from __future__ import annotations
import re
from collections import namedtuple
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