chore: bump version to v6.0.0rc4

When percentage is zero, the progress bar looks the same as it does when
no generation is in progress. Render it as indeterminate (pulsing) when
percentage is zero to indicate that somethign is happenign.

.github/actions/install-frontend-deps/action.yml

@@ -3,15 +3,15 @@ description: Installs frontend dependencies with pnpm, with caching
 runs:
   using: 'composite'
   steps:
-    - name: setup node 18
+    - name: setup node 20
       uses: actions/setup-node@v4
       with:
-        node-version: '18'
+        node-version: '20'
 
     - name: setup pnpm
       uses: pnpm/action-setup@v4
       with:
-        version: 8.15.6
+        version: 10
         run_install: false
 
     - name: get pnpm store directory

docs/contributing/frontend/workflows.md

@@ -297,7 +297,7 @@ Migration logic is in [migrations.ts].
 <!-- links -->
 
 [pydantic]: https://github.com/pydantic/pydantic 'pydantic'
-[zod]: https://github.com/colinhacks/zod 'zod'
+[zod]: https://github.com/colinhacks/zod 'zod/v4'
 [openapi-types]: https://github.com/kogosoftwarellc/open-api/tree/main/packages/openapi-types 'openapi-types'
 [reactflow]: https://github.com/xyflow/xyflow 'reactflow'
 [reactflow-concepts]: https://reactflow.dev/learn/concepts/terms-and-definitions

docs/installation/quick_start.md

@@ -35,7 +35,7 @@ More detail on system requirements can be found [here](./requirements.md).
 
 ## Step 2: Download
 
-Download the most launcher for your operating system:
+Download the most recent launcher for your operating system:
 
 - [Download for Windows](https://download.invoke.ai/Invoke%20Community%20Edition.exe)
 - [Download for macOS](https://download.invoke.ai/Invoke%20Community%20Edition.dmg)

invokeai/app/api/routers/images.py

@@ -1,7 +1,7 @@
 import io
 import json
 import traceback
-from typing import ClassVar, Literal, Optional
+from typing import ClassVar, Optional
 
 from fastapi import BackgroundTasks, Body, HTTPException, Path, Query, Request, Response, UploadFile
 from fastapi.responses import FileResponse
@@ -14,7 +14,7 @@ from invokeai.app.api.extract_metadata_from_image import extract_metadata_from_i
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
-    ImageCollectionCounts,
+    ImageNamesResult,
     ImageRecordChanges,
     ResourceOrigin,
 )
@@ -565,67 +565,6 @@ async def get_bulk_download_item(
         raise HTTPException(status_code=404)
 
 
-@images_router.get(
-    "/collections/counts", operation_id="get_image_collection_counts", response_model=ImageCollectionCounts
-)
-async def get_image_collection_counts(
-    image_origin: Optional[ResourceOrigin] = Query(default=None, description="The origin of images to count."),
-    categories: Optional[list[ImageCategory]] = Query(default=None, description="The categories of image to include."),
-    is_intermediate: Optional[bool] = Query(default=None, description="Whether to include intermediate images."),
-    board_id: Optional[str] = Query(
-        default=None,
-        description="The board id to filter by. Use 'none' to find images without a board.",
-    ),
-    search_term: Optional[str] = Query(default=None, description="The term to search for"),
-) -> ImageCollectionCounts:
-    """Gets counts for starred and unstarred image collections"""
-
-    try:
-        return ApiDependencies.invoker.services.images.get_collection_counts(
-            image_origin=image_origin,
-            categories=categories,
-            is_intermediate=is_intermediate,
-            board_id=board_id,
-            search_term=search_term,
-        )
-    except Exception:
-        raise HTTPException(status_code=500, detail="Failed to get collection counts")
-
-
-@images_router.get("/collections/{collection}", operation_id="get_image_collection")
-async def get_image_collection(
-    collection: Literal["starred", "unstarred"] = Path(..., description="The collection to retrieve from"),
-    image_origin: Optional[ResourceOrigin] = Query(default=None, description="The origin of images to list."),
-    categories: Optional[list[ImageCategory]] = Query(default=None, description="The categories of image to include."),
-    is_intermediate: Optional[bool] = Query(default=None, description="Whether to list intermediate images."),
-    board_id: Optional[str] = Query(
-        default=None,
-        description="The board id to filter by. Use 'none' to find images without a board.",
-    ),
-    offset: int = Query(default=0, description="The offset within the collection"),
-    limit: int = Query(default=50, description="The number of images to return"),
-    order_dir: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The order of sort"),
-    search_term: Optional[str] = Query(default=None, description="The term to search for"),
-) -> OffsetPaginatedResults[ImageDTO]:
-    """Gets images from a specific collection (starred or unstarred)"""
-
-    try:
-        image_dtos = ApiDependencies.invoker.services.images.get_collection_images(
-            collection=collection,
-            offset=offset,
-            limit=limit,
-            order_dir=order_dir,
-            image_origin=image_origin,
-            categories=categories,
-            is_intermediate=is_intermediate,
-            board_id=board_id,
-            search_term=search_term,
-        )
-        return image_dtos
-    except Exception:
-        raise HTTPException(status_code=500, detail="Failed to get collection images")
-
-
 @images_router.get("/names", operation_id="get_image_names")
 async def get_image_names(
     image_origin: Optional[ResourceOrigin] = Query(default=None, description="The origin of images to list."),
@@ -636,12 +575,14 @@ async def get_image_names(
         description="The board id to filter by. Use 'none' to find images without a board.",
     ),
     order_dir: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The order of sort"),
+    starred_first: bool = Query(default=True, description="Whether to sort by starred images first"),
     search_term: Optional[str] = Query(default=None, description="The term to search for"),
-) -> list[str]:
-    """Gets ordered list of all image names (starred first, then unstarred)"""
+) -> ImageNamesResult:
+    """Gets ordered list of image names with metadata for optimistic updates"""
 
     try:
-        image_names = ApiDependencies.invoker.services.images.get_image_names(
+        result = ApiDependencies.invoker.services.images.get_image_names(
+            starred_first=starred_first,
             order_dir=order_dir,
             image_origin=image_origin,
             categories=categories,
@@ -649,6 +590,34 @@ async def get_image_names(
             board_id=board_id,
             search_term=search_term,
         )
-        return image_names
+        return result
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to get image names")
+
+
+@images_router.post(
+    "/images_by_names",
+    operation_id="get_images_by_names",
+    responses={200: {"model": list[ImageDTO]}},
+)
+async def get_images_by_names(
+    image_names: list[str] = Body(embed=True, description="Object containing list of image names to fetch DTOs for"),
+) -> list[ImageDTO]:
+    """Gets image DTOs for the specified image names. Maintains order of input names."""
+
+    try:
+        image_service = ApiDependencies.invoker.services.images
+
+        # Fetch DTOs preserving the order of requested names
+        image_dtos: list[ImageDTO] = []
+        for name in image_names:
+            try:
+                dto = image_service.get_dto(name)
+                image_dtos.append(dto)
+            except Exception:
+                # Skip missing images - they may have been deleted between name fetch and DTO fetch
+                continue
+
+        return image_dtos
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to get image DTOs")

invokeai/app/api/routers/model_manager.py

@@ -41,6 +41,7 @@ from invokeai.backend.model_manager.starter_models import (
     STARTER_BUNDLES,
     STARTER_MODELS,
     StarterModel,
+    StarterModelBundle,
     StarterModelWithoutDependencies,
 )
 
@@ -799,7 +800,7 @@ async def convert_model(
 
 class StarterModelResponse(BaseModel):
     starter_models: list[StarterModel]
-    starter_bundles: dict[str, list[StarterModel]]
+    starter_bundles: dict[str, StarterModelBundle]
 
 
 def get_is_installed(
@@ -833,7 +834,7 @@ async def get_starter_models() -> StarterModelResponse:
         model.dependencies = missing_deps
 
     for bundle in starter_bundles.values():
-        for model in bundle:
+        for model in bundle.models:
             model.is_installed = get_is_installed(model, installed_models)
             # Remove already-installed dependencies
             missing_deps: list[StarterModelWithoutDependencies] = []

invokeai/app/api/routers/session_queue.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from fastapi import Body, Path, Query
+from fastapi import Body, HTTPException, Path, Query
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
 
@@ -22,6 +22,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     RetryItemsResult,
     SessionQueueCountsByDestination,
     SessionQueueItem,
+    SessionQueueItemNotFoundError,
     SessionQueueStatus,
 )
 from invokeai.app.services.shared.pagination import CursorPaginatedResults
@@ -59,10 +60,12 @@ async def enqueue_batch(
     ),
 ) -> EnqueueBatchResult:
     """Processes a batch and enqueues the output graphs for execution."""
-
-    return await ApiDependencies.invoker.services.session_queue.enqueue_batch(
-        queue_id=queue_id, batch=batch, prepend=prepend
-    )
+    try:
+        return await ApiDependencies.invoker.services.session_queue.enqueue_batch(
+            queue_id=queue_id, batch=batch, prepend=prepend
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while enqueuing batch: {e}")
 
 
 @session_queue_router.get(
@@ -82,14 +85,17 @@ async def list_queue_items(
 ) -> CursorPaginatedResults[SessionQueueItem]:
     """Gets cursor-paginated queue items"""
 
-    return ApiDependencies.invoker.services.session_queue.list_queue_items(
-        queue_id=queue_id,
-        limit=limit,
-        status=status,
-        cursor=cursor,
-        priority=priority,
-        destination=destination,
-    )
+    try:
+        return ApiDependencies.invoker.services.session_queue.list_queue_items(
+            queue_id=queue_id,
+            limit=limit,
+            status=status,
+            cursor=cursor,
+            priority=priority,
+            destination=destination,
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while listing all items: {e}")
 
 
 @session_queue_router.get(
@@ -104,11 +110,13 @@ async def list_all_queue_items(
     destination: Optional[str] = Query(default=None, description="The destination of queue items to fetch"),
 ) -> list[SessionQueueItem]:
     """Gets all queue items"""
-
-    return ApiDependencies.invoker.services.session_queue.list_all_queue_items(
-        queue_id=queue_id,
-        destination=destination,
-    )
+    try:
+        return ApiDependencies.invoker.services.session_queue.list_all_queue_items(
+            queue_id=queue_id,
+            destination=destination,
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while listing all queue items: {e}")
 
 
 @session_queue_router.put(
@@ -120,7 +128,10 @@ async def resume(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> SessionProcessorStatus:
     """Resumes session processor"""
-    return ApiDependencies.invoker.services.session_processor.resume()
+    try:
+        return ApiDependencies.invoker.services.session_processor.resume()
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while resuming queue: {e}")
 
 
 @session_queue_router.put(
@@ -132,7 +143,10 @@ async def Pause(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> SessionProcessorStatus:
     """Pauses session processor"""
-    return ApiDependencies.invoker.services.session_processor.pause()
+    try:
+        return ApiDependencies.invoker.services.session_processor.pause()
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while pausing queue: {e}")
 
 
 @session_queue_router.put(
@@ -144,7 +158,10 @@ async def cancel_all_except_current(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> CancelAllExceptCurrentResult:
     """Immediately cancels all queue items except in-processing items"""
-    return ApiDependencies.invoker.services.session_queue.cancel_all_except_current(queue_id=queue_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.cancel_all_except_current(queue_id=queue_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while canceling all except current: {e}")
 
 
 @session_queue_router.put(
@@ -156,7 +173,10 @@ async def delete_all_except_current(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> DeleteAllExceptCurrentResult:
     """Immediately deletes all queue items except in-processing items"""
-    return ApiDependencies.invoker.services.session_queue.delete_all_except_current(queue_id=queue_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.delete_all_except_current(queue_id=queue_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while deleting all except current: {e}")
 
 
 @session_queue_router.put(
@@ -169,7 +189,12 @@ async def cancel_by_batch_ids(
     batch_ids: list[str] = Body(description="The list of batch_ids to cancel all queue items for", embed=True),
 ) -> CancelByBatchIDsResult:
     """Immediately cancels all queue items from the given batch ids"""
-    return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(queue_id=queue_id, batch_ids=batch_ids)
+    try:
+        return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(
+            queue_id=queue_id, batch_ids=batch_ids
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while canceling by batch id: {e}")
 
 
 @session_queue_router.put(
@@ -182,9 +207,12 @@ async def cancel_by_destination(
     destination: str = Query(description="The destination to cancel all queue items for"),
 ) -> CancelByDestinationResult:
     """Immediately cancels all queue items with the given origin"""
-    return ApiDependencies.invoker.services.session_queue.cancel_by_destination(
-        queue_id=queue_id, destination=destination
-    )
+    try:
+        return ApiDependencies.invoker.services.session_queue.cancel_by_destination(
+            queue_id=queue_id, destination=destination
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while canceling by destination: {e}")
 
 
 @session_queue_router.put(
@@ -197,7 +225,10 @@ async def retry_items_by_id(
     item_ids: list[int] = Body(description="The queue item ids to retry"),
 ) -> RetryItemsResult:
     """Immediately cancels all queue items with the given origin"""
-    return ApiDependencies.invoker.services.session_queue.retry_items_by_id(queue_id=queue_id, item_ids=item_ids)
+    try:
+        return ApiDependencies.invoker.services.session_queue.retry_items_by_id(queue_id=queue_id, item_ids=item_ids)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while retrying queue items: {e}")
 
 
 @session_queue_router.put(
@@ -211,11 +242,14 @@ async def clear(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> ClearResult:
     """Clears the queue entirely, immediately canceling the currently-executing session"""
-    queue_item = ApiDependencies.invoker.services.session_queue.get_current(queue_id)
-    if queue_item is not None:
-        ApiDependencies.invoker.services.session_queue.cancel_queue_item(queue_item.item_id)
-    clear_result = ApiDependencies.invoker.services.session_queue.clear(queue_id)
-    return clear_result
+    try:
+        queue_item = ApiDependencies.invoker.services.session_queue.get_current(queue_id)
+        if queue_item is not None:
+            ApiDependencies.invoker.services.session_queue.cancel_queue_item(queue_item.item_id)
+        clear_result = ApiDependencies.invoker.services.session_queue.clear(queue_id)
+        return clear_result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while clearing queue: {e}")
 
 
 @session_queue_router.put(
@@ -229,7 +263,10 @@ async def prune(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> PruneResult:
     """Prunes all completed or errored queue items"""
-    return ApiDependencies.invoker.services.session_queue.prune(queue_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.prune(queue_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while pruning queue: {e}")
 
 
 @session_queue_router.get(
@@ -243,7 +280,10 @@ async def get_current_queue_item(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> Optional[SessionQueueItem]:
     """Gets the currently execution queue item"""
-    return ApiDependencies.invoker.services.session_queue.get_current(queue_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.get_current(queue_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while getting current queue item: {e}")
 
 
 @session_queue_router.get(
@@ -257,7 +297,10 @@ async def get_next_queue_item(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> Optional[SessionQueueItem]:
     """Gets the next queue item, without executing it"""
-    return ApiDependencies.invoker.services.session_queue.get_next(queue_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.get_next(queue_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while getting next queue item: {e}")
 
 
 @session_queue_router.get(
@@ -271,9 +314,12 @@ async def get_queue_status(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> SessionQueueAndProcessorStatus:
     """Gets the status of the session queue"""
-    queue = ApiDependencies.invoker.services.session_queue.get_queue_status(queue_id)
-    processor = ApiDependencies.invoker.services.session_processor.get_status()
-    return SessionQueueAndProcessorStatus(queue=queue, processor=processor)
+    try:
+        queue = ApiDependencies.invoker.services.session_queue.get_queue_status(queue_id)
+        processor = ApiDependencies.invoker.services.session_processor.get_status()
+        return SessionQueueAndProcessorStatus(queue=queue, processor=processor)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while getting queue status: {e}")
 
 
 @session_queue_router.get(
@@ -288,7 +334,10 @@ async def get_batch_status(
     batch_id: str = Path(description="The batch to get the status of"),
 ) -> BatchStatus:
     """Gets the status of the session queue"""
-    return ApiDependencies.invoker.services.session_queue.get_batch_status(queue_id=queue_id, batch_id=batch_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.get_batch_status(queue_id=queue_id, batch_id=batch_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while getting batch status: {e}")
 
 
 @session_queue_router.get(
@@ -304,7 +353,12 @@ async def get_queue_item(
     item_id: int = Path(description="The queue item to get"),
 ) -> SessionQueueItem:
     """Gets a queue item"""
-    return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
+    except SessionQueueItemNotFoundError:
+        raise HTTPException(status_code=404, detail=f"Queue item with id {item_id} not found in queue {queue_id}")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while fetching queue item: {e}")
 
 
 @session_queue_router.delete(
@@ -316,7 +370,10 @@ async def delete_queue_item(
     item_id: int = Path(description="The queue item to delete"),
 ) -> None:
     """Deletes a queue item"""
-    ApiDependencies.invoker.services.session_queue.delete_queue_item(item_id)
+    try:
+        ApiDependencies.invoker.services.session_queue.delete_queue_item(item_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while deleting queue item: {e}")
 
 
 @session_queue_router.put(
@@ -331,8 +388,12 @@ async def cancel_queue_item(
     item_id: int = Path(description="The queue item to cancel"),
 ) -> SessionQueueItem:
     """Deletes a queue item"""
-
-    return ApiDependencies.invoker.services.session_queue.cancel_queue_item(item_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.cancel_queue_item(item_id)
+    except SessionQueueItemNotFoundError:
+        raise HTTPException(status_code=404, detail=f"Queue item with id {item_id} not found in queue {queue_id}")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while canceling queue item: {e}")
 
 
 @session_queue_router.get(
@@ -345,9 +406,12 @@ async def counts_by_destination(
     destination: str = Query(description="The destination to query"),
 ) -> SessionQueueCountsByDestination:
     """Gets the counts of queue items by destination"""
-    return ApiDependencies.invoker.services.session_queue.get_counts_by_destination(
-        queue_id=queue_id, destination=destination
-    )
+    try:
+        return ApiDependencies.invoker.services.session_queue.get_counts_by_destination(
+            queue_id=queue_id, destination=destination
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while fetching counts by destination: {e}")
 
 
 @session_queue_router.delete(
@@ -360,6 +424,9 @@ async def delete_by_destination(
     destination: str = Path(description="The destination to query"),
 ) -> DeleteByDestinationResult:
     """Deletes all items with the given destination"""
-    return ApiDependencies.invoker.services.session_queue.delete_by_destination(
-        queue_id=queue_id, destination=destination
-    )
+    try:
+        return ApiDependencies.invoker.services.session_queue.delete_by_destination(
+            queue_id=queue_id, destination=destination
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while deleting by destination: {e}")

invokeai/app/invocations/fields.py

@@ -64,6 +64,7 @@ class UIType(str, Enum, metaclass=MetaEnum):
     Imagen3Model = "Imagen3ModelField"
     Imagen4Model = "Imagen4ModelField"
     ChatGPT4oModel = "ChatGPT4oModelField"
+    FluxKontextModel = "FluxKontextModelField"
     # endregion
 
     # region Misc Field Types
@@ -214,6 +215,7 @@ class FieldDescriptions:
     flux_redux_conditioning = "FLUX Redux conditioning tensor"
     vllm_model = "The VLLM model to use"
     flux_fill_conditioning = "FLUX Fill conditioning tensor"
+    flux_kontext_conditioning = "FLUX Kontext conditioning (reference image)"
 
 
 class ImageField(BaseModel):
@@ -290,6 +292,12 @@ class FluxFillConditioningField(BaseModel):
     mask: TensorField = Field(description="The FLUX Fill inpaint mask.")
 
 
+class FluxKontextConditioningField(BaseModel):
+    """A conditioning field for FLUX Kontext (reference image)."""
+
+    image: ImageField = Field(description="The Kontext reference image.")
+
+
 class SD3ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 

invokeai/app/invocations/flux_denoise.py

@@ -16,13 +16,12 @@ from invokeai.app.invocations.fields import (
     FieldDescriptions,
     FluxConditioningField,
     FluxFillConditioningField,
+    FluxKontextConditioningField,
     FluxReduxConditioningField,
     ImageField,
     Input,
     InputField,
     LatentsField,
-    WithBoard,
-    WithMetadata,
 )
 from invokeai.app.invocations.flux_controlnet import FluxControlNetField
 from invokeai.app.invocations.flux_vae_encode import FluxVaeEncodeInvocation
@@ -34,6 +33,7 @@ from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXCo
 from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlNetFlux
 from invokeai.backend.flux.denoise import denoise
 from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
+from invokeai.backend.flux.extensions.kontext_extension import KontextExtension
 from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
@@ -63,9 +63,9 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.3.0",
+    version="4.0.0",
 )
-class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
+class FluxDenoiseInvocation(BaseInvocation):
     """Run denoising process with a FLUX transformer model."""
 
     # If latents is provided, this means we are doing image-to-image.
@@ -145,11 +145,20 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         description=FieldDescriptions.vae,
         input=Input.Connection,
     )
+    # This node accepts a images for features like FLUX Fill, ControlNet, and Kontext, but needs to operate on them in
+    # latent space. We'll run the VAE to encode them in this node instead of requiring the user to run the VAE in
+    # upstream nodes.
 
     ip_adapter: IPAdapterField | list[IPAdapterField] | None = InputField(
         description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection
     )
 
+    kontext_conditioning: Optional[FluxKontextConditioningField] = InputField(
+        default=None,
+        description="FLUX Kontext conditioning (reference image).",
+        input=Input.Connection,
+    )
+
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents = self._run_diffusion(context)
@@ -376,6 +385,27 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 dtype=inference_dtype,
             )
 
+            kontext_extension = None
+            if self.kontext_conditioning is not None:
+                if not self.controlnet_vae:
+                    raise ValueError("A VAE (e.g., controlnet_vae) must be provided to use Kontext conditioning.")
+
+                kontext_extension = KontextExtension(
+                    context=context,
+                    kontext_conditioning=self.kontext_conditioning,
+                    vae_field=self.controlnet_vae,
+                    device=TorchDevice.choose_torch_device(),
+                    dtype=inference_dtype,
+                )
+
+            # Prepare Kontext conditioning if provided
+            img_cond_seq = None
+            img_cond_seq_ids = None
+            if kontext_extension is not None:
+                # Ensure batch sizes match
+                kontext_extension.ensure_batch_size(x.shape[0])
+                img_cond_seq, img_cond_seq_ids = kontext_extension.kontext_latents, kontext_extension.kontext_ids
+
             x = denoise(
                 model=transformer,
                 img=x,
@@ -391,6 +421,8 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 pos_ip_adapter_extensions=pos_ip_adapter_extensions,
                 neg_ip_adapter_extensions=neg_ip_adapter_extensions,
                 img_cond=img_cond,
+                img_cond_seq=img_cond_seq,
+                img_cond_seq_ids=img_cond_seq_ids,
             )
 
         x = unpack(x.float(), self.height, self.width)
@@ -865,7 +897,10 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     def _build_step_callback(self, context: InvocationContext) -> Callable[[PipelineIntermediateState], None]:
         def step_callback(state: PipelineIntermediateState) -> None:
-            state.latents = unpack(state.latents.float(), self.height, self.width).squeeze()
+            # The denoise function now handles Kontext conditioning correctly,
+            # so we don't need to slice the latents here
+            latents = state.latents.float()
+            state.latents = unpack(latents, self.height, self.width).squeeze()
             context.util.flux_step_callback(state)
 
         return step_callback

invokeai/app/invocations/flux_kontext.py

@@ -0,0 +1,40 @@
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    FluxKontextConditioningField,
+    InputField,
+    OutputField,
+)
+from invokeai.app.invocations.primitives import ImageField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+
+
+@invocation_output("flux_kontext_output")
+class FluxKontextOutput(BaseInvocationOutput):
+    """The conditioning output of a FLUX Kontext invocation."""
+
+    kontext_cond: FluxKontextConditioningField = OutputField(
+        description=FieldDescriptions.flux_kontext_conditioning, title="Kontext Conditioning"
+    )
+
+
+@invocation(
+    "flux_kontext",
+    title="Kontext Conditioning - FLUX",
+    tags=["conditioning", "kontext", "flux"],
+    category="conditioning",
+    version="1.0.0",
+)
+class FluxKontextInvocation(BaseInvocation):
+    """Prepares a reference image for FLUX Kontext conditioning."""
+
+    image: ImageField = InputField(description="The Kontext reference image.")
+
+    def invoke(self, context: InvocationContext) -> FluxKontextOutput:
+        """Packages the provided image into a Kontext conditioning field."""
+        return FluxKontextOutput(kontext_cond=FluxKontextConditioningField(image=self.image))

invokeai/app/invocations/flux_text_encoder.py

@@ -1,5 +1,5 @@
 from contextlib import ExitStack
-from typing import Iterator, Literal, Optional, Tuple
+from typing import Iterator, Literal, Optional, Tuple, Union
 
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer, T5TokenizerFast
@@ -111,6 +111,9 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
 
+            if context.config.get().log_tokenization:
+                self._log_t5_tokenization(context, t5_tokenizer)
+
             context.util.signal_progress("Running T5 encoder")
             prompt_embeds = t5_encoder(prompt)
 
@@ -151,6 +154,9 @@ class FluxTextEncoderInvocation(BaseInvocation):
 
             clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True, 77)
 
+            if context.config.get().log_tokenization:
+                self._log_clip_tokenization(context, clip_tokenizer)
+
             context.util.signal_progress("Running CLIP encoder")
             pooled_prompt_embeds = clip_encoder(prompt)
 
