chore(ui): lint

When the invocation cache is used, we might skip all progress images. This can prevent auto-switch-on-first-progress from working, as we don't get any of those events.

It's much easier to only support auto-switch on complete.

.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 20
+    - name: setup node 18
       uses: actions/setup-node@v4
       with:
-        node-version: '20'
+        node-version: '18'
 
     - name: setup pnpm
       uses: pnpm/action-setup@v4
       with:
-        version: 10
+        version: 8.15.6
         run_install: false
 
     - name: get pnpm store directory

.gitignore

@@ -180,7 +180,6 @@ cython_debug/
 # Scratch folder
 .scratch/
 .vscode/
-.zed/
 
 # source installer files
 installer/*zip

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/v4'
+[zod]: https://github.com/colinhacks/zod 'zod'
 [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 recent launcher for your operating system:
+Download the most 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/board_images.py

@@ -1,12 +1,21 @@
 from fastapi import Body, HTTPException
 from fastapi.routing import APIRouter
+from pydantic import BaseModel, Field
 
 from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.services.images.images_common import AddImagesToBoardResult, RemoveImagesFromBoardResult
 
 board_images_router = APIRouter(prefix="/v1/board_images", tags=["boards"])
 
 
+class AddImagesToBoardResult(BaseModel):
+    board_id: str = Field(description="The id of the board the images were added to")
+    added_image_names: list[str] = Field(description="The image names that were added to the board")
+
+
+class RemoveImagesFromBoardResult(BaseModel):
+    removed_image_names: list[str] = Field(description="The image names that were removed from their board")
+
+
 @board_images_router.post(
     "/",
     operation_id="add_image_to_board",
@@ -14,26 +23,17 @@ board_images_router = APIRouter(prefix="/v1/board_images", tags=["boards"])
         201: {"description": "The image was added to a board successfully"},
     },
     status_code=201,
-    response_model=AddImagesToBoardResult,
 )
 async def add_image_to_board(
     board_id: str = Body(description="The id of the board to add to"),
     image_name: str = Body(description="The name of the image to add"),
-) -> AddImagesToBoardResult:
+):
     """Creates a board_image"""
     try:
-        added_images: set[str] = set()
-        affected_boards: set[str] = set()
-        old_board_id = ApiDependencies.invoker.services.images.get_dto(image_name).board_id or "none"
-        ApiDependencies.invoker.services.board_images.add_image_to_board(board_id=board_id, image_name=image_name)
-        added_images.add(image_name)
-        affected_boards.add(board_id)
-        affected_boards.add(old_board_id)
-
-        return AddImagesToBoardResult(
-            added_images=list(added_images),
-            affected_boards=list(affected_boards),
+        result = ApiDependencies.invoker.services.board_images.add_image_to_board(
+            board_id=board_id, image_name=image_name
         )
+        return result
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to add image to board")
 
@@ -45,25 +45,14 @@ async def add_image_to_board(
         201: {"description": "The image was removed from the board successfully"},
     },
     status_code=201,
-    response_model=RemoveImagesFromBoardResult,
 )
 async def remove_image_from_board(
     image_name: str = Body(description="The name of the image to remove", embed=True),
-) -> RemoveImagesFromBoardResult:
+):
     """Removes an image from its board, if it had one"""
     try:
-        removed_images: set[str] = set()
-        affected_boards: set[str] = set()
-        old_board_id = ApiDependencies.invoker.services.images.get_dto(image_name).board_id or "none"
-        ApiDependencies.invoker.services.board_images.remove_image_from_board(image_name=image_name)
-        removed_images.add(image_name)
-        affected_boards.add("none")
-        affected_boards.add(old_board_id)
-        return RemoveImagesFromBoardResult(
-            removed_images=list(removed_images),
-            affected_boards=list(affected_boards),
-        )
-
+        result = ApiDependencies.invoker.services.board_images.remove_image_from_board(image_name=image_name)
+        return result
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to remove image from board")
 
@@ -83,25 +72,16 @@ async def add_images_to_board(
 ) -> AddImagesToBoardResult:
     """Adds a list of images to a board"""
     try:
-        added_images: set[str] = set()
-        affected_boards: set[str] = set()
+        added_image_names: list[str] = []
         for image_name in image_names:
             try:
-                old_board_id = ApiDependencies.invoker.services.images.get_dto(image_name).board_id or "none"
                 ApiDependencies.invoker.services.board_images.add_image_to_board(
-                    board_id=board_id,
-                    image_name=image_name,
+                    board_id=board_id, image_name=image_name
                 )
-                added_images.add(image_name)
-                affected_boards.add(board_id)
-                affected_boards.add(old_board_id)
-
+                added_image_names.append(image_name)
             except Exception:
                 pass
-        return AddImagesToBoardResult(
-            added_images=list(added_images),
-            affected_boards=list(affected_boards),
-        )
+        return AddImagesToBoardResult(board_id=board_id, added_image_names=added_image_names)
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to add images to board")
 
@@ -120,20 +100,13 @@ async def remove_images_from_board(
 ) -> RemoveImagesFromBoardResult:
     """Removes a list of images from their board, if they had one"""
     try:
-        removed_images: set[str] = set()
-        affected_boards: set[str] = set()
+        removed_image_names: list[str] = []
         for image_name in image_names:
             try:
-                old_board_id = ApiDependencies.invoker.services.images.get_dto(image_name).board_id or "none"
                 ApiDependencies.invoker.services.board_images.remove_image_from_board(image_name=image_name)
-                removed_images.add(image_name)
-                affected_boards.add("none")
-                affected_boards.add(old_board_id)
+                removed_image_names.append(image_name)
             except Exception:
                 pass
-        return RemoveImagesFromBoardResult(
-            removed_images=list(removed_images),
-            affected_boards=list(affected_boards),
-        )
+        return RemoveImagesFromBoardResult(removed_image_names=removed_image_names)
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to remove images from board")

invokeai/app/api/routers/images.py

@@ -14,17 +14,10 @@ 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,
-    ImageNamesResult,
     ImageRecordChanges,
     ResourceOrigin,
 )
-from invokeai.app.services.images.images_common import (
-    DeleteImagesResult,
-    ImageDTO,
-    ImageUrlsDTO,
-    StarredImagesResult,
-    UnstarredImagesResult,
-)
+from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 from invokeai.app.util.controlnet_utils import heuristic_resize_fast
@@ -72,7 +65,7 @@ async def upload_image(
     resize_to: Optional[str] = Body(
         default=None,
         description=f"Dimensions to resize the image to, must be stringified tuple of 2 integers. Max total pixel count: {ResizeToDimensions.MAX_SIZE}",
-        examples=['"[1024,1024]"'],
+        example='"[1024,1024]"',
     ),
     metadata: Optional[str] = Body(
         default=None,
@@ -160,30 +153,18 @@ async def create_image_upload_entry(
     raise HTTPException(status_code=501, detail="Not implemented")
 
 
-@images_router.delete("/i/{image_name}", operation_id="delete_image", response_model=DeleteImagesResult)
+@images_router.delete("/i/{image_name}", operation_id="delete_image")
 async def delete_image(
     image_name: str = Path(description="The name of the image to delete"),
-) -> DeleteImagesResult:
+) -> None:
     """Deletes an image"""
 
-    deleted_images: set[str] = set()
-    affected_boards: set[str] = set()
-
     try:
-        image_dto = ApiDependencies.invoker.services.images.get_dto(image_name)
-        board_id = image_dto.board_id or "none"
         ApiDependencies.invoker.services.images.delete(image_name)
-        deleted_images.add(image_name)
-        affected_boards.add(board_id)
     except Exception:
         # TODO: Does this need any exception handling at all?
         pass
 
-    return DeleteImagesResult(
-        deleted_images=list(deleted_images),
-        affected_boards=list(affected_boards),
-    )
-
 
 @images_router.delete("/intermediates", operation_id="clear_intermediates")
 async def clear_intermediates() -> int:
@@ -395,32 +376,31 @@ async def list_image_dtos(
     return image_dtos
 
 
-@images_router.post("/delete", operation_id="delete_images_from_list", response_model=DeleteImagesResult)
+class DeleteImagesFromListResult(BaseModel):
+    deleted_images: list[str]
+
+
+@images_router.post("/delete", operation_id="delete_images_from_list", response_model=DeleteImagesFromListResult)
 async def delete_images_from_list(
     image_names: list[str] = Body(description="The list of names of images to delete", embed=True),
-) -> DeleteImagesResult:
+) -> DeleteImagesFromListResult:
     try:
-        deleted_images: set[str] = set()
-        affected_boards: set[str] = set()
+        deleted_images: list[str] = []
         for image_name in image_names:
             try:
-                image_dto = ApiDependencies.invoker.services.images.get_dto(image_name)
-                board_id = image_dto.board_id or "none"
                 ApiDependencies.invoker.services.images.delete(image_name)
-                deleted_images.add(image_name)
-                affected_boards.add(board_id)
+                deleted_images.append(image_name)
             except Exception:
                 pass
-        return DeleteImagesResult(
-            deleted_images=list(deleted_images),
-            affected_boards=list(affected_boards),
-        )
+        return DeleteImagesFromListResult(deleted_images=deleted_images)
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to delete images")
 
 
-@images_router.delete("/uncategorized", operation_id="delete_uncategorized_images", response_model=DeleteImagesResult)
-async def delete_uncategorized_images() -> DeleteImagesResult:
+@images_router.delete(
+    "/uncategorized", operation_id="delete_uncategorized_images", response_model=DeleteImagesFromListResult
+)
+async def delete_uncategorized_images() -> DeleteImagesFromListResult:
     """Deletes all images that are uncategorized"""
 
     image_names = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
@@ -428,19 +408,14 @@ async def delete_uncategorized_images() -> DeleteImagesResult:
     )
 
     try:
-        deleted_images: set[str] = set()
-        affected_boards: set[str] = set()
+        deleted_images: list[str] = []
         for image_name in image_names:
             try:
                 ApiDependencies.invoker.services.images.delete(image_name)
-                deleted_images.add(image_name)
-                affected_boards.add("none")
+                deleted_images.append(image_name)
             except Exception:
                 pass
-        return DeleteImagesResult(
-            deleted_images=list(deleted_images),
-            affected_boards=list(affected_boards),
-        )
+        return DeleteImagesFromListResult(deleted_images=deleted_images)
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to delete images")
 
@@ -449,50 +424,36 @@ class ImagesUpdatedFromListResult(BaseModel):
     updated_image_names: list[str] = Field(description="The image names that were updated")
 
 
-@images_router.post("/star", operation_id="star_images_in_list", response_model=StarredImagesResult)
+@images_router.post("/star", operation_id="star_images_in_list", response_model=ImagesUpdatedFromListResult)
 async def star_images_in_list(
     image_names: list[str] = Body(description="The list of names of images to star", embed=True),
-) -> StarredImagesResult:
+) -> ImagesUpdatedFromListResult:
     try:
-        starred_images: set[str] = set()
-        affected_boards: set[str] = set()
+        updated_image_names: list[str] = []
         for image_name in image_names:
             try:
-                updated_image_dto = ApiDependencies.invoker.services.images.update(
-                    image_name, changes=ImageRecordChanges(starred=True)
-                )
-                starred_images.add(image_name)
-                affected_boards.add(updated_image_dto.board_id or "none")
+                ApiDependencies.invoker.services.images.update(image_name, changes=ImageRecordChanges(starred=True))
+                updated_image_names.append(image_name)
             except Exception:
                 pass
-        return StarredImagesResult(
-            starred_images=list(starred_images),
-            affected_boards=list(affected_boards),
-        )
+        return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to star images")
 
 
-@images_router.post("/unstar", operation_id="unstar_images_in_list", response_model=UnstarredImagesResult)
+@images_router.post("/unstar", operation_id="unstar_images_in_list", response_model=ImagesUpdatedFromListResult)
 async def unstar_images_in_list(
     image_names: list[str] = Body(description="The list of names of images to unstar", embed=True),
-) -> UnstarredImagesResult:
+) -> ImagesUpdatedFromListResult:
     try:
-        unstarred_images: set[str] = set()
-        affected_boards: set[str] = set()
+        updated_image_names: list[str] = []
         for image_name in image_names:
             try:
-                updated_image_dto = ApiDependencies.invoker.services.images.update(
-                    image_name, changes=ImageRecordChanges(starred=False)
-                )
-                unstarred_images.add(image_name)
-                affected_boards.add(updated_image_dto.board_id or "none")
+                ApiDependencies.invoker.services.images.update(image_name, changes=ImageRecordChanges(starred=False))
+                updated_image_names.append(image_name)
             except Exception:
                 pass
-        return UnstarredImagesResult(
-            unstarred_images=list(unstarred_images),
-            affected_boards=list(affected_boards),
-        )
+        return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to unstar images")
 
@@ -563,61 +524,3 @@ async def get_bulk_download_item(
         return response
     except Exception:
         raise HTTPException(status_code=404)
-
-
-@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."),
-    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.",
-    ),
-    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"),
-) -> ImageNamesResult:
-    """Gets ordered list of image names with metadata for optimistic updates"""
-
-    try:
-        result = ApiDependencies.invoker.services.images.get_image_names(
-            starred_first=starred_first,
-            order_dir=order_dir,
-            image_origin=image_origin,
-            categories=categories,
-            is_intermediate=is_intermediate,
-            board_id=board_id,
-            search_term=search_term,
-        )
-        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

@@ -41,7 +41,6 @@ from invokeai.backend.model_manager.starter_models import (
     STARTER_BUNDLES,
     STARTER_MODELS,
     StarterModel,
-    StarterModelBundle,
     StarterModelWithoutDependencies,
 )
 
