351 lines
12 KiB
Python
351 lines
12 KiB
Python
import re
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from collections import namedtuple
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from typing import List
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import lark
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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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schedule_parser = lark.Lark(r"""
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!start: (prompt | /[][():]/+)*
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prompt: (emphasized | scheduled | plain | WHITESPACE)*
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!emphasized: "(" prompt ")"
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| "(" prompt ":" prompt ")"
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| "[" prompt "]"
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scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
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WHITESPACE: /\s+/
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plain: /([^\\\[\]():]|\\.)+/
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%import common.SIGNED_NUMBER -> NUMBER
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""")
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def get_learned_conditioning_prompt_schedules(prompts, steps):
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"""
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>>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
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>>> g("test")
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[[10, 'test']]
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>>> g("a [b:3]")
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[[3, 'a '], [10, 'a b']]
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>>> g("a [b: 3]")
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[[3, 'a '], [10, 'a b']]
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>>> g("a [[[b]]:2]")
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[[2, 'a '], [10, 'a [[b]]']]
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>>> g("[(a:2):3]")
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[[3, ''], [10, '(a:2)']]
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>>> g("a [b : c : 1] d")
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[[1, 'a b d'], [10, 'a c d']]
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>>> g("a[b:[c:d:2]:1]e")
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[[1, 'abe'], [2, 'ace'], [10, 'ade']]
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>>> g("a [unbalanced")
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[[10, 'a [unbalanced']]
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>>> g("a [b:.5] c")
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[[5, 'a c'], [10, 'a b c']]
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>>> g("a [{b|d{:.5] c") # not handling this right now
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[[5, 'a c'], [10, 'a {b|d{ c']]
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>>> g("((a][:b:c [d:3]")
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[[3, '((a][:b:c '], [10, '((a][:b:c d']]
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"""
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def collect_steps(steps, tree):
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l = [steps]
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class CollectSteps(lark.Visitor):
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def scheduled(self, tree):
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tree.children[-1] = float(tree.children[-1])
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if tree.children[-1] < 1:
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tree.children[-1] *= steps
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tree.children[-1] = min(steps, int(tree.children[-1]))
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l.append(tree.children[-1])
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CollectSteps().visit(tree)
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return sorted(set(l))
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def at_step(step, tree):
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class AtStep(lark.Transformer):
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def scheduled(self, args):
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before, after, _, when = args
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yield before or () if step <= when else after
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def start(self, args):
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def flatten(x):
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if type(x) == str:
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yield x
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else:
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for gen in x:
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yield from flatten(gen)
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return ''.join(flatten(args))
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def plain(self, args):
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yield args[0].value
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def __default__(self, data, children, meta):
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for child in children:
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yield from child
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return AtStep().transform(tree)
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def get_schedule(prompt):
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try:
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tree = schedule_parser.parse(prompt)
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except lark.exceptions.LarkError as e:
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if 0:
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import traceback
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traceback.print_exc()
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return [[steps, prompt]]
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return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
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promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
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return [promptdict[prompt] for prompt in prompts]
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ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
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def get_learned_conditioning(model, prompts, steps):
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"""converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
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and the sampling step at which this condition is to be replaced by the next one.
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Input:
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(model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
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Output:
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[
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[
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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'))
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],
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[
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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')),
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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'))
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]
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]
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"""
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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 = 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 res
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re_AND = re.compile(r"\bAND\b")
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re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
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def get_multicond_prompt_list(prompts):
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res_indexes = []
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prompt_flat_list = []
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prompt_indexes = {}
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for prompt in prompts:
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subprompts = re_AND.split(prompt)
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indexes = []
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for subprompt in subprompts:
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text, weight = re_weight.search(subprompt).groups()
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weight = float(weight) if weight is not None else 1.0
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index = prompt_indexes.get(text, None)
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if index is None:
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index = len(prompt_flat_list)
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prompt_flat_list.append(text)
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prompt_indexes[text] = index
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indexes.append((index, weight))
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res_indexes.append(indexes)
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return res_indexes, prompt_flat_list, prompt_indexes
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class ComposableScheduledPromptConditioning:
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def __init__(self, schedules, weight=1.0):
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self.schedules: List[ScheduledPromptConditioning] = schedules
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self.weight: float = weight
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class MulticondLearnedConditioning:
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def __init__(self, shape, batch):
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self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
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self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
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def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
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"""same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
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For each prompt, the list is obtained by splitting the prompt using the AND separator.