@@ -170,3 +176,88 @@ class FluxTextEncoderInvocation(BaseInvocation):
             assert isinstance(lora_info.model, ModelPatchRaw)
             yield (lora_info.model, lora.weight)
             del lora_info
+
+    def _log_t5_tokenization(
+        self,
+        context: InvocationContext,
+        tokenizer: Union[T5Tokenizer, T5TokenizerFast],
+    ) -> None:
+        """Logs the tokenization of a prompt for a T5-based model like FLUX."""
+
+        # Tokenize the prompt using the same parameters as the model's text encoder.
+        # T5 tokenizers add an EOS token (</s>) and then pad to max_length.
+        tokenized_output = tokenizer(
+            self.prompt,
+            padding="max_length",
+            max_length=self.t5_max_seq_len,
+            truncation=True,
+            add_special_tokens=True,  # This is important for T5 to add the EOS token.
+            return_tensors="pt",
+        )
+
+        input_ids = tokenized_output.input_ids[0]
+        tokens = tokenizer.convert_ids_to_tokens(input_ids)
+
+        # The T5 tokenizer uses a space-like character ' ' (U+2581) to denote spaces.
+        # We'll replace it with a regular space for readability.
+        tokens = [t.replace("\u2581", " ") for t in tokens]
+
+        tokenized_str = ""
+        used_tokens = 0
+        for token in tokens:
+            if token == tokenizer.eos_token:
+                tokenized_str += f"\x1b[0;31m{token}\x1b[0m"  # Red for EOS
+                used_tokens += 1
+            elif token == tokenizer.pad_token:
+                # tokenized_str += f"\x1b[0;34m{token}\x1b[0m"  # Blue for PAD
+                continue
+            else:
+                color = (used_tokens % 6) + 1  # Cycle through 6 colors
+                tokenized_str += f"\x1b[0;3{color}m{token}\x1b[0m"
+                used_tokens += 1
+
+        context.logger.info(f">> [T5 TOKENLOG] Tokens ({used_tokens}/{self.t5_max_seq_len}):")
+        context.logger.info(f"{tokenized_str}\x1b[0m")
+
+    def _log_clip_tokenization(
+        self,
+        context: InvocationContext,
+        tokenizer: CLIPTokenizer,
+    ) -> None:
+        """Logs the tokenization of a prompt for a CLIP-based model."""
+        max_length = tokenizer.model_max_length
+
+        tokenized_output = tokenizer(
+            self.prompt,
+            padding="max_length",
+            max_length=max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        input_ids = tokenized_output.input_ids[0]
+        attention_mask = tokenized_output.attention_mask[0]
+        tokens = tokenizer.convert_ids_to_tokens(input_ids)
+
+        # The CLIP tokenizer uses '</w>' to denote spaces.
+        # We'll replace it with a regular space for readability.
+        tokens = [t.replace("</w>", " ") for t in tokens]
+
+        tokenized_str = ""
+        used_tokens = 0
+        for i, token in enumerate(tokens):
+            if attention_mask[i] == 0:
+                # Do not log padding tokens.
+                continue
+
+            if token == tokenizer.bos_token:
+                tokenized_str += f"\x1b[0;32m{token}\x1b[0m"  # Green for BOS
+            elif token == tokenizer.eos_token:
+                tokenized_str += f"\x1b[0;31m{token}\x1b[0m"  # Red for EOS
+            else:
+                color = (used_tokens % 6) + 1  # Cycle through 6 colors
+                tokenized_str += f"\x1b[0;3{color}m{token}\x1b[0m"
+            used_tokens += 1
+
+        context.logger.info(f">> [CLIP TOKENLOG] Tokens ({used_tokens}/{max_length}):")
+        context.logger.info(f"{tokenized_str}\x1b[0m")

invokeai/app/services/image_records/image_records_base.py

@@ -1,11 +1,11 @@
 from abc import ABC, abstractmethod
 from datetime import datetime
-from typing import Literal, Optional
+from typing import Optional
 
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
-    ImageCollectionCounts,
+    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ResourceOrigin,
@@ -99,43 +99,16 @@ class ImageRecordStorageBase(ABC):
         """Gets the most recent image for a board."""
         pass
 
-    @abstractmethod
-    def get_collection_counts(
-        self,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> ImageCollectionCounts:
-        """Gets counts for starred and unstarred image collections."""
-        pass
-
-    @abstractmethod
-    def get_collection_images(
-        self,
-        collection: Literal["starred", "unstarred"],
-        offset: int = 0,
-        limit: int = 10,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> OffsetPaginatedResults[ImageRecord]:
-        """Gets images from a specific collection (starred or unstarred)."""
-        pass
-
     @abstractmethod
     def get_image_names(
         self,
+        starred_first: bool = True,
         order_dir: SQLiteDirection = SQLiteDirection.Descending,
         image_origin: Optional[ResourceOrigin] = None,
         categories: Optional[list[ImageCategory]] = None,
         is_intermediate: Optional[bool] = None,
         board_id: Optional[str] = None,
         search_term: Optional[str] = None,
-    ) -> list[str]:
-        """Gets ordered list of all image names (starred first, then unstarred)."""
+    ) -> ImageNamesResult:
+        """Gets ordered list of image names with metadata for optimistic updates."""
         pass

invokeai/app/services/image_records/image_records_common.py

@@ -212,3 +212,11 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
 class ImageCollectionCounts(BaseModel):
     starred_count: int = Field(description="The number of starred images in the collection.")
     unstarred_count: int = Field(description="The number of unstarred images in the collection.")
+
+
+class ImageNamesResult(BaseModel):
+    """Response containing ordered image names with metadata for optimistic updates."""
+
+    image_names: list[str] = Field(description="Ordered list of image names")
+    starred_count: int = Field(description="Number of starred images (when starred_first=True)")
+    total_count: int = Field(description="Total number of images matching the query")

invokeai/app/services/image_records/image_records_sqlite.py

@@ -1,13 +1,13 @@
 import sqlite3
 from datetime import datetime
-from typing import Literal, Optional, Union, cast
+from typing import Optional, Union, cast
 
 from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
 from invokeai.app.services.image_records.image_records_base import ImageRecordStorageBase
 from invokeai.app.services.image_records.image_records_common import (
     IMAGE_DTO_COLS,
     ImageCategory,
-    ImageCollectionCounts,
+    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ImageRecordDeleteException,
@@ -388,199 +388,19 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
 
         return deserialize_image_record(dict(result))
 
-    def get_collection_counts(
-        self,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> ImageCollectionCounts:
-        cursor = self._conn.cursor()
-
-        # Build the base query conditions (same as get_many)
-        base_query = """--sql
-        FROM images
-        LEFT JOIN board_images ON board_images.image_name = images.image_name
-        WHERE 1=1
-        """
-
-        query_conditions = ""
-        query_params: list[Union[int, str, bool]] = []
-
-        if image_origin is not None:
-            query_conditions += """--sql
-            AND images.image_origin = ?
-            """
-            query_params.append(image_origin.value)
-
-        if categories is not None:
-            category_strings = [c.value for c in set(categories)]
-            placeholders = ",".join("?" * len(category_strings))
-            query_conditions += f"""--sql
-            AND images.image_category IN ( {placeholders} )
-            """
-            for c in category_strings:
-                query_params.append(c)
-
-        if is_intermediate is not None:
-            query_conditions += """--sql
-            AND images.is_intermediate = ?
-            """
-            query_params.append(is_intermediate)
-
-        if board_id == "none":
-            query_conditions += """--sql
-            AND board_images.board_id IS NULL
-            """
-        elif board_id is not None:
-            query_conditions += """--sql
-            AND board_images.board_id = ?
-            """
-            query_params.append(board_id)
-
-        if search_term:
-            query_conditions += """--sql
-            AND (
-                images.metadata LIKE ?
-                OR images.created_at LIKE ?
-            )
-            """
-            query_params.append(f"%{search_term.lower()}%")
-            query_params.append(f"%{search_term.lower()}%")
-
-        # Get starred count
-        starred_query = f"SELECT COUNT(*) {base_query} {query_conditions} AND images.starred = TRUE;"
-        cursor.execute(starred_query, query_params)
-        starred_count = cast(int, cursor.fetchone()[0])
-
-        # Get unstarred count
-        unstarred_query = f"SELECT COUNT(*) {base_query} {query_conditions} AND images.starred = FALSE;"
-        cursor.execute(unstarred_query, query_params)
-        unstarred_count = cast(int, cursor.fetchone()[0])
-
-        return ImageCollectionCounts(starred_count=starred_count, unstarred_count=unstarred_count)
-
-    def get_collection_images(
-        self,
-        collection: Literal["starred", "unstarred"],
-        offset: int = 0,
-        limit: int = 10,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> OffsetPaginatedResults[ImageRecord]:
-        cursor = self._conn.cursor()
-
-        # Base queries
-        count_query = """--sql
-        SELECT COUNT(*)
-        FROM images
-        LEFT JOIN board_images ON board_images.image_name = images.image_name
-        WHERE 1=1
-        """
-
-        images_query = f"""--sql
-        SELECT {IMAGE_DTO_COLS}
-        FROM images
-        LEFT JOIN board_images ON board_images.image_name = images.image_name
-        WHERE 1=1
-        """
-
-        query_conditions = ""
-        query_params: list[Union[int, str, bool]] = []
-
-        # Add starred/unstarred filter
-        is_starred = collection == "starred"
-        query_conditions += """--sql
-        AND images.starred = ?
-        """
-        query_params.append(is_starred)
-
-        if image_origin is not None:
-            query_conditions += """--sql
-            AND images.image_origin = ?
-            """
-            query_params.append(image_origin.value)
-
-        if categories is not None:
-            category_strings = [c.value for c in set(categories)]
-            placeholders = ",".join("?" * len(category_strings))
-            query_conditions += f"""--sql
-            AND images.image_category IN ( {placeholders} )
-            """
-            for c in category_strings:
-                query_params.append(c)
-
-        if is_intermediate is not None:
-            query_conditions += """--sql
-            AND images.is_intermediate = ?
-            """
-            query_params.append(is_intermediate)
-
-        if board_id == "none":
-            query_conditions += """--sql
-            AND board_images.board_id IS NULL
-            """
-        elif board_id is not None:
-            query_conditions += """--sql
-            AND board_images.board_id = ?
-            """
-            query_params.append(board_id)
-
-        if search_term:
-            query_conditions += """--sql
-            AND (
-                images.metadata LIKE ?
-                OR images.created_at LIKE ?
-            )
-            """
-            query_params.append(f"%{search_term.lower()}%")
-            query_params.append(f"%{search_term.lower()}%")
-
-        # Add ordering and pagination
-        query_pagination = f"""--sql
-        ORDER BY images.created_at {order_dir.value} LIMIT ? OFFSET ?
-        """
-
-        # Execute images query
-        images_query += query_conditions + query_pagination + ";"
-        images_params = query_params.copy()
-        images_params.extend([limit, offset])
-
-        cursor.execute(images_query, images_params)
-        result = cast(list[sqlite3.Row], cursor.fetchall())
-        images = [deserialize_image_record(dict(r)) for r in result]
-
-        # Execute count query
-        count_query += query_conditions + ";"
-        cursor.execute(count_query, query_params)
-        count = cast(int, cursor.fetchone()[0])
-
-        return OffsetPaginatedResults(items=images, offset=offset, limit=limit, total=count)
-
     def get_image_names(
         self,
+        starred_first: bool = True,
         order_dir: SQLiteDirection = SQLiteDirection.Descending,
         image_origin: Optional[ResourceOrigin] = None,
         categories: Optional[list[ImageCategory]] = None,
         is_intermediate: Optional[bool] = None,
         board_id: Optional[str] = None,
         search_term: Optional[str] = None,
-    ) -> list[str]:
+    ) -> ImageNamesResult:
         cursor = self._conn.cursor()
 
-        # Base query to get image names in order (starred first, then unstarred)
-        query = """--sql
-        SELECT images.image_name
-        FROM images
-        LEFT JOIN board_images ON board_images.image_name = images.image_name
-        WHERE 1=1
-        """
-
+        # Build query conditions (reused for both starred count and image names queries)
         query_conditions = ""
         query_params: list[Union[int, str, bool]] = []
 
@@ -625,15 +445,38 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             query_params.append(f"%{search_term.lower()}%")
             query_params.append(f"%{search_term.lower()}%")
 
-        # Order by starred first, then by created_at
-        query += (
-            query_conditions
-            + f"""--sql
-        ORDER BY images.starred DESC, images.created_at {order_dir.value}
-        """
-        )
+        # Get starred count if starred_first is enabled
+        starred_count = 0
+        if starred_first:
+            starred_count_query = f"""--sql
+            SELECT COUNT(*)
+            FROM images
+            LEFT JOIN board_images ON board_images.image_name = images.image_name
+            WHERE images.starred = TRUE AND (1=1{query_conditions})
+            """
+            cursor.execute(starred_count_query, query_params)
+            starred_count = cast(int, cursor.fetchone()[0])
 
-        cursor.execute(query, query_params)
+        # Get all image names with proper ordering
+        if starred_first:
+            names_query = f"""--sql
+            SELECT images.image_name
+            FROM images
+            LEFT JOIN board_images ON board_images.image_name = images.image_name
+            WHERE 1=1{query_conditions}
+            ORDER BY images.starred DESC, images.created_at {order_dir.value}
+            """
+        else:
+            names_query = f"""--sql
+            SELECT images.image_name
+            FROM images
+            LEFT JOIN board_images ON board_images.image_name = images.image_name
+            WHERE 1=1{query_conditions}
+            ORDER BY images.created_at {order_dir.value}
+            """
+
+        cursor.execute(names_query, query_params)
         result = cast(list[sqlite3.Row], cursor.fetchall())
+        image_names = [row[0] for row in result]
 
-        return [row[0] for row in result]
+        return ImageNamesResult(image_names=image_names, starred_count=starred_count, total_count=len(image_names))

invokeai/app/services/images/images_base.py

@@ -1,12 +1,12 @@
 from abc import ABC, abstractmethod
-from typing import Callable, Literal, Optional
+from typing import Callable, Optional
 
 from PIL.Image import Image as PILImageType
 
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
-    ImageCollectionCounts,
+    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ResourceOrigin,
@@ -149,43 +149,16 @@ class ImageServiceABC(ABC):
         """Deletes all images on a board."""
         pass
 
-    @abstractmethod
-    def get_collection_counts(
-        self,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> ImageCollectionCounts:
-        """Gets counts for starred and unstarred image collections."""
-        pass
-
-    @abstractmethod
-    def get_collection_images(
-        self,
-        collection: Literal["starred", "unstarred"],
-        offset: int = 0,
-        limit: int = 10,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> OffsetPaginatedResults[ImageDTO]:
-        """Gets images from a specific collection (starred or unstarred)."""
-        pass
-
     @abstractmethod
     def get_image_names(
         self,
+        starred_first: bool = True,
         order_dir: SQLiteDirection = SQLiteDirection.Descending,
         image_origin: Optional[ResourceOrigin] = None,
         categories: Optional[list[ImageCategory]] = None,
         is_intermediate: Optional[bool] = None,
         board_id: Optional[str] = None,
         search_term: Optional[str] = None,
-    ) -> list[str]:
-        """Gets ordered list of all image names (starred first, then unstarred)."""
+    ) -> ImageNamesResult:
+        """Gets ordered list of image names with metadata for optimistic updates."""
         pass

invokeai/app/services/images/images_default.py

@@ -1,4 +1,4 @@
-from typing import Literal, Optional
+from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
@@ -10,7 +10,7 @@ from invokeai.app.services.image_files.image_files_common import (
 )
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
-    ImageCollectionCounts,
+    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ImageRecordDeleteException,
@@ -311,82 +311,19 @@ class ImageService(ImageServiceABC):
             self.__invoker.services.logger.error("Problem getting intermediates count")
             raise e
 
-    def get_collection_counts(
-        self,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> ImageCollectionCounts:
-        try:
-            return self.__invoker.services.image_records.get_collection_counts(
-                image_origin=image_origin,
-                categories=categories,
-                is_intermediate=is_intermediate,
-                board_id=board_id,
-                search_term=search_term,
-            )
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting collection counts")
-            raise e
-
-    def get_collection_images(
-        self,
-        collection: Literal["starred", "unstarred"],
-        offset: int = 0,
-        limit: int = 10,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-        board_id: Optional[str] = None,
-        search_term: Optional[str] = None,
-    ) -> OffsetPaginatedResults[ImageDTO]:
-        try:
-            results = self.__invoker.services.image_records.get_collection_images(
-                collection=collection,
-                offset=offset,
-                limit=limit,
-                order_dir=order_dir,
-                image_origin=image_origin,
-                categories=categories,
-                is_intermediate=is_intermediate,
-                board_id=board_id,
-                search_term=search_term,
-            )
-
-            image_dtos = [
-                image_record_to_dto(
-                    image_record=r,
-                    image_url=self.__invoker.services.urls.get_image_url(r.image_name),
-                    thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
-                    board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
-                )
-                for r in results.items
-            ]
-
-            return OffsetPaginatedResults[ImageDTO](
-                items=image_dtos,
-                offset=results.offset,
-                limit=results.limit,
-                total=results.total,
-            )
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting collection images")
-            raise e
-
     def get_image_names(
         self,
+        starred_first: bool = True,
         order_dir: SQLiteDirection = SQLiteDirection.Descending,
         image_origin: Optional[ResourceOrigin] = None,
         categories: Optional[list[ImageCategory]] = None,
         is_intermediate: Optional[bool] = None,
         board_id: Optional[str] = None,
         search_term: Optional[str] = None,
-    ) -> list[str]:
+    ) -> ImageNamesResult:
         try:
             return self.__invoker.services.image_records.get_image_names(
+                starred_first=starred_first,
                 order_dir=order_dir,
                 image_origin=image_origin,
                 categories=categories,

invokeai/app/services/session_queue/session_queue_common.py

@@ -205,6 +205,7 @@ class FieldIdentifier(BaseModel):
     kind: Literal["input", "output"] = Field(description="The kind of field")
     node_id: str = Field(description="The ID of the node")
     field_name: str = Field(description="The name of the field")
+    user_label: str | None = Field(description="The user label of the field, if any")
 
 
 class SessionQueueItem(BaseModel):
@@ -331,6 +332,7 @@ class EnqueueBatchResult(BaseModel):
     requested: int = Field(description="The total number of queue items requested to be enqueued")
     batch: Batch = Field(description="The batch that was enqueued")
     priority: int = Field(description="The priority of the enqueued batch")
+    item_ids: list[int] = Field(description="The IDs of the queue items that were enqueued")
 
 
 class RetryItemsResult(BaseModel):

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -133,6 +133,18 @@ class SqliteSessionQueue(SessionQueueBase):
                     """,
                     values_to_insert,
                 )
+            with self._conn:
+                cursor = self._conn.cursor()
+                cursor.execute(
+                    """--sql
+                    SELECT item_id
+                    FROM session_queue
+                    WHERE batch_id = ?
+                    ORDER BY item_id DESC;
+                    """,
+                    (batch.batch_id,),
+                )
+                item_ids = [row[0] for row in cursor.fetchall()]
         except Exception:
             raise
         enqueue_result = EnqueueBatchResult(
@@ -141,6 +153,7 @@ class SqliteSessionQueue(SessionQueueBase):
             enqueued=enqueued_count,
             batch=batch,
             priority=priority,
+            item_ids=item_ids,
         )
         self.__invoker.services.events.emit_batch_enqueued(enqueue_result)
         return enqueue_result
@@ -727,7 +740,7 @@ class SqliteSessionQueue(SessionQueueBase):
         counts_result = cast(list[sqlite3.Row], cursor.fetchall())
 
         current_item = self.get_current(queue_id=queue_id)
-        total = sum(row[1] for row in counts_result)
+        total = sum(row[1] or 0 for row in counts_result)
         counts: dict[str, int] = {row[0]: row[1] for row in counts_result}
         return SessionQueueStatus(
             queue_id=queue_id,
@@ -756,7 +769,7 @@ class SqliteSessionQueue(SessionQueueBase):
             (queue_id, batch_id),
         )
         result = cast(list[sqlite3.Row], cursor.fetchall())
-        total = sum(row[1] for row in result)
+        total = sum(row[1] or 0 for row in result)
         counts: dict[str, int] = {row[0]: row[1] for row in result}
         origin = result[0]["origin"] if result else None
         destination = result[0]["destination"] if result else None
@@ -788,7 +801,7 @@ class SqliteSessionQueue(SessionQueueBase):
         )
         counts_result = cast(list[sqlite3.Row], cursor.fetchall())
 
-        total = sum(row[1] for row in counts_result)
+        total = sum(row[1] or 0 for row in counts_result)
         counts: dict[str, int] = {row[0]: row[1] for row in counts_result}
 
         return SessionQueueCountsByDestination(

invokeai/app/services/shared/graph.py

@@ -2,7 +2,7 @@
 
 import copy
 import itertools
-from typing import Any, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
+from typing import Any, Optional, TypeVar, Union, get_args, get_origin
 
 import networkx as nx
 from pydantic import (
@@ -58,17 +58,32 @@ class Edge(BaseModel):
 
 
 def get_output_field_type(node: BaseInvocation, field: str) -> Any:
-    node_type = type(node)
-    node_outputs = get_type_hints(node_type.get_output_annotation())
-    node_output_field = node_outputs.get(field) or None
-    return node_output_field
+    # TODO(psyche): This is awkward - if field_info is None, it means the field is not defined in the output, which
+    # really should raise. The consumers of this utility expect it to never raise, and return None instead. Fixing this
+    # would require some fairly significant changes and I don't want risk breaking anything.
+    try:
+        invocation_class = type(node)
+        invocation_output_class = invocation_class.get_output_annotation()
+        field_info = invocation_output_class.model_fields.get(field)
+        assert field_info is not None, f"Output field '{field}' not found in {invocation_output_class.get_type()}"
+        output_field_type = field_info.annotation
+        return output_field_type
+    except Exception:
+        return None
 
 
 def get_input_field_type(node: BaseInvocation, field: str) -> Any:
-    node_type = type(node)
-    node_inputs = get_type_hints(node_type)
-    node_input_field = node_inputs.get(field) or None
-    return node_input_field
+    # TODO(psyche): This is awkward - if field_info is None, it means the field is not defined in the output, which
+    # really should raise. The consumers of this utility expect it to never raise, and return None instead. Fixing this
+    # would require some fairly significant changes and I don't want risk breaking anything.
+    try:
+        invocation_class = type(node)
+        field_info = invocation_class.model_fields.get(field)
+        assert field_info is not None, f"Input field '{field}' not found in {invocation_class.get_type()}"
+        input_field_type = field_info.annotation
+        return input_field_type
+    except Exception:
+        return None
 
 
 def is_union_subtype(t1, t2):
@@ -992,10 +1007,11 @@ class GraphExecutionState(BaseModel):
         new_node_ids = []
         if isinstance(next_node, CollectInvocation):
             # Collapse all iterator input mappings and create a single execution node for the collect invocation
-            all_iteration_mappings = list(
-                itertools.chain(*(((s, p) for p in self.source_prepared_mapping[s]) for s in next_node_parents))
-            )
-            # all_iteration_mappings = list(set(itertools.chain(*prepared_parent_mappings)))
+            all_iteration_mappings = []
+            for source_node_id in next_node_parents:
+                prepared_nodes = self.source_prepared_mapping[source_node_id]
+                all_iteration_mappings.extend([(source_node_id, p) for p in prepared_nodes])
+
             create_results = self._create_execution_node(next_node_id, all_iteration_mappings)
             if create_results is not None:
                 new_node_ids.extend(create_results)

invokeai/app/util/step_callback.py

@@ -123,7 +123,11 @@ def calc_percentage(intermediate_state: PipelineIntermediateState) -> float:
     if total_steps == 0:
         return 0.0
     if order == 2:
-        return floor(step / 2) / floor(total_steps / 2)
+        # Prevent division by zero when total_steps is 1 or 2
+        denominator = floor(total_steps / 2)
+        if denominator == 0:
+            return 0.0
+        return floor(step / 2) / denominator
     # order == 1
     return step / total_steps
 

invokeai/backend/flux/denoise.py

@@ -30,8 +30,11 @@ def denoise(
     controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension],
     pos_ip_adapter_extensions: list[XLabsIPAdapterExtension],
     neg_ip_adapter_extensions: list[XLabsIPAdapterExtension],
-    # extra img tokens
+    # extra img tokens (channel-wise)
     img_cond: torch.Tensor | None,
+    # extra img tokens (sequence-wise) - for Kontext conditioning
+    img_cond_seq: torch.Tensor | None = None,
+    img_cond_seq_ids: torch.Tensor | None = None,
 ):
     # step 0 is the initial state
     total_steps = len(timesteps) - 1
@@ -46,6 +49,10 @@ def denoise(
     )
     # guidance_vec is ignored for schnell.
     guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
+
+    # Store original sequence length for slicing predictions
+    original_seq_len = img.shape[1]
+
     for step_index, (t_curr, t_prev) in tqdm(list(enumerate(zip(timesteps[:-1], timesteps[1:], strict=True)))):
         t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
 
@@ -71,10 +78,26 @@ def denoise(
         # controlnet_residuals datastructure is efficient in that it likely contains multiple references to the same
         # tensors. Calculating the sum materializes each tensor into its own instance.
         merged_controlnet_residuals = sum_controlnet_flux_outputs(controlnet_residuals)
-        pred_img = torch.cat((img, img_cond), dim=-1) if img_cond is not None else img
+
+        # Prepare input for model - concatenate fresh each step
+        img_input = img
+        img_input_ids = img_ids
+
+        # Add channel-wise conditioning (for ControlNet, FLUX Fill, etc.)
+        if img_cond is not None:
+            img_input = torch.cat((img_input, img_cond), dim=-1)
+
+        # Add sequence-wise conditioning (for Kontext)
+        if img_cond_seq is not None:
+            assert img_cond_seq_ids is not None, (
+                "You need to provide either both or neither of the sequence conditioning"
+            )
+            img_input = torch.cat((img_input, img_cond_seq), dim=1)
+            img_input_ids = torch.cat((img_input_ids, img_cond_seq_ids), dim=1)
+
         pred = model(
-            img=pred_img,
-            img_ids=img_ids,
+            img=img_input,
+            img_ids=img_input_ids,
             txt=pos_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
             txt_ids=pos_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
             y=pos_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
@@ -88,6 +111,10 @@ def denoise(
             regional_prompting_extension=pos_regional_prompting_extension,
         )
 
+        # Slice prediction to only include the main image tokens
+        if img_input_ids is not None:
+            pred = pred[:, :original_seq_len]
+
         step_cfg_scale = cfg_scale[step_index]
 