@@ -292,7 +291,7 @@ async def get_hugging_face_models(
 )
 async def update_model_record(
     key: Annotated[str, Path(description="Unique key of model")],
-    changes: Annotated[ModelRecordChanges, Body(description="Model config", examples=[example_model_input])],
+    changes: Annotated[ModelRecordChanges, Body(description="Model config", example=example_model_input)],
 ) -> AnyModelConfig:
     """Update a model's config."""
     logger = ApiDependencies.invoker.services.logger
@@ -450,7 +449,7 @@ async def install_model(
     access_token: Optional[str] = Query(description="access token for the remote resource", default=None),
     config: ModelRecordChanges = Body(
         description="Object containing fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
-        examples=[{"name": "string", "description": "string"}],
+        example={"name": "string", "description": "string"},
     ),
 ) -> ModelInstallJob:
     """Install a model using a string identifier.
@@ -800,7 +799,7 @@ async def convert_model(
 
 class StarterModelResponse(BaseModel):
     starter_models: list[StarterModel]
-    starter_bundles: dict[str, StarterModelBundle]
+    starter_bundles: dict[str, list[StarterModel]]
 
 
 def get_is_installed(
@@ -834,7 +833,7 @@ async def get_starter_models() -> StarterModelResponse:
         model.dependencies = missing_deps
 
     for bundle in starter_bundles.values():
-        for model in bundle.models:
+        for model in bundle:
             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, HTTPException, Path, Query
+from fastapi import Body, Path, Query
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
 
@@ -22,7 +22,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     RetryItemsResult,
     SessionQueueCountsByDestination,
     SessionQueueItem,
-    SessionQueueItemNotFoundError,
     SessionQueueStatus,
 )
 from invokeai.app.services.shared.pagination import CursorPaginatedResults
@@ -60,12 +59,10 @@ async def enqueue_batch(
     ),
 ) -> EnqueueBatchResult:
     """Processes a batch and enqueues the output graphs for execution."""
-    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}")
+
+    return await ApiDependencies.invoker.services.session_queue.enqueue_batch(
+        queue_id=queue_id, batch=batch, prepend=prepend
+    )
 
 
 @session_queue_router.get(
@@ -85,17 +82,14 @@ async def list_queue_items(
 ) -> CursorPaginatedResults[SessionQueueItem]:
     """Gets cursor-paginated queue items"""
 
-    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}")
+    return ApiDependencies.invoker.services.session_queue.list_queue_items(
+        queue_id=queue_id,
+        limit=limit,
+        status=status,
+        cursor=cursor,
+        priority=priority,
+        destination=destination,
+    )
 
 
 @session_queue_router.get(
@@ -110,13 +104,11 @@ 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"""
-    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}")
+
+    return ApiDependencies.invoker.services.session_queue.list_all_queue_items(
+        queue_id=queue_id,
+        destination=destination,
+    )
 
 
 @session_queue_router.put(
@@ -128,10 +120,7 @@ async def resume(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> SessionProcessorStatus:
     """Resumes session processor"""
-    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}")
+    return ApiDependencies.invoker.services.session_processor.resume()
 
 
 @session_queue_router.put(
@@ -143,10 +132,7 @@ async def Pause(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> SessionProcessorStatus:
     """Pauses session processor"""
-    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}")
+    return ApiDependencies.invoker.services.session_processor.pause()
 
 
 @session_queue_router.put(
@@ -158,10 +144,7 @@ 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"""
-    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}")
+    return ApiDependencies.invoker.services.session_queue.cancel_all_except_current(queue_id=queue_id)
 
 
 @session_queue_router.put(
@@ -173,10 +156,7 @@ 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"""
-    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}")
+    return ApiDependencies.invoker.services.session_queue.delete_all_except_current(queue_id=queue_id)
 
 
 @session_queue_router.put(
@@ -189,12 +169,7 @@ 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"""
-    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}")
+    return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(queue_id=queue_id, batch_ids=batch_ids)
 
 
 @session_queue_router.put(
@@ -207,12 +182,9 @@ 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"""
-    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}")
+    return ApiDependencies.invoker.services.session_queue.cancel_by_destination(
+        queue_id=queue_id, destination=destination
+    )
 
 
 @session_queue_router.put(
@@ -225,10 +197,7 @@ 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"""
-    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}")
+    return ApiDependencies.invoker.services.session_queue.retry_items_by_id(queue_id=queue_id, item_ids=item_ids)
 
 
 @session_queue_router.put(
@@ -242,14 +211,11 @@ 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"""
-    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}")
+    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
 
 
 @session_queue_router.put(
@@ -263,10 +229,7 @@ async def prune(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> PruneResult:
     """Prunes all completed or errored queue items"""
-    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}")
+    return ApiDependencies.invoker.services.session_queue.prune(queue_id)
 
 
 @session_queue_router.get(
@@ -280,10 +243,7 @@ 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"""
-    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}")
+    return ApiDependencies.invoker.services.session_queue.get_current(queue_id)
 
 
 @session_queue_router.get(
@@ -297,10 +257,7 @@ 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"""
-    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}")
+    return ApiDependencies.invoker.services.session_queue.get_next(queue_id)
 
 
 @session_queue_router.get(
@@ -314,12 +271,9 @@ 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"""
-    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}")
+    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)
 
 
 @session_queue_router.get(
@@ -334,10 +288,7 @@ 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"""
-    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}")
+    return ApiDependencies.invoker.services.session_queue.get_batch_status(queue_id=queue_id, batch_id=batch_id)
 
 
 @session_queue_router.get(
@@ -353,12 +304,7 @@ async def get_queue_item(
     item_id: int = Path(description="The queue item to get"),
 ) -> SessionQueueItem:
     """Gets a queue item"""
-    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}")
+    return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
 
 
 @session_queue_router.delete(
@@ -370,10 +316,7 @@ async def delete_queue_item(
     item_id: int = Path(description="The queue item to delete"),
 ) -> None:
     """Deletes a queue item"""
-    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}")
+    ApiDependencies.invoker.services.session_queue.delete_queue_item(item_id)
 
 
 @session_queue_router.put(
@@ -388,12 +331,8 @@ async def cancel_queue_item(
     item_id: int = Path(description="The queue item to cancel"),
 ) -> SessionQueueItem:
     """Deletes a queue item"""
-    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}")
+
+    return ApiDependencies.invoker.services.session_queue.cancel_queue_item(item_id)
 
 
 @session_queue_router.get(
@@ -406,12 +345,9 @@ async def counts_by_destination(
     destination: str = Query(description="The destination to query"),
 ) -> SessionQueueCountsByDestination:
     """Gets the counts of queue items by 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}")
+    return ApiDependencies.invoker.services.session_queue.get_counts_by_destination(
+        queue_id=queue_id, destination=destination
+    )
 
 
 @session_queue_router.delete(
@@ -424,9 +360,6 @@ async def delete_by_destination(
     destination: str = Path(description="The destination to query"),
 ) -> DeleteByDestinationResult:
     """Deletes all items with the given 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}")
+    return ApiDependencies.invoker.services.session_queue.delete_by_destination(
+        queue_id=queue_id, destination=destination
+    )

invokeai/app/api_app.py

@@ -158,7 +158,7 @@ web_root_path = Path(list(web_dir.__path__)[0])
 try:
     app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
 except RuntimeError:
-    logger.warning(f"No UI found at {web_root_path}/dist, skipping UI mount")
+    logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
 app.mount(
     "/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
 )  # docs favicon is in here

invokeai/app/invocations/baseinvocation.py

@@ -499,7 +499,7 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
 
         ui_type = field.json_schema_extra.get("ui_type", None)
         if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
-            logger.warning(f'"UIType.{ui_type.split("_")[-1]}" is deprecated, ignoring')
+            logger.warn(f'"UIType.{ui_type.split("_")[-1]}" is deprecated, ignoring')
             field.json_schema_extra.pop("ui_type")
     return None
 
@@ -582,8 +582,6 @@ def invocation(
 
         fields: dict[str, tuple[Any, FieldInfo]] = {}
 
-        original_model_fields: dict[str, OriginalModelField] = {}
-
         for field_name, field_info in cls.model_fields.items():
             annotation = field_info.annotation
             assert annotation is not None, f"{field_name} on invocation {invocation_type} has no type annotation."
@@ -591,7 +589,7 @@ def invocation(
                 f"{field_name} on invocation {invocation_type} has a non-dict json_schema_extra, did you forget to use InputField?"
             )
 
-            original_model_fields[field_name] = OriginalModelField(annotation=annotation, field_info=field_info)
+            cls._original_model_fields[field_name] = OriginalModelField(annotation=annotation, field_info=field_info)
 
             validate_field_default(cls.__name__, field_name, invocation_type, annotation, field_info)
 
@@ -615,7 +613,7 @@ def invocation(
                 raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
             uiconfig["version"] = version
         else:
-            logger.warning(f'No version specified for node "{invocation_type}", using "1.0.0"')
+            logger.warn(f'No version specified for node "{invocation_type}", using "1.0.0"')
             uiconfig["version"] = "1.0.0"
 
         cls.UIConfig = UIConfigBase(**uiconfig)
@@ -678,7 +676,6 @@ def invocation(
         docstring = cls.__doc__
         new_class = create_model(cls.__qualname__, __base__=cls, __module__=cls.__module__, **fields)  # type: ignore
         new_class.__doc__ = docstring
-        new_class._original_model_fields = original_model_fields
 
         InvocationRegistry.register_invocation(new_class)
 

invokeai/app/invocations/compel.py

@@ -114,13 +114,6 @@ class CompelInvocation(BaseInvocation):
 
             c, _options = compel.build_conditioning_tensor_for_conjunction(conjunction)
 
-        del compel
-        del patched_tokenizer
-        del tokenizer
-        del ti_manager
-        del text_encoder
-        del text_encoder_info
-
         c = c.detach().to("cpu")
 
         conditioning_data = ConditioningFieldData(conditionings=[BasicConditioningInfo(embeds=c)])
@@ -229,10 +222,7 @@ class SDXLPromptInvocationBase:
             else:
                 c_pooled = None
 
-        del compel
-        del patched_tokenizer
         del tokenizer
-        del ti_manager
         del text_encoder
         del text_encoder_info
 

invokeai/app/invocations/fields.py

@@ -64,7 +64,6 @@ class UIType(str, Enum, metaclass=MetaEnum):
     Imagen3Model = "Imagen3ModelField"
     Imagen4Model = "Imagen4ModelField"
     ChatGPT4oModel = "ChatGPT4oModelField"
-    FluxKontextModel = "FluxKontextModelField"
     # endregion
 
     # region Misc Field Types
@@ -215,7 +214,6 @@ 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):
@@ -292,12 +290,6 @@ 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"""
 
@@ -445,7 +437,7 @@ class WithWorkflow:
     workflow = None
 
     def __init_subclass__(cls) -> None:
-        logger.warning(
+        logger.warn(
             f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
         )
         super().__init_subclass__()
@@ -586,7 +578,7 @@ def InputField(
 
     if default_factory is not _Unset and default_factory is not None:
         default = default_factory()
-        logger.warning('"default_factory" is not supported, calling it now to set "default"')
+        logger.warn('"default_factory" is not supported, calling it now to set "default"')
 
     # These are the args we may wish pass to the pydantic `Field()` function
     field_args = {

invokeai/app/invocations/flux_denoise.py

@@ -16,12 +16,13 @@ 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
@@ -33,7 +34,6 @@ 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="4.0.0",
+    version="3.3.0",
 )
-class FluxDenoiseInvocation(BaseInvocation):
+class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Run denoising process with a FLUX transformer model."""
 
     # If latents is provided, this means we are doing image-to-image.
@@ -145,20 +145,11 @@ class FluxDenoiseInvocation(BaseInvocation):
         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)
@@ -385,27 +376,6 @@ class FluxDenoiseInvocation(BaseInvocation):
                 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,
@@ -421,8 +391,6 @@ class FluxDenoiseInvocation(BaseInvocation):
                 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)
@@ -897,10 +865,7 @@ class FluxDenoiseInvocation(BaseInvocation):
 
     def _build_step_callback(self, context: InvocationContext) -> Callable[[PipelineIntermediateState], None]:
         def step_callback(state: PipelineIntermediateState) -> None:
-            # 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()
+            state.latents = unpack(state.latents.float(), self.height, self.width).squeeze()
             context.util.flux_step_callback(state)
 
         return step_callback

invokeai/app/invocations/flux_kontext.py

@@ -1,40 +0,0 @@
-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, Union
+from typing import Iterator, Literal, Optional, Tuple
 
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer, T5TokenizerFast
@@ -111,9 +111,6 @@ 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)
 
@@ -154,9 +151,6 @@ 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)
 
@@ -176,88 +170,3 @@ 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/config/config_default.py

@@ -24,6 +24,7 @@ from invokeai.frontend.cli.arg_parser import InvokeAIArgs
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
 LEGACY_INIT_FILE = Path("invokeai.init")
+DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
 PRECISION = Literal["auto", "float16", "bfloat16", "float32"]
 ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
 ATTENTION_SLICE_SIZE = Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8]
@@ -92,7 +93,7 @@ class InvokeAIAppConfig(BaseSettings):
         vram: DEPRECATED: This setting is no longer used. It has been replaced by `max_cache_vram_gb`, but most users will not need to use this config since automatic cache size limits should work well in most cases. This config setting will be removed once the new model cache behavior is stable.
         lazy_offload: DEPRECATED: This setting is no longer used. Lazy-offloading is enabled by default. This config setting will be removed once the new model cache behavior is stable.
         pytorch_cuda_alloc_conf: Configure the Torch CUDA memory allocator. This will impact peak reserved VRAM usage and performance. Setting to "backend:cudaMallocAsync" works well on many systems. The optimal configuration is highly dependent on the system configuration (device type, VRAM, CUDA driver version, etc.), so must be tuned experimentally.
-        device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `mps`, `cuda:N` (where N is a device number)
+        device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
         precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`
         sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
         attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
@@ -175,7 +176,7 @@ class InvokeAIAppConfig(BaseSettings):
     pytorch_cuda_alloc_conf: Optional[str] = Field(default=None,            description="Configure the Torch CUDA memory allocator. This will impact peak reserved VRAM usage and performance. Setting to \"backend:cudaMallocAsync\" works well on many systems. The optimal configuration is highly dependent on the system configuration (device type, VRAM, CUDA driver version, etc.), so must be tuned experimentally.")
 
