feat(nodes): add config to limit size of images in denoising

This serves as a relatively crude way to prevent OOM errors during denoising (and any operations downstream of the denoising step, like the VAE decode in Linear UI graphs).

- Add `max_image_size` config options - this is the total number of pixels eg the area
- Add logic to `denoise_latents` to scale the `latents` and `noise` to fit this
- Add logic to `color_correct` to scale the reference and mask to fit the image

invokeai/app/api/routers/images.py

@@ -45,17 +45,13 @@ async def upload_image(
     if not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
 
-    metadata: Optional[str] = None
-    workflow: Optional[str] = None
-
     contents = await file.read()
+
     try:
         pil_image = Image.open(io.BytesIO(contents))
         if crop_visible:
             bbox = pil_image.getbbox()
             pil_image = pil_image.crop(bbox)
-        metadata = pil_image.info.get("invokeai_metadata", None)
-        workflow = pil_image.info.get("invokeai_workflow", None)
     except Exception:
         # Error opening the image
         raise HTTPException(status_code=415, detail="Failed to read image")
@@ -67,8 +63,6 @@ async def upload_image(
             image_category=image_category,
             session_id=session_id,
             board_id=board_id,
-            metadata=metadata,
-            workflow=workflow,
             is_intermediate=is_intermediate,
         )
 

invokeai/app/invocations/image.py

@@ -645,13 +645,19 @@ class ColorCorrectInvocation(BaseInvocation):
     mask_blur_radius: float = InputField(default=8, description="Mask blur radius")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
+        result = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
+
+        init_image = context.services.images.get_pil_image(self.reference.image_name)
+        # fit reference image to the input image
+        if init_image.size != result.size:
+            init_image = init_image.resize((result.width, result.height), Image.BILINEAR)
+
         pil_init_mask = None
         if self.mask is not None:
             pil_init_mask = context.services.images.get_pil_image(self.mask.image_name).convert("L")
-
-        init_image = context.services.images.get_pil_image(self.reference.image_name)
-
-        result = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
+            # fit mask to the input image
+            if pil_init_mask.size != result.size:
+                pil_init_mask = pil_init_mask.resize((result.width, result.height), Image.BILINEAR)
 
         # if init_image is None or init_mask is None:
         #    return result

invokeai/app/invocations/latent.py

@@ -77,6 +77,25 @@ DEFAULT_PRECISION = choose_precision(choose_torch_device())
 SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
 
 
+def fit_latents(latents: torch.Tensor, max_latents_size: int, device: torch.device) -> torch.Tensor:
+    if max_latents_size == 0:
+        return latents
+
+    latents_area = latents.shape[2] * latents.shape[3]
+    if latents_area <= max_latents_size:
+        return latents
+
+    scale_factor = np.sqrt(max_latents_size / latents_area)
+    scaled_latents = torch.nn.functional.interpolate(
+        latents.to(device),
+        scale_factor=scale_factor,
+        mode="bilinear",
+        antialias=True,
+    )
+
+    return scaled_latents
+
+
 @invocation_output("scheduler_output")
 class SchedulerOutput(BaseInvocationOutput):
     scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
@@ -500,14 +519,20 @@ class DenoiseLatentsInvocation(BaseInvocation):
         with SilenceWarnings():  # this quenches NSFW nag from diffusers
             seed = None
             noise = None
+            max_image_size = context.services.configuration.max_image_size
+
             if self.noise is not None:
                 noise = context.services.latents.get(self.noise.latents_name)
                 seed = self.noise.seed
+                noise = fit_latents(latents=noise, max_latents_size=max_image_size // 64, device=choose_torch_device())
 
             if self.latents is not None:
                 latents = context.services.latents.get(self.latents.latents_name)
                 if seed is None:
                     seed = self.latents.seed
+                latents = fit_latents(
+                    latents=latents, max_latents_size=max_image_size // 64, device=choose_torch_device()
+                )
 
