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on
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Running
on
Zero
SunderAli17
commited on
Commit
•
0fca383
1
Parent(s):
2e62d1b
Create attention_processor.py
Browse files- toonmage/attention_processor.py +422 -0
toonmage/attention_processor.py
ADDED
@@ -0,0 +1,422 @@
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1 |
+
# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
|
2 |
+
import torch
|
3 |
+
import torch.nn as nn
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4 |
+
import torch.nn.functional as F
|
5 |
+
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6 |
+
NUM_ZERO = 0
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7 |
+
ORTHO = False
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8 |
+
ORTHO_v2 = False
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9 |
+
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10 |
+
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11 |
+
class AttnProcessor(nn.Module):
|
12 |
+
def __init__(self):
|
13 |
+
super().__init__()
|
14 |
+
|
15 |
+
def __call__(
|
16 |
+
self,
|
17 |
+
attn,
|
18 |
+
hidden_states,
|
19 |
+
encoder_hidden_states=None,
|
20 |
+
attention_mask=None,
|
21 |
+
temb=None,
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22 |
+
id_embedding=None,
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23 |
+
id_scale=1.0,
|
24 |
+
):
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25 |
+
residual = hidden_states
|
26 |
+
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27 |
+
if attn.spatial_norm is not None:
|
28 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
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29 |
+
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30 |
+
input_ndim = hidden_states.ndim
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31 |
+
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32 |
+
if input_ndim == 4:
|
33 |
+
batch_size, channel, height, width = hidden_states.shape
|
34 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
35 |
+
|
36 |
+
batch_size, sequence_length, _ = (
|
37 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
38 |
+
)
|
39 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
40 |
+
|
41 |
+
if attn.group_norm is not None:
|
42 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
43 |
+
|
44 |
+
query = attn.to_q(hidden_states)
|
45 |
+
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46 |
+
if encoder_hidden_states is None:
|
47 |
+
encoder_hidden_states = hidden_states
|
48 |
+
elif attn.norm_cross:
|
49 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
50 |
+
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51 |
+
key = attn.to_k(encoder_hidden_states)
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52 |
+
value = attn.to_v(encoder_hidden_states)
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53 |
+
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54 |
+
query = attn.head_to_batch_dim(query)
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55 |
+
key = attn.head_to_batch_dim(key)
|
56 |
+
value = attn.head_to_batch_dim(value)
|
57 |
+
|
58 |
+
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
59 |
+
hidden_states = torch.bmm(attention_probs, value)
|
60 |
+
hidden_states = attn.batch_to_head_dim(hidden_states)
|
61 |
+
|
62 |
+
# linear proj
|
63 |
+
hidden_states = attn.to_out[0](hidden_states)
|
64 |
+
# dropout
|
65 |
+
hidden_states = attn.to_out[1](hidden_states)
|
66 |
+
|
67 |
+
if input_ndim == 4:
|
68 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
69 |
+
|
70 |
+
if attn.residual_connection:
|
71 |
+
hidden_states = hidden_states + residual
|
72 |
+
|
73 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
74 |
+
|
75 |
+
return hidden_states
|
76 |
+
|
77 |
+
|
78 |
+
class IDAttnProcessor(nn.Module):
|
79 |
+
r"""
|
80 |
+
Attention processor for ID-Adapater.
|
81 |
+
Args:
|
82 |
+
hidden_size (`int`):
|
83 |
+
The hidden size of the attention layer.
|
84 |
+
cross_attention_dim (`int`):
|
85 |
+
The number of channels in the `encoder_hidden_states`.
|
86 |
+
scale (`float`, defaults to 1.0):
|
87 |
+
the weight scale of image prompt.