     # DEVICE
-    device:                      str = Field(default="auto",                description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `mps`, `cuda:N` (where N is a device number)", pattern=r"^(auto|cpu|mps|cuda(:\d+)?)$")
+    device:                      DEVICE = Field(default="auto",             description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.")
     precision:                PRECISION = Field(default="auto",             description="Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.")
 
     # GENERATION

invokeai/app/services/image_records/image_records_base.py

@@ -5,7 +5,6 @@ from typing import Optional
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
-    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ResourceOrigin,
@@ -98,17 +97,3 @@ class ImageRecordStorageBase(ABC):
     def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
         """Gets the most recent image for a board."""
         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,
-    ) -> ImageNamesResult:
-        """Gets ordered list of image names with metadata for optimistic updates."""
-        pass

invokeai/app/services/image_records/image_records_common.py

@@ -3,7 +3,7 @@ import datetime
 from enum import Enum
 from typing import Optional, Union
 
-from pydantic import BaseModel, Field, StrictBool, StrictStr
+from pydantic import Field, StrictBool, StrictStr
 
 from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import get_iso_timestamp
@@ -207,16 +207,3 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
         starred=starred,
         has_workflow=has_workflow,
     )
-
-
-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

@@ -7,7 +7,6 @@ from invokeai.app.services.image_records.image_records_base import ImageRecordSt
 from invokeai.app.services.image_records.image_records_common import (
     IMAGE_DTO_COLS,
     ImageCategory,
-    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ImageRecordDeleteException,
@@ -387,96 +386,3 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             return None
 
         return deserialize_image_record(dict(result))
-
-    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,
-    ) -> ImageNamesResult:
-        cursor = self._conn.cursor()
-
-        # Build query conditions (reused for both starred count and image names queries)
-        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 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])
-
-        # 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 ImageNamesResult(image_names=image_names, starred_count=starred_count, total_count=len(image_names))

invokeai/app/services/images/images_base.py

@@ -6,7 +6,6 @@ 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,
-    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ResourceOrigin,
@@ -126,7 +125,7 @@ class ImageServiceABC(ABC):
         board_id: Optional[str] = None,
         search_term: Optional[str] = None,
     ) -> OffsetPaginatedResults[ImageDTO]:
-        """Gets a paginated list of image DTOs with starred images first when starred_first=True."""
+        """Gets a paginated list of image DTOs."""
         pass
 
     @abstractmethod
@@ -148,17 +147,3 @@ class ImageServiceABC(ABC):
     def delete_images_on_board(self, board_id: str):
         """Deletes all images on a board."""
         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,
-    ) -> ImageNamesResult:
-        """Gets ordered list of image names with metadata for optimistic updates."""
-        pass

invokeai/app/services/images/images_common.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import Field
 
 from invokeai.app.services.image_records.image_records_common import ImageRecord
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
@@ -39,27 +39,3 @@ def image_record_to_dto(
         thumbnail_url=thumbnail_url,
         board_id=board_id,
     )
-
-
-class ResultWithAffectedBoards(BaseModel):
-    affected_boards: list[str] = Field(description="The ids of boards affected by the delete operation")
-
-
-class DeleteImagesResult(ResultWithAffectedBoards):
-    deleted_images: list[str] = Field(description="The names of the images that were deleted")
-
-
-class StarredImagesResult(ResultWithAffectedBoards):
-    starred_images: list[str] = Field(description="The names of the images that were starred")
-
-
-class UnstarredImagesResult(ResultWithAffectedBoards):
-    unstarred_images: list[str] = Field(description="The names of the images that were unstarred")
-
-
-class AddImagesToBoardResult(ResultWithAffectedBoards):
-    added_images: list[str] = Field(description="The image names that were added to the board")
-
-
-class RemoveImagesFromBoardResult(ResultWithAffectedBoards):
-    removed_images: list[str] = Field(description="The image names that were removed from their board")

invokeai/app/services/images/images_default.py

@@ -10,7 +10,6 @@ from invokeai.app.services.image_files.image_files_common import (
 )
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
-    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ImageRecordDeleteException,
@@ -79,7 +78,7 @@ class ImageService(ImageServiceABC):
                         board_id=board_id, image_name=image_name
                     )
                 except Exception as e:
-                    self.__invoker.services.logger.warning(f"Failed to add image to board {board_id}: {str(e)}")
+                    self.__invoker.services.logger.warn(f"Failed to add image to board {board_id}: {str(e)}")
             self.__invoker.services.image_files.save(
                 image_name=image_name, image=image, metadata=metadata, workflow=workflow, graph=graph
             )
@@ -310,27 +309,3 @@ class ImageService(ImageServiceABC):
         except Exception as e:
             self.__invoker.services.logger.error("Problem getting intermediates count")
             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,
-    ) -> 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,
-                is_intermediate=is_intermediate,
-                board_id=board_id,
-                search_term=search_term,
-            )
-        except Exception as e:
-            self.__invoker.services.logger.error("Problem getting image names")
-            raise e

invokeai/app/services/model_install/model_install_default.py

@@ -148,7 +148,7 @@ class ModelInstallService(ModelInstallServiceBase):
     def _clear_pending_jobs(self) -> None:
         for job in self.list_jobs():
             if not job.in_terminal_state:
-                self._logger.warning(f"Cancelling job {job.id}")
+                self._logger.warning("Cancelling job {job.id}")
                 self.cancel_job(job)
         while True:
             try:

invokeai/app/services/session_processor/session_processor_default.py

@@ -1,4 +1,3 @@
-import gc
 import traceback
 from contextlib import suppress
 from threading import BoundedSemaphore, Thread
@@ -440,12 +439,6 @@ class DefaultSessionProcessor(SessionProcessorBase):
                         poll_now_event.wait(self._polling_interval)
                         continue
 
-                    # GC-ing here can reduce peak memory usage of the invoke process by freeing allocated memory blocks.
-                    # Most queue items take seconds to execute, so the relative cost of a GC is very small.
-                    # Python will never cede allocated memory back to the OS, so anything we can do to reduce the peak
-                    # allocation is well worth it.
-                    gc.collect()
-
                     self._invoker.services.logger.info(
                         f"Executing queue item {self._queue_item.item_id}, session {self._queue_item.session_id}"
                     )

invokeai/app/services/session_queue/session_queue_common.py

@@ -205,7 +205,6 @@ 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):
@@ -332,7 +331,6 @@ 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

@@ -101,7 +101,11 @@ class SqliteSessionQueue(SessionQueueBase):
         return cast(Union[int, None], cursor.fetchone()[0]) or 0
 
     async def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
+        return await asyncio.to_thread(self._enqueue_batch, queue_id, batch, prepend)
+
+    def _enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
         try:
+            cursor = self._conn.cursor()
             # TODO: how does this work in a multi-user scenario?
             current_queue_size = self._get_current_queue_size(queue_id)
             max_queue_size = self.__invoker.services.configuration.max_queue_size
@@ -111,12 +115,8 @@ class SqliteSessionQueue(SessionQueueBase):
             if prepend:
                 priority = self._get_highest_priority(queue_id) + 1
 
-            requested_count = await asyncio.to_thread(
-                calc_session_count,
-                batch=batch,
-            )
-            values_to_insert = await asyncio.to_thread(
-                prepare_values_to_insert,
+            requested_count = calc_session_count(batch)
+            values_to_insert = prepare_values_to_insert(
                 queue_id=queue_id,
                 batch=batch,
                 priority=priority,
@@ -124,28 +124,19 @@ class SqliteSessionQueue(SessionQueueBase):
             )
             enqueued_count = len(values_to_insert)
 
-            with self._conn:
-                cursor = self._conn.cursor()
-                cursor.executemany(
-                    """--sql
-                    INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin, destination, retried_from_item_id)
-                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
-                    """,
-                    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()]
+            if requested_count > enqueued_count:
+                values_to_insert = values_to_insert[:max_new_queue_items]
+
+            cursor.executemany(
+                """--sql
+                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin, destination, retried_from_item_id)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                """,
+                values_to_insert,
+            )
+            self._conn.commit()
         except Exception:
+            self._conn.rollback()
             raise
         enqueue_result = EnqueueBatchResult(
             queue_id=queue_id,
@@ -153,7 +144,6 @@ 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
@@ -404,8 +394,6 @@ class SqliteSessionQueue(SessionQueueBase):
                   AND status != 'canceled'
                   AND status != 'completed'
                   AND status != 'failed'
-                  -- We will cancel the current item separately below - skip it here
-                  AND status != 'in_progress'
                 """
             params = [queue_id] + batch_ids
             cursor.execute(
@@ -444,8 +432,6 @@ class SqliteSessionQueue(SessionQueueBase):
                   AND status != 'canceled'
                   AND status != 'completed'
                   AND status != 'failed'
-                  -- We will cancel the current item separately below - skip it here
-                  AND status != 'in_progress'
                 """
             params = (queue_id, destination)
             cursor.execute(
@@ -548,8 +534,6 @@ class SqliteSessionQueue(SessionQueueBase):
                   AND status != 'canceled'
                   AND status != 'completed'
                   AND status != 'failed'
-                  -- We will cancel the current item separately below - skip it here
-                  AND status != 'in_progress'
                 """
             params = [queue_id]
             cursor.execute(
@@ -570,9 +554,12 @@ class SqliteSessionQueue(SessionQueueBase):
                 tuple(params),
             )
             self._conn.commit()
-
             if current_queue_item is not None and current_queue_item.queue_id == queue_id:
-                self._set_queue_item_status(current_queue_item.item_id, "canceled")
+                batch_status = self.get_batch_status(queue_id=queue_id, batch_id=current_queue_item.batch_id)
+                queue_status = self.get_queue_status(queue_id=queue_id)
+                self.__invoker.services.events.emit_queue_item_status_changed(
+                    current_queue_item, batch_status, queue_status
+                )
         except Exception:
             self._conn.rollback()
             raise
@@ -743,7 +730,7 @@ class SqliteSessionQueue(SessionQueueBase):
         counts_result = cast(list[sqlite3.Row], cursor.fetchall())
 
         current_item = self.get_current(queue_id=queue_id)
-        total = sum(row[1] or 0 for row in counts_result)
+        total = sum(row[1] for row in counts_result)
         counts: dict[str, int] = {row[0]: row[1] for row in counts_result}
         return SessionQueueStatus(
             queue_id=queue_id,
@@ -772,7 +759,7 @@ class SqliteSessionQueue(SessionQueueBase):
             (queue_id, batch_id),
         )
         result = cast(list[sqlite3.Row], cursor.fetchall())
-        total = sum(row[1] or 0 for row in result)
+        total = sum(row[1] 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
@@ -804,7 +791,7 @@ class SqliteSessionQueue(SessionQueueBase):
         )
         counts_result = cast(list[sqlite3.Row], cursor.fetchall())
 
-        total = sum(row[1] or 0 for row in counts_result)
+        total = sum(row[1] 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
+from typing import Any, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
 
 import networkx as nx
 from pydantic import (
@@ -58,32 +58,17 @@ class Edge(BaseModel):
 
 
 def get_output_field_type(node: BaseInvocation, field: str) -> Any:
-    # 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
+    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
 
 
 def get_input_field_type(node: BaseInvocation, field: str) -> Any:
-    # 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
+    node_type = type(node)
+    node_inputs = get_type_hints(node_type)
+    node_input_field = node_inputs.get(field) or None
+    return node_input_field
 
 
 def is_union_subtype(t1, t2):
@@ -1007,11 +992,10 @@ 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 = []
-            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])
-
+            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)))
             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

@@ -123,11 +123,7 @@ def calc_percentage(intermediate_state: PipelineIntermediateState) -> float:
     if total_steps == 0:
         return 0.0
     if order == 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
+        return floor(step / 2) / floor(total_steps / 2)
     # order == 1
     return step / total_steps
 

invokeai/backend/flux/denoise.py

@@ -30,11 +30,8 @@ def denoise(
     controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension],
     pos_ip_adapter_extensions: list[XLabsIPAdapterExtension],
     neg_ip_adapter_extensions: list[XLabsIPAdapterExtension],
-    # extra img tokens (channel-wise)
+    # extra img tokens
     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
@@ -49,10 +46,6 @@ 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)
 
@@ -78,26 +71,10 @@ 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)
-
-        # 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_img = torch.cat((img, img_cond), dim=-1) if img_cond is not None else img
         pred = model(
-            img=img_input,
-            img_ids=img_input_ids,
+            img=pred_img,
+            img_ids=img_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,
@@ -111,10 +88,6 @@ 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

@@ -1,149 +0,0 @@
-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

@@ -174,13 +174,11 @@ 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 = img_ids.to(orig_dtype)
+        img_ids.to(orig_dtype)
 
     return img_ids

invokeai/backend/flux/util.py

@@ -18,29 +18,6 @@ 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

@@ -37,7 +37,6 @@ 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,
@@ -297,7 +296,7 @@ class LoRAConfigBase(ABC, BaseModel):
         from invokeai.backend.patches.lora_conversions.formats import flux_format_from_state_dict
 
         sd = mod.load_state_dict(mod.path)
-        value = flux_format_from_state_dict(sd, mod.metadata())
+        value = flux_format_from_state_dict(sd)
         mod.cache[key] = value
         return value
 
@@ -335,36 +334,6 @@ 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."""
 
