diff --git a/invokeai/app/invocations/flux_lora_loader.py b/invokeai/app/invocations/flux_lora_loader.py
index f4181032d72..ef364bd0fd6 100644
--- a/invokeai/app/invocations/flux_lora_loader.py
+++ b/invokeai/app/invocations/flux_lora_loader.py
@@ -110,14 +110,14 @@ def invoke(self, context: InvocationContext) -> FluxLoRALoaderOutput:
     title="FLUX LoRA Collection Loader",
     tags=["lora", "model", "flux"],
     category="model",
-    version="1.2.0",
+    version="1.3.0",
     classification=Classification.Prototype,
 )
 class FLUXLoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to a FLUX transformer."""
 
-    loras: LoRAField | list[LoRAField] = InputField(
-        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    loras: Optional[LoRAField | list[LoRAField]] = InputField(
+        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
 
     transformer: Optional[TransformerField] = InputField(
@@ -144,7 +144,19 @@ def invoke(self, context: InvocationContext) -> FluxLoRALoaderOutput:
         loras = self.loras if isinstance(self.loras, list) else [self.loras]
         added_loras: list[str] = []
 
+        if self.transformer is not None:
+            output.transformer = self.transformer.model_copy(deep=True)
+
+        if self.clip is not None:
+            output.clip = self.clip.model_copy(deep=True)
+
+        if self.t5_encoder is not None:
+            output.t5_encoder = self.t5_encoder.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
 
@@ -155,19 +167,13 @@ def invoke(self, context: InvocationContext) -> FluxLoRALoaderOutput:
 
             added_loras.append(lora.lora.key)
 
-            if self.transformer is not None:
-                if output.transformer is None:
-                    output.transformer = self.transformer.model_copy(deep=True)
+            if self.transformer is not None and output.transformer is not None:
                 output.transformer.loras.append(lora)
 
-            if self.clip is not None:
-                if output.clip is None:
-                    output.clip = self.clip.model_copy(deep=True)
+            if self.clip is not None and output.clip is not None:
                 output.clip.loras.append(lora)
 
-            if self.t5_encoder is not None:
-                if output.t5_encoder is None:
-                    output.t5_encoder = self.t5_encoder.model_copy(deep=True)
+            if self.t5_encoder is not None and output.t5_encoder is None:
                 output.t5_encoder.loras.append(lora)
 
         return output
diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py
index cdb4b5ccca6..a893d13fdfd 100644
--- a/invokeai/app/invocations/model.py
+++ b/invokeai/app/invocations/model.py
@@ -206,7 +206,7 @@ def invoke(self, context: InvocationContext) -> LoRALoaderOutput:
         lora_key = self.lora.key
 
         if not context.models.exists(lora_key):
-            raise Exception(f"Unkown lora: {lora_key}!")
+            raise Exception(f"Unknown lora: {lora_key}!")
 
         if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras):
             raise Exception(f'LoRA "{lora_key}" already applied to unet')
@@ -257,12 +257,12 @@ def invoke(self, context: InvocationContext) -> LoRASelectorOutput:
         return LoRASelectorOutput(lora=LoRAField(lora=self.lora, weight=self.weight))
 
 
-@invocation("lora_collection_loader", title="LoRA Collection Loader", tags=["model"], category="model", version="1.0.0")
+@invocation("lora_collection_loader", title="LoRA Collection Loader", tags=["model"], category="model", version="1.1.0")
 class LoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to the provided UNet and CLIP models."""
 
-    loras: LoRAField | list[LoRAField] = InputField(
-        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    loras: Optional[LoRAField | list[LoRAField]] = InputField(
+        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -282,7 +282,15 @@ def invoke(self, context: InvocationContext) -> LoRALoaderOutput:
         loras = self.loras if isinstance(self.loras, list) else [self.loras]
         added_loras: list[str] = []
 
+        if self.unet is not None:
+            output.unet = self.unet.model_copy(deep=True)
+        if self.clip is not None:
+            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
 
@@ -293,14 +301,10 @@ def invoke(self, context: InvocationContext) -> LoRALoaderOutput:
 
             added_loras.append(lora.lora.key)
 