|
88 |
+
"""
|
89 |
+
|
90 |
+
def __init__(self, hidden_size, cross_attention_dim=None):
|
91 |
+
super().__init__()
|
92 |
+
self.id_to_k = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
93 |
+
self.id_to_v = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
94 |
+
|
95 |
+
def __call__(
|
96 |
+
self,
|
97 |
+
attn,
|
98 |
+
hidden_states,
|
99 |
+
encoder_hidden_states=None,
|
100 |
+
attention_mask=None,
|
101 |
+
temb=None,
|
102 |
+
id_embedding=None,
|
103 |
+
id_scale=1.0,
|
104 |
+
):
|
105 |
+
residual = hidden_states
|
106 |
+
|
107 |
+
if attn.spatial_norm is not None:
|
108 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
109 |
+
|
110 |
+
input_ndim = hidden_states.ndim
|
111 |
+
|
112 |
+
if input_ndim == 4:
|
113 |
+
batch_size, channel, height, width = hidden_states.shape
|
114 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
115 |
+
|
116 |
+
batch_size, sequence_length, _ = (
|
117 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
118 |
+
)
|
119 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
120 |
+
|
121 |
+
if attn.group_norm is not None:
|
122 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
123 |
+
|
124 |
+
query = attn.to_q(hidden_states)
|
125 |
+
|
126 |
+
if encoder_hidden_states is None:
|
127 |
+
encoder_hidden_states = hidden_states
|
128 |
+
elif attn.norm_cross:
|
129 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
130 |
+
|
131 |
+
key = attn.to_k(encoder_hidden_states)
|
132 |
+
value = attn.to_v(encoder_hidden_states)
|
133 |
+
|
134 |
+
query = attn.head_to_batch_dim(query)
|
135 |
+
key = attn.head_to_batch_dim(key)
|
136 |
+
value = attn.head_to_batch_dim(value)
|
137 |
+
|
138 |
+
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
139 |
+
hidden_states = torch.bmm(attention_probs, value)
|
140 |
+
hidden_states = attn.batch_to_head_dim(hidden_states)
|
141 |
+
|
142 |
+
# for id-adapter
|
143 |
+
if id_embedding is not None:
|
144 |
+
if NUM_ZERO == 0:
|
145 |
+
id_key = self.id_to_k(id_embedding)
|
146 |
+
id_value = self.id_to_v(id_embedding)
|
147 |
+
else:
|
148 |
+
zero_tensor = torch.zeros(
|
149 |
+
(id_embedding.size(0), NUM_ZERO, id_embedding.size(-1)),
|
150 |
+
dtype=id_embedding.dtype,
|
151 |
+
device=id_embedding.device,
|
152 |
+
)
|
153 |
+
id_key = self.id_to_k(torch.cat((id_embedding, zero_tensor), dim=1))
|
154 |
+
id_value = self.id_to_v(torch.cat((id_embedding, zero_tensor), dim=1))
|
155 |
+
|
156 |
+
id_key = attn.head_to_batch_dim(id_key).to(query.dtype)
|
157 |
+
id_value = attn.head_to_batch_dim(id_value).to(query.dtype)
|
158 |
+
|
159 |
+
id_attention_probs = attn.get_attention_scores(query, id_key, None)
|
160 |
+
id_hidden_states = torch.bmm(id_attention_probs, id_value)
|
161 |
+
id_hidden_states = attn.batch_to_head_dim(id_hidden_states)
|
162 |
+
|
163 |
+
if not ORTHO:
|
164 |
+
hidden_states = hidden_states + id_scale * id_hidden_states
|
165 |
+
else:
|
166 |
+
projection = (
|
167 |
+
torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True)
|
168 |
+
/ torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True)
|
169 |
+
* hidden_states
|
170 |
+
)
|
171 |
+
orthogonal = id_hidden_states - projection
|
172 |
+
hidden_states = hidden_states + id_scale * orthogonal
|
173 |
+
|
174 |
+
# linear proj
|
175 |
+
hidden_states = attn.to_out[0](hidden_states)
|
176 |
+
# dropout
|
177 |
+
hidden_states = attn.to_out[1](hidden_states)
|
178 |
+
|
179 |
+
if input_ndim == 4:
|
180 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
181 |
+
|
182 |
+
if attn.residual_connection:
|
183 |
+
hidden_states = hidden_states + residual
|
184 |
+
|
185 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
186 |
+
|
187 |
+
return hidden_states
|
188 |
+
|
189 |
+
|
190 |
+
class AttnProcessor2_0(nn.Module):
|
191 |
+
r"""
|
192 |
+
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
|
193 |
+
"""
|
194 |
+
|
195 |
+
def __init__(self):
|
196 |
+
super().__init__()
|
197 |
+
if not hasattr(F, "scaled_dot_product_attention"):
|
198 |
+
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
199 |
+
|
200 |
+
def __call__(
|
201 |
+
self,
|
202 |
+
attn,
|
203 |
+
hidden_states,
|
204 |
+
encoder_hidden_states=None,
|
205 |
+
attention_mask=None,