@@ -381,7 +350,7 @@ class LoRALyCORISConfig(LoRAConfigBase, ModelConfigBase):
 
         state_dict = mod.load_state_dict()
         for key in state_dict.keys():
-            if isinstance(key, int):
+            if type(key) is int:
                 continue
 
             if key.startswith(("lora_te_", "lora_unet_", "lora_te1_", "lora_te2_", "lora_transformer_")):
@@ -699,7 +668,6 @@ 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

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

@@ -13,7 +13,6 @@ 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,
@@ -21,10 +20,6 @@ from invokeai.backend.model_manager.taxonomy import (
     ModelType,
     SubModelType,
 )
-from invokeai.backend.patches.lora_conversions.flux_aitoolkit_lora_conversion_utils import (
-    is_state_dict_likely_in_flux_aitoolkit_format,
-    lora_model_from_flux_aitoolkit_state_dict,
-)
 from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import (
     is_state_dict_likely_flux_control,
     lora_model_from_flux_control_state_dict,
@@ -44,8 +39,6 @@ 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)
@@ -80,23 +73,12 @@ 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 in [ModelFormat.Diffusers, ModelFormat.OMI]:
+            if config.format == ModelFormat.Diffusers:
                 # 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
@@ -110,10 +92,8 @@ class LoRALoader(ModelLoader):
                     model = lora_model_from_flux_onetrainer_state_dict(state_dict=state_dict)
                 elif is_state_dict_likely_flux_control(state_dict=state_dict):
                     model = lora_model_from_flux_control_state_dict(state_dict=state_dict)
-                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("LoRA model is in unsupported FLUX format")
+                    raise ValueError(f"LoRA model is in unsupported FLUX format: {config.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

@@ -1,7 +0,0 @@
-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

@@ -1,21 +0,0 @@
-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

@@ -1,20 +0,0 @@
-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

@@ -1,84 +0,0 @@
-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

@@ -1,217 +0,0 @@
-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

@@ -1,125 +0,0 @@
-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

@@ -1,19 +0,0 @@
-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

@@ -1,31 +0,0 @@
-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 StarterModelBundle(BaseModel):
+class StarterModelBundles(BaseModel):
     name: str
     models: list[StarterModel]
 
@@ -109,7 +109,7 @@ flux_vae = StarterModel(
 
 # region: Main
 flux_schnell_quantized = StarterModel(
-    name="FLUX.1 schnell (quantized)",
+    name="FLUX 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.1 dev (quantized)",
+    name="FLUX 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.1 schnell",
+    name="FLUX Schnell",
     base=BaseModelType.Flux,
     source="InvokeAI/flux_schnell::transformer/base/flux1-schnell.safetensors",
     description="FLUX schnell transformer in bfloat16. Total size with dependencies: ~33GB",
@@ -133,29 +133,13 @@ flux_schnell = StarterModel(
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
 flux_dev = StarterModel(
-    name="FLUX.1 dev",
+    name="FLUX 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="https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev/resolve/main/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],
-)
-flux_kontext_quantized = StarterModel(
-    name="FLUX.1 Kontext dev (Quantized)",
-    base=BaseModelType.Flux,
-    source="https://huggingface.co/unsloth/FLUX.1-Kontext-dev-GGUF/resolve/main/flux1-kontext-dev-Q4_K_M.gguf",
-    description="FLUX.1 Kontext dev quantized (q4_k_m). Total size with dependencies: ~14GB",
-    type=ModelType.Main,
-    dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
-)
 sd35_medium = StarterModel(
     name="SD3.5 Medium",
     base=BaseModelType.StableDiffusion3,
@@ -672,7 +656,6 @@ 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_quantized,
     flux_schnell_quantized,
     flux_dev_quantized,
     flux_schnell,
@@ -793,13 +776,12 @@ flux_bundle: list[StarterModel] = [
     flux_depth_control_lora,
     flux_redux,
     flux_fill,
-    flux_kontext_quantized,
 ]
 
-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),
+STARTER_BUNDLES: dict[str, list[StarterModel]] = {
+    BaseModelType.StableDiffusion1: sd1_bundle,
+    BaseModelType.StableDiffusionXL: sdxl_bundle,
+    BaseModelType.Flux: flux_bundle,
 }
 
 assert len(STARTER_MODELS) == len({m.source for m in STARTER_MODELS}), "Duplicate starter models"

invokeai/backend/model_manager/taxonomy.py

@@ -29,7 +29,6 @@ class BaseModelType(str, Enum):
     Imagen3 = "imagen3"
     Imagen4 = "imagen4"
     ChatGPT4o = "chatgpt-4o"
-    FluxKontext = "flux-kontext"
 
 
 class ModelType(str, Enum):
@@ -89,7 +88,6 @@ class ModelVariantType(str, Enum):
 class ModelFormat(str, Enum):
     """Storage format of model."""
 
-    OMI = "omi"
     Diffusers = "diffusers"
     Checkpoint = "checkpoint"
     LyCORIS = "lycoris"
@@ -139,7 +137,6 @@ class FluxLoRAFormat(str, Enum):
     Kohya = "flux.kohya"
     OneTrainer = "flux.onetrainer"
     Control = "flux.control"
-    AIToolkit = "flux.aitoolkit"
 
 
 AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]

invokeai/backend/model_patcher.py

@@ -46,10 +46,6 @@ class ModelPatcher:
         text_encoder: Union[CLIPTextModel, CLIPTextModelWithProjection],
         ti_list: List[Tuple[str, TextualInversionModelRaw]],
     ) -> Iterator[Tuple[CLIPTokenizer, TextualInversionManager]]:
-        if len(ti_list) == 0:
-            yield tokenizer, TextualInversionManager(tokenizer)
-            return
-
         init_tokens_count = None
         new_tokens_added = None
 

invokeai/backend/patches/lora_conversions/flux_aitoolkit_lora_conversion_utils.py

@@ -1,63 +0,0 @@
-import json
-from dataclasses import dataclass, field
-from typing import Any
-
-import torch
-
-from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
-from invokeai.backend.patches.layers.utils import any_lora_layer_from_state_dict
-from invokeai.backend.patches.lora_conversions.flux_diffusers_lora_conversion_utils import _group_by_layer
-from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
-from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
-from invokeai.backend.util import InvokeAILogger
-
-
-def is_state_dict_likely_in_flux_aitoolkit_format(state_dict: dict[str, Any], metadata: dict[str, Any] = None) -> bool:
-    if metadata:
-        try:
-            software = json.loads(metadata.get("software", "{}"))
-        except json.JSONDecodeError:
-            return False
-        return software.get("name") == "ai-toolkit"
-    # metadata got lost somewhere
-    return any("diffusion_model" == k.split(".", 1)[0] for k in state_dict.keys())
-
-
-@dataclass
-class GroupedStateDict:
-    transformer: dict[str, Any] = field(default_factory=dict)
-    # might also grow CLIP and T5 submodels
-
-
-def _group_state_by_submodel(state_dict: dict[str, Any]) -> GroupedStateDict:
-    logger = InvokeAILogger.get_logger()
-    grouped = GroupedStateDict()
-    for key, value in state_dict.items():
-        submodel_name, param_name = key.split(".", 1)
-        match submodel_name:
-            case "diffusion_model":
-                grouped.transformer[param_name] = value
-            case _:
-                logger.warning(f"Unexpected submodel name: {submodel_name}")
-    return grouped
-
-
-def _rename_peft_lora_keys(state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
-    """Renames keys from the PEFT LoRA format to the InvokeAI format."""
-    renamed_state_dict = {}
-    for key, value in state_dict.items():
-        renamed_key = key.replace(".lora_A.", ".lora_down.").replace(".lora_B.", ".lora_up.")
-        renamed_state_dict[renamed_key] = value
-    return renamed_state_dict
-
-
-def lora_model_from_flux_aitoolkit_state_dict(state_dict: dict[str, torch.Tensor]) -> ModelPatchRaw:
-    state_dict = _rename_peft_lora_keys(state_dict)
-    by_layer = _group_by_layer(state_dict)
-    by_model = _group_state_by_submodel(by_layer)
-
-    layers: dict[str, BaseLayerPatch] = {}
-    for layer_key, layer_state_dict in by_model.transformer.items():
-        layers[FLUX_LORA_TRANSFORMER_PREFIX + layer_key] = any_lora_layer_from_state_dict(layer_state_dict)
-
-    return ModelPatchRaw(layers=layers)

invokeai/backend/patches/lora_conversions/formats.py

@@ -1,7 +1,4 @@
 from invokeai.backend.model_manager.taxonomy import FluxLoRAFormat
-from invokeai.backend.patches.lora_conversions.flux_aitoolkit_lora_conversion_utils import (
-    is_state_dict_likely_in_flux_aitoolkit_format,
-)
 from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import is_state_dict_likely_flux_control
 from invokeai.backend.patches.lora_conversions.flux_diffusers_lora_conversion_utils import (
     is_state_dict_likely_in_flux_diffusers_format,
@@ -14,7 +11,7 @@ from invokeai.backend.patches.lora_conversions.flux_onetrainer_lora_conversion_u
 )
 
 
-def flux_format_from_state_dict(state_dict: dict, metadata: dict | None = None) -> FluxLoRAFormat | None:
+def flux_format_from_state_dict(state_dict):
     if is_state_dict_likely_in_flux_kohya_format(state_dict):
         return FluxLoRAFormat.Kohya
     elif is_state_dict_likely_in_flux_onetrainer_format(state_dict):
@@ -23,7 +20,5 @@ def flux_format_from_state_dict(state_dict: dict, metadata: dict | None = None)
         return FluxLoRAFormat.Diffusers
     elif is_state_dict_likely_flux_control(state_dict):
         return FluxLoRAFormat.Control
-    elif is_state_dict_likely_in_flux_aitoolkit_format(state_dict, metadata):
-        return FluxLoRAFormat.AIToolkit
     else:
         return None

invokeai/frontend/web/.eslintrc.js

@@ -12,22 +12,11 @@ module.exports = {
     // TODO: ENABLE THIS RULE BEFORE v6.0.0
     // 'i18next/no-literal-string': 'error',
     // https://eslint.org/docs/latest/rules/no-console
-    'no-console': 'warn',
+    'no-console': 'error',
     // https://eslint.org/docs/latest/rules/no-promise-executor-return
     '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': [
       'error',
       {
@@ -42,38 +31,8 @@ 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

@@ -3,6 +3,8 @@ 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
@@ -12,8 +14,10 @@ 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',
-    // Will be using this
-    'src/common/hooks/useAsyncState.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

@@ -38,60 +38,70 @@
     "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": "^1.7.4",
-    "@atlaskit/pragmatic-drag-and-drop-auto-scroll": "^2.1.1",
-    "@atlaskit/pragmatic-drag-and-drop-hitbox": "^1.1.0",
-    "@dagrejs/dagre": "^1.1.5",
+    "@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",
     "@dagrejs/graphlib": "^2.2.4",
-    "@fontsource-variable/inter": "^5.2.6",
+    "@fontsource-variable/inter": "^5.2.5",
     "@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": "^12.7.1",
-    "ag-psd": "^28.2.1",
+    "@xyflow/react": "^12.6.0",
     "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.2",
     "cmdk": "^1.1.1",
     "compare-versions": "^6.1.1",
-    "dockview": "^4.4.0",
-    "es-toolkit": "^1.39.5",
     "filesize": "^10.1.6",
-    "fracturedjsonjs": "^4.1.0",
+    "fracturedjsonjs": "^4.0.2",
     "framer-motion": "^11.10.0",
-    "i18next": "^25.2.1",
+    "i18next": "^25.0.1",
     "i18next-http-backend": "^3.0.2",
-    "idb-keyval": "^6.2.2",
+    "idb-keyval": "^6.2.1",
     "jsondiffpatch": "^0.7.3",
     "konva": "^9.3.20",
-    "linkify-react": "^4.3.1",
-    "linkifyjs": "^4.3.1",
+    "linkify-react": "^4.2.0",
+    "linkifyjs": "^4.2.0",
+    "lodash-es": "^4.17.21",
     "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": "^2.11.4",
+    "overlayscrollbars": "^2.11.1",
     "overlayscrollbars-react": "^0.5.6",
     "perfect-freehand": "^1.2.2",
-    "query-string": "^9.2.1",
+    "query-string": "^9.1.1",
     "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": "^7.58.1",
+    "react-hook-form": "^7.56.1",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^15.5.3",
+    "react-i18next": "^15.5.1",
     "react-icons": "^5.5.0",
     "react-redux": "9.2.0",
-    "react-resizable-panels": "^3.0.3",
+    "react-resizable-panels": "^2.1.8",
     "react-textarea-autosize": "^8.5.9",
     "react-use": "^17.6.0",
-    "react-virtuoso": "^4.13.0",
+    "react-virtuoso": "^4.12.6",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.2.0",
     "redux-undo": "^1.1.0",
@@ -99,12 +109,12 @@
     "roarr": "^7.21.1",
     "serialize-error": "^12.0.0",
     "socket.io-client": "^4.8.1",
-    "stable-hash": "^0.0.6",
-    "use-debounce": "^10.0.5",
+    "stable-hash": "^0.0.5",
+    "use-debounce": "^10.0.4",
     "use-device-pixel-ratio": "^1.1.2",
     "uuid": "^11.1.0",
-    "zod": "^3.25.67",
-    "zod-validation-error": "^3.5.2"
+    "zod": "^3.24.3",
+    "zod-validation-error": "^3.4.0"
   },
   "peerDependencies": {
     "react": "^18.2.0",
@@ -121,6 +131,7 @@
     "@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",
@@ -134,7 +145,7 @@
     "eslint": "^8.57.1",
     "eslint-plugin-i18next": "^6.1.1",
     "eslint-plugin-path": "^1.3.0",
-    "knip": "^5.61.3",
+    "knip": "^5.50.5",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^7.6.1",
     "prettier": "^3.5.3",
@@ -143,7 +154,7 @@
     "tsafe": "^1.8.5",
     "type-fest": "^4.40.0",
     "typescript": "^5.8.3",
-    "vite": "^7.0.2",
+    "vite": "^6.3.3",
     "vite-plugin-css-injected-by-js": "^3.5.2",
     "vite-plugin-dts": "^4.5.3",
     "vite-plugin-eslint": "^1.8.1",
@@ -151,7 +162,7 @@
     "vitest": "^3.1.2"
   },
   "engines": {
-    "pnpm": "10"
+    "pnpm": "8"
   },
-  "packageManager": "pnpm@10.12.4"
+  "packageManager": "pnpm@8.15.9+sha512.499434c9d8fdd1a2794ebf4552b3b25c0a633abcee5bb15e7b5de90f32f47b513aca98cd5cfd001c31f0db454bc3804edccd578501e4ca293a6816166bbd9f81"
 }

