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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
#get_learned_conditioning_prompt_schedules(["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]"], 100)
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