|
206 |
+
temb=None,
|
207 |
+
id_embedding=None,
|
208 |
+
id_scale=1.0,
|
209 |
+
):
|
210 |
+
residual = hidden_states
|
211 |
+
|
212 |
+
if attn.spatial_norm is not None:
|
213 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
214 |
+
|
215 |
+
input_ndim = hidden_states.ndim
|
216 |
+
|
217 |
+
if input_ndim == 4:
|
218 |
+
batch_size, channel, height, width = hidden_states.shape
|
219 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
220 |
+
|
221 |
+
batch_size, sequence_length, _ = (
|
222 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
223 |
+
)
|
224 |
+
|
225 |
+
if attention_mask is not None:
|
226 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
227 |
+
# scaled_dot_product_attention expects attention_mask shape to be
|
228 |
+
# (batch, heads, source_length, target_length)
|
229 |
+
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
230 |
+
|
231 |
+
if attn.group_norm is not None:
|
232 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
233 |
+
|
234 |
+
query = attn.to_q(hidden_states)
|
235 |
+
|
236 |
+
if encoder_hidden_states is None:
|
237 |
+
encoder_hidden_states = hidden_states
|
238 |
+
elif attn.norm_cross:
|
239 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
240 |
+
|
241 |
+
key = attn.to_k(encoder_hidden_states)
|
242 |
+
value = attn.to_v(encoder_hidden_states)
|
243 |
+
|
244 |
+
inner_dim = key.shape[-1]
|
245 |
+
head_dim = inner_dim // attn.heads
|
246 |
+
|
247 |
+
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
248 |
+
|
249 |
+
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
250 |
+
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
251 |
+
|
252 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
253 |
+
hidden_states = F.scaled_dot_product_attention(
|
254 |
+
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
255 |
+
)
|
256 |
+
|
257 |
+
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
258 |
+
hidden_states = hidden_states.to(query.dtype)
|
259 |
+
|
260 |
+
# linear proj
|
261 |
+
hidden_states = attn.to_out[0](hidden_states)
|
262 |
+
# dropout
|
263 |
+
hidden_states = attn.to_out[1](hidden_states)
|
264 |
+
|
265 |
+
if input_ndim == 4:
|
266 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
267 |
+
|
268 |
+
if attn.residual_connection:
|
269 |
+
hidden_states = hidden_states + residual
|
270 |
+
|
271 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
272 |
+
|
273 |
+
return hidden_states
|
274 |
+
|
275 |
+
|
276 |
+
class IDAttnProcessor2_0(torch.nn.Module):
|
277 |
+
r"""
|
278 |
+
Attention processor for ID-Adapater for PyTorch 2.0.
|
279 |
+
Args:
|
280 |
+
hidden_size (`int`):
|
281 |
+
The hidden size of the attention layer.
|
282 |
+
cross_attention_dim (`int`):
|
283 |
+
The number of channels in the `encoder_hidden_states`.
|
284 |
+
"""
|
285 |
+
|
286 |
+
def __init__(self, hidden_size, cross_attention_dim=None):
|
287 |
+
super().__init__()
|
288 |
+
if not hasattr(F, "scaled_dot_product_attention"):
|
289 |
+
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
290 |
+
|
291 |
+
self.id_to_k = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
292 |
+
self.id_to_v = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
293 |
+
|
294 |
+
def __call__(
|
295 |
+
self,
|
296 |
+
attn,
|
297 |
+
hidden_states,
|
298 |
+
encoder_hidden_states=None,
|
299 |
+
attention_mask=None,
|
300 |
+
temb=None,
|
301 |
+
id_embedding=None,
|
302 |
+
id_scale=1.0,
|
303 |
+
):
|
304 |
+
residual = hidden_states
|
305 |
+
|
306 |
+
if attn.spatial_norm is not None:
|
307 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
308 |
+
|
309 |
+
input_ndim = hidden_states.ndim
|
310 |
+
|
311 |
+
if input_ndim == 4:
|
312 |
+
batch_size, channel, height, width = hidden_states.shape
|
313 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
314 |
+
|
315 |
+
batch_size, sequence_length, _ = (
|
316 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
317 |
+
)
|
318 |
+
|
319 |
+
if attention_mask is not None:
|
320 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
321 |
+
# scaled_dot_product_attention expects attention_mask shape to be
|
322 |
+
# (batch, heads, source_length, target_length)
|
323 |
+
