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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)
if len(res) == 0:
res = [["", 1.0]]
return res
|
