stable-diffusion-webui/modules/prompt_parser.py

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)
    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.to(shared.device)



#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)