InvokeAI/invokeai/app/invocations/ip_adapter.py
2023-09-14 16:48:47 -04:00

74 lines
2.8 KiB
Python

from pydantic import BaseModel, Field
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
FieldDescriptions,
Input,
InputField,
InvocationContext,
OutputField,
UIType,
invocation,
invocation_output,
)
from invokeai.app.invocations.primitives import ImageField
from invokeai.backend.model_management.models.base import BaseModelType, ModelType
class IPAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the IP-Adapter model")
base_model: BaseModelType = Field(description="Base model")
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')")
class IPAdapterField(BaseModel):
image: ImageField = Field(description="The IP-Adapter image prompt.")
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: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
@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="1.0.0")
class IPAdapterInvocation(BaseInvocation):
"""Collects IP-Adapter info to pass to other nodes."""
# Inputs
image: ImageField = InputField(description="The IP-Adapter image prompt.")
ip_adapter_model: IPAdapterModelField = InputField(
description="The IP-Adapter model.",
title="IP-Adapter Model",
input=Input.Direct,
)
weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
def invoke(self, context: InvocationContext) -> IPAdapterOutput:
# Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
ip_adapter_info = context.services.model_manager.model_info(
self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
)
image_encoder_model_name = ip_adapter_info["image_encoder_model"].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,
),
)