mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
196 lines
10 KiB
Python
196 lines
10 KiB
Python
'''
|
|
This module handles the generation of the conditioning tensors.
|
|
|
|
Useful function exports:
|
|
|
|
get_uc_and_c_and_ec() get the conditioned and unconditioned latent, and edited conditioning if we're doing cross-attention control
|
|
|
|
'''
|
|
import re
|
|
from difflib import SequenceMatcher
|
|
from typing import Union
|
|
|
|
import torch
|
|
|
|
from .prompt_parser import PromptParser, Blend, FlattenedPrompt, \
|
|
CrossAttentionControlledFragment, CrossAttentionControlSubstitute, Fragment, log_tokenization
|
|
from ..models.diffusion.cross_attention_control import CrossAttentionControl
|
|
from ..models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
|
|
from ..modules.encoders.modules import WeightedFrozenCLIPEmbedder
|
|
|
|
|
|
def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_normalize=False):
|
|
|
|
# Extract Unconditioned Words From Prompt
|
|
unconditioned_words = ''
|
|
unconditional_regex = r'\[(.*?)\]'
|
|
unconditionals = re.findall(unconditional_regex, prompt_string_uncleaned)
|
|
|
|
if len(unconditionals) > 0:
|
|
unconditioned_words = ' '.join(unconditionals)
|
|
|
|
# Remove Unconditioned Words From Prompt
|
|
unconditional_regex_compile = re.compile(unconditional_regex)
|
|
clean_prompt = unconditional_regex_compile.sub(' ', prompt_string_uncleaned)
|
|
prompt_string_cleaned = re.sub(' +', ' ', clean_prompt)
|
|
else:
|
|
prompt_string_cleaned = prompt_string_uncleaned
|
|
|
|
pp = PromptParser()
|
|
|
|
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(f">> Parsed prompt to {parsed_prompt}")
|
|
|
|
conditioning = None
|
|
cac_args:CrossAttentionControl.Arguments = None
|
|
|
|
if type(parsed_prompt) is Blend:
|
|
blend: Blend = parsed_prompt
|
|
embeddings_to_blend = None
|
|
for i,flattened_prompt in enumerate(blend.prompts):
|
|
this_embedding, _ = build_embeddings_and_tokens_for_flattened_prompt(model,
|
|
flattened_prompt,
|
|
log_tokens=log_tokens,
|
|
log_display_label=f"(blend part {i+1}, weight={blend.weights[i]})" )
|
|
embeddings_to_blend = this_embedding if embeddings_to_blend is None else torch.cat(
|
|
(embeddings_to_blend, this_embedding))
|
|
conditioning = WeightedFrozenCLIPEmbedder.apply_embedding_weights(embeddings_to_blend.unsqueeze(0),
|
|
blend.weights,
|
|
normalize=blend.normalize_weights)
|
|
else:
|
|
flattened_prompt: FlattenedPrompt = parsed_prompt
|
|
wants_cross_attention_control = type(flattened_prompt) is not Blend \
|
|
and any([issubclass(type(x), CrossAttentionControlledFragment) for x in flattened_prompt.children])
|
|
if wants_cross_attention_control:
|
|
original_prompt = FlattenedPrompt()
|
|
edited_prompt = FlattenedPrompt()
|
|
# for name, a0, a1, b0, b1 in edit_opcodes: only name == 'equal' is currently parsed
|
|
original_token_count = 0
|
|
edited_token_count = 0
|
|
edit_opcodes = []
|
|
edit_options = []
|
|
for fragment in flattened_prompt.children:
|
|
if type(fragment) is CrossAttentionControlSubstitute:
|
|
original_prompt.append(fragment.original)
|
|
edited_prompt.append(fragment.edited)
|
|
|
|
to_replace_token_count = get_tokens_length(model, fragment.original)
|
|
replacement_token_count = get_tokens_length(model, fragment.edited)
|
|
edit_opcodes.append(('replace',
|
|
original_token_count, original_token_count + to_replace_token_count,
|
|
edited_token_count, edited_token_count + replacement_token_count
|
|
))
|
|
original_token_count += to_replace_token_count
|
|
edited_token_count += replacement_token_count
|
|
edit_options.append(fragment.options)
|
|
#elif type(fragment) is CrossAttentionControlAppend:
|
|
# edited_prompt.append(fragment.fragment)
|
|
else:
|
|
# regular fragment
|
|
original_prompt.append(fragment)
|
|
edited_prompt.append(fragment)
|
|
|
|
count = get_tokens_length(model, [fragment])
|
|
edit_opcodes.append(('equal', original_token_count, original_token_count+count, edited_token_count, edited_token_count+count))
|
|
edit_options.append(None)
|
|
original_token_count += count
|
|
edited_token_count += count
|
|
original_embeddings, original_tokens = build_embeddings_and_tokens_for_flattened_prompt(model,
|
|
original_prompt,
|
|
log_tokens=log_tokens,
|
|
log_display_label="(.swap originals)")