                 if noise is not None and noise.shape[1:] != latents.shape[1:]:
                     raise Exception(f"Incompatable 'noise' and 'latents' shapes: {latents.shape=} {noise.shape=}")
@@ -532,8 +557,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             def _lora_loader():
                 for lora in self.unet.loras:
                     lora_info = context.services.model_manager.get_model(
-                        **lora.dict(exclude={"weight"}),
-                        context=context,
+                        **lora.dict(exclude={"weight"}), context=context
                     )
                     yield (lora_info.context.model, lora.weight)
                     del lora_info

invokeai/app/invocations/metadata.py

@@ -85,8 +85,11 @@ class CoreMetadata(BaseModelExcludeNull):
 class ImageMetadata(BaseModelExcludeNull):
     """An image's generation metadata"""
 
-    metadata: Optional[dict] = Field(default=None, description="The metadata associated with the image")
-    workflow: Optional[dict] = Field(default=None, description="The workflow associated with the image")
+    metadata: Optional[dict] = Field(
+        default=None,
+        description="The image's core metadata, if it was created in the Linear or Canvas UI",
+    )
+    graph: Optional[dict] = Field(default=None, description="The graph that created the image")
 
 
 @invocation_output("metadata_accumulator_output")

invokeai/app/services/config/invokeai_config.py

@@ -255,6 +255,7 @@ class InvokeAIAppConfig(InvokeAISettings):
     attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
     force_tiled_decode  : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
     force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
+    max_image_size      : int = Field(default=512 * 512, description="The maximum size of images, in pixels. The maximum size for latents is inferred from this evaluating `max_image_size // 8`. If the size is exceeded during denoising, the latents will be resized.", category="Generation", )
 
     # QUEUE
     max_queue_size      : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue", category="Queue", )

invokeai/app/services/image_file_storage.py

@@ -59,7 +59,7 @@ class ImageFileStorageBase(ABC):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[Union[str, dict]] = None,
+        metadata: Optional[dict] = None,
         workflow: Optional[str] = None,
         thumbnail_size: int = 256,
     ) -> None:
@@ -109,7 +109,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
         self,
         image: PILImageType,
         image_name: str,
-        metadata: Optional[Union[str, dict]] = None,
+        metadata: Optional[dict] = None,
         workflow: Optional[str] = None,
         thumbnail_size: int = 256,
     ) -> None:
@@ -119,10 +119,20 @@ class DiskImageFileStorage(ImageFileStorageBase):
 
             pnginfo = PngImagePlugin.PngInfo()
 
-            if metadata is not None:
-                pnginfo.add_text("invokeai_metadata", json.dumps(metadata) if type(metadata) is dict else metadata)
-            if workflow is not None:
-                pnginfo.add_text("invokeai_workflow", workflow)
+            if metadata is not None or workflow is not None:
+                if metadata is not None:
+                    pnginfo.add_text("invokeai_metadata", json.dumps(metadata))
+                if workflow is not None:
+                    pnginfo.add_text("invokeai_workflow", workflow)
+            else:
+                # For uploaded images, we want to retain metadata. PIL strips it on save; manually add it back
+                # TODO: retain non-invokeai metadata on save...
+                original_metadata = image.info.get("invokeai_metadata", None)
+                if original_metadata is not None:
+                    pnginfo.add_text("invokeai_metadata", original_metadata)
+                original_workflow = image.info.get("invokeai_workflow", None)
+                if original_workflow is not None:
+                    pnginfo.add_text("invokeai_workflow", original_workflow)
 
             image.save(image_path, "PNG", pnginfo=pnginfo)
 

invokeai/app/services/image_record_storage.py

@@ -3,12 +3,11 @@ import sqlite3
 import threading
 from abc import ABC, abstractmethod
 from datetime import datetime
-from typing import Generic, Optional, TypeVar, Union, cast
+from typing import Generic, Optional, TypeVar, cast
 
 from pydantic import BaseModel, Field
 from pydantic.generics import GenericModel
 
