fix 1:1 ratio (#8127)

Co-authored-by: Mary Hipp <maryhipp@Marys-Air.lan>

.github/ISSUE_TEMPLATE/BUG_REPORT.yml

@@ -21,20 +21,6 @@ body:
         - label: I have searched the existing issues
           required: true
 
-  - type: dropdown
-    id: install_method
-    attributes:
-      label: Install method
-      description: How did you install Invoke?
-      multiple: false
-      options:
-        - "Invoke's Launcher"
-        - 'Stability Matrix'
-        - 'Pinokio'
-        - 'Manual'
-    validations:
-      required: true
-
   - type: markdown
     attributes:
       value: __Describe your environment__
@@ -90,8 +76,8 @@ body:
     attributes:
       label: Version number
       description: |
-        The version of Invoke you have installed. If it is not the [latest version](https://github.com/invoke-ai/InvokeAI/releases/latest), please update and try again to confirm the issue still exists. If you are testing main, please include the commit hash instead.
-      placeholder: ex. v6.0.2
+        The version of Invoke you have installed. If it is not the latest version, please update and try again to confirm the issue still exists. If you are testing main, please include the commit hash instead.
+      placeholder: ex. 3.6.1
     validations:
       required: true
 
@@ -99,17 +85,17 @@ body:
     id: browser-version
     attributes:
       label: Browser
-      description: Your web browser and version, if you do not use the Launcher's provided GUI.
+      description: Your web browser and version.
       placeholder: ex. Firefox 123.0b3
     validations:
-      required: false
+      required: true
 
   - type: textarea
     id: python-deps
     attributes:
-      label: System Information
+      label: Python dependencies
       description: |
-        Click the gear icon at the bottom left corner, then click "About". Click the copy button and then paste here.
+        If the problem occurred during image generation, click the gear icon at the bottom left corner, click "About", click the copy button and then paste here.
     validations:
       required: false
 

.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
@@ -190,5 +189,3 @@ installer/update.bat
 installer/update.sh
 installer/InvokeAI-Installer/
 .aider*
-
-.claude/

docker/Dockerfile

@@ -5,7 +5,8 @@
 FROM docker.io/node:22-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
 ENV PATH="$PNPM_HOME:$PATH"
-RUN corepack use pnpm@10.x && corepack enable
+RUN corepack use pnpm@8.x
+RUN corepack enable
 
 WORKDIR /build
 COPY invokeai/frontend/web/ ./

docs/contributing/dev-environment.md

@@ -41,7 +41,7 @@ If you just want to use Invoke, you should use the [launcher][launcher link].
      With the modifications made, the install command should look something like this:
 
       ```sh
-      uv pip install -e ".[dev,test,docs,xformers]" --python 3.12 --python-preference only-managed --index=https://download.pytorch.org/whl/cu128 --reinstall
+      uv pip install -e ".[dev,test,docs,xformers]" --python 3.12 --python-preference only-managed --index=https://download.pytorch.org/whl/cu126 --reinstall
       ```
 
 6. At this point, you should have Invoke installed, a venv set up and activated, and the server running. But you will see a warning in the terminal that no UI was found. If you go to the URL for the server, you won't get a UI.
@@ -50,11 +50,11 @@ If you just want to use Invoke, you should use the [launcher][launcher link].
 
       If you only want to edit the docs, you can stop here and skip to the **Documentation** section below.
 
-7. Install the frontend dev toolchain, paying attention to versions:
+7. Install the frontend dev toolchain:
 
-      - [`nodejs`](https://nodejs.org/) (tested on LTS, v22)
+      - [`nodejs`](https://nodejs.org/) (v20+)
 
-      - [`pnpm`](https://pnpm.io/installation) (tested on v10)
+      - [`pnpm`](https://pnpm.io/8.x/installation) (must be v8 - not v9!)
 
 8. Do a production build of the frontend:
 

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,
@@ -106,9 +99,7 @@ async def upload_image(
             raise HTTPException(status_code=400, detail="Invalid resize_to format or size")
 
         try:
-            # heuristic_resize_fast expects an RGB or RGBA image
-            pil_rgba = pil_image.convert("RGBA")
-            np_image = pil_to_np(pil_rgba)
+            np_image = pil_to_np(pil_image)
             np_image = heuristic_resize_fast(np_image, (resize_dims.width, resize_dims.height))
             pil_image = np_to_pil(np_image)
         except Exception:
@@ -160,30 +151,18 @@ 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 +374,31 @@ 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 +406,14 @@ 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 +422,36 @@ 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 +522,3 @@ 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
 
@@ -14,15 +14,13 @@ from invokeai.app.services.session_queue.session_queue_common import (
     CancelByBatchIDsResult,
     CancelByDestinationResult,
     ClearResult,
-    DeleteAllExceptCurrentResult,
-    DeleteByDestinationResult,
     EnqueueBatchResult,
     FieldIdentifier,
     PruneResult,
     RetryItemsResult,
     SessionQueueCountsByDestination,
     SessionQueueItem,
-    SessionQueueItemNotFoundError,
+    SessionQueueItemDTO,
     SessionQueueStatus,
 )
 from invokeai.app.services.shared.pagination import CursorPaginatedResults
@@ -60,19 +58,17 @@ 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(
     "/{queue_id}/list",
     operation_id="list_queue_items",
     responses={
-        200: {"model": CursorPaginatedResults[SessionQueueItem]},
+        200: {"model": CursorPaginatedResults[SessionQueueItemDTO]},
     },
 )
 async def list_queue_items(
@@ -81,42 +77,12 @@ async def list_queue_items(
     status: Optional[QUEUE_ITEM_STATUS] = Query(default=None, description="The status of items to fetch"),
     cursor: Optional[int] = Query(default=None, description="The pagination cursor"),
     priority: int = Query(default=0, description="The pagination cursor priority"),
-    destination: Optional[str] = Query(default=None, description="The destination of queue items to fetch"),
-) -> CursorPaginatedResults[SessionQueueItem]:
-    """Gets cursor-paginated queue items"""
+) -> CursorPaginatedResults[SessionQueueItemDTO]:
+    """Gets all queue items (without graphs)"""
 
-    try:
-        return ApiDependencies.invoker.services.session_queue.list_queue_items(
-            queue_id=queue_id,
-            limit=limit,
-            status=status,
-            cursor=cursor,
-            priority=priority,
-            destination=destination,
-        )
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Unexpected error while listing all items: {e}")
-
-
-@session_queue_router.get(
-    "/{queue_id}/list_all",
-    operation_id="list_all_queue_items",
-    responses={
-        200: {"model": list[SessionQueueItem]},
-    },
-)
-async def list_all_queue_items(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    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_queue_items(
+        queue_id=queue_id, limit=limit, status=status, cursor=cursor, priority=priority
+    )
 
 
 @session_queue_router.put(
@@ -128,10 +94,7 @@ 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 +106,7 @@ 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(
@@ -158,25 +118,7 @@ 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}")
-
-
-@session_queue_router.put(
-    "/{queue_id}/delete_all_except_current",
-    operation_id="delete_all_except_current",
-    responses={200: {"model": DeleteAllExceptCurrentResult}},
-)
-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.cancel_all_except_current(queue_id=queue_id)
 
 
 @session_queue_router.put(
@@ -189,12 +131,7 @@ 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 +144,9 @@ 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 +159,7 @@ 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 +173,11 @@ 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 +191,7 @@ 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 +205,7 @@ 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 +219,7 @@ 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 +233,9 @@ 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 +250,7 @@ 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(
@@ -353,27 +266,7 @@ 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}")
-
-
-@session_queue_router.delete(
-    "/{queue_id}/i/{item_id}",
-    operation_id="delete_queue_item",
-)
-async def delete_queue_item(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    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}")
+    return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
 
 
 @session_queue_router.put(
@@ -388,12 +281,8 @@ 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(
@@ -406,27 +295,6 @@ 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}")
-
-
-@session_queue_router.delete(
-    "/{queue_id}/d/{destination}",
-    operation_id="delete_by_destination",
-    responses={200: {"model": DeleteByDestinationResult}},
-)
-async def delete_by_destination(
-    queue_id: str = Path(description="The queue id to query"),
-    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.get_counts_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
 
@@ -615,7 +615,7 @@ 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)

invokeai/app/invocations/bria_controlnet.py

@@ -1,158 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-from pydantic import BaseModel, Field
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    ImageField,
-    InputField,
-    OutputField,
-    UIType,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.bria.controlnet_aux.open_pose import Body, Face, Hand, OpenposeDetector
-from invokeai.backend.bria.controlnet_bria import BRIA_CONTROL_MODES
-from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
-from invokeai.invocation_api import Classification, ImageOutput
-
-DEPTH_SMALL_V2_URL = "depth-anything/Depth-Anything-V2-Small-hf"
-HF_LLLYASVIEL = "https://huggingface.co/lllyasviel/Annotators/resolve/main/"
-
-
-class BriaControlNetField(BaseModel):
-    image: ImageField = Field(description="The control image")
-    model: ModelIdentifierField = Field(description="The ControlNet model to use")
-    mode: BRIA_CONTROL_MODES = Field(description="The mode of the ControlNet")
-    conditioning_scale: float = Field(description="The weight given to the ControlNet")
-
-
-@invocation_output("bria_controlnet_output")
-class BriaControlNetOutput(BaseInvocationOutput):
-    """Bria ControlNet info"""
-
-    control: BriaControlNetField = OutputField(description=FieldDescriptions.control)
-    preprocessed_images: ImageField = OutputField(description="The preprocessed control image")
-
-
-@invocation(
-    "bria_controlnet",
-    title="ControlNet - Bria",
-    tags=["controlnet", "bria"],
-    category="controlnet",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class BriaControlNetInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Collect Bria ControlNet info to pass to denoiser node."""
-
-    control_image: ImageField = InputField(description="The control image")
-    control_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.controlnet_model, ui_type=UIType.BriaControlNetModel
-    )
-    control_mode: BRIA_CONTROL_MODES = InputField(default="depth", description="The mode of the ControlNet")
-    control_weight: float = InputField(default=1.0, ge=-1, le=2, description="The weight given to the ControlNet")
-
-    def invoke(self, context: InvocationContext) -> BriaControlNetOutput:
-        image_in = resize_img(context.images.get_pil(self.control_image.image_name))
-        if self.control_mode == "canny":
-            control_image = extract_canny(image_in)
-        elif self.control_mode == "depth":
-            control_image = extract_depth(image_in, context)
-        elif self.control_mode == "pose":
-            control_image = extract_openpose(image_in, context)
-        elif self.control_mode == "colorgrid":
-            control_image = tile(64, image_in)
-        elif self.control_mode == "recolor":
-            control_image = convert_to_grayscale(image_in)
-        elif self.control_mode == "tile":
-            control_image = tile(16, image_in)
-
-        control_image = resize_img(control_image)
-        image_dto = context.images.save(image=control_image)
-        image_output = ImageOutput.build(image_dto)
-        return BriaControlNetOutput(
-            preprocessed_images=image_output.image,
-            control=BriaControlNetField(
-                image=ImageField(image_name=image_dto.image_name),
-                model=self.control_model,
-                mode=self.control_mode,
-                conditioning_scale=self.control_weight,
-            ),
-        )
-
-
-RATIO_CONFIGS_1024 = {
-    0.6666666666666666: {"width": 832, "height": 1248},
-    0.7432432432432432: {"width": 880, "height": 1184},
-    0.8028169014084507: {"width": 912, "height": 1136},
-    1.0: {"width": 1024, "height": 1024},
-    1.2456140350877194: {"width": 1136, "height": 912},
-    1.3454545454545455: {"width": 1184, "height": 880},
-    1.4339622641509433: {"width": 1216, "height": 848},
-    1.5: {"width": 1248, "height": 832},
-    1.5490196078431373: {"width": 1264, "height": 816},
-    1.62: {"width": 1296, "height": 800},
-    1.7708333333333333: {"width": 1360, "height": 768},
-}
-
-
-def extract_depth(image: Image.Image, context: InvocationContext):
-    loaded_model = context.models.load_remote_model(DEPTH_SMALL_V2_URL, DepthAnythingPipeline.load_model)
-
-    with loaded_model as depth_anything_detector:
-        assert isinstance(depth_anything_detector, DepthAnythingPipeline)
-        depth_map = depth_anything_detector.generate_depth(image)
-    return depth_map
-
-
-def extract_openpose(image: Image.Image, context: InvocationContext):
-    body_model = context.models.load_remote_model(f"{HF_LLLYASVIEL}body_pose_model.pth", Body)
-    hand_model = context.models.load_remote_model(f"{HF_LLLYASVIEL}hand_pose_model.pth", Hand)
-    face_model = context.models.load_remote_model(f"{HF_LLLYASVIEL}facenet.pth", Face)
-
-    with body_model as body_model, hand_model as hand_model, face_model as face_model:
-        open_pose_model = OpenposeDetector(body_model, hand_model, face_model)
-        processed_image_open_pose = open_pose_model(image, hand_and_face=True)
-
-    processed_image_open_pose = processed_image_open_pose.resize(image.size)
-    return processed_image_open_pose
-
-
-def extract_canny(input_image):
-    image = np.array(input_image)
-    image = cv2.Canny(image, 100, 200)
-    image = image[:, :, None]
-    image = np.concatenate([image, image, image], axis=2)
-    canny_image = Image.fromarray(image)
-    return canny_image
-
-
-def convert_to_grayscale(image):
-    gray_image = image.convert("L").convert("RGB")
-    return gray_image
-
-
-def tile(downscale_factor, input_image):
-    control_image = input_image.resize(
-        (input_image.size[0] // downscale_factor, input_image.size[1] // downscale_factor)
-    ).resize(input_image.size, Image.Resampling.NEAREST)
-    return control_image
-
-
-def resize_img(control_image):
-    image_ratio = control_image.width / control_image.height
-    ratio = min(RATIO_CONFIGS_1024.keys(), key=lambda k: abs(k - image_ratio))
-    to_height = RATIO_CONFIGS_1024[ratio]["height"]
-    to_width = RATIO_CONFIGS_1024[ratio]["width"]
-    resized_image = control_image.resize((to_width, to_height), resample=Image.Resampling.LANCZOS)
-    return resized_image

invokeai/app/invocations/bria_decoder.py

@@ -1,46 +0,0 @@
-import torch
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from PIL import Image
-
-from invokeai.app.invocations.model import VAEField
-from invokeai.app.invocations.primitives import FieldDescriptions, Input, InputField, LatentsField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.invocation_api import BaseInvocation, Classification, ImageOutput, invocation
-
-
-@invocation(
-    "bria_decoder",
-    title="Decoder - Bria",
-    tags=["image", "bria"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class BriaDecoderInvocation(BaseInvocation):
-    vae: VAEField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-    latents: LatentsField = InputField(
-        description=FieldDescriptions.latents,
-        input=Input.Connection,
-    )
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.tensors.load(self.latents.latents_name)
-        latents = latents.view(1, 64, 64, 4, 2, 2).permute(0, 3, 1, 4, 2, 5).reshape(1, 4, 128, 128)
-
-        with context.models.load(self.vae.vae) as vae:
-            assert isinstance(vae, AutoencoderKL)
-            latents = latents / vae.config.scaling_factor
-            latents = latents.to(device=vae.device, dtype=vae.dtype)
-
-            decoded_output = vae.decode(latents)
-            image = decoded_output.sample
-
-        # Convert to numpy with proper gradient handling
-        image = ((image.clamp(-1, 1) + 1) / 2 * 255).cpu().detach().permute(0, 2, 3, 1).numpy().astype("uint8")[0]
-        img = Image.fromarray(image)
-        image_dto = context.images.save(image=img)
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/bria_denoiser.py