         # If step_cfg_scale, is 1.0, then we don't need to run the negative prediction.

invokeai/backend/flux/extensions/kontext_extension.py

@@ -0,0 +1,149 @@
+import einops
+import numpy as np
+import torch
+from einops import repeat
+from PIL import Image
+
+from invokeai.app.invocations.fields import FluxKontextConditioningField
+from invokeai.app.invocations.flux_vae_encode import FluxVaeEncodeInvocation
+from invokeai.app.invocations.model import VAEField
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.sampling_utils import pack
+from invokeai.backend.flux.util import PREFERED_KONTEXT_RESOLUTIONS
+
+
+def generate_img_ids_with_offset(
+    latent_height: int,
+    latent_width: int,
+    batch_size: int,
+    device: torch.device,
+    dtype: torch.dtype,
+    idx_offset: int = 0,
+) -> torch.Tensor:
+    """Generate tensor of image position ids with an optional offset.
+
+    Args:
+        latent_height (int): Height of image in latent space (after packing, this becomes h//2).
+        latent_width (int): Width of image in latent space (after packing, this becomes w//2).
+        batch_size (int): Number of images in the batch.
+        device (torch.device): Device to create tensors on.
+        dtype (torch.dtype): Data type for the tensors.
+        idx_offset (int): Offset to add to the first dimension of the image ids.
+
+    Returns:
+        torch.Tensor: Image position ids with shape [batch_size, (latent_height//2 * latent_width//2), 3].
+    """
+
+    if device.type == "mps":
+        orig_dtype = dtype
+        dtype = torch.float16
+
+    # After packing, the spatial dimensions are halved due to the 2x2 patch structure
+    packed_height = latent_height // 2
+    packed_width = latent_width // 2
+
+    # Create base tensor for position IDs with shape [packed_height, packed_width, 3]
+    # The 3 channels represent: [batch_offset, y_position, x_position]
+    img_ids = torch.zeros(packed_height, packed_width, 3, device=device, dtype=dtype)
+
+    # Set the batch offset for all positions
+    img_ids[..., 0] = idx_offset
+
+    # Create y-coordinate indices (vertical positions)
+    y_indices = torch.arange(packed_height, device=device, dtype=dtype)
+    # Broadcast y_indices to match the spatial dimensions [packed_height, 1]
+    img_ids[..., 1] = y_indices[:, None]
+
+    # Create x-coordinate indices (horizontal positions)
+    x_indices = torch.arange(packed_width, device=device, dtype=dtype)
+    # Broadcast x_indices to match the spatial dimensions [1, packed_width]
+    img_ids[..., 2] = x_indices[None, :]
+
+    # Expand to include batch dimension: [batch_size, (packed_height * packed_width), 3]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size)
+
+    if device.type == "mps":
+        img_ids = img_ids.to(orig_dtype)
+
+    return img_ids
+
+
+class KontextExtension:
+    """Applies FLUX Kontext (reference image) conditioning."""
+
+    def __init__(
+        self,
+        kontext_conditioning: FluxKontextConditioningField,
+        context: InvocationContext,
+        vae_field: VAEField,
+        device: torch.device,
+        dtype: torch.dtype,
+    ):
+        """
+        Initializes the KontextExtension, pre-processing the reference image
+        into latents and positional IDs.
+        """
+        self._context = context
+        self._device = device
+        self._dtype = dtype
+        self._vae_field = vae_field
+        self.kontext_conditioning = kontext_conditioning
+
+        # Pre-process and cache the kontext latents and ids upon initialization.
+        self.kontext_latents, self.kontext_ids = self._prepare_kontext()
+
+    def _prepare_kontext(self) -> tuple[torch.Tensor, torch.Tensor]:
+        """Encodes the reference image and prepares its latents and IDs."""
+        image = self._context.images.get_pil(self.kontext_conditioning.image.image_name)
+
+        # Calculate aspect ratio of input image
+        width, height = image.size
+        aspect_ratio = width / height
+
+        # Find the closest preferred resolution by aspect ratio
+        _, target_width, target_height = min(
+            ((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS), key=lambda x: x[0]
+        )
+
+        # Apply BFL's scaling formula
+        # This ensures compatibility with the model's training
+        scaled_width = 2 * int(target_width / 16)
+        scaled_height = 2 * int(target_height / 16)
+
+        # Resize to the exact resolution used during training
+        image = image.convert("RGB")
+        final_width = 8 * scaled_width
+        final_height = 8 * scaled_height
+        image = image.resize((final_width, final_height), Image.Resampling.LANCZOS)
+
+        # Convert to tensor with same normalization as BFL
+        image_np = np.array(image)
+        image_tensor = torch.from_numpy(image_np).float() / 127.5 - 1.0
+        image_tensor = einops.rearrange(image_tensor, "h w c -> 1 c h w")
+        image_tensor = image_tensor.to(self._device)
+
+        # Continue with VAE encoding
+        vae_info = self._context.models.load(self._vae_field.vae)
+        kontext_latents_unpacked = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
+
+        # Extract tensor dimensions
+        batch_size, _, latent_height, latent_width = kontext_latents_unpacked.shape
+
+        # Pack the latents and generate IDs
+        kontext_latents_packed = pack(kontext_latents_unpacked).to(self._device, self._dtype)
+        kontext_ids = generate_img_ids_with_offset(
+            latent_height=latent_height,
+            latent_width=latent_width,
+            batch_size=batch_size,
+            device=self._device,
+            dtype=self._dtype,
+            idx_offset=1,
+        )
+
+        return kontext_latents_packed, kontext_ids
+
+    def ensure_batch_size(self, target_batch_size: int) -> None:
+        """Ensures the kontext latents and IDs match the target batch size by repeating if necessary."""
+        if self.kontext_latents.shape[0] != target_batch_size:
+            self.kontext_latents = self.kontext_latents.repeat(target_batch_size, 1, 1)
+            self.kontext_ids = self.kontext_ids.repeat(target_batch_size, 1, 1)

invokeai/backend/flux/sampling_utils.py

@@ -174,11 +174,13 @@ def generate_img_ids(h: int, w: int, batch_size: int, device: torch.device, dtyp
         dtype = torch.float16
 
     img_ids = torch.zeros(h // 2, w // 2, 3, device=device, dtype=dtype)
+    # Set batch offset to 0 for main image tokens
+    img_ids[..., 0] = 0
     img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=device, dtype=dtype)[:, None]
     img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=device, dtype=dtype)[None, :]
     img_ids = repeat(img_ids, "h w c -> b (h w) c", b=batch_size)
 
     if device.type == "mps":
-        img_ids.to(orig_dtype)
+        img_ids = img_ids.to(orig_dtype)
 
     return img_ids

invokeai/backend/flux/util.py

@@ -18,6 +18,29 @@ class ModelSpec:
     repo_ae: str | None
 
 
+# Preferred resolutions for Kontext models to avoid tiling artifacts
+# These are the specific resolutions the model was trained on
+PREFERED_KONTEXT_RESOLUTIONS = [
+    (672, 1568),
+    (688, 1504),
+    (720, 1456),
+    (752, 1392),
+    (800, 1328),
+    (832, 1248),
+    (880, 1184),
+    (944, 1104),
+    (1024, 1024),
+    (1104, 944),
+    (1184, 880),
+    (1248, 832),
+    (1328, 800),
+    (1392, 752),
+    (1456, 720),
+    (1504, 688),
+    (1568, 672),
+]
+
+
 max_seq_lengths: Dict[str, Literal[256, 512]] = {
     "flux-dev": 512,
     "flux-dev-fill": 512,

invokeai/backend/model_manager/config.py

@@ -37,6 +37,7 @@ from invokeai.app.util.misc import uuid_string
 from invokeai.backend.model_hash.hash_validator import validate_hash
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS
 from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
+from invokeai.backend.model_manager.omi import flux_dev_1_lora, stable_diffusion_xl_1_lora
 from invokeai.backend.model_manager.taxonomy import (
     AnyVariant,
     BaseModelType,
@@ -334,6 +335,36 @@ class T5EncoderBnbQuantizedLlmInt8bConfig(T5EncoderConfigBase, LegacyProbeMixin,
     format: Literal[ModelFormat.BnbQuantizedLlmInt8b] = ModelFormat.BnbQuantizedLlmInt8b
 
 
+class LoRAOmiConfig(LoRAConfigBase, ModelConfigBase):
+    format: Literal[ModelFormat.OMI] = ModelFormat.OMI
+
+    @classmethod
+    def matches(cls, mod: ModelOnDisk) -> bool:
+        if mod.path.is_dir():
+            return False
+
+        metadata = mod.metadata()
+        return (
+            metadata.get("modelspec.sai_model_spec")
+            and metadata.get("ot_branch") == "omi_format"
+            and metadata["modelspec.architecture"].split("/")[1].lower() == "lora"
+        )
+
+    @classmethod
+    def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
+        metadata = mod.metadata()
+        architecture = metadata["modelspec.architecture"]
+
+        if architecture == stable_diffusion_xl_1_lora:
+            base = BaseModelType.StableDiffusionXL
+        elif architecture == flux_dev_1_lora:
+            base = BaseModelType.Flux
+        else:
+            raise InvalidModelConfigException(f"Unrecognised/unsupported architecture for OMI LoRA: {architecture}")
+
+        return {"base": base}
+
+
 class LoRALyCORISConfig(LoRAConfigBase, ModelConfigBase):
     """Model config for LoRA/Lycoris models."""
 
@@ -350,7 +381,7 @@ class LoRALyCORISConfig(LoRAConfigBase, ModelConfigBase):
 
         state_dict = mod.load_state_dict()
         for key in state_dict.keys():
-            if type(key) is int:
+            if isinstance(key, int):
                 continue
 
             if key.startswith(("lora_te_", "lora_unet_", "lora_te1_", "lora_te2_", "lora_transformer_")):
@@ -668,6 +699,7 @@ AnyModelConfig = Annotated[
         Annotated[ControlNetDiffusersConfig, ControlNetDiffusersConfig.get_tag()],
         Annotated[ControlNetCheckpointConfig, ControlNetCheckpointConfig.get_tag()],
         Annotated[LoRALyCORISConfig, LoRALyCORISConfig.get_tag()],
+        Annotated[LoRAOmiConfig, LoRAOmiConfig.get_tag()],
         Annotated[ControlLoRALyCORISConfig, ControlLoRALyCORISConfig.get_tag()],
         Annotated[ControlLoRADiffusersConfig, ControlLoRADiffusersConfig.get_tag()],
         Annotated[LoRADiffusersConfig, LoRADiffusersConfig.get_tag()],

invokeai/backend/model_manager/load/model_loaders/flux.py

@@ -7,7 +7,14 @@ from typing import Optional
 import accelerate
 import torch
 from safetensors.torch import load_file
-from transformers import AutoConfig, AutoModelForTextEncoding, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
+from transformers import (
+    AutoConfig,
+    AutoModelForTextEncoding,
+    CLIPTextModel,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5TokenizerFast,
+)
 
 from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXControlNetFlux
@@ -139,7 +146,7 @@ class BnbQuantizedLlmInt8bCheckpointModel(ModelLoader):
             )
         match submodel_type:
             case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
-                return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
+                return T5TokenizerFast.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
             case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 te2_model_path = Path(config.path) / "text_encoder_2"
                 model_config = AutoConfig.from_pretrained(te2_model_path)
@@ -183,7 +190,7 @@ class T5EncoderCheckpointModel(ModelLoader):
 
         match submodel_type:
             case SubModelType.Tokenizer2 | SubModelType.Tokenizer3:
-                return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
+                return T5TokenizerFast.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
             case SubModelType.TextEncoder2 | SubModelType.TextEncoder3:
                 return T5EncoderModel.from_pretrained(
                     Path(config.path) / "text_encoder_2", torch_dtype="auto", low_cpu_mem_usage=True

invokeai/backend/model_manager/load/model_loaders/lora.py

@@ -13,6 +13,7 @@ from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.omi.omi import convert_from_omi
 from invokeai.backend.model_manager.taxonomy import (
     AnyModel,
     BaseModelType,
@@ -43,6 +44,8 @@ from invokeai.backend.patches.lora_conversions.sd_lora_conversion_utils import l
 from invokeai.backend.patches.lora_conversions.sdxl_lora_conversion_utils import convert_sdxl_keys_to_diffusers_format
 
 
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.LoRA, format=ModelFormat.OMI)
+@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.LoRA, format=ModelFormat.OMI)
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.LyCORIS)
 @ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.ControlLoRa, format=ModelFormat.LyCORIS)
@@ -77,12 +80,23 @@ class LoRALoader(ModelLoader):
         else:
             state_dict = torch.load(model_path, map_location="cpu")
 
+        # Strip 'bundle_emb' keys - these are unused and currently cause downstream errors.
+        # To revisit later to determine if they're needed/useful.
+        state_dict = {k: v for k, v in state_dict.items() if not k.startswith("bundle_emb")}
+
+        # At the time of writing, we support the OMI standard for base models Flux and SDXL
+        if config.format == ModelFormat.OMI and self._model_base in [
+            BaseModelType.StableDiffusionXL,
+            BaseModelType.Flux,
+        ]:
+            state_dict = convert_from_omi(state_dict, config.base)  # type: ignore
+
         # Apply state_dict key conversions, if necessary.
         if self._model_base == BaseModelType.StableDiffusionXL:
             state_dict = convert_sdxl_keys_to_diffusers_format(state_dict)
             model = lora_model_from_sd_state_dict(state_dict=state_dict)
         elif self._model_base == BaseModelType.Flux:
-            if config.format == ModelFormat.Diffusers:
+            if config.format in [ModelFormat.Diffusers, ModelFormat.OMI]:
                 # HACK(ryand): We set alpha=None for diffusers PEFT format models. These models are typically
                 # distributed as a single file without the associated metadata containing the alpha value. We chose
                 # alpha=None, because this is treated as alpha=rank internally in `LoRALayerBase.scale()`. alpha=rank
@@ -99,7 +113,7 @@ class LoRALoader(ModelLoader):
                 elif is_state_dict_likely_in_flux_aitoolkit_format(state_dict=state_dict):
                     model = lora_model_from_flux_aitoolkit_state_dict(state_dict=state_dict)
                 else:
-                    raise ValueError(f"LoRA model is in unsupported FLUX format: {config.format}")
+                    raise ValueError("LoRA model is in unsupported FLUX format")
             else:
                 raise ValueError(f"LoRA model is in unsupported FLUX format: {config.format}")
         elif self._model_base in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:

invokeai/backend/model_manager/omi/__init__.py

@@ -0,0 +1,7 @@
+from invokeai.backend.model_manager.omi.omi import convert_from_omi
+from invokeai.backend.model_manager.omi.vendor.model_spec.architecture import (
+    flux_dev_1_lora,
+    stable_diffusion_xl_1_lora,
+)
+
+__all__ = ["flux_dev_1_lora", "stable_diffusion_xl_1_lora", "convert_from_omi"]

invokeai/backend/model_manager/omi/omi.py

@@ -0,0 +1,21 @@
+from invokeai.backend.model_manager.model_on_disk import StateDict
+from invokeai.backend.model_manager.omi.vendor.convert.lora import (
+    convert_flux_lora as omi_flux,
+)
+from invokeai.backend.model_manager.omi.vendor.convert.lora import (
+    convert_lora_util as lora_util,
+)
+from invokeai.backend.model_manager.omi.vendor.convert.lora import (
+    convert_sdxl_lora as omi_sdxl,
+)
+from invokeai.backend.model_manager.taxonomy import BaseModelType
+
+
+def convert_from_omi(weights_sd: StateDict, base: BaseModelType):
+    keyset = {
+        BaseModelType.Flux: omi_flux.convert_flux_lora_key_sets(),
+        BaseModelType.StableDiffusionXL: omi_sdxl.convert_sdxl_lora_key_sets(),
+    }[base]
+    source = "omi"
+    target = "legacy_diffusers"
+    return lora_util.__convert(weights_sd, keyset, source, target)  # type: ignore

invokeai/backend/model_manager/omi/vendor/convert/lora/convert_clip.py

@@ -0,0 +1,20 @@
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import (
+    LoraConversionKeySet,
+    map_prefix_range,
+)
+
+
+def map_clip(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += [LoraConversionKeySet("text_projection", "text_projection", parent=key_prefix)]
+
+    for k in map_prefix_range("text_model.encoder.layers", "text_model.encoder.layers", parent=key_prefix):
+        keys += [LoraConversionKeySet("mlp.fc1", "mlp.fc1", parent=k)]
+        keys += [LoraConversionKeySet("mlp.fc2", "mlp.fc2", parent=k)]
+        keys += [LoraConversionKeySet("self_attn.k_proj", "self_attn.k_proj", parent=k)]
+        keys += [LoraConversionKeySet("self_attn.out_proj", "self_attn.out_proj", parent=k)]
+        keys += [LoraConversionKeySet("self_attn.q_proj", "self_attn.q_proj", parent=k)]
+        keys += [LoraConversionKeySet("self_attn.v_proj", "self_attn.v_proj", parent=k)]
+
+    return keys

invokeai/backend/model_manager/omi/vendor/convert/lora/convert_flux_lora.py

@@ -0,0 +1,84 @@
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_clip import map_clip
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import (
+    LoraConversionKeySet,
+    map_prefix_range,
+)
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_t5 import map_t5
+
+
+def __map_double_transformer_block(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += [LoraConversionKeySet("img_attn.qkv.0", "attn.to_q", parent=key_prefix)]
+    keys += [LoraConversionKeySet("img_attn.qkv.1", "attn.to_k", parent=key_prefix)]
+    keys += [LoraConversionKeySet("img_attn.qkv.2", "attn.to_v", parent=key_prefix)]
+
+    keys += [LoraConversionKeySet("txt_attn.qkv.0", "attn.add_q_proj", parent=key_prefix)]
+    keys += [LoraConversionKeySet("txt_attn.qkv.1", "attn.add_k_proj", parent=key_prefix)]
+    keys += [LoraConversionKeySet("txt_attn.qkv.2", "attn.add_v_proj", parent=key_prefix)]
+
+    keys += [LoraConversionKeySet("img_attn.proj", "attn.to_out.0", parent=key_prefix)]
+    keys += [LoraConversionKeySet("img_mlp.0", "ff.net.0.proj", parent=key_prefix)]
+    keys += [LoraConversionKeySet("img_mlp.2", "ff.net.2", parent=key_prefix)]
+    keys += [LoraConversionKeySet("img_mod.lin", "norm1.linear", parent=key_prefix)]
+
+    keys += [LoraConversionKeySet("txt_attn.proj", "attn.to_add_out", parent=key_prefix)]
+    keys += [LoraConversionKeySet("txt_mlp.0", "ff_context.net.0.proj", parent=key_prefix)]
+    keys += [LoraConversionKeySet("txt_mlp.2", "ff_context.net.2", parent=key_prefix)]
+    keys += [LoraConversionKeySet("txt_mod.lin", "norm1_context.linear", parent=key_prefix)]
+
+    return keys
+
+
+def __map_single_transformer_block(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += [LoraConversionKeySet("linear1.0", "attn.to_q", parent=key_prefix)]
+    keys += [LoraConversionKeySet("linear1.1", "attn.to_k", parent=key_prefix)]
+    keys += [LoraConversionKeySet("linear1.2", "attn.to_v", parent=key_prefix)]
+    keys += [LoraConversionKeySet("linear1.3", "proj_mlp", parent=key_prefix)]
+
+    keys += [LoraConversionKeySet("linear2", "proj_out", parent=key_prefix)]
+    keys += [LoraConversionKeySet("modulation.lin", "norm.linear", parent=key_prefix)]
+
+    return keys
+
+
+def __map_transformer(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += [LoraConversionKeySet("txt_in", "context_embedder", parent=key_prefix)]
+    keys += [
+        LoraConversionKeySet("final_layer.adaLN_modulation.1", "norm_out.linear", parent=key_prefix, swap_chunks=True)
+    ]
+    keys += [LoraConversionKeySet("final_layer.linear", "proj_out", parent=key_prefix)]
+    keys += [
+        LoraConversionKeySet("guidance_in.in_layer", "time_text_embed.guidance_embedder.linear_1", parent=key_prefix)
+    ]
+    keys += [
+        LoraConversionKeySet("guidance_in.out_layer", "time_text_embed.guidance_embedder.linear_2", parent=key_prefix)
+    ]
+    keys += [LoraConversionKeySet("vector_in.in_layer", "time_text_embed.text_embedder.linear_1", parent=key_prefix)]
+    keys += [LoraConversionKeySet("vector_in.out_layer", "time_text_embed.text_embedder.linear_2", parent=key_prefix)]
+    keys += [LoraConversionKeySet("time_in.in_layer", "time_text_embed.timestep_embedder.linear_1", parent=key_prefix)]
+    keys += [LoraConversionKeySet("time_in.out_layer", "time_text_embed.timestep_embedder.linear_2", parent=key_prefix)]
+    keys += [LoraConversionKeySet("img_in.proj", "x_embedder", parent=key_prefix)]
+
+    for k in map_prefix_range("double_blocks", "transformer_blocks", parent=key_prefix):
+        keys += __map_double_transformer_block(k)
+
+    for k in map_prefix_range("single_blocks", "single_transformer_blocks", parent=key_prefix):
+        keys += __map_single_transformer_block(k)
+
+    return keys
+
+
+def convert_flux_lora_key_sets() -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += [LoraConversionKeySet("bundle_emb", "bundle_emb")]
+    keys += __map_transformer(LoraConversionKeySet("transformer", "lora_transformer"))
+    keys += map_clip(LoraConversionKeySet("clip_l", "lora_te1"))
+    keys += map_t5(LoraConversionKeySet("t5", "lora_te2"))
+
+    return keys

invokeai/backend/model_manager/omi/vendor/convert/lora/convert_lora_util.py

@@ -0,0 +1,217 @@
+import torch
+from torch import Tensor
+from typing_extensions import Self
+
+
+class LoraConversionKeySet:
+    def __init__(
+        self,
+        omi_prefix: str,
+        diffusers_prefix: str,
+        legacy_diffusers_prefix: str | None = None,
+        parent: Self | None = None,
+        swap_chunks: bool = False,
+        filter_is_last: bool | None = None,
+        next_omi_prefix: str | None = None,
+        next_diffusers_prefix: str | None = None,
+    ):
+        if parent is not None:
+            self.omi_prefix = combine(parent.omi_prefix, omi_prefix)
+            self.diffusers_prefix = combine(parent.diffusers_prefix, diffusers_prefix)
+        else:
+            self.omi_prefix = omi_prefix
+            self.diffusers_prefix = diffusers_prefix
+
+        if legacy_diffusers_prefix is None:
+            self.legacy_diffusers_prefix = self.diffusers_prefix.replace(".", "_")
+        elif parent is not None:
+            self.legacy_diffusers_prefix = combine(parent.legacy_diffusers_prefix, legacy_diffusers_prefix).replace(
+                ".", "_"
+            )
+        else:
+            self.legacy_diffusers_prefix = legacy_diffusers_prefix
+
+        self.parent = parent
+        self.swap_chunks = swap_chunks
+        self.filter_is_last = filter_is_last
+        self.prefix = parent
+
+        if next_omi_prefix is None and parent is not None:
+            self.next_omi_prefix = parent.next_omi_prefix
+            self.next_diffusers_prefix = parent.next_diffusers_prefix
+            self.next_legacy_diffusers_prefix = parent.next_legacy_diffusers_prefix
+        elif next_omi_prefix is not None and parent is not None:
+            self.next_omi_prefix = combine(parent.omi_prefix, next_omi_prefix)
+            self.next_diffusers_prefix = combine(parent.diffusers_prefix, next_diffusers_prefix)
+            self.next_legacy_diffusers_prefix = combine(parent.legacy_diffusers_prefix, next_diffusers_prefix).replace(
+                ".", "_"
+            )
+        elif next_omi_prefix is not None and parent is None:
+            self.next_omi_prefix = next_omi_prefix
+            self.next_diffusers_prefix = next_diffusers_prefix
+            self.next_legacy_diffusers_prefix = next_diffusers_prefix.replace(".", "_")
+        else:
+            self.next_omi_prefix = None
+            self.next_diffusers_prefix = None
+            self.next_legacy_diffusers_prefix = None
+
+    def __get_omi(self, in_prefix: str, key: str) -> str:
+        return self.omi_prefix + key.removeprefix(in_prefix)
+
+    def __get_diffusers(self, in_prefix: str, key: str) -> str:
+        return self.diffusers_prefix + key.removeprefix(in_prefix)
+
+    def __get_legacy_diffusers(self, in_prefix: str, key: str) -> str:
+        key = self.legacy_diffusers_prefix + key.removeprefix(in_prefix)
+
+        suffix = key[key.rfind(".") :]
+        if suffix not in [".alpha", ".dora_scale"]:  # some keys only have a single . in the suffix
+            suffix = key[key.removesuffix(suffix).rfind(".") :]
+        key = key.removesuffix(suffix)
+
+        return key.replace(".", "_") + suffix
+
+    def get_key(self, in_prefix: str, key: str, target: str) -> str:
+        if target == "omi":
+            return self.__get_omi(in_prefix, key)
+        elif target == "diffusers":
+            return self.__get_diffusers(in_prefix, key)
+        elif target == "legacy_diffusers":
+            return self.__get_legacy_diffusers(in_prefix, key)
+        return key
+
+    def __str__(self) -> str:
+        return f"omi: {self.omi_prefix}, diffusers: {self.diffusers_prefix}, legacy: {self.legacy_diffusers_prefix}"
+
+
+def combine(left: str, right: str) -> str:
+    left = left.rstrip(".")
+    right = right.lstrip(".")
+    if left == "" or left is None:
+        return right
+    elif right == "" or right is None:
+        return left
+    else:
+        return left + "." + right
+
+
+def map_prefix_range(
+    omi_prefix: str,
+    diffusers_prefix: str,
+    parent: LoraConversionKeySet,
+) -> list[LoraConversionKeySet]:
+    # 100 should be a safe upper bound. increase if it's not enough in the future
+    return [
+        LoraConversionKeySet(
+            omi_prefix=f"{omi_prefix}.{i}",
+            diffusers_prefix=f"{diffusers_prefix}.{i}",
+            parent=parent,
+            next_omi_prefix=f"{omi_prefix}.{i + 1}",
+            next_diffusers_prefix=f"{diffusers_prefix}.{i + 1}",
+        )
+        for i in range(100)
+    ]
+
+
+def __convert(
+    state_dict: dict[str, Tensor],
+    key_sets: list[LoraConversionKeySet],
+    source: str,
+    target: str,
+) -> dict[str, Tensor]:
+    out_states = {}
+
+    if source == target:
+        return dict(state_dict)
+
+    # TODO: maybe replace with a non O(n^2) algorithm
+    for key, tensor in state_dict.items():
+        for key_set in key_sets:
+            in_prefix = ""
+
+            if source == "omi":
+                in_prefix = key_set.omi_prefix
+            elif source == "diffusers":
+                in_prefix = key_set.diffusers_prefix
+            elif source == "legacy_diffusers":
+                in_prefix = key_set.legacy_diffusers_prefix
+
+            if not key.startswith(in_prefix):
+                continue
+
+            if key_set.filter_is_last is not None:
+                next_prefix = None
+                if source == "omi":
+                    next_prefix = key_set.next_omi_prefix
+                elif source == "diffusers":
+                    next_prefix = key_set.next_diffusers_prefix
+                elif source == "legacy_diffusers":
+                    next_prefix = key_set.next_legacy_diffusers_prefix
+
+                is_last = not any(k.startswith(next_prefix) for k in state_dict)
+                if key_set.filter_is_last != is_last:
+                    continue
+
+            name = key_set.get_key(in_prefix, key, target)
+
+            can_swap_chunks = target == "omi" or source == "omi"
+            if key_set.swap_chunks and name.endswith(".lora_up.weight") and can_swap_chunks:
+                chunk_0, chunk_1 = tensor.chunk(2, dim=0)
+                tensor = torch.cat([chunk_1, chunk_0], dim=0)
+
+            out_states[name] = tensor
+
+            break  # only map the first matching key set
+
+    return out_states
+
+
+def __detect_source(
+    state_dict: dict[str, Tensor],
+    key_sets: list[LoraConversionKeySet],
+) -> str:
+    omi_count = 0
+    diffusers_count = 0
+    legacy_diffusers_count = 0
+
+    for key in state_dict:
+        for key_set in key_sets:
+            if key.startswith(key_set.omi_prefix):
+                omi_count += 1
+            if key.startswith(key_set.diffusers_prefix):
+                diffusers_count += 1
+            if key.startswith(key_set.legacy_diffusers_prefix):
+                legacy_diffusers_count += 1
+
+    if omi_count > diffusers_count and omi_count > legacy_diffusers_count:
+        return "omi"
+    if diffusers_count > omi_count and diffusers_count > legacy_diffusers_count:
+        return "diffusers"
+    if legacy_diffusers_count > omi_count and legacy_diffusers_count > diffusers_count:
+        return "legacy_diffusers"
+
+    return ""
+
+
+def convert_to_omi(
+    state_dict: dict[str, Tensor],
+    key_sets: list[LoraConversionKeySet],
+) -> dict[str, Tensor]:
+    source = __detect_source(state_dict, key_sets)
+    return __convert(state_dict, key_sets, source, "omi")
+
+
+def convert_to_diffusers(
+    state_dict: dict[str, Tensor],
+    key_sets: list[LoraConversionKeySet],
+) -> dict[str, Tensor]:
+    source = __detect_source(state_dict, key_sets)
+    return __convert(state_dict, key_sets, source, "diffusers")
+
+
+def convert_to_legacy_diffusers(
+    state_dict: dict[str, Tensor],
+    key_sets: list[LoraConversionKeySet],
+) -> dict[str, Tensor]:
+    source = __detect_source(state_dict, key_sets)
+    return __convert(state_dict, key_sets, source, "legacy_diffusers")

