch_dim_to_heads(out)
41 return self.to_out(out)
42
43 return forward
44
45 def register_recr(net_, count, place_in_unet):
46 if net_.__class__.__name__ == "CrossAttention":
47 net_.forward = ca_forward(net_, place_in_unet)
48 return count + 1
49 elif hasattr(net_, "children"):
50 for net__ in net_.children():
51 count = register_recr(net__, count, place_in_unet)
52 return count
53
54 cross_att_count = 0
55 sub_nets = model.unet.named_children()
56 for net in sub_nets:
57 if "down" in net[0]:
58 cross_att_count += register_recr(net[1], 0, "down")
59 elif "up" in net[0]:
60 cross_att_count += register_recr(net[1], 0, "up")
61 elif "mid" in net[0]:
62 cross_att_count += register_recr(net[1], 0, "mid")
63 controller.num_att_layers = cross_att_count
diff --git a/models/attention/structured.py b/models/attention/structured.py
deleted file mode 100644
index 5bbbc06..0000000
--- a/models/attention/structured.py
+++ /dev/null
@@ -1,145 +0,0 @@
1import torch
2
3from .control import AttentionControl
4
5
6class StructuredAttentionControl(AttentionControl):
7 def forward(self, attn, is_cross: bool, place_in_unet: str):
8 return attn
9
10 def forward(self, x, context=None, mask=None):
11 h = self.heads
12
13 q = self.to_q(x)
14
15 if isinstance(context, list):
16 if self.struct_attn:
17 out = self.struct_qkv(q, context, mask)
18 else:
19 context = torch.cat(
20 [context[0], context[1]["k"][0]], dim=0
21 ) # use key tensor for context
22 out = self.normal_qkv(q, context, mask)
23 else:
24 context = default(context, x)
25 out = self.normal_qkv(q, context, mask)
26
27 return self.to_out(out)
28
29 def struct_qkv(self, q, context, mask):
30 """
31 context: list of [uc, list of conditional context]
32 """
33 uc_context = context[0]
34 context_k, context_v = context[1]["k"], context[1]["v"]
35
36 if isinstance(context_k, list) and isinstance(context_v, list):
37 out = self.multi_qkv(q, uc_context, context_k, context_v, mask)
38 elif isinstance(context_k, torch.Tensor) and isinstance(
39 context_v, torch.Tensor
40 ):
41 out = self.heterogeous_qkv(q, uc_context, context_k, context_v, mask)
42 else:
43 raise NotImplementedError
44
45 return out
46
47 def multi_qkv(self, q, uc_context, context_k, context_v, mask):
48 h = self.heads
49
50 assert uc_context.size(0) == context_k[0].size(0) == context_v[0].size(0)
51 true_bs = uc_context.size(0) * h
52
53 k_uc, v_uc = self.get_kv(uc_context)
54 k_c = [self.to_k(c_k) for c_k in context_k]
55 v_c = [self.to_v(c_v) for c_v in context_v]
56
57 q = rearrange(q, "b n (h d) -> (b h) n d", h=h)
58
59 k_uc = rearrange(k_uc, "b n (h d) -> (b h) n d", h=h)
60 v_uc = rearrange(v_uc, "b n (h d) -> (b h) n d", h=h)
61
62 k_c = [
63 rearrange(k, "b n (h d) -> (b h) n d", h=h) for k in k_c
64 ] # NOTE: modification point
65 v_c = [rearrange(v, "b n (h d) -> (b h) n d", h=h) for v in v_c]
66
67 # get composition
68 sim_uc = einsum("b i d, b j d -> b i j", q[:true_bs], k_uc) * self.scale
69 sim_c = [
70 einsum("b i d, b j d -> b i j", q[true_bs:], k) * self.scale for k in k_c
71 ]
72
73 attn_uc = sim_uc.softmax(dim=-1)
74 attn_c = [sim.softmax(dim=-1) for sim in sim_c]
75
76 # get uc output
77 out_uc = einsum("b i j, b j d -> b i d", attn_uc, v_uc)
78
79 # get c output
80 if len(v_c) == 1:
81 out_c_collect = []
82 for attn in attn_c:
83 for v in v_c:
84 out_c_collect.append(einsum("b i j, b j d -> b i d", attn, v))
85 out_c = sum(out_c_collect) / len(out_c_collect)
86 else:
87 out_c = sum(
88 [
89 einsum("b i j, b j d -> b i d", attn, v)
90 for attn, v in zip(attn_c, v_c)
91 ]
92 ) / len(v_c)
93
94 out = torch.cat([out_uc, out_c], dim=0)
95 out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
96
97 return out
98
99 def normal_qkv(self, q, context, mask):
100 h = self.heads
101 k = self.to_k(context)
102 v = self.to_v(context)
103
104 q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
105
106 sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
107
108 if exists(mask):
109 mask = rearrange(mask, "b ... -> b (...)")