@@ -1,180 +0,0 @@
-from typing import List, Tuple
-
-import torch
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from diffusers.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
-
-from invokeai.app.invocations.bria_controlnet import BriaControlNetField
-from invokeai.app.invocations.fields import Input, InputField, LatentsField, OutputField
-from invokeai.app.invocations.model import SubModelType, T5EncoderField, TransformerField, VAEField
-from invokeai.app.invocations.primitives import BaseInvocationOutput, FieldDescriptions
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.bria.controlnet_bria import BriaControlModes, BriaMultiControlNetModel
-from invokeai.backend.bria.controlnet_utils import prepare_control_images
-from invokeai.backend.bria.pipeline_bria_controlnet import BriaControlNetPipeline
-from invokeai.backend.bria.transformer_bria import BriaTransformer2DModel
-from invokeai.invocation_api import BaseInvocation, Classification, invocation, invocation_output
-
-
-@invocation_output("bria_denoise_output")
-class BriaDenoiseInvocationOutput(BaseInvocationOutput):
-    latents: LatentsField = OutputField(description=FieldDescriptions.latents)
-
-
-@invocation(
-    "bria_denoise",
-    title="Denoise - Bria",
-    tags=["image", "bria"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class BriaDenoiseInvocation(BaseInvocation):
-    num_steps: int = InputField(
-        default=30, title="Number of Steps", description="The number of steps to use for the denoiser"
-    )
-    guidance_scale: float = InputField(
-        default=5.0, title="Guidance Scale", description="The guidance scale to use for the denoiser"
-    )
-
-    transformer: TransformerField = InputField(
-        description="Bria model (Transformer) to load",
-        input=Input.Connection,
-        title="Transformer",
-    )
-    t5_encoder: T5EncoderField = InputField(
-        title="T5Encoder",
-        description=FieldDescriptions.t5_encoder,
-        input=Input.Connection,
-    )
-    vae: VAEField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-        title="VAE",
-    )
-    latents: LatentsField = InputField(
-        description="Latents to denoise",
-        input=Input.Connection,
-        title="Latents",
-    )
-    latent_image_ids: LatentsField = InputField(
-        description="Latent Image IDs to denoise",
-        input=Input.Connection,
-        title="Latent Image IDs",
-    )
-    pos_embeds: LatentsField = InputField(
-        description="Positive Prompt Embeds",
-        input=Input.Connection,
-        title="Positive Prompt Embeds",
-    )
-    neg_embeds: LatentsField = InputField(
-        description="Negative Prompt Embeds",
-        input=Input.Connection,
-        title="Negative Prompt Embeds",
-    )
-    text_ids: LatentsField = InputField(
-        description="Text IDs",
-        input=Input.Connection,
-        title="Text IDs",
-    )
-    control: BriaControlNetField | list[BriaControlNetField] | None = InputField(
-        description="ControlNet",
-        input=Input.Connection,
-        title="ControlNet",
-        default=None,
-    )
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> BriaDenoiseInvocationOutput:
-        latents = context.tensors.load(self.latents.latents_name)
-        pos_embeds = context.tensors.load(self.pos_embeds.latents_name)
-        neg_embeds = context.tensors.load(self.neg_embeds.latents_name)
-        text_ids = context.tensors.load(self.text_ids.latents_name)
-        latent_image_ids = context.tensors.load(self.latent_image_ids.latents_name)
-        scheduler_identifier = self.transformer.transformer.model_copy(update={"submodel_type": SubModelType.Scheduler})
-
-        device = None
-        dtype = None
-        with (
-            context.models.load(self.transformer.transformer) as transformer,
-            context.models.load(scheduler_identifier) as scheduler,
-            context.models.load(self.vae.vae) as vae,
-            context.models.load(self.t5_encoder.text_encoder) as t5_encoder,
-            context.models.load(self.t5_encoder.tokenizer) as t5_tokenizer,
-        ):
-            assert isinstance(transformer, BriaTransformer2DModel)
-            assert isinstance(scheduler, FlowMatchEulerDiscreteScheduler)
-            assert isinstance(vae, AutoencoderKL)
-            dtype = transformer.dtype
-            device = transformer.device
-            latents, pos_embeds, neg_embeds = (x.to(device, dtype) for x in (latents, pos_embeds, neg_embeds))
-
-            control_model, control_images, control_modes, control_scales = None, None, None, None
-            if self.control is not None:
-                control_model, control_images, control_modes, control_scales = self._prepare_multi_control(
-                    context=context,
-                    vae=vae,
-                    width=1024,
-                    height=1024,
-                    device=vae.device,
-                )
-
-            pipeline = BriaControlNetPipeline(
-                transformer=transformer,
-                scheduler=scheduler,
-                vae=vae,
-                text_encoder=t5_encoder,
-                tokenizer=t5_tokenizer,
-                controlnet=control_model,
-            )
-            pipeline.to(device=transformer.device, dtype=transformer.dtype)
-
-            latents = pipeline(
-                control_image=control_images,
-                control_mode=control_modes,
-                width=1024,
-                height=1024,
-                controlnet_conditioning_scale=control_scales,
-                num_inference_steps=self.num_steps,
-                max_sequence_length=128,
-                guidance_scale=self.guidance_scale,
-                latents=latents,
-                latent_image_ids=latent_image_ids,
-                text_ids=text_ids,
-                prompt_embeds=pos_embeds,
-                negative_prompt_embeds=neg_embeds,
-                output_type="latent",
-            )[0]
-
-        assert isinstance(latents, torch.Tensor)
-        saved_input_latents_tensor = context.tensors.save(latents)
-        latents_output = LatentsField(latents_name=saved_input_latents_tensor)
-        return BriaDenoiseInvocationOutput(latents=latents_output)
-
-    def _prepare_multi_control(
-        self, context: InvocationContext, vae: AutoencoderKL, width: int, height: int, device: torch.device
-    ) -> Tuple[BriaMultiControlNetModel, List[torch.Tensor], List[torch.Tensor], List[float]]:
-        control = self.control if isinstance(self.control, list) else [self.control]
-        control_images, control_models, control_modes, control_scales = [], [], [], []
-        for controlnet in control:
-            if controlnet is not None:
-                control_models.append(context.models.load(controlnet.model).model)
-                control_modes.append(BriaControlModes[controlnet.mode].value)
-                control_scales.append(controlnet.conditioning_scale)
-                try:
-                    control_images.append(context.images.get_pil(controlnet.image.image_name))
-                except Exception:
-                    raise FileNotFoundError(
-                        f"Control image {controlnet.image.image_name} not found. Make sure not to delete the preprocessed image before finishing the pipeline."
-                    )
-
-        control_model = BriaMultiControlNetModel(control_models).to(device)
-        tensored_control_images, tensored_control_modes = prepare_control_images(
-            vae=vae,
-            control_images=control_images,
-            control_modes=control_modes,
-            width=width,
-            height=height,
-            device=device,
-        )
-        return control_model, tensored_control_images, tensored_control_modes, control_scales

invokeai/app/invocations/bria_latent_sampler.py

@@ -1,76 +0,0 @@
-import torch
-
-from invokeai.app.invocations.fields import Input, InputField, OutputField
-from invokeai.app.invocations.model import TransformerField
-from invokeai.app.invocations.primitives import (
-    BaseInvocationOutput,
-    FieldDescriptions,
-    LatentsField,
-)
-from invokeai.backend.bria.pipeline_bria_controlnet import prepare_latents
-from invokeai.invocation_api import (
-    BaseInvocation,
-    Classification,
-    InvocationContext,
-    invocation,
-    invocation_output,
-)
-
-
-@invocation_output("bria_latent_sampler_output")
-class BriaLatentSamplerInvocationOutput(BaseInvocationOutput):
-    """Base class for nodes that output a CogView text conditioning tensor."""
-
-    latents: LatentsField = OutputField(description=FieldDescriptions.cond)
-    latent_image_ids: LatentsField = OutputField(description=FieldDescriptions.cond)
-
-
-@invocation(
-    "bria_latent_sampler",
-    title="Latent Sampler - Bria",
-    tags=["image", "bria"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class BriaLatentSamplerInvocation(BaseInvocation):
-    seed: int = InputField(
-        default=42,
-        title="Seed",
-        description="The seed to use for the latent sampler",
-    )
-    transformer: TransformerField = InputField(
-        description="Bria model (Transformer) to load",
-        input=Input.Connection,
-        title="Transformer",
-    )
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> BriaLatentSamplerInvocationOutput:
-        with context.models.load(self.transformer.transformer) as transformer:
-            device = transformer.device
-            dtype = transformer.dtype
-
-        height, width = 1024, 1024
-        generator = torch.Generator(device=device).manual_seed(self.seed)
-
-        num_channels_latents = 4
-        latents, latent_image_ids = prepare_latents(
-            batch_size=1,
-            num_channels_latents=num_channels_latents,
-            height=height,
-            width=width,
-            dtype=dtype,
-            device=device,
-            generator=generator,
-        )
-
-        saved_latents_tensor = context.tensors.save(latents)
-        saved_latent_image_ids_tensor = context.tensors.save(latent_image_ids)
-        latents_output = LatentsField(latents_name=saved_latents_tensor)
-        latent_image_ids_output = LatentsField(latents_name=saved_latent_image_ids_tensor)
-
-        return BriaLatentSamplerInvocationOutput(
-            latents=latents_output,
-            latent_image_ids=latent_image_ids_output,
-        )

invokeai/app/invocations/bria_model_loader.py

@@ -1,58 +0,0 @@
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
-from invokeai.app.invocations.model import (
-    ModelIdentifierField,
-    SubModelType,
-    T5EncoderField,
-    TransformerField,
-    VAEField,
-)
-from invokeai.invocation_api import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    InvocationContext,
-    invocation,
-    invocation_output,
-)
-
-
-@invocation_output("bria_model_loader_output")
-class BriaModelLoaderOutput(BaseInvocationOutput):
-    """Bria base model loader output"""
-
-    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
-    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
-    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
-
-
-@invocation(
-    "bria_model_loader",
-    title="Main Model - Bria",
-    tags=["model", "bria"],
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class BriaModelLoaderInvocation(BaseInvocation):
-    """Loads a bria base model, outputting its submodels."""
-
-    model: ModelIdentifierField = InputField(
-        description="Bria model (Transformer) to load",
-        ui_type=UIType.BriaMainModel,
-        input=Input.Direct,
-    )
-
-    def invoke(self, context: InvocationContext) -> BriaModelLoaderOutput:
-        for key in [self.model.key]:
-            if not context.models.exists(key):
-                raise ValueError(f"Unknown model: {key}")
-
-        transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-        text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
-        tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
-        vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
-
-        return BriaModelLoaderOutput(
-            transformer=TransformerField(transformer=transformer, loras=[]),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[]),
-            vae=VAEField(vae=vae),
-        )

invokeai/app/invocations/bria_text_encoder.py

@@ -1,93 +0,0 @@
-from typing import Optional
-
-import torch
-from transformers import (
-    T5EncoderModel,
-    T5TokenizerFast,
-)
-
-from invokeai.app.invocations.model import T5EncoderField
-from invokeai.app.invocations.primitives import BaseInvocationOutput, FieldDescriptions, Input, OutputField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.bria.pipeline_bria_controlnet import encode_prompt
-from invokeai.invocation_api import (
-    BaseInvocation,
-    Classification,
-    InputField,
-    LatentsField,
-    invocation,
-    invocation_output,
-)
-
-
-@invocation_output("bria_text_encoder_output")
-class BriaTextEncoderInvocationOutput(BaseInvocationOutput):
-    """Base class for nodes that output a CogView text conditioning tensor."""
-
-    pos_embeds: LatentsField = OutputField(description=FieldDescriptions.cond)
-    neg_embeds: LatentsField = OutputField(description=FieldDescriptions.cond)
-    text_ids: LatentsField = OutputField(description=FieldDescriptions.cond)
-
-
-@invocation(
-    "bria_text_encoder",
-    title="Prompt - Bria",
-    tags=["prompt", "conditioning", "bria"],
-    category="conditioning",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class BriaTextEncoderInvocation(BaseInvocation):
-    prompt: str = InputField(
-        title="Prompt",
-        description="The prompt to encode",
-    )
-    negative_prompt: Optional[str] = InputField(
-        title="Negative Prompt",
-        description="The negative prompt to encode",
-        default="Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate",
-    )
-    max_length: int = InputField(
-        default=128,
-        title="Max Length",
-        description="The maximum length of the prompt",
-    )
-    t5_encoder: T5EncoderField = InputField(
-        title="T5Encoder",
-        description=FieldDescriptions.t5_encoder,
-        input=Input.Connection,
-    )
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> BriaTextEncoderInvocationOutput:
-        t5_encoder_info = context.models.load(self.t5_encoder.text_encoder)
-        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
-        with (
-            t5_encoder_info as text_encoder,
-            t5_tokenizer_info as tokenizer,
-        ):
-            assert isinstance(tokenizer, T5TokenizerFast)
-            assert isinstance(text_encoder, T5EncoderModel)
-
-        (prompt_embeds, negative_prompt_embeds, text_ids) = encode_prompt(
-            prompt=self.prompt,
-            tokenizer=tokenizer,
-            text_encoder=text_encoder,
-            negative_prompt=self.negative_prompt,
-            device=text_encoder.device,
-            num_images_per_prompt=1,
-            max_sequence_length=self.max_length,
-            lora_scale=1.0,
-        )
-
-        saved_pos_tensor = context.tensors.save(prompt_embeds)
-        saved_neg_tensor = context.tensors.save(negative_prompt_embeds)
-        saved_text_ids_tensor = context.tensors.save(text_ids)
-        pos_embeds_output = LatentsField(latents_name=saved_pos_tensor)
-        neg_embeds_output = LatentsField(latents_name=saved_neg_tensor)
-        text_ids_output = LatentsField(latents_name=saved_text_ids_tensor)
-        return BriaTextEncoderInvocationOutput(
-            pos_embeds=pos_embeds_output,
-            neg_embeds=neg_embeds_output,
-            text_ids=text_ids_output,
-        )

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

@@ -42,8 +42,6 @@ class UIType(str, Enum, metaclass=MetaEnum):
     MainModel = "MainModelField"
     CogView4MainModel = "CogView4MainModelField"
     FluxMainModel = "FluxMainModelField"
-    BriaMainModel = "BriaMainModelField"
-    BriaControlNetModel = "BriaControlNetModelField"
     SD3MainModel = "SD3MainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
@@ -217,7 +215,6 @@ 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):
@@ -294,12 +291,6 @@ 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"""
 
@@ -447,7 +438,7 @@ 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__()
@@ -588,7 +579,7 @@ 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/invocations/primitives.py

@@ -430,15 +430,6 @@ class FluxConditioningOutput(BaseInvocationOutput):
         return cls(conditioning=FluxConditioningField(conditioning_name=conditioning_name))
 
 
-@invocation_output("flux_conditioning_collection_output")
-class FluxConditioningCollectionOutput(BaseInvocationOutput):
-    """Base class for nodes that output a collection of conditioning tensors"""
-
-    collection: list[FluxConditioningField] = OutputField(
-        description="The output conditioning tensors",
-    )
-
-
 @invocation_output("sd3_conditioning_output")
 class SD3ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single SD3 conditioning tensor"""

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -14,14 +14,15 @@ from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._db = db
+        self._conn = db.conn
 
     def add_image_to_board(
         self,
         board_id: str,
         image_name: str,
     ) -> None:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 INSERT INTO board_images (board_id, image_name)
@@ -30,12 +31,17 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
                 """,
                 (board_id, image_name, board_id),
             )
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise e
 
     def remove_image_from_board(
         self,
         image_name: str,
     ) -> None:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 DELETE FROM board_images
@@ -43,6 +49,10 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
                 """,
                 (image_name,),
             )
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise e
 
     def get_images_for_board(
         self,
@@ -50,26 +60,27 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
         offset: int = 0,
         limit: int = 10,
     ) -> OffsetPaginatedResults[ImageRecord]:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT images.*
-                FROM board_images
-                INNER JOIN images ON board_images.image_name = images.image_name
-                WHERE board_images.board_id = ?
-                ORDER BY board_images.updated_at DESC;
-                """,
-                (board_id,),
-            )
-            result = cast(list[sqlite3.Row], cursor.fetchall())
-            images = [deserialize_image_record(dict(r)) for r in result]
+        # TODO: this isn't paginated yet?
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT images.*
+            FROM board_images
+            INNER JOIN images ON board_images.image_name = images.image_name
+            WHERE board_images.board_id = ?
+            ORDER BY board_images.updated_at DESC;
+            """,
+            (board_id,),
+        )
+        result = cast(list[sqlite3.Row], cursor.fetchall())
+        images = [deserialize_image_record(dict(r)) for r in result]
 
-            cursor.execute(
-                """--sql
-                SELECT COUNT(*) FROM images WHERE 1=1;
-                """
-            )
-            count = cast(int, cursor.fetchone()[0])
+        cursor.execute(
+            """--sql
+            SELECT COUNT(*) FROM images WHERE 1=1;
+            """
+        )
+        count = cast(int, cursor.fetchone()[0])
 
         return OffsetPaginatedResults(items=images, offset=offset, limit=limit, total=count)
 
@@ -79,55 +90,56 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
         categories: list[ImageCategory] | None,
         is_intermediate: bool | None,
     ) -> list[str]:
-        with self._db.transaction() as cursor:
-            params: list[str | bool] = []
+        params: list[str | bool] = []
 
-            # Base query is a join between images and board_images
-            stmt = """
-                    SELECT images.image_name
-                    FROM images
-                    LEFT JOIN board_images ON board_images.image_name = images.image_name
-                    WHERE 1=1
-                    """
+        # Base query is a join between images and board_images
+        stmt = """
+                SELECT images.image_name
+                FROM images
+                LEFT JOIN board_images ON board_images.image_name = images.image_name
+                WHERE 1=1
+                """
 
-            # Handle board_id filter
-            if board_id == "none":
-                stmt += """--sql
-                    AND board_images.board_id IS NULL
-                    """
-            else:
-                stmt += """--sql
-                    AND board_images.board_id = ?
-                    """
-                params.append(board_id)
+        # Handle board_id filter
+        if board_id == "none":
+            stmt += """--sql
+                AND board_images.board_id IS NULL
+                """
+        else:
+            stmt += """--sql
+                AND board_images.board_id = ?
+                """
+            params.append(board_id)
 
-            # Add the category filter
-            if categories is not None:
-                # Convert the enum values to unique list of strings
-                category_strings = [c.value for c in set(categories)]
-                # Create the correct length of placeholders
-                placeholders = ",".join("?" * len(category_strings))
-                stmt += f"""--sql
-                    AND images.image_category IN ( {placeholders} )
-                    """
+        # Add the category filter
+        if categories is not None:
+            # Convert the enum values to unique list of strings
+            category_strings = [c.value for c in set(categories)]
+            # Create the correct length of placeholders
+            placeholders = ",".join("?" * len(category_strings))
+            stmt += f"""--sql
+                AND images.image_category IN ( {placeholders} )
+                """
 
-                # Unpack the included categories into the query params
-                for c in category_strings:
-                    params.append(c)
+            # Unpack the included categories into the query params
+            for c in category_strings:
+                params.append(c)
 
-            # Add the is_intermediate filter
-            if is_intermediate is not None:
-                stmt += """--sql
-                    AND images.is_intermediate = ?
-                    """
-                params.append(is_intermediate)
+        # Add the is_intermediate filter
+        if is_intermediate is not None:
+            stmt += """--sql
+                AND images.is_intermediate = ?
+                """
+            params.append(is_intermediate)
 
-            # Put a ring on it
-            stmt += ";"
+        # Put a ring on it
+        stmt += ";"
 
-            cursor.execute(stmt, params)
+        # Execute the query
+        cursor = self._conn.cursor()
+        cursor.execute(stmt, params)
 
-            result = cast(list[sqlite3.Row], cursor.fetchall())
+        result = cast(list[sqlite3.Row], cursor.fetchall())
         image_names = [r[0] for r in result]
         return image_names
 