invokeai/backend/model_manager/omi/vendor/convert/lora/convert_sdxl_lora.py

@@ -0,0 +1,125 @@
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_clip import map_clip
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import (
+    LoraConversionKeySet,
+    map_prefix_range,
+)
+
+
+def __map_unet_resnet_block(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += [LoraConversionKeySet("emb_layers.1", "time_emb_proj", parent=key_prefix)]
+    keys += [LoraConversionKeySet("in_layers.2", "conv1", parent=key_prefix)]
+    keys += [LoraConversionKeySet("out_layers.3", "conv2", parent=key_prefix)]
+    keys += [LoraConversionKeySet("skip_connection", "conv_shortcut", parent=key_prefix)]
+
+    return keys
+
+
+def __map_unet_attention_block(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += [LoraConversionKeySet("proj_in", "proj_in", parent=key_prefix)]
+    keys += [LoraConversionKeySet("proj_out", "proj_out", parent=key_prefix)]
+    for k in map_prefix_range("transformer_blocks", "transformer_blocks", parent=key_prefix):
+        keys += [LoraConversionKeySet("attn1.to_q", "attn1.to_q", parent=k)]
+        keys += [LoraConversionKeySet("attn1.to_k", "attn1.to_k", parent=k)]
+        keys += [LoraConversionKeySet("attn1.to_v", "attn1.to_v", parent=k)]
+        keys += [LoraConversionKeySet("attn1.to_out.0", "attn1.to_out.0", parent=k)]
+        keys += [LoraConversionKeySet("attn2.to_q", "attn2.to_q", parent=k)]
+        keys += [LoraConversionKeySet("attn2.to_k", "attn2.to_k", parent=k)]
+        keys += [LoraConversionKeySet("attn2.to_v", "attn2.to_v", parent=k)]
+        keys += [LoraConversionKeySet("attn2.to_out.0", "attn2.to_out.0", parent=k)]
+        keys += [LoraConversionKeySet("ff.net.0.proj", "ff.net.0.proj", parent=k)]
+        keys += [LoraConversionKeySet("ff.net.2", "ff.net.2", parent=k)]
+
+    return keys
+
+
+def __map_unet_down_blocks(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += __map_unet_resnet_block(LoraConversionKeySet("1.0", "0.resnets.0", parent=key_prefix))
+    keys += __map_unet_resnet_block(LoraConversionKeySet("2.0", "0.resnets.1", parent=key_prefix))
+    keys += [LoraConversionKeySet("3.0.op", "0.downsamplers.0.conv", parent=key_prefix)]
+
+    keys += __map_unet_resnet_block(LoraConversionKeySet("4.0", "1.resnets.0", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("4.1", "1.attentions.0", parent=key_prefix))
+    keys += __map_unet_resnet_block(LoraConversionKeySet("5.0", "1.resnets.1", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("5.1", "1.attentions.1", parent=key_prefix))
+    keys += [LoraConversionKeySet("6.0.op", "1.downsamplers.0.conv", parent=key_prefix)]
+
+    keys += __map_unet_resnet_block(LoraConversionKeySet("7.0", "2.resnets.0", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("7.1", "2.attentions.0", parent=key_prefix))
+    keys += __map_unet_resnet_block(LoraConversionKeySet("8.0", "2.resnets.1", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("8.1", "2.attentions.1", parent=key_prefix))
+
+    return keys
+
+
+def __map_unet_mid_block(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += __map_unet_resnet_block(LoraConversionKeySet("0", "resnets.0", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("1", "attentions.0", parent=key_prefix))
+    keys += __map_unet_resnet_block(LoraConversionKeySet("2", "resnets.1", parent=key_prefix))
+
+    return keys
+
+
+def __map_unet_up_block(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += __map_unet_resnet_block(LoraConversionKeySet("0.0", "0.resnets.0", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("0.1", "0.attentions.0", parent=key_prefix))
+    keys += __map_unet_resnet_block(LoraConversionKeySet("1.0", "0.resnets.1", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("1.1", "0.attentions.1", parent=key_prefix))
+    keys += __map_unet_resnet_block(LoraConversionKeySet("2.0", "0.resnets.2", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("2.1", "0.attentions.2", parent=key_prefix))
+    keys += [LoraConversionKeySet("2.2.conv", "0.upsamplers.0.conv", parent=key_prefix)]
+
+    keys += __map_unet_resnet_block(LoraConversionKeySet("3.0", "1.resnets.0", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("3.1", "1.attentions.0", parent=key_prefix))
+    keys += __map_unet_resnet_block(LoraConversionKeySet("4.0", "1.resnets.1", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("4.1", "1.attentions.1", parent=key_prefix))
+    keys += __map_unet_resnet_block(LoraConversionKeySet("5.0", "1.resnets.2", parent=key_prefix))
+    keys += __map_unet_attention_block(LoraConversionKeySet("5.1", "1.attentions.2", parent=key_prefix))
+    keys += [LoraConversionKeySet("5.2.conv", "1.upsamplers.0.conv", parent=key_prefix)]
+
+    keys += __map_unet_resnet_block(LoraConversionKeySet("6.0", "2.resnets.0", parent=key_prefix))
+    keys += __map_unet_resnet_block(LoraConversionKeySet("7.0", "2.resnets.1", parent=key_prefix))
+    keys += __map_unet_resnet_block(LoraConversionKeySet("8.0", "2.resnets.2", parent=key_prefix))
+
+    return keys
+
+
+def __map_unet(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += [LoraConversionKeySet("input_blocks.0.0", "conv_in", parent=key_prefix)]
+
+    keys += [LoraConversionKeySet("time_embed.0", "time_embedding.linear_1", parent=key_prefix)]
+    keys += [LoraConversionKeySet("time_embed.2", "time_embedding.linear_2", parent=key_prefix)]
+
+    keys += [LoraConversionKeySet("label_emb.0.0", "add_embedding.linear_1", parent=key_prefix)]
+    keys += [LoraConversionKeySet("label_emb.0.2", "add_embedding.linear_2", parent=key_prefix)]
+
+    keys += __map_unet_down_blocks(LoraConversionKeySet("input_blocks", "down_blocks", parent=key_prefix))
+    keys += __map_unet_mid_block(LoraConversionKeySet("middle_block", "mid_block", parent=key_prefix))
+    keys += __map_unet_up_block(LoraConversionKeySet("output_blocks", "up_blocks", parent=key_prefix))
+
+    keys += [LoraConversionKeySet("out.0", "conv_norm_out", parent=key_prefix)]
+    keys += [LoraConversionKeySet("out.2", "conv_out", parent=key_prefix)]
+
+    return keys
+
+
+def convert_sdxl_lora_key_sets() -> list[LoraConversionKeySet]:
+    keys = []
+
+    keys += [LoraConversionKeySet("bundle_emb", "bundle_emb")]
+    keys += __map_unet(LoraConversionKeySet("unet", "lora_unet"))
+    keys += map_clip(LoraConversionKeySet("clip_l", "lora_te1"))
+    keys += map_clip(LoraConversionKeySet("clip_g", "lora_te2"))
+
+    return keys

invokeai/backend/model_manager/omi/vendor/convert/lora/convert_t5.py

@@ -0,0 +1,19 @@
+from invokeai.backend.model_manager.omi.vendor.convert.lora.convert_lora_util import (
+    LoraConversionKeySet,
+    map_prefix_range,
+)
+
+
+def map_t5(key_prefix: LoraConversionKeySet) -> list[LoraConversionKeySet]:
+    keys = []
+
+    for k in map_prefix_range("encoder.block", "encoder.block", parent=key_prefix):
+        keys += [LoraConversionKeySet("layer.0.SelfAttention.k", "layer.0.SelfAttention.k", parent=k)]
+        keys += [LoraConversionKeySet("layer.0.SelfAttention.o", "layer.0.SelfAttention.o", parent=k)]
+        keys += [LoraConversionKeySet("layer.0.SelfAttention.q", "layer.0.SelfAttention.q", parent=k)]
+        keys += [LoraConversionKeySet("layer.0.SelfAttention.v", "layer.0.SelfAttention.v", parent=k)]
+        keys += [LoraConversionKeySet("layer.1.DenseReluDense.wi_0", "layer.1.DenseReluDense.wi_0", parent=k)]
+        keys += [LoraConversionKeySet("layer.1.DenseReluDense.wi_1", "layer.1.DenseReluDense.wi_1", parent=k)]
+        keys += [LoraConversionKeySet("layer.1.DenseReluDense.wo", "layer.1.DenseReluDense.wo", parent=k)]
+
+    return keys

invokeai/backend/model_manager/omi/vendor/model_spec/architecture.py

@@ -0,0 +1,31 @@
+stable_diffusion_1_lora = "stable-diffusion-v1/lora"
+stable_diffusion_1_inpainting_lora = "stable-diffusion-v1-inpainting/lora"
+
+stable_diffusion_2_512_lora = "stable-diffusion-v2-512/lora"
+stable_diffusion_2_768_v_lora = "stable-diffusion-v2-768-v/lora"
+stable_diffusion_2_depth_lora = "stable-diffusion-v2-depth/lora"
+stable_diffusion_2_inpainting_lora = "stable-diffusion-v2-inpainting/lora"
+
+stable_diffusion_3_medium_lora = "stable-diffusion-v3-medium/lora"
+stable_diffusion_35_medium_lora = "stable-diffusion-v3.5-medium/lora"
+stable_diffusion_35_large_lora = "stable-diffusion-v3.5-large/lora"
+
+stable_diffusion_xl_1_lora = "stable-diffusion-xl-v1-base/lora"
+stable_diffusion_xl_1_inpainting_lora = "stable-diffusion-xl-v1-base-inpainting/lora"
+
+wuerstchen_2_lora = "wuerstchen-v2-prior/lora"
+stable_cascade_1_stage_a_lora = "stable-cascade-v1-stage-a/lora"
+stable_cascade_1_stage_b_lora = "stable-cascade-v1-stage-b/lora"
+stable_cascade_1_stage_c_lora = "stable-cascade-v1-stage-c/lora"
+
+pixart_alpha_lora = "pixart-alpha/lora"
+pixart_sigma_lora = "pixart-sigma/lora"
+
+flux_dev_1_lora = "Flux.1-dev/lora"
+flux_fill_dev_1_lora = "Flux.1-fill-dev/lora"
+
+sana_lora = "sana/lora"
+
+hunyuan_video_lora = "hunyuan-video/lora"
+
+hi_dream_i1_lora = "hidream-i1/lora"

invokeai/backend/model_manager/starter_models.py

@@ -23,7 +23,7 @@ class StarterModel(StarterModelWithoutDependencies):
     dependencies: Optional[list[StarterModelWithoutDependencies]] = None
 
 
-class StarterModelBundles(BaseModel):
+class StarterModelBundle(BaseModel):
     name: str
     models: list[StarterModel]
 
@@ -109,7 +109,7 @@ flux_vae = StarterModel(
 
 # region: Main
 flux_schnell_quantized = StarterModel(
-    name="FLUX Schnell (Quantized)",
+    name="FLUX.1 schnell (quantized)",
     base=BaseModelType.Flux,
     source="InvokeAI/flux_schnell::transformer/bnb_nf4/flux1-schnell-bnb_nf4.safetensors",
     description="FLUX schnell transformer quantized to bitsandbytes NF4 format. Total size with dependencies: ~12GB",
@@ -117,7 +117,7 @@ flux_schnell_quantized = StarterModel(
     dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
 )
 flux_dev_quantized = StarterModel(
-    name="FLUX Dev (Quantized)",
+    name="FLUX.1 dev (quantized)",
     base=BaseModelType.Flux,
     source="InvokeAI/flux_dev::transformer/bnb_nf4/flux1-dev-bnb_nf4.safetensors",
     description="FLUX dev transformer quantized to bitsandbytes NF4 format. Total size with dependencies: ~12GB",
@@ -125,7 +125,7 @@ flux_dev_quantized = StarterModel(
     dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
 )
 flux_schnell = StarterModel(
-    name="FLUX Schnell",
+    name="FLUX.1 schnell",
     base=BaseModelType.Flux,
     source="InvokeAI/flux_schnell::transformer/base/flux1-schnell.safetensors",
     description="FLUX schnell transformer in bfloat16. Total size with dependencies: ~33GB",
@@ -133,13 +133,21 @@ flux_schnell = StarterModel(
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
 flux_dev = StarterModel(
-    name="FLUX Dev",
+    name="FLUX.1 dev",
     base=BaseModelType.Flux,
     source="InvokeAI/flux_dev::transformer/base/flux1-dev.safetensors",
     description="FLUX dev transformer in bfloat16. Total size with dependencies: ~33GB",
     type=ModelType.Main,
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
+flux_kontext = StarterModel(
+    name="FLUX.1 Kontext dev",
+    base=BaseModelType.Flux,
+    source="black-forest-labs/FLUX.1-Kontext-dev::flux1-kontext-dev.safetensors",
+    description="FLUX.1 Kontext dev transformer in bfloat16. Total size with dependencies: ~33GB",
+    type=ModelType.Main,
+    dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
+)
 sd35_medium = StarterModel(
     name="SD3.5 Medium",
     base=BaseModelType.StableDiffusion3,
@@ -656,6 +664,7 @@ flux_fill = StarterModel(
 # List of starter models, displayed on the frontend.
 # The order/sort of this list is not changed by the frontend - set it how you want it here.
 STARTER_MODELS: list[StarterModel] = [
+    flux_kontext,
     flux_schnell_quantized,
     flux_dev_quantized,
     flux_schnell,
@@ -776,12 +785,13 @@ flux_bundle: list[StarterModel] = [
     flux_depth_control_lora,
     flux_redux,
     flux_fill,
+    flux_kontext,
 ]
 
-STARTER_BUNDLES: dict[str, list[StarterModel]] = {
-    BaseModelType.StableDiffusion1: sd1_bundle,
-    BaseModelType.StableDiffusionXL: sdxl_bundle,
-    BaseModelType.Flux: flux_bundle,
+STARTER_BUNDLES: dict[str, StarterModelBundle] = {
+    BaseModelType.StableDiffusion1: StarterModelBundle(name="Stable Diffusion 1.5", models=sd1_bundle),
+    BaseModelType.StableDiffusionXL: StarterModelBundle(name="SDXL", models=sdxl_bundle),
+    BaseModelType.Flux: StarterModelBundle(name="FLUX.1 dev", models=flux_bundle),
 }
 
 assert len(STARTER_MODELS) == len({m.source for m in STARTER_MODELS}), "Duplicate starter models"

invokeai/backend/model_manager/taxonomy.py

@@ -29,6 +29,7 @@ class BaseModelType(str, Enum):
     Imagen3 = "imagen3"
     Imagen4 = "imagen4"
     ChatGPT4o = "chatgpt-4o"
+    FluxKontext = "flux-kontext"
 
 
 class ModelType(str, Enum):
@@ -88,6 +89,7 @@ class ModelVariantType(str, Enum):
 class ModelFormat(str, Enum):
     """Storage format of model."""
 
+    OMI = "omi"
     Diffusers = "diffusers"
     Checkpoint = "checkpoint"
     LyCORIS = "lycoris"

invokeai/frontend/web/.eslintrc.js

@@ -17,6 +17,15 @@ module.exports = {
     'no-promise-executor-return': 'error',
     // https://eslint.org/docs/latest/rules/require-await
     'require-await': 'error',
+    // Restrict setActiveTab calls to only use-navigation-api.tsx
+    'no-restricted-syntax': [
+      'error',
+      {
+        selector: 'CallExpression[callee.name="setActiveTab"]',
+        message:
+          'setActiveTab() can only be called from use-navigation-api.tsx. Use navigationApi.switchToTab() instead.',
+      },
+    ],
     // TODO: ENABLE THIS RULE BEFORE v6.0.0
     'react/display-name': 'off',
     'no-restricted-properties': [
@@ -33,8 +42,38 @@ module.exports = {
           'The Clipboard API is not available by default in Firefox. Use the `useClipboard` hook instead, which wraps clipboard access to prevent errors.',
       },
     ],
+    'no-restricted-imports': [
+      'error',
+      {
+        paths: [
+          {
+            name: 'lodash-es',
+            importNames: ['isEqual'],
+            message: 'Please use objectEquals from @observ33r/object-equals instead.',
+          },
+          {
+            name: 'lodash-es',
+            message: 'Please use es-toolkit instead.',
+          },
+          {
+            name: 'es-toolkit',
+            importNames: ['isEqual'],
+            message: 'Please use objectEquals from @observ33r/object-equals instead.',
+          },
+        ],
+      },
+    ],
   },
   overrides: [
+    /**
+     * Allow setActiveTab calls only in use-navigation-api.tsx
+     */
+    {
+      files: ['**/use-navigation-api.tsx'],
+      rules: {
+        'no-restricted-syntax': 'off',
+      },
+    },
     /**
      * Overrides for stories
      */

invokeai/frontend/web/knip.ts

@@ -3,8 +3,6 @@ import type { KnipConfig } from 'knip';
 const config: KnipConfig = {
   project: ['src/**/*.{ts,tsx}!'],
   ignore: [
-    // TODO(psyche): temporarily ignored all files for test build purposes
-    'src/**',
     // This file is only used during debugging
     'src/app/store/middleware/debugLoggerMiddleware.ts',
     // Autogenerated types - shouldn't ever touch these
@@ -14,10 +12,6 @@ const config: KnipConfig = {
     'src/features/parameters/types/parameterSchemas.ts',
     // TODO(psyche): maybe we can clean up these utils after canvas v2 release
     'src/features/controlLayers/konva/util.ts',
-    // TODO(psyche): restore HRF functionality?
-    'src/features/hrf/**',
-    // This feature is (temprarily?) disabled
-    'src/features/controlLayers/components/InpaintMask/InpaintMaskAddButtons.tsx',
   ],
   ignoreBinaries: ['only-allow'],
   paths: {

invokeai/frontend/web/package.json

@@ -38,71 +38,60 @@
     "test:ui": "vitest --coverage --ui",
     "test:no-watch": "vitest --no-watch"
   },
-  "madge": {
-    "excludeRegExp": [
-      "^index.ts$"
-    ],
-    "detectiveOptions": {
-      "ts": {
-        "skipTypeImports": true
-      },
-      "tsx": {
-        "skipTypeImports": true
-      }
-    }
-  },
   "dependencies": {
-    "@atlaskit/pragmatic-drag-and-drop": "^1.5.3",
-    "@atlaskit/pragmatic-drag-and-drop-auto-scroll": "^2.1.0",
-    "@atlaskit/pragmatic-drag-and-drop-hitbox": "^1.0.3",
-    "@dagrejs/dagre": "^1.1.4",
+    "@atlaskit/pragmatic-drag-and-drop": "^1.7.4",
+    "@atlaskit/pragmatic-drag-and-drop-auto-scroll": "^2.1.1",
+    "@atlaskit/pragmatic-drag-and-drop-hitbox": "^1.1.0",
+    "@dagrejs/dagre": "^1.1.5",
     "@dagrejs/graphlib": "^2.2.4",
-    "@fontsource-variable/inter": "^5.2.5",
+    "@fontsource-variable/inter": "^5.2.6",
     "@invoke-ai/ui-library": "^0.0.46",
     "@nanostores/react": "^1.0.0",
+    "@observ33r/object-equals": "^1.1.4",
     "@reduxjs/toolkit": "2.8.2",
     "@roarr/browser-log-writer": "^1.3.0",
-    "@xyflow/react": "^12.6.0",
+    "@xyflow/react": "^12.7.1",
+    "ag-psd": "^28.2.1",
     "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.2",
     "cmdk": "^1.1.1",
     "compare-versions": "^6.1.1",
-    "dockview": "^4.3.1",
+    "dockview": "^4.4.0",
+    "es-toolkit": "^1.39.5",
     "filesize": "^10.1.6",
     "fracturedjsonjs": "^4.1.0",
     "framer-motion": "^11.10.0",
-    "i18next": "^25.0.1",
+    "i18next": "^25.2.1",
     "i18next-http-backend": "^3.0.2",
-    "idb-keyval": "^6.2.1",
+    "idb-keyval": "^6.2.2",
     "jsondiffpatch": "^0.7.3",
     "konva": "^9.3.20",
-    "linkify-react": "^4.2.0",
-    "linkifyjs": "^4.2.0",
-    "lodash-es": "^4.17.21",
+    "linkify-react": "^4.3.1",
+    "linkifyjs": "^4.3.1",
     "lru-cache": "^11.1.0",
     "mtwist": "^1.0.2",
     "nanoid": "^5.1.5",
     "nanostores": "^1.0.1",
     "new-github-issue-url": "^1.1.0",
-    "overlayscrollbars": "^2.11.1",
+    "overlayscrollbars": "^2.11.4",
     "overlayscrollbars-react": "^0.5.6",
     "perfect-freehand": "^1.2.2",
-    "query-string": "^9.1.1",
+    "query-string": "^9.2.1",
     "raf-throttle": "^2.0.6",
     "react": "^18.3.1",
     "react-colorful": "^5.6.1",
     "react-dom": "^18.3.1",
     "react-dropzone": "^14.3.8",
     "react-error-boundary": "^5.0.0",
-    "react-hook-form": "^7.56.1",
+    "react-hook-form": "^7.58.1",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^15.5.1",
+    "react-i18next": "^15.5.3",
     "react-icons": "^5.5.0",
     "react-redux": "9.2.0",
-    "react-resizable-panels": "^2.1.8",
+    "react-resizable-panels": "^3.0.3",
     "react-textarea-autosize": "^8.5.9",
     "react-use": "^17.6.0",
-    "react-virtuoso": "^4.12.6",
+    "react-virtuoso": "^4.13.0",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.2.0",
     "redux-undo": "^1.1.0",
@@ -110,12 +99,12 @@
     "roarr": "^7.21.1",
     "serialize-error": "^12.0.0",
     "socket.io-client": "^4.8.1",
-    "stable-hash": "^0.0.5",
-    "use-debounce": "^10.0.4",
+    "stable-hash": "^0.0.6",
+    "use-debounce": "^10.0.5",
     "use-device-pixel-ratio": "^1.1.2",
     "uuid": "^11.1.0",
-    "zod": "^3.24.3",
-    "zod-validation-error": "^3.4.0"
+    "zod": "^3.25.67",
+    "zod-validation-error": "^3.5.2"
   },
   "peerDependencies": {
     "react": "^18.2.0",
@@ -132,7 +121,6 @@
     "@storybook/react": "^8.6.12",
     "@storybook/react-vite": "^8.6.12",
     "@storybook/theming": "^8.6.12",
-    "@types/lodash-es": "^4.17.12",
     "@types/node": "^22.15.1",
     "@types/react": "^18.3.11",
     "@types/react-dom": "^18.3.0",
@@ -146,7 +134,7 @@
     "eslint": "^8.57.1",
     "eslint-plugin-i18next": "^6.1.1",
     "eslint-plugin-path": "^1.3.0",
-    "knip": "^5.50.5",
+    "knip": "^5.61.3",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^7.6.1",
     "prettier": "^3.5.3",
@@ -155,7 +143,7 @@
     "tsafe": "^1.8.5",
     "type-fest": "^4.40.0",
     "typescript": "^5.8.3",
-    "vite": "^6.3.3",
+    "vite": "^7.0.2",
     "vite-plugin-css-injected-by-js": "^3.5.2",
     "vite-plugin-dts": "^4.5.3",
     "vite-plugin-eslint": "^1.8.1",
@@ -163,7 +151,7 @@
     "vitest": "^3.1.2"
   },
   "engines": {
-    "pnpm": "8"
+    "pnpm": "10"
   },
-  "packageManager": "pnpm@8.15.9+sha512.499434c9d8fdd1a2794ebf4552b3b25c0a633abcee5bb15e7b5de90f32f47b513aca98cd5cfd001c31f0db454bc3804edccd578501e4ca293a6816166bbd9f81"
+  "packageManager": "pnpm@10.12.4"
 }