-from invokeai.app.invocations.metadata import ImageMetadata
 from invokeai.app.models.image import ImageCategory, ResourceOrigin
 from invokeai.app.services.models.image_record import ImageRecord, ImageRecordChanges, deserialize_image_record
 
@@ -82,7 +81,7 @@ class ImageRecordStorageBase(ABC):
         pass
 
     @abstractmethod
-    def get_metadata(self, image_name: str) -> ImageMetadata:
+    def get_metadata(self, image_name: str) -> Optional[dict]:
         """Gets an image's metadata'."""
         pass
 
@@ -135,8 +134,7 @@ class ImageRecordStorageBase(ABC):
         height: int,
         session_id: Optional[str],
         node_id: Optional[str],
-        metadata: Optional[Union[str, dict]],
-        workflow: Optional[str],
+        metadata: Optional[dict],
         is_intermediate: bool = False,
         starred: bool = False,
     ) -> datetime:
@@ -206,13 +204,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                 """
             )
 
-        if "workflow" not in columns:
-            self._cursor.execute(
-                """--sql
-                ALTER TABLE images ADD COLUMN workflow TEXT;
-                """
-            )
-
         # Create the `images` table indices.
         self._cursor.execute(
             """--sql
@@ -278,31 +269,22 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
 
         return deserialize_image_record(dict(result))
 
-    def get_metadata(self, image_name: str) -> ImageMetadata:
+    def get_metadata(self, image_name: str) -> Optional[dict]:
         try:
             self._lock.acquire()
 
             self._cursor.execute(
                 """--sql
-                SELECT metadata, workflow FROM images
+                SELECT images.metadata FROM images
                 WHERE image_name = ?;
                 """,
                 (image_name,),
             )
 
             result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
-
-            if not result:
-                return ImageMetadata()
-
-            as_dict = dict(result)
-            metadata_raw = cast(Optional[str], as_dict.get("metadata", None))
-            workflow_raw = cast(Optional[str], as_dict.get("workflow", None))
-
-            return ImageMetadata(
-                metadata=json.loads(metadata_raw) if metadata_raw is not None else None,
-                workflow=json.loads(workflow_raw) if workflow_raw is not None else None,
-            )
+            if not result or not result[0]:
+                return None
+            return json.loads(result[0])
         except sqlite3.Error as e:
             self._conn.rollback()
             raise ImageRecordNotFoundException from e
@@ -537,15 +519,12 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         width: int,
         height: int,
         node_id: Optional[str],
-        metadata: Optional[Union[str, dict]],
-        workflow: Optional[str],
+        metadata: Optional[dict],
         is_intermediate: bool = False,
         starred: bool = False,
     ) -> datetime:
         try:
-            metadata_json: Optional[str] = None
-            if metadata is not None:
-                metadata_json = metadata if type(metadata) is str else json.dumps(metadata)
+            metadata_json = None if metadata is None else json.dumps(metadata)
             self._lock.acquire()
             self._cursor.execute(
                 """--sql
@@ -558,11 +537,10 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     node_id,
                     session_id,
                     metadata,
-                    workflow,
                     is_intermediate,
                     starred
                     )
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
                 """,
                 (
                     image_name,
@@ -573,7 +551,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
                     node_id,
                     session_id,
                     metadata_json,
-                    workflow,
                     is_intermediate,
                     starred,
                 ),

invokeai/app/services/images.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 from logging import Logger
-from typing import TYPE_CHECKING, Callable, Optional, Union
+from typing import TYPE_CHECKING, Callable, Optional
 