@@ -135,31 +147,31 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
         self,
         image_name: str,
     ) -> Optional[str]:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                    SELECT board_id
-                    FROM board_images
-                    WHERE image_name = ?;
-                    """,
-                (image_name,),
-            )
-            result = cursor.fetchone()
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+                SELECT board_id
+                FROM board_images
+                WHERE image_name = ?;
+                """,
+            (image_name,),
+        )
+        result = cursor.fetchone()
         if result is None:
             return None
         return cast(str, result[0])
 
     def get_image_count_for_board(self, board_id: str) -> int:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                    SELECT COUNT(*)
-                    FROM board_images
-                    INNER JOIN images ON board_images.image_name = images.image_name
-                    WHERE images.is_intermediate = FALSE
-                    AND board_images.board_id = ?;
-                    """,
-                (board_id,),
-            )
-            count = cast(int, cursor.fetchone()[0])
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+                SELECT COUNT(*)
+                FROM board_images
+                INNER JOIN images ON board_images.image_name = images.image_name
+                WHERE images.is_intermediate = FALSE
+                AND board_images.board_id = ?;
+                """,
+            (board_id,),
+        )
+        count = cast(int, cursor.fetchone()[0])
         return count

invokeai/app/services/board_records/board_records_sqlite.py

@@ -20,57 +20,61 @@ from invokeai.app.util.misc import uuid_string
 class SqliteBoardRecordStorage(BoardRecordStorageBase):
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._db = db
+        self._conn = db.conn
 
     def delete(self, board_id: str) -> None:
-        with self._db.transaction() as cursor:
-            try:
-                cursor.execute(
-                    """--sql
-                    DELETE FROM boards
-                    WHERE board_id = ?;
-                    """,
-                    (board_id,),
-                )
-            except Exception as e:
-                raise BoardRecordDeleteException from e
+        try:
+            cursor = self._conn.cursor()
+            cursor.execute(
+                """--sql
+                DELETE FROM boards
+                WHERE board_id = ?;
+                """,
+                (board_id,),
+            )
+            self._conn.commit()
+        except Exception as e:
+            self._conn.rollback()
+            raise BoardRecordDeleteException from e
 
     def save(
         self,
         board_name: str,
     ) -> BoardRecord:
-        with self._db.transaction() as cursor:
-            try:
-                board_id = uuid_string()
-                cursor.execute(
-                    """--sql
-                    INSERT OR IGNORE INTO boards (board_id, board_name)
-                    VALUES (?, ?);
-                    """,
-                    (board_id, board_name),
-                )
-            except sqlite3.Error as e:
-                raise BoardRecordSaveException from e
+        try:
+            board_id = uuid_string()
+            cursor = self._conn.cursor()
+            cursor.execute(
+                """--sql
+                INSERT OR IGNORE INTO boards (board_id, board_name)
+                VALUES (?, ?);
+                """,
+                (board_id, board_name),
+            )
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise BoardRecordSaveException from e
         return self.get(board_id)
 
     def get(
         self,
         board_id: str,
     ) -> BoardRecord:
-        with self._db.transaction() as cursor:
-            try:
-                cursor.execute(
-                    """--sql
-                    SELECT *
-                    FROM boards
-                    WHERE board_id = ?;
-                    """,
-                    (board_id,),
-                )
+        try:
+            cursor = self._conn.cursor()
+            cursor.execute(
+                """--sql
+                SELECT *
+                FROM boards
+                WHERE board_id = ?;
+                """,
+                (board_id,),
+            )
 
-                result = cast(Union[sqlite3.Row, None], cursor.fetchone())
-            except sqlite3.Error as e:
-                raise BoardRecordNotFoundException from e
+            result = cast(Union[sqlite3.Row, None], cursor.fetchone())
+        except sqlite3.Error as e:
+            raise BoardRecordNotFoundException from e
         if result is None:
             raise BoardRecordNotFoundException
         return BoardRecord(**dict(result))
@@ -80,43 +84,45 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
         board_id: str,
         changes: BoardChanges,
     ) -> BoardRecord:
-        with self._db.transaction() as cursor:
-            try:
-                # Change the name of a board
-                if changes.board_name is not None:
-                    cursor.execute(
-                        """--sql
-                        UPDATE boards
-                        SET board_name = ?
-                        WHERE board_id = ?;
-                        """,
-                        (changes.board_name, board_id),
-                    )
+        try:
+            cursor = self._conn.cursor()
+            # Change the name of a board
+            if changes.board_name is not None:
+                cursor.execute(
+                    """--sql
+                    UPDATE boards
+                    SET board_name = ?
+                    WHERE board_id = ?;
+                    """,
+                    (changes.board_name, board_id),
+                )
 
-                # Change the cover image of a board
-                if changes.cover_image_name is not None:
-                    cursor.execute(
-                        """--sql
-                        UPDATE boards
-                        SET cover_image_name = ?
-                        WHERE board_id = ?;
-                        """,
-                        (changes.cover_image_name, board_id),
-                    )
+            # Change the cover image of a board
+            if changes.cover_image_name is not None:
+                cursor.execute(
+                    """--sql
+                    UPDATE boards
+                    SET cover_image_name = ?
+                    WHERE board_id = ?;
+                    """,
+                    (changes.cover_image_name, board_id),
+                )
 
-                # Change the archived status of a board
-                if changes.archived is not None:
-                    cursor.execute(
-                        """--sql
-                        UPDATE boards
-                        SET archived = ?
-                        WHERE board_id = ?;
-                        """,
-                        (changes.archived, board_id),
-                    )
+            # Change the archived status of a board
+            if changes.archived is not None:
+                cursor.execute(
+                    """--sql
+                    UPDATE boards
+                    SET archived = ?
+                    WHERE board_id = ?;
+                    """,
+                    (changes.archived, board_id),
+                )
 
-            except sqlite3.Error as e:
-                raise BoardRecordSaveException from e
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise BoardRecordSaveException from e
         return self.get(board_id)
 
     def get_many(
@@ -127,77 +133,78 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
         limit: int = 10,
         include_archived: bool = False,
     ) -> OffsetPaginatedResults[BoardRecord]:
-        with self._db.transaction() as cursor:
-            # Build base query
-            base_query = """
-                    SELECT *
+        cursor = self._conn.cursor()
+
+        # Build base query
+        base_query = """
+                SELECT *
+                FROM boards
+                {archived_filter}
+                ORDER BY {order_by} {direction}
+                LIMIT ? OFFSET ?;
+            """
+
+        # Determine archived filter condition
+        archived_filter = "" if include_archived else "WHERE archived = 0"
+
+        final_query = base_query.format(
+            archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
+        )
+
+        # Execute query to fetch boards
+        cursor.execute(final_query, (limit, offset))
+
+        result = cast(list[sqlite3.Row], cursor.fetchall())
+        boards = [deserialize_board_record(dict(r)) for r in result]
+
+        # Determine count query
+        if include_archived:
+            count_query = """
+                    SELECT COUNT(*)
+                    FROM boards;
+                """
+        else:
+            count_query = """
+                    SELECT COUNT(*)
                     FROM boards
-                    {archived_filter}
-                    ORDER BY {order_by} {direction}
-                    LIMIT ? OFFSET ?;
+                    WHERE archived = 0;
                 """
 
-            # Determine archived filter condition
-            archived_filter = "" if include_archived else "WHERE archived = 0"
+        # Execute count query
+        cursor.execute(count_query)
 
-            final_query = base_query.format(
-                archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
-            )
-
-            # Execute query to fetch boards
-            cursor.execute(final_query, (limit, offset))
-
-            result = cast(list[sqlite3.Row], cursor.fetchall())
-            boards = [deserialize_board_record(dict(r)) for r in result]
-
-            # Determine count query
-            if include_archived:
-                count_query = """
-                        SELECT COUNT(*)
-                        FROM boards;
-                    """
-            else:
-                count_query = """
-                        SELECT COUNT(*)
-                        FROM boards
-                        WHERE archived = 0;
-                    """
-
-            # Execute count query
-            cursor.execute(count_query)
-
-            count = cast(int, cursor.fetchone()[0])
+        count = cast(int, cursor.fetchone()[0])
 
         return OffsetPaginatedResults[BoardRecord](items=boards, offset=offset, limit=limit, total=count)
 
     def get_all(
         self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
     ) -> list[BoardRecord]:
-        with self._db.transaction() as cursor:
-            if order_by == BoardRecordOrderBy.Name:
-                base_query = """
-                        SELECT *
-                        FROM boards
-                        {archived_filter}
-                        ORDER BY LOWER(board_name) {direction}
-                    """
-            else:
-                base_query = """
-                        SELECT *
-                        FROM boards
-                        {archived_filter}
-                        ORDER BY {order_by} {direction}
-                    """
+        cursor = self._conn.cursor()
+        if order_by == BoardRecordOrderBy.Name:
+            base_query = """
+                    SELECT *
+                    FROM boards
+                    {archived_filter}
+                    ORDER BY LOWER(board_name) {direction}
+                """
+        else:
+            base_query = """
+                    SELECT *
+                    FROM boards
+                    {archived_filter}
+                    ORDER BY {order_by} {direction}
+                """
 
-            archived_filter = "" if include_archived else "WHERE archived = 0"
+        archived_filter = "" if include_archived else "WHERE archived = 0"
 
-            final_query = base_query.format(
-                archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
-            )
+        final_query = base_query.format(
+            archived_filter=archived_filter, order_by=order_by.value, direction=direction.value
+        )
 
-            cursor.execute(final_query)
+        cursor.execute(final_query)
 
-            result = cast(list[sqlite3.Row], cursor.fetchall())
+        result = cast(list[sqlite3.Row], cursor.fetchall())
         boards = [deserialize_board_record(dict(r)) for r in result]
 
         return boards

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/download/download_default.py

@@ -8,7 +8,6 @@ import time
 import traceback
 from pathlib import Path
 from queue import Empty, PriorityQueue
-from shutil import disk_usage
 from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Set
 
 import requests
@@ -336,14 +335,6 @@ class DownloadQueueService(DownloadQueueServiceBase):
 
         assert job.download_path
 
-        free_space = disk_usage(job.download_path.parent).free
-        GB = 2**30
-        self._logger.debug(f"Download is {job.total_bytes / GB:.2f} GB of {free_space / GB:.2f} GB free.")
-        if free_space < job.total_bytes:
-            raise RuntimeError(
-                f"Free disk space {free_space / GB:.2f} GB is not enough for download of {job.total_bytes / GB:.2f} GB."
-            )
-
         # Don't clobber an existing file. See commit 82c2c85202f88c6d24ff84710f297cfc6ae174af
         # for code that instead resumes an interrupted download.
         if job.download_path.exists():

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,
@@ -24,22 +23,22 @@ from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 class SqliteImageRecordStorage(ImageRecordStorageBase):
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._db = db
+        self._conn = db.conn
 
     def get(self, image_name: str) -> ImageRecord:
-        with self._db.transaction() as cursor:
-            try:
-                cursor.execute(
-                    f"""--sql
-                    SELECT {IMAGE_DTO_COLS} FROM images
-                    WHERE image_name = ?;
-                    """,
-                    (image_name,),
-                )
+        try:
+            cursor = self._conn.cursor()
+            cursor.execute(
+                f"""--sql
+                SELECT {IMAGE_DTO_COLS} FROM images
+                WHERE image_name = ?;
+                """,
+                (image_name,),
+            )
 
-                result = cast(Optional[sqlite3.Row], cursor.fetchone())
-            except sqlite3.Error as e:
-                raise ImageRecordNotFoundException from e
+            result = cast(Optional[sqlite3.Row], cursor.fetchone())
+        except sqlite3.Error as e:
+            raise ImageRecordNotFoundException from e
 
         if not result:
             raise ImageRecordNotFoundException
@@ -47,20 +46,17 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         return deserialize_image_record(dict(result))
 
     def get_metadata(self, image_name: str) -> Optional[MetadataField]:
-        with self._db.transaction() as cursor:
-            try:
-                cursor.execute(
-                    """--sql
-                    SELECT metadata FROM images
-                    WHERE image_name = ?;
-                    """,
-                    (image_name,),
-                )
+        try:
+            cursor = self._conn.cursor()
+            cursor.execute(
+                """--sql
+                SELECT metadata FROM images
+                WHERE image_name = ?;
+                """,
+                (image_name,),
+            )
 
-                result = cast(Optional[sqlite3.Row], cursor.fetchone())
-
-            except sqlite3.Error as e:
-                raise ImageRecordNotFoundException from e
+            result = cast(Optional[sqlite3.Row], cursor.fetchone())
 
             if not result:
                 raise ImageRecordNotFoundException
@@ -68,60 +64,64 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             as_dict = dict(result)
             metadata_raw = cast(Optional[str], as_dict.get("metadata", None))
             return MetadataFieldValidator.validate_json(metadata_raw) if metadata_raw is not None else None
+        except sqlite3.Error as e:
+            raise ImageRecordNotFoundException from e
 
     def update(
         self,
         image_name: str,
         changes: ImageRecordChanges,
     ) -> None:
-        with self._db.transaction() as cursor:
-            try:
-                # Change the category of the image
-                if changes.image_category is not None:
-                    cursor.execute(
-                        """--sql
-                        UPDATE images
-                        SET image_category = ?
-                        WHERE image_name = ?;
-                        """,
-                        (changes.image_category, image_name),
-                    )
+        try:
+            cursor = self._conn.cursor()
+            # Change the category of the image
+            if changes.image_category is not None:
+                cursor.execute(
+                    """--sql
+                    UPDATE images
+                    SET image_category = ?
+                    WHERE image_name = ?;
+                    """,
+                    (changes.image_category, image_name),
+                )
 
-                # Change the session associated with the image
-                if changes.session_id is not None:
-                    cursor.execute(
-                        """--sql
-                        UPDATE images
-                        SET session_id = ?
-                        WHERE image_name = ?;
-                        """,
-                        (changes.session_id, image_name),
-                    )
+            # Change the session associated with the image
+            if changes.session_id is not None:
+                cursor.execute(
+                    """--sql
+                    UPDATE images
+                    SET session_id = ?
+                    WHERE image_name = ?;
+                    """,
+                    (changes.session_id, image_name),
+                )
 
-                # Change the image's `is_intermediate`` flag
-                if changes.is_intermediate is not None:
-                    cursor.execute(
-                        """--sql
-                        UPDATE images
-                        SET is_intermediate = ?
-                        WHERE image_name = ?;
-                        """,
-                        (changes.is_intermediate, image_name),
-                    )
+            # Change the image's `is_intermediate`` flag
+            if changes.is_intermediate is not None:
+                cursor.execute(
+                    """--sql
+                    UPDATE images
+                    SET is_intermediate = ?
+                    WHERE image_name = ?;
+                    """,
+                    (changes.is_intermediate, image_name),
+                )
 
-                # Change the image's `starred`` state
-                if changes.starred is not None:
-                    cursor.execute(
-                        """--sql
-                        UPDATE images
-                        SET starred = ?
-                        WHERE image_name = ?;
-                        """,
-                        (changes.starred, image_name),
-                    )
+            # Change the image's `starred`` state
+            if changes.starred is not None:
+                cursor.execute(
+                    """--sql
+                    UPDATE images
+                    SET starred = ?
+                    WHERE image_name = ?;
+                    """,
+                    (changes.starred, image_name),
+                )
 
-            except sqlite3.Error as e:
-                raise ImageRecordSaveException from e
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise ImageRecordSaveException from e
 
     def get_many(
         self,
@@ -135,162 +135,166 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         board_id: Optional[str] = None,
         search_term: Optional[str] = None,
     ) -> OffsetPaginatedResults[ImageRecord]:
-        with self._db.transaction() as cursor:
-            # Manually build two queries - one for the count, one for the records
-            count_query = """--sql
-            SELECT COUNT(*)
-            FROM images
-            LEFT JOIN board_images ON board_images.image_name = images.image_name
-            WHERE 1=1
+        cursor = self._conn.cursor()
+
+        # Manually build two queries - one for the count, one for the records
+        count_query = """--sql
+        SELECT COUNT(*)
+        FROM images
+        LEFT JOIN board_images ON board_images.image_name = images.image_name
+        WHERE 1=1
+        """
+
+        images_query = f"""--sql
+        SELECT {IMAGE_DTO_COLS}
+        FROM images
+        LEFT JOIN board_images ON board_images.image_name = images.image_name
+        WHERE 1=1
+        """
+
+        query_conditions = ""
+        query_params: list[Union[int, str, bool]] = []
+
+        if image_origin is not None:
+            query_conditions += """--sql
+            AND images.image_origin = ?
+            """
+            query_params.append(image_origin.value)
+
+        if categories is not None:
+            # Convert the enum values to unique list of strings
+            category_strings = [c.value for c in set(categories)]
+            # Create the correct length of placeholders
+            placeholders = ",".join("?" * len(category_strings))
+
+            query_conditions += f"""--sql
+            AND images.image_category IN ( {placeholders} )
             """
 
-            images_query = f"""--sql
-            SELECT {IMAGE_DTO_COLS}
-            FROM images
-            LEFT JOIN board_images ON board_images.image_name = images.image_name
-            WHERE 1=1
+            # Unpack the included categories into the query params
+            for c in category_strings:
+                query_params.append(c)
+
+        if is_intermediate is not None:
+            query_conditions += """--sql
+            AND images.is_intermediate = ?
             """
 
-            query_conditions = ""
-            query_params: list[Union[int, str, bool]] = []
+            query_params.append(is_intermediate)
 
-            if image_origin is not None:
-                query_conditions += """--sql
-                AND images.image_origin = ?
-                """
-                query_params.append(image_origin.value)
+        # board_id of "none" is reserved for images without a board
+        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 categories is not None:
-                # Convert the enum values to unique list of strings
-                category_strings = [c.value for c in set(categories)]
-                # Create the correct length of placeholders
-                placeholders = ",".join("?" * len(category_strings))
+        # Search term condition
+        if search_term:
+            query_conditions += """--sql
+            AND images.metadata LIKE ?
+            """
+            query_params.append(f"%{search_term.lower()}%")
 
-                query_conditions += f"""--sql
-                AND images.image_category IN ( {placeholders} )
-                """
+        if starred_first:
+            query_pagination = f"""--sql
+            ORDER BY images.starred DESC, images.created_at {order_dir.value} LIMIT ? OFFSET ?
+            """
+        else:
+            query_pagination = f"""--sql
+            ORDER BY images.created_at {order_dir.value} LIMIT ? OFFSET ?
+            """
 
-                # Unpack the included categories into the query params
-                for c in category_strings:
-                    query_params.append(c)
+        # Final images query with pagination
+        images_query += query_conditions + query_pagination + ";"
+        # Add all the parameters
+        images_params = query_params.copy()
+        # Add the pagination parameters
+        images_params.extend([limit, offset])
 
-            if is_intermediate is not None:
-                query_conditions += """--sql
-                AND images.is_intermediate = ?
-                """
+        # Build the list of images, deserializing each row
+        cursor.execute(images_query, images_params)
+        result = cast(list[sqlite3.Row], cursor.fetchall())
+        images = [deserialize_image_record(dict(r)) for r in result]
 
