mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
model manager now running as a service
This commit is contained in:
@ -1,7 +1,6 @@
|
||||
from typing import Literal, Optional, Union
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from invokeai.app.invocations.util.choose_model import choose_model
|
||||
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
|
||||
|
||||
from ...backend.util.devices import choose_torch_device, torch_dtype
|
||||
@ -58,74 +57,74 @@ class CompelInvocation(BaseInvocation):
|
||||
def invoke(self, context: InvocationContext) -> CompelOutput:
|
||||
|
||||
# TODO: load without model
|
||||
model = choose_model(context.services.model_manager, self.model)
|
||||
pipeline = model.context.model
|
||||
tokenizer = pipeline.tokenizer
|
||||
text_encoder = pipeline.text_encoder
|
||||
model = context.services.model_manager.get_model(self.model)
|
||||
with model.context as pipeline:
|
||||
tokenizer = pipeline.tokenizer
|
||||
text_encoder = pipeline.text_encoder
|
||||
|
||||
# TODO: global? input?
|
||||
#use_full_precision = precision == "float32" or precision == "autocast"
|
||||
#use_full_precision = False
|
||||
# TODO: global? input?
|
||||
#use_full_precision = precision == "float32" or precision == "autocast"
|
||||
#use_full_precision = False
|
||||
|
||||
# TODO: redo TI when separate model loding implemented
|
||||
#textual_inversion_manager = TextualInversionManager(
|
||||
# tokenizer=tokenizer,
|
||||
# text_encoder=text_encoder,
|
||||
# full_precision=use_full_precision,
|
||||
#)
|
||||
# TODO: redo TI when separate model loding implemented
|
||||
#textual_inversion_manager = TextualInversionManager(
|
||||
# tokenizer=tokenizer,
|
||||
# text_encoder=text_encoder,
|
||||
# full_precision=use_full_precision,
|
||||
#)
|
||||
|
||||
def load_huggingface_concepts(concepts: list[str]):
|
||||
pipeline.textual_inversion_manager.load_huggingface_concepts(concepts)
|
||||
def load_huggingface_concepts(concepts: list[str]):
|
||||
pipeline.textual_inversion_manager.load_huggingface_concepts(concepts)
|
||||
|
||||
# apply the concepts library to the prompt
|
||||
prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers(
|
||||
self.prompt,
|
||||
lambda concepts: load_huggingface_concepts(concepts),
|
||||
pipeline.textual_inversion_manager.get_all_trigger_strings(),
|
||||
)
|
||||
# apply the concepts library to the prompt
|
||||
prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers(
|
||||
self.prompt,
|
||||
lambda concepts: load_huggingface_concepts(concepts),
|
||||
pipeline.textual_inversion_manager.get_all_trigger_strings(),
|
||||
)
|
||||
|
||||
# lazy-load any deferred textual inversions.
|
||||
# this might take a couple of seconds the first time a textual inversion is used.
|
||||
pipeline.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
|
||||
prompt_str
|
||||
)
|
||||
# lazy-load any deferred textual inversions.
|
||||
# this might take a couple of seconds the first time a textual inversion is used.
|
||||
pipeline.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
|
||||
prompt_str
|
||||
)
|
||||
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
text_encoder=text_encoder,
|
||||
textual_inversion_manager=pipeline.textual_inversion_manager,
|
||||
dtype_for_device_getter=torch_dtype,
|
||||
truncate_long_prompts=True, # TODO:
|
||||
)
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
text_encoder=text_encoder,
|
||||
textual_inversion_manager=pipeline.textual_inversion_manager,
|
||||
dtype_for_device_getter=torch_dtype,
|
||||
truncate_long_prompts=True, # TODO:
|
||||
)
|
||||
|
||||
# TODO: support legacy blend?
|
||||
# TODO: support legacy blend?
|
||||
|
||||
prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str)
|
||||
prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str)
|
||||
|
||||
if getattr(Globals, "log_tokenization", False):
|
||||
log_tokenization_for_prompt_object(prompt, tokenizer)
|
||||
if getattr(Globals, "log_tokenization", False):
|
||||
log_tokenization_for_prompt_object(prompt, tokenizer)
|
||||
|
||||
c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
|
||||
c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
|
||||
|
||||
# TODO: long prompt support
|
||||
#if not self.truncate_long_prompts:
|
||||
# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
|
||||
# TODO: long prompt support
|
||||
#if not self.truncate_long_prompts:
|
||||
# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
|
||||
|
||||
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
|
||||
tokens_count_including_eos_bos=get_max_token_count(tokenizer, prompt),
|
||||
cross_attention_control_args=options.get("cross_attention_control", None),
|
||||
)
|
||||
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
|
||||
tokens_count_including_eos_bos=get_max_token_count(tokenizer, prompt),
|
||||
cross_attention_control_args=options.get("cross_attention_control", None),
|
||||
)
|
||||
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
|
||||
|
||||
# TODO: hacky but works ;D maybe rename latents somehow?
|
||||
context.services.latents.set(conditioning_name, (c, ec))
|
||||
# TODO: hacky but works ;D maybe rename latents somehow?
|
||||
context.services.latents.set(conditioning_name, (c, ec))
|
||||
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
return CompelOutput(
|
||||
conditioning=ConditioningField(
|
||||
conditioning_name=conditioning_name,
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def get_max_token_count(
|
||||
|
Reference in New Issue
Block a user