invokeai/frontend/web/pnpm-workspace.yaml

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

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

@@ -225,16 +225,7 @@
     "prompt": {
         "addPromptTrigger": "Add Prompt Trigger",
         "compatibleEmbeddings": "Compatible Embeddings",
-        "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"
+        "noMatchingTriggers": "No matching triggers"
     },
     "queue": {
         "queue": "Queue",
@@ -344,14 +335,14 @@
         "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": "Drop or Upload",
+        "dropOrUpload": "$t(gallery.drop) or Upload",
         "dropToUpload": "$t(gallery.drop) to Upload",
         "deleteImage_one": "Delete Image",
         "deleteImage_other": "Delete {{count}} Images",
@@ -366,7 +357,7 @@
         "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",
@@ -405,8 +396,7 @@
         "compareHelp4": "Press <Kbd>Z</Kbd> or <Kbd>Esc</Kbd> to exit.",
         "openViewer": "Open Viewer",
         "closeViewer": "Close Viewer",
-        "move": "Move",
-        "useForPromptGeneration": "Use for Prompt Generation"
+        "move": "Move"
     },
     "hotkeys": {
         "hotkeys": "Hotkeys",
@@ -589,16 +579,6 @@
             "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": {
@@ -762,7 +742,7 @@
         "vae": "VAE",
         "width": "Width",
         "workflow": "Workflow",
-        "canvasV2Metadata": "Canvas Layers"
+        "canvasV2Metadata": "Canvas"
     },
     "modelManager": {
         "active": "active",
@@ -783,7 +763,7 @@
         "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 the 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 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?",
@@ -826,11 +806,7 @@
         "urlUnauthorizedErrorMessage": "You may need to configure an API token to access this model.",
         "urlUnauthorizedErrorMessage2": "Learn how here.",
         "imageEncoderModelId": "Image Encoder Model ID",
-        "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",
+        "includesNModels": "Includes {{n}} models and their dependencies",
         "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.",
@@ -893,25 +869,6 @@
         "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",
@@ -948,8 +905,7 @@
         "selectModel": "Select a Model",
         "noLoRAsInstalled": "No LoRAs installed",
         "noRefinerModelsInstalled": "No SDXL Refiner models installed",
-        "defaultVAE": "Default VAE",
-        "noCompatibleLoRAs": "No Compatible LoRAs"
+        "defaultVAE": "Default VAE"
     },
     "nodes": {
         "arithmeticSequence": "Arithmetic Sequence",
@@ -1191,7 +1147,6 @@
             "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)",
@@ -1199,9 +1154,7 @@
             "canvasIsSelectingObject": "Canvas is busy (selecting object)",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
-            "systemDisconnected": "System disconnected",
-            "promptExpansionPending": "Prompt expansion in progress",
-            "promptExpansionResultPending": "Please accept or discard your prompt expansion result"
+            "systemDisconnected": "System disconnected"
         },
         "maskBlur": "Mask Blur",
         "negativePromptPlaceholder": "Negative Prompt",
@@ -1359,21 +1312,6 @@
         "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",
@@ -1399,15 +1337,9 @@
         "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 does not support generation from images placed on the canvas. Re-try using the Reference Image section and disable any Raster Layers.",
         "problemUnpublishingWorkflow": "Problem Unpublishing Workflow",
         "problemUnpublishingWorkflowDescription": "There was a problem unpublishing the workflow. Please try again.",
-        "workflowUnpublished": "Workflow Unpublished",
-        "sentToCanvas": "Sent to Canvas",
-        "sentToUpscale": "Sent to Upscale",
-        "promptGenerationStarted": "Prompt generation started",
-        "uploadAndPromptGenerationFailed": "Failed to upload image and generate prompt",
-        "promptExpansionFailed": "We ran into an issue. Please try prompt expansion again."
+        "workflowUnpublished": "Workflow Unpublished"
     },
     "popovers": {
         "clipSkip": {
@@ -1930,7 +1862,6 @@
         "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",
@@ -1956,13 +1887,11 @@
         "mergingLayers": "Merging layers",
         "clearHistory": "Clear History",
         "bboxOverlay": "Show Bbox Overlay",
-        "ruleOfThirds": "Show Rule of Thirds",
         "newSession": "New Session",
         "clearCaches": "Clear Caches",
         "recalculateRects": "Recalculate Rects",
         "clipToBbox": "Clip Strokes to Bbox",
         "outputOnlyMaskedRegions": "Output Only Generated Regions",
-        "saveAllImagesToGallery": "Save All Images to Gallery",
         "addLayer": "Add Layer",
         "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
@@ -2063,8 +1992,6 @@
         "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",
@@ -2331,9 +2258,6 @@
                 "label": "Preserve Masked Region",
                 "alert": "Preserving Masked Region"
             },
-            "saveAllImagesToGallery": {
-                "alert": "Saving All Images to Gallery"
-            },
             "isolatedStagingPreview": "Isolated Staging Preview",
             "isolatedPreview": "Isolated Preview",
             "isolatedLayerPreview": "Isolated Layer Preview",
@@ -2362,7 +2286,6 @@
             "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",
@@ -2380,11 +2303,6 @@
             "saveToGallery": "Save To Gallery",
             "showResultsOn": "Showing Results",
             "showResultsOff": "Hiding Results"
-        },
-        "autoSwitch": {
-            "off": "Off",
-            "switchOnStart": "On Start",
-            "switchOnFinish": "On Finish"
         }
     },
     "upscaling": {
@@ -2451,8 +2369,7 @@
         "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.",
-        "togglePromptPreviews": "Toggle Prompt Previews"
+        "viewModeTooltip": "This is how your prompt will look with your currently selected template. To edit your prompt, click anywhere in the text box."
     },
     "upsell": {
         "inviteTeammates": "Invite Teammates",
@@ -2472,55 +2389,6 @@
             "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": {
@@ -2560,9 +2428,8 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "Generate images faster with new Launchpads and a simplified Generate tab.",
-            "Edit with prompts using Flux Kontext Dev.",
-            "Export to PSD, bulk-hide overlays, organize models & images — all in a reimagined interface built for control."
+            "Inpainting: Per-mask noise levels and denoise limits.",
+            "Canvas: Smarter aspect ratios for SDXL and improved scroll-to-zoom."
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",
@@ -2571,16 +2438,62 @@
     "supportVideos": {
         "supportVideos": "Support Videos",
         "gettingStarted": "Getting Started",
+        "controlCanvas": "Control Canvas",
         "watch": "Watch",
-        "studioSessionsDesc": "Join our <DiscordLink /> to participate in the live sessions and ask questions. Sessions are uploaded to the playlist the following week.",
+        "studioSessionsDesc1": "Check out the <StudioSessionsPlaylistLink /> for Invoke deep dives.",
+        "studioSessionsDesc2": "Join our <DiscordLink /> to participate in the live sessions and ask questions. Sessions are uploaded to the playlist the following week.",
         "videos": {
-            "gettingStarted": {
-                "title": "Getting Started with Invoke",
-                "description": "Complete video series covering everything you need to know to get started with Invoke, from creating your first image to advanced techniques."
+            "creatingYourFirstImage": {
+                "title": "Creating Your First Image",
+                "description": "Introduction to creating an image from scratch using Invoke's tools."
             },
-            "studioSessions": {
-                "title": "Studio Sessions",
-                "description": "Deep dive sessions exploring advanced Invoke features, creative workflows, and community discussions."
+            "usingControlLayersAndReferenceGuides": {
+                "title": "Using Control Layers and Reference Guides",
+                "description": "Learn how to guide your image creation with control layers and reference images."
+            },
+            "understandingImageToImageAndDenoising": {
+                "title": "Understanding Image-to-Image and Denoising",
+                "description": "Overview of image-to-image transformations and denoising in Invoke."
+            },
+            "exploringAIModelsAndConceptAdapters": {
+                "title": "Exploring AI Models and Concept Adapters",
+                "description": "Dive into AI models and how to use concept adapters for creative control."
+            },
+            "creatingAndComposingOnInvokesControlCanvas": {
+                "title": "Creating and Composing on Invoke's Control Canvas",
+                "description": "Learn to compose images using Invoke's control canvas."
+            },
+            "upscaling": {
+                "title": "Upscaling",
+                "description": "How to upscale images with Invoke's tools to enhance resolution."
+            },
+            "howDoIGenerateAndSaveToTheGallery": {
+                "title": "How Do I Generate and Save to the Gallery?",
+                "description": "Steps to generate and save images to the gallery."
+            },
+            "howDoIEditOnTheCanvas": {
+                "title": "How Do I Edit on the Canvas?",
+                "description": "Guide to editing images directly on the canvas."
+            },
+            "howDoIDoImageToImageTransformation": {
+                "title": "How Do I Do Image-to-Image Transformation?",
+                "description": "Tutorial on performing image-to-image transformations in Invoke."
+            },
+            "howDoIUseControlNetsAndControlLayers": {
+                "title": "How Do I Use Control Nets and Control Layers?",
+                "description": "Learn to apply control layers and controlnets to your images."
+            },
+            "howDoIUseGlobalIPAdaptersAndReferenceImages": {
+                "title": "How Do I Use Global IP Adapters and Reference Images?",
+                "description": "Introduction to adding reference images and global IP adapters."
+            },
+            "howDoIUseInpaintMasks": {
+                "title": "How Do I Use Inpaint Masks?",
+                "description": "How to apply inpaint masks for image correction and variation."
+            },
+            "howDoIOutpaint": {
+                "title": "How Do I Outpaint?",
+                "description": "Guide to outpainting beyond the original image borders."
             }
         }
     }

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

@@ -2,7 +2,8 @@ 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 { $didStudioInit, type StudioInitAction } from 'app/hooks/useStudioInitAction';
+import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
+import { $didStudioInit } from 'app/hooks/useStudioInitAction';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import Loading from 'common/components/Loading/Loading';
 import { useClearStorage } from 'common/hooks/useClearStorage';
@@ -11,7 +12,6 @@ import { memo, useCallback } from 'react';
 import { ErrorBoundary } from 'react-error-boundary';
 
 import AppErrorBoundaryFallback from './AppErrorBoundaryFallback';
-import ThemeLocaleProvider from './ThemeLocaleProvider';
 const DEFAULT_CONFIG = {};
 
 interface Props {
@@ -31,14 +31,12 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
 
   return (
     <ErrorBoundary onReset={handleReset} FallbackComponent={AppErrorBoundaryFallback}>
-      <ThemeLocaleProvider>
-        <Box id="invoke-app-wrapper" w="100dvw" h="100dvh" position="relative" overflow="hidden">
-          <AppContent />
-          {!didStudioInit && <Loading />}
-        </Box>
-        <GlobalHookIsolator config={config} studioInitAction={studioInitAction} />
-        <GlobalModalIsolator />
-      </ThemeLocaleProvider>
+      <Box id="invoke-app-wrapper" w="100dvw" h="100dvh" position="relative" overflow="hidden">
+        <AppContent />
+        {!didStudioInit && <Loading />}
+      </Box>
+      <GlobalHookIsolator config={config} studioInitAction={studioInitAction} />
+      <GlobalModalIsolator />
     </ErrorBoundary>
   );
 };

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

@@ -1,5 +1,4 @@
 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';
@@ -11,15 +10,16 @@ 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 { toggleImageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
 import { useWorkflowBuilderWatcher } from 'features/nodes/components/sidePanel/workflow/IsolatedWorkflowBuilderWatcher';
 import { useReadinessWatcher } from 'features/queue/store/readiness';
+import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
 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';
@@ -45,8 +45,6 @@ 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.
@@ -57,18 +55,16 @@ export const GlobalHookIsolator = memo(
     }, [language]);
 
     useEffect(() => {
-      logger.info({ config }, 'Received config');
-      dispatch(configChanged(config));
+      if (size(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();
@@ -76,6 +72,12 @@ export const GlobalHookIsolator = memo(
     useWorkflowBuilderWatcher();
     useDynamicPromptsWatcher();
 