-                query_params.append(is_intermediate)
-
-            # board_id of "none" is reserved for images without a board
-            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)
-
-            # Search term condition
-            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()}%")
-
-            if starred_first:
-                query_pagination = f"""--sql
-                ORDER BY images.starred DESC, images.created_at {order_dir.value} LIMIT ? OFFSET ?
-                """
-            else:
-                query_pagination = f"""--sql
-                ORDER BY images.created_at {order_dir.value} LIMIT ? OFFSET ?
-                """
-
-            # Final images query with pagination
-            images_query += query_conditions + query_pagination + ";"
-            # Add all the parameters
-            images_params = query_params.copy()
-            # Add the pagination parameters
-            images_params.extend([limit, offset])
-
-            # Build the list of images, deserializing each row
-            cursor.execute(images_query, images_params)
-            result = cast(list[sqlite3.Row], cursor.fetchall())
-
-            images = [deserialize_image_record(dict(r)) for r in result]
-
-            # Set up and execute the count query, without pagination
-            count_query += query_conditions + ";"
-            count_params = query_params.copy()
-            cursor.execute(count_query, count_params)
-            count = cast(int, cursor.fetchone()[0])
+        # Set up and execute the count query, without pagination
+        count_query += query_conditions + ";"
+        count_params = query_params.copy()
+        cursor.execute(count_query, count_params)
+        count = cast(int, cursor.fetchone()[0])
 
         return OffsetPaginatedResults(items=images, offset=offset, limit=limit, total=count)
 
     def delete(self, image_name: str) -> None:
-        with self._db.transaction() as cursor:
-            try:
-                cursor.execute(
-                    """--sql
-                    DELETE FROM images
-                    WHERE image_name = ?;
-                    """,
-                    (image_name,),
-                )
-            except sqlite3.Error as e:
-                raise ImageRecordDeleteException from e
-
-    def delete_many(self, image_names: list[str]) -> None:
-        with self._db.transaction() as cursor:
-            try:
-                placeholders = ",".join("?" for _ in image_names)
-
-                # Construct the SQLite query with the placeholders
-                query = f"DELETE FROM images WHERE image_name IN ({placeholders})"
-
-                # Execute the query with the list of IDs as parameters
-                cursor.execute(query, image_names)
-
-            except sqlite3.Error as e:
-                raise ImageRecordDeleteException from e
-
-    def get_intermediates_count(self) -> int:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
-                SELECT COUNT(*) FROM images
-                WHERE is_intermediate = TRUE;
-                """
+                DELETE FROM images
+                WHERE image_name = ?;
+                """,
+                (image_name,),
             )
-            count = cast(int, cursor.fetchone()[0])
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise ImageRecordDeleteException from e
+
+    def delete_many(self, image_names: list[str]) -> None:
+        try:
+            cursor = self._conn.cursor()
+
+            placeholders = ",".join("?" for _ in image_names)
+
+            # Construct the SQLite query with the placeholders
+            query = f"DELETE FROM images WHERE image_name IN ({placeholders})"
+
+            # Execute the query with the list of IDs as parameters
+            cursor.execute(query, image_names)
+
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise ImageRecordDeleteException from e
+
+    def get_intermediates_count(self) -> int:
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT COUNT(*) FROM images
+            WHERE is_intermediate = TRUE;
+            """
+        )
+        count = cast(int, cursor.fetchone()[0])
+        self._conn.commit()
         return count
 
     def delete_intermediates(self) -> list[str]:
-        with self._db.transaction() as cursor:
-            try:
-                cursor.execute(
-                    """--sql
-                    SELECT image_name FROM images
-                    WHERE is_intermediate = TRUE;
-                    """
-                )
-                result = cast(list[sqlite3.Row], cursor.fetchall())
-                image_names = [r[0] for r in result]
-                cursor.execute(
-                    """--sql
-                    DELETE FROM images
-                    WHERE is_intermediate = TRUE;
-                    """
-                )
-            except sqlite3.Error as e:
-                raise ImageRecordDeleteException from e
-        return image_names
+        try:
+            cursor = self._conn.cursor()
+            cursor.execute(
+                """--sql
+                SELECT image_name FROM images
+                WHERE is_intermediate = TRUE;
+                """
+            )
+            result = cast(list[sqlite3.Row], cursor.fetchall())
+            image_names = [r[0] for r in result]
+            cursor.execute(
+                """--sql
+                DELETE FROM images
+                WHERE is_intermediate = TRUE;
+                """
+            )
+            self._conn.commit()
+            return image_names
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise ImageRecordDeleteException from e
 
     def save(
         self,
@@ -306,165 +310,75 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         node_id: Optional[str] = None,
         metadata: Optional[str] = None,
     ) -> datetime:
-        with self._db.transaction() as cursor:
-            try:
-                cursor.execute(
-                    """--sql
-                    INSERT OR IGNORE INTO images (
-                        image_name,
-                        image_origin,
-                        image_category,
-                        width,
-                        height,
-                        node_id,
-                        session_id,
-                        metadata,
-                        is_intermediate,
-                        starred,
-                        has_workflow
-                        )
-                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
-                    """,
-                    (
-                        image_name,
-                        image_origin.value,
-                        image_category.value,
-                        width,
-                        height,
-                        node_id,
-                        session_id,
-                        metadata,
-                        is_intermediate,
-                        starred,
-                        has_workflow,
-                    ),
-                )
-
-                cursor.execute(
-                    """--sql
-                    SELECT created_at
-                    FROM images
-                    WHERE image_name = ?;
-                    """,
-                    (image_name,),
-                )
-
-                created_at = datetime.fromisoformat(cursor.fetchone()[0])
-
-            except sqlite3.Error as e:
-                raise ImageRecordSaveException from e
-        return created_at
-
-    def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
-                SELECT images.*
-                FROM images
-                JOIN board_images ON images.image_name = board_images.image_name
-                WHERE board_images.board_id = ?
-                AND images.is_intermediate = FALSE
-                ORDER BY images.starred DESC, images.created_at DESC
-                LIMIT 1;
+                INSERT OR IGNORE INTO images (
+                    image_name,
+                    image_origin,
+                    image_category,
+                    width,
+                    height,
+                    node_id,
+                    session_id,
+                    metadata,
+                    is_intermediate,
+                    starred,
+                    has_workflow
+                    )
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?);
                 """,
-                (board_id,),
+                (
+                    image_name,
+                    image_origin.value,
+                    image_category.value,
+                    width,
+                    height,
+                    node_id,
+                    session_id,
+                    metadata,
+                    is_intermediate,
+                    starred,
+                    has_workflow,
+                ),
+            )
+            self._conn.commit()
+
+            cursor.execute(
+                """--sql
+                SELECT created_at
+                FROM images
+                WHERE image_name = ?;
+                """,
+                (image_name,),
             )
 
-            result = cast(Optional[sqlite3.Row], cursor.fetchone())
+            created_at = datetime.fromisoformat(cursor.fetchone()[0])
+
+            return created_at
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise ImageRecordSaveException from e
+
+    def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT images.*
+            FROM images
+            JOIN board_images ON images.image_name = board_images.image_name
+            WHERE board_images.board_id = ?
+            AND images.is_intermediate = FALSE
+            ORDER BY images.starred DESC, images.created_at DESC
+            LIMIT 1;
+            """,
+            (board_id,),
+        )
+
+        result = cast(Optional[sqlite3.Row], cursor.fetchone())
 
         if result is None:
             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:
-        with self._db.transaction() as 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

@@ -51,7 +51,6 @@ from invokeai.backend.model_manager.metadata import (
 from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMetadata
 from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.model_manager.taxonomy import ModelRepoVariant, ModelSourceType
-from invokeai.backend.model_manager.util.lora_metadata_extractor import apply_lora_metadata
 from invokeai.backend.util import InvokeAILogger
 from invokeai.backend.util.catch_sigint import catch_sigint
 from invokeai.backend.util.devices import TorchDevice
@@ -149,7 +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:
@@ -668,10 +667,6 @@ class ModelInstallService(ModelInstallServiceBase):
 
         info = info or self._probe(model_path, config)
 
-        # Apply LoRA metadata if applicable
-        model_images_path = self.app_config.models_path / "model_images"
-        apply_lora_metadata(info, model_path.resolve(), model_images_path)
-
         model_path = model_path.resolve()
 
         # Models in the Invoke-managed models dir should use relative paths.

invokeai/app/services/model_records/model_records_sql.py

@@ -78,6 +78,11 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         self._db = db
         self._logger = logger
 
+    @property
+    def db(self) -> SqliteDatabase:
+        """Return the underlying database."""
+        return self._db
+
     def add_model(self, config: AnyModelConfig) -> AnyModelConfig:
         """
         Add a model to the database.
@@ -88,33 +93,38 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
 
         Can raise DuplicateModelException and InvalidModelConfigException exceptions.
         """
-        with self._db.transaction() as cursor:
-            try:
-                cursor.execute(
-                    """--sql
-                    INSERT INTO models (
-                        id,
-                        config
-                        )
-                    VALUES (?,?);
-                    """,
-                    (
-                        config.key,
-                        config.model_dump_json(),
-                    ),
-                )
+        try:
+            cursor = self._db.conn.cursor()
+            cursor.execute(
+                """--sql
+                INSERT INTO models (
+                    id,
+                    config
+                    )
+                VALUES (?,?);
+                """,
+                (
+                    config.key,
+                    config.model_dump_json(),
+                ),
+            )
+            self._db.conn.commit()
 
-            except sqlite3.IntegrityError as e:
-                if "UNIQUE constraint failed" in str(e):
-                    if "models.path" in str(e):
-                        msg = f"A model with path '{config.path}' is already installed"
-                    elif "models.name" in str(e):
-                        msg = f"A model with name='{config.name}', type='{config.type}', base='{config.base}' is already installed"
-                    else:
-                        msg = f"A model with key '{config.key}' is already installed"
-                    raise DuplicateModelException(msg) from e
+        except sqlite3.IntegrityError as e:
+            self._db.conn.rollback()
+            if "UNIQUE constraint failed" in str(e):
+                if "models.path" in str(e):
+                    msg = f"A model with path '{config.path}' is already installed"
+                elif "models.name" in str(e):
+                    msg = f"A model with name='{config.name}', type='{config.type}', base='{config.base}' is already installed"
                 else:
-                    raise e
+                    msg = f"A model with key '{config.key}' is already installed"
+                raise DuplicateModelException(msg) from e
+            else:
+                raise e
+        except sqlite3.Error as e:
+            self._db.conn.rollback()
+            raise e
 
         return self.get_model(config.key)
 
@@ -126,7 +136,8 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
 
         Can raise an UnknownModelException
         """
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._db.conn.cursor()
             cursor.execute(
                 """--sql
                 DELETE FROM models
@@ -136,17 +147,22 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             )
             if cursor.rowcount == 0:
                 raise UnknownModelException("model not found")
+            self._db.conn.commit()
+        except sqlite3.Error as e:
+            self._db.conn.rollback()
+            raise e
 
     def update_model(self, key: str, changes: ModelRecordChanges) -> AnyModelConfig:
-        with self._db.transaction() as cursor:
-            record = self.get_model(key)
+        record = self.get_model(key)
 
-            # Model configs use pydantic's `validate_assignment`, so each change is validated by pydantic.
-            for field_name in changes.model_fields_set:
-                setattr(record, field_name, getattr(changes, field_name))
+        # Model configs use pydantic's `validate_assignment`, so each change is validated by pydantic.
+        for field_name in changes.model_fields_set:
+            setattr(record, field_name, getattr(changes, field_name))
 
-            json_serialized = record.model_dump_json()
+        json_serialized = record.model_dump_json()
 
+        try:
+            cursor = self._db.conn.cursor()
             cursor.execute(
                 """--sql
                 UPDATE models
@@ -158,6 +174,10 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             )
             if cursor.rowcount == 0:
                 raise UnknownModelException("model not found")
+            self._db.conn.commit()
+        except sqlite3.Error as e:
+            self._db.conn.rollback()
+            raise e
 
         return self.get_model(key)
 
@@ -169,30 +189,30 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
 
         Exceptions: UnknownModelException
         """
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT config, strftime('%s',updated_at) FROM models
-                WHERE id=?;
-                """,
-                (key,),
-            )
-            rows = cursor.fetchone()
+        cursor = self._db.conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT config, strftime('%s',updated_at) FROM models
+            WHERE id=?;
+            """,
+            (key,),
+        )
+        rows = cursor.fetchone()
         if not rows:
             raise UnknownModelException("model not found")
         model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
         return model
 
     def get_model_by_hash(self, hash: str) -> AnyModelConfig:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT config, strftime('%s',updated_at) FROM models
-                WHERE hash=?;
-                """,
-                (hash,),
-            )
-            rows = cursor.fetchone()
+        cursor = self._db.conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT config, strftime('%s',updated_at) FROM models
+            WHERE hash=?;
+            """,
+            (hash,),
+        )
+        rows = cursor.fetchone()
         if not rows:
             raise UnknownModelException("model not found")
         model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
@@ -204,15 +224,15 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
 
         :param key: Unique key for the model to be deleted
         """
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                select count(*) FROM models
-                WHERE id=?;
-                """,
-                (key,),
-            )
-            count = cursor.fetchone()[0]
+        cursor = self._db.conn.cursor()
+        cursor.execute(
+            """--sql
+            select count(*) FROM models
+            WHERE id=?;
+            """,
+            (key,),
+        )
+        count = cursor.fetchone()[0]
         return count > 0
 
     def search_by_attr(
@@ -235,42 +255,43 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         If none of the optional filters are passed, will return all
         models in the database.
         """
-        with self._db.transaction() as cursor:
-            assert isinstance(order_by, ModelRecordOrderBy)
-            ordering = {
-                ModelRecordOrderBy.Default: "type, base, name, format",
-                ModelRecordOrderBy.Type: "type",
-                ModelRecordOrderBy.Base: "base",
-                ModelRecordOrderBy.Name: "name",
-                ModelRecordOrderBy.Format: "format",
-            }
 
-            where_clause: list[str] = []
-            bindings: list[str] = []
-            if model_name:
-                where_clause.append("name=?")
-                bindings.append(model_name)
-            if base_model:
-                where_clause.append("base=?")
-                bindings.append(base_model)
-            if model_type:
-                where_clause.append("type=?")
-                bindings.append(model_type)
-            if model_format:
-                where_clause.append("format=?")
-                bindings.append(model_format)
-            where = f"WHERE {' AND '.join(where_clause)}" if where_clause else ""
+        assert isinstance(order_by, ModelRecordOrderBy)
+        ordering = {
+            ModelRecordOrderBy.Default: "type, base, name, format",
+            ModelRecordOrderBy.Type: "type",
+            ModelRecordOrderBy.Base: "base",
+            ModelRecordOrderBy.Name: "name",
+            ModelRecordOrderBy.Format: "format",
+        }
 
-            cursor.execute(
-                f"""--sql
-                SELECT config, strftime('%s',updated_at)
-                FROM models
-                {where}
-                ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason;
-                """,
-                tuple(bindings),
-            )
-            result = cursor.fetchall()
+        where_clause: list[str] = []
+        bindings: list[str] = []
+        if model_name:
+            where_clause.append("name=?")
+            bindings.append(model_name)
+        if base_model:
+            where_clause.append("base=?")
+            bindings.append(base_model)
+        if model_type:
+            where_clause.append("type=?")
+            bindings.append(model_type)
+        if model_format:
+            where_clause.append("format=?")
+            bindings.append(model_format)
+        where = f"WHERE {' AND '.join(where_clause)}" if where_clause else ""
+
+        cursor = self._db.conn.cursor()
+        cursor.execute(
+            f"""--sql
+            SELECT config, strftime('%s',updated_at)
+            FROM models
+            {where}
+            ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason;
+            """,
+            tuple(bindings),
+        )
+        result = cursor.fetchall()
 
         # Parse the model configs.
         results: list[AnyModelConfig] = []
@@ -292,68 +313,69 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
 
     def search_by_path(self, path: Union[str, Path]) -> List[AnyModelConfig]:
         """Return models with the indicated path."""
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT config, strftime('%s',updated_at) FROM models
-                WHERE path=?;
-                """,
-                (str(path),),
-            )
-            results = [ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in cursor.fetchall()]
+        cursor = self._db.conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT config, strftime('%s',updated_at) FROM models
+            WHERE path=?;
+            """,
+            (str(path),),
+        )
+        results = [ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in cursor.fetchall()]
         return results
 
     def search_by_hash(self, hash: str) -> List[AnyModelConfig]:
         """Return models with the indicated hash."""
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT config, strftime('%s',updated_at) FROM models
-                WHERE hash=?;
-                """,
-                (hash,),
-            )
-            results = [ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in cursor.fetchall()]
+        cursor = self._db.conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT config, strftime('%s',updated_at) FROM models
+            WHERE hash=?;
+            """,
+            (hash,),
+        )
+        results = [ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in cursor.fetchall()]
         return results
 
     def list_models(
         self, page: int = 0, per_page: int = 10, order_by: ModelRecordOrderBy = ModelRecordOrderBy.Default
     ) -> PaginatedResults[ModelSummary]:
         """Return a paginated summary listing of each model in the database."""
-        with self._db.transaction() as cursor:
-            assert isinstance(order_by, ModelRecordOrderBy)
-            ordering = {
-                ModelRecordOrderBy.Default: "type, base, name, format",
-                ModelRecordOrderBy.Type: "type",
-                ModelRecordOrderBy.Base: "base",
-                ModelRecordOrderBy.Name: "name",
-                ModelRecordOrderBy.Format: "format",
-            }
+        assert isinstance(order_by, ModelRecordOrderBy)
+        ordering = {
+            ModelRecordOrderBy.Default: "type, base, name, format",
+            ModelRecordOrderBy.Type: "type",
+            ModelRecordOrderBy.Base: "base",
+            ModelRecordOrderBy.Name: "name",
+            ModelRecordOrderBy.Format: "format",
+        }
 
-            # Lock so that the database isn't updated while we're doing the two queries.
-            # query1: get the total number of model configs
-            cursor.execute(
-                """--sql
-                select count(*) from models;
-                """,
-                (),
-            )
-            total = int(cursor.fetchone()[0])
+        cursor = self._db.conn.cursor()
 
-            # query2: fetch key fields
-            cursor.execute(
-                f"""--sql
-                SELECT config
-                FROM models
-                ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason
-                LIMIT ?
-                OFFSET ?;
-                """,
-                (
-                    per_page,
-                    page * per_page,
-                ),
-            )
-            rows = cursor.fetchall()
+        # Lock so that the database isn't updated while we're doing the two queries.
+        # query1: get the total number of model configs
+        cursor.execute(
+            """--sql
+            select count(*) from models;
+            """,
+            (),
+        )
+        total = int(cursor.fetchone()[0])
+
+        # query2: fetch key fields
+        cursor.execute(
+            f"""--sql
+            SELECT config
+            FROM models
+            ORDER BY {ordering[order_by]} -- using ? to bind doesn't work here for some reason
+            LIMIT ?
+            OFFSET ?;
+            """,
+            (
+                per_page,
+                page * per_page,
+            ),
+        )
+        rows = cursor.fetchall()
         items = [ModelSummary.model_validate(dict(x)) for x in rows]
         return PaginatedResults(page=page, pages=ceil(total / per_page), per_page=per_page, total=total, items=items)

invokeai/app/services/model_relationship_records/model_relationship_records_sqlite.py

