434 lines
15 KiB
Python
434 lines
15 KiB
Python
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 "]"
|
|
alternate: "[" prompt ("|" prompt)+ "]"
|
|
WHITESPACE: /\s+/
|
|
plain: /([^\\\[\]():|]|\\.)+/
|
|
%import common.SIGNED_NUMBER -> NUMBER
|
|
""")
|
|
|
|
def get_learned_conditioning_prompt_schedules(prompts, steps):
|
|
"""
|
|
>>> 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)']]
|
|
"""
|
|
|
|
def collect_steps(steps, tree):
|
|
res = [steps]
|
|
|
|
class CollectSteps(lark.Visitor):
|
|
def scheduled(self, tree):
|
|
tree.children[-1] = float(tree.children[-1])
|
|
if tree.children[-1] < 1:
|
|
tree.children[-1] *= steps
|
|
tree.children[-1] = min(steps, int(tree.children[-1]))
|
|
res.append(tree.children[-1])
|
|
|
|
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):
|
|
yield next(args[(step - 1)%len(args)])
|
|
def start(self, args):
|
|
def flatten(x):
|
|
if type(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, width=None, height=None):
|
|
super().__init__()
|
|
self.extend(prompts)
|
|
self.width = width or getattr(prompts, 'width', None)
|
|
self.height = height or getattr(prompts, 'height', None)
|
|
|
|
|
|
def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
|
|
"""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)
|
|
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 = 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*([-+]?(?:\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) -> 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)
|
|
|
|
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"""
|
|
\\\(|
|
|
\\\)|
|
|
\\\[|
|
|
\\]|
|
|
\\\\|
|
|
\\|
|
|
\(|
|
|
\[|
|
|
:([+-]?[.\d]+)\)|
|
|
\)|
|
|
]|
|
|
[^\\()\[\]:]+|
|
|
:
|
|
""", 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
|