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
324 |
+
|
325 |
+
if attn.group_norm is not None:
|
326 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
327 |
+
|
328 |
+
query = attn.to_q(hidden_states)
|
329 |
+
|
330 |
+
if encoder_hidden_states is None:
|
331 |
+
encoder_hidden_states = hidden_states
|
332 |
+
elif attn.norm_cross:
|
333 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
334 |
+
|
335 |
+
key = attn.to_k(encoder_hidden_states)
|
336 |
+
value = attn.to_v(encoder_hidden_states)
|
337 |
+
|
338 |
+
inner_dim = key.shape[-1]
|
339 |
+
head_dim = inner_dim // attn.heads
|
340 |
+
|
341 |
+
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
342 |
+
|
343 |
+
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
344 |
+
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
345 |
+
|
346 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
347 |
+
hidden_states = F.scaled_dot_product_attention(
|
348 |
+
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
349 |
+
)
|
350 |
+
|
351 |
+
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
352 |
+
hidden_states = hidden_states.to(query.dtype)
|
353 |
+
|
354 |
+
# for id embedding
|
355 |
+
if id_embedding is not None:
|
356 |
+
if NUM_ZERO == 0:
|
357 |
+
id_key = self.id_to_k(id_embedding).to(query.dtype)
|
358 |
+
id_value = self.id_to_v(id_embedding).to(query.dtype)
|
359 |
+
else:
|
360 |
+
zero_tensor = torch.zeros(
|
361 |
+
(id_embedding.size(0), NUM_ZERO, id_embedding.size(-1)),
|
362 |
+
dtype=id_embedding.dtype,
|
363 |
+
device=id_embedding.device,
|
364 |
+
)
|
365 |
+
id_key = self.id_to_k(torch.cat((id_embedding, zero_tensor), dim=1)).to(query.dtype)
|
366 |
+
id_value = self.id_to_v(torch.cat((id_embedding, zero_tensor), dim=1)).to(query.dtype)
|
367 |
+
|
368 |
+
id_key = id_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
369 |
+
id_value = id_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
370 |
+
|
371 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
372 |
+
id_hidden_states = F.scaled_dot_product_attention(
|
373 |
+
query, id_key, id_value, attn_mask=None, dropout_p=0.0, is_causal=False
|
374 |
+
)
|
375 |
+
|
376 |
+
id_hidden_states = id_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
377 |
+
id_hidden_states = id_hidden_states.to(query.dtype)
|
378 |
+
|
379 |
+
if not ORTHO and not ORTHO_v2:
|
380 |
+
hidden_states = hidden_states + id_scale * id_hidden_states
|
381 |
+
elif ORTHO_v2:
|
382 |
+
orig_dtype = hidden_states.dtype
|
383 |
+
hidden_states = hidden_states.to(torch.float32)
|
384 |
+
id_hidden_states = id_hidden_states.to(torch.float32)
|
385 |
+
attn_map = query @ id_key.transpose(-2, -1)
|
386 |
+
attn_mean = attn_map.softmax(dim=-1).mean(dim=1)
|
387 |
+
attn_mean = attn_mean[:, :, :5].sum(dim=-1, keepdim=True)
|
388 |
+
projection = (
|
389 |
+
torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True)
|
390 |
+
/ torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True)
|
391 |
+
* hidden_states
|
392 |
+
)
|
393 |
+
orthogonal = id_hidden_states + (attn_mean - 1) * projection
|
394 |
+
hidden_states = hidden_states + id_scale * orthogonal
|
395 |
+
hidden_states = hidden_states.to(orig_dtype)
|
396 |
+
else:
|
397 |
+
orig_dtype = hidden_states.dtype
|
398 |
+
hidden_states = hidden_states.to(torch.float32)
|
399 |
+
id_hidden_states = id_hidden_states.to(torch.float32)
|
400 |
+
projection = (
|
401 |
+
torch.sum((hidden_states * id_hidden_states), dim=-2, keepdim=True)
|
402 |
+
/ torch.sum((hidden_states * hidden_states), dim=-2, keepdim=True)
|
403 |
+
* hidden_states
|
404 |
+
)
|
405 |
+
orthogonal = id_hidden_states - projection
|
406 |
+
hidden_states = hidden_states + id_scale * orthogonal
|
407 |
+
hidden_states = hidden_states.to(orig_dtype)
|
408 |
+
|
409 |
+
# linear proj
|
410 |
+
hidden_states = attn.to_out[0](hidden_states)
|
411 |
+
# dropout
|
412 |
+
hidden_states = attn.to_out[1](hidden_states)
|
413 |
+
|
414 |
+
if input_ndim == 4:
|
415 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
416 |
+
|
417 |
+
if attn.residual_connection:
|
418 |
+
hidden_states = hidden_states + residual
|
419 |
+
|
420 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
421 |
+
|
422 |
+
return hidden_states
|