|
|
# naïvely building a single edited_embeddings like this disregards the effects of changing the absolute location of
|
|
# subsequent tokens when there is >1 edit and earlier edits change the total token count.
|
|
# eg "a cat.swap(smiling dog, s_start=0.5) eating a hotdog.swap(pizza)" - when the 'pizza' edit is active but the
|
|
# 'cat' edit is not, the 'pizza' feature vector will nevertheless be affected by the introduction of the extra
|
|
# token 'smiling' in the inactive 'cat' edit.
|
|
# todo: build multiple edited_embeddings, one for each edit, and pass just the edited fragments through to the CrossAttentionControl functions
|
|
edited_embeddings, edited_tokens = build_embeddings_and_tokens_for_flattened_prompt(model,
|
|
edited_prompt,
|
|
log_tokens=log_tokens,
|
|
log_display_label="(.swap replacements)")
|
|
|
|
conditioning = original_embeddings
|
|
edited_conditioning = edited_embeddings
|
|
#print('>> got edit_opcodes', edit_opcodes, 'options', edit_options)
|
|
cac_args = CrossAttentionControl.Arguments(
|
|
edited_conditioning = edited_conditioning,
|
|
edit_opcodes = edit_opcodes,
|
|
edit_options = edit_options
|
|
)
|
|
else:
|
|
conditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model,
|
|
flattened_prompt,
|
|
log_tokens=log_tokens,
|
|
log_display_label="(prompt)")
|
|
|
|
unconditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model,
|
|
parsed_negative_prompt,
|
|
log_tokens=log_tokens,
|
|
log_display_label="(unconditioning)")
|
|
if isinstance(conditioning, dict):
|
|
# hybrid conditioning is in play
|
|
unconditioning, conditioning = flatten_hybrid_conditioning(unconditioning, conditioning)
|
|
if cac_args is not None:
|
|
print(">> Hybrid conditioning cannot currently be combined with cross attention control. Cross attention control will be ignored.")
|
|
cac_args = None
|
|
|
|
return (
|
|
unconditioning, conditioning, InvokeAIDiffuserComponent.ExtraConditioningInfo(
|
|
cross_attention_control_args=cac_args
|
|
)
|
|
)
|
|
|
|
|
|
def build_token_edit_opcodes(original_tokens, edited_tokens):
|
|
original_tokens = original_tokens.cpu().numpy()[0]
|
|
edited_tokens = edited_tokens.cpu().numpy()[0]
|
|
|
|
return SequenceMatcher(None, original_tokens, edited_tokens).get_opcodes()
|
|
|
|
def build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt: FlattenedPrompt, log_tokens: bool=False, log_display_label: str=None):
|
|
if type(flattened_prompt) is not FlattenedPrompt:
|
|
raise Exception(f"embeddings can only be made from FlattenedPrompts, got {type(flattened_prompt)} instead")
|
|
fragments = [x.text for x in flattened_prompt.children]
|
|
weights = [x.weight for x in flattened_prompt.children]
|
|
embeddings, tokens = model.get_learned_conditioning([fragments], return_tokens=True, fragment_weights=[weights])
|
|
if log_tokens:
|
|
text = " ".join(fragments)
|
|
log_tokenization(text, model, display_label=log_display_label)
|
|
|
|
return embeddings, tokens
|
|
|
|
def get_tokens_length(model, fragments: list[Fragment]):
|
|
fragment_texts = [x.text for x in fragments]
|
|
tokens = model.cond_stage_model.get_tokens(fragment_texts, include_start_and_end_markers=False)
|
|
return sum([len(x) for x in tokens])
|
|
|
|
def flatten_hybrid_conditioning(uncond, cond):
|
|
'''
|
|
This handles the choice between a conditional conditioning
|
|
that is a tensor (used by cross attention) vs one that has additional
|
|
dimensions as well, as used by 'hybrid'
|
|
'''
|
|
assert isinstance(uncond, dict)
|
|
assert isinstance(cond, dict)
|
|
cond_flattened = dict()
|
|
for k in cond:
|
|
if isinstance(cond[k], list):
|
|
cond_flattened[k] = [
|
|
torch.cat([uncond[k][i], cond[k][i]])
|
|
for i in range(len(cond[k]))
|
|
]
|
|
else:
|
|
cond_flattened[k] = torch.cat([uncond[k], cond[k]])
|
|
return uncond, cond_flattened
|
|
|
|
|