@@ -1,3 +1,5 @@
+import sqlite3
+
 from invokeai.app.services.model_relationship_records.model_relationship_records_base import (
     ModelRelationshipRecordStorageBase,
 )
@@ -7,49 +9,58 @@ from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 class SqliteModelRelationshipRecordStorage(ModelRelationshipRecordStorageBase):
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._db = db
+        self._conn = db.conn
 
     def add_model_relationship(self, model_key_1: str, model_key_2: str) -> None:
-        with self._db.transaction() as cursor:
-            if model_key_1 == model_key_2:
-                raise ValueError("Cannot relate a model to itself.")
-            a, b = sorted([model_key_1, model_key_2])
+        if model_key_1 == model_key_2:
+            raise ValueError("Cannot relate a model to itself.")
+        a, b = sorted([model_key_1, model_key_2])
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 "INSERT OR IGNORE INTO model_relationships (model_key_1, model_key_2) VALUES (?, ?)",
                 (a, b),
             )
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise e
 
     def remove_model_relationship(self, model_key_1: str, model_key_2: str) -> None:
-        with self._db.transaction() as cursor:
-            a, b = sorted([model_key_1, model_key_2])
+        a, b = sorted([model_key_1, model_key_2])
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 "DELETE FROM model_relationships WHERE model_key_1 = ? AND model_key_2 = ?",
                 (a, b),
             )
+            self._conn.commit()
+        except sqlite3.Error as e:
+            self._conn.rollback()
+            raise e
 
     def get_related_model_keys(self, model_key: str) -> list[str]:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """
-                SELECT model_key_2 FROM model_relationships WHERE model_key_1 = ?
-                UNION
-                SELECT model_key_1 FROM model_relationships WHERE model_key_2 = ?
-                """,
-                (model_key, model_key),
-            )
-            result = [row[0] for row in cursor.fetchall()]
-        return result
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """
+            SELECT model_key_2 FROM model_relationships WHERE model_key_1 = ?
+            UNION
+            SELECT model_key_1 FROM model_relationships WHERE model_key_2 = ?
+            """,
+            (model_key, model_key),
+        )
+        return [row[0] for row in cursor.fetchall()]
 
     def get_related_model_keys_batch(self, model_keys: list[str]) -> list[str]:
-        with self._db.transaction() as cursor:
-            key_list = ",".join("?" for _ in model_keys)
-            cursor.execute(
-                f"""
-                SELECT model_key_2 FROM model_relationships WHERE model_key_1 IN ({key_list})
-                UNION
-                SELECT model_key_1 FROM model_relationships WHERE model_key_2 IN ({key_list})
-                """,
-                model_keys + model_keys,
-            )
-            result = [row[0] for row in cursor.fetchall()]
-        return result
+        cursor = self._conn.cursor()
+
+        key_list = ",".join("?" for _ in model_keys)
+        cursor.execute(
+            f"""
+            SELECT model_key_2 FROM model_relationships WHERE model_key_1 IN ({key_list})
+            UNION
+            SELECT model_key_1 FROM model_relationships WHERE model_key_2 IN ({key_list})
+            """,
+            model_keys + model_keys,
+        )
+        return [row[0] for row in cursor.fetchall()]

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_base.py

@@ -10,8 +10,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     CancelByDestinationResult,
     CancelByQueueIDResult,
     ClearResult,
-    DeleteAllExceptCurrentResult,
-    DeleteByDestinationResult,
     EnqueueBatchResult,
     IsEmptyResult,
     IsFullResult,
@@ -19,6 +17,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     RetryItemsResult,
     SessionQueueCountsByDestination,
     SessionQueueItem,
+    SessionQueueItemDTO,
     SessionQueueStatus,
 )
 from invokeai.app.services.shared.graph import GraphExecutionState
@@ -93,11 +92,6 @@ class SessionQueueBase(ABC):
         """Cancels a session queue item"""
         pass
 
-    @abstractmethod
-    def delete_queue_item(self, item_id: int) -> None:
-        """Deletes a session queue item"""
-        pass
-
     @abstractmethod
     def fail_queue_item(
         self, item_id: int, error_type: str, error_message: str, error_traceback: str
@@ -115,11 +109,6 @@ class SessionQueueBase(ABC):
         """Cancels all queue items with the given batch destination"""
         pass
 
-    @abstractmethod
-    def delete_by_destination(self, queue_id: str, destination: str) -> DeleteByDestinationResult:
-        """Deletes all queue items with the given batch destination"""
-        pass
-
     @abstractmethod
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         """Cancels all queue items with matching queue ID"""
@@ -130,11 +119,6 @@ class SessionQueueBase(ABC):
         """Cancels all queue items except in-progress items"""
         pass
 
-    @abstractmethod
-    def delete_all_except_current(self, queue_id: str) -> DeleteAllExceptCurrentResult:
-        """Deletes all queue items except in-progress items"""
-        pass
-
     @abstractmethod
     def list_queue_items(
         self,
@@ -143,20 +127,10 @@ class SessionQueueBase(ABC):
         priority: int,
         cursor: Optional[int] = None,
         status: Optional[QUEUE_ITEM_STATUS] = None,
-        destination: Optional[str] = None,
-    ) -> CursorPaginatedResults[SessionQueueItem]:
+    ) -> CursorPaginatedResults[SessionQueueItemDTO]:
         """Gets a page of session queue items"""
         pass
 
-    @abstractmethod
-    def list_all_queue_items(
-        self,
-        queue_id: str,
-        destination: Optional[str] = None,
-    ) -> list[SessionQueueItem]:
-        """Gets all queue items that match the given parameters"""
-        pass
-
     @abstractmethod
     def get_queue_item(self, item_id: int) -> SessionQueueItem:
         """Gets a session queue item by ID"""

invokeai/app/services/session_queue/session_queue_common.py

@@ -208,7 +208,7 @@ class FieldIdentifier(BaseModel):
     user_label: str | None = Field(description="The user label of the field, if any")
 
 
-class SessionQueueItem(BaseModel):
+class SessionQueueItemWithoutGraph(BaseModel):
     """Session queue item without the full graph. Used for serialization."""
 
     item_id: int = Field(description="The identifier of the session queue item")
@@ -252,7 +252,42 @@ class SessionQueueItem(BaseModel):
         default=None,
         description="The ID of the published workflow associated with this queue item",
     )
+    api_input_fields: Optional[list[FieldIdentifier]] = Field(
+        default=None, description="The fields that were used as input to the API"
+    )
+    api_output_fields: Optional[list[FieldIdentifier]] = Field(
+        default=None, description="The nodes that were used as output from the API"
+    )
     credits: Optional[float] = Field(default=None, description="The total credits used for this queue item")
+
+    @classmethod
+    def queue_item_dto_from_dict(cls, queue_item_dict: dict) -> "SessionQueueItemDTO":
+        # must parse these manually
+        queue_item_dict["field_values"] = get_field_values(queue_item_dict)
+        return SessionQueueItemDTO(**queue_item_dict)
+
+    model_config = ConfigDict(
+        json_schema_extra={
+            "required": [
+                "item_id",
+                "status",
+                "batch_id",
+                "queue_id",
+                "session_id",
+                "priority",
+                "session_id",
+                "created_at",
+                "updated_at",
+            ]
+        }
+    )
+
+
+class SessionQueueItemDTO(SessionQueueItemWithoutGraph):
+    pass
+
+
+class SessionQueueItem(SessionQueueItemWithoutGraph):
     session: GraphExecutionState = Field(description="The fully-populated session to be executed")
     workflow: Optional[WorkflowWithoutID] = Field(
         default=None, description="The workflow associated with this queue item"
@@ -332,7 +367,6 @@ 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):
@@ -364,18 +398,6 @@ class CancelByDestinationResult(CancelByBatchIDsResult):
     pass
 
 
-class DeleteByDestinationResult(BaseModel):
-    """Result of deleting by a destination"""
-
-    deleted: int = Field(..., description="Number of queue items deleted")
-
-
-class DeleteAllExceptCurrentResult(DeleteByDestinationResult):
-    """Result of deleting all except current"""
-
-    pass
-
-
 class CancelByQueueIDResult(CancelByBatchIDsResult):
     """Result of canceling by queue id"""
 

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -17,8 +17,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     CancelByDestinationResult,
     CancelByQueueIDResult,
     ClearResult,
-    DeleteAllExceptCurrentResult,
-    DeleteByDestinationResult,
     EnqueueBatchResult,
     IsEmptyResult,
     IsFullResult,
@@ -26,6 +24,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     RetryItemsResult,
     SessionQueueCountsByDestination,
     SessionQueueItem,
+    SessionQueueItemDTO,
     SessionQueueItemNotFoundError,
     SessionQueueStatus,
     ValueToInsertTuple,
@@ -47,17 +46,22 @@ class SqliteSessionQueue(SessionQueueBase):
             clear_result = self.clear(DEFAULT_QUEUE_ID)
             if clear_result.deleted > 0:
                 self.__invoker.services.logger.info(f"Cleared all {clear_result.deleted} queue items")
+        else:
+            prune_result = self.prune(DEFAULT_QUEUE_ID)
+            if prune_result.deleted > 0:
+                self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
 
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._db = db
+        self._conn = db.conn
 
     def _set_in_progress_to_canceled(self) -> None:
         """
         Sets all in_progress queue items to canceled. Run on app startup, not associated with any queue.
         This is necessary because the invoker may have been killed while processing a queue item.
         """
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 UPDATE session_queue
@@ -65,104 +69,102 @@ class SqliteSessionQueue(SessionQueueBase):
                 WHERE status = 'in_progress';
                 """
             )
+        except Exception:
+            self._conn.rollback()
+            raise
 
     def _get_current_queue_size(self, queue_id: str) -> int:
         """Gets the current number of pending queue items"""
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT count(*)
-                FROM session_queue
-                WHERE
-                queue_id = ?
-                AND status = 'pending'
-                """,
-                (queue_id,),
-            )
-            count = cast(int, cursor.fetchone()[0])
-        return count
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT count(*)
+            FROM session_queue
+            WHERE
+              queue_id = ?
+              AND status = 'pending'
+            """,
+            (queue_id,),
+        )
+        return cast(int, cursor.fetchone()[0])
 
     def _get_highest_priority(self, queue_id: str) -> int:
         """Gets the highest priority value in the queue"""
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT MAX(priority)
-                FROM session_queue
-                WHERE
-                queue_id = ?
-                AND status = 'pending'
-                """,
-                (queue_id,),
-            )
-            priority = cast(Union[int, None], cursor.fetchone()[0]) or 0
-        return priority
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT MAX(priority)
+            FROM session_queue
+            WHERE
+              queue_id = ?
+              AND status = 'pending'
+            """,
+            (queue_id,),
+        )
+        return cast(Union[int, None], cursor.fetchone()[0]) or 0
 
     async def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
-        current_queue_size = self._get_current_queue_size(queue_id)
-        max_queue_size = self.__invoker.services.configuration.max_queue_size
-        max_new_queue_items = max_queue_size - current_queue_size
+        return await asyncio.to_thread(self._enqueue_batch, queue_id, batch, prepend)
 
-        priority = 0
-        if prepend:
-            priority = self._get_highest_priority(queue_id) + 1
+    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
+            max_new_queue_items = max_queue_size - current_queue_size
 
-        requested_count = await asyncio.to_thread(
-            calc_session_count,
-            batch=batch,
-        )
-        values_to_insert = await asyncio.to_thread(
-            prepare_values_to_insert,
-            queue_id=queue_id,
-            batch=batch,
-            priority=priority,
-            max_new_queue_items=max_new_queue_items,
-        )
-        enqueued_count = len(values_to_insert)
+            priority = 0
+            if prepend:
+                priority = self._get_highest_priority(queue_id) + 1
+
+            requested_count = calc_session_count(batch)
+            values_to_insert = prepare_values_to_insert(
+                queue_id=queue_id,
+                batch=batch,
+                priority=priority,
+                max_new_queue_items=max_new_queue_items,
+            )
+            enqueued_count = len(values_to_insert)
+
+            if requested_count > enqueued_count:
+                values_to_insert = values_to_insert[:max_new_queue_items]
 
-        with self._db.transaction() as 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 (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
-                    """,
+                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,
             )
-            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()]
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         enqueue_result = EnqueueBatchResult(
             queue_id=queue_id,
             requested=requested_count,
             enqueued=enqueued_count,
             batch=batch,
             priority=priority,
-            item_ids=item_ids,
         )
         self.__invoker.services.events.emit_batch_enqueued(enqueue_result)
         return enqueue_result
 
     def dequeue(self) -> Optional[SessionQueueItem]:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT *
-                FROM session_queue
-                WHERE status = 'pending'
-                ORDER BY
-                    priority DESC,
-                    item_id ASC
-                LIMIT 1
-                """
-            )
-            result = cast(Union[sqlite3.Row, None], cursor.fetchone())
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT *
+            FROM session_queue
+            WHERE status = 'pending'
+            ORDER BY
+                priority DESC,
+                item_id ASC
+            LIMIT 1
+            """
+        )
+        result = cast(Union[sqlite3.Row, None], cursor.fetchone())
         if result is None:
             return None
         queue_item = SessionQueueItem.queue_item_from_dict(dict(result))
@@ -170,40 +172,40 @@ class SqliteSessionQueue(SessionQueueBase):
         return queue_item
 
     def get_next(self, queue_id: str) -> Optional[SessionQueueItem]:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT *
-                FROM session_queue
-                WHERE
-                    queue_id = ?
-                    AND status = 'pending'
-                ORDER BY
-                    priority DESC,
-                    created_at ASC
-                LIMIT 1
-                """,
-                (queue_id,),
-            )
-            result = cast(Union[sqlite3.Row, None], cursor.fetchone())
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT *
+            FROM session_queue
+            WHERE
+                queue_id = ?
+                AND status = 'pending'
+            ORDER BY
+                priority DESC,
+                created_at ASC
+            LIMIT 1
+            """,
+            (queue_id,),
+        )
+        result = cast(Union[sqlite3.Row, None], cursor.fetchone())
         if result is None:
             return None
         return SessionQueueItem.queue_item_from_dict(dict(result))
 
     def get_current(self, queue_id: str) -> Optional[SessionQueueItem]:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT *
-                FROM session_queue
-                WHERE
-                    queue_id = ?
-                    AND status = 'in_progress'
-                LIMIT 1
-                """,
-                (queue_id,),
-            )
-            result = cast(Union[sqlite3.Row, None], cursor.fetchone())
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT *
+            FROM session_queue
+            WHERE
+                queue_id = ?
+                AND status = 'in_progress'
+            LIMIT 1
+            """,
+            (queue_id,),
+        )
+        result = cast(Union[sqlite3.Row, None], cursor.fetchone())
         if result is None:
             return None
         return SessionQueueItem.queue_item_from_dict(dict(result))
@@ -216,23 +218,8 @@ class SqliteSessionQueue(SessionQueueBase):
         error_message: Optional[str] = None,
         error_traceback: Optional[str] = None,
     ) -> SessionQueueItem:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT status FROM session_queue WHERE item_id = ?
-                """,
-                (item_id,),
-            )
-            row = cursor.fetchone()
-        if row is None:
-            raise SessionQueueItemNotFoundError(f"No queue item with id {item_id}")
-        current_status = row[0]
-
-        # Only update if not already finished (completed, failed or canceled)
-        if current_status in ("completed", "failed", "canceled"):
-            return self.get_queue_item(item_id)
-
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 UPDATE session_queue
@@ -241,7 +228,10 @@ class SqliteSessionQueue(SessionQueueBase):
                 """,
                 (status, error_type, error_message, error_traceback, item_id),
             )
-
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         queue_item = self.get_queue_item(item_id)
         batch_status = self.get_batch_status(queue_id=queue_item.queue_id, batch_id=queue_item.batch_id)
         queue_status = self.get_queue_status(queue_id=queue_item.queue_id)
@@ -249,34 +239,35 @@ class SqliteSessionQueue(SessionQueueBase):
         return queue_item
 
     def is_empty(self, queue_id: str) -> IsEmptyResult:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT count(*)
-                FROM session_queue
-                WHERE queue_id = ?
-                """,
-                (queue_id,),
-            )
-            is_empty = cast(int, cursor.fetchone()[0]) == 0
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT count(*)
+            FROM session_queue
+            WHERE queue_id = ?
+            """,
+            (queue_id,),
+        )
+        is_empty = cast(int, cursor.fetchone()[0]) == 0
         return IsEmptyResult(is_empty=is_empty)
 
     def is_full(self, queue_id: str) -> IsFullResult:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT count(*)
-                FROM session_queue
-                WHERE queue_id = ?
-                """,
-                (queue_id,),
-            )
-            max_queue_size = self.__invoker.services.configuration.max_queue_size
-            is_full = cast(int, cursor.fetchone()[0]) >= max_queue_size
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT count(*)
+            FROM session_queue
+            WHERE queue_id = ?
+            """,
+            (queue_id,),
+        )
+        max_queue_size = self.__invoker.services.configuration.max_queue_size
+        is_full = cast(int, cursor.fetchone()[0]) >= max_queue_size
         return IsFullResult(is_full=is_full)
 
     def clear(self, queue_id: str) -> ClearResult:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 SELECT COUNT(*)
@@ -294,19 +285,24 @@ class SqliteSessionQueue(SessionQueueBase):
                 """,
                 (queue_id,),
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         self.__invoker.services.events.emit_queue_cleared(queue_id)
         return ClearResult(deleted=count)
 
     def prune(self, queue_id: str) -> PruneResult:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             where = """--sql
                 WHERE
-                queue_id = ?
-                AND (
+                  queue_id = ?
+                  AND (
                     status = 'completed'
                     OR status = 'failed'
                     OR status = 'canceled'
-                )
+                  )
                 """
             cursor.execute(
                 f"""--sql
@@ -325,28 +321,16 @@ class SqliteSessionQueue(SessionQueueBase):
                 """,
                 (queue_id,),
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         return PruneResult(deleted=count)
 
     def cancel_queue_item(self, item_id: int) -> SessionQueueItem:
         queue_item = self._set_queue_item_status(item_id=item_id, status="canceled")
         return queue_item
 
