mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
7e5ba2795e
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
123 lines
5.2 KiB
Python
123 lines
5.2 KiB
Python
import os
|
|
from builtins import float
|
|
from typing import List, 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.fields import FieldDescriptions, Input, InputField, OutputField
|
|
from invokeai.app.invocations.primitives import ImageField
|
|
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
|
|
from invokeai.backend.model_management.models.base import BaseModelType, ModelType
|
|
from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
|
|
|
|
|
|
class IPAdapterModelField(BaseModel):
|
|
model_name: str = Field(description="Name of the IP-Adapter model")
|
|
base_model: BaseModelType = Field(description="Base model")
|
|
|
|
model_config = ConfigDict(protected_namespaces=())
|
|
|
|
|
|
class CLIPVisionModelField(BaseModel):
|
|
model_name: str = Field(description="Name of the CLIP Vision image encoder model")
|
|
base_model: BaseModelType = Field(description="Base model (usually 'Any')")
|
|
|
|
model_config = ConfigDict(protected_namespaces=())
|
|
|
|
|
|
class IPAdapterField(BaseModel):
|
|
image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
|
|
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
|
|
image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
|
|
weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
|
|
begin_step_percent: float = Field(
|
|
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
|
|
)
|
|
end_step_percent: float = Field(
|
|
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
|
|
)
|
|
|
|
@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("ip_adapter_output")
|
|
class IPAdapterOutput(BaseInvocationOutput):
|
|
# Outputs
|
|
ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
|
|
|
|
|
|
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.2")
|
|
class IPAdapterInvocation(BaseInvocation):
|
|
"""Collects IP-Adapter info to pass to other nodes."""
|
|
|
|
# Inputs
|
|
image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
|
|
ip_adapter_model: IPAdapterModelField = InputField(
|
|
description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
|
|
)
|
|
|
|
weight: Union[float, List[float]] = InputField(
|
|
default=1, description="The weight given to the IP-Adapter", title="Weight"
|
|
)
|
|
begin_step_percent: float = InputField(
|
|
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
|
|
)
|
|
end_step_percent: float = InputField(
|
|
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
|
|
)
|
|
|
|
@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) -> IPAdapterOutput:
|
|
# Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
|
|
ip_adapter_info = context.models.get_info(
|
|
self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
|
|
)
|
|
# HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
|
|
# directly, and 2) we are reading from disk every time this invocation is called without caching the result.
|
|
# A better solution would be to store the image encoder model reference in the IP-Adapter model info, but this
|
|
# is currently messy due to differences between how the model info is generated when installing a model from
|
|
# disk vs. downloading the model.
|
|
image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
|
|
os.path.join(context.config.get().models_path, ip_adapter_info["path"])
|
|
)
|
|
image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
|
|
image_encoder_model = CLIPVisionModelField(
|
|
model_name=image_encoder_model_name,
|
|
base_model=BaseModelType.Any,
|
|
)
|
|
return IPAdapterOutput(
|
|
ip_adapter=IPAdapterField(
|
|
image=self.image,
|
|
ip_adapter_model=self.ip_adapter_model,
|
|
image_encoder_model=image_encoder_model,
|
|
weight=self.weight,
|
|
begin_step_percent=self.begin_step_percent,
|
|
end_step_percent=self.end_step_percent,
|
|
),
|
|
)
|