mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
121 lines
5.3 KiB
Python
121 lines
5.3 KiB
Python
from builtins import float
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from typing import List, Union
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from pydantic import BaseModel, Field, field_validator, model_validator
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from typing_extensions import Self
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from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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invocation,
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invocation_output,
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)
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from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
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from invokeai.app.invocations.model import ModelIdentifierField
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from invokeai.app.invocations.primitives import ImageField
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from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, IPAdapterConfig, ModelType
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class IPAdapterField(BaseModel):
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image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
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ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model to use.")
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image_encoder_model: ModelIdentifierField = Field(description="The name of the CLIP image encoder model.")
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weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
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begin_step_percent: float = Field(
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default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
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)
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end_step_percent: float = Field(
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default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
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)
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@field_validator("weight")
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@classmethod
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def validate_ip_adapter_weight(cls, v: float) -> float:
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validate_weights(v)
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return v
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@model_validator(mode="after")
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def validate_begin_end_step_percent(self) -> Self:
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validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
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return self
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@invocation_output("ip_adapter_output")
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class IPAdapterOutput(BaseInvocationOutput):
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# Outputs
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ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
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@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.2")
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class IPAdapterInvocation(BaseInvocation):
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"""Collects IP-Adapter info to pass to other nodes."""
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# Inputs
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image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
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ip_adapter_model: ModelIdentifierField = InputField(
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description="The IP-Adapter model.",
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title="IP-Adapter Model",
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input=Input.Direct,
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ui_order=-1,
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ui_type=UIType.IPAdapterModel,
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)
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weight: Union[float, List[float]] = InputField(
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default=1, description="The weight given to the IP-Adapter", title="Weight"
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)
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begin_step_percent: float = InputField(
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default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
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)
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end_step_percent: float = InputField(
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default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
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)
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@field_validator("weight")
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@classmethod
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def validate_ip_adapter_weight(cls, v: float) -> float:
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validate_weights(v)
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return v
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@model_validator(mode="after")
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def validate_begin_end_step_percent(self) -> Self:
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validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
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return self
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def invoke(self, context: InvocationContext) -> IPAdapterOutput:
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# Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
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ip_adapter_info = context.models.get_config(self.ip_adapter_model.key)
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assert isinstance(ip_adapter_info, IPAdapterConfig)
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image_encoder_model_id = ip_adapter_info.image_encoder_model_id
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image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
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image_encoder_model = self._get_image_encoder(context, image_encoder_model_name)
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return IPAdapterOutput(
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ip_adapter=IPAdapterField(
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image=self.image,
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ip_adapter_model=self.ip_adapter_model,
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image_encoder_model=ModelIdentifierField.from_config(image_encoder_model),
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weight=self.weight,
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begin_step_percent=self.begin_step_percent,
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end_step_percent=self.end_step_percent,
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),
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)
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def _get_image_encoder(self, context: InvocationContext, image_encoder_model_name: str) -> AnyModelConfig:
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found = False
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while not found:
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image_encoder_models = context.models.search_by_attrs(
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name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision
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)
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found = len(image_encoder_models) > 0
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if not found:
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context.logger.warning(
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f"The image encoder required by this IP Adapter ({image_encoder_model_name}) is not installed."
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)
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context.logger.warning("Downloading and installing now. This may take a while.")
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installer = context._services.model_manager.install
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job = installer.heuristic_import(f"InvokeAI/{image_encoder_model_name}")
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installer.wait_for_job(job, timeout=600) # wait up to 10 minutes - then raise a TimeoutException
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assert len(image_encoder_models) == 1
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return image_encoder_models[0]
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