Merge branch 'main' into fix-inpainting

invokeai/frontend/web/src/features/lora/components/ParamLora.tsx

@@ -37,7 +37,7 @@ const ParamLora = (props: Props) => {
   return (
     <Flex sx={{ gap: 2.5, alignItems: 'flex-end' }}>
       <IAISlider
-        label={lora.name}
+        label={lora.model_name}
         value={lora.weight}
         onChange={handleChange}
         min={-1}

invokeai/frontend/web/src/features/lora/components/ParamLoraList.tsx

@@ -18,7 +18,7 @@ const selector = createSelector(
 const ParamLoraList = () => {
   const { loras } = useAppSelector(selector);
 
-  return map(loras, (lora) => <ParamLora key={lora.name} lora={lora} />);
+  return map(loras, (lora) => <ParamLora key={lora.model_name} lora={lora} />);
 };
 
 export default ParamLoraList;

invokeai/frontend/web/src/features/lora/store/loraSlice.ts

@@ -1,12 +1,8 @@
 import { PayloadAction, createSlice } from '@reduxjs/toolkit';
 import { LoRAModelParam } from 'features/parameters/store/parameterZodSchemas';
 import { LoRAModelConfigEntity } from 'services/api/endpoints/models';
-import { BaseModelType } from 'services/api/types';
 
-export type Lora = {
+export type Lora = LoRAModelParam & {
-  id: string;
-  base_model: BaseModelType;
-  name: string;
   weight: number;
 };
 
@@ -27,8 +23,8 @@ export const loraSlice = createSlice({
   initialState: intialLoraState,
   reducers: {
     loraAdded: (state, action: PayloadAction<LoRAModelConfigEntity>) => {
-      const { name, id, base_model } = action.payload;
+      const { model_name, id, base_model } = action.payload;
-      state.loras[id] = { id, name, base_model, ...defaultLoRAConfig };
+      state.loras[id] = { id, model_name, base_model, ...defaultLoRAConfig };
     },
     loraRemoved: (state, action: PayloadAction<string>) => {
       const id = action.payload;

invokeai/frontend/web/src/features/nodes/components/fields/LoRAModelInputFieldComponent.tsx

@@ -45,7 +45,7 @@ const LoRAModelInputFieldComponent = (
 
       data.push({
         value: id,
-        label: model.name,
+        label: model.model_name,
         group: BASE_MODEL_NAME_MAP[model.base_model],
       });
     });

invokeai/frontend/web/src/features/nodes/components/fields/ModelInputFieldComponent.tsx

@@ -38,7 +38,7 @@ const ModelInputFieldComponent = (
 
       data.push({
         value: id,
-        label: model.name,
+        label: model.model_name,
         group: BASE_MODEL_NAME_MAP[model.base_model],
       });
     });

invokeai/frontend/web/src/features/nodes/components/fields/VaeModelInputFieldComponent.tsx

@@ -45,7 +45,7 @@ const VaeModelInputFieldComponent = (
 
       data.push({
         value: id,
-        label: model.name,
+        label: model.model_name,
         group: BASE_MODEL_NAME_MAP[model.base_model],
       });
     });

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addControlNetToLinearGraph.ts

@@ -19,9 +19,9 @@ export const addControlNetToLinearGraph = (
 
   const validControlNets = getValidControlNets(controlNets);
 
-  const metadataAccumulator = graph.nodes[
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    METADATA_ACCUMULATOR
+    | MetadataAccumulatorInvocation
-  ] as MetadataAccumulatorInvocation;
+    | undefined;
 
   if (isControlNetEnabled && Boolean(validControlNets.length)) {
     if (validControlNets.length) {
@@ -79,13 +79,15 @@ export const addControlNetToLinearGraph = (
 
         graph.nodes[controlNetNode.id] = controlNetNode;
 
-        // metadata accumulator only needs a control field - not the whole node
+        if (metadataAccumulator) {
-        // extract what we need and add to the accumulator
+          // metadata accumulator only needs a control field - not the whole node
-        const controlField = omit(controlNetNode, [
+          // extract what we need and add to the accumulator
-          'id',
+          const controlField = omit(controlNetNode, [
-          'type',
+            'id',
-        ]) as ControlField;
+            'type',
-        metadataAccumulator.controlnets.push(controlField);
+          ]) as ControlField;
+          metadataAccumulator.controlnets.push(controlField);
+        }
 
         graph.edges.push({
           source: { node_id: controlNetNode.id, field: 'control' },

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addDynamicPromptsToGraph.ts

@@ -32,9 +32,9 @@ export const addDynamicPromptsToGraph = (
     maxPrompts,
   } = state.dynamicPrompts;
 
