from __future__ import annotations import re from collections import namedtuple from typing import List import lark # 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'] schedule_parser = lark.Lark(r""" !start: (prompt | /[][():]/+)* prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)* !emphasized: "(" prompt ")" | "(" prompt ":" prompt ")" | "[" prompt "]" scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER [WHITESPACE] "]" alternate: "[" prompt ("|" [prompt])+ "]" WHITESPACE: /\s+/ plain: /([^\\\[\]():|]|\\.)+/ %import common.SIGNED_NUMBER -> NUMBER """) def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=None, use_old_scheduling=False): """ >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0] >>> g("test") [[10, 'test']] >>> g("a [b:3]") [[3, 'a '], [10, 'a b']] >>> g("a [b: 3]") [[3, 'a '], [10, 'a b']] >>> g("a [[[b]]:2]") [[2, 'a '], [10, 'a [[b]]']] >>> g("[(a:2):3]") [[3, ''], [10, '(a:2)']] >>> g("a [b : c : 1] d") [[1, 'a b d'], [10, 'a c d']] >>> g("a[b:[c:d:2]:1]e") [[1, 'abe'], [2, 'ace'], [10, 'ade']] >>> g("a [unbalanced") [[10, 'a [unbalanced']] >>> g("a [b:.5] c") [[5, 'a c'], [10, 'a b c']] >>> g("a [{b|d{:.5] c") # not handling this right now [[5, 'a c'], [10, 'a {b|d{ c']] >>> g("((a][:b:c [d:3]") [[3, '((a][:b:c '], [10, '((a][:b:c d']] >>> g("[a|(b:1.1)]") [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']] >>> g("[fe|]male") [[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']] >>> g("[fe|||]male") [[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']] >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10, 10)[0] >>> g("a [b:.5] c") [[10, 'a b c']] >>> g("a [b:1.5] c") [[5, 'a c'], [10, 'a b c']] """ if hires_steps is None or use_old_scheduling: int_offset = 0 flt_offset = 0 steps = base_steps else: int_offset = base_steps flt_offset = 1.0 steps = hires_steps def collect_steps(steps, tree): res = [steps] class CollectSteps(lark.Visitor): def scheduled(self, tree): s = tree.children[-2] v = float(s) if use_old_scheduling: v = v*steps if v<1 else v else: if "." in s: v = (v - flt_offset) * steps else: v = (v - int_offset) tree.children[-2] = min(steps, int(v)) if tree.children[-2] >= 1: res.append(tree.children[-2]) def alternate(self, tree): res.extend(range(1, steps+1)) CollectSteps().visit(tree) return sorted(set(res)) def at_step(step, tree): class AtStep(lark.Transformer): def scheduled(self, args): before, after, _, when, _ = args yield before or () if step <= when else after def alternate(self, args): args = ["" if not arg else arg for arg in args] yield args[(step - 1) % len(args)] def start(self, args): def flatten(x): if isinstance(x, str): yield x else: for gen in x: yield from flatten(gen) return ''.join(flatten(args)) def plain(self, args): yield args[0].value def __default__(self, data, children, meta): for child in children: yield child return AtStep().transform(tree) def get_schedule(prompt): try: tree = schedule_parser.parse(prompt) except lark.exceptions.LarkError: if 0: import traceback traceback.print_exc() return [[steps, prompt]] return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)] promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)} return [promptdict[prompt] for prompt in prompts] ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"]) class SdConditioning(list): """ A list with prompts for stable diffusion's conditioner model. Can also specify width and height of created image - SDXL needs it. """ def __init__(self, prompts, is_negative_prompt=False, width=None, height=None, copy_from=None): super().__init__() self.extend(prompts) if copy_from is None: copy_from = prompts self.is_negative_prompt = is_negative_prompt or getattr(copy_from, 'is_negative_prompt', False) self.width = width or getattr(copy_from, 'width', None) self.height = height or getattr(copy_from, 'height', None) def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps, hires_steps=None, use_old_scheduling=False): """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond), and the sampling step at which this condition is to be replaced by the next one. Input: (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20) Output: [ [ ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0523, ..., -0.4901, -0.3066, 0.0674], ..., [ 0.3317, -0.5102, -0.4066, ..., 0.4119, -0.7647, -1.0160]], device='cuda:0')) ], [ ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.0192, 0.3867, -0.4644, ..., 0.1135, -0.3696, -0.4625]], device='cuda:0')), ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.7352, -0.4356, -0.7888, ..., 0.6994, -0.4312, -1.2593]], device='cuda:0')) ] ] """ res = [] prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps, hires_steps, use_old_scheduling) 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 = SdConditioning([x[1] for x in prompt_schedule], copy_from=prompts) conds = model.get_learned_conditioning(texts) cond_schedule = [] for i, (end_at_step, _) in enumerate(prompt_schedule): if isinstance(conds, dict): cond = {k: v[i] for k, v in conds.items()} else: cond = conds[i] cond_schedule.append(ScheduledPromptConditioning(end_at_step, cond)) cache[prompt] = cond_schedule res.append(cond_schedule) return res re_AND = re.compile(r"\bAND\b") re_weight = re.compile(r"^((?:\s|.)