+    useRegisteredHotkeys({
+      id: 'toggleViewer',
+      category: 'viewer',
+      callback: toggleImageViewer,
+    });
+
     return null;
   }
 );

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

@@ -1,22 +1,17 @@
 import { useAppSelector } from 'app/store/storeHooks';
 import { useIsRegionFocused } from 'common/hooks/focus';
 import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
-import { useLoadWorkflow } from 'features/gallery/hooks/useLoadWorkflow';
-import { useRecallAll } from 'features/gallery/hooks/useRecallAll';
-import { useRecallDimensions } from 'features/gallery/hooks/useRecallDimensions';
-import { useRecallPrompts } from 'features/gallery/hooks/useRecallPrompts';
-import { useRecallRemix } from 'features/gallery/hooks/useRecallRemix';
-import { useRecallSeed } from 'features/gallery/hooks/useRecallSeed';
+import { selectIsStaging } from 'features/controlLayers/store/canvasStagingAreaSlice';
+import { useImageActions } from 'features/gallery/hooks/useImageActions';
 import { selectLastSelectedImage } from 'features/gallery/store/gallerySelectors';
 import { useRegisteredHotkeys } from 'features/system/components/HotkeysModal/useHotkeyData';
+import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
 import { memo } from 'react';
-import { useImageDTO } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
 
 export const GlobalImageHotkeys = memo(() => {
   useAssertSingleton('GlobalImageHotkeys');
-  const imageName = useAppSelector(selectLastSelectedImage);
-  const imageDTO = useImageDTO(imageName);
+  const imageDTO = useAppSelector(selectLastSelectedImage);
 
   if (!imageDTO) {
     return null;
@@ -30,64 +25,59 @@ GlobalImageHotkeys.displayName = 'GlobalImageHotkeys';
 const GlobalImageHotkeysInternal = memo(({ imageDTO }: { imageDTO: ImageDTO }) => {
   const isGalleryFocused = useIsRegionFocused('gallery');
   const isViewerFocused = useIsRegionFocused('viewer');
-
-  const isFocusOK = isGalleryFocused || isViewerFocused;
-
-  const recallAll = useRecallAll(imageDTO);
-  const recallRemix = useRecallRemix(imageDTO);
-  const recallPrompts = useRecallPrompts(imageDTO);
-  const recallSeed = useRecallSeed(imageDTO);
-  const recallDimensions = useRecallDimensions(imageDTO);
-  const loadWorkflow = useLoadWorkflow(imageDTO);
+  const imageActions = useImageActions(imageDTO);
+  const isStaging = useAppSelector(selectIsStaging);
+  const isUpscalingEnabled = useFeatureStatus('upscaling');
 
   useRegisteredHotkeys({
     id: 'loadWorkflow',
     category: 'viewer',
-    callback: loadWorkflow.load,
-    options: { enabled: loadWorkflow.isEnabled && isFocusOK },
-    dependencies: [loadWorkflow, isFocusOK],
+    callback: imageActions.loadWorkflow,
+    options: { enabled: isGalleryFocused || isViewerFocused },
+    dependencies: [imageActions.loadWorkflow, isGalleryFocused, isViewerFocused],
   });
-
   useRegisteredHotkeys({
     id: 'recallAll',
     category: 'viewer',
-    callback: recallAll.recall,
-    options: { enabled: recallAll.isEnabled && isFocusOK },
-    dependencies: [recallAll, isFocusOK],
+    callback: imageActions.recallAll,
+    options: { enabled: !isStaging && (isGalleryFocused || isViewerFocused) },
+    dependencies: [imageActions.recallAll, isStaging, isGalleryFocused, isViewerFocused],
   });
-
   useRegisteredHotkeys({
     id: 'recallSeed',
     category: 'viewer',
-    callback: recallSeed.recall,
-    options: { enabled: recallSeed.isEnabled && isFocusOK },
-    dependencies: [recallSeed, isFocusOK],
+    callback: imageActions.recallSeed,
+    options: { enabled: isGalleryFocused || isViewerFocused },
+    dependencies: [imageActions.recallSeed, isGalleryFocused, isViewerFocused],
   });
-
   useRegisteredHotkeys({
     id: 'recallPrompts',
     category: 'viewer',
-    callback: recallPrompts.recall,
-    options: { enabled: recallPrompts.isEnabled && isFocusOK },
-    dependencies: [recallPrompts, isFocusOK],
+    callback: imageActions.recallPrompts,
+    options: { enabled: isGalleryFocused || isViewerFocused },
+    dependencies: [imageActions.recallPrompts, isGalleryFocused, isViewerFocused],
   });
-
   useRegisteredHotkeys({
     id: 'remix',
     category: 'viewer',
-    callback: recallRemix.recall,
-    options: { enabled: recallRemix.isEnabled && isFocusOK },
-    dependencies: [recallRemix, isFocusOK],
+    callback: imageActions.remix,
+    options: { enabled: isGalleryFocused || isViewerFocused },
+    dependencies: [imageActions.remix, isGalleryFocused, isViewerFocused],
   });
-
   useRegisteredHotkeys({
     id: 'useSize',
     category: 'viewer',
-    callback: recallDimensions.recall,
-    options: { enabled: recallDimensions.isEnabled && isFocusOK },
-    dependencies: [recallDimensions, isFocusOK],
+    callback: imageActions.recallSize,
+    options: { enabled: !isStaging && (isGalleryFocused || isViewerFocused) },
+    dependencies: [imageActions.recallSize, isStaging, isGalleryFocused, isViewerFocused],
+  });
+  useRegisteredHotkeys({
+    id: 'runPostprocessing',
+    category: 'viewer',
+    callback: imageActions.upscale,
+    options: { enabled: isUpscalingEnabled && isViewerFocused },
+    dependencies: [isUpscalingEnabled, imageDTO, isViewerFocused],
   });
-
   return null;
 });
 

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

@@ -11,6 +11,7 @@ import { FullscreenDropzone } from 'features/dnd/FullscreenDropzone';
 import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';
 import DeleteBoardModal from 'features/gallery/components/Boards/DeleteBoardModal';
 import { ImageContextMenu } from 'features/gallery/components/ImageContextMenu/ImageContextMenu';
+import { ImageViewerModal } from 'features/gallery/components/ImageViewer/ImageViewer';
 import { ShareWorkflowModal } from 'features/nodes/components/sidePanel/workflow/WorkflowLibrary/ShareWorkflowModal';
 import { WorkflowLibraryModal } from 'features/nodes/components/sidePanel/workflow/WorkflowLibrary/WorkflowLibraryModal';
 import { CancelAllExceptCurrentQueueItemConfirmationAlertDialog } from 'features/queue/components/CancelAllExceptCurrentQueueItemConfirmationAlertDialog';
@@ -60,6 +61,7 @@ export const GlobalModalIsolator = memo(() => {
         <CanvasPasteModal />
       </CanvasManagerProviderGate>
       <LoadWorkflowFromGraphModal />
+      <ImageViewerModal />
     </>
   );
 });

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

@@ -42,6 +42,7 @@ import { $socketOptions } from 'services/events/stores';
 import type { ManagerOptions, SocketOptions } from 'socket.io-client';
 
 const App = lazy(() => import('./App'));
+const ThemeLocaleProvider = lazy(() => import('./ThemeLocaleProvider'));
 
 interface Props extends PropsWithChildren {
   apiUrl?: string;
@@ -329,7 +330,9 @@ const InvokeAIUI = ({
     <React.StrictMode>
       <Provider store={store}>
         <React.Suspense fallback={<Loading />}>
-          <App config={config} studioInitAction={studioInitAction} />
+          <ThemeLocaleProvider>
+            <App config={config} studioInitAction={studioInitAction} />
+          </ThemeLocaleProvider>
         </React.Suspense>
       </Provider>
     </React.StrictMode>

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

@@ -1,7 +1,6 @@
 import '@fontsource-variable/inter';
 import 'overlayscrollbars/overlayscrollbars.css';
 import '@xyflow/react/dist/base.css';
-import 'common/components/OverlayScrollbars/overlayscrollbars.css';
 
 import { ChakraProvider, DarkMode, extendTheme, theme as _theme, TOAST_OPTIONS } from '@invoke-ai/ui-library';
 import type { ReactNode } from 'react';

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

@@ -7,8 +7,9 @@ import { rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
 import { paramsReset } from 'features/controlLayers/store/paramsSlice';
 import type { CanvasRasterLayerState } from 'features/controlLayers/store/types';
 import { imageDTOToImageObject } from 'features/controlLayers/store/util';
+import { $imageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { sentImageToCanvas } from 'features/gallery/store/actions';
-import { MetadataUtils } from 'features/metadata/parsing';
+import { parseAndRecallAllMetadata } from 'features/metadata/util/handlers';
 import { $hasTemplates } from 'features/nodes/store/nodesSlice';
 import { $isWorkflowLibraryModalOpen } from 'features/nodes/store/workflowLibraryModal';
 import {
@@ -19,9 +20,7 @@ import {
 } from 'features/nodes/store/workflowLibrarySlice';
 import { $isStylePresetsMenuOpen, activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import { toast } from 'features/toast/toast';
-import { navigationApi } from 'features/ui/layouts/navigation-api';
-import { LAUNCHPAD_PANEL_ID, WORKSPACE_PANEL_ID } from 'features/ui/layouts/shared';
-import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
+import { activeTabCanvasRightPanelChanged, setActiveTab } from 'features/ui/store/uiSlice';
 import { useLoadWorkflowWithDialog } from 'features/workflowLibrary/components/LoadWorkflowConfirmationAlertDialog';
 import { atom } from 'nanostores';
 import { useCallback, useEffect } from 'react';
@@ -92,10 +91,10 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
       const overrides: Partial<CanvasRasterLayerState> = {
         objects: [imageObject],
       };
-      await navigationApi.focusPanel('canvas', WORKSPACE_PANEL_ID);
       store.dispatch(canvasReset());
       store.dispatch(rasterLayerAdded({ overrides, isSelected: true }));
       store.dispatch(sentImageToCanvas());
+      $imageViewer.set(false);
       toast({
         title: t('toast.sentToCanvas'),
         status: 'info',
@@ -119,23 +118,23 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
       const metadata = getImageMetadataResult.value;
       store.dispatch(canvasReset());
       // This shows a toast
-      await MetadataUtils.recallAll(metadata, store);
+      await parseAndRecallAllMetadata(metadata, true);
     },
     [store, t]
   );
 
   const handleLoadWorkflow = useCallback(
-    (workflowId: string) => {
+    async (workflowId: string) => {
       // This shows a toast
-      loadWorkflowWithDialog({
+      await loadWorkflowWithDialog({
         type: 'library',
         data: workflowId,
         onSuccess: () => {
-          navigationApi.switchToTab('workflows');
+          store.dispatch(setActiveTab('workflows'));
         },
       });
     },
-    [loadWorkflowWithDialog]
+    [loadWorkflowWithDialog, store]
   );
 
   const handleSelectStylePreset = useCallback(
@@ -149,7 +148,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
         return;
       }
       store.dispatch(activeStylePresetIdChanged(stylePresetId));
-      navigationApi.switchToTab('canvas');
+      store.dispatch(setActiveTab('canvas'));
       toast({
         title: t('toast.stylePresetLoaded'),
         status: 'info',
@@ -159,34 +158,35 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
   );
 
   const handleGoToDestination = useCallback(
-    async (destination: StudioDestinationAction['data']['destination']) => {
+    (destination: StudioDestinationAction['data']['destination']) => {
       switch (destination) {
         case 'generation':
-          // Go to the generate tab, open the launchpad
-          await navigationApi.focusPanel('generate', LAUNCHPAD_PANEL_ID);
+          // Go to the canvas tab, open the image viewer, and enable send-to-gallery mode
           store.dispatch(paramsReset());
           store.dispatch(activeTabCanvasRightPanelChanged('gallery'));
+          $imageViewer.set(true);
           break;
         case 'canvas':
-          // Go to the canvas tab, open the launchpad
-          await navigationApi.focusPanel('canvas', WORKSPACE_PANEL_ID);
+          // Go to the canvas tab, close the image viewer, and disable send-to-gallery mode
+          store.dispatch(canvasReset());
+          $imageViewer.set(false);
           break;
         case 'workflows':
           // Go to the workflows tab
-          navigationApi.switchToTab('workflows');
+          store.dispatch(setActiveTab('workflows'));
           break;
         case 'upscaling':
           // Go to the upscaling tab
-          navigationApi.switchToTab('upscaling');
+          store.dispatch(setActiveTab('upscaling'));
           break;
         case 'viewAllWorkflows':
           // Go to the workflows tab and open the workflow library modal
-          navigationApi.switchToTab('workflows');
+          store.dispatch(setActiveTab('workflows'));
           $isWorkflowLibraryModalOpen.set(true);
           break;
         case 'viewAllWorkflowsRecommended':
           // Go to the workflows tab and open the workflow library modal with the recommended workflows view
-          navigationApi.switchToTab('workflows');
+          store.dispatch(setActiveTab('workflows'));
           $isWorkflowLibraryModalOpen.set(true);
           store.dispatch(workflowLibraryViewChanged('defaults'));
           store.dispatch(workflowLibraryTagsReset());
@@ -198,7 +198,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
           break;
         case 'viewAllStylePresets':
           // Go to the canvas tab and open the style presets menu
-          navigationApi.switchToTab('canvas');
+          store.dispatch(setActiveTab('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/v4';
+import { z } from 'zod';
 
