fix typegen error

fix typegen

invokeai/app/invocations/flux_lora_loader.py

@@ -117,7 +117,7 @@ class FLUXLoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to a FLUX transformer."""
 
     loras: Optional[LoRAField | list[LoRAField]] = InputField(
-        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+        default=[], description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
 
     transformer: Optional[TransformerField] = InputField(
@@ -151,8 +151,6 @@ class FLUXLoRACollectionLoader(BaseInvocation):
             output.clip = self.clip.model_copy(deep=True)
 
         for lora in loras:
-            if lora is None:
-                continue
             assert type(lora) is LoRAField
             if lora.lora.key in added_loras:
                 continue

invokeai/app/invocations/model.py

@@ -262,7 +262,7 @@ class LoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to the provided UNet and CLIP models."""
 
     loras: Optional[LoRAField | list[LoRAField]] = InputField(
-        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+        default=[], description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -288,8 +288,6 @@ class LoRACollectionLoader(BaseInvocation):
             output.clip = self.clip.model_copy(deep=True)
 
         for lora in loras:
-            if lora is None:
-                continue
             assert type(lora) is LoRAField
             if lora.lora.key in added_loras:
                 continue
@@ -410,7 +408,7 @@ class SDXLLoRACollectionLoader(BaseInvocation):
     """Applies a collection of SDXL LoRAs to the provided UNet and CLIP models."""
 
     loras: Optional[LoRAField | list[LoRAField]] = InputField(
-        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+        default=[], description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -446,8 +444,6 @@ class SDXLLoRACollectionLoader(BaseInvocation):
             output.clip2 = self.clip2.model_copy(deep=True)
 
         for lora in loras:
-            if lora is None:
-                continue
             assert type(lora) is LoRAField
             if lora.lora.key in added_loras:
                 continue