invokeai/frontend/web/pnpm-workspace.yaml

@@ -0,0 +1,3 @@
+onlyBuiltDependencies:
+  - '@swc/core'
+  - esbuild

invokeai/frontend/web/public/locales/en.json

@@ -225,7 +225,16 @@
     "prompt": {
         "addPromptTrigger": "Add Prompt Trigger",
         "compatibleEmbeddings": "Compatible Embeddings",
-        "noMatchingTriggers": "No matching triggers"
+        "noMatchingTriggers": "No matching triggers",
+        "generateFromImage": "Generate prompt from image",
+        "expandCurrentPrompt": "Expand Current Prompt",
+        "uploadImageForPromptGeneration": "Upload Image for Prompt Generation",
+        "expandingPrompt": "Expanding prompt...",
+        "resultTitle": "Prompt Expansion Complete",
+        "resultSubtitle": "Choose how to handle the expanded prompt:",
+        "replace": "Replace",
+        "insert": "Insert",
+        "discard": "Discard"
     },
     "queue": {
         "queue": "Queue",
@@ -335,14 +344,14 @@
         "images": "Images",
         "assets": "Assets",
         "alwaysShowImageSizeBadge": "Always Show Image Size Badge",
-        "assetsTab": "Files you’ve uploaded for use in your projects.",
+        "assetsTab": "Files you've uploaded for use in your projects.",
         "autoAssignBoardOnClick": "Auto-Assign Board on Click",
         "autoSwitchNewImages": "Auto-Switch to New Images",
         "boardsSettings": "Boards Settings",
         "copy": "Copy",
         "currentlyInUse": "This image is currently in use in the following features:",
         "drop": "Drop",
-        "dropOrUpload": "$t(gallery.drop) or Upload",
+        "dropOrUpload": "Drop or Upload",
         "dropToUpload": "$t(gallery.drop) to Upload",
         "deleteImage_one": "Delete Image",
         "deleteImage_other": "Delete {{count}} Images",
@@ -357,7 +366,7 @@
         "gallerySettings": "Gallery Settings",
         "go": "Go",
         "image": "image",
-        "imagesTab": "Images you’ve created and saved within Invoke.",
+        "imagesTab": "Images you've created and saved within Invoke.",
         "imagesSettings": "Gallery Images Settings",
         "jump": "Jump",
         "loading": "Loading",
@@ -396,7 +405,8 @@
         "compareHelp4": "Press <Kbd>Z</Kbd> or <Kbd>Esc</Kbd> to exit.",
         "openViewer": "Open Viewer",
         "closeViewer": "Close Viewer",
-        "move": "Move"
+        "move": "Move",
+        "useForPromptGeneration": "Use for Prompt Generation"
     },
     "hotkeys": {
         "hotkeys": "Hotkeys",
@@ -579,6 +589,16 @@
             "cancelTransform": {
                 "title": "Cancel Transform",
                 "desc": "Cancel the pending transform."
+            },
+            "settings": {
+                "behavior": "Behavior",
+                "display": "Display",
+                "grid": "Grid",
+                "debug": "Debug"
+            },
+            "toggleNonRasterLayers": {
+                "title": "Toggle Non-Raster Layers",
+                "desc": "Show or hide all non-raster layer categories (Control Layers, Inpaint Masks, Regional Guidance)."
             }
         },
         "workflows": {
@@ -742,7 +762,7 @@
         "vae": "VAE",
         "width": "Width",
         "workflow": "Workflow",
-        "canvasV2Metadata": "Canvas"
+        "canvasV2Metadata": "Canvas Layers"
     },
     "modelManager": {
         "active": "active",
@@ -763,7 +783,7 @@
         "convertToDiffusers": "Convert To Diffusers",
         "convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
         "convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
-        "convertToDiffusersHelpText3": "Your checkpoint file on disk WILL be deleted if it is in InvokeAI root folder. If it is in a custom location, then it WILL NOT be deleted.",
+        "convertToDiffusersHelpText3": "Your checkpoint file on disk WILL be deleted if it is in the InvokeAI root folder. If it is in a custom location, then it WILL NOT be deleted.",
         "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
         "convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 2GB-7GB in size.",
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
@@ -806,7 +826,11 @@
         "urlUnauthorizedErrorMessage": "You may need to configure an API token to access this model.",
         "urlUnauthorizedErrorMessage2": "Learn how here.",
         "imageEncoderModelId": "Image Encoder Model ID",
-        "includesNModels": "Includes {{n}} models and their dependencies",
+        "installedModelsCount": "{{installed}} of {{total}} models installed.",
+        "includesNModels": "Includes {{n}} models and their dependencies.",
+        "allNModelsInstalled": "All {{count}} models installed",
+        "nToInstall": "{{count}} to install",
+        "nAlreadyInstalled": "{{count}} already installed",
         "installQueue": "Install Queue",
         "inplaceInstall": "In-place install",
         "inplaceInstallDesc": "Install models without copying the files. When using the model, it will be loaded from its this location. If disabled, the model file(s) will be copied into the Invoke-managed models directory during installation.",
@@ -869,6 +893,25 @@
         "starterBundleHelpText": "Easily install all models needed to get started with a base model, including a main model, controlnets, IP adapters, and more. Selecting a bundle will skip any models that you already have installed.",
         "starterModels": "Starter Models",
         "starterModelsInModelManager": "Starter Models can be found in Model Manager",
+        "bundleAlreadyInstalled": "Bundle already installed",
+        "bundleAlreadyInstalledDesc": "All models in the {{bundleName}} bundle are already installed.",
+        "launchpadTab": "Launchpad",
+        "launchpad": {
+            "welcome": "Welcome to Model Management",
+            "description": "Invoke requires models to be installed to utilize most features of the platform. Choose from manual installation options or explore curated starter models.",
+            "manualInstall": "Manual Installation",
+            "urlDescription": "Install models from a URL or local file path. Perfect for specific models you want to add.",
+            "huggingFaceDescription": "Browse and install models directly from HuggingFace repositories.",
+            "scanFolderDescription": "Scan a local folder to automatically detect and install models.",
+            "recommendedModels": "Recommended Models",
+            "exploreStarter": "Or browse all available starter models",
+            "quickStart": "Quick Start Bundles",
+            "bundleDescription": "Each bundle includes essential models for each model family and curated base models to get started.",
+            "browseAll": "Or browse all available models:",
+            "stableDiffusion15": "Stable Diffusion 1.5",
+            "sdxl": "SDXL",
+            "fluxDev": "FLUX.1 dev"
+        },
         "controlLora": "Control LoRA",
         "llavaOnevision": "LLaVA OneVision",
         "syncModels": "Sync Models",
@@ -905,7 +948,8 @@
         "selectModel": "Select a Model",
         "noLoRAsInstalled": "No LoRAs installed",
         "noRefinerModelsInstalled": "No SDXL Refiner models installed",
-        "defaultVAE": "Default VAE"
+        "defaultVAE": "Default VAE",
+        "noCompatibleLoRAs": "No Compatible LoRAs"
     },
     "nodes": {
         "arithmeticSequence": "Arithmetic Sequence",
@@ -1147,6 +1191,7 @@
             "modelIncompatibleScaledBboxWidth": "Scaled bbox width is {{width}} but {{model}} requires multiple of {{multiple}}",
             "modelIncompatibleScaledBboxHeight": "Scaled bbox height is {{height}} but {{model}} requires multiple of {{multiple}}",
             "fluxModelMultipleControlLoRAs": "Can only use 1 Control LoRA at a time",
+            "fluxKontextMultipleReferenceImages": "Can only use 1 Reference Image at a time with Flux Kontext",
             "canvasIsFiltering": "Canvas is busy (filtering)",
             "canvasIsTransforming": "Canvas is busy (transforming)",
             "canvasIsRasterizing": "Canvas is busy (rasterizing)",
@@ -1154,7 +1199,9 @@
             "canvasIsSelectingObject": "Canvas is busy (selecting object)",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
-            "systemDisconnected": "System disconnected"
+            "systemDisconnected": "System disconnected",
+            "promptExpansionPending": "Prompt expansion in progress",
+            "promptExpansionResultPending": "Please accept or discard your prompt expansion result"
         },
         "maskBlur": "Mask Blur",
         "negativePromptPlaceholder": "Negative Prompt",
@@ -1312,6 +1359,21 @@
         "problemCopyingLayer": "Unable to Copy Layer",
         "problemSavingLayer": "Unable to Save Layer",
         "problemDownloadingImage": "Unable to Download Image",
+        "noRasterLayers": "No Raster Layers Found",
+        "noRasterLayersDesc": "Create at least one raster layer to export to PSD",
+        "noActiveRasterLayers": "No Active Raster Layers",
+        "noActiveRasterLayersDesc": "Enable at least one raster layer to export to PSD",
+        "noVisibleRasterLayers": "No Visible Raster Layers",
+        "noVisibleRasterLayersDesc": "Enable at least one raster layer to export to PSD",
+        "invalidCanvasDimensions": "Invalid Canvas Dimensions",
+        "canvasTooLarge": "Canvas Too Large",
+        "canvasTooLargeDesc": "Canvas dimensions exceed the maximum allowed size for PSD export. Reduce the total width and height of the canvas of the canvas and try again.",
+        "failedToProcessLayers": "Failed to Process Layers",
+        "psdExportSuccess": "PSD Export Complete",
+        "psdExportSuccessDesc": "Successfully exported {{count}} layers to PSD file",
+        "problemExportingPSD": "Problem Exporting PSD",
+        "canvasManagerNotAvailable": "Canvas Manager Not Available",
+        "noValidLayerAdapters": "No Valid Layer Adapters Found",
         "pasteSuccess": "Pasted to {{destination}}",
         "pasteFailed": "Paste Failed",
         "prunedQueue": "Pruned Queue",
@@ -1337,9 +1399,15 @@
         "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fill is not compatible with Text to Image or Image to Image. Use other FLUX models for these tasks.",
         "imagenIncompatibleGenerationMode": "Google {{model}} supports Text to Image only. Use other models for Image to Image, Inpainting and Outpainting tasks.",
         "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4o supports Text to Image and Image to Image only. Use other models Inpainting and Outpainting tasks.",
+        "fluxKontextIncompatibleGenerationMode": "FLUX Kontext supports Text to Image only. Use other models for Image to Image, Inpainting and Outpainting tasks.",
         "problemUnpublishingWorkflow": "Problem Unpublishing Workflow",
         "problemUnpublishingWorkflowDescription": "There was a problem unpublishing the workflow. Please try again.",
-        "workflowUnpublished": "Workflow Unpublished"
+        "workflowUnpublished": "Workflow Unpublished",
+        "sentToCanvas": "Sent to Canvas",
+        "sentToUpscale": "Sent to Upscale",
+        "promptGenerationStarted": "Prompt generation started",
+        "uploadAndPromptGenerationFailed": "Failed to upload image and generate prompt",
+        "promptExpansionFailed": "Prompt expansion failed"
     },
     "popovers": {
         "clipSkip": {
@@ -1862,6 +1930,7 @@
         "saveCanvasToGallery": "Save Canvas to Gallery",
         "saveBboxToGallery": "Save Bbox to Gallery",
         "saveLayerToAssets": "Save Layer to Assets",
+        "exportCanvasToPSD": "Export Canvas to PSD",
         "cropLayerToBbox": "Crop Layer to Bbox",
         "savedToGalleryOk": "Saved to Gallery",
         "savedToGalleryError": "Error saving to gallery",
@@ -1887,6 +1956,7 @@
         "mergingLayers": "Merging layers",
         "clearHistory": "Clear History",
         "bboxOverlay": "Show Bbox Overlay",
+        "ruleOfThirds": "Show Rule of Thirds",
         "newSession": "New Session",
         "clearCaches": "Clear Caches",
         "recalculateRects": "Recalculate Rects",
@@ -1992,6 +2062,8 @@
         "disableTransparencyEffect": "Disable Transparency Effect",
         "hidingType": "Hiding {{type}}",
         "showingType": "Showing {{type}}",
+        "showNonRasterLayers": "Show Non-Raster Layers (Shift+H)",
+        "hideNonRasterLayers": "Hide Non-Raster Layers (Shift+H)",
         "dynamicGrid": "Dynamic Grid",
         "logDebugInfo": "Log Debug Info",
         "locked": "Locked",
@@ -2286,6 +2358,7 @@
             "newGlobalReferenceImage": "New Global Reference Image",
             "newRegionalReferenceImage": "New Regional Reference Image",
             "newControlLayer": "New Control Layer",
+            "newResizedControlLayer": "New Resized Control Layer",
             "newRasterLayer": "New Raster Layer",
             "newInpaintMask": "New Inpaint Mask",
             "newRegionalGuidance": "New Regional Guidance",
@@ -2369,7 +2442,8 @@
         "uploadImage": "Upload Image",
         "useForTemplate": "Use For Prompt Template",
         "viewList": "View Template List",
-        "viewModeTooltip": "This is how your prompt will look with your currently selected template. To edit your prompt, click anywhere in the text box."
+        "viewModeTooltip": "This is how your prompt will look with your currently selected template. To edit your prompt, click anywhere in the text box.",
+        "togglePromptPreviews": "Toggle Prompt Previews"
     },
     "upsell": {
         "inviteTeammates": "Invite Teammates",
@@ -2389,6 +2463,55 @@
             "upscaling": "Upscaling",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
             "gallery": "Gallery"
+        },
+        "launchpad": {
+            "workflowsTitle": "Go deep with Workflows.",
+            "upscalingTitle": "Upscale and add detail.",
+            "canvasTitle": "Edit and refine on Canvas.",
+            "generateTitle": "Generate images from text prompts.",
+            "modelGuideText": "Want to learn what prompts work best for each model?",
+            "modelGuideLink": "Check out our Model Guide.",
+            "workflows": {
+                "description": "Workflows are reusable templates that automate image generation tasks, allowing you to quickly perform complex operations and get consistent results.",
+                "learnMoreLink": "Learn more about creating workflows",
+                "browseTemplates": {
+                    "title": "Browse Workflow Templates",
+                    "description": "Choose from pre-built workflows for common tasks"
+                },
+                "createNew": {
+                    "title": "Create a new Workflow",
+                    "description": "Start a new workflow from scratch"
+                },
+                "loadFromFile": {
+                    "title": "Load workflow from file",
+                    "description": "Upload a workflow to start with an existing setup"
+                }
+            },
+            "upscaling": {
+                "uploadImage": {
+                    "title": "Upload Image to Upscale",
+                    "description": "Click or drag an image to upscale (JPG, PNG, WebP up to 100MB)"
+                },
+                "replaceImage": {
+                    "title": "Replace Current Image",
+                    "description": "Click or drag a new image to replace the current one"
+                },
+                "imageReady": {
+                    "title": "Image Ready",
+                    "description": "Press Invoke to begin upscaling"
+                },
+                "readyToUpscale": {
+                    "title": "Ready to upscale!",
+                    "description": "Configure your settings below, then click the Invoke button to begin upscaling your image."
+                },
+                "upscaleModel": "Upscale Model",
+                "model": "Model",
+                "scale": "Scale",
+                "helpText": {
+                    "promptAdvice": "When upscaling, use a prompt that describes the medium and style. Avoid describing specific content details in the image.",
+                    "styleAdvice": "Upscaling works best with the general style of your image."
+                }
+            }
         }
     },
     "system": {

invokeai/frontend/web/src/app/components/App.tsx

@@ -2,8 +2,7 @@ import { Box } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { GlobalHookIsolator } from 'app/components/GlobalHookIsolator';
 import { GlobalModalIsolator } from 'app/components/GlobalModalIsolator';
-import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
-import { $globalIsLoading } from 'app/store/nanostores/globalIsLoading';
+import { $didStudioInit, type StudioInitAction } from 'app/hooks/useStudioInitAction';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import Loading from 'common/components/Loading/Loading';
 import { useClearStorage } from 'common/hooks/useClearStorage';
@@ -20,7 +19,7 @@ interface Props {
 }
 
 const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
-  const globalIsLoading = useStore($globalIsLoading);
+  const didStudioInit = useStore($didStudioInit);
   const clearStorage = useClearStorage();
 
   const handleReset = useCallback(() => {
@@ -33,7 +32,7 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
     <ErrorBoundary onReset={handleReset} FallbackComponent={AppErrorBoundaryFallback}>
       <Box id="invoke-app-wrapper" w="100dvw" h="100dvh" position="relative" overflow="hidden">
         <AppContent />
-        {globalIsLoading && <Loading />}
+        {!didStudioInit && <Loading />}
       </Box>
       <GlobalHookIsolator config={config} studioInitAction={studioInitAction} />
       <GlobalModalIsolator />

invokeai/frontend/web/src/app/components/GlobalHookIsolator.tsx

@@ -1,4 +1,5 @@
 import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
+import { setupListeners } from '@reduxjs/toolkit/query';
 import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
 import { useStudioInitAction } from 'app/hooks/useStudioInitAction';
 import { useSyncQueueStatus } from 'app/hooks/useSyncQueueStatus';
@@ -10,14 +11,15 @@ import type { PartialAppConfig } from 'app/types/invokeai';
 import { useFocusRegionWatcher } from 'common/hooks/focus';
 import { useCloseChakraTooltipsOnDragFix } from 'common/hooks/useCloseChakraTooltipsOnDragFix';
 import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
+import { useDndMonitor } from 'features/dnd/useDndMonitor';
 import { useDynamicPromptsWatcher } from 'features/dynamicPrompts/hooks/useDynamicPromptsWatcher';
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
 import { useWorkflowBuilderWatcher } from 'features/nodes/components/sidePanel/workflow/IsolatedWorkflowBuilderWatcher';
 import { useReadinessWatcher } from 'features/queue/store/readiness';
 import { configChanged } from 'features/system/store/configSlice';
 import { selectLanguage } from 'features/system/store/systemSelectors';
+import { useNavigationApi } from 'features/ui/layouts/use-navigation-api';
 import i18n from 'i18n';
-import { size } from 'lodash-es';
 import { memo, useEffect } from 'react';
 import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
 import { useGetQueueCountsByDestinationQuery } from 'services/api/endpoints/queue';
@@ -43,6 +45,8 @@ export const GlobalHookIsolator = memo(
     useGetOpenAPISchemaQuery();
     useSyncLoggingConfig();
     useCloseChakraTooltipsOnDragFix();
+    useNavigationApi();
+    useDndMonitor();
 
     // Persistent subscription to the queue counts query - canvas relies on this to know if there are pending
     // and/or in progress canvas sessions.
@@ -53,16 +57,18 @@ export const GlobalHookIsolator = memo(
     }, [language]);
 
     useEffect(() => {
-      if (size(config)) {
-        logger.info({ config }, 'Received config');
-        dispatch(configChanged(config));
-      }
+      logger.info({ config }, 'Received config');
+      dispatch(configChanged(config));
     }, [dispatch, config, logger]);
 
     useEffect(() => {
       dispatch(appStarted());
     }, [dispatch]);
 
+    useEffect(() => {
+      return setupListeners(dispatch);
+    }, [dispatch]);
+
     useStudioInitAction(studioInitAction);
     useStarterModelsToast();
     useSyncQueueStatus();

invokeai/frontend/web/src/app/hooks/useStudioInitAction.ts

@@ -8,7 +8,7 @@ import { paramsReset } from 'features/controlLayers/store/paramsSlice';
 import type { CanvasRasterLayerState } from 'features/controlLayers/store/types';
 import { imageDTOToImageObject } from 'features/controlLayers/store/util';
 import { sentImageToCanvas } from 'features/gallery/store/actions';
-import { parseAndRecallAllMetadata } from 'features/metadata/util/handlers';
+import { MetadataUtils } from 'features/metadata/parsing';
 import { $hasTemplates } from 'features/nodes/store/nodesSlice';
 import { $isWorkflowLibraryModalOpen } from 'features/nodes/store/workflowLibraryModal';
 import {
@@ -19,7 +19,8 @@ import {
 } from 'features/nodes/store/workflowLibrarySlice';
 import { $isStylePresetsMenuOpen, activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import { toast } from 'features/toast/toast';
-import { activeTabCanvasRightPanelChanged, setActiveTab } from 'features/ui/store/uiSlice';
+import { navigationApi } from 'features/ui/layouts/navigation-api';
+import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
 import { useLoadWorkflowWithDialog } from 'features/workflowLibrary/components/LoadWorkflowConfirmationAlertDialog';
 import { atom } from 'nanostores';
 import { useCallback, useEffect } from 'react';
@@ -116,23 +117,23 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
       const metadata = getImageMetadataResult.value;
       store.dispatch(canvasReset());
       // This shows a toast
-      await parseAndRecallAllMetadata(metadata, true);
+      await MetadataUtils.recallAll(metadata, store);
     },
     [store, t]
   );
 
   const handleLoadWorkflow = useCallback(
-    async (workflowId: string) => {
+    (workflowId: string) => {
       // This shows a toast
-      await loadWorkflowWithDialog({
+      loadWorkflowWithDialog({
         type: 'library',
         data: workflowId,
         onSuccess: () => {
-          store.dispatch(setActiveTab('workflows'));
+          navigationApi.switchToTab('workflows');
         },
       });
     },
-    [loadWorkflowWithDialog, store]
+    [loadWorkflowWithDialog]
   );
 
   const handleSelectStylePreset = useCallback(
@@ -146,7 +147,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
         return;
       }
       store.dispatch(activeStylePresetIdChanged(stylePresetId));
-      store.dispatch(setActiveTab('canvas'));
+      navigationApi.switchToTab('canvas');
       toast({
         title: t('toast.stylePresetLoaded'),
         status: 'info',
@@ -169,20 +170,20 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
           break;
         case 'workflows':
           // Go to the workflows tab
-          store.dispatch(setActiveTab('workflows'));
+          navigationApi.switchToTab('workflows');
           break;
         case 'upscaling':
           // Go to the upscaling tab
-          store.dispatch(setActiveTab('upscaling'));
+          navigationApi.switchToTab('upscaling');
           break;
         case 'viewAllWorkflows':
           // Go to the workflows tab and open the workflow library modal
-          store.dispatch(setActiveTab('workflows'));
+          navigationApi.switchToTab('workflows');
           $isWorkflowLibraryModalOpen.set(true);
           break;
         case 'viewAllWorkflowsRecommended':
           // Go to the workflows tab and open the workflow library modal with the recommended workflows view
-          store.dispatch(setActiveTab('workflows'));
+          navigationApi.switchToTab('workflows');
           $isWorkflowLibraryModalOpen.set(true);
           store.dispatch(workflowLibraryViewChanged('defaults'));
           store.dispatch(workflowLibraryTagsReset());
@@ -194,7 +195,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
           break;
         case 'viewAllStylePresets':
           // Go to the canvas tab and open the style presets menu
-          store.dispatch(setActiveTab('canvas'));
+          navigationApi.switchToTab('canvas');
           $isStylePresetsMenuOpen.set(true);
           break;
       }

invokeai/frontend/web/src/app/logging/logger.ts

@@ -2,7 +2,7 @@ import { createLogWriter } from '@roarr/browser-log-writer';
 import { atom } from 'nanostores';
 import type { Logger, MessageSerializer } from 'roarr';
 import { ROARR, Roarr } from 'roarr';
-import { z } from 'zod';
+import { z } from 'zod/v4';
 
 const serializeMessage: MessageSerializer = (message) => {
   return JSON.stringify(message);

invokeai/frontend/web/src/app/store/createMemoizedSelector.ts

@@ -1,13 +1,13 @@
+import { objectEquals } from '@observ33r/object-equals';
 import { createDraftSafeSelectorCreator, createSelectorCreator, lruMemoize } from '@reduxjs/toolkit';
-import { isEqual } from 'lodash-es';
 
 /**
- * A memoized selector creator that uses LRU cache and lodash's isEqual for equality check.
+ * A memoized selector creator that uses LRU cache and @observ33r/object-equals's objectEquals for equality check.
  */
 export const createMemoizedSelector = createSelectorCreator({
   memoize: lruMemoize,
   memoizeOptions: {
-    resultEqualityCheck: isEqual,
+    resultEqualityCheck: objectEquals,
   },
   argsMemoize: lruMemoize,
 });

invokeai/frontend/web/src/app/store/middleware/debugLoggerMiddleware.ts

@@ -8,10 +8,13 @@ import { diff } from 'jsondiffpatch';
  * Super simple logger middleware. Useful for debugging when the redux devtools are awkward.
  */
 export const getDebugLoggerMiddleware =
-  (options?: { withDiff?: boolean; withNextState?: boolean }): Middleware =>
+  (options?: { filter?: (action: unknown) => boolean; withDiff?: boolean; withNextState?: boolean }): Middleware =>
   (api: MiddlewareAPI) =>
   (next) =>
   (action) => {
+    if (options?.filter?.(action)) {
+      return next(action);
+    }
     const originalState = api.getState();
     console.log('REDUX: dispatching', action);
     const result = next(action);

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/index.ts

@@ -8,10 +8,6 @@ import { addBatchEnqueuedListener } from 'app/store/middleware/listenerMiddlewar
 import { addDeleteBoardAndImagesFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted';
 import { addBoardIdSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/boardIdSelected';
 import { addBulkDownloadListeners } from 'app/store/middleware/listenerMiddleware/listeners/bulkDownload';
-import { addEnqueueRequestedLinear } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedLinear';
-import { addEnsureImageIsSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/ensureImageIsSelectedListener';
-import { addGalleryImageClickedListener } from 'app/store/middleware/listenerMiddleware/listeners/galleryImageClicked';
-import { addGalleryOffsetChangedListener } from 'app/store/middleware/listenerMiddleware/listeners/galleryOffsetChanged';
 import { addGetOpenAPISchemaListener } from 'app/store/middleware/listenerMiddleware/listeners/getOpenAPISchema';
 import { addImageAddedToBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageAddedToBoard';
 import { addImageRemovedFromBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageRemovedFromBoard';
@@ -23,7 +19,6 @@ import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMi
 import type { AppDispatch, RootState } from 'app/store/store';
 
 import { addArchivedOrDeletedBoardListener } from './listeners/addArchivedOrDeletedBoardListener';
-import { addEnqueueRequestedUpscale } from './listeners/enqueueRequestedUpscale';
 
 export const listenerMiddleware = createListenerMiddleware();
 
@@ -45,13 +40,7 @@ addImageUploadedFulfilledListener(startAppListening);
 // Image deleted
 addDeleteBoardAndImagesFulfilledListener(startAppListening);
 
