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re-enable legacy blend syntax

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Damian at mba 2022-10-24 11:16:52 +02:00
parent 0564397ee6
commit 44e4090909
6 changed files with 134 additions and 71 deletions
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2
backend/invoke_ai_web_server.py
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@ -14,7 +14,7 @@ from threading import Event
from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
from ldm.invoke.conditioning import split_weighted_subprompts
from ldm.invoke.prompt_parser import split_weighted_subprompts
from backend.modules.parameters import parameters_to_command

2
backend/server.py
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@ -33,7 +33,7 @@ from ldm.generate import Generate
from ldm.invoke.restoration import Restoration
from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
from ldm.invoke.args import APP_ID, APP_VERSION, calculate_init_img_hash
from ldm.invoke.conditioning import split_weighted_subprompts
from ldm.invoke.prompt_parser import split_weighted_subprompts
from modules.parameters import parameters_to_command

2
ldm/invoke/args.py
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@ -92,7 +92,7 @@ import copy
import base64
import functools
import ldm.invoke.pngwriter
from ldm.invoke.conditioning import split_weighted_subprompts
from ldm.invoke.prompt_parser import split_weighted_subprompts
SAMPLER_CHOICES = [
'ddim',

70
ldm/invoke/conditioning.py
View File

@ -41,9 +41,15 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
pp = PromptParser()
# we don't support conjunctions for now
parsed_prompt: Union[FlattenedPrompt, Blend] = pp.parse(prompt_string_cleaned).prompts[0]
parsed_negative_prompt: FlattenedPrompt = pp.parse(unconditioned_words).prompts[0]
parsed_prompt: Union[FlattenedPrompt, Blend] = None
legacy_blend: Blend = pp.parse_legacy_blend(prompt_string_cleaned)
if legacy_blend is not None:
parsed_prompt = legacy_blend
else:
# we don't support conjunctions for now
parsed_prompt = pp.parse_conjunction(prompt_string_cleaned).prompts[0]
parsed_negative_prompt: FlattenedPrompt = pp.parse_conjunction(unconditioned_words).prompts[0]
print("parsed prompt to", parsed_prompt)
conditioning = None
@ -146,61 +152,3 @@ def get_tokens_length(model, fragments: list[Fragment]):
return sum([len(x) for x in tokens])
def split_weighted_subprompts(text, skip_normalize=False)->list:
"""
grabs all text up to the first occurrence of ':'
uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
if ':' has no value defined, defaults to 1.0
repeats until no text remaining
"""
prompt_parser = re.compile("""
(?P<prompt> # capture group for 'prompt'
(?:\\\:|[^:])+ # match one or more non ':' characters or escaped colons '\:'
) # end 'prompt'
(?: # non-capture group
:+ # match one or more ':' characters
(?P<weight> # capture group for 'weight'
-?\d+(?:\.\d+)? # match positive or negative integer or decimal number
)? # end weight capture group, make optional
\s* # strip spaces after weight
| # OR
$ # else, if no ':' then match end of line
) # end non-capture group
""", re.VERBOSE)
parsed_prompts = [(match.group("prompt").replace("\\:", ":"), float(
match.group("weight") or 1)) for match in re.finditer(prompt_parser, text)]
if skip_normalize:
return parsed_prompts
weight_sum = sum(map(lambda x: x[1], parsed_prompts))
if weight_sum == 0:
print(
"Warning: Subprompt weights add up to zero. Discarding and using even weights instead.")
equal_weight = 1 / max(len(parsed_prompts), 1)
return [(x[0], equal_weight) for x in parsed_prompts]
return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
# shows how the prompt is tokenized
# usually tokens have '</w>' to indicate end-of-word,
# but for readability it has been replaced with ' '
def log_tokenization(text, model, log=False, weight=1):
if not log:
return
tokens = model.cond_stage_model.tokenizer._tokenize(text)
tokenized = ""
discarded = ""
usedTokens = 0
totalTokens = len(tokens)
for i in range(0, totalTokens):
token = tokens[i].replace('</w>', ' ')
# alternate color
s = (usedTokens % 6) + 1
if i < model.cond_stage_model.max_length:
tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
usedTokens += 1
else: # over max token length
discarded = discarded + f"\x1b[0;3{s};40m{token}"
print(f"\n>> Tokens ({usedTokens}), Weight ({weight:.2f}):\n{tokenized}\x1b[0m")
if discarded != "":
print(
f">> Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m"
)