 from PIL.Image import Image as PILImageType
 
@@ -29,6 +29,7 @@ from invokeai.app.services.item_storage import ItemStorageABC
 from invokeai.app.services.models.image_record import ImageDTO, ImageRecord, ImageRecordChanges, image_record_to_dto
 from invokeai.app.services.resource_name import NameServiceBase
 from invokeai.app.services.urls import UrlServiceBase
+from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
 
 if TYPE_CHECKING:
     from invokeai.app.services.graph import GraphExecutionState
@@ -70,7 +71,7 @@ class ImageServiceABC(ABC):
         session_id: Optional[str] = None,
         board_id: Optional[str] = None,
         is_intermediate: bool = False,
-        metadata: Optional[Union[str, dict]] = None,
+        metadata: Optional[dict] = None,
         workflow: Optional[str] = None,
     ) -> ImageDTO:
         """Creates an image, storing the file and its metadata."""
@@ -195,7 +196,7 @@ class ImageService(ImageServiceABC):
         session_id: Optional[str] = None,
         board_id: Optional[str] = None,
         is_intermediate: bool = False,
-        metadata: Optional[Union[str, dict]] = None,
+        metadata: Optional[dict] = None,
         workflow: Optional[str] = None,
     ) -> ImageDTO:
         if image_origin not in ResourceOrigin:
@@ -233,7 +234,6 @@ class ImageService(ImageServiceABC):
                 # Nullable fields
                 node_id=node_id,
                 metadata=metadata,
-                workflow=workflow,
                 session_id=session_id,
             )
             if board_id is not None:
@@ -311,7 +311,23 @@ class ImageService(ImageServiceABC):
 
     def get_metadata(self, image_name: str) -> Optional[ImageMetadata]:
         try:
-            return self._services.image_records.get_metadata(image_name)
+            image_record = self._services.image_records.get(image_name)
+            metadata = self._services.image_records.get_metadata(image_name)
+
+            if not image_record.session_id:
+                return ImageMetadata(metadata=metadata)
+
+            session_raw = self._services.graph_execution_manager.get_raw(image_record.session_id)
+            graph = None
+
+            if session_raw:
+                try:
+                    graph = get_metadata_graph_from_raw_session(session_raw)
+                except Exception as e:
+                    self._services.logger.warn(f"Failed to parse session graph: {e}")
+                    graph = None
+
+            return ImageMetadata(graph=graph, metadata=metadata)
         except ImageRecordNotFoundException:
             self._services.logger.error("Image record not found")
             raise

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

@@ -28,7 +28,7 @@ import {
   setShouldShowImageDetails,
   setShouldShowProgressInViewer,
 } from 'features/ui/store/uiSlice';
-import { memo, useCallback } from 'react';
+import { memo, useCallback, useMemo } from 'react';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
 import {
@@ -41,10 +41,9 @@ import {
 import { FaCircleNodes, FaEllipsis } from 'react-icons/fa6';
 import {
   useGetImageDTOQuery,
-  useGetImageMetadataQuery,
+  useGetImageMetadataFromFileQuery,
 } from 'services/api/endpoints/images';
 import { menuListMotionProps } from 'theme/components/menu';
-import { useDebounce } from 'use-debounce';
 import { sentImageToImg2Img } from '../../store/actions';
 import SingleSelectionMenuItems from '../ImageContextMenu/SingleSelectionMenuItems';
 
@@ -93,6 +92,7 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
     shouldShowImageDetails,
     lastSelectedImage,
     shouldShowProgressInViewer,
+    shouldFetchMetadataFromApi,
   } = useAppSelector(currentImageButtonsSelector);
 
   const isUpscalingEnabled = useFeatureStatus('upscaling').isFeatureEnabled;
@@ -107,10 +107,16 @@ const CurrentImageButtons = (props: CurrentImageButtonsProps) => {
     lastSelectedImage?.image_name ?? skipToken
   );
 