-// Gallery
-addGalleryImageClickedListener(startAppListening);
-addGalleryOffsetChangedListener(startAppListening);
-
 // User Invoked
-addEnqueueRequestedLinear(startAppListening);
-addEnqueueRequestedUpscale(startAppListening);
 addAnyEnqueuedListener(startAppListening);
 addBatchEnqueuedListener(startAppListening);
 
@@ -82,5 +71,3 @@ addAppConfigReceivedListener(startAppListening);
 addAdHocPostProcessingRequestedListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);
-
-addEnsureImageIsSelectedListener(startAppListening);

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/appStarted.ts

@@ -1,15 +1,29 @@
 import { createAction } from '@reduxjs/toolkit';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { selectLastSelectedImage } from 'features/gallery/store/gallerySelectors';
+import { imageSelected } from 'features/gallery/store/gallerySlice';
+import { imagesApi } from 'services/api/endpoints/images';
 
 export const appStarted = createAction('app/appStarted');
 
 export const addAppStartedListener = (startAppListening: AppStartListening) => {
   startAppListening({
     actionCreator: appStarted,
-    effect: (action, { unsubscribe, cancelActiveListeners }) => {
+    effect: async (action, { unsubscribe, cancelActiveListeners, take, getState, dispatch }) => {
       // this should only run once
       cancelActiveListeners();
       unsubscribe();
+
+      // ensure an image is selected when we load the first board
+      const firstImageLoad = await take(imagesApi.endpoints.getImageNames.matchFulfilled);
+      if (firstImageLoad !== null) {
+        const [{ payload }] = firstImageLoad;
+        const selectedImage = selectLastSelectedImage(getState());
+        if (selectedImage) {
+          return;
+        }
+        dispatch(imageSelected(payload.image_names.at(0) ?? null));
+      }
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/batchEnqueued.ts

@@ -1,9 +1,9 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { truncate } from 'es-toolkit/compat';
 import { zPydanticValidationError } from 'features/system/store/zodSchemas';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
-import { truncate } from 'lodash-es';
 import { serializeError } from 'serialize-error';
 import { queueApi } from 'services/api/endpoints/queue';
 import type { JsonObject } from 'type-fest';

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/boardIdSelected.ts

@@ -1,6 +1,6 @@
 import { isAnyOf } from '@reduxjs/toolkit';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
+import { selectGetImageNamesQueryArgs, selectSelectedBoardId } from 'features/gallery/store/gallerySelectors';
 import { boardIdSelected, galleryViewChanged, imageSelected } from 'features/gallery/store/gallerySlice';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -11,36 +11,35 @@ export const addBoardIdSelectedListener = (startAppListening: AppStartListening)
       // Cancel any in-progress instances of this listener, we don't want to select an image from a previous board
       cancelActiveListeners();
 
+      if (boardIdSelected.match(action) && action.payload.selectedImageName) {
+        // This action already has a selected image name, we trust it is valid
+        return;
+      }
+
       const state = getState();
 
-      const queryArgs = selectListImagesQueryArgs(state);
+      const board_id = selectSelectedBoardId(state);
+
+      const queryArgs = { ...selectGetImageNamesQueryArgs(state), board_id };
 
       // wait until the board has some images - maybe it already has some from a previous fetch
       // must use getState() to ensure we do not have stale state
       const isSuccess = await condition(
-        () => imagesApi.endpoints.listImages.select(queryArgs)(getState()).isSuccess,
+        () => imagesApi.endpoints.getImageNames.select(queryArgs)(getState()).isSuccess,
         5000
       );
 
-      if (isSuccess) {
-        // the board was just changed - we can select the first image
-        const { data: boardImagesData } = imagesApi.endpoints.listImages.select(queryArgs)(getState());
-
-        if (boardImagesData && boardIdSelected.match(action) && action.payload.selectedImageName) {
-          const selectedImage = boardImagesData.items.find(
-            (item) => item.image_name === action.payload.selectedImageName
-          );
-          dispatch(imageSelected(selectedImage?.image_name ?? null));
-        } else if (boardImagesData) {
-          dispatch(imageSelected(boardImagesData.items[0]?.image_name ?? null));
-        } else {
-          // board has no images - deselect
-          dispatch(imageSelected(null));
-        }
-      } else {
-        // fallback - deselect
+      if (!isSuccess) {
         dispatch(imageSelected(null));
+        return;
       }
+
+      // the board was just changed - we can select the first image
+      const imageNames = imagesApi.endpoints.getImageNames.select(queryArgs)(getState()).data?.image_names;
+
+      const imageToSelect = imageNames?.at(0) ?? null;
+
+      dispatch(imageSelected(imageToSelect));
     },
   });
 };

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/enqueueRequestedLinear.ts

@@ -1,151 +0,0 @@
-import type { AlertStatus } from '@invoke-ai/ui-library';
-import { createAction } from '@reduxjs/toolkit';
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { extractMessageFromAssertionError } from 'common/util/extractMessageFromAssertionError';
-import { withResult, withResultAsync } from 'common/util/result';
-import { parseify } from 'common/util/serialize';
-import {
-  canvasSessionIdCreated,
-  generateSessionIdCreated,
-  selectCanvasSessionId,
-  selectGenerateSessionId,
-} from 'features/controlLayers/store/canvasStagingAreaSlice';
-import { $canvasManager } from 'features/controlLayers/store/ephemeral';
-import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildChatGPT4oGraph } from 'features/nodes/util/graph/generation/buildChatGPT4oGraph';
-import { buildCogView4Graph } from 'features/nodes/util/graph/generation/buildCogView4Graph';
-import { buildFLUXGraph } from 'features/nodes/util/graph/generation/buildFLUXGraph';
-import { buildImagen3Graph } from 'features/nodes/util/graph/generation/buildImagen3Graph';
-import { buildImagen4Graph } from 'features/nodes/util/graph/generation/buildImagen4Graph';
-import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
-import { buildSD3Graph } from 'features/nodes/util/graph/generation/buildSD3Graph';
-import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGraph';
-import { UnsupportedGenerationModeError } from 'features/nodes/util/graph/types';
-import { toast } from 'features/toast/toast';
-import { selectActiveTab } from 'features/ui/store/uiSelectors';
-import { serializeError } from 'serialize-error';
-import { enqueueMutationFixedCacheKeyOptions, queueApi } from 'services/api/endpoints/queue';
-import { assert, AssertionError } from 'tsafe';
-
-const log = logger('generation');
-
-export const enqueueRequestedCanvas = createAction<{ prepend: boolean }>('app/enqueueRequestedCanvas');
-
-export const addEnqueueRequestedLinear = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: enqueueRequestedCanvas,
-    effect: async (action, { getState, dispatch }) => {
-      log.debug('Enqueue requested');
-
-      const tab = selectActiveTab(getState());
-      let sessionId = null;
-      if (tab === 'generate') {
-        sessionId = selectGenerateSessionId(getState());
-        if (!sessionId) {
-          dispatch(generateSessionIdCreated());
-          sessionId = selectGenerateSessionId(getState());
-        }
-      } else if (tab === 'canvas') {
-        sessionId = selectCanvasSessionId(getState());
-        if (!sessionId) {
-          dispatch(canvasSessionIdCreated());
-          sessionId = selectCanvasSessionId(getState());
-        }
-      } else {
-        log.warn(`Enqueue requested in unsupported tab ${tab}`);
-        return;
-      }
-
-      const state = getState();
-      const destination = sessionId;
-      assert(destination !== null);
-
-      const { prepend } = action.payload;
-
-      const manager = $canvasManager.get();
-      // assert(manager, 'No canvas manager');
-
-      const model = state.params.model;
-      assert(model, 'No model found in state');
-      const base = model.base;
-
-      const buildGraphResult = await withResultAsync(async () => {
-        switch (base) {
-          case 'sdxl':
-            return await buildSDXLGraph(state, manager);
-          case 'sd-1':
-          case `sd-2`:
-            return await buildSD1Graph(state, manager);
-          case `sd-3`:
-            return await buildSD3Graph(state, manager);
-          case `flux`:
-            return await buildFLUXGraph(state, manager);
-          case 'cogview4':
-            return await buildCogView4Graph(state, manager);
-          case 'imagen3':
-            return await buildImagen3Graph(state, manager);
-          case 'imagen4':
-            return await buildImagen4Graph(state, manager);
-          case 'chatgpt-4o':
-            return await buildChatGPT4oGraph(state, manager);
-          default:
-            assert(false, `No graph builders for base ${base}`);
-        }
-      });
-
-      if (buildGraphResult.isErr()) {
-        let title = 'Failed to build graph';
-        let status: AlertStatus = 'error';
-        let description: string | null = null;
-        if (buildGraphResult.error instanceof AssertionError) {
-          description = extractMessageFromAssertionError(buildGraphResult.error);
-        } else if (buildGraphResult.error instanceof UnsupportedGenerationModeError) {
-          title = 'Unsupported generation mode';
-          description = buildGraphResult.error.message;
-          status = 'warning';
-        }
-        const error = serializeError(buildGraphResult.error);
-        log.error({ error }, 'Failed to build graph');
-        toast({
-          status,
-          title,
-          description,
-        });
-        return;
-      }
-
-      const { g, seedFieldIdentifier, positivePromptFieldIdentifier } = buildGraphResult.value;
-
-      const prepareBatchResult = withResult(() =>
-        prepareLinearUIBatch({
-          state,
-          g,
-          prepend,
-          seedFieldIdentifier,
-          positivePromptFieldIdentifier,
-          origin: 'canvas',
-          destination,
-        })
-      );
-
-      if (prepareBatchResult.isErr()) {
-        log.error({ error: serializeError(prepareBatchResult.error) }, 'Failed to prepare batch');
-        return;
-      }
-
-      const req = dispatch(
-        queueApi.endpoints.enqueueBatch.initiate(prepareBatchResult.value, enqueueMutationFixedCacheKeyOptions)
-      );
-
-      try {
-        await req.unwrap();
-        log.debug(parseify({ batchConfig: prepareBatchResult.value }), 'Enqueued batch');
-      } catch (error) {
-        log.error({ error: serializeError(error as Error) }, 'Failed to enqueue batch');
-      } finally {
-        req.reset();
-      }
-    },
-  });
-};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/enqueueRequestedUpscale.ts

@@ -1,44 +0,0 @@
-import { createAction } from '@reduxjs/toolkit';
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { parseify } from 'common/util/serialize';
-import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildMultidiffusionUpscaleGraph } from 'features/nodes/util/graph/buildMultidiffusionUpscaleGraph';
-import { serializeError } from 'serialize-error';
-import { enqueueMutationFixedCacheKeyOptions, queueApi } from 'services/api/endpoints/queue';
-
-const log = logger('generation');
-
-export const enqueueRequestedUpscaling = createAction<{ prepend: boolean }>('app/enqueueRequestedUpscaling');
-
-export const addEnqueueRequestedUpscale = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: enqueueRequestedUpscaling,
-    effect: async (action, { getState, dispatch }) => {
-      const state = getState();
-      const { prepend } = action.payload;
-
-      const { g, seedFieldIdentifier, positivePromptFieldIdentifier } = await buildMultidiffusionUpscaleGraph(state);
-
-      const batchConfig = prepareLinearUIBatch({
-        state,
-        g,
-        prepend,
-        seedFieldIdentifier,
-        positivePromptFieldIdentifier,
-        origin: 'upscaling',
-        destination: 'gallery',
-      });
-
-      const req = dispatch(queueApi.endpoints.enqueueBatch.initiate(batchConfig, enqueueMutationFixedCacheKeyOptions));
-      try {
-        await req.unwrap();
-        log.debug(parseify({ batchConfig }), 'Enqueued batch');
-      } catch (error) {
-        log.error({ error: serializeError(error as Error) }, 'Failed to enqueue batch');
-      } finally {
-        req.reset();
-      }
-    },
-  });
-};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/ensureImageIsSelectedListener.ts

@@ -1,16 +0,0 @@
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { imageSelected } from 'features/gallery/store/gallerySlice';
-import { imagesApi } from 'services/api/endpoints/images';
-
-export const addEnsureImageIsSelectedListener = (startAppListening: AppStartListening) => {
-  // When we list images, if no images is selected, select the first one.
-  startAppListening({
-    matcher: imagesApi.endpoints.listImages.matchFulfilled,
-    effect: (action, { dispatch, getState }) => {
-      const selection = getState().gallery.selection;
-      if (selection.length === 0) {
-        dispatch(imageSelected(action.payload.items[0]?.image_name ?? null));
-      }
-    },
-  });
-};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/galleryImageClicked.ts

@@ -1,99 +0,0 @@
-import { createAction } from '@reduxjs/toolkit';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { RootState } from 'app/store/store';
-import { selectImageCollectionQueryArgs } from 'features/gallery/store/gallerySelectors';
-import { imageToCompareChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
-import { uniq } from 'lodash-es';
-import { imagesApi } from 'services/api/endpoints/images';
-import type { ImageCategory, SQLiteDirection } from 'services/api/types';
-
-// Type for image collection query arguments
-type ImageCollectionQueryArgs = {
-  board_id?: string;
-  categories?: ImageCategory[];
-  search_term?: string;
-  order_dir?: SQLiteDirection;
-  is_intermediate: boolean;
-};
-
-/**
- * Helper function to get cached image names list for selection operations
- * Returns an ordered array of image names (starred first, then unstarred)
- */
-const getCachedImageNames = (state: RootState, queryArgs: ImageCollectionQueryArgs): string[] => {
-  const queryResult = imagesApi.endpoints.getImageNames.select(queryArgs)(state);
-  return queryResult.data || [];
-};
-
-export const galleryImageClicked = createAction<{
-  imageName: string;
-  shiftKey: boolean;
-  ctrlKey: boolean;
-  metaKey: boolean;
-  altKey: boolean;
-}>('gallery/imageClicked');
-
-/**
- * This listener handles the logic for selecting images in the gallery.
- *
- * Previously, this logic was in a `useCallback` with the whole gallery selection as a dependency. Every time
- * the selection changed, the callback got recreated and all images rerendered. This could easily block for
- * hundreds of ms, more for lower end devices.
- *
- * Moving this logic into a listener means we don't need to recalculate anything dynamically and the gallery
- * is much more responsive.
- */
-
-export const addGalleryImageClickedListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: galleryImageClicked,
-    effect: (action, { dispatch, getState }) => {
-      const { imageName, shiftKey, ctrlKey, metaKey, altKey } = action.payload;
-      const state = getState();
-      const queryArgs = selectImageCollectionQueryArgs(state);
-
-      // Get cached image names for selection operations
-      const imageNames = getCachedImageNames(state, queryArgs);
-
-      // If we don't have the image names cached, we can't perform selection operations
-      // This can happen if the user clicks on an image before the names are loaded
-      if (imageNames.length === 0) {
-        // For basic click without modifiers, we can still set selection
-        if (!shiftKey && !ctrlKey && !metaKey && !altKey) {
-          dispatch(selectionChanged([imageName]));
-        }
-        return;
-      }
-
-      const selection = state.gallery.selection;
-
-      if (altKey) {
-        if (state.gallery.imageToCompare === imageName) {
-          dispatch(imageToCompareChanged(null));
-        } else {
-          dispatch(imageToCompareChanged(imageName));
-        }
-      } else if (shiftKey) {
-        const rangeEndImageName = imageName;
-        const lastSelectedImage = selection.at(-1);
-        const lastClickedIndex = imageNames.findIndex((name) => name === lastSelectedImage);
-        const currentClickedIndex = imageNames.findIndex((name) => name === rangeEndImageName);
-        if (lastClickedIndex > -1 && currentClickedIndex > -1) {
-          // We have a valid range!
-          const start = Math.min(lastClickedIndex, currentClickedIndex);
-          const end = Math.max(lastClickedIndex, currentClickedIndex);
-          const imagesToSelect = imageNames.slice(start, end + 1);
-          dispatch(selectionChanged(uniq(selection.concat(imagesToSelect))));
-        }
-      } else if (ctrlKey || metaKey) {
-        if (selection.some((n) => n === imageName) && selection.length > 1) {
-          dispatch(selectionChanged(uniq(selection.filter((n) => n !== imageName))));
-        } else {
-          dispatch(selectionChanged(uniq(selection.concat(imageName))));
-        }
-      } else {
-        dispatch(selectionChanged([imageName]));
-      }
-    },
-  });
-};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/galleryOffsetChanged.ts

@@ -1,119 +0,0 @@
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
-import { imageToCompareChanged, offsetChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
-import { imagesApi } from 'services/api/endpoints/images';
-
-export const addGalleryOffsetChangedListener = (startAppListening: AppStartListening) => {
-  /**
-   * When the user changes pages in the gallery, we need to wait until the next page of images is loaded, then maybe
-   * update the selection.
-   *
-   * There are a three scenarios:
-   *
-   * 1. The page is changed by clicking the pagination buttons. No changes to selection are needed.
-   *
-   * 2. The page is changed by using the arrow keys (without alt).
-   * - When going backwards, select the last image.
-   * - When going forwards, select the first image.
-   *
-   * 3. The page is changed by using the arrows keys with alt. This means the user is changing the comparison image.
-   * - When going backwards, select the last image _as the comparison image_.
-   * - When going forwards, select the first image _as the comparison image_.
-   */
-  startAppListening({
-    actionCreator: offsetChanged,
-    effect: async (action, { dispatch, getState, getOriginalState, take, cancelActiveListeners }) => {
-      // Cancel any active listeners to prevent the selection from changing without user input
-      cancelActiveListeners();
-
-      const { withHotkey } = action.payload;
-
-      if (!withHotkey) {
-        // User changed pages by clicking the pagination buttons - no changes to selection
-        return;
-      }
-
-      const originalState = getOriginalState();
-      const prevOffset = originalState.gallery.offset;
-      const offset = getState().gallery.offset;
-
-      if (offset === prevOffset) {
-        // The page didn't change - bail
-        return;
-      }
-
-      /**
-       * We need to wait until the next page of images is loaded before updating the selection, so we use the correct
-       * page of images.
-       *
-       * The simplest way to do it would be to use `take` to wait for the next fulfilled action, but RTK-Q doesn't
-       * dispatch an action on cache hits. This means the `take` will only return if the cache is empty. If the user
-       * changes to a cached page - a common situation - the `take` will never resolve.
-       *
-       * So we need to take a two-step approach. First, check if we have data in the cache for the page of images. If
-       * we have data cached, use it to update the selection. If we don't have data cached, wait for the next fulfilled
-       * action, which updates the cache, then use the cache to update the selection.
-       */
-
-      // Check if we have data in the cache for the page of images
-      const queryArgs = selectListImagesQueryArgs(getState());
-      let { data } = imagesApi.endpoints.listImages.select(queryArgs)(getState());
-
-      // No data yet - wait for the network request to complete
-      if (!data) {
-        const takeResult = await take(imagesApi.endpoints.listImages.matchFulfilled, 5000);
-        if (!takeResult) {
-          // The request didn't complete in time - bail
-          return;
-        }
-        data = takeResult[0].payload;
-      }
-
-      // We awaited a network request - state could have changed, get fresh state
-      const state = getState();
-      const { selection, imageToCompare } = state.gallery;
-      const imageDTOs = data?.items;
-
-      if (!imageDTOs) {
-        // The page didn't load - bail
-        return;
-      }
-
-      if (withHotkey === 'arrow') {
-        // User changed pages by using the arrow keys - selection changes to first or last image depending
-        if (offset < prevOffset) {
-          // We've gone backwards
-          const lastImage = imageDTOs[imageDTOs.length - 1];
-          if (!selection.some((selectedImage) => selectedImage === lastImage?.image_name)) {
-            dispatch(selectionChanged(lastImage ? [lastImage.image_name] : []));
-          }
-        } else {
-          // We've gone forwards
-          const firstImage = imageDTOs[0];
-          if (!selection.some((selectedImage) => selectedImage === firstImage?.image_name)) {
-            dispatch(selectionChanged(firstImage ? [firstImage.image_name] : []));
-          }
-        }
-        return;
-      }
-
-      if (withHotkey === 'alt+arrow') {
-        // User changed pages by using the arrow keys with alt - comparison image changes to first or last depending
-        if (offset < prevOffset) {
-          // We've gone backwards
-          const lastImage = imageDTOs[imageDTOs.length - 1];
-          if (lastImage && imageToCompare !== lastImage.image_name) {
-            dispatch(imageToCompareChanged(lastImage.image_name));
-          }
-        } else {
-          // We've gone forwards
-          const firstImage = imageDTOs[0];
-          if (firstImage && imageToCompare !== firstImage.image_name) {
-            dispatch(imageToCompareChanged(firstImage.image_name));
-          }
-        }
-        return;
-      }
-    },
-  });
-};

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/getOpenAPISchema.ts

@@ -1,9 +1,9 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { parseify } from 'common/util/serialize';
+import { size } from 'es-toolkit/compat';
 import { $templates } from 'features/nodes/store/nodesSlice';
 import { parseSchema } from 'features/nodes/util/schema/parseSchema';
-import { size } from 'lodash-es';
 import { serializeError } from 'serialize-error';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
 import type { JsonObject } from 'type-fest';

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/imageUploaded.ts

@@ -2,12 +2,12 @@ import { isAnyOf } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import type { RootState } from 'app/store/store';
+import { omit } from 'es-toolkit/compat';
 import { imageUploadedClientSide } from 'features/gallery/store/actions';
 import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
 import { boardIdSelected, galleryViewChanged } from 'features/gallery/store/gallerySlice';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
-import { omit } from 'lodash-es';
 import { boardsApi } from 'services/api/endpoints/boards';
 import { imagesApi } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/modelSelected.ts

@@ -3,7 +3,7 @@ import type { AppStartListening } from 'app/store/middleware/listenerMiddleware'
 import { bboxSyncedToOptimalDimension } from 'features/controlLayers/store/canvasSlice';
 import { selectIsStaging } from 'features/controlLayers/store/canvasStagingAreaSlice';
 import { loraDeleted } from 'features/controlLayers/store/lorasSlice';
-import { modelChanged, vaeSelected } from 'features/controlLayers/store/paramsSlice';
+import { modelChanged, syncedToOptimalDimension, vaeSelected } from 'features/controlLayers/store/paramsSlice';
 import { selectBboxModelBase } from 'features/controlLayers/store/selectors';
 import { modelSelected } from 'features/parameters/store/actions';
 import { zParameterModel } from 'features/parameters/types/parameterSchemas';
@@ -71,9 +71,16 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
       }
 
       dispatch(modelChanged({ model: newModel, previousModel: state.params.model }));
+
       const modelBase = selectBboxModelBase(state);
-      if (!selectIsStaging(state) && modelBase !== state.params.model?.base) {
-        dispatch(bboxSyncedToOptimalDimension());
+
+      if (modelBase !== state.params.model?.base) {
+        // Sync generate tab settings whenever the model base changes
+        dispatch(syncedToOptimalDimension());
+        if (!selectIsStaging(state)) {
+          // Canvas tab only syncs if not staging
+          dispatch(bboxSyncedToOptimalDimension());
+        }
       }
     },
   });

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/setDefaultSettings.ts

@@ -1,4 +1,5 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { isNil } from 'es-toolkit';
 import { bboxHeightChanged, bboxWidthChanged } from 'features/controlLayers/store/canvasSlice';
 import { selectIsStaging } from 'features/controlLayers/store/canvasStagingAreaSlice';
 import {
@@ -86,10 +87,16 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
           }
         }
 
-        if (cfg_rescale_multiplier) {
+        if (!isNil(cfg_rescale_multiplier)) {
           if (isParameterCFGRescaleMultiplier(cfg_rescale_multiplier)) {
             dispatch(setCfgRescaleMultiplier(cfg_rescale_multiplier));
           }
+        } else {
+          // Set this to 0 if it doesn't have a default. This value is
+          // easy to miss in the UI when users are resetting defaults
+          // and leaving it non-zero could lead to detrimental
+          // effects.
+          dispatch(setCfgRescaleMultiplier(0));
         }
 
         if (steps) {

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/socketConnected.ts

@@ -1,8 +1,8 @@
+import { objectEquals } from '@observ33r/object-equals';
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
-import { isEqual } from 'lodash-es';
 import { atom } from 'nanostores';
 import { api } from 'services/api';
 import { modelsApi } from 'services/api/endpoints/models';
@@ -64,7 +64,7 @@ export const addSocketConnectedEventListener = (startAppListening: AppStartListe
         const nextQueueStatusData = await queueStatusRequest.unwrap();
 
         // If the queue hasn't changed, we don't need to do anything.
-        if (isEqual(prevQueueStatusData?.queue, nextQueueStatusData.queue)) {
+        if (objectEquals(prevQueueStatusData?.queue, nextQueueStatusData.queue)) {
           return;
         }
 

invokeai/frontend/web/src/app/store/nanostores/globalIsLoading.ts

@@ -1,13 +0,0 @@
-import { $didStudioInit } from 'app/hooks/useStudioInitAction';
-import { atom, computed } from 'nanostores';
-import { flushSync } from 'react-dom';
-
-export const $isLayoutLoading = atom(false);
-export const setIsLayoutLoading = (isLoading: boolean) => {
-  flushSync(() => {
-    $isLayoutLoading.set(isLoading);
-  });
-};
-export const $globalIsLoading = computed([$didStudioInit, $isLayoutLoading], (didStudioInit, isLayoutLoading) => {
-  return !didStudioInit || isLayoutLoading;
-});

invokeai/frontend/web/src/app/store/nanostores/store.ts

@@ -1,4 +1,3 @@
-import { useStore } from '@nanostores/react';
 import type { AppStore } from 'app/store/store';
 import { atom } from 'nanostores';
 