 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 @observ33r/object-equals's objectEquals for equality check.
+ * A memoized selector creator that uses LRU cache and lodash's isEqual for equality check.
  */
 export const createMemoizedSelector = createSelectorCreator({
   memoize: lruMemoize,
   memoizeOptions: {
-    resultEqualityCheck: objectEquals,
+    resultEqualityCheck: isEqual,
   },
   argsMemoize: lruMemoize,
 });

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

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

@@ -8,9 +8,15 @@ 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';
+import { addImagesStarredListener } from 'app/store/middleware/listenerMiddleware/listeners/imagesStarred';
+import { addImagesUnstarredListener } from 'app/store/middleware/listenerMiddleware/listeners/imagesUnstarred';
 import { addImageUploadedFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageUploaded';
 import { addModelSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelSelected';
 import { addModelsLoadedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelsLoaded';
@@ -19,6 +25,7 @@ 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();
 
@@ -40,7 +47,17 @@ addImageUploadedFulfilledListener(startAppListening);
 // Image deleted
 addDeleteBoardAndImagesFulfilledListener(startAppListening);
 
+// Image starred
+addImagesStarredListener(startAppListening);
+addImagesUnstarredListener(startAppListening);
+
+// Gallery
+addGalleryImageClickedListener(startAppListening);
+addGalleryOffsetChangedListener(startAppListening);
+
 // User Invoked
+addEnqueueRequestedLinear(startAppListening);
+addEnqueueRequestedUpscale(startAppListening);
 addAnyEnqueuedListener(startAppListening);
 addBatchEnqueuedListener(startAppListening);
 
@@ -71,3 +88,5 @@ addAppConfigReceivedListener(startAppListening);
 addAdHocPostProcessingRequestedListener(startAppListening);
 
 addSetDefaultSettingsListener(startAppListening);
+
+addEnsureImageIsSelectedListener(startAppListening);

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

@@ -25,7 +25,7 @@ export const addArchivedOrDeletedBoardListener = (startAppListening: AppStartLis
     matcher: matchAnyBoardDeleted,
     effect: (action, { dispatch, getState }) => {
       const state = getState();
-      const deletedBoardId = action.meta.arg.originalArgs.board_id;
+      const deletedBoardId = action.meta.arg.originalArgs;
       const { autoAddBoardId, selectedBoardId } = state.gallery;
 
       // If the deleted board was currently selected, we should reset the selected board to uncategorized

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

@@ -1,29 +1,15 @@
 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: async (action, { unsubscribe, cancelActiveListeners, take, getState, dispatch }) => {
+    effect: (action, { unsubscribe, cancelActiveListeners }) => {
       // 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 { selectGetImageNamesQueryArgs, selectSelectedBoardId } from 'features/gallery/store/gallerySelectors';
+import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
 import { boardIdSelected, galleryViewChanged, imageSelected } from 'features/gallery/store/gallerySlice';
 import { imagesApi } from 'services/api/endpoints/images';
 
@@ -11,35 +11,36 @@ 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 board_id = selectSelectedBoardId(state);
-
-      const queryArgs = { ...selectGetImageNamesQueryArgs(state), board_id };
+      const queryArgs = selectListImagesQueryArgs(state);
 
       // 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.getImageNames.select(queryArgs)(getState()).isSuccess,
+        () => imagesApi.endpoints.listImages.select(queryArgs)(getState()).isSuccess,
         5000
       );
 
-      if (!isSuccess) {
+      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 || null));
+        } else if (boardImagesData) {
+          dispatch(imageSelected(boardImagesData.items[0] || null));
+        } else {
+          // board has no images - deselect
+          dispatch(imageSelected(null));
+        }
+      } else {
+        // fallback - deselect
         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

@@ -0,0 +1,151 @@
+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

@@ -0,0 +1,44 @@
+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

@@ -0,0 +1,16 @@
+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] ?? null));
+      }
+    },
+  });
+};

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

@@ -0,0 +1,73 @@
+import { createAction } from '@reduxjs/toolkit';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { selectListImagesQueryArgs } from 'features/gallery/store/gallerySelectors';
+import { imageToCompareChanged, selectionChanged } from 'features/gallery/store/gallerySlice';
+import { imagesApi } from 'services/api/endpoints/images';
+import type { ImageDTO } from 'services/api/types';
+
+export const galleryImageClicked = createAction<{
+  imageDTO: ImageDTO;
+  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 { imageDTO, shiftKey, ctrlKey, metaKey, altKey } = action.payload;
+      const state = getState();
+      const queryArgs = selectListImagesQueryArgs(state);
+      const queryResult = imagesApi.endpoints.listImages.select(queryArgs)(state);
+
+      if (!queryResult.data) {
+        // Should never happen if we have clicked a gallery image
+        return;
+      }
+
+      const imageDTOs = queryResult.data.items;
+      const selection = state.gallery.selection;
+
+      if (altKey) {
+        if (state.gallery.imageToCompare?.image_name === imageDTO.image_name) {
+          dispatch(imageToCompareChanged(null));
+        } else {
+          dispatch(imageToCompareChanged(imageDTO));
+        }
+      } else if (shiftKey) {
+        const rangeEndImageName = imageDTO.image_name;
+        const lastSelectedImage = selection[selection.length - 1]?.image_name;
+        const lastClickedIndex = imageDTOs.findIndex((n) => n.image_name === lastSelectedImage);
+        const currentClickedIndex = imageDTOs.findIndex((n) => n.image_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 = imageDTOs.slice(start, end + 1);
+          dispatch(selectionChanged(selection.concat(imagesToSelect)));
+        }
+      } else if (ctrlKey || metaKey) {
+        if (selection.some((i) => i.image_name === imageDTO.image_name) && selection.length > 1) {
+          dispatch(selectionChanged(selection.filter((n) => n.image_name !== imageDTO.image_name)));
+        } else {
+          dispatch(selectionChanged(selection.concat(imageDTO)));
+        }
+      } else {
+        dispatch(selectionChanged([imageDTO]));
+      }
+    },
+  });
+};

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

@@ -0,0 +1,119 @@
+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.image_name === lastImage?.image_name)) {
+            dispatch(selectionChanged(lastImage ? [lastImage] : []));
+          }
+        } else {
+          // We've gone forwards
+          const firstImage = imageDTOs[0];
+          if (!selection.some((selectedImage) => selectedImage.image_name === firstImage?.image_name)) {
+            dispatch(selectionChanged(firstImage ? [firstImage] : []));
+          }
+        }
+        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?.image_name !== lastImage.image_name) {
+            dispatch(imageToCompareChanged(lastImage));
+          }
+        } else {
+          // We've gone forwards
+          const firstImage = imageDTOs[0];
+          if (firstImage && imageToCompare?.image_name !== firstImage.image_name) {
+            dispatch(imageToCompareChanged(firstImage));
+          }
+        }
+        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/imageAddedToBoard.ts

@@ -8,16 +8,16 @@ export const addImageAddedToBoardFulfilledListener = (startAppListening: AppStar
   startAppListening({
     matcher: imagesApi.endpoints.addImageToBoard.matchFulfilled,
     effect: (action) => {
-      const { board_id, image_name } = action.meta.arg.originalArgs;
-      log.debug({ board_id, image_name }, 'Image added to board');
+      const { board_id, imageDTO } = action.meta.arg.originalArgs;
+      log.debug({ board_id, imageDTO }, 'Image added to board');
     },
   });
 
   startAppListening({
     matcher: imagesApi.endpoints.addImageToBoard.matchRejected,
     effect: (action) => {
-      const { board_id, image_name } = action.meta.arg.originalArgs;
-      log.debug({ board_id, image_name }, 'Problem adding image to board');
+      const { board_id, imageDTO } = action.meta.arg.originalArgs;
+      log.debug({ board_id, imageDTO }, 'Problem adding image to board');
     },
   });
 };

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/imagesStarred.ts

@@ -0,0 +1,30 @@
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { selectionChanged } from 'features/gallery/store/gallerySlice';
+import { imagesApi } from 'services/api/endpoints/images';
+import type { ImageDTO } from 'services/api/types';
+
+export const addImagesStarredListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    matcher: imagesApi.endpoints.starImages.matchFulfilled,
+    effect: (action, { dispatch, getState }) => {
+      const { updated_image_names: starredImages } = action.payload;
+
+      const state = getState();
+
+      const { selection } = state.gallery;
+      const updatedSelection: ImageDTO[] = [];
+
+      selection.forEach((selectedImageDTO) => {
+        if (starredImages.includes(selectedImageDTO.image_name)) {
+          updatedSelection.push({
+            ...selectedImageDTO,
+            starred: true,
+          });
+        } else {
+          updatedSelection.push(selectedImageDTO);
+        }
+      });
+      dispatch(selectionChanged(updatedSelection));
+    },
+  });
+};

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

@@ -0,0 +1,30 @@
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { selectionChanged } from 'features/gallery/store/gallerySlice';
+import { imagesApi } from 'services/api/endpoints/images';
+import type { ImageDTO } from 'services/api/types';
+
+export const addImagesUnstarredListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    matcher: imagesApi.endpoints.unstarImages.matchFulfilled,
+    effect: (action, { dispatch, getState }) => {
+      const { updated_image_names: unstarredImages } = action.payload;
+
+      const state = getState();
+
+      const { selection } = state.gallery;
+      const updatedSelection: ImageDTO[] = [];
+
+      selection.forEach((selectedImageDTO) => {
+        if (unstarredImages.includes(selectedImageDTO.image_name)) {
+          updatedSelection.push({
+            ...selectedImageDTO,
+            starred: false,
+          });
+        } else {
+          updatedSelection.push(selectedImageDTO);
+        }
+      });
+      dispatch(selectionChanged(updatedSelection));
+    },
+  });
+};

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

@@ -1,28 +1,14 @@
 import { logger } from 'app/logging/logger';
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { bboxSyncedToOptimalDimension, rgRefImageModelChanged } from 'features/controlLayers/store/canvasSlice';
+import { bboxSyncedToOptimalDimension } from 'features/controlLayers/store/canvasSlice';
 import { selectIsStaging } from 'features/controlLayers/store/canvasStagingAreaSlice';
 import { loraDeleted } from 'features/controlLayers/store/lorasSlice';
-import { modelChanged, syncedToOptimalDimension, vaeSelected } from 'features/controlLayers/store/paramsSlice';
-import { refImageModelChanged, selectReferenceImageEntities } from 'features/controlLayers/store/refImagesSlice';
-import {
-  selectAllEntitiesOfType,
-  selectBboxModelBase,
-  selectCanvasSlice,
-} from 'features/controlLayers/store/selectors';
-import { getEntityIdentifier } from 'features/controlLayers/store/types';
+import { modelChanged, 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';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
-import { selectGlobalRefImageModels, selectRegionalRefImageModels } from 'services/api/hooks/modelsByType';
-import type { AnyModelConfig } from 'services/api/types';
-import {
-  isChatGPT4oModelConfig,
-  isFluxKontextApiModelConfig,
-  isFluxKontextModelConfig,
-  isFluxReduxModelConfig,
-} from 'services/api/types';
 
 const log = logger('models');
 
@@ -39,8 +25,9 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
       }
 
       const newModel = result.data;
-      const newBase = newModel.base;
-      const didBaseModelChange = state.params.model?.base !== newBase;
+
+      const newBaseModel = newModel.base;
+      const didBaseModelChange = state.params.model?.base !== newBaseModel;
 
       if (didBaseModelChange) {
         // we may need to reset some incompatible submodels
@@ -48,7 +35,7 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
 
         // handle incompatible loras
         state.loras.loras.forEach((lora) => {
-          if (lora.model.base !== newBase) {
+          if (lora.model.base !== newBaseModel) {
             dispatch(loraDeleted({ id: lora.id }));
             modelsCleared += 1;
           }
@@ -56,82 +43,20 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
 
         // handle incompatible vae
         const { vae } = state.params;
-        if (vae && vae.base !== newBase) {
+        if (vae && vae.base !== newBaseModel) {
           dispatch(vaeSelected(null));
           modelsCleared += 1;
         }
 
