Merge branch 'main' into chore/clean-up-unused-files

Author: blessedcoolant

invokeai/app/invocations/compel.py

@@ -279,8 +279,8 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     crop_left: int = InputField(default=0, description="")
     target_width: int = InputField(default=1024, description="")
     target_height: int = InputField(default=1024, description="")
-    clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
-    clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
+    clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1")
+    clip2: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2")
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:

invokeai/app/invocations/metadata.py

@@ -72,10 +72,10 @@ class CoreMetadata(BaseModelExcludeNull):
     )
     refiner_steps: Optional[int] = Field(default=None, description="The number of steps used for the refiner")
     refiner_scheduler: Optional[str] = Field(default=None, description="The scheduler used for the refiner")
-    refiner_positive_aesthetic_store: Optional[float] = Field(
+    refiner_positive_aesthetic_score: Optional[float] = Field(
         default=None, description="The aesthetic score used for the refiner"
     )
-    refiner_negative_aesthetic_store: Optional[float] = Field(
+    refiner_negative_aesthetic_score: Optional[float] = Field(
         default=None, description="The aesthetic score used for the refiner"
     )
     refiner_start: Optional[float] = Field(default=None, description="The start value used for refiner denoising")
@@ -160,11 +160,11 @@ class MetadataAccumulatorInvocation(BaseInvocation):
         default=None,
         description="The scheduler used for the refiner",
     )
-    refiner_positive_aesthetic_store: Optional[float] = InputField(
+    refiner_positive_aesthetic_score: Optional[float] = InputField(
         default=None,
         description="The aesthetic score used for the refiner",
     )
-    refiner_negative_aesthetic_store: Optional[float] = InputField(
+    refiner_negative_aesthetic_score: Optional[float] = InputField(
         default=None,
         description="The aesthetic score used for the refiner",
     )

invokeai/app/invocations/model.py

@@ -250,13 +250,13 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
 
     lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
     weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
-    unet: Optional[UNetField] = Field(
-        default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNET"
+    unet: Optional[UNetField] = InputField(
+        default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
     )
-    clip: Optional[ClipField] = Field(
+    clip: Optional[ClipField] = InputField(
         default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 1"
     )
-    clip2: Optional[ClipField] = Field(
+    clip2: Optional[ClipField] = InputField(
         default=None, description=FieldDescriptions.clip, input=Input.Connection, title="CLIP 2"
     )
 

invokeai/backend/model_management/model_probe.py

@@ -50,6 +50,7 @@ class ModelProbe(object):
         "StableDiffusionInpaintPipeline": ModelType.Main,
         "StableDiffusionXLPipeline": ModelType.Main,
         "StableDiffusionXLImg2ImgPipeline": ModelType.Main,
+        "StableDiffusionXLInpaintPipeline": ModelType.Main,
         "AutoencoderKL": ModelType.Vae,
         "ControlNetModel": ModelType.ControlNet,
     }

invokeai/frontend/web/src/features/gallery/components/CurrentImage/CurrentImageButtons.tsx

@@ -110,7 +110,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
   );
 
   const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
-    lastSelectedImage?.image_name ?? skipToken,
+    lastSelectedImage ?? skipToken,
     {
       selectFromResult: (res) => ({
         isLoading: res.isFetching,

invokeai/frontend/web/src/features/gallery/components/ImageContextMenu/SingleSelectionMenuItems.tsx

@@ -52,7 +52,7 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
   const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
 
   const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
-    imageDTO.image_name,
+    imageDTO,
     {
       selectFromResult: (res) => ({
         isLoading: res.isFetching,

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx

@@ -101,13 +101,15 @@ const ImageMetadataActions = (props: Props) => {
           onClick={handleRecallSeed}
         />
       )}
-      {metadata.model !== undefined && metadata.model !== null && (
-        <ImageMetadataItem
-          label="Model"
-          value={metadata.model.model_name}
-          onClick={handleRecallModel}
-        />
-      )}
+      {metadata.model !== undefined &&
+        metadata.model !== null &&
+        metadata.model.model_name && (
+          <ImageMetadataItem
+            label="Model"
+            value={metadata.model.model_name}
+            onClick={handleRecallModel}
+          />
+        )}
       {metadata.width && (
         <ImageMetadataItem
           label="Width"

