InvokeAI/invokeai/app/invocations/t2i_adapter.py
psychedelicious 7e5ba2795e feat(nodes): update all invocations to use new invocation context
Update all invocations to use the new context. The changes are all fairly simple, but there are a lot of them.

Supporting minor changes:
- Patch bump for all nodes that use the context
- Update invocation processor to provide new context
- Minor change to `EventServiceBase` to accept a node's ID instead of the dict version of a node
- Minor change to `ModelManagerService` to support the new wrapped context
- Fanagling of imports to avoid circular dependencies
2024-02-15 17:30:02 +11:00

103 lines
3.9 KiB
Python

from typing import Union
from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
invocation,
invocation_output,
)
from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.backend.model_management.models.base import BaseModelType
class T2IAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the T2I-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
class T2IAdapterField(BaseModel):
image: ImageField = Field(description="The T2I-Adapter image prompt.")
t2i_adapter_model: T2IAdapterModelField = Field(description="The T2I-Adapter model to use.")
weight: Union[float, list[float]] = Field(default=1, description="The weight given to the T2I-Adapter")
begin_step_percent: float = Field(
default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
)
end_step_percent: float = Field(
default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
)
resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
@field_validator("weight")
@classmethod
def validate_ip_adapter_weight(cls, v):
validate_weights(v)
return v
@model_validator(mode="after")
def validate_begin_end_step_percent(self):
validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
return self
@invocation_output("t2i_adapter_output")
class T2IAdapterOutput(BaseInvocationOutput):
t2i_adapter: T2IAdapterField = OutputField(description=FieldDescriptions.t2i_adapter, title="T2I Adapter")
@invocation(
"t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.1"
)
class T2IAdapterInvocation(BaseInvocation):
"""Collects T2I-Adapter info to pass to other nodes."""
# Inputs
image: ImageField = InputField(description="The IP-Adapter image prompt.")
t2i_adapter_model: T2IAdapterModelField = InputField(
description="The T2I-Adapter model.",
title="T2I-Adapter Model",
input=Input.Direct,
ui_order=-1,
)
weight: Union[float, list[float]] = InputField(
default=1, ge=0, description="The weight given to the T2I-Adapter", title="Weight"
)
begin_step_percent: float = InputField(
default=0, ge=0, le=1, description="When the T2I-Adapter is first applied (% of total steps)"
)
end_step_percent: float = InputField(
default=1, ge=0, le=1, description="When the T2I-Adapter is last applied (% of total steps)"
)
resize_mode: CONTROLNET_RESIZE_VALUES = InputField(
default="just_resize",
description="The resize mode applied to the T2I-Adapter input image so that it matches the target output size.",
)
@field_validator("weight")
@classmethod
def validate_ip_adapter_weight(cls, v):
validate_weights(v)
return v
@model_validator(mode="after")
def validate_begin_end_step_percent(self):
validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
return self
def invoke(self, context) -> T2IAdapterOutput:
return T2IAdapterOutput(
t2i_adapter=T2IAdapterField(
image=self.image,
t2i_adapter_model=self.t2i_adapter_model,
weight=self.weight,
begin_step_percent=self.begin_step_percent,
end_step_percent=self.end_step_percent,
resize_mode=self.resize_mode,
)
)