mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
make conditioning.py work with compel 1.1.5
This commit is contained in:
Lincoln Stein
parent
fb6ef61a4d
commit
5d5157fc65
@ -16,6 +16,7 @@ from compel.prompt_parser import (
|
||||
FlattenedPrompt,
|
||||
Fragment,
|
||||
PromptParser,
|
||||
Conjunction,
|
||||
)
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
@ -25,58 +26,51 @@ from ..stable_diffusion import InvokeAIDiffuserComponent
|
||||
from ..util import torch_dtype
|
||||
|
||||
|
||||
def get_uc_and_c_and_ec(
|
||||
prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False
|
||||
):
|
||||
def get_uc_and_c_and_ec(prompt_string,
|
||||
model: InvokeAIDiffuserComponent,
|
||||
log_tokens=False, skip_normalize_legacy_blend=False):
|
||||
# lazy-load any deferred textual inversions.
|
||||
# this might take a couple of seconds the first time a textual inversion is used.
|
||||
model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
|
||||
prompt_string
|
||||
)
|
||||
model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(prompt_string)
|
||||
|
||||
tokenizer = model.tokenizer
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
text_encoder=model.text_encoder,
|
||||
textual_inversion_manager=model.textual_inversion_manager,
|
||||
dtype_for_device_getter=torch_dtype,
|
||||
truncate_long_prompts=False
|
||||
)
|
||||
compel = Compel(tokenizer=model.tokenizer,
|
||||
text_encoder=model.text_encoder,
|
||||
textual_inversion_manager=model.textual_inversion_manager,
|
||||
dtype_for_device_getter=torch_dtype,
|
||||
truncate_long_prompts=False,
|
||||
)
|
||||
|
||||
# get rid of any newline characters
|
||||
prompt_string = prompt_string.replace("\n", " ")
|
||||
(
|
||||
positive_prompt_string,
|
||||
negative_prompt_string,
|
||||
) = split_prompt_to_positive_and_negative(prompt_string)
|
||||
legacy_blend = try_parse_legacy_blend(
|
||||
positive_prompt_string, skip_normalize_legacy_blend
|
||||
)
|
||||
positive_prompt: Union[FlattenedPrompt, Blend]
|
||||
positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
|
||||
|
||||
legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
|
||||
positive_conjunction: Conjunction
|
||||
if legacy_blend is not None:
|
||||
positive_prompt = legacy_blend
|
||||
positive_conjunction = legacy_blend
|
||||
else:
|
||||
positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
|
||||
negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
|
||||
negative_prompt_string
|
||||
)
|
||||
positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
|
||||
positive_prompt = positive_conjunction.prompts[0]
|
||||
|
||||
negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
|
||||
negative_prompt: FlattenedPrompt | Blend = negative_conjunction.prompts[0]
|
||||
|
||||
tokens_count = get_max_token_count(model.tokenizer, positive_prompt)
|
||||
if log_tokens or getattr(Globals, "log_tokenization", False):
|
||||
log_tokenization(positive_prompt, negative_prompt, tokenizer=tokenizer)
|
||||
log_tokenization(positive_prompt, negative_prompt, tokenizer=model.tokenizer)
|
||||
|
||||
c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
|
||||
uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
|
||||
[c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
|
||||
with InvokeAIDiffuserComponent.custom_attention_context(model.unet,
|
||||
extra_conditioning_info=None,
|
||||
step_count=-1):
|
||||
c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
|
||||
uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
|
||||
|
||||
tokens_count = get_max_token_count(tokenizer, positive_prompt)
|
||||
|
||||
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
|
||||
tokens_count_including_eos_bos=tokens_count,
|
||||
cross_attention_control_args=options.get("cross_attention_control", None),
|
||||
)
|
||||
# now build the "real" ec
|
||||
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
|
||||
cross_attention_control_args=options.get(
|
||||
'cross_attention_control', None))
|
||||
return uc, c, ec
|
||||
|
||||
|
||||
def get_prompt_structure(
|
||||
prompt_string, skip_normalize_legacy_blend: bool = False
|
||||
) -> (Union[FlattenedPrompt, Blend], FlattenedPrompt):
|
||||
@ -87,18 +81,17 @@ def get_prompt_structure(
|
||||
legacy_blend = try_parse_legacy_blend(
|
||||
positive_prompt_string, skip_normalize_legacy_blend
|
||||
)
|
||||
positive_prompt: Union[FlattenedPrompt, Blend]
|
||||
positive_prompt: Conjunction
|
||||
if legacy_blend is not None:
|
||||
positive_prompt = legacy_blend
|
||||
positive_conjunction = legacy_blend
|
||||
else:
|
||||
positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
|
||||
negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
|
||||
negative_prompt_string
|
||||
)
|
||||
positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
|
||||
positive_prompt = positive_conjunction.prompts[0]
|
||||
negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
|
||||
negative_prompt: FlattenedPrompt|Blend = negative_conjunction.prompts[0]
|
||||
|
||||
return positive_prompt, negative_prompt
|
||||
|
||||
|
||||
def get_max_token_count(
|
||||
tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
|
||||
) -> int:
|
||||
@ -245,22 +238,21 @@ def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_t
|
||||
logger.info(f"[TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
|
||||
logger.debug(f"{discarded}\x1b[0m")
|
||||
|
||||
|
||||
def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Blend]:
|
||||
def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Conjunction]:
|
||||
weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
|
||||
if len(weighted_subprompts) <= 1:
|
||||
return None
|
||||
strings = [x[0] for x in weighted_subprompts]
|
||||
weights = [x[1] for x in weighted_subprompts]
|
||||
|
||||
pp = PromptParser()
|
||||
parsed_conjunctions = [pp.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=not skip_normalize
|
||||
)
|
||||
|
||||
flattened_prompts = []
|
||||
weights = []
|
||||
for i, x in enumerate(parsed_conjunctions):
|
||||
if len(x.prompts)>0:
|
||||
flattened_prompts.append(x.prompts[0])
|
||||
weights.append(weighted_subprompts[i][1])
|
||||
return Conjunction([Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)])
|
||||
|
||||
def split_weighted_subprompts(text, skip_normalize=False) -> list:
|
||||
"""
|
||||
|
||||
Loading…
Reference in New Issue
Block a user