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https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
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"""
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res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
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learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
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res = []
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for indexes in res_indexes:
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res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
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return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
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def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
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param = c[0][0].cond
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res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
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for i, cond_schedule in enumerate(c):
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target_index = 0
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for current, (end_at, cond) in enumerate(cond_schedule):
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if current_step <= end_at:
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target_index = current
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break
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res[i] = cond_schedule[target_index].cond
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return res
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def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
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param = c.batch[0][0].schedules[0].cond
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tensors = []
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conds_list = []
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for batch_no, composable_prompts in enumerate(c.batch):
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conds_for_batch = []
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for cond_index, composable_prompt in enumerate(composable_prompts):
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target_index = 0
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for current, (end_at, cond) in enumerate(composable_prompt.schedules):
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if current_step <= end_at:
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target_index = current
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break
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conds_for_batch.append((len(tensors), composable_prompt.weight))
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tensors.append(composable_prompt.schedules[target_index].cond)
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conds_list.append(conds_for_batch)
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return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
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re_attention = re.compile(r"""
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\\\(|
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\\\)|
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\\\[|
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\\]|
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\\\\|
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\\|
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\(|
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\[|
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:([+-]?[.\d]+)\)|
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\)|
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]|
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[^\\()\[\]:]+|
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:
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""", re.X)
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def parse_prompt_attention(text):
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"""
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Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
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Accepted tokens are:
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(abc) - increases attention to abc by a multiplier of 1.1
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(abc:3.12) - increases attention to abc by a multiplier of 3.12
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[abc] - decreases attention to abc by a multiplier of 1.1
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\( - literal character '('
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\[ - literal character '['
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\) - literal character ')'
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\] - literal character ']'
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\\ - literal character '\'
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anything else - just text
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>>> parse_prompt_attention('normal text')
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[['normal text', 1.0]]
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>>> parse_prompt_attention('an (important) word')
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[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
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>>> parse_prompt_attention('(unbalanced')
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[['unbalanced', 1.1]]
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>>> parse_prompt_attention('\(literal\]')
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[['(literal]', 1.0]]
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>>> parse_prompt_attention('(unnecessary)(parens)')
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[['unnecessaryparens', 1.1]]
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>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
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[['a ', 1.0],
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['house', 1.5730000000000004],
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[' ', 1.1],
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['on', 1.0],
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[' a ', 1.1],
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['hill', 0.55],
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[', sun, ', 1.1],
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['sky', 1.4641000000000006],
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['.', 1.1]]
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"""
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res = []
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round_brackets = []
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square_brackets = []
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round_bracket_multiplier = 1.1
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square_bracket_multiplier = 1 / 1.1
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def multiply_range(start_position, multiplier):
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for p in range(start_position, len(res)):
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res[p][1] *= multiplier
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for m in re_attention.finditer(text):
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text = m.group(0)
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weight = m.group(1)
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if text.startswith('\\'):
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res.append([text[1:], 1.0])
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elif text == '(':
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round_brackets.append(len(res))
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elif text == '[':
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square_brackets.append(len(res))
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elif weight is not None and len(round_brackets) > 0:
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multiply_range(round_brackets.pop(), float(weight))
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elif text == ')' and len(round_brackets) > 0:
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multiply_range(round_brackets.pop(), round_bracket_multiplier)
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elif text == ']' and len(square_brackets) > 0:
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multiply_range(square_brackets.pop(), square_bracket_multiplier)
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else:
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res.append([text, 1.0])
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for pos in round_brackets:
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multiply_range(pos, round_bracket_multiplier)
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for pos in square_brackets:
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multiply_range(pos, square_bracket_multiplier)
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if len(res) == 0:
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res = [["", 1.0]]
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# merge runs of identical weights
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i = 0
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while i + 1 < len(res):
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if res[i][1] == res[i + 1][1]:
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res[i][0] += res[i + 1][0]
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res.pop(i + 1)
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else:
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i += 1
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return res
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if __name__ == "__main__":
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import doctest
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doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
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else:
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import torch # doctest faster
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