-  const [debouncedImageName] = useDebounce(lastSelectedImage?.image_name, 300);
+  const getMetadataArg = useMemo(() => {
+    if (lastSelectedImage) {
+      return { image: lastSelectedImage, shouldFetchMetadataFromApi };
+    } else {
+      return skipToken;
+    }
+  }, [lastSelectedImage, shouldFetchMetadataFromApi]);
 
-  const { metadata, workflow, isLoading } = useGetImageMetadataQuery(
-    debouncedImageName ?? skipToken,
+  const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
+    getMetadataArg,
     {
       selectFromResult: (res) => ({
         isLoading: res.isFetching,

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

@@ -1,9 +1,8 @@
 import { Flex, MenuItem, Spinner } from '@chakra-ui/react';
 import { useStore } from '@nanostores/react';
-import { skipToken } from '@reduxjs/toolkit/dist/query';
 import { useAppToaster } from 'app/components/Toaster';
 import { $customStarUI } from 'app/store/nanostores/customStarUI';
-import { useAppDispatch } from 'app/store/storeHooks';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
 import {
   imagesToChangeSelected,
@@ -33,12 +32,12 @@ import {
 import { FaCircleNodes } from 'react-icons/fa6';
 import { MdStar, MdStarBorder } from 'react-icons/md';
 import {
-  useGetImageMetadataQuery,
+  useGetImageMetadataFromFileQuery,
   useStarImagesMutation,
   useUnstarImagesMutation,
 } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
-import { useDebounce } from 'use-debounce';
+import { configSelector } from '../../../system/store/configSelectors';
 import { sentImageToCanvas, sentImageToImg2Img } from '../../store/actions';
 
 type SingleSelectionMenuItemsProps = {
@@ -54,12 +53,11 @@ const SingleSelectionMenuItems = (props: SingleSelectionMenuItemsProps) => {
   const toaster = useAppToaster();
 
   const isCanvasEnabled = useFeatureStatus('unifiedCanvas').isFeatureEnabled;
+  const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
   const customStarUi = useStore($customStarUI);
 
-  const [debouncedImageName] = useDebounce(imageDTO.image_name, 300);
-
-  const { metadata, workflow, isLoading } = useGetImageMetadataQuery(
-    debouncedImageName ?? skipToken,
+  const { metadata, workflow, isLoading } = useGetImageMetadataFromFileQuery(
+    { image: imageDTO, shouldFetchMetadataFromApi },
     {
       selectFromResult: (res) => ({
         isLoading: res.isFetching,

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

@@ -9,15 +9,15 @@ import {
   Tabs,
   Text,
 } from '@chakra-ui/react';
-import { skipToken } from '@reduxjs/toolkit/dist/query';
 import { IAINoContentFallback } from 'common/components/IAIImageFallback';
 import { memo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { useGetImageMetadataQuery } from 'services/api/endpoints/images';
+import { useGetImageMetadataFromFileQuery } from 'services/api/endpoints/images';
 import { ImageDTO } from 'services/api/types';
-import { useDebounce } from 'use-debounce';
 import DataViewer from './DataViewer';
 import ImageMetadataActions from './ImageMetadataActions';
+import { useAppSelector } from '../../../../app/store/storeHooks';
+import { configSelector } from '../../../system/store/configSelectors';
+import { useTranslation } from 'react-i18next';
 
 type ImageMetadataViewerProps = {
   image: ImageDTO;
@@ -31,10 +31,10 @@ const ImageMetadataViewer = ({ image }: ImageMetadataViewerProps) => {
   // });
   const { t } = useTranslation();
 
-  const [debouncedImageName] = useDebounce(image.image_name, 300);
+  const { shouldFetchMetadataFromApi } = useAppSelector(configSelector);
 
