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50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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Classification,
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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, ImageField, InputField, OutputField, UIType
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from invokeai.app.invocations.model import ControlLoRAField, ModelIdentifierField
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from invokeai.app.services.shared.invocation_context import InvocationContext
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@invocation_output("flux_control_lora_loader_output")
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class FluxControlLoRALoaderOutput(BaseInvocationOutput):
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"""Flux Control LoRA Loader Output"""
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control_lora: ControlLoRAField = OutputField(
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title="Flux Control LoRA", description="Control LoRAs to apply on model loading", default=None
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)
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@invocation(
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"flux_control_lora_loader",
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title="Flux Control LoRA",
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tags=["lora", "model", "flux"],
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category="model",
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version="1.1.0",
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classification=Classification.Prototype,
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)
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class FluxControlLoRALoaderInvocation(BaseInvocation):
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"""LoRA model and Image to use with FLUX transformer generation."""
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lora: ModelIdentifierField = InputField(
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description=FieldDescriptions.control_lora_model, title="Control LoRA", ui_type=UIType.ControlLoRAModel
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)
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image: ImageField = InputField(description="The image to encode.")
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weight: float = InputField(description="The weight of the LoRA.", default=1.0)
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def invoke(self, context: InvocationContext) -> FluxControlLoRALoaderOutput:
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if not context.models.exists(self.lora.key):
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raise ValueError(f"Unknown lora: {self.lora.key}!")
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return FluxControlLoRALoaderOutput(
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control_lora=ControlLoRAField(
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lora=self.lora,
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img=self.image,
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weight=self.weight,
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)
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)
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