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
|
import re
from collections import namedtuple
import torch
import modules.shared as shared
re_prompt = re.compile(r'''
(.*?)
\[
([^]:]+):
(?:([^]:]*):)?
([0-9]*\.?[0-9]+)
]
|
(.+)
''', re.X)
# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
# will be represented with prompt_schedule like this (assuming steps=100):
# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
# [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
# [60, 'fantasy landscape with a lake and an oak in foreground in background masterful']
# [75, 'fantasy landscape with a lake and an oak in background masterful']
# [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
def get_learned_conditioning_prompt_schedules(prompts, steps):
res = []
cache = {}
for prompt in prompts:
prompt_schedule: list[list[str | int]] = [[steps, ""]]
cached = cache.get(prompt, None)
if cached is not None:
res.append(cached)
continue
for m in re_prompt.finditer(prompt):
plaintext = m.group(1) if m.group(5) is None else m.group(5)
concept_from = m.group(2)
concept_to = m.group(3)
if concept_to is None:
concept_to = concept_from
concept_from = ""
swap_position = float(m.group(4)) if m.group(4) is not None else None
if swap_position is not None:
if swap_position < 1:
swap_position = swap_position * steps
swap_position = int(min(swap_position, steps))
swap_index = None
found_exact_index = False
for i in range(len(prompt_schedule)):
end_step = prompt_schedule[i][0]
prompt_schedule[i][1] += plaintext
if swap_position is not None and swap_index is None:
if swap_position == end_step:
swap_index = i
found_exact_index = True
if swap_position < end_step:
swap_index = i
if swap_index is not None:
if not found_exact_index:
prompt_schedule.insert(swap_index, [swap_position, prompt_schedule[swap_index][1]])
for i in range(len(prompt_schedule)):
end_step = prompt_schedule[i][0]
must_replace = swap_position < end_step
prompt_schedule[i][1] += concept_to if must_replace else concept_from
res.append(prompt_schedule)
cache[prompt] = prompt_schedule
#for t in prompt_schedule:
# print(t)
return res
ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
def get_learned_conditioning(prompts, steps):
res = []
prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
cache = {}
for prompt, prompt_schedule in zip(prompts, prompt_schedules):
cached = cache.get(prompt, None)
if cached is not None:
res.append(cached)
continue
texts = [x[1] for x in prompt_schedule]
conds = shared.sd_model.get_learned_conditioning(texts)
cond_schedule = []
for i, (end_at_step, text) in enumerate(prompt_schedule):
cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
cache[prompt] = cond_schedule
res.append(cond_schedule)
return ScheduledPromptBatch((len(prompts),) + res[0][0].cond.shape, res)
def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
res = torch.zeros(c.shape, device=shared.device, dtype=next(shared.sd_model.parameters()).dtype)
for i, cond_schedule in enumerate(c.schedules):
target_index = 0
for curret_index, (end_at, cond) in enumerate(cond_schedule):
if current_step <= end_at:
target_index = curret_index
break
res[i] = cond_schedule[target_index].cond
return res
re_attention = re.compile(r"""
\\\(|
\\\)|
\\\[|
\\]|
\\\\|
\\|
\(|
\[|
:([+-]?[.\d]+)\)|
\)|
]|
[^\\()\[\]:]+|
:
""", re.X)
def parse_prompt_attention(text):
"""
Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
Accepted tokens are:
(abc) - increases attention to abc by a multiplier of 1.1
(abc:3.12) - increases attention to abc by a multiplier of 3.12
[abc] - decreases attention to abc by a multiplier of 1.1
\( - literal character '('
\[ - literal character '['
\) - literal character ')'
\] - literal character ']'
\\ - literal character '\'
anything else - just text
Example:
'a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).'
produces:
[
['a ', 1.0],
['house', 1.5730000000000004],
[' ', 1.1],
['on', 1.0],
[' a ', 1.1],
['hill', 0.55],
[', sun, ', 1.1],
['sky', 1.4641000000000006],
['.', 1.1]
]
"""
res = []
round_brackets = []
square_brackets = []
round_bracket_multiplier = 1.1
square_bracket_multiplier = 1 / 1.1
def multiply_range(start_position, multiplier):
for p in range(start_position, len(res)):
res[p][1] *= multiplier
for m in re_attention.finditer(text):
text = m.group(0)
weight = m.group(1)
if text.startswith('\\'):
res.append([text[1:], 1.0])
elif text == '(':
round_brackets.append(len(res))
elif text == '[':
square_brackets.append(len(res))
elif weight is not None and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), float(weight))
elif text == ')' and len(round_brackets) > 0:
multiply_range(round_brackets.pop(), round_bracket_multiplier)
elif text == ']' and len(square_brackets) > 0:
multiply_range(square_brackets.pop(), square_bracket_multiplier)
else:
res.append([text, 1.0])
for pos in round_brackets:
multiply_range(pos, round_bracket_multiplier)
for pos in square_brackets:
multiply_range(pos, square_bracket_multiplier)
return res
|
