diff --git a/invokeai/app/invocations/model.py b/invokeai/app/invocations/model.py
index c3dde5e725..548ff3cb4b 100644
--- a/invokeai/app/invocations/model.py
+++ b/invokeai/app/invocations/model.py
@@ -249,7 +249,7 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
     lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
-    weight: float = Field(default=0.75, description=FieldDescriptions.lora_weight)
+    weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
     unet: Optional[UNetField] = Field(
         default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNET"
     )
diff --git a/invokeai/backend/util/mps_fixes.py b/invokeai/backend/util/mps_fixes.py
index 8a4e6baab5..ce21d33b88 100644
--- a/invokeai/backend/util/mps_fixes.py
+++ b/invokeai/backend/util/mps_fixes.py
@@ -1,7 +1,7 @@
 import math
-import torch
-import diffusers
 
+import diffusers
+import torch
 
 if torch.backends.mps.is_available():
     torch.empty = torch.zeros
diff --git a/invokeai/frontend/web/src/features/controlNet/components/ControlNetImagePreview.tsx b/invokeai/frontend/web/src/features/controlNet/components/ControlNetImagePreview.tsx
index d3ab3c3817..d2c37445a3 100644
--- a/invokeai/frontend/web/src/features/controlNet/components/ControlNetImagePreview.tsx
+++ b/invokeai/frontend/web/src/features/controlNet/components/ControlNetImagePreview.tsx
@@ -104,22 +104,22 @@ const ControlNetImagePreview = ({ isSmall, controlNet }: Props) => {
   ]);
 
   const handleSetControlImageToDimensions = useCallback(() => {
-    if (!processedControlImage) {
+    if (!controlImage) {
       return;
     }
 
     if (activeTabName === 'unifiedCanvas') {
       dispatch(
         setBoundingBoxDimensions({
-          width: processedControlImage.width,
-          height: processedControlImage.height,
+          width: controlImage.width,
+          height: controlImage.height,
         })
       );
     } else {
-      dispatch(setWidth(processedControlImage.width));
-      dispatch(setHeight(processedControlImage.height));
+      dispatch(setWidth(controlImage.width));
+      dispatch(setHeight(controlImage.height));
     }
-  }, [processedControlImage, activeTabName, dispatch]);
+  }, [controlImage, activeTabName, dispatch]);
 
   const handleMouseEnter = useCallback(() => {
     setIsMouseOverImage(true);
diff --git a/invokeai/frontend/web/src/features/nodes/types/types.ts b/invokeai/frontend/web/src/features/nodes/types/types.ts
index 7783393d6f..a2de44cb38 100644
--- a/invokeai/frontend/web/src/features/nodes/types/types.ts
+++ b/invokeai/frontend/web/src/features/nodes/types/types.ts
@@ -1,4 +1,3 @@
-import { store } from 'app/store/store';
 import {
   SchedulerParam,
   zBaseModel,
@@ -10,7 +9,6 @@ import { keyBy } from 'lodash-es';
 import { OpenAPIV3 } from 'openapi-types';
 import { RgbaColor } from 'react-colorful';
 import { Node } from 'reactflow';
-import { JsonObject } from 'type-fest';
 import { Graph, ImageDTO, _InputField, _OutputField } from 'services/api/types';
 import {
   AnyInvocationType,
@@ -18,6 +16,7 @@ import {
   ProgressImage,
 } from 'services/events/types';
 import { O } from 'ts-toolbelt';
+import { JsonObject } from 'type-fest';
 import { z } from 'zod';
 
 export type NonNullableGraph = O.Required<Graph, 'nodes' | 'edges'>;
@@ -936,22 +935,10 @@ export const zWorkflow = z.object({
 });
 
 export const zValidatedWorkflow = zWorkflow.transform((workflow) => {
-  const nodeTemplates = store.getState().nodes.nodeTemplates;
   const { nodes, edges } = workflow;
   const warnings: WorkflowWarning[] = [];
   const invocationNodes = nodes.filter(isWorkflowInvocationNode);
   const keyedNodes = keyBy(invocationNodes, 'id');
-  invocationNodes.forEach((node, i) => {
-    const nodeTemplate = nodeTemplates[node.data.type];
-    if (!nodeTemplate) {
-      warnings.push({
-        message: `Node "${node.data.label || node.data.id}" skipped`,
-        issues: [`Unable to find template for type "${node.data.type}"`],
-        data: node,
-      });
-      delete nodes[i];
-    }
-  });
   edges.forEach((edge, i) => {
     const sourceNode = keyedNodes[edge.source];
     const targetNode = keyedNodes[edge.target];