@@ -32,11 +31,3 @@ export const getStore = () => {
   }
   return store;
 };
-
-export const useAppStore = () => {
-  const store = useStore($store);
-  if (!store) {
-    throw new ReduxStoreNotInitialized();
-  }
-  return store;
-};

invokeai/frontend/web/src/app/store/nanostores/util.ts

@@ -11,5 +11,7 @@ export const $false: ReadableAtom<boolean> = atom(false);
 /**
  * A fallback non-writable atom that always returns `true`, used when a nanostores atom is only conditionally available
  * in a hook or component.
+ *
+ * @knipignore
  */
 export const $true: ReadableAtom<boolean> = atom(true);

invokeai/frontend/web/src/app/store/store.ts

@@ -4,6 +4,7 @@ import { logger } from 'app/logging/logger';
 import { idbKeyValDriver } from 'app/store/enhancers/reduxRemember/driver';
 import { errorHandler } from 'app/store/enhancers/reduxRemember/errors';
 import { deepClone } from 'common/util/deepClone';
+import { keys, mergeWith, omit, pick } from 'es-toolkit/compat';
 import { changeBoardModalSlice } from 'features/changeBoardModal/store/slice';
 import { canvasSettingsPersistConfig, canvasSettingsSlice } from 'features/controlLayers/store/canvasSettingsSlice';
 import { canvasPersistConfig, canvasSlice, canvasUndoableConfig } from 'features/controlLayers/store/canvasSlice';
@@ -16,7 +17,6 @@ import { paramsPersistConfig, paramsSlice } from 'features/controlLayers/store/p
 import { refImagesPersistConfig, refImagesSlice } from 'features/controlLayers/store/refImagesSlice';
 import { dynamicPromptsPersistConfig, dynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
 import { galleryPersistConfig, gallerySlice } from 'features/gallery/store/gallerySlice';
-import { hrfPersistConfig, hrfSlice } from 'features/hrf/store/hrfSlice';
 import { modelManagerV2PersistConfig, modelManagerV2Slice } from 'features/modelManagerV2/store/modelManagerV2Slice';
 import { nodesPersistConfig, nodesSlice, nodesUndoableConfig } from 'features/nodes/store/nodesSlice';
 import { workflowLibraryPersistConfig, workflowLibrarySlice } from 'features/nodes/store/workflowLibrarySlice';
@@ -28,7 +28,6 @@ import { configSlice } from 'features/system/store/configSlice';
 import { systemPersistConfig, systemSlice } from 'features/system/store/systemSlice';
 import { uiPersistConfig, uiSlice } from 'features/ui/store/uiSlice';
 import { diff } from 'jsondiffpatch';
-import { keys, mergeWith, omit, pick } from 'lodash-es';
 import dynamicMiddlewares from 'redux-dynamic-middlewares';
 import type { SerializeFunction, UnserializeFunction } from 'redux-remember';
 import { rememberEnhancer, rememberReducer } from 'redux-remember';
@@ -57,7 +56,6 @@ const allReducers = {
   [changeBoardModalSlice.name]: changeBoardModalSlice.reducer,
   [modelManagerV2Slice.name]: modelManagerV2Slice.reducer,
   [queueSlice.name]: queueSlice.reducer,
-  [hrfSlice.name]: hrfSlice.reducer,
   [canvasSlice.name]: undoable(canvasSlice.reducer, canvasUndoableConfig),
   [workflowSettingsSlice.name]: workflowSettingsSlice.reducer,
   [upscaleSlice.name]: upscaleSlice.reducer,
@@ -103,7 +101,6 @@ const persistConfigs: { [key in keyof typeof allReducers]?: PersistConfig } = {
   [uiPersistConfig.name]: uiPersistConfig,
   [dynamicPromptsPersistConfig.name]: dynamicPromptsPersistConfig,
   [modelManagerV2PersistConfig.name]: modelManagerV2PersistConfig,
-  [hrfPersistConfig.name]: hrfPersistConfig,
   [canvasPersistConfig.name]: canvasPersistConfig,
   [workflowSettingsPersistConfig.name]: workflowSettingsPersistConfig,
   [upscalePersistConfig.name]: upscalePersistConfig,

invokeai/frontend/web/src/app/store/storeHooks.ts

@@ -1,8 +1,8 @@
-import type { AppThunkDispatch, RootState } from 'app/store/store';
+import type { AppStore, AppThunkDispatch, RootState } from 'app/store/store';
 import type { TypedUseSelectorHook } from 'react-redux';
 import { useDispatch, useSelector, useStore } from 'react-redux';
 
 // Use throughout your app instead of plain `useDispatch` and `useSelector`
 export const useAppDispatch = () => useDispatch<AppThunkDispatch>();
 export const useAppSelector: TypedUseSelectorHook<RootState> = useSelector;
-export const useAppStore = () => useStore<RootState>();
+export const useAppStore = () => useStore.withTypes<AppStore>()();

invokeai/frontend/web/src/app/store/use-debounced-app-selector.ts

@@ -1,6 +1,6 @@
 import type { Selector } from '@reduxjs/toolkit';
-import { useAppStore } from 'app/store/nanostores/store';
 import type { RootState } from 'app/store/store';
+import { useAppStore } from 'app/store/storeHooks';
 import { useEffect, useState } from 'react';
 
 /**

invokeai/frontend/web/src/app/types/invokeai.ts

@@ -14,6 +14,7 @@ export type AppFeature =
   | 'githubLink'
   | 'discordLink'
   | 'bugLink'
+  | 'aboutModal'
   | 'localization'
   | 'consoleLogging'
   | 'dynamicPrompting'
@@ -29,7 +30,8 @@ export type AppFeature =
   | 'hfToken'
   | 'retryQueueItem'
   | 'cancelAndClearAll'
-  | 'chatGPT4oHigh';
+  | 'chatGPT4oHigh'
+  | 'modelRelationships';
 /**
  * A disable-able Stable Diffusion feature
  */
@@ -76,6 +78,7 @@ export type AppConfig = {
   allowPrivateStylePresets: boolean;
   allowClientSideUpload: boolean;
   allowPublishWorkflows: boolean;
+  allowPromptExpansion: boolean;
   disabledTabs: TabName[];
   disabledFeatures: AppFeature[];
   disabledSDFeatures: SDFeature[];

invokeai/frontend/web/src/common/components/FocusRegionWrapper.tsx

@@ -1,56 +0,0 @@
-import { Box, type BoxProps, type SystemStyleObject } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { type FocusRegionName, useFocusRegion, useIsRegionFocused } from 'common/hooks/focus';
-import { selectSystemShouldEnableHighlightFocusedRegions } from 'features/system/store/systemSlice';
-import { memo, useMemo, useRef } from 'react';
-
-interface FocusRegionWrapperProps extends BoxProps {
-  region: FocusRegionName;
-  focusOnMount?: boolean;
-}
-
-const FOCUS_REGION_STYLES: SystemStyleObject = {
-  position: 'relative',
-  '&[data-highlighted="true"]::after': {
-    borderColor: 'blue.700',
-  },
-  '&::after': {
-    content: '""',
-    position: 'absolute',
-    inset: 0,
-    zIndex: 1,
-    borderRadius: 'base',
-    border: '2px solid',
-    borderColor: 'transparent',
-    pointerEvents: 'none',
-    transition: 'border-color 0.1s ease-in-out',
-  },
-};
-
-export const FocusRegionWrapper = memo(
-  ({ region, focusOnMount = false, sx, children, ...boxProps }: FocusRegionWrapperProps) => {
-    const shouldHighlightFocusedRegions = useAppSelector(selectSystemShouldEnableHighlightFocusedRegions);
-
-    const ref = useRef<HTMLDivElement>(null);
-
-    const options = useMemo(() => ({ focusOnMount }), [focusOnMount]);
-
-    useFocusRegion(region, ref, options);
-    const isFocused = useIsRegionFocused(region);
-    const isHighlighted = isFocused && shouldHighlightFocusedRegions;
-
-    return (
-      <Box
-        ref={ref}
-        tabIndex={-1}
-        sx={useMemo(() => ({ ...FOCUS_REGION_STYLES, ...sx }), [sx])}
-        data-highlighted={isHighlighted}
-        {...boxProps}
-      >
-        {children}
-      </Box>
-    );
-  }
-);
-
-FocusRegionWrapper.displayName = 'FocusRegionWrapper';

invokeai/frontend/web/src/common/components/InformationalPopover/InformationalPopover.tsx

@@ -15,9 +15,9 @@ import {
 } from '@invoke-ai/ui-library';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { merge, omit } from 'es-toolkit/compat';
 import { selectSystemSlice, setShouldEnableInformationalPopovers } from 'features/system/store/systemSlice';
 import { toast } from 'features/toast/toast';
-import { merge, omit } from 'lodash-es';
 import type { ReactElement } from 'react';
 import { memo, useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';

invokeai/frontend/web/src/common/components/Loading/Loading.tsx

@@ -8,21 +8,16 @@ const Loading = () => {
   return (
     <Flex
       position="absolute"
-      width="100dvw"
-      height="100dvh"
       alignItems="center"
       justifyContent="center"
-      bg="#151519"
-      top={0}
-      right={0}
-      bottom={0}
-      left={0}
+      bg="hsl(220 12% 10% / 1)" // base.900
+      inset={0}
       zIndex={99999}
     >
       <Image src={InvokeLogoWhite} w="8rem" h="8rem" />
       <Spinner
         label="Loading"
-        color="grey"
+        color="hsl(220 12% 68% / 1)" // base.300
         position="absolute"
         size="sm"
         width="24px !important"

invokeai/frontend/web/src/common/components/OverlayScrollbars/constants.ts

@@ -1,5 +1,5 @@
 import { deepClone } from 'common/util/deepClone';
-import { merge } from 'lodash-es';
+import { merge } from 'es-toolkit/compat';
 import { ClickScrollPlugin, OverlayScrollbars } from 'overlayscrollbars';
 import type { UseOverlayScrollbarsParams } from 'overlayscrollbars-react';
 import type { CSSProperties } from 'react';

invokeai/frontend/web/src/common/components/Picker/Picker.tsx

@@ -91,6 +91,10 @@ const isGroup = <T extends object>(optionOrGroup: OptionOrGroup<T>): optionOrGro
   return uniqueGroupKey in optionOrGroup && optionOrGroup[uniqueGroupKey] === true;
 };
 
+export const isOption = <T extends object>(optionOrGroup: OptionOrGroup<T>): optionOrGroup is T => {
+  return !(uniqueGroupKey in optionOrGroup);
+};
+
 const DefaultOptionComponent = typedMemo(<T extends object>({ option }: { option: T }) => {
   const { getOptionId } = usePickerContext();
   return <Text fontWeight="bold">{getOptionId(option)}</Text>;
@@ -198,6 +202,10 @@ type PickerProps<T extends object> = {
    * Whether the picker should be searchable. If true, renders a search input.
    */
   searchable?: boolean;
+  /**
+   * Initial state for group toggles. If provided, groups will start with these states instead of all being disabled.
+   */
+  initialGroupStates?: GroupStatusMap;
 };
 
 export type PickerContextState<T extends object> = {
@@ -310,9 +318,9 @@ const flattenOptions = <T extends object>(options: OptionOrGroup<T>[]): T[] => {
   return flattened;
 };
 
-type GroupStatusMap = Record<string, boolean>;
+export type GroupStatusMap = Record<string, boolean>;
 
-const useTogglableGroups = <T extends object>(options: OptionOrGroup<T>[]) => {
+const useTogglableGroups = <T extends object>(options: OptionOrGroup<T>[], initialGroupStates?: GroupStatusMap) => {
   const groupsWithOptions = useMemo(() => {
     const ids: string[] = [];
     for (const optionOrGroup of options) {
@@ -332,14 +340,16 @@ const useTogglableGroups = <T extends object>(options: OptionOrGroup<T>[]) => {
     const groupStatusMap = $groupStatusMap.get();
     const newMap: GroupStatusMap = {};
     for (const id of groupsWithOptions) {
-      if (newMap[id] === undefined) {
-        newMap[id] = false;
+      if (initialGroupStates && initialGroupStates[id] !== undefined) {
+        newMap[id] = initialGroupStates[id];
       } else if (groupStatusMap[id] !== undefined) {
         newMap[id] = groupStatusMap[id];
+      } else {
+        newMap[id] = false;
       }
     }
     $groupStatusMap.set(newMap);
-  }, [groupsWithOptions, $groupStatusMap]);
+  }, [groupsWithOptions, $groupStatusMap, initialGroupStates]);
 
   const toggleGroup = useCallback(
     (idToToggle: string) => {
@@ -511,10 +521,14 @@ export const Picker = typedMemo(<T extends object>(props: PickerProps<T>) => {
     OptionComponent = DefaultOptionComponent,
     NextToSearchBar,
     searchable,
+    initialGroupStates,
   } = props;
   const rootRef = useRef<HTMLDivElement>(null);
   const inputRef = useRef<HTMLInputElement>(null);
-  const { $groupStatusMap, $areAllGroupsDisabled, toggleGroup } = useTogglableGroups(optionsOrGroups);
+  const { $groupStatusMap, $areAllGroupsDisabled, toggleGroup } = useTogglableGroups(
+    optionsOrGroups,
+    initialGroupStates
+  );
   const $activeOptionId = useAtom(getFirstOptionId(optionsOrGroups, getOptionId));
   const $compactView = useAtom(true);
   const $optionsOrGroups = useAtom(optionsOrGroups);

invokeai/frontend/web/src/common/components/SessionMenuItems.tsx

@@ -1,20 +1,15 @@
 import { MenuItem } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
-import {
-  useNewCanvasSession,
-  useNewGallerySession,
-} from 'features/controlLayers/components/NewSessionConfirmationAlertDialog';
 import { allEntitiesDeleted } from 'features/controlLayers/store/canvasSlice';
 import { paramsReset } from 'features/controlLayers/store/paramsSlice';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiArrowsCounterClockwiseBold, PiFilePlusBold } from 'react-icons/pi';
+import { PiArrowsCounterClockwiseBold } from 'react-icons/pi';
 
 export const SessionMenuItems = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
-  const { newGallerySessionWithDialog } = useNewGallerySession();
-  const { newCanvasSessionWithDialog } = useNewCanvasSession();
+
   const resetCanvasLayers = useCallback(() => {
     dispatch(allEntitiesDeleted());
   }, [dispatch]);
@@ -23,12 +18,6 @@ export const SessionMenuItems = memo(() => {
   }, [dispatch]);
   return (
     <>
-      <MenuItem icon={<PiFilePlusBold />} onClick={newGallerySessionWithDialog}>
-        {t('controlLayers.newGallerySession')}
-      </MenuItem>
-      <MenuItem icon={<PiFilePlusBold />} onClick={newCanvasSessionWithDialog}>
-        {t('controlLayers.newCanvasSession')}
-      </MenuItem>
       <MenuItem icon={<PiArrowsCounterClockwiseBold />} onClick={resetCanvasLayers}>
         {t('controlLayers.resetCanvasLayers')}
       </MenuItem>

invokeai/frontend/web/src/common/hooks/focus.ts

@@ -6,6 +6,7 @@ import { atom, computed } from 'nanostores';
 import type { RefObject } from 'react';
 import { useEffect } from 'react';
 import { objectKeys } from 'tsafe';
+import z from 'zod/v4';
 
 /**
  * We need to manage focus regions to conditionally enable hotkeys:
@@ -30,23 +31,34 @@ const log = logger('system');
 /**
  * The names of the focus regions.
  */
-export type FocusRegionName = 'gallery' | 'layers' | 'canvas' | 'workflows' | 'viewer';
+const zFocusRegionName = z.enum([
+  'launchpad',
+  'viewer',
+  'gallery',
+  'boards',
+  'layers',
+  'canvas',
+  'workflows',
+  'progress',
+  'settings',
+]);
+export type FocusRegionName = z.infer<typeof zFocusRegionName>;
 
 /**
  * A map of focus regions to the elements that are part of that region.
  */
-const REGION_TARGETS: Record<FocusRegionName, Set<HTMLElement>> = {
-  gallery: new Set<HTMLElement>(),
-  layers: new Set<HTMLElement>(),
-  canvas: new Set<HTMLElement>(),
-  workflows: new Set<HTMLElement>(),
-  viewer: new Set<HTMLElement>(),
-} as const;
+const REGION_TARGETS: Record<FocusRegionName, Set<HTMLElement>> = zFocusRegionName.options.values().reduce(
+  (acc, region) => {
+    acc[region] = new Set<HTMLElement>();
+    return acc;
+  },
+  {} as Record<FocusRegionName, Set<HTMLElement>>
+);
 
 /**
  * The currently-focused region or `null` if no region is focused.
  */
-export const $focusedRegion = atom<FocusRegionName | null>(null);
+const $focusedRegion = atom<FocusRegionName | null>(null);
 
 /**
  * A map of focus regions to atoms that indicate if that region is focused.
@@ -62,11 +74,13 @@ const FOCUS_REGIONS = objectKeys(REGION_TARGETS).reduce(
 /**
  * Sets the focused region, logging a trace level message.
  */
-const setFocus = (region: FocusRegionName | null) => {
+export const setFocusedRegion = (region: FocusRegionName | null) => {
   $focusedRegion.set(region);
   log.trace(`Focus changed: ${region}`);
 };
 
+export const getFocusedRegion = () => $focusedRegion.get();
+
 type UseFocusRegionOptions = {
   focusOnMount?: boolean;
 };
@@ -99,14 +113,14 @@ export const useFocusRegion = (
     REGION_TARGETS[region].add(element);
 
     if (focusOnMount) {
-      setFocus(region);
+      setFocusedRegion(region);
     }
 
     return () => {
       REGION_TARGETS[region].delete(element);
 
       if (REGION_TARGETS[region].size === 0 && $focusedRegion.get() === region) {
-        setFocus(null);
+        setFocusedRegion(null);
       }
     };
   }, [options, ref, region]);
@@ -163,7 +177,7 @@ const onFocus = (_: FocusEvent) => {
     return;
   }
 
-  setFocus(focusedRegion);
+  setFocusedRegion(focusedRegion);
 };
 
 /**

invokeai/frontend/web/src/common/hooks/useBoolean.ts

@@ -73,7 +73,7 @@ export const useBoolean = (initialValue: boolean): UseBoolean => {
   };
 };
 
-export type UseDisclosure = {
+type UseDisclosure = {
   isOpen: boolean;
   open: () => void;
   close: () => void;

invokeai/frontend/web/src/common/hooks/useFilterableOutsideClick.ts

@@ -1,165 +0,0 @@
-/* eslint-disable @typescript-eslint/no-explicit-any */
-
-/**
- * Adapted from https://github.com/chakra-ui/chakra-ui/blob/v2/packages/hooks/src/use-outside-click.ts
- *
- * The main change here is to support filtering of outside clicks via a `filter` function.
- *
- * This lets us work around issues with portals and components like popovers, which typically close on an outside click.
- *
- * For example, consider a popover that has a custom drop-down component inside it, which uses a portal to render
- * the drop-down options. The original outside click handler would close the popover when clicking on the drop-down options,
- * because the click is outside the popover - but we expect the popover to stay open in this case.
- *
- * A filter function like this can fix that:
- *
- * ```ts
- * const filter = (el: HTMLElement) => el.className.includes('chakra-portal') || el.id.includes('react-select')
- * ```
- *
- * This ignores clicks on react-select-based drop-downs and Chakra UI portals and is used as the default filter.
- */
-
-import { useCallback, useEffect, useRef } from 'react';
-
-type FilterFunction = (el: HTMLElement | SVGElement) => boolean;
-
-export function useCallbackRef<T extends (...args: any[]) => any>(
-  callback: T | undefined,
-  deps: React.DependencyList = []
-) {
-  const callbackRef = useRef(callback);
-
-  useEffect(() => {
-    callbackRef.current = callback;
-  });
-
-  // eslint-disable-next-line react-hooks/exhaustive-deps
-  return useCallback(((...args) => callbackRef.current?.(...args)) as T, deps);
-}
-
-export interface UseOutsideClickProps {
-  /**
-   * Whether the hook is enabled
-   */
-  enabled?: boolean;
-  /**
-   * The reference to a DOM element.
-   */
-  ref: React.RefObject<HTMLElement | null>;
-  /**
-   * Function invoked when a click is triggered outside the referenced element.
-   */
-  handler?: (e: Event) => void;
-  /**
-   * A function that filters the elements that should be considered as outside clicks.
-   *
-   * If omitted, a default filter function that ignores clicks in Chakra UI portals and react-select components is used.
-   */
-  filter?: FilterFunction;
-}
-
-export const DEFAULT_FILTER: FilterFunction = (el) => {
-  if (el instanceof SVGElement) {
-    // SVGElement's type appears to be incorrect. Its className is not a string, which causes `includes` to fail.
-    // Let's assume that SVG elements with a class name are not part of the portal and should not be filtered.
-    return false;
-  }
-  return el.className.includes('chakra-portal') || el.id.includes('react-select');
-};
-
-/**
- * Example, used in components like Dialogs and Popovers, so they can close
- * when a user clicks outside them.
- */
-export function useFilterableOutsideClick(props: UseOutsideClickProps) {
-  const { ref, handler, enabled = true, filter = DEFAULT_FILTER } = props;
-  const savedHandler = useCallbackRef(handler);
-
-  const stateRef = useRef({
-    isPointerDown: false,
-    ignoreEmulatedMouseEvents: false,
-  });
-
-  const state = stateRef.current;
-
-  useEffect(() => {
-    if (!enabled) {
-      return;
-    }
-    const onPointerDown: any = (e: PointerEvent) => {
-      if (isValidEvent(e, ref, filter)) {
-        state.isPointerDown = true;
-      }
-    };
-
-    const onMouseUp: any = (event: MouseEvent) => {
-      if (state.ignoreEmulatedMouseEvents) {
-        state.ignoreEmulatedMouseEvents = false;
-        return;
-      }
-
-      if (state.isPointerDown && handler && isValidEvent(event, ref)) {
-        state.isPointerDown = false;
-        savedHandler(event);
-      }
-    };
-
-    const onTouchEnd = (event: TouchEvent) => {
-      state.ignoreEmulatedMouseEvents = true;
-      if (handler && state.isPointerDown && isValidEvent(event, ref)) {
-        state.isPointerDown = false;
-        savedHandler(event);
-      }
-    };
-
-    const doc = getOwnerDocument(ref.current);
-    doc.addEventListener('mousedown', onPointerDown, true);
-    doc.addEventListener('mouseup', onMouseUp, true);
-    doc.addEventListener('touchstart', onPointerDown, true);
-    doc.addEventListener('touchend', onTouchEnd, true);
-
-    return () => {
-      doc.removeEventListener('mousedown', onPointerDown, true);
-      doc.removeEventListener('mouseup', onMouseUp, true);
-      doc.removeEventListener('touchstart', onPointerDown, true);
-      doc.removeEventListener('touchend', onTouchEnd, true);
-    };
-  }, [handler, ref, savedHandler, state, enabled, filter]);
-}
-
-function isValidEvent(event: Event, ref: React.RefObject<HTMLElement | null>, filter?: FilterFunction): boolean {
-  const target = (event.composedPath?.()[0] ?? event.target) as HTMLElement;
-
-  if (target) {
-    const doc = getOwnerDocument(target);
-    if (!doc.contains(target)) {
-      return false;
-    }
-  }
-
-  if (ref.current?.contains(target)) {
-    return false;
-  }
-
-  // This is the main logic change from the original hook.
-  if (filter) {
-    // Check if the click is inside an element matching the filter.
-    // This is used for portal-awareness or other general exclusion cases.
-    let currentElement: HTMLElement | null = target;
-    // Traverse up the DOM tree from the target element.
-    while (currentElement && currentElement !== document.body) {
-      if (filter(currentElement)) {
-        return false;
-      }
-      currentElement = currentElement.parentElement;
-    }
-  }
-
-  // If the click is not inside the ref and not inside a portal, it's a valid outside click.
-  return true;
-}
-
-function getOwnerDocument(node?: Element | null): Document {
-  return node?.ownerDocument ?? document;
-}

invokeai/frontend/web/src/common/hooks/useGlobalHotkeys.ts

@@ -1,13 +1,17 @@
-import { useAppDispatch } from 'app/store/storeHooks';
+import { useAppStore } from 'app/store/storeHooks';
+import { useDeleteImageModalApi } from 'features/deleteImageModal/store/state';
+import { selectSelection } from 'features/gallery/store/gallerySelectors';
 import { useClearQueue } from 'features/queue/hooks/useClearQueue';
 import { useDeleteCurrentQueueItem } from 'features/queue/hooks/useDeleteCurrentQueueItem';
 import { useInvoke } from 'features/queue/hooks/useInvoke';
 import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
-import { setActiveTab } from 'features/ui/store/uiSlice';
+import { navigationApi } from 'features/ui/layouts/navigation-api';
+
+import { getFocusedRegion } from './focus';
 
 export const useGlobalHotkeys = () => {
-  const dispatch = useAppDispatch();
+  const { dispatch, getState } = useAppStore();
   const isModelManagerEnabled = useFeatureStatus('modelManager');
   const queue = useInvoke();
 
