mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-11-21 03:11:40 +08:00
465 lines
16 KiB
Python
465 lines
16 KiB
Python
from __future__ import annotations
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import re
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from collections import namedtuple
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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 | alternate | 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 [WHITESPACE] "]"
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alternate: "[" prompt ("|" [prompt])+ "]"
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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, base_steps, hires_steps=None, use_old_scheduling=False):
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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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>>> g("[a|(b:1.1)]")
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[[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)']]
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>>> g("[fe|]male")
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[[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]
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>>> g("[fe|||]male")
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[[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]
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>>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10, 10)[0]
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>>> g("a [b:.5] c")
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[[10, 'a b c']]
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>>> g("a [b:1.5] c")
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[[5, 'a c'], [10, 'a b c']]
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"""
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if hires_steps is None or use_old_scheduling:
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int_offset = 0
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flt_offset = 0
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steps = base_steps
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else:
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int_offset = base_steps
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flt_offset = 1.0
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steps = hires_steps
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def collect_steps(steps, tree):
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res = [steps]
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class CollectSteps(lark.Visitor):
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def scheduled(self, tree):
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s = tree.children[-2]
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v = float(s)
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if use_old_scheduling:
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v = v*steps if v<1 else v
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else:
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if "." in s:
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v = (v - flt_offset) * steps
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else:
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v = (v - int_offset)
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tree.children[-2] = min(steps, int(v))
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if tree.children[-2] >= 1:
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res.append(tree.children[-2])
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def alternate(self, tree):
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res.extend(range(1, steps+1))
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CollectSteps().visit(tree)
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return sorted(set(res))
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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 alternate(self, args):
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args = ["" if not arg else arg for arg in args]
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yield args[(step - 1) % len(args)]
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def start(self, args):
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def flatten(x):
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if isinstance(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 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:
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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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class SdConditioning(list):
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"""
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A list with prompts for stable diffusion's conditioner model.
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Can also specify width and height of created image - SDXL needs it.
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"""
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def __init__(self, prompts, is_negative_prompt=False, width=None, height=None, copy_from=None):
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super().__init__()
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self.extend(prompts)
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if copy_from is None:
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copy_from = prompts
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self.is_negative_prompt = is_negative_prompt or getattr(copy_from, 'is_negative_prompt', False)
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self.width = width or getattr(copy_from, 'width', None)
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self.height = height or getattr(copy_from, 'height', None)
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def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps, hires_steps=None, use_old_scheduling=False):
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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, hires_steps, use_old_scheduling)
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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 = SdConditioning([x[1] for x in prompt_schedule], copy_from=prompts)
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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, _) in enumerate(prompt_schedule):
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if isinstance(conds, dict):
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cond = {k: v[i] for k, v in conds.items()}
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else:
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cond = conds[i]
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cond_schedule.append(ScheduledPromptConditioning(end_at_step, cond))
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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*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
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def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
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res_indexes = []
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prompt_indexes = {}
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prompt_flat_list = SdConditioning(prompts)
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prompt_flat_list.clear()
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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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match = re_weight.search(subprompt)
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text, weight = match.groups() if match is not None else (subprompt, 1.0)
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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, hires_steps=None, use_old_scheduling=False) -> 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, hires_steps, use_old_scheduling)
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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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class DictWithShape(dict):
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def __init__(self, x, shape):
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super().__init__()
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self.update(x)
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@property
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def shape(self):
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return self["crossattn"].shape
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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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is_dict = isinstance(param, dict)
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if is_dict:
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dict_cond = param
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res = {k: torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype) for k, param in dict_cond.items()}
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res = DictWithShape(res, (len(c),) + dict_cond['crossattn'].shape)
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else:
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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, entry in enumerate(cond_schedule):
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if current_step <= entry.end_at_step:
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target_index = current
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break
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if is_dict:
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for k, param in cond_schedule[target_index].cond.items():
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res[k][i] = param
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else:
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res[i] = cond_schedule[target_index].cond
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return res
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def stack_conds(tensors):
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# if prompts have wildly different lengths above the limit we'll get tensors of different shapes
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# and won't be able to torch.stack them. So this fixes that.
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token_count = max([x.shape[0] for x in tensors])
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for i in range(len(tensors)):
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if tensors[i].shape[0] != token_count:
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last_vector = tensors[i][-1:]
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last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
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tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
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return torch.stack(tensors)
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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 composable_prompts in c.batch:
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conds_for_batch = []
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for composable_prompt in composable_prompts:
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target_index = 0
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for current, entry in enumerate(composable_prompt.schedules):
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if current_step <= entry.end_at_step:
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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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if isinstance(tensors[0], dict):
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keys = list(tensors[0].keys())
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stacked = {k: stack_conds([x[k] for x in tensors]) for k in keys}
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stacked = DictWithShape(stacked, stacked['crossattn'].shape)
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else:
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stacked = stack_conds(tensors).to(device=param.device, dtype=param.dtype)
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return conds_list, stacked
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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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:\s*([+-]?[.\d]+)\s*\)|
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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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re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
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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 associated 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 round_brackets:
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multiply_range(round_brackets.pop(), float(weight))
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elif text == ')' and round_brackets:
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multiply_range(round_brackets.pop(), round_bracket_multiplier)
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elif text == ']' and square_brackets:
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multiply_range(square_brackets.pop(), square_bracket_multiplier)
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else:
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parts = re.split(re_break, text)
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for i, part in enumerate(parts):
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if i > 0:
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res.append(["BREAK", -1])
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res.append([part, 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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