131 lines
4.5 KiB
Python
131 lines
4.5 KiB
Python
import re
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from collections import namedtuple
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import torch
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import modules.shared as shared
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re_prompt = re.compile(r'''
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(.*?)
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\[
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([^]:]+):
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(?:([^]:]*):)?
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([0-9]*\.?[0-9]+)
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]
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(.+)
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''', re.X)
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# 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]"
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# will be represented with prompt_schedule like this (assuming steps=100):
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# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
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# [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
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# [60, 'fantasy landscape with a lake and an oak in foreground in background masterful']
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# [75, 'fantasy landscape with a lake and an oak in background masterful']
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# [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
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def get_learned_conditioning_prompt_schedules(prompts, steps):
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res = []
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cache = {}
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for prompt in prompts:
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prompt_schedule: list[list[str | int]] = [[steps, ""]]
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cached = cache.get(prompt, None)
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if cached is not None:
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res.append(cached)
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continue
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for m in re_prompt.finditer(prompt):
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plaintext = m.group(1) if m.group(5) is None else m.group(5)
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concept_from = m.group(2)
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concept_to = m.group(3)
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if concept_to is None:
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concept_to = concept_from
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concept_from = ""
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swap_position = float(m.group(4)) if m.group(4) is not None else None
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if swap_position is not None:
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if swap_position < 1:
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swap_position = swap_position * steps
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swap_position = int(min(swap_position, steps))
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swap_index = None
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found_exact_index = False
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for i in range(len(prompt_schedule)):
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end_step = prompt_schedule[i][0]
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prompt_schedule[i][1] += plaintext
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if swap_position is not None and swap_index is None:
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if swap_position == end_step:
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swap_index = i
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found_exact_index = True
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if swap_position < end_step:
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swap_index = i
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if swap_index is not None:
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if not found_exact_index:
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prompt_schedule.insert(swap_index, [swap_position, prompt_schedule[swap_index][1]])
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for i in range(len(prompt_schedule)):
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end_step = prompt_schedule[i][0]
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must_replace = swap_position < end_step
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prompt_schedule[i][1] += concept_to if must_replace else concept_from
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res.append(prompt_schedule)
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cache[prompt] = prompt_schedule
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#for t in prompt_schedule:
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# print(t)
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return res
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ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
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ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
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def get_learned_conditioning(prompts, steps):
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res = []
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prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
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cache = {}
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for prompt, prompt_schedule in zip(prompts, prompt_schedules):
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cached = cache.get(prompt, None)
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if cached is not None:
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res.append(cached)
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continue
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texts = [x[1] for x in prompt_schedule]
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conds = shared.sd_model.get_learned_conditioning(texts)
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cond_schedule = []
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for i, (end_at_step, text) in enumerate(prompt_schedule):
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cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
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cache[prompt] = cond_schedule
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res.append(cond_schedule)
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return ScheduledPromptBatch((len(prompts),) + res[0][0].cond.shape, res)
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def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
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res = torch.zeros(c.shape)
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for i, cond_schedule in enumerate(c.schedules):
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target_index = 0
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for curret_index, (end_at, cond) in enumerate(cond_schedule):
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if current_step <= end_at:
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target_index = curret_index
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break
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res[i] = cond_schedule[target_index].cond
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return res.to(shared.device)
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#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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