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataViewer.tsx

@@ -27,15 +27,12 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
   //   dispatch(setShouldShowImageDetails(false));
   // });
 
-  const { metadata, workflow } = useGetImageMetadataFromFileQuery(
-    image.image_name,
-    {
-      selectFromResult: (res) => ({
-        metadata: res?.currentData?.metadata,
-        workflow: res?.currentData?.workflow,
-      }),
-    }
-  );
+  const { metadata, workflow } = useGetImageMetadataFromFileQuery(image, {
+    selectFromResult: (res) => ({
+      metadata: res?.currentData?.metadata,
+      workflow: res?.currentData?.workflow,
+    }),
+  });
 
   return (
     <Flex

invokeai/frontend/web/src/features/nodes/types/types.ts

@@ -1,7 +1,9 @@
 import {
   SchedulerParam,
   zBaseModel,
+  zMainModel,
   zMainOrOnnxModel,
+  zOnnxModel,
   zSDXLRefinerModel,
   zScheduler,
 } from 'features/parameters/types/parameterSchemas';
@@ -769,12 +771,14 @@ export const zCoreMetadata = z
     steps: z.number().int().nullish(),
     scheduler: z.string().nullish(),
     clip_skip: z.number().int().nullish(),
-    model: zMainOrOnnxModel.nullish(),
-    controlnets: z.array(zControlField).nullish(),
+    model: z
+      .union([zMainModel.deepPartial(), zOnnxModel.deepPartial()])
+      .nullish(),
+    controlnets: z.array(zControlField.deepPartial()).nullish(),
     loras: z
       .array(
         z.object({
-          lora: zLoRAModelField,
+          lora: zLoRAModelField.deepPartial(),
           weight: z.number(),
         })
       )
@@ -784,15 +788,15 @@ export const zCoreMetadata = z
     init_image: z.string().nullish(),
     positive_style_prompt: z.string().nullish(),
     negative_style_prompt: z.string().nullish(),
-    refiner_model: zSDXLRefinerModel.nullish(),
+    refiner_model: zSDXLRefinerModel.deepPartial().nullish(),
     refiner_cfg_scale: z.number().nullish(),
     refiner_steps: z.number().int().nullish(),
     refiner_scheduler: z.string().nullish(),
-    refiner_positive_aesthetic_store: z.number().nullish(),
-    refiner_negative_aesthetic_store: z.number().nullish(),
+    refiner_positive_aesthetic_score: z.number().nullish(),
+    refiner_negative_aesthetic_score: z.number().nullish(),
     refiner_start: z.number().nullish(),
   })
-  .catchall(z.record(z.any()));
+  .passthrough();
 
 export type CoreMetadata = z.infer<typeof zCoreMetadata>;
 

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

@@ -1,4 +1,6 @@
 import * as png from '@stevebel/png';
+import { logger } from 'app/logging/logger';
+import { parseify } from 'common/util/serialize';
 import {
   ImageMetadataAndWorkflow,
   zCoreMetadata,
@@ -18,6 +20,11 @@ export const getMetadataAndWorkflowFromImageBlob = async (
     const metadataResult = zCoreMetadata.safeParse(JSON.parse(rawMetadata));
     if (metadataResult.success) {
       data.metadata = metadataResult.data;
+    } else {
+      logger('system').error(
+        { error: parseify(metadataResult.error) },
+        'Problem reading metadata from image'
+      );
     }
   }
 
@@ -26,6 +33,11 @@ export const getMetadataAndWorkflowFromImageBlob = async (
     const workflowResult = zWorkflow.safeParse(JSON.parse(rawWorkflow));
     if (workflowResult.success) {
       data.workflow = workflowResult.data;
+    } else {
+      logger('system').error(
+        { error: parseify(workflowResult.error) },
+        'Problem reading workflow from image'
+      );
     }
   }