110 max_neg_value = -torch.finfo(sim.dtype).max
111 mask = repeat(mask, "b j -> (b h) () j", h=h)
112 sim.masked_fill_(~mask, max_neg_value)
113
114 # attention, what we cannot get enough of
115 attn = sim.softmax(dim=-1)
116
117 out = einsum("b i j, b j d -> b i d", attn, v)
118 out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
119
120 return out
121
122 def heterogeous_qkv(self, q, uc_context, context_k, context_v, mask):
123 h = self.heads
124 k = self.to_k(torch.cat([uc_context, context_k], dim=0))
125 v = self.to_v(torch.cat([uc_context, context_v], dim=0))
126
127 q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))
128
129 sim = einsum("b i d, b j d -> b i j", q, k) * self.scale
130
131 if exists(mask):
132 mask = rearrange(mask, "b ... -> b (...)")
133 max_neg_value = -torch.finfo(sim.dtype).max
134 mask = repeat(mask, "b j -> (b h) () j", h=h)
135 sim.masked_fill_(~mask, max_neg_value)
136
137 # attention, what we cannot get enough of
138 attn = sim.softmax(dim=-1)
139
140 out = einsum("b i j, b j d -> b i d", attn, v)
141 out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
142 return out
143
144 def get_kv(self, context):
145 return self.to_k(context), self.to_v(context)
diff --git a/models/convnext/discriminator.py b/models/convnext/discriminator.py
deleted file mode 100644
index 5798bcf..0000000
--- a/models/convnext/discriminator.py
+++ /dev/null
@@ -1,34 +0,0 @@
1import torch
2from timm.models import ConvNeXt
3from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
4
5from torch.nn import functional as F
6
7
8class ConvNeXtDiscriminator:
9 def __init__(self, model: ConvNeXt, input_size: int) -> None:
10 self.net = model
11
12 self.input_size = input_size
13
14 self.img_mean = torch.tensor(IMAGENET_DEFAULT_MEAN).view(1, -1, 1, 1)
15 self.img_std = torch.tensor(IMAGENET_DEFAULT_STD).view(1, -1, 1, 1)
16
17 def get_score(self, img):
18 pred = self.get_all(img)
19 return torch.softmax(pred, dim=-1)[:, 1]
20
21 def get_all(self, img):
22 img_mean = self.img_mean.to(device=img.device, dtype=img.dtype)
23 img_std = self.img_std.to(device=img.device, dtype=img.dtype)
24
25 img = ((img + 1.0) / 2.0).sub(img_mean).div(img_std)
26
27 img = F.interpolate(
28 img,
29 size=(self.input_size, self.input_size),
30 mode="bicubic",
31 align_corners=True,
32 )
33 pred = self.net(img)
34 return pred
generated by cgit v1.2.3-54-g00ecf (git 2.44.0) at 2024-06-26 15:40:57 +0000