Make FluxControlLoRALoaderOutput.control_lora non-optional.

invokeai/app/invocations/flux_control_lora_loader.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -16,8 +14,8 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 class FluxControlLoRALoaderOutput(BaseInvocationOutput):
     """Flux Control LoRA Loader Output"""
 
-    control_lora: Optional[ControlLoRAField] = OutputField(
-        title="Flux Control Lora", description="Control LoRAs to apply on model loading", default=None
+    control_lora: ControlLoRAField = OutputField(
+        title="Flux Control LoRA", description="Control LoRAs to apply on model loading", default=None
     )
 
 
@@ -26,7 +24,7 @@ class FluxControlLoRALoaderOutput(BaseInvocationOutput):
     title="Flux Control LoRA",
     tags=["lora", "model", "flux"],
     category="model",
-    version="1.1.0",
+    version="1.0.0",
     classification=Classification.Prototype,
 )
 class FluxControlLoRALoaderInvocation(BaseInvocation):
@@ -35,9 +33,7 @@ class FluxControlLoRALoaderInvocation(BaseInvocation):
     lora: ModelIdentifierField = InputField(
         description=FieldDescriptions.control_lora_model, title="Control LoRA", ui_type=UIType.ControlLoRAModel
     )
-    image: ImageField = InputField(
-        description="The image to encode.",
-    )
+    image: ImageField = InputField(description="The image to encode.")
 
     def invoke(self, context: InvocationContext) -> FluxControlLoRALoaderOutput:
         lora_key = self.lora.key
@@ -45,11 +41,10 @@ class FluxControlLoRALoaderInvocation(BaseInvocation):
         if not context.models.exists(lora_key):
             raise ValueError(f"Unknown lora: {lora_key}!")
 
-        output = FluxControlLoRALoaderOutput()
-
-        output.control_lora = ControlLoRAField(
-            lora=self.lora,
-            img=self.image,
-            weight=1,
+        return FluxControlLoRALoaderOutput(
+            control_lora=ControlLoRAField(
+                lora=self.lora,
+                img=self.image,
+                weight=1,
+            )
         )
-        return output