Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

Allow TIs to be either a key or a name in the prompt during our transition to using keys

Browse Source
This commit is contained in:
Brandon Rising 2024-02-27 12:26:51 -05:00 committed by psychedelicious
parent 94e3857110
commit 5922cee541
1 changed files with 47 additions and 25 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

72
invokeai/app/invocations/compel.py
View File

@ -3,7 +3,7 @@ from typing import Iterator, List, Optional, Tuple, Union
import torch
from compel import Compel, ReturnedEmbeddingsType
from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
from transformers import CLIPTokenizer
from transformers import CLIPTokenizer, CLIPTextModel
import invokeai.backend.util.logging as logger
from invokeai.app.invocations.fields import (
@ -18,7 +18,7 @@ from invokeai.app.services.model_records import UnknownModelException
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.ti_utils import extract_ti_triggers_from_prompt
from invokeai.backend.lora import LoRAModelRaw
from invokeai.backend.model_manager import ModelType
from invokeai.backend.model_manager.config import ModelType
from invokeai.backend.model_patcher import ModelPatcher
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
BasicConditioningInfo,
@ -70,7 +70,11 @@ class CompelInvocation(BaseInvocation):
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ConditioningOutput:
tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
tokenizer_model = tokenizer_info.model
assert isinstance(tokenizer_model, CLIPTokenizer)
text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
text_encoder_model = text_encoder_info.model
assert isinstance(text_encoder_model, CLIPTextModel)
def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
for lora in self.clip.loras:
@ -82,21 +86,29 @@ class CompelInvocation(BaseInvocation):
# loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
ti_list = []
ti_list: List[Tuple[str, TextualInversionModelRaw]] = []
for trigger in extract_ti_triggers_from_prompt(self.prompt):
name = trigger[1:-1]
name_or_key = trigger[1:-1]
try:
loaded_model = context.models.load(key=name).model
assert isinstance(loaded_model, TextualInversionModelRaw)
ti_list.append((name, loaded_model))
loaded_model = context.models.load(key=name_or_key)
model = loaded_model.model
assert isinstance(model, TextualInversionModelRaw)
ti_list.append((name_or_key, model))
except UnknownModelException:
# print(e)
# import traceback
# print(traceback.format_exc())
print(f'Warn: trigger: "{trigger}" not found')
try:
loaded_model = context.models.load_by_attrs(
model_name=name_or_key, base_model=text_encoder_info.config.base, model_type=ModelType.TextualInversion
)
model = loaded_model.model
assert isinstance(model, TextualInversionModelRaw)
ti_list.append((name_or_key, model))
except UnknownModelException:
logger.warning(f'trigger: "{trigger}" not found')
except ValueError:
logger.warning(f'trigger: "{trigger}" more than one similarly-named textual inversion models')
with (
ModelPatcher.apply_ti(tokenizer_info.model, text_encoder_info.model, ti_list) as (
ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
tokenizer,
ti_manager,
),
@ -106,6 +118,7 @@ class CompelInvocation(BaseInvocation):
# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
ModelPatcher.apply_clip_skip(text_encoder_info.model, self.clip.skipped_layers),
):
assert isinstance(text_encoder, CLIPTextModel)
compel = Compel(
tokenizer=tokenizer,
text_encoder=text_encoder,
@ -155,7 +168,11 @@ class SDXLPromptInvocationBase:
zero_on_empty: bool,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[ExtraConditioningInfo]]:
tokenizer_info = context.models.load(**clip_field.tokenizer.model_dump())
tokenizer_model = tokenizer_info.model
assert isinstance(tokenizer_model, CLIPTokenizer)
text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
text_encoder_model = text_encoder_info.model
assert isinstance(text_encoder_model, CLIPTextModel)
# return zero on empty
if prompt == "" and zero_on_empty:
@ -189,25 +206,29 @@ class SDXLPromptInvocationBase:
# loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
ti_list = []
ti_list: List[Tuple[str, TextualInversionModelRaw]] = []
for trigger in extract_ti_triggers_from_prompt(prompt):
name = trigger[1:-1]
name_or_key = trigger[1:-1]
try:
ti_model = context.models.load_by_attrs(
model_name=name, base_model=text_encoder_info.config.base, model_type=ModelType.TextualInversion
).model
assert isinstance(ti_model, TextualInversionModelRaw)
ti_list.append((name, ti_model))
loaded_model = context.models.load(key=name_or_key)
model = loaded_model.model
assert isinstance(model, TextualInversionModelRaw)
ti_list.append((name_or_key, model))
except UnknownModelException:
# print(e)
# import traceback
# print(traceback.format_exc())
logger.warning(f'trigger: "{trigger}" not found')
try:
loaded_model = context.models.load_by_attrs(
model_name=name_or_key, base_model=text_encoder_info.config.base, model_type=ModelType.TextualInversion
)
model = loaded_model.model
assert isinstance(model, TextualInversionModelRaw)
ti_list.append((name_or_key, model))
except UnknownModelException:
logger.warning(f'trigger: "{trigger}" not found')
except ValueError:
logger.warning(f'trigger: "{trigger}" more than one similarly-named textual inversion models')
with (
ModelPatcher.apply_ti(tokenizer_info.model, text_encoder_info.model, ti_list) as (
ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
tokenizer,
ti_manager,
),
@ -215,8 +236,9 @@ class SDXLPromptInvocationBase:
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
# Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
ModelPatcher.apply_clip_skip(text_encoder_info.model, clip_field.skipped_layers),
ModelPatcher.apply_clip_skip(text_encoder_model, clip_field.skipped_layers),
):
assert isinstance(text_encoder, CLIPTextModel)
compel = Compel(
tokenizer=tokenizer,
text_encoder=text_encoder,