-    def delete_queue_item(self, item_id: int) -> None:
-        """Deletes a session queue item"""
-        try:
-            self.cancel_queue_item(item_id)
-        except SessionQueueItemNotFoundError:
-            pass
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                DELETE
-                FROM session_queue
-                WHERE item_id = ?
-                """,
-                (item_id,),
-            )
-
     def complete_queue_item(self, item_id: int) -> SessionQueueItem:
         queue_item = self._set_queue_item_status(item_id=item_id, status="completed")
         return queue_item
@@ -368,7 +352,8 @@ class SqliteSessionQueue(SessionQueueBase):
         return queue_item
 
     def cancel_by_batch_ids(self, queue_id: str, batch_ids: list[str]) -> CancelByBatchIDsResult:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             current_queue_item = self.get_current(queue_id)
             placeholders = ", ".join(["?" for _ in batch_ids])
             where = f"""--sql
@@ -378,8 +363,6 @@ 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(
@@ -399,14 +382,17 @@ class SqliteSessionQueue(SessionQueueBase):
                 """,
                 tuple(params),
             )
-
-        if current_queue_item is not None and current_queue_item.batch_id in batch_ids:
-            self._set_queue_item_status(current_queue_item.item_id, "canceled")
-
+            self._conn.commit()
+            if current_queue_item is not None and current_queue_item.batch_id in batch_ids:
+                self._set_queue_item_status(current_queue_item.item_id, "canceled")
+        except Exception:
+            self._conn.rollback()
+            raise
         return CancelByBatchIDsResult(canceled=count)
 
     def cancel_by_destination(self, queue_id: str, destination: str) -> CancelByDestinationResult:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             current_queue_item = self.get_current(queue_id)
             where = """--sql
                 WHERE
@@ -415,8 +401,6 @@ 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(
@@ -436,67 +420,17 @@ class SqliteSessionQueue(SessionQueueBase):
                 """,
                 params,
             )
-        if current_queue_item is not None and current_queue_item.destination == destination:
-            self._set_queue_item_status(current_queue_item.item_id, "canceled")
+            self._conn.commit()
+            if current_queue_item is not None and current_queue_item.destination == destination:
+                self._set_queue_item_status(current_queue_item.item_id, "canceled")
+        except Exception:
+            self._conn.rollback()
+            raise
         return CancelByDestinationResult(canceled=count)
 
-    def delete_by_destination(self, queue_id: str, destination: str) -> DeleteByDestinationResult:
-        with self._db.transaction() as cursor:
-            current_queue_item = self.get_current(queue_id)
-            if current_queue_item is not None and current_queue_item.destination == destination:
-                self.cancel_queue_item(current_queue_item.item_id)
-            params = (queue_id, destination)
-            cursor.execute(
-                """--sql
-                SELECT COUNT(*)
-                FROM session_queue
-                WHERE
-                  queue_id = ?
-                  AND destination = ?;
-                """,
-                params,
-            )
-            count = cursor.fetchone()[0]
-            cursor.execute(
-                """--sql
-                DELETE
-                FROM session_queue
-                WHERE
-                  queue_id = ?
-                  AND destination = ?;
-                """,
-                params,
-            )
-        return DeleteByDestinationResult(deleted=count)
-
-    def delete_all_except_current(self, queue_id: str) -> DeleteAllExceptCurrentResult:
-        with self._db.transaction() as cursor:
-            where = """--sql
-                WHERE
-                  queue_id == ?
-                  AND status == 'pending'
-                """
-            cursor.execute(
-                f"""--sql
-                SELECT COUNT(*)
-                FROM session_queue
-                {where};
-                """,
-                (queue_id,),
-            )
-            count = cursor.fetchone()[0]
-            cursor.execute(
-                f"""--sql
-                DELETE
-                FROM session_queue
-                {where};
-                """,
-                (queue_id,),
-            )
-        return DeleteAllExceptCurrentResult(deleted=count)
-
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             current_queue_item = self.get_current(queue_id)
             where = """--sql
                 WHERE
@@ -504,8 +438,6 @@ 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(
@@ -525,13 +457,21 @@ class SqliteSessionQueue(SessionQueueBase):
                 """,
                 tuple(params),
             )
-
-        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")
+            self._conn.commit()
+            if current_queue_item is not None and current_queue_item.queue_id == queue_id:
+                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
         return CancelByQueueIDResult(canceled=count)
 
     def cancel_all_except_current(self, queue_id: str) -> CancelAllExceptCurrentResult:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             where = """--sql
                 WHERE
                   queue_id == ?
@@ -554,25 +494,30 @@ class SqliteSessionQueue(SessionQueueBase):
                 """,
                 (queue_id,),
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         return CancelAllExceptCurrentResult(canceled=count)
 
     def get_queue_item(self, item_id: int) -> SessionQueueItem:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT * FROM session_queue
-                WHERE
-                    item_id = ?
-                """,
-                (item_id,),
-            )
-            result = cast(Union[sqlite3.Row, None], cursor.fetchone())
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT * FROM session_queue
+            WHERE
+                item_id = ?
+            """,
+            (item_id,),
+        )
+        result = cast(Union[sqlite3.Row, None], cursor.fetchone())
         if result is None:
             raise SessionQueueItemNotFoundError(f"No queue item with id {item_id}")
         return SessionQueueItem.queue_item_from_dict(dict(result))
 
     def set_queue_item_session(self, item_id: int, session: GraphExecutionState) -> SessionQueueItem:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             # Use exclude_none so we don't end up with a bunch of nulls in the graph - this can cause validation errors
             # when the graph is loaded. Graph execution occurs purely in memory - the session saved here is not referenced
             # during execution.
@@ -585,6 +530,10 @@ class SqliteSessionQueue(SessionQueueBase):
                 """,
                 (session_json, item_id),
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         return self.get_queue_item(item_id)
 
     def list_queue_items(
@@ -594,45 +543,53 @@ class SqliteSessionQueue(SessionQueueBase):
         priority: int,
         cursor: Optional[int] = None,
         status: Optional[QUEUE_ITEM_STATUS] = None,
-        destination: Optional[str] = None,
-    ) -> CursorPaginatedResults[SessionQueueItem]:
-        with self._db.transaction() as cursor_:
-            item_id = cursor
-            query = """--sql
-                SELECT *
-                FROM session_queue
-                WHERE queue_id = ?
-            """
-            params: list[Union[str, int]] = [queue_id]
-
-            if status is not None:
-                query += """--sql
-                    AND status = ?
-                    """
-                params.append(status)
-
-            if destination is not None:
-                query += """---sql
-                    AND destination = ?
-                """
-                params.append(destination)
-
-            if item_id is not None:
-                query += """--sql
-                    AND (priority < ?) OR (priority = ? AND item_id > ?)
-                    """
-                params.extend([priority, priority, item_id])
+    ) -> CursorPaginatedResults[SessionQueueItemDTO]:
+        cursor_ = self._conn.cursor()
+        item_id = cursor
+        query = """--sql
+            SELECT item_id,
+                status,
+                priority,
+                field_values,
+                error_type,
+                error_message,
+                error_traceback,
+                created_at,
+                updated_at,
+                completed_at,
+                started_at,
+                session_id,
+                batch_id,
+                queue_id,
+                origin,
+                destination
+            FROM session_queue
+            WHERE queue_id = ?
+        """
+        params: list[Union[str, int]] = [queue_id]
 
+        if status is not None:
             query += """--sql
-                ORDER BY
-                    priority DESC,
-                    item_id ASC
-                LIMIT ?
+                AND status = ?
                 """
-            params.append(limit + 1)
-            cursor_.execute(query, params)
-            results = cast(list[sqlite3.Row], cursor_.fetchall())
-        items = [SessionQueueItem.queue_item_from_dict(dict(result)) for result in results]
+            params.append(status)
+
+        if item_id is not None:
+            query += """--sql
+                AND (priority < ?) OR (priority = ? AND item_id > ?)
+                """
+            params.extend([priority, priority, item_id])
+
+        query += """--sql
+            ORDER BY
+                priority DESC,
+                item_id ASC
+            LIMIT ?
+            """
+        params.append(limit + 1)
+        cursor_.execute(query, params)
+        results = cast(list[sqlite3.Row], cursor_.fetchall())
+        items = [SessionQueueItemDTO.queue_item_dto_from_dict(dict(result)) for result in results]
         has_more = False
         if len(items) > limit:
             # remove the extra item
@@ -640,52 +597,21 @@ class SqliteSessionQueue(SessionQueueBase):
             has_more = True
         return CursorPaginatedResults(items=items, limit=limit, has_more=has_more)
 
-    def list_all_queue_items(
-        self,
-        queue_id: str,
-        destination: Optional[str] = None,
-    ) -> list[SessionQueueItem]:
-        """Gets all queue items that match the given parameters"""
-        with self._db.transaction() as cursor:
-            query = """--sql
-                SELECT *
-                FROM session_queue
-                WHERE queue_id = ?
-            """
-            params: list[Union[str, int]] = [queue_id]
-
-            if destination is not None:
-                query += """---sql
-                    AND destination = ?
-                """
-                params.append(destination)
-
-            query += """--sql
-                ORDER BY
-                    priority DESC,
-                    item_id ASC
-                ;
-                """
-            cursor.execute(query, params)
-            results = cast(list[sqlite3.Row], cursor.fetchall())
-        items = [SessionQueueItem.queue_item_from_dict(dict(result)) for result in results]
-        return items
-
     def get_queue_status(self, queue_id: str) -> SessionQueueStatus:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT status, count(*)
-                FROM session_queue
-                WHERE queue_id = ?
-                GROUP BY status
-                """,
-                (queue_id,),
-            )
-            counts_result = cast(list[sqlite3.Row], cursor.fetchall())
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT status, count(*)
+            FROM session_queue
+            WHERE queue_id = ?
+            GROUP BY status
+            """,
+            (queue_id,),
+        )
+        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,
@@ -701,20 +627,20 @@ class SqliteSessionQueue(SessionQueueBase):
         )
 
     def get_batch_status(self, queue_id: str, batch_id: str) -> BatchStatus:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT status, count(*), origin, destination
-                FROM session_queue
-                WHERE
-                    queue_id = ?
-                    AND batch_id = ?
-                GROUP BY status
-                """,
-                (queue_id, batch_id),
-            )
-            result = cast(list[sqlite3.Row], cursor.fetchall())
-        total = sum(row[1] or 0 for row in result)
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT status, count(*), origin, destination
+            FROM session_queue
+            WHERE
+                queue_id = ?
+                AND batch_id = ?
+            GROUP BY status
+            """,
+            (queue_id, batch_id),
+        )
+        result = cast(list[sqlite3.Row], cursor.fetchall())
+        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
@@ -733,20 +659,20 @@ class SqliteSessionQueue(SessionQueueBase):
         )
 
     def get_counts_by_destination(self, queue_id: str, destination: str) -> SessionQueueCountsByDestination:
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT status, count(*)
-                FROM session_queue
-                WHERE queue_id = ?
-                AND destination = ?
-                GROUP BY status
-                """,
-                (queue_id, destination),
-            )
-            counts_result = cast(list[sqlite3.Row], cursor.fetchall())
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT status, count(*)
+            FROM session_queue
+            WHERE queue_id = ?
+            AND destination = ?
+            GROUP BY status
+            """,
+            (queue_id, destination),
+        )
+        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(
@@ -762,7 +688,8 @@ class SqliteSessionQueue(SessionQueueBase):
 
     def retry_items_by_id(self, queue_id: str, item_ids: list[int]) -> RetryItemsResult:
         """Retries the given queue items"""
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             values_to_insert: list[ValueToInsertTuple] = []
             retried_item_ids: list[int] = []
 
@@ -813,6 +740,10 @@ class SqliteSessionQueue(SessionQueueBase):
                 values_to_insert,
             )
 
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         retry_result = RetryItemsResult(
             queue_id=queue_id,
             retried_item_ids=retried_item_ids,

invokeai/app/services/shared/graph.py

@@ -2,12 +2,11 @@
 
 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 (
     BaseModel,
-    ConfigDict,
     GetCoreSchemaHandler,
     GetJsonSchemaHandler,
     ValidationError,
@@ -58,32 +57,17 @@ 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):
@@ -803,22 +787,6 @@ class GraphExecutionState(BaseModel):
         default_factory=dict,
     )
 
-    model_config = ConfigDict(
-        json_schema_extra={
-            "required": [
-                "id",
-                "graph",
-                "execution_graph",
-                "executed",
-                "executed_history",
-                "results",
-                "errors",
-                "prepared_source_mapping",
-                "source_prepared_mapping",
-            ]
-        }
-    )
-
     @field_validator("graph")
     def graph_is_valid(cls, v: Graph):
         """Validates that the graph is valid"""
@@ -1007,11 +975,10 @@ 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/services/shared/sqlite/sqlite_database.py

@@ -1,7 +1,4 @@
 import sqlite3
-import threading
-from collections.abc import Generator
-from contextlib import contextmanager
 from logging import Logger
 from pathlib import Path
 
@@ -29,65 +26,46 @@ class SqliteDatabase:
 
     def __init__(self, db_path: Path | None, logger: Logger, verbose: bool = False) -> None:
         """Initializes the database. This is used internally by the class constructor."""
-        self._logger = logger
-        self._db_path = db_path
-        self._verbose = verbose
-        self._lock = threading.RLock()
+        self.logger = logger
+        self.db_path = db_path
+        self.verbose = verbose
 
-        if not self._db_path:
+        if not self.db_path:
             logger.info("Initializing in-memory database")
         else:
-            self._db_path.parent.mkdir(parents=True, exist_ok=True)
-            self._logger.info(f"Initializing database at {self._db_path}")
+            self.db_path.parent.mkdir(parents=True, exist_ok=True)
+            self.logger.info(f"Initializing database at {self.db_path}")
 
-        self._conn = sqlite3.connect(database=self._db_path or sqlite_memory, check_same_thread=False)
-        self._conn.row_factory = sqlite3.Row
+        self.conn = sqlite3.connect(database=self.db_path or sqlite_memory, check_same_thread=False)
+        self.conn.row_factory = sqlite3.Row
 
-        if self._verbose:
-            self._conn.set_trace_callback(self._logger.debug)
+        if self.verbose:
+            self.conn.set_trace_callback(self.logger.debug)
 
         # Enable foreign key constraints
-        self._conn.execute("PRAGMA foreign_keys = ON;")
+        self.conn.execute("PRAGMA foreign_keys = ON;")
 
         # Enable Write-Ahead Logging (WAL) mode for better concurrency
-        self._conn.execute("PRAGMA journal_mode = WAL;")
+        self.conn.execute("PRAGMA journal_mode = WAL;")
 
         # Set a busy timeout to prevent database lockups during writes
-        self._conn.execute("PRAGMA busy_timeout = 5000;")  # 5 seconds
+        self.conn.execute("PRAGMA busy_timeout = 5000;")  # 5 seconds
 
     def clean(self) -> None:
         """
         Cleans the database by running the VACUUM command, reporting on the freed space.
         """
         # No need to clean in-memory database
-        if not self._db_path:
+        if not self.db_path:
             return
         try:
-            with self._conn as conn:
-                initial_db_size = Path(self._db_path).stat().st_size
-                conn.execute("VACUUM;")
-                conn.commit()
-                final_db_size = Path(self._db_path).stat().st_size
-                freed_space_in_mb = round((initial_db_size - final_db_size) / 1024 / 1024, 2)
-                if freed_space_in_mb > 0:
-                    self._logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
+            initial_db_size = Path(self.db_path).stat().st_size
+            self.conn.execute("VACUUM;")
+            self.conn.commit()
+            final_db_size = Path(self.db_path).stat().st_size
+            freed_space_in_mb = round((initial_db_size - final_db_size) / 1024 / 1024, 2)
+            if freed_space_in_mb > 0:
+                self.logger.info(f"Cleaned database (freed {freed_space_in_mb}MB)")
         except Exception as e:
-            self._logger.error(f"Error cleaning database: {e}")
+            self.logger.error(f"Error cleaning database: {e}")
             raise
-
-    @contextmanager
-    def transaction(self) -> Generator[sqlite3.Cursor, None, None]:
-        """
-        Thread-safe context manager for DB work.
-        Acquires the RLock, yields a Cursor, then commits or rolls back.
-        """
-        with self._lock:
-            cursor = self._conn.cursor()
-            try:
-                yield cursor
-                self._conn.commit()
-            except:
-                self._conn.rollback()
-                raise
-            finally:
-                cursor.close()

invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_impl.py

@@ -32,7 +32,7 @@ class SqliteMigrator:
 
     def __init__(self, db: SqliteDatabase) -> None:
         self._db = db
-        self._logger = db._logger
+        self._logger = db.logger
         self._migration_set = MigrationSet()
         self._backup_path: Optional[Path] = None
 
@@ -45,7 +45,7 @@ class SqliteMigrator:
         """Migrates the database to the latest version."""
         # This throws if there is a problem.
         self._migration_set.validate_migration_chain()
-        cursor = self._db._conn.cursor()
+        cursor = self._db.conn.cursor()
         self._create_migrations_table(cursor=cursor)
 
         if self._migration_set.count == 0:
@@ -59,13 +59,13 @@ class SqliteMigrator:
         self._logger.info("Database update needed")
 
         # Make a backup of the db if it needs to be updated and is a file db
-        if self._db._db_path is not None:
+        if self._db.db_path is not None:
             timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
-            self._backup_path = self._db._db_path.parent / f"{self._db._db_path.stem}_backup_{timestamp}.db"
+            self._backup_path = self._db.db_path.parent / f"{self._db.db_path.stem}_backup_{timestamp}.db"
             self._logger.info(f"Backing up database to {str(self._backup_path)}")
             # Use SQLite to do the backup
             with closing(sqlite3.connect(self._backup_path)) as backup_conn:
-                self._db._conn.backup(backup_conn)
+                self._db.conn.backup(backup_conn)
         else:
             self._logger.info("Using in-memory database, no backup needed")
 
@@ -81,7 +81,7 @@ class SqliteMigrator:
         try:
             # Using sqlite3.Connection as a context manager commits a the transaction on exit, or rolls it back if an
             # exception is raised.
-            with self._db._conn as conn:
+            with self._db.conn as conn:
                 cursor = conn.cursor()
                 if self._get_current_version(cursor) != migration.from_version:
                     raise MigrationError(

invokeai/app/services/style_preset_records/style_preset_records_sqlite.py

@@ -17,7 +17,7 @@ from invokeai.app.util.misc import uuid_string
 class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._db = db
+        self._conn = db.conn
 
     def start(self, invoker: Invoker) -> None:
         self._invoker = invoker
@@ -25,23 +25,24 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
 
     def get(self, style_preset_id: str) -> StylePresetRecordDTO:
         """Gets a style preset by ID."""
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT *
-                FROM style_presets
-                WHERE id = ?;
-                """,
-                (style_preset_id,),
-            )
-            row = cursor.fetchone()
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT *
+            FROM style_presets
+            WHERE id = ?;
+            """,
+            (style_preset_id,),
+        )
+        row = cursor.fetchone()
         if row is None:
             raise StylePresetNotFoundError(f"Style preset with id {style_preset_id} not found")
         return StylePresetRecordDTO.from_dict(dict(row))
 
     def create(self, style_preset: StylePresetWithoutId) -> StylePresetRecordDTO:
         style_preset_id = uuid_string()
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 INSERT OR IGNORE INTO style_presets (
@@ -59,11 +60,16 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
                     style_preset.type,
                 ),
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         return self.get(style_preset_id)
 
     def create_many(self, style_presets: list[StylePresetWithoutId]) -> None:
         style_preset_ids = []
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             for style_preset in style_presets:
                 style_preset_id = uuid_string()
                 style_preset_ids.append(style_preset_id)
@@ -84,11 +90,16 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
                         style_preset.type,
                     ),
                 )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
 
         return None
 
     def update(self, style_preset_id: str, changes: StylePresetChanges) -> StylePresetRecordDTO:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             # Change the name of a style preset
             if changes.name is not None:
                 cursor.execute(
@@ -111,10 +122,15 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
                     (changes.preset_data.model_dump_json(), style_preset_id),
                 )
 