-  const metadataAccumulator = graph.nodes[
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    METADATA_ACCUMULATOR
+    | MetadataAccumulatorInvocation
-  ] as MetadataAccumulatorInvocation;
+    | undefined;
 
   if (isDynamicPromptsEnabled) {
     // iteration is handled via dynamic prompts
@@ -116,11 +116,15 @@ export const addDynamicPromptsToGraph = (
       (graph.nodes[NOISE] as NoiseInvocation).seed = seed;
 
       // hook up seed to metadata
-      metadataAccumulator.seed = seed;
+      if (metadataAccumulator) {
+        metadataAccumulator.seed = seed;
+      }
     }
   } else {
     // no dynamic prompt - hook up positive prompt
-    metadataAccumulator.positive_prompt = positivePrompt;
+    if (metadataAccumulator) {
+      metadataAccumulator.positive_prompt = positivePrompt;
+    }
 
     const rangeOfSizeNode: RangeOfSizeInvocation = {
       id: RANGE_OF_SIZE,

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts

@@ -30,9 +30,9 @@ export const addLoRAsToGraph = (
 
   const { loras } = state.lora;
   const loraCount = size(loras);
-  const metadataAccumulator = graph.nodes[
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    METADATA_ACCUMULATOR
+    | MetadataAccumulatorInvocation
-  ] as MetadataAccumulatorInvocation;
+    | undefined;
 
   if (loraCount > 0) {
     // Remove MAIN_MODEL_LOADER unet connection to feed it to LoRAs
@@ -70,7 +70,9 @@ export const addLoRAsToGraph = (
     };
 
     // add the lora to the metadata accumulator
-    metadataAccumulator.loras.push({ lora: loraField, weight });
+    if (metadataAccumulator) {
+      metadataAccumulator.loras.push({ lora: loraField, weight });
+    }
 
     // add to graph
     graph.nodes[currentLoraNodeId] = loraLoaderNode;

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts

@@ -22,9 +22,9 @@ export const addVAEToGraph = (
   const vae_model = modelIdToVAEModelField(vae?.id || '');
 
   const isAutoVae = !vae;
-  const metadataAccumulator = graph.nodes[
+  const metadataAccumulator = graph.nodes[METADATA_ACCUMULATOR] as
-    METADATA_ACCUMULATOR
+    | MetadataAccumulatorInvocation
-  ] as MetadataAccumulatorInvocation;
+    | undefined;
 
   if (!isAutoVae) {
     graph.nodes[VAE_LOADER] = {
@@ -73,7 +73,7 @@ export const addVAEToGraph = (
     });
   }
 
-  if (vae) {
+  if (vae && metadataAccumulator) {
     metadataAccumulator.vae = vae_model;
   }
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts

@@ -7,7 +7,6 @@ import {
   RandomIntInvocation,
   RangeOfSizeInvocation,
 } from 'services/api/types';
-import { modelIdToMainModelField } from '../modelIdToMainModelField';
 import { addLoRAsToGraph } from './addLoRAsToGraph';
 import { addVAEToGraph } from './addVAEToGraph';
 import {
@@ -35,7 +34,7 @@ export const buildCanvasInpaintGraph = (
   const {
     positivePrompt,
     negativePrompt,
-    model: currentModel,
+    model,
     cfgScale: cfg_scale,
     scheduler,
     steps,
@@ -53,14 +52,17 @@ export const buildCanvasInpaintGraph = (
     clipSkip,
   } = state.generation;
 
+  if (!model) {
+    moduleLog.error('No model found in state');
+    throw new Error('No model found in state');
+  }
+
   // The bounding box determines width and height, not the width and height params
   const { width, height } = state.canvas.boundingBoxDimensions;
 
   // We may need to set the inpaint width and height to scale the image
   const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
-  const model = modelIdToMainModelField(currentModel?.id || '');
-
   const graph: NonNullableGraph = {
     id: INPAINT_GRAPH,
     nodes: {

invokeai/frontend/web/src/features/nodes/util/parseSchema.ts

@@ -13,7 +13,7 @@ import {
   buildOutputFieldTemplates,
 } from './fieldTemplateBuilders';
 
-const RESERVED_FIELD_NAMES = ['id', 'type', 'is_intermediate', 'core_metadata'];
+const RESERVED_FIELD_NAMES = ['id', 'type', 'is_intermediate', 'metadata'];
 
 const invocationDenylist = [
   'Graph',

invokeai/frontend/web/src/features/parameters/store/parameterZodSchemas.ts

@@ -170,7 +170,7 @@ export const isValidVaeModel = (val: unknown): val is VaeModelParam =>
  */
 export const zLoRAModel = z.object({
   id: z.string(),
-  name: z.string(),
+  model_name: z.string(),
   base_model: zBaseModel,
 });
 /**