-  const { metadata, workflow } = useGetImageMetadataQuery(
-    debouncedImageName ?? skipToken,
+  const { metadata, workflow } = useGetImageMetadataFromFileQuery(
+    { image, shouldFetchMetadataFromApi },
     {
       selectFromResult: (res) => ({
         metadata: res?.currentData?.metadata,

invokeai/frontend/web/src/services/api/endpoints/images.ts

@@ -10,7 +10,6 @@ import {
 import {
   ImageMetadataAndWorkflow,
   zCoreMetadata,
-  zWorkflow,
 } from 'features/nodes/types/types';
 import { getMetadataAndWorkflowFromImageBlob } from 'features/nodes/util/getMetadataAndWorkflowFromImageBlob';
 import { keyBy } from 'lodash-es';
@@ -24,6 +23,7 @@ import {
   ListImagesArgs,
   OffsetPaginatedResults_ImageDTO_,
   PostUploadAction,
+  UnsafeImageMetadata,
 } from '../types';
 import {
   getCategories,
@@ -33,7 +33,6 @@ import {
   imagesSelectors,
 } from '../util';
 import { boardsApi } from './boards';
-import { logger } from 'app/logging/logger';
 
 export const imagesApi = api.injectEndpoints({
   endpoints: (build) => ({
@@ -114,33 +113,11 @@ export const imagesApi = api.injectEndpoints({
       ],
       keepUnusedDataFor: 86400, // 24 hours
     }),
-    getImageMetadata: build.query<ImageMetadataAndWorkflow, string>({
+    getImageMetadata: build.query<UnsafeImageMetadata, string>({
       query: (image_name) => ({ url: `images/i/${image_name}/metadata` }),
       providesTags: (result, error, image_name) => [
         { type: 'ImageMetadata', id: image_name },
       ],
-      transformResponse: (
-        response: paths['/api/v1/images/i/{image_name}/metadata']['get']['responses']['200']['content']['application/json']
-      ) => {
-        const imageMetadataAndWorkflow: ImageMetadataAndWorkflow = {};
-        if (response?.metadata) {
-          const metadataResult = zCoreMetadata.safeParse(response.metadata);
-          if (metadataResult.success) {
-            imageMetadataAndWorkflow.metadata = metadataResult.data;
-          } else {
-            logger('images').warn('Problem parsing metadata');
-          }
-        }
-        if (response?.workflow) {
-          const workflowResult = zWorkflow.safeParse(response.workflow);
-          if (workflowResult.success) {
-            imageMetadataAndWorkflow.workflow = workflowResult.data;
-          } else {
-            logger('images').warn('Problem parsing workflow');
-          }
-        }
-        return imageMetadataAndWorkflow;
-      },
       keepUnusedDataFor: 86400, // 24 hours
     }),
     getImageMetadataFromFile: build.query<