-            if self.unet is not None:
-                if output.unet is None:
-                    output.unet = self.unet.model_copy(deep=True)
+            if self.unet is not None and output.unet is not None:
                 output.unet.loras.append(lora)
 
-            if self.clip is not None:
-                if output.clip is None:
-                    output.clip = self.clip.model_copy(deep=True)
+            if self.clip is not None and output.clip is not None:
                 output.clip.loras.append(lora)
 
         return output
@@ -400,13 +404,13 @@ def invoke(self, context: InvocationContext) -> SDXLLoRALoaderOutput:
     title="SDXL LoRA Collection Loader",
     tags=["model"],
     category="model",
-    version="1.0.0",
+    version="1.1.0",
 )
 class SDXLLoRACollectionLoader(BaseInvocation):
     """Applies a collection of SDXL LoRAs to the provided UNet and CLIP models."""
 
-    loras: LoRAField | list[LoRAField] = InputField(
-        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    loras: Optional[LoRAField | list[LoRAField]] = InputField(
+        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -432,7 +436,19 @@ def invoke(self, context: InvocationContext) -> SDXLLoRALoaderOutput:
         loras = self.loras if isinstance(self.loras, list) else [self.loras]
         added_loras: list[str] = []
 
+        if self.unet is not None:
+            output.unet = self.unet.model_copy(deep=True)
+
+        if self.clip is not None:
+            output.clip = self.clip.model_copy(deep=True)
+
+        if self.clip2 is not None:
+            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
 
@@ -443,19 +459,13 @@ def invoke(self, context: InvocationContext) -> SDXLLoRALoaderOutput:
 
             added_loras.append(lora.lora.key)
 
-            if self.unet is not None:
-                if output.unet is None:
-                    output.unet = self.unet.model_copy(deep=True)
+            if self.unet is not None and output.unet is not None:
                 output.unet.loras.append(lora)
 
-            if self.clip is not None:
-                if output.clip is None:
-                    output.clip = self.clip.model_copy(deep=True)
+            if self.clip is not None and output.clip is not None:
                 output.clip.loras.append(lora)
 
-            if self.clip2 is not None:
-                if output.clip2 is None:
-                    output.clip2 = self.clip2.model_copy(deep=True)
+            if self.clip2 is not None and output.clip2 is not None:
                 output.clip2.loras.append(lora)
 
         return output
@@ -473,7 +483,7 @@ def invoke(self, context: InvocationContext) -> VAEOutput:
         key = self.vae_model.key
 
         if not context.models.exists(key):
-            raise Exception(f"Unkown vae: {key}!")
+            raise Exception(f"Unknown vae: {key}!")
 
         return VAEOutput(vae=VAEField(vae=self.vae_model))
 
diff --git a/invokeai/frontend/web/src/services/api/schema.ts b/invokeai/frontend/web/src/services/api/schema.ts
index c8a506a370e..174e937421d 100644
--- a/invokeai/frontend/web/src/services/api/schema.ts
+++ b/invokeai/frontend/web/src/services/api/schema.ts
@@ -6201,7 +6201,7 @@ export type components = {
              * @description LoRA models and weights. May be a single LoRA or collection.
              * @default null
              */
-            loras?: components["schemas"]["LoRAField"] | components["schemas"]["LoRAField"][];
+            loras?: components["schemas"]["LoRAField"] | components["schemas"]["LoRAField"][] | null;
             /**
              * Transformer
              * @description Transformer
@@ -12206,7 +12206,7 @@ export type components = {
              * @description LoRA models and weights. May be a single LoRA or collection.
              * @default null
              */
-            loras?: components["schemas"]["LoRAField"] | components["schemas"]["LoRAField"][];
+            loras?: components["schemas"]["LoRAField"] | components["schemas"]["LoRAField"][] | null;
             /**
              * UNet
              * @description UNet (scheduler, LoRAs)
@@ -16054,7 +16054,7 @@ export type components = {
              * @description LoRA models and weights. May be a single LoRA or collection.
              * @default null
              */
-            loras?: components["schemas"]["LoRAField"] | components["schemas"]["LoRAField"][];
+            loras?: components["schemas"]["LoRAField"] | components["schemas"]["LoRAField"][] | null;
             /**
              * UNet
              * @description UNet (scheduler, LoRAs)