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lint
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Haoming
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@ -2,17 +2,20 @@ 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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import lark
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import torch
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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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# [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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schedule_parser = lark.Lark(
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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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@ -23,10 +26,12 @@ 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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"""
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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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r"""
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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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@ -80,18 +85,18 @@ def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=N
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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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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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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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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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@ -101,9 +106,11 @@ def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=N
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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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@ -111,12 +118,16 @@ def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=N
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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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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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@ -125,6 +136,7 @@ def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=N
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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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@ -141,6 +153,7 @@ class SdConditioning(list):
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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, distilled_cfg_scale=None):
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super().__init__()
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self.extend(prompts)
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@ -148,16 +161,16 @@ class SdConditioning(list):
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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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self.distilled_cfg_scale = distilled_cfg_scale or getattr(copy_from, 'distilled_cfg_scale', None)
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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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self.distilled_cfg_scale = distilled_cfg_scale or getattr(copy_from, "distilled_cfg_scale", 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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r"""
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converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning,
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specifying the condition (cond), 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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@ -251,10 +264,9 @@ class MulticondLearnedConditioning:
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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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"""
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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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@ -365,7 +377,8 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
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return conds_list, stacked
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re_attention = re.compile(r"""
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re_attention = re.compile(
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r"""
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\\\(|
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\\\)|
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\\\[|
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@ -379,12 +392,15 @@ re_attention = re.compile(r"""
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]|
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[^\\()\[\]:]+|
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:
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""", re.X)
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""",
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re.X,
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)
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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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r"""
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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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@ -434,17 +450,17 @@ def parse_prompt_attention(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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if text.startswith("\\"):
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res.append([text[1:], 1.0])
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elif text == '(':
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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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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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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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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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@ -472,9 +488,3 @@ def parse_prompt_attention(text):
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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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