88
ldm/invoke/prompt_parser.py
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@ -1,6 +1,6 @@
import string
from typing import Union
from typing import Union, Optional
import re
import pyparsing as pp
class Prompt():
@ -223,10 +223,10 @@ class PromptParser():
def __init__(self, attention_plus_base=1.1, attention_minus_base=0.9):
self.root = build_parser_syntax(attention_plus_base, attention_minus_base)
self.conjunction, self.prompt = build_parser_syntax(attention_plus_base, attention_minus_base)
def parse(self, prompt: str) -> Conjunction:
def parse_conjunction(self, prompt: str) -> Conjunction:
'''
:param prompt: The prompt string to parse
:return: a Conjunction representing the parsed results.
@ -236,13 +236,25 @@ class PromptParser():
if len(prompt.strip()) == 0:
return Conjunction(prompts=[FlattenedPrompt([('', 1.0)])], weights=[1.0])
root = self.root.parse_string(prompt)
root = self.conjunction.parse_string(prompt)
#print(f"'{prompt}' parsed to root", root)
#fused = fuse_fragments(parts)
#print("fused to", fused)
return self.flatten(root[0])
def parse_legacy_blend(self, text: str) -> Optional[Blend]:
weighted_subprompts = split_weighted_subprompts(text, skip_normalize=False)
if len(weighted_subprompts) == 1:
return None
strings = [x[0] for x in weighted_subprompts]
weights = [x[1] for x in weighted_subprompts]
parsed_conjunctions = [self.parse_conjunction(x) for x in strings]
flattened_prompts = [x.prompts[0] for x in parsed_conjunctions]
return Blend(prompts=flattened_prompts, weights=weights, normalize_weights=True)
def flatten(self, root: Conjunction) -> Conjunction:
"""
@ -596,4 +608,68 @@ def build_parser_syntax(attention_plus_base: float, attention_minus_base: float)
conjunction.set_debug(False)
# top-level is a conjunction of one or more blends or prompts
return conjunction
return conjunction, prompt
def split_weighted_subprompts(text, skip_normalize=False)->list:
"""
Legacy blend parsing.
grabs all text up to the first occurrence of ':'
uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
if ':' has no value defined, defaults to 1.0
repeats until no text remaining
"""
prompt_parser = re.compile("""
(?P<prompt> # capture group for 'prompt'
(?:\\\:|[^:])+ # match one or more non ':' characters or escaped colons '\:'
) # end 'prompt'
(?: # non-capture group
:+ # match one or more ':' characters
(?P<weight> # capture group for 'weight'
-?\d+(?:\.\d+)? # match positive or negative integer or decimal number
)? # end weight capture group, make optional
\s* # strip spaces after weight
| # OR
$ # else, if no ':' then match end of line
) # end non-capture group
""", re.VERBOSE)
parsed_prompts = [(match.group("prompt").replace("\\:", ":"), float(
match.group("weight") or 1)) for match in re.finditer(prompt_parser, text)]
if skip_normalize:
return parsed_prompts
weight_sum = sum(map(lambda x: x[1], parsed_prompts))
if weight_sum == 0:
print(
"Warning: Subprompt weights add up to zero. Discarding and using even weights instead.")
equal_weight = 1 / max(len(parsed_prompts), 1)
return [(x[0], equal_weight) for x in parsed_prompts]
return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
# shows how the prompt is tokenized
# usually tokens have '</w>' to indicate end-of-word,
# but for readability it has been replaced with ' '
def log_tokenization(text, model, log=False, weight=1):
if not log:
return
tokens = model.cond_stage_model.tokenizer._tokenize(text)
tokenized = ""
discarded = ""
usedTokens = 0
totalTokens = len(tokens)
for i in range(0, totalTokens):
token = tokens[i].replace('</w>', 'x` ')
# alternate color
s = (usedTokens % 6) + 1
if i < model.cond_stage_model.max_length:
tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
usedTokens += 1
else: # over max token length
discarded = discarded + f"\x1b[0;3{s};40m{token}"
print(f"\n>> Tokens ({usedTokens}), Weight ({weight:.2f}):\n{tokenized}\x1b[0m")
if discarded != "":
print(
f">> Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m"
)

41
tests/test_prompt_parser.py
View File

@ -9,7 +9,7 @@ from ldm.invoke.prompt_parser import PromptParser, Blend, Conjunction, Flattened
def parse_prompt(prompt_string):
pp = PromptParser()
#print(f"parsing '{prompt_string}'")
parse_result = pp.parse(prompt_string)
parse_result = pp.parse_conjunction(prompt_string)
#print(f"-> parsed '{prompt_string}' to {parse_result}")
return parse_result
@ -351,6 +351,45 @@ class PromptParserTestCase(unittest.TestCase):
self.assertEqual(Conjunction([FlattenedPrompt([('mountain', 1), CrossAttentionControlSubstitute([Fragment('(((', 1)], [Fragment('m(on))key', 1)])])]),
parse_prompt('mountain (\(\(\().swap(m\(on\)\)key)'))
def test_legacy_blend(self):
pp = PromptParser()
self.assertEqual(Blend([FlattenedPrompt([('mountain man', 1)]),
FlattenedPrompt([('man mountain', 1)])],
weights=[0.5,0.5]),
pp.parse_legacy_blend('mountain man:1 man mountain:1'))
self.assertEqual(Blend([FlattenedPrompt([('mountain', 1.1), ('man', 1)]),
FlattenedPrompt([('man', 1), ('mountain', 0.9)])],
weights=[0.5,0.5]),
pp.parse_legacy_blend('mountain+ man:1 man mountain-:1'))
self.assertEqual(Blend([FlattenedPrompt([('mountain', 1.1), ('man', 1)]),
FlattenedPrompt([('man', 1), ('mountain', 0.9)])],
weights=[0.5,0.5]),
pp.parse_legacy_blend('mountain+ man:1 man mountain-'))
self.assertEqual(Blend([FlattenedPrompt([('mountain', 1.1), ('man', 1)]),
FlattenedPrompt([('man', 1), ('mountain', 0.9)])],
weights=[0.5,0.5]),
pp.parse_legacy_blend('mountain+ man: man mountain-:'))
self.assertEqual(Blend([FlattenedPrompt([('mountain man', 1)]),
FlattenedPrompt([('man mountain', 1)])],
weights=[0.75,0.25]),
pp.parse_legacy_blend('mountain man:3 man mountain:1'))
self.assertEqual(Blend([FlattenedPrompt([('mountain man', 1)]),
FlattenedPrompt([('man mountain', 1)])],
weights=[1.0,0.0]),
pp.parse_legacy_blend('mountain man:3 man mountain:0'))
self.assertEqual(Blend([FlattenedPrompt([('mountain man', 1)]),
FlattenedPrompt([('man mountain', 1)])],
weights=[0.8,0.2]),
pp.parse_legacy_blend('"mountain man":4 man mountain'))
def test_single(self):
# todo handle this
#self.assertEqual(make_basic_conjunction(['a badly formed +test prompt']),