@@ -65,7 +69,7 @@ export const useGlobalHotkeys = () => {
     id: 'selectGenerateTab',
     category: 'app',
     callback: () => {
-      dispatch(setActiveTab('generate'));
+      navigationApi.switchToTab('generate');
     },
     dependencies: [dispatch],
   });
@@ -74,7 +78,7 @@ export const useGlobalHotkeys = () => {
     id: 'selectCanvasTab',
     category: 'app',
     callback: () => {
-      dispatch(setActiveTab('canvas'));
+      navigationApi.switchToTab('canvas');
     },
     dependencies: [dispatch],
   });
@@ -83,7 +87,7 @@ export const useGlobalHotkeys = () => {
     id: 'selectUpscalingTab',
     category: 'app',
     callback: () => {
-      dispatch(setActiveTab('upscaling'));
+      navigationApi.switchToTab('upscaling');
     },
     dependencies: [dispatch],
   });
@@ -92,7 +96,7 @@ export const useGlobalHotkeys = () => {
     id: 'selectWorkflowsTab',
     category: 'app',
     callback: () => {
-      dispatch(setActiveTab('workflows'));
+      navigationApi.switchToTab('workflows');
     },
     dependencies: [dispatch],
   });
@@ -101,7 +105,7 @@ export const useGlobalHotkeys = () => {
     id: 'selectModelsTab',
     category: 'app',
     callback: () => {
-      dispatch(setActiveTab('models'));
+      navigationApi.switchToTab('models');
     },
     options: {
       enabled: isModelManagerEnabled,
@@ -113,8 +117,26 @@ export const useGlobalHotkeys = () => {
     id: 'selectQueueTab',
     category: 'app',
     callback: () => {
-      dispatch(setActiveTab('queue'));
+      navigationApi.switchToTab('queue');
     },
     dependencies: [dispatch, isModelManagerEnabled],
   });
+
+  const deleteImageModalApi = useDeleteImageModalApi();
+  useRegisteredHotkeys({
+    id: 'deleteSelection',
+    category: 'gallery',
+    callback: () => {
+      const focusedRegion = getFocusedRegion();
+      if (focusedRegion !== 'gallery' && focusedRegion !== 'viewer') {
+        return;
+      }
+      const selection = selectSelection(getState());
+      if (!selection.length) {
+        return;
+      }
+      deleteImageModalApi.delete(selection);
+    },
+    dependencies: [getState, deleteImageModalApi],
+  });
 };

invokeai/frontend/web/src/common/hooks/useGroupedModelCombobox.ts

@@ -2,10 +2,10 @@ import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppSelector } from 'app/store/storeHooks';
 import type { GroupBase } from 'chakra-react-select';
+import { groupBy, reduce } from 'es-toolkit/compat';
 import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
 import type { ModelIdentifierField } from 'features/nodes/types/common';
 import { selectSystemShouldEnableModelDescriptions } from 'features/system/store/systemSlice';
-import { groupBy, reduce } from 'lodash-es';
 import { useCallback, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
 import type { AnyModelConfig } from 'services/api/types';

invokeai/frontend/web/src/common/hooks/useImageUploadButton.tsx

@@ -21,11 +21,15 @@ type UseImageUploadButtonArgs =
       isDisabled?: boolean;
       allowMultiple: false;
       onUpload?: (imageDTO: ImageDTO) => void;
+      onUploadStarted?: (files: File) => void;
+      onError?: (error: unknown) => void;
     }
   | {
       isDisabled?: boolean;
       allowMultiple: true;
       onUpload?: (imageDTOs: ImageDTO[]) => void;
+      onUploadStarted?: (files: File[]) => void;
+      onError?: (error: unknown) => void;
     };
 
 const log = logger('gallery');
@@ -49,7 +53,13 @@ const log = logger('gallery');
  * <Button {...getUploadButtonProps()} /> // will open the file dialog on click
  * <input {...getUploadInputProps()} /> // hidden, handles native upload functionality
  */
-export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: UseImageUploadButtonArgs) => {
+export const useImageUploadButton = ({
+  onUpload,
+  isDisabled,
+  allowMultiple,
+  onUploadStarted,
+  onError,
+}: UseImageUploadButtonArgs) => {
   const autoAddBoardId = useAppSelector(selectAutoAddBoardId);
   const isClientSideUploadEnabled = useAppSelector(selectIsClientSideUploadEnabled);
   const [uploadImage, request] = useUploadImageMutation();
@@ -71,6 +81,7 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
           }
           const file = files[0];
           assert(file !== undefined); // should never happen
+          onUploadStarted?.(file);
           const imageDTO = await uploadImage({
             file,
             image_category: 'user',
@@ -82,6 +93,8 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
             onUpload(imageDTO);
           }
         } else {
+          onUploadStarted?.(files);
+
           let imageDTOs: ImageDTO[] = [];
           if (isClientSideUploadEnabled && files.length > 1) {
             imageDTOs = await Promise.all(files.map((file, i) => clientSideUpload(file, i)));
@@ -102,6 +115,7 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
           }
         }
       } catch (error) {
+        onError?.(error);
         toast({
           id: 'UPLOAD_FAILED',
           title: t('toast.imageUploadFailed'),
@@ -109,7 +123,17 @@ export const useImageUploadButton = ({ onUpload, isDisabled, allowMultiple }: Us
         });
       }
     },
-    [allowMultiple, autoAddBoardId, onUpload, uploadImage, isClientSideUploadEnabled, clientSideUpload, t]
+    [
+      allowMultiple,
+      onUploadStarted,
+      uploadImage,
+      autoAddBoardId,
+      onUpload,
+      isClientSideUploadEnabled,
+      clientSideUpload,
+      onError,
+      t,
+    ]
   );
 
   const onDropRejected = useCallback(

invokeai/frontend/web/src/common/hooks/usePersistedTextareaSize.ts

@@ -1,7 +1,7 @@
-import { useAppStore } from 'app/store/nanostores/store';
+import { useAppStore } from 'app/store/storeHooks';
+import { debounce } from 'es-toolkit/compat';
 import type { Dimensions } from 'features/controlLayers/store/types';
 import { selectUiSlice, textAreaSizesStateChanged } from 'features/ui/store/uiSlice';
-import { debounce } from 'lodash-es';
 import { type RefObject, useCallback, useEffect, useMemo } from 'react';
 
 type Options = {

invokeai/frontend/web/src/common/hooks/useRelatedGroupedModelCombobox.ts

@@ -1,132 +0,0 @@
-import type { ComboboxOnChange, ComboboxOption } from '@invoke-ai/ui-library';
-import { EMPTY_ARRAY } from 'app/store/constants';
-import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
-import { useAppSelector } from 'app/store/storeHooks';
-import type { GroupBase } from 'chakra-react-select';
-import { selectLoRAsSlice } from 'features/controlLayers/store/lorasSlice';
-import { selectParamsSlice } from 'features/controlLayers/store/paramsSlice';
-import type { ModelIdentifierField } from 'features/nodes/types/common';
-import { uniq } from 'lodash-es';
-import { useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { useGetRelatedModelIdsBatchQuery } from 'services/api/endpoints/modelRelationships';
-import type { AnyModelConfig } from 'services/api/types';
-
-import { useGroupedModelCombobox } from './useGroupedModelCombobox';
-
-type UseRelatedGroupedModelComboboxArg<T extends AnyModelConfig> = {
-  modelConfigs: T[];
-  selectedModel?: ModelIdentifierField | null;
-  onChange: (value: T | null) => void;
-  getIsDisabled?: (model: T) => boolean;
-  isLoading?: boolean;
-  groupByType?: boolean;
-};
-
-// Custom hook to overlay the grouped model combobox with related models on top!
-// Cleaner than hooking into useGroupedModelCombobox with a flag to enable/disable the related models
-// Also allows for related models to be shown conditionally with some pretty simple logic if it ends up as a config flag.
-
-type UseRelatedGroupedModelComboboxReturn = {
-  value: ComboboxOption | undefined | null;
-  options: GroupBase<ComboboxOption>[];
-  onChange: ComboboxOnChange;
-  placeholder: string;
-  noOptionsMessage: () => string;
-};
-
-const selectSelectedModelKeys = createMemoizedSelector(selectParamsSlice, selectLoRAsSlice, (params, loras) => {
-  const keys: string[] = [];
-  const main = params.model;
-  const vae = params.vae;
-  const refiner = params.refinerModel;
-  const controlnet = params.controlLora;
-
-  if (main) {
-    keys.push(main.key);
-  }
-  if (vae) {
-    keys.push(vae.key);
-  }
-  if (refiner) {
-    keys.push(refiner.key);
-  }
-  if (controlnet) {
-    keys.push(controlnet.key);
-  }
-  for (const { model } of loras.loras) {
-    keys.push(model.key);
-  }
-
-  return uniq(keys);
-});
-
-export function useRelatedGroupedModelCombobox<T extends AnyModelConfig>({
-  modelConfigs,
-  selectedModel,
-  onChange,
-  isLoading = false,
-  getIsDisabled,
-  groupByType,
-}: UseRelatedGroupedModelComboboxArg<T>): UseRelatedGroupedModelComboboxReturn {
-  const { t } = useTranslation();
-
-  const selectedKeys = useAppSelector(selectSelectedModelKeys);
-  const { relatedKeys } = useGetRelatedModelIdsBatchQuery(selectedKeys, {
-    selectFromResult: ({ data }) => {
-      if (!data) {
-        return { relatedKeys: EMPTY_ARRAY };
-      }
-      return { relatedKeys: data };
-    },
-  });
-
-  // Base grouped options
-  const base = useGroupedModelCombobox({
-    modelConfigs,
-    selectedModel,
-    onChange,
-    getIsDisabled,
-    isLoading,
-    groupByType,
-  });
-
-  const options = useMemo(() => {
-    if (relatedKeys.length === 0) {
-      return base.options;
-    }
-
-    const relatedOptions: ComboboxOption[] = [];
-    const updatedGroups: GroupBase<ComboboxOption>[] = [];
-
-    for (const group of base.options) {
-      const remainingOptions: ComboboxOption[] = [];
-
-      for (const option of group.options) {
-        if (relatedKeys.includes(option.value)) {
-          relatedOptions.push({ ...option, label: `* ${option.label}` });
-        } else {
-          remainingOptions.push(option);
-        }
-      }
-
-      if (remainingOptions.length > 0) {
-        updatedGroups.push({
-          label: group.label,
-          options: remainingOptions,
-        });
-      }
-    }
-
-    if (relatedOptions.length > 0) {
-      return [{ label: t('modelManager.relatedModels'), options: relatedOptions }, ...updatedGroups];
-    } else {
-      return updatedGroups;
-    }
-  }, [base.options, relatedKeys, t]);
-
-  return {
-    ...base,
-    options,
-  };
-}

invokeai/frontend/web/src/common/hooks/useSelectorAsAtom.ts

@@ -1,28 +0,0 @@
-import type { Selector } from '@reduxjs/toolkit';
-import type { RootState } from 'app/store/store';
-import { useAppStore } from 'app/store/storeHooks';
-import type { Atom, WritableAtom } from 'nanostores';
-import { atom } from 'nanostores';
-import { useEffect, useState } from 'react';
-
-/* eslint-disable-next-line @typescript-eslint/no-explicit-any */
-export const useSelectorAsAtom = <T extends Selector<RootState, any>>(selector: T): Atom<ReturnType<T>> => {
-  const store = useAppStore();
-  const $atom = useState<WritableAtom<ReturnType<T>>>(() => atom<ReturnType<T>>(selector(store.getState())))[0];
-
-  useEffect(() => {
-    const unsubscribe = store.subscribe(() => {
-      const prev = $atom.get();
-      const next = selector(store.getState());
-      if (prev !== next) {
-        $atom.set(next);
-      }
-    });
-
-    return () => {
-      unsubscribe();
-    };
-  }, [$atom, selector, store]);
-
-  return $atom;
-};

invokeai/frontend/web/src/common/util/createDeferredPromise.ts

@@ -0,0 +1,20 @@
+export type Deferred<T> = {
+  promise: Promise<T>;
+  resolve: (value: T) => void;
+  reject: (error: Error) => void;
+};
+
+/**
+ * Create a promise and expose its resolve and reject callbacks.
+ */
+export const createDeferredPromise = <T>(): Deferred<T> => {
+  let resolve!: (value: T) => void;
+  let reject!: (error: Error) => void;
+
+  const promise = new Promise<T>((res, rej) => {
+    resolve = res;
+    reject = rej;
+  });
+
+  return { promise, resolve, reject };
+};

invokeai/frontend/web/src/common/util/generateSeeds.ts

@@ -1,5 +1,5 @@
 import { NUMPY_RAND_MAX, NUMPY_RAND_MIN } from 'app/constants';
-import { random } from 'lodash-es';
+import { random } from 'es-toolkit/compat';
 
 type GenerateSeedsArg = {
   count: number;

invokeai/frontend/web/src/common/util/objectKeys.ts

@@ -1,6 +0,0 @@
-/**
- * Get the keys of an object. This is a wrapper around `Object.keys` that types the result as an array of the keys of the object.
- * @param obj The object to get the keys of.
- * @returns The keys of the object.
- */
-export const objectKeys = <T extends Record<string, unknown>>(obj: T) => Object.keys(obj) as Array<keyof T>;

invokeai/frontend/web/src/common/util/result.ts

@@ -89,7 +89,7 @@ export function withResult<T>(fn: () => T): Result<T> {
   try {
     return new Ok(fn());
   } catch (error) {
-    return new Err(error instanceof Error ? error : new Error(String(error)));
+    return new Err(error instanceof Error ? error : new WrappedError(error));
   }
 }
 
@@ -104,6 +104,23 @@ export async function withResultAsync<T>(fn: () => Promise<T>): Promise<Result<T
     const result = await fn();
     return new Ok(result);
   } catch (error) {
-    return new Err(error instanceof Error ? error : new Error(String(error)));
+    return new Err(error instanceof Error ? error : new WrappedError(error));
+  }
+}
+
+export class WrappedError extends Error {
+  error: unknown;
+
+  constructor(error: unknown) {
+    super('Wrapped Error');
+    this.name = this.constructor.name;
+    this.error = error;
+  }
+
+  static wrap(error: unknown): Error | WrappedError {
+    if (error instanceof Error) {
+      return error;
+    }
+    return new WrappedError(error);
   }
 }

invokeai/frontend/web/src/common/util/zodUtils.ts

@@ -1,4 +1,4 @@
-import type { z } from 'zod';
+import type { z } from 'zod/v4';
 
 /**
  * Helper to create a type guard from a zod schema. The type guard will infer the schema's TS type.

invokeai/frontend/web/src/features/controlLayers/components/AdvancedSession/AdvancedSession.tsx

@@ -1,182 +0,0 @@
-import type { SystemStyleObject } from '@invoke-ai/ui-library';
-import {
-  ContextMenu,
-  Divider,
-  Flex,
-  IconButton,
-  Menu,
-  MenuButton,
-  MenuList,
-  Tab,
-  TabList,
-  TabPanel,
-  TabPanels,
-  Tabs,
-} from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { FocusRegionWrapper } from 'common/components/FocusRegionWrapper';
-import { CanvasAlertsInvocationProgress } from 'features/controlLayers/components/CanvasAlerts/CanvasAlertsInvocationProgress';
-import { CanvasAlertsPreserveMask } from 'features/controlLayers/components/CanvasAlerts/CanvasAlertsPreserveMask';
-import { CanvasAlertsSelectedEntityStatus } from 'features/controlLayers/components/CanvasAlerts/CanvasAlertsSelectedEntityStatus';
-import { CanvasContextMenuGlobalMenuItems } from 'features/controlLayers/components/CanvasContextMenu/CanvasContextMenuGlobalMenuItems';
-import { CanvasContextMenuSelectedEntityMenuItems } from 'features/controlLayers/components/CanvasContextMenu/CanvasContextMenuSelectedEntityMenuItems';
-import { CanvasDropArea } from 'features/controlLayers/components/CanvasDropArea';
-import { Filter } from 'features/controlLayers/components/Filters/Filter';
-import { CanvasHUD } from 'features/controlLayers/components/HUD/CanvasHUD';
-import { InvokeCanvasComponent } from 'features/controlLayers/components/InvokeCanvasComponent';
-import { SelectObject } from 'features/controlLayers/components/SelectObject/SelectObject';
-import { CanvasSessionContextProvider } from 'features/controlLayers/components/SimpleSession/context';
-import { GenerateLaunchpadPanel } from 'features/controlLayers/components/SimpleSession/GenerateLaunchpadPanel';
-import { StagingAreaItemsList } from 'features/controlLayers/components/SimpleSession/StagingAreaItemsList';
-import { StagingAreaToolbar } from 'features/controlLayers/components/StagingArea/StagingAreaToolbar';
-import { CanvasToolbar } from 'features/controlLayers/components/Toolbar/CanvasToolbar';
-import { Transform } from 'features/controlLayers/components/Transform/Transform';
-import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import { selectDynamicGrid, selectShowHUD } from 'features/controlLayers/store/canvasSettingsSlice';
-import { ImageViewer } from 'features/gallery/components/ImageViewer/ImageViewer';
-import { ViewerToolbar } from 'features/gallery/components/ImageViewer/ViewerToolbar';
-import { memo, useCallback } from 'react';
-import { PiDotsThreeOutlineVerticalFill } from 'react-icons/pi';
-
-const FOCUS_REGION_STYLES: SystemStyleObject = {
-  width: 'full',
-  height: 'full',
-};
-
-const MenuContent = memo(() => {
-  return (
-    <CanvasManagerProviderGate>
-      <MenuList>
-        <CanvasContextMenuSelectedEntityMenuItems />
-        <CanvasContextMenuGlobalMenuItems />
-      </MenuList>
-    </CanvasManagerProviderGate>
-  );
-});
-MenuContent.displayName = 'MenuContent';
-
-const canvasBgSx = {
-  position: 'relative',
-  w: 'full',
-  h: 'full',
-  borderRadius: 'base',
-  overflow: 'hidden',
-  bg: 'base.900',
-  '&[data-dynamic-grid="true"]': {
-    bg: 'base.850',
-  },
-};
-
-export const AdvancedSession = memo(({ id }: { id: string | null }) => {
-  const dynamicGrid = useAppSelector(selectDynamicGrid);
-  const showHUD = useAppSelector(selectShowHUD);
-
-  const renderMenu = useCallback(() => {
-    return <MenuContent />;
-  }, []);
-
-  return (
-    <Tabs w="full" h="full">
-      <TabList>
-        <Tab>Welcome</Tab>
-        <Tab>Workspace</Tab>
-        <Tab>Viewer</Tab>
-      </TabList>
-      <TabPanels w="full" h="full">
-        <TabPanel w="full" h="full" justifyContent="center">
-          <GenerateLaunchpadPanel />
-        </TabPanel>
-        <TabPanel w="full" h="full">
-          <FocusRegionWrapper region="canvas" sx={FOCUS_REGION_STYLES}>
-            <Flex
-              tabIndex={-1}
-              borderRadius="base"
-              position="relative"
-              flexDirection="column"
-              height="full"
-              width="full"
-              gap={2}
-              alignItems="center"
-              justifyContent="center"
-              overflow="hidden"
-            >
-              <CanvasManagerProviderGate>
-                <CanvasToolbar />
-              </CanvasManagerProviderGate>
-              <Divider />
-              <ContextMenu<HTMLDivElement> renderMenu={renderMenu} withLongPress={false}>
-                {(ref) => (
-                  <Flex ref={ref} sx={canvasBgSx} data-dynamic-grid={dynamicGrid}>
-                    <InvokeCanvasComponent />
-                    <CanvasManagerProviderGate>
-                      <Flex
-                        position="absolute"
-                        flexDir="column"
-                        top={1}
-                        insetInlineStart={1}
-                        pointerEvents="none"
-                        gap={2}
-                        alignItems="flex-start"
-                      >
-                        {showHUD && <CanvasHUD />}
-                        <CanvasAlertsSelectedEntityStatus />
-                        <CanvasAlertsPreserveMask />
-                        <CanvasAlertsInvocationProgress />
-                      </Flex>
-                      <Flex position="absolute" top={1} insetInlineEnd={1}>
-                        <Menu>
-                          <MenuButton as={IconButton} icon={<PiDotsThreeOutlineVerticalFill />} colorScheme="base" />
-                          <MenuContent />
-                        </Menu>
-                      </Flex>
-                    </CanvasManagerProviderGate>
-                  </Flex>
-                )}
-              </ContextMenu>
-              {id !== null && (
-                <CanvasManagerProviderGate>
-                  <CanvasSessionContextProvider type="advanced" id={id}>
-                    <Flex
-                      position="absolute"
-                      flexDir="column"
-                      bottom={4}
-                      gap={2}
-                      align="center"
-                      justify="center"
-                      left={4}
-                      right={4}
-                    >
-                      <Flex position="relative" maxW="full" w="full" h={108}>
-                        <StagingAreaItemsList />
-                      </Flex>
-                      <Flex gap={2}>
-                        <StagingAreaToolbar />
-                      </Flex>
-                    </Flex>
-                  </CanvasSessionContextProvider>
-                </CanvasManagerProviderGate>
-              )}
-              <Flex position="absolute" bottom={4}>
-                <CanvasManagerProviderGate>
-                  <Filter />
-                  <Transform />
-                  <SelectObject />
-                </CanvasManagerProviderGate>
-              </Flex>
-              <CanvasManagerProviderGate>
-                <CanvasDropArea />
-              </CanvasManagerProviderGate>
-            </Flex>
-          </FocusRegionWrapper>
-        </TabPanel>
-        <TabPanel w="full" h="full">
-          <Flex flexDir="column" w="full" h="full">
-            <ViewerToolbar />
-            <ImageViewer />
-          </Flex>
-        </TabPanel>
-      </TabPanels>
-    </Tabs>
-  );
-});
-AdvancedSession.displayName = 'AdvancedSession';

invokeai/frontend/web/src/features/controlLayers/components/CanvasBusySpinner.tsx

@@ -1,3 +1,4 @@
+import type { SpinnerProps } from '@invoke-ai/ui-library';
 import { Spinner } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
@@ -5,7 +6,7 @@ import { useAllEntityAdapters } from 'features/controlLayers/contexts/EntityAdap
 import { computed } from 'nanostores';
 import { memo, useMemo } from 'react';
 
-export const CanvasBusySpinner = memo(() => {
+export const CanvasBusySpinner = memo((props: SpinnerProps) => {
   const canvasManager = useCanvasManager();
   const allEntityAdapters = useAllEntityAdapters();
   const $isPendingRectCalculation = useMemo(
@@ -21,7 +22,7 @@ export const CanvasBusySpinner = memo(() => {
   const isCompositing = useStore(canvasManager.compositor.$isBusy);
 
   if (isRasterizing || isCompositing || isPendingRectCalculation) {
-    return <Spinner opacity={0.3} />;
+    return <Spinner opacity={0.3} {...props} />;
   }
   return null;
 });

invokeai/frontend/web/src/features/controlLayers/components/CanvasDropArea.tsx

@@ -12,6 +12,10 @@ const addControlLayerFromImageDndTargetData = newCanvasEntityFromImageDndTarget.
 const addRegionalGuidanceReferenceImageFromImageDndTargetData = newCanvasEntityFromImageDndTarget.getData({
   type: 'regional_guidance_with_reference_image',
 });
+const addResizedControlLayerFromImageDndTargetData = newCanvasEntityFromImageDndTarget.getData({
+  type: 'control_layer',
+  withResize: true,
+});
 
 export const CanvasDropArea = memo(() => {
   const { t } = useTranslation();
@@ -45,7 +49,6 @@ export const CanvasDropArea = memo(() => {
             isDisabled={isBusy}
           />
         </GridItem>
-
         <GridItem position="relative">
           <DndDropTarget
             dndTarget={newCanvasEntityFromImageDndTarget}
@@ -54,6 +57,14 @@ export const CanvasDropArea = memo(() => {
             isDisabled={isBusy}
           />
         </GridItem>
+        <GridItem position="relative">
+          <DndDropTarget
+            dndTarget={newCanvasEntityFromImageDndTarget}
+            dndTargetData={addResizedControlLayerFromImageDndTargetData}
+            label={t('controlLayers.canvasContextMenu.newResizedControlLayer')}
+            isDisabled={isBusy}
+          />
+        </GridItem>
       </Grid>
     </>
   );

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/CanvasEntityGroupList.tsx

@@ -10,6 +10,7 @@ import { fixTooltipCloseOnScrollStyles } from 'common/util/fixTooltipCloseOnScro
 import { CanvasEntityAddOfTypeButton } from 'features/controlLayers/components/common/CanvasEntityAddOfTypeButton';
 import { CanvasEntityMergeVisibleButton } from 'features/controlLayers/components/common/CanvasEntityMergeVisibleButton';
 import { CanvasEntityTypeIsHiddenToggle } from 'features/controlLayers/components/common/CanvasEntityTypeIsHiddenToggle';
+import { RasterLayerExportPSDButton } from 'features/controlLayers/components/RasterLayer/RasterLayerExportPSDButton';
 import { useEntityTypeInformationalPopover } from 'features/controlLayers/hooks/useEntityTypeInformationalPopover';
 import { useEntityTypeTitle } from 'features/controlLayers/hooks/useEntityTypeTitle';
 import { entitiesReordered } from 'features/controlLayers/store/canvasSlice';
@@ -118,7 +119,7 @@ export const CanvasEntityGroupList = memo(({ isSelected, type, children, entityI
 
   return (
     <Flex flexDir="column" w="full">
-      <Flex w="full">
+      <Flex w="full" ps={2}>
         <Flex
           flexGrow={1}
           as={Button}
@@ -166,6 +167,7 @@ export const CanvasEntityGroupList = memo(({ isSelected, type, children, entityI
         </Flex>
         <CanvasEntityMergeVisibleButton type={type} />
         <CanvasEntityTypeIsHiddenToggle type={type} />
+        {type === 'raster_layer' && <RasterLayerExportPSDButton />}
         <CanvasEntityAddOfTypeButton type={type} />
       </Flex>
       <Collapse in={collapse.isTrue} style={fixTooltipCloseOnScrollStyles}>

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBar.tsx

@@ -6,6 +6,7 @@ import { EntityListSelectedEntityActionBarFilterButton } from 'features/controlL
 import { EntityListSelectedEntityActionBarOpacity } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarOpacity';
 import { EntityListSelectedEntityActionBarSelectObjectButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarSelectObjectButton';
 import { EntityListSelectedEntityActionBarTransformButton } from 'features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarTransformButton';
+import { EntityListNonRasterLayerToggle } from 'features/controlLayers/components/common/CanvasNonRasterLayersIsHiddenToggle';
 import { memo } from 'react';
 
 import { EntityListSelectedEntityActionBarSaveToAssetsButton } from './EntityListSelectedEntityActionBarSaveToAssetsButton';
@@ -22,6 +23,7 @@ export const EntityListSelectedEntityActionBar = memo(() => {
         <EntityListSelectedEntityActionBarTransformButton />
         <EntityListSelectedEntityActionBarSaveToAssetsButton />
         <EntityListSelectedEntityActionBarDuplicateButton />
+        <EntityListNonRasterLayerToggle />
         <EntityListGlobalActionBarAddLayerMenu />
       </Flex>
     </Flex>

invokeai/frontend/web/src/features/controlLayers/components/CanvasEntityList/EntityListSelectedEntityActionBarOpacity.tsx

@@ -15,6 +15,7 @@ import {
 } from '@invoke-ai/ui-library';
 import { createSelector } from '@reduxjs/toolkit';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { clamp, round } from 'es-toolkit/compat';
 import { snapToNearest } from 'features/controlLayers/konva/util';
 import { entityOpacityChanged } from 'features/controlLayers/store/canvasSlice';
 import {
@@ -22,7 +23,6 @@ import {
   selectEntity,
   selectSelectedEntityIdentifier,
 } from 'features/controlLayers/store/selectors';
-import { clamp, round } from 'lodash-es';
 import type { KeyboardEvent } from 'react';
 import { memo, useCallback, useEffect, useState } from 'react';
 import { useTranslation } from 'react-i18next';

invokeai/frontend/web/src/features/controlLayers/components/CanvasLayersPanelContent.tsx

@@ -14,7 +14,7 @@ export const CanvasLayersPanel = memo(() => {
 
   return (
     <CanvasManagerProviderGate>
-      <Flex flexDir="column" gap={2} w="full" h="full" p={2}>
+      <Flex flexDir="column" gap={2} w="full" h="full">
         <EntityListSelectedEntityActionBar />
         <Divider py={0} />
         <ParamDenoisingStrength />