-        // Handle incompatible reference image models - switch to first compatible model, with some smart logic
-        // to choose the best available model based on the new main model.
-        const allRefImageModels = selectGlobalRefImageModels(state).filter(({ base }) => base === newBase);
-
-        let newGlobalRefImageModel = null;
-
-        // Certain models require the ref image model to be the same as the main model - others just need a matching
-        // base. Helper to grab the first exact match or the first available model if no exact match is found.
-        const exactMatchOrFirst = <T extends AnyModelConfig>(candidates: T[]): T | null =>
-          candidates.find(({ key }) => key === newModel.key) ?? candidates[0] ?? null;
-
-        // The only way we can differentiate between FLUX and FLUX Kontext is to check for "kontext" in the name
-        if (newModel.base === 'flux' && newModel.name.toLowerCase().includes('kontext')) {
-          const fluxKontextDevModels = allRefImageModels.filter(isFluxKontextModelConfig);
-          newGlobalRefImageModel = exactMatchOrFirst(fluxKontextDevModels);
-        } else if (newModel.base === 'chatgpt-4o') {
-          const chatGPT4oModels = allRefImageModels.filter(isChatGPT4oModelConfig);
-          newGlobalRefImageModel = exactMatchOrFirst(chatGPT4oModels);
-        } else if (newModel.base === 'flux-kontext') {
-          const fluxKontextApiModels = allRefImageModels.filter(isFluxKontextApiModelConfig);
-          newGlobalRefImageModel = exactMatchOrFirst(fluxKontextApiModels);
-        } else if (newModel.base === 'flux') {
-          const fluxReduxModels = allRefImageModels.filter(isFluxReduxModelConfig);
-          newGlobalRefImageModel = fluxReduxModels[0] ?? null;
-        } else {
-          newGlobalRefImageModel = allRefImageModels[0] ?? null;
-        }
-
-        // All ref image entities are updated to use the same new model
-        const refImageEntities = selectReferenceImageEntities(state);
-        for (const entity of refImageEntities) {
-          const shouldUpdateModel =
-            (entity.config.model && entity.config.model.base !== newBase) ||
-            (!entity.config.model && newGlobalRefImageModel);
-
-          if (shouldUpdateModel) {
-            dispatch(
-              refImageModelChanged({
-                id: entity.id,
-                modelConfig: newGlobalRefImageModel,
-              })
-            );
-            modelsCleared += 1;
-          }
-        }
-
-        // For regional guidance, there is no smart logic - we just pick the first available model.
-        const newRegionalRefImageModel = selectRegionalRefImageModels(state)[0] ?? null;
-
-        // All regional guidance entities are updated to use the same new model.
-        const canvasState = selectCanvasSlice(state);
-        const canvasRegionalGuidanceEntities = selectAllEntitiesOfType(canvasState, 'regional_guidance');
-        for (const entity of canvasRegionalGuidanceEntities) {
-          for (const refImage of entity.referenceImages) {
-            // Only change the model if the current one is not compatible with the new base model.
-            const shouldUpdateModel =
-              (refImage.config.model && refImage.config.model.base !== newBase) ||
-              (!refImage.config.model && newRegionalRefImageModel);
-
-            if (shouldUpdateModel) {
-              dispatch(
-                rgRefImageModelChanged({
-                  entityIdentifier: getEntityIdentifier(entity),
-                  referenceImageId: refImage.id,
-                  modelConfig: newRegionalRefImageModel,
-                })
-              );
-              modelsCleared += 1;
-            }
-          }
-        }
+        // handle incompatible controlnets
+        // state.canvas.present.controlAdapters.entities.forEach((ca) => {
+        //   if (ca.model?.base !== newBaseModel) {
+        //     modelsCleared += 1;
+        //     if (ca.isEnabled) {
+        //       dispatch(entityIsEnabledToggled({ entityIdentifier: { id: ca.id, type: 'control_adapter' } }));
+        //     }
+        //   }
+        // });
 
         if (modelsCleared > 0) {
           toast({
@@ -146,16 +71,9 @@ export const addModelSelectedListener = (startAppListening: AppStartListening) =
       }
 
       dispatch(modelChanged({ model: newModel, previousModel: state.params.model }));
-
       const modelBase = selectBboxModelBase(state);
-
-      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());
-        }
+      if (!selectIsStaging(state) && modelBase !== state.params.model?.base) {
+        dispatch(bboxSyncedToOptimalDimension());
       }
     },
   });

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

@@ -1,9 +1,7 @@
 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 {
-  heightChanged,
   setCfgRescaleMultiplier,
   setCfgScale,
   setGuidance,
@@ -11,7 +9,6 @@ import {
   setSteps,
   vaePrecisionChanged,
   vaeSelected,
-  widthChanged,
 } from 'features/controlLayers/store/paramsSlice';
 import { setDefaultSettings } from 'features/parameters/store/actions';
 import {
@@ -26,7 +23,6 @@ import {
   zParameterVAEModel,
 } from 'features/parameters/types/parameterSchemas';
 import { toast } from 'features/toast/toast';
-import { selectActiveTab } from 'features/ui/store/uiSelectors';
 import { t } from 'i18next';
 import { modelConfigsAdapterSelectors, modelsApi } from 'services/api/endpoints/models';
 import { isNonRefinerMainModelConfig } from 'services/api/types';
@@ -90,16 +86,10 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
           }
         }
 
-        if (!isNil(cfg_rescale_multiplier)) {
+        if (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) {
@@ -116,24 +106,15 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
         const setSizeOptions = { updateAspectRatio: true, clamp: true };
 
         const isStaging = selectIsStaging(getState());
-        const activeTab = selectActiveTab(getState());
-        if (activeTab === 'generate') {
+        if (!isStaging && width) {
           if (isParameterWidth(width)) {
-            dispatch(widthChanged({ width, ...setSizeOptions }));
-          }
-          if (isParameterHeight(height)) {
-            dispatch(heightChanged({ height, ...setSizeOptions }));
+            dispatch(bboxWidthChanged({ width, ...setSizeOptions }));
           }
         }
 
-        if (activeTab === 'canvas') {
-          if (!isStaging) {
-            if (isParameterWidth(width)) {
-              dispatch(bboxWidthChanged({ width, ...setSizeOptions }));
-            }
-            if (isParameterHeight(height)) {
-              dispatch(bboxHeightChanged({ height, ...setSizeOptions }));
-            }
+        if (!isStaging && height) {
+          if (isParameterHeight(height)) {
+            dispatch(bboxHeightChanged({ height, ...setSizeOptions }));
           }
         }
 

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 (objectEquals(prevQueueStatusData?.queue, nextQueueStatusData.queue)) {
+        if (isEqual(prevQueueStatusData?.queue, nextQueueStatusData.queue)) {
           return;
         }
 

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

@@ -1,3 +1,4 @@
+import { useStore } from '@nanostores/react';
 import type { AppStore } from 'app/store/store';
 import { atom } from 'nanostores';
 
@@ -31,3 +32,11 @@ 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

@@ -11,7 +11,5 @@ 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

@@ -4,7 +4,6 @@ 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';
@@ -17,6 +16,7 @@ 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';
@@ -28,6 +28,7 @@ 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';
@@ -56,6 +57,7 @@ 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,
@@ -101,6 +103,7 @@ 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 { AppStore, AppThunkDispatch, RootState } from 'app/store/store';
+import type { 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.withTypes<AppStore>()();
+export const useAppStore = () => useStore<RootState>();

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

@@ -14,7 +14,6 @@ export type AppFeature =
   | 'githubLink'
   | 'discordLink'
   | 'bugLink'
-  | 'aboutModal'
   | 'localization'
   | 'consoleLogging'
   | 'dynamicPrompting'
@@ -30,8 +29,7 @@ export type AppFeature =
   | 'hfToken'
   | 'retryQueueItem'
   | 'cancelAndClearAll'
-  | 'chatGPT4oHigh'
-  | 'modelRelationships';
+  | 'chatGPT4oHigh';
 /**
  * A disable-able Stable Diffusion feature
  */
@@ -78,7 +76,6 @@ 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

@@ -0,0 +1,56 @@
+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

@@ -8,16 +8,20 @@ const Loading = () => {
   return (
     <Flex
       position="absolute"
+      width="100dvw"
+      height="100dvh"
       alignItems="center"
       justifyContent="center"
-      bg="hsl(220 12% 10% / 1)" // base.900
-      inset={0}
-      zIndex={99999}
+      bg="#151519"
+      top={0}
+      right={0}
+      bottom={0}
+      left={0}
     >
       <Image src={InvokeLogoWhite} w="8rem" h="8rem" />
       <Spinner
         label="Loading"
-        color="hsl(220 12% 68% / 1)" // base.300
+        color="grey"
         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 'es-toolkit/compat';
+import { merge } from 'lodash-es';
 import { ClickScrollPlugin, OverlayScrollbars } from 'overlayscrollbars';
 import type { UseOverlayScrollbarsParams } from 'overlayscrollbars-react';
 import type { CSSProperties } from 'react';

invokeai/frontend/web/src/common/components/OverlayScrollbars/overlayscrollbars.css

@@ -1,6 +1,6 @@
 .os-scrollbar {
   /*      The size of the scrollbar */
-  --os-size: 8px;
+  --os-size: 9px;
   /*      The axis-perpedicular padding of the scrollbar (horizontal: padding-y, vertical: padding-x) */
   /* --os-padding-perpendicular: 0; */
   /*      The axis padding of the scrollbar (horizontal: padding-x, vertical: padding-y) */
@@ -8,11 +8,11 @@
   /*      The border radius of the scrollbar track */
   /* --os-track-border-radius: 0; */
   /*      The background of the scrollbar track */
-  --os-track-bg: rgba(0, 0, 0, 0.5);
+  /* --os-track-bg: rgba(0, 0, 0, 0.3); */
   /*      The :hover background of the scrollbar track */
-  --os-track-bg-hover: rgba(0, 0, 0, 0.5);
+  /* --os-track-bg-hover: rgba(0, 0, 0, 0.3); */
   /*      The :active background of the scrollbar track */
-  --os-track-bg-active: rgba(0, 0, 0, 0.6);
+  /* --os-track-bg-active: rgba(0, 0, 0, 0.3); */
   /*      The border of the scrollbar track */
   /* --os-track-border: none; */
   /*      The :hover background of the scrollbar track */
@@ -22,11 +22,11 @@
   /*      The border radius of the scrollbar handle */
   /* --os-handle-border-radius: 2px; */
   /*      The background of the scrollbar handle */
-  --os-handle-bg: var(--invoke-colors-base-400);
+  /* --os-handle-bg: var(--invokeai-colors-accentAlpha-500); */
   /*      The :hover background of the scrollbar handle */
-  --os-handle-bg-hover: var(--invoke-colors-base-300);
+  /* --os-handle-bg-hover: var(--invokeai-colors-accentAlpha-700); */
   /*      The :active background of the scrollbar handle */
-  --os-handle-bg-active: var(--invoke-colors-base-250);
+  /* --os-handle-bg-active: var(--invokeai-colors-accentAlpha-800); */
   /*      The border of the scrollbar handle */
   /* --os-handle-border: none; */
   /*      The :hover border of the scrollbar handle */
@@ -34,23 +34,23 @@
   /*      The :active border of the scrollbar handle */
   /* --os-handle-border-active: none; */
   /*      The min size of the scrollbar handle */
-  --os-handle-min-size: 32px;
+  --os-handle-min-size: 50px;
   /*      The max size of the scrollbar handle */
   /* --os-handle-max-size: none; */
   /*      The axis-perpedicular size of the scrollbar handle (horizontal: height, vertical: width) */
   /* --os-handle-perpendicular-size: 100%; */
   /*      The :hover axis-perpedicular size of the scrollbar handle (horizontal: height, vertical: width) */
-  --os-handle-perpendicular-size-hover: 100%;
+  /* --os-handle-perpendicular-size-hover: 100%; */
   /*      The :active axis-perpedicular size of the scrollbar handle (horizontal: height, vertical: width) */
   /* --os-handle-perpendicular-size-active: 100%; */
   /*      Increases the interactive area of the scrollbar handle. */
-  --os-handle-interactive-area-offset: -1px;
+  /* --os-handle-interactive-area-offset: 0; */
 }
 
 .os-scrollbar-handle {
-  /* cursor: grab; */
+  cursor: grab;
 }
 
 .os-scrollbar-handle:active {
-  /* cursor: grabbing; */
+  cursor: grabbing;
 }

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

@@ -87,7 +87,7 @@ export const buildGroup = <T extends object>(group: Omit<Group<T>, typeof unique
   [uniqueGroupKey]: true,
 });
 
-export const isGroup = <T extends object>(optionOrGroup: OptionOrGroup<T>): optionOrGroup is Group<T> => {
+const isGroup = <T extends object>(optionOrGroup: OptionOrGroup<T>): optionOrGroup is Group<T> => {
   return uniqueGroupKey in optionOrGroup && optionOrGroup[uniqueGroupKey] === true;
 };
 
@@ -198,10 +198,6 @@ 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> = {
@@ -314,9 +310,9 @@ const flattenOptions = <T extends object>(options: OptionOrGroup<T>[]): T[] => {
   return flattened;
 };
 
-export type GroupStatusMap = Record<string, boolean>;
+type GroupStatusMap = Record<string, boolean>;
 
-const useTogglableGroups = <T extends object>(options: OptionOrGroup<T>[], initialGroupStates?: GroupStatusMap) => {
+const useTogglableGroups = <T extends object>(options: OptionOrGroup<T>[]) => {
   const groupsWithOptions = useMemo(() => {
     const ids: string[] = [];
     for (const optionOrGroup of options) {
@@ -336,16 +332,14 @@ const useTogglableGroups = <T extends object>(options: OptionOrGroup<T>[], initi
     const groupStatusMap = $groupStatusMap.get();
     const newMap: GroupStatusMap = {};
     for (const id of groupsWithOptions) {
-      if (initialGroupStates && initialGroupStates[id] !== undefined) {
-        newMap[id] = initialGroupStates[id];
+      if (newMap[id] === undefined) {
+        newMap[id] = false;
       } else if (groupStatusMap[id] !== undefined) {
         newMap[id] = groupStatusMap[id];
-      } else {
-        newMap[id] = false;
       }
     }
     $groupStatusMap.set(newMap);
-  }, [groupsWithOptions, $groupStatusMap, initialGroupStates]);
+  }, [groupsWithOptions, $groupStatusMap]);
 
   const toggleGroup = useCallback(
     (idToToggle: string) => {
@@ -517,14 +511,10 @@ 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,
-    initialGroupStates
-  );
+  const { $groupStatusMap, $areAllGroupsDisabled, toggleGroup } = useTogglableGroups(optionsOrGroups);
   const $activeOptionId = useAtom(getFirstOptionId(optionsOrGroups, getOptionId));
   const $compactView = useAtom(true);
   const $optionsOrGroups = useAtom(optionsOrGroups);

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

@@ -1,15 +1,20 @@
 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 } from 'react-icons/pi';
+import { PiArrowsCounterClockwiseBold, PiFilePlusBold } 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]);
@@ -18,6 +23,12 @@ 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

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