invokeai/frontend/web/src/services/api/schema.d.ts

@@ -1287,11 +1287,6 @@ export type components = {
        * @default true
        */
       use_cache?: boolean;
-      /**
-       * CLIP
-       * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count
-       */
-      clip?: components["schemas"]["ClipField"];
       /**
        * Skipped Layers
        * @description Number of layers to skip in text encoder
@@ -1304,6 +1299,11 @@ export type components = {
        * @enum {string}
        */
       type: "clip_skip";
+      /**
+       * CLIP
+       * @description CLIP (tokenizer, text encoder, LoRAs) and skipped layer count
+       */
+      clip?: components["schemas"]["ClipField"];
     };
     /**
      * ClipSkipInvocationOutput
@@ -3916,14 +3916,14 @@ export type components = {
     ImageMetadata: {
       /**
        * Metadata
-       * @description The metadata associated with the image
+       * @description The image's core metadata, if it was created in the Linear or Canvas UI
        */
       metadata?: Record<string, never>;
       /**
-       * Workflow
-       * @description The workflow associated with the image
+       * Graph
+       * @description The graph that created the image
        */
-      workflow?: Record<string, never>;
+      graph?: Record<string, never>;
     };
     /**
      * Multiply Images
@@ -7550,11 +7550,6 @@ export type components = {
        * @default false
        */
       use_cache?: boolean;
-      /**
-       * Image
-       * @description The image to load
-       */
-      image?: components["schemas"]["ImageField"];
       /**
        * Metadata
        * @description Optional core metadata to be written to image
@@ -7566,6 +7561,11 @@ export type components = {
        * @enum {string}
        */
       type: "save_image";
+      /**
+       * Image
+       * @description The image to load
+       */
+      image?: components["schemas"]["ImageField"];
     };
     /**
      * Scale Latents
@@ -7862,6 +7862,16 @@ export type components = {
        * @description The ID of the session associated with this queue item. The session doesn't exist in graph_executions until the queue item is executed.
        */
       session_id: string;
+      /**
+       * Field Values
+       * @description The field values that were used for this queue item
+       */
+      field_values?: components["schemas"]["NodeFieldValue"][];
+      /**
+       * Queue Id
+       * @description The id of the queue with which this item is associated
+       */
+      queue_id: string;
       /**
        * Error
        * @description The error message if this queue item errored
@@ -7887,16 +7897,6 @@ export type components = {
        * @description When this queue item was completed
        */
       completed_at?: string;
-      /**
-       * Queue Id
-       * @description The id of the queue with which this item is associated
-       */
-      queue_id: string;
-      /**
-       * Field Values
-       * @description The field values that were used for this queue item
-       */
-      field_values?: components["schemas"]["NodeFieldValue"][];
       /**
        * Session
        * @description The fully-populated session to be executed
@@ -7936,6 +7936,16 @@ export type components = {
        * @description The ID of the session associated with this queue item. The session doesn't exist in graph_executions until the queue item is executed.
        */
       session_id: string;
+      /**
+       * Field Values
+       * @description The field values that were used for this queue item
+       */
+      field_values?: components["schemas"]["NodeFieldValue"][];
+      /**
+       * Queue Id
+       * @description The id of the queue with which this item is associated
+       */
+      queue_id: string;
       /**
        * Error
        * @description The error message if this queue item errored
@@ -7961,16 +7971,6 @@ export type components = {
        * @description When this queue item was completed
        */
       completed_at?: string;
-      /**
-       * Queue Id
-       * @description The id of the queue with which this item is associated
-       */
-      queue_id: string;
-      /**
-       * Field Values
-       * @description The field values that were used for this queue item
-       */
-      field_values?: components["schemas"]["NodeFieldValue"][];
     };
     /** SessionQueueStatus */
     SessionQueueStatus: {
@@ -9095,12 +9095,6 @@ export type components = {
       /** Ui Order */
       ui_order?: number;
     };
-    /**
-     * ControlNetModelFormat
-     * @description An enumeration.
-     * @enum {string}
-     */
-    ControlNetModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusionOnnxModelFormat
      * @description An enumeration.
@@ -9113,18 +9107,18 @@ export type components = {
      * @enum {string}
      */
     StableDiffusion2ModelFormat: "checkpoint" | "diffusers";
-    /**
-     * StableDiffusion1ModelFormat
-     * @description An enumeration.
-     * @enum {string}
-     */
-    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
     /**
      * CLIPVisionModelFormat
      * @description An enumeration.
      * @enum {string}
      */
     CLIPVisionModelFormat: "diffusers";
+    /**
+     * StableDiffusion1ModelFormat
+     * @description An enumeration.
+     * @enum {string}
+     */
+    StableDiffusion1ModelFormat: "checkpoint" | "diffusers";
     /**
      * StableDiffusionXLModelFormat
      * @description An enumeration.
@@ -9137,6 +9131,12 @@ export type components = {
      * @enum {string}
      */
     IPAdapterModelFormat: "invokeai";
+    /**
+     * ControlNetModelFormat
+     * @description An enumeration.
+     * @enum {string}
+     */
+    ControlNetModelFormat: "checkpoint" | "diffusers";
   };
   responses: never;
   parameters: never;