*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$") def get_multicond_prompt_list(prompts: SdConditioning | list[str]): res_indexes = [] prompt_indexes = {} prompt_flat_list = SdConditioning(prompts) prompt_flat_list.clear() for prompt in prompts: subprompts = re_AND.split(prompt) indexes = [] for subprompt in subprompts: match = re_weight.search(subprompt) text, weight = match.groups() if match is not None else (subprompt, 1.0) weight = float(weight) if weight is not None else 1.0 index = prompt_indexes.get(text, None) if index is None: index = len(prompt_flat_list) prompt_flat_list.append(text) prompt_indexes[text] = index indexes.append((index, weight)) res_indexes.append(indexes) return res_indexes, prompt_flat_list, prompt_indexes class ComposableScheduledPromptConditioning: def __init__(self, schedules, weight=1.0): self.schedules: List[ScheduledPromptConditioning] = schedules self.weight: float = weight class MulticondLearnedConditioning: def __init__(self, shape, batch): self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS self.batch: List[List[ComposableScheduledPromptConditioning]] = batch def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning: """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt. For each prompt, the list is obtained by splitting the prompt using the AND separator. https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/ """ res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts) learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling) res = [] for indexes in res_indexes: res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes]) return MulticondLearnedConditioning(shape=(len(prompts),), batch=res) class DictWithShape(dict): def __init__(self, x, shape): super().__init__() self.update(x) @property def shape(self): return self["crossattn"].shape def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step): param = c[0][0].cond is_dict = isinstance(param, dict) if is_dict: dict_cond = param res = {k: torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for k, param in dict_cond.items()} res = DictWithShape(res, (len(c),) + dict_cond['crossattn'].shape) else: res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for i, cond_schedule in enumerate(c): target_index = 0 for current, entry in enumerate(cond_schedule): if current_step <= entry.end_at_step: target_index = current break if is_dict: for k, param in cond_schedule[target_index].cond.items(): res[k][i] = param else: res[i] = cond_schedule[target_index].cond return res def stack_conds(tensors): # if prompts have wildly different lengths above the limit we'll get tensors of different shapes # and won't be able to torch.stack them. So this fixes that. token_count = max([x.shape[0] for x in tensors]) for i in range(len(tensors)): if tensors[i].shape[0] != token_count: last_vector = tensors[i][-1:] last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1]) tensors[i] = torch.vstack([tensors[i], last_vector_repeated]) return torch.stack(tensors) def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step): param = c.batch[0][0].schedules[0].cond tensors = [] conds_list = [] for composable_prompts in c.batch: conds_for_batch = [] for composable_prompt in composable_prompts: target_index = 0 for current, entry in enumerate(composable_prompt.schedules): if current_step <= entry.end_at_step: target_index = current break conds_for_batch.append((len(tensors), composable_prompt.weight)) tensors.append(composable_prompt.schedules[target_index].cond) conds_list.append(conds_for_batch) if isinstance(tensors[0], dict): keys = list(tensors[0].keys()) stacked = {k: stack_conds([x[k] for x in tensors]) for k in keys} stacked = DictWithShape(stacked, stacked['crossattn'].shape) else: stacked = stack_conds(tensors).to(device=param.device, dtype=param.dtype) return conds_list, stacked re_attention = re.compile(r""" \\\(| \\\)| \\\[| \\]| \\\\| \\| \(| \[| :\s*([+-]?[.\d]+)\s*\)| \)| ]| [^\\()\[\]:]+| : """, re.X) re_break = re.compile(r"\s*\bBREAK\b\s*", re.S) def parse_prompt_attention(text): """ Parses a string with attention tokens and returns a list of pairs: text and its associated 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 >>> parse_prompt_attention('normal text') [['normal text', 1.0]] >>> parse_prompt_attention('an (important) word') [['an ', 1.0], ['important', 1.1], [' word', 1.0]] >>> parse_prompt_attention('(unbalanced') [['unbalanced', 1.1]] >>> parse_prompt_attention('\(literal\]') [['(literal]', 1.0]] >>> parse_prompt_attention('(unnecessary)(parens)') [['unnecessaryparens', 1.1]] >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).') [['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 round_brackets: multiply_range(round_brackets.pop(), float(weight)) elif text == ')' and round_brackets: multiply_range(round_brackets.pop(), round_bracket_multiplier) elif text == ']' and square_brackets: multiply_range(square_brackets.pop(), square_bracket_multiplier) else: parts = re.split(re_break, text) for i, part in enumerate(parts): if i > 0: res.append(["BREAK", -1]) res.append([part, 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]] # merge runs of identical weights i = 0 while i + 1 < len(res): if res[i][1] == res[i + 1][1]: res[i][0] += res[i + 1][0] res.pop(i + 1) else: i += 1 return res if __name__ == "__main__": import doctest doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE) else: import torch # doctest faster