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         return self.get(style_preset_id)
 
     def delete(self, style_preset_id: str) -> None:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 DELETE from style_presets
@@ -122,41 +138,51 @@ class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
                 """,
                 (style_preset_id,),
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         return None
 
     def get_many(self, type: PresetType | None = None) -> list[StylePresetRecordDTO]:
-        with self._db.transaction() as cursor:
-            main_query = """
-                SELECT
-                    *
-                FROM style_presets
-                """
+        main_query = """
+            SELECT
+                *
+            FROM style_presets
+            """
 
-            if type is not None:
-                main_query += "WHERE type = ? "
+        if type is not None:
+            main_query += "WHERE type = ? "
 
-            main_query += "ORDER BY LOWER(name) ASC"
+        main_query += "ORDER BY LOWER(name) ASC"
 
-            if type is not None:
-                cursor.execute(main_query, (type,))
-            else:
-                cursor.execute(main_query)
+        cursor = self._conn.cursor()
+        if type is not None:
+            cursor.execute(main_query, (type,))
+        else:
+            cursor.execute(main_query)
 
-            rows = cursor.fetchall()
+        rows = cursor.fetchall()
         style_presets = [StylePresetRecordDTO.from_dict(dict(row)) for row in rows]
 
         return style_presets
 
     def _sync_default_style_presets(self) -> None:
         """Syncs default style presets to the database. Internal use only."""
-        with self._db.transaction() as cursor:
-            # First delete all existing default style presets
+
+        # First delete all existing default style presets
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 DELETE FROM style_presets
                 WHERE type = "default";
                 """
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         # Next, parse and create the default style presets
         with open(Path(__file__).parent / Path("default_style_presets.json"), "r") as file:
             presets = json.load(file)

invokeai/app/services/workflow_records/workflow_records_sqlite.py

@@ -25,7 +25,7 @@ SQL_TIME_FORMAT = "%Y-%m-%d %H:%M:%f"
 class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
-        self._db = db
+        self._conn = db.conn
 
     def start(self, invoker: Invoker) -> None:
         self._invoker = invoker
@@ -33,16 +33,16 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
 
     def get(self, workflow_id: str) -> WorkflowRecordDTO:
         """Gets a workflow by ID. Updates the opened_at column."""
-        with self._db.transaction() as cursor:
-            cursor.execute(
-                """--sql
-                SELECT workflow_id, workflow, name, created_at, updated_at, opened_at
-                FROM workflow_library
-                WHERE workflow_id = ?;
-                """,
-                (workflow_id,),
-            )
-            row = cursor.fetchone()
+        cursor = self._conn.cursor()
+        cursor.execute(
+            """--sql
+            SELECT workflow_id, workflow, name, created_at, updated_at, opened_at
+            FROM workflow_library
+            WHERE workflow_id = ?;
+            """,
+            (workflow_id,),
+        )
+        row = cursor.fetchone()
         if row is None:
             raise WorkflowNotFoundError(f"Workflow with id {workflow_id} not found")
         return WorkflowRecordDTO.from_dict(dict(row))
@@ -51,8 +51,9 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         if workflow.meta.category is WorkflowCategory.Default:
             raise ValueError("Default workflows cannot be created via this method")
 
-        with self._db.transaction() as cursor:
+        try:
             workflow_with_id = Workflow(**workflow.model_dump(), id=uuid_string())
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 INSERT OR IGNORE INTO workflow_library (
@@ -63,13 +64,18 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                 """,
                 (workflow_with_id.id, workflow_with_id.model_dump_json()),
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         return self.get(workflow_with_id.id)
 
     def update(self, workflow: Workflow) -> WorkflowRecordDTO:
         if workflow.meta.category is WorkflowCategory.Default:
             raise ValueError("Default workflows cannot be updated")
 
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 UPDATE workflow_library
@@ -78,13 +84,18 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                 """,
                 (workflow.model_dump_json(), workflow.id),
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         return self.get(workflow.id)
 
     def delete(self, workflow_id: str) -> None:
         if self.get(workflow_id).workflow.meta.category is WorkflowCategory.Default:
             raise ValueError("Default workflows cannot be deleted")
 
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 """--sql
                 DELETE from workflow_library
@@ -92,6 +103,10 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                 """,
                 (workflow_id,),
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
         return None
 
     def get_many(
@@ -106,108 +121,108 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         has_been_opened: Optional[bool] = None,
         is_published: Optional[bool] = None,
     ) -> PaginatedResults[WorkflowRecordListItemDTO]:
-        with self._db.transaction() as cursor:
-            # sanitize!
-            assert order_by in WorkflowRecordOrderBy
-            assert direction in SQLiteDirection
+        # sanitize!
+        assert order_by in WorkflowRecordOrderBy
+        assert direction in SQLiteDirection
 
-            # We will construct the query dynamically based on the query params
+        # We will construct the query dynamically based on the query params
 
-            # The main query to get the workflows / counts
-            main_query = """
-                    SELECT
-                        workflow_id,
-                        category,
-                        name,
-                        description,
-                        created_at,
-                        updated_at,
-                        opened_at,
-                        tags
-                    FROM workflow_library
-                    """
-            count_query = "SELECT COUNT(*) FROM workflow_library"
+        # The main query to get the workflows / counts
+        main_query = """
+                SELECT
+                    workflow_id,
+                    category,
+                    name,
+                    description,
+                    created_at,
+                    updated_at,
+                    opened_at,
+                    tags
+                FROM workflow_library
+                """
+        count_query = "SELECT COUNT(*) FROM workflow_library"
 
-            # Start with an empty list of conditions and params
-            conditions: list[str] = []
-            params: list[str | int] = []
+        # Start with an empty list of conditions and params
+        conditions: list[str] = []
+        params: list[str | int] = []
 
-            if categories:
-                # Categories is a list of WorkflowCategory enum values, and a single string in the DB
+        if categories:
+            # Categories is a list of WorkflowCategory enum values, and a single string in the DB
 
-                # Ensure all categories are valid (is this necessary?)
-                assert all(c in WorkflowCategory for c in categories)
+            # Ensure all categories are valid (is this necessary?)
+            assert all(c in WorkflowCategory for c in categories)
 
-                # Construct a placeholder string for the number of categories
-                placeholders = ", ".join("?" for _ in categories)
+            # Construct a placeholder string for the number of categories
+            placeholders = ", ".join("?" for _ in categories)
 
-                # Construct the condition string & params
-                category_condition = f"category IN ({placeholders})"
-                category_params = [category.value for category in categories]
+            # Construct the condition string & params
+            category_condition = f"category IN ({placeholders})"
+            category_params = [category.value for category in categories]
 
-                conditions.append(category_condition)
-                params.extend(category_params)
+            conditions.append(category_condition)
+            params.extend(category_params)
 
-            if tags:
-                # Tags is a list of strings, and a single string in the DB
-                # The string in the DB has no guaranteed format
+        if tags:
+            # Tags is a list of strings, and a single string in the DB
+            # The string in the DB has no guaranteed format
 
-                # Construct a list of conditions for each tag
-                tags_conditions = ["tags LIKE ?" for _ in tags]
-                tags_conditions_joined = " OR ".join(tags_conditions)
-                tags_condition = f"({tags_conditions_joined})"
+            # Construct a list of conditions for each tag
+            tags_conditions = ["tags LIKE ?" for _ in tags]
+            tags_conditions_joined = " OR ".join(tags_conditions)
+            tags_condition = f"({tags_conditions_joined})"
 
-                # And the params for the tags, case-insensitive
-                tags_params = [f"%{t.strip()}%" for t in tags]
+            # And the params for the tags, case-insensitive
+            tags_params = [f"%{t.strip()}%" for t in tags]
 
-                conditions.append(tags_condition)
-                params.extend(tags_params)
+            conditions.append(tags_condition)
+            params.extend(tags_params)
 
-            if has_been_opened:
-                conditions.append("opened_at IS NOT NULL")
-            elif has_been_opened is False:
-                conditions.append("opened_at IS NULL")
+        if has_been_opened:
+            conditions.append("opened_at IS NOT NULL")
+        elif has_been_opened is False:
+            conditions.append("opened_at IS NULL")
 
-            # Ignore whitespace in the query
-            stripped_query = query.strip() if query else None
-            if stripped_query:
-                # Construct a wildcard query for the name, description, and tags
-                wildcard_query = "%" + stripped_query + "%"
-                query_condition = "(name LIKE ? OR description LIKE ? OR tags LIKE ?)"
+        # Ignore whitespace in the query
+        stripped_query = query.strip() if query else None
+        if stripped_query:
+            # Construct a wildcard query for the name, description, and tags
+            wildcard_query = "%" + stripped_query + "%"
+            query_condition = "(name LIKE ? OR description LIKE ? OR tags LIKE ?)"
 
-                conditions.append(query_condition)
-                params.extend([wildcard_query, wildcard_query, wildcard_query])
+            conditions.append(query_condition)
+            params.extend([wildcard_query, wildcard_query, wildcard_query])
 
-            if conditions:
-                # If there are conditions, add a WHERE clause and then join the conditions
-                main_query += " WHERE "
-                count_query += " WHERE "
+        if conditions:
+            # If there are conditions, add a WHERE clause and then join the conditions
+            main_query += " WHERE "
+            count_query += " WHERE "
 
-                all_conditions = " AND ".join(conditions)
-                main_query += all_conditions
-                count_query += all_conditions
+            all_conditions = " AND ".join(conditions)
+            main_query += all_conditions
+            count_query += all_conditions
 
-            # After this point, the query and params differ for the main query and the count query
-            main_params = params.copy()
-            count_params = params.copy()
+        # After this point, the query and params differ for the main query and the count query
+        main_params = params.copy()
+        count_params = params.copy()
 
-            # Main query also gets ORDER BY and LIMIT/OFFSET
-            main_query += f" ORDER BY {order_by.value} {direction.value}"
+        # Main query also gets ORDER BY and LIMIT/OFFSET
+        main_query += f" ORDER BY {order_by.value} {direction.value}"
 
-            if per_page:
-                main_query += " LIMIT ? OFFSET ?"
-                main_params.extend([per_page, page * per_page])
+        if per_page:
+            main_query += " LIMIT ? OFFSET ?"
+            main_params.extend([per_page, page * per_page])
 
-            # Put a ring on it
-            main_query += ";"
-            count_query += ";"
+        # Put a ring on it
+        main_query += ";"
+        count_query += ";"
 
-            cursor.execute(main_query, main_params)
-            rows = cursor.fetchall()
-            workflows = [WorkflowRecordListItemDTOValidator.validate_python(dict(row)) for row in rows]
+        cursor = self._conn.cursor()
+        cursor.execute(main_query, main_params)
+        rows = cursor.fetchall()
+        workflows = [WorkflowRecordListItemDTOValidator.validate_python(dict(row)) for row in rows]
 
-            cursor.execute(count_query, count_params)
-            total = cursor.fetchone()[0]
+        cursor.execute(count_query, count_params)
+        total = cursor.fetchone()[0]
 
         if per_page:
             pages = total // per_page + (total % per_page > 0)
@@ -232,46 +247,46 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         if not tags:
             return {}
 
-        with self._db.transaction() as cursor:
-            result: dict[str, int] = {}
-            # Base conditions for categories and selected tags
-            base_conditions: list[str] = []
-            base_params: list[str | int] = []
+        cursor = self._conn.cursor()
+        result: dict[str, int] = {}
+        # Base conditions for categories and selected tags
+        base_conditions: list[str] = []
+        base_params: list[str | int] = []
 
-            # Add category conditions
-            if categories:
-                assert all(c in WorkflowCategory for c in categories)
-                placeholders = ", ".join("?" for _ in categories)
-                base_conditions.append(f"category IN ({placeholders})")
-                base_params.extend([category.value for category in categories])
+        # Add category conditions
+        if categories:
+            assert all(c in WorkflowCategory for c in categories)
+            placeholders = ", ".join("?" for _ in categories)
+            base_conditions.append(f"category IN ({placeholders})")
+            base_params.extend([category.value for category in categories])
 
-            if has_been_opened:
-                base_conditions.append("opened_at IS NOT NULL")
-            elif has_been_opened is False:
-                base_conditions.append("opened_at IS NULL")
+        if has_been_opened:
+            base_conditions.append("opened_at IS NOT NULL")
+        elif has_been_opened is False:
+            base_conditions.append("opened_at IS NULL")
 
-            # For each tag to count, run a separate query
-            for tag in tags:
-                # Start with the base conditions
-                conditions = base_conditions.copy()
-                params = base_params.copy()
+        # For each tag to count, run a separate query
+        for tag in tags:
+            # Start with the base conditions
+            conditions = base_conditions.copy()
+            params = base_params.copy()
 
-                # Add this specific tag condition
-                conditions.append("tags LIKE ?")
-                params.append(f"%{tag.strip()}%")
+            # Add this specific tag condition
+            conditions.append("tags LIKE ?")
+            params.append(f"%{tag.strip()}%")
 
-                # Construct the full query
-                stmt = """--sql
-                    SELECT COUNT(*)
-                    FROM workflow_library
-                    """
+            # Construct the full query
+            stmt = """--sql
+                SELECT COUNT(*)
+                FROM workflow_library
+                """
 
-                if conditions:
-                    stmt += " WHERE " + " AND ".join(conditions)
+            if conditions:
+                stmt += " WHERE " + " AND ".join(conditions)
 
-                cursor.execute(stmt, params)
-                count = cursor.fetchone()[0]
-                result[tag] = count
+            cursor.execute(stmt, params)
+            count = cursor.fetchone()[0]
+            result[tag] = count
 
         return result
 
@@ -281,51 +296,52 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         has_been_opened: Optional[bool] = None,
         is_published: Optional[bool] = None,
     ) -> dict[str, int]:
-        with self._db.transaction() as cursor:
-            result: dict[str, int] = {}
-            # Base conditions for categories
-            base_conditions: list[str] = []
-            base_params: list[str | int] = []
+        cursor = self._conn.cursor()
+        result: dict[str, int] = {}
+        # Base conditions for categories
+        base_conditions: list[str] = []
+        base_params: list[str | int] = []
 
-            # Add category conditions
-            if categories:
-                assert all(c in WorkflowCategory for c in categories)
-                placeholders = ", ".join("?" for _ in categories)
-                base_conditions.append(f"category IN ({placeholders})")
-                base_params.extend([category.value for category in categories])
+        # Add category conditions
+        if categories:
+            assert all(c in WorkflowCategory for c in categories)
+            placeholders = ", ".join("?" for _ in categories)
+            base_conditions.append(f"category IN ({placeholders})")
+            base_params.extend([category.value for category in categories])
 
-            if has_been_opened:
-                base_conditions.append("opened_at IS NOT NULL")
-            elif has_been_opened is False:
-                base_conditions.append("opened_at IS NULL")
+        if has_been_opened:
+            base_conditions.append("opened_at IS NOT NULL")
+        elif has_been_opened is False:
+            base_conditions.append("opened_at IS NULL")
 
-            # For each category to count, run a separate query
-            for category in categories:
-                # Start with the base conditions
-                conditions = base_conditions.copy()
-                params = base_params.copy()
+        # For each category to count, run a separate query
+        for category in categories:
+            # Start with the base conditions
+            conditions = base_conditions.copy()
+            params = base_params.copy()
 
-                # Add this specific category condition
-                conditions.append("category = ?")
-                params.append(category.value)
+            # Add this specific category condition
+            conditions.append("category = ?")
+            params.append(category.value)
 
-                # Construct the full query
-                stmt = """--sql
-                    SELECT COUNT(*)
-                    FROM workflow_library
-                    """
+            # Construct the full query
+            stmt = """--sql
+                SELECT COUNT(*)
+                FROM workflow_library
+                """
 
-                if conditions:
-                    stmt += " WHERE " + " AND ".join(conditions)
+            if conditions:
+                stmt += " WHERE " + " AND ".join(conditions)
 
-                cursor.execute(stmt, params)
-                count = cursor.fetchone()[0]
-                result[category.value] = count
+            cursor.execute(stmt, params)
+            count = cursor.fetchone()[0]
+            result[category.value] = count
 
         return result
 
     def update_opened_at(self, workflow_id: str) -> None:
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             cursor.execute(
                 f"""--sql
                 UPDATE workflow_library
@@ -334,6 +350,10 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                 """,
                 (workflow_id,),
             )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
 
     def _sync_default_workflows(self) -> None:
         """Syncs default workflows to the database. Internal use only."""
@@ -348,7 +368,8 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
         meaningless, as they are overwritten every time the server starts.
         """
 
-        with self._db.transaction() as cursor:
+        try:
+            cursor = self._conn.cursor()
             workflows_from_file: list[Workflow] = []
             workflows_to_update: list[Workflow] = []
             workflows_to_add: list[Workflow] = []
@@ -428,3 +449,8 @@ class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
                     """,
                     (w.model_dump_json(), w.id),
                 )
+
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise

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/bria/bria_utils.py

@@ -1,314 +0,0 @@
-import math
-import os
-from typing import List, Optional, Union
-
-import numpy as np
-import torch
-import torch.distributed as dist
-from diffusers.utils import logging
-from transformers import (
-    CLIPTextModel,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    T5EncoderModel,
-    T5TokenizerFast,
-)
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def get_t5_prompt_embeds(
-    tokenizer: T5TokenizerFast,
-    text_encoder: T5EncoderModel,
-    prompt: Union[str, List[str], None] = None,
-    num_images_per_prompt: int = 1,
-    max_sequence_length: int = 128,
-    device: Optional[torch.device] = None,
-):
-    device = device or text_encoder.device
-
-    if prompt is None:
-        prompt = ""
-
-    prompt = [prompt] if isinstance(prompt, str) else prompt
-    batch_size = len(prompt)
-
-    text_inputs = tokenizer(
-        prompt,
-        # padding="max_length",
-        max_length=max_sequence_length,
-        truncation=True,
-        add_special_tokens=True,
-        return_tensors="pt",
-    )
-    text_input_ids = text_inputs.input_ids
-    untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-    if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
-        removed_text = tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
-        logger.warning(
-            "The following part of your input was truncated because `max_sequence_length` is set to "
-            f" {max_sequence_length} tokens: {removed_text}"
-        )
-
-    prompt_embeds = text_encoder(text_input_ids.to(device))[0]
-
-    # Concat zeros to max_sequence
-    b, seq_len, dim = prompt_embeds.shape
-    if seq_len < max_sequence_length:
-        padding = torch.zeros(
-            (b, max_sequence_length - seq_len, dim), dtype=prompt_embeds.dtype, device=prompt_embeds.device
-        )
-        prompt_embeds = torch.concat([prompt_embeds, padding], dim=1)
-
-    prompt_embeds = prompt_embeds.to(device=device)
-
-    _, seq_len, _ = prompt_embeds.shape
-
-    # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
-    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-    prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-    return prompt_embeds
-
-
-# in order the get the same sigmas as in training and sample from them
-def get_original_sigmas(num_train_timesteps=1000, num_inference_steps=1000):
-    timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
-    sigmas = timesteps / num_train_timesteps
-
-    inds = [int(ind) for ind in np.linspace(0, num_train_timesteps - 1, num_inference_steps)]
-    new_sigmas = sigmas[inds]
-    return new_sigmas
-
-
-def is_ng_none(negative_prompt):
-    return (
-        negative_prompt is None
-        or negative_prompt == ""
-        or (isinstance(negative_prompt, list) and negative_prompt[0] is None)
-        or (isinstance(negative_prompt, list) and negative_prompt[0] == "")
-    )
-
-
-class CudaTimerContext:
-    def __init__(self, times_arr):
-        self.times_arr = times_arr
-
-    def __enter__(self):
-        self.before_event = torch.cuda.Event(enable_timing=True)
-        self.after_event = torch.cuda.Event(enable_timing=True)
-        self.before_event.record()
-
-    def __exit__(self, type, value, traceback):
-        self.after_event.record()
-        torch.cuda.synchronize()
-        elapsed_time = self.before_event.elapsed_time(self.after_event) / 1000
-        self.times_arr.append(elapsed_time)
-
-
-def get_env_prefix():
-    env = os.environ.get("CLOUD_PROVIDER", "AWS").upper()
-    if env == "AWS":
-        return "SM_CHANNEL"
-    elif env == "AZURE":
-        return "AZUREML_DATAREFERENCE"
-
-    raise Exception(f"Env {env} not supported")
-
-
-def compute_density_for_timestep_sampling(
-    weighting_scheme: str, batch_size: int, logit_mean: float = None, logit_std: float = None, mode_scale: float = None
-):
-    """Compute the density for sampling the timesteps when doing SD3 training.
-
-    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
-
-    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
-    """
-    if weighting_scheme == "logit_normal":
-        # See 3.1 in the SD3 paper ($rf/lognorm(0.00,1.00)$).
-        u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device="cpu")
-        u = torch.nn.functional.sigmoid(u)
-    elif weighting_scheme == "mode":
-        u = torch.rand(size=(batch_size,), device="cpu")
-        u = 1 - u - mode_scale * (torch.cos(math.pi * u / 2) ** 2 - 1 + u)
-    else:
-        u = torch.rand(size=(batch_size,), device="cpu")
-    return u
-
-
-def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
-    """Computes loss weighting scheme for SD3 training.
-
-    Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
-
-    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
-    """
-    if weighting_scheme == "sigma_sqrt":
-        weighting = (sigmas**-2.0).float()
-    elif weighting_scheme == "cosmap":
-        bot = 1 - 2 * sigmas + 2 * sigmas**2
-        weighting = 2 / (math.pi * bot)
-    else:
-        weighting = torch.ones_like(sigmas)
-    return weighting
-
-
-def initialize_distributed():
-    # Initialize the process group for distributed training
-    dist.init_process_group("nccl")
-
-    # Get the current process's rank (ID) and the total number of processes (world size)
-    rank = dist.get_rank()
-    world_size = dist.get_world_size()
-
-    print(f"Initialized distributed training: Rank {rank}/{world_size}")
-
-
-def get_clip_prompt_embeds(
-    text_encoder: CLIPTextModel,
-    text_encoder_2: CLIPTextModelWithProjection,
-    tokenizer: CLIPTokenizer,
-    tokenizer_2: CLIPTokenizer,
-    prompt: Union[str, List[str]] = None,
-    num_images_per_prompt: int = 1,
-    max_sequence_length: int = 77,
-    device: Optional[torch.device] = None,
-):
-    device = device or text_encoder.device
-    assert max_sequence_length == tokenizer.model_max_length
-    prompt = [prompt] if isinstance(prompt, str) else prompt
-
-    # Define tokenizers and text encoders
-    tokenizers = [tokenizer, tokenizer_2]
-    text_encoders = [text_encoder, text_encoder_2]
-
-    # textual inversion: process multi-vector tokens if necessary
-    prompt_embeds_list = []
-    prompts = [prompt, prompt]
-    for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders, strict=False):
-        text_inputs = tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-
-        text_input_ids = text_inputs.input_ids
-        prompt_embeds = text_encoder(text_input_ids.to(text_encoder.device), output_hidden_states=True)
-
-        # We are only ALWAYS interested in the pooled output of the final text encoder
-        pooled_prompt_embeds = prompt_embeds[0]
-        prompt_embeds = prompt_embeds.hidden_states[-2]
-
-        prompt_embeds_list.append(prompt_embeds)
-
-    prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
-
-    bs_embed, seq_len, _ = prompt_embeds.shape
-    # duplicate text embeddings for each generation per prompt, using mps friendly method
-    prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-    prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-    pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
-        bs_embed * num_images_per_prompt, -1
-    )
-
-    return prompt_embeds, pooled_prompt_embeds
-
-
-def get_1d_rotary_pos_embed(
-    dim: int,
-    pos: Union[np.ndarray, int],
-    theta: float = 10000.0,
-    use_real=False,
-    linear_factor=1.0,
-    ntk_factor=1.0,
-    repeat_interleave_real=True,
-    freqs_dtype=torch.float32,  #  torch.float32, torch.float64 (flux)
-):
-    """
-    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
-
-    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
-    index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
-    data type.
-
-    Args:
-        dim (`int`): Dimension of the frequency tensor.
-        pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
-        theta (`float`, *optional*, defaults to 10000.0):
-            Scaling factor for frequency computation. Defaults to 10000.0.
-        use_real (`bool`, *optional*):
-            If True, return real part and imaginary part separately. Otherwise, return complex numbers.
-        linear_factor (`float`, *optional*, defaults to 1.0):
-            Scaling factor for the context extrapolation. Defaults to 1.0.
-        ntk_factor (`float`, *optional*, defaults to 1.0):
-            Scaling factor for the NTK-Aware RoPE. Defaults to 1.0.
-        repeat_interleave_real (`bool`, *optional*, defaults to `True`):
-            If `True` and `use_real`, real part and imaginary part are each interleaved with themselves to reach `dim`.
-            Otherwise, they are concateanted with themselves.
-        freqs_dtype (`torch.float32` or `torch.float64`, *optional*, defaults to `torch.float32`):
-            the dtype of the frequency tensor.
-    Returns:
-        `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
-    """
-    assert dim % 2 == 0
-
-    if isinstance(pos, int):
-        pos = torch.arange(pos)
-    if isinstance(pos, np.ndarray):
-        pos = torch.from_numpy(pos)  # type: ignore  # [S]
-
-    theta = theta * ntk_factor
-    freqs = (
-        1.0
-        / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=pos.device)[: (dim // 2)] / dim))
-        / linear_factor
-    )  # [D/2]
-    freqs = torch.outer(pos, freqs)  # type: ignore   # [S, D/2]
-    if use_real and repeat_interleave_real:
-        # flux, hunyuan-dit, cogvideox
-        freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float()  # [S, D]
-        freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float()  # [S, D]
-        return freqs_cos, freqs_sin
-    elif use_real:
-        # stable audio, allegro
-        freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float()  # [S, D]
-        freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float()  # [S, D]
-        return freqs_cos, freqs_sin
-    else:
-        # lumina
-        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
-        return freqs_cis
-
-
-class FluxPosEmbed(torch.nn.Module):
-    # modified from https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/modules/layers.py#L11
-    def __init__(self, theta: int, axes_dim: List[int]):
-        super().__init__()
-        self.theta = theta
-        self.axes_dim = axes_dim
-
-    def forward(self, ids: torch.Tensor) -> torch.Tensor:
-        n_axes = ids.shape[-1]
-        cos_out = []
-        sin_out = []
-        pos = ids.float()
-        is_mps = ids.device.type == "mps"
-        freqs_dtype = torch.float32 if is_mps else torch.float64
-        for i in range(n_axes):
-            cos, sin = get_1d_rotary_pos_embed(
-                self.axes_dim[i],
-                pos[:, i],
-                theta=self.theta,
-                repeat_interleave_real=True,
-                use_real=True,
-                freqs_dtype=freqs_dtype,
-            )
-            cos_out.append(cos)
-            sin_out.append(sin)
-        freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
-        freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
-        return freqs_cos, freqs_sin

invokeai/backend/bria/controlnet_aux/__init__.py

@@ -1,6 +0,0 @@
-__version__ = "0.0.9"
-
-from invokeai.backend.bria.controlnet_aux.canny import CannyDetector as CannyDetector
-from invokeai.backend.bria.controlnet_aux.open_pose import OpenposeDetector as OpenposeDetector
-
-__all__ = ["CannyDetector", "OpenposeDetector"]

invokeai/backend/bria/controlnet_aux/canny/__init__.py

@@ -1,48 +0,0 @@
-import warnings
-
-import cv2
-import numpy as np
-from PIL import Image
-
-from invokeai.backend.bria.controlnet_aux.util import HWC3, resize_image
-
-
-class CannyDetector:
-    def __call__(
-        self,
-        input_image=None,
-        low_threshold=100,
-        high_threshold=200,
-        detect_resolution=512,
-        image_resolution=512,
-        output_type=None,
-        **kwargs,
-    ):
-        if "img" in kwargs:
-            warnings.warn("img is deprecated, please use `input_image=...` instead.", DeprecationWarning, stacklevel=2)
-            input_image = kwargs.pop("img")
-
-        if input_image is None:
-            raise ValueError("input_image must be defined.")
-
-        if not isinstance(input_image, np.ndarray):
-            input_image = np.array(input_image, dtype=np.uint8)
-            output_type = output_type or "pil"
-        else:
-            output_type = output_type or "np"
-
-        input_image = HWC3(input_image)
-        input_image = resize_image(input_image, detect_resolution)
-
-        detected_map = cv2.Canny(input_image, low_threshold, high_threshold)
-        detected_map = HWC3(detected_map)
-
-        img = resize_image(input_image, image_resolution)
-        H, W, C = img.shape
-
-        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
-
-        if output_type == "pil":
-            detected_map = Image.fromarray(detected_map)
-
-        return detected_map

invokeai/backend/bria/controlnet_aux/open_pose/LICENSE

@@ -1,108 +0,0 @@
-OPENPOSE: MULTIPERSON KEYPOINT DETECTION
-SOFTWARE LICENSE AGREEMENT
-ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
-
-BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.  IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
-
-This is a license agreement ("Agreement") between your academic institution or non-profit organization or self (called "Licensee" or "You" in this Agreement) and Carnegie Mellon University (called "Licensor" in this Agreement).  All rights not specifically granted to you in this Agreement are reserved for Licensor. 
-
-RESERVATION OF OWNERSHIP AND GRANT OF LICENSE: 
-Licensor retains exclusive ownership of any copy of the Software (as defined below) licensed under this Agreement and hereby grants to Licensee a personal, non-exclusive, 
-non-transferable license to use the Software for noncommercial research purposes, without the right to sublicense, pursuant to the terms and conditions of this Agreement.  As used in this Agreement, the term "Software" means (i) the actual copy of all or any portion of code for program routines made accessible to Licensee by Licensor pursuant to this Agreement, inclusive of backups, updates, and/or merged copies permitted hereunder or subsequently supplied by Licensor,  including all or any file structures, programming instructions, user interfaces and screen formats and sequences as well as any and all documentation and instructions related to it, and (ii) all or any derivatives and/or modifications created or made by You to any of the items specified in (i).
-
-CONFIDENTIALITY: Licensee acknowledges that the Software is proprietary to Licensor, and as such, Licensee agrees to receive all such materials in confidence and use the Software only in accordance with the terms of this Agreement.  Licensee agrees to use reasonable effort to protect the Software from unauthorized use, reproduction, distribution, or publication.
-
-COPYRIGHT: The Software is owned by Licensor and is protected by United 
-States copyright laws and applicable international treaties and/or conventions.
-
-PERMITTED USES:  The Software may be used for your own noncommercial internal research purposes. You understand and agree that Licensor is not obligated to implement any suggestions and/or feedback you might provide regarding the Software, but to the extent Licensor does so, you are not entitled to any compensation related thereto.
-
-DERIVATIVES: You may create derivatives of or make modifications to the Software, however, You agree that all and any such derivatives and modifications will be owned by Licensor and become a part of the Software licensed to You under this Agreement.  You may only use such derivatives and modifications for your own noncommercial internal research purposes, and you may not otherwise use, distribute or copy such derivatives and modifications in violation of this Agreement.
-
-BACKUPS:  If Licensee is an organization, it may make that number of copies of the Software necessary for internal noncommercial use at a single site within its organization provided that all information appearing in or on the original labels, including the copyright and trademark notices are copied onto the labels of the copies.
-
-USES NOT PERMITTED:  You may not distribute, copy or use the Software except as explicitly permitted herein. Licensee has not been granted any trademark license as part of this Agreement and may not use the name or mark “OpenPose", "Carnegie Mellon" or any renditions thereof without the prior written permission of Licensor.
-
-You may not sell, rent, lease, sublicense, lend, time-share or transfer, in whole or in part, or provide third parties access to prior or present versions (or any parts thereof) of the Software.
-
-ASSIGNMENT: You may not assign this Agreement or your rights hereunder without the prior written consent of Licensor. Any attempted assignment without such consent shall be null and void.
-
-TERM: The term of the license granted by this Agreement is from Licensee's acceptance of this Agreement by downloading the Software or by using the Software until terminated as provided below.
-
-The Agreement automatically terminates without notice if you fail to comply with any provision of this Agreement.  Licensee may terminate this Agreement by ceasing using the Software.  Upon any termination of this Agreement, Licensee will delete any and all copies of the Software. You agree that all provisions which operate to protect the proprietary rights of Licensor shall remain in force should breach occur and that the obligation of confidentiality described in this Agreement is binding in perpetuity and, as such, survives the term of the Agreement.
-
-FEE: Provided Licensee abides completely by the terms and conditions of this Agreement, there is no fee due to Licensor for Licensee's use of the Software in accordance with this Agreement.
-
-DISCLAIMER OF WARRANTIES:  THE SOFTWARE IS PROVIDED "AS-IS" WITHOUT WARRANTY OF ANY KIND INCLUDING ANY WARRANTIES OF PERFORMANCE OR MERCHANTABILITY OR FITNESS FOR A PARTICULAR USE OR PURPOSE OR OF NON-INFRINGEMENT.  LICENSEE BEARS ALL RISK RELATING TO QUALITY AND PERFORMANCE OF THE SOFTWARE AND RELATED MATERIALS.
-
-SUPPORT AND MAINTENANCE: No Software support or training by the Licensor is provided as part of this Agreement.  
-
-EXCLUSIVE REMEDY AND LIMITATION OF LIABILITY: To the maximum extent permitted under applicable law, Licensor shall not be liable for direct, indirect, special, incidental, or consequential damages or lost profits related to Licensee's use of and/or inability to use the Software, even if Licensor is advised of the possibility of such damage.
-
-EXPORT REGULATION: Licensee agrees to comply with any and all applicable 
-U.S. export control laws, regulations, and/or other laws related to embargoes and sanction programs administered by the Office of Foreign Assets Control.
-
-SEVERABILITY: If any provision(s) of this Agreement shall be held to be invalid, illegal, or unenforceable by a court or other tribunal of competent jurisdiction, the validity, legality and enforceability of the remaining provisions shall not in any way be affected or impaired thereby.
-
-NO IMPLIED WAIVERS: No failure or delay by Licensor in enforcing any right or remedy under this Agreement shall be construed as a waiver of any future or other exercise of such right or remedy by Licensor.
-
-GOVERNING LAW: This Agreement shall be construed and enforced in accordance with the laws of the Commonwealth of Pennsylvania without reference to conflict of laws principles.  You consent to the personal jurisdiction of the courts of this County and waive their rights to venue outside of Allegheny County, Pennsylvania.
-
-ENTIRE AGREEMENT AND AMENDMENTS: This Agreement constitutes the sole and entire agreement between Licensee and Licensor as to the matter set forth herein and supersedes any previous agreements, understandings, and arrangements between the parties relating hereto.
-
-
-
-************************************************************************
-
-THIRD-PARTY SOFTWARE NOTICES AND INFORMATION
-
-This project incorporates material from the project(s) listed below (collectively, "Third Party Code").  This Third Party Code is licensed to you under their original license terms set forth below.  We reserves all other rights not expressly granted, whether by implication, estoppel or otherwise.
- 
-1.	Caffe, version 1.0.0, (https://github.com/BVLC/caffe/)
-
-COPYRIGHT
-
-All contributions by the University of California:
-Copyright (c) 2014-2017 The Regents of the University of California (Regents)
-All rights reserved.
-
-All other contributions:
-Copyright (c) 2014-2017, the respective contributors
-All rights reserved.
-
-Caffe uses a shared copyright model: each contributor holds copyright over
-their contributions to Caffe. The project versioning records all such
-contribution and copyright details. If a contributor wants to further mark
-their specific copyright on a particular contribution, they should indicate
-their copyright solely in the commit message of the change when it is
-committed.
-
-LICENSE
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met: 
-
-1. Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer. 
-2. Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution. 
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
-ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-CONTRIBUTION AGREEMENT
-
-By contributing to the BVLC/caffe repository through pull-request, comment,
-or otherwise, the contributor releases their content to the
-license and copyright terms herein.
-
-************END OF THIRD-PARTY SOFTWARE NOTICES AND INFORMATION**********

invokeai/backend/bria/controlnet_aux/open_pose/__init__.py

@@ -1,267 +0,0 @@
-# Openpose
-# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
-# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
-# 3rd Edited by ControlNet
-# 4th Edited by ControlNet (added face and correct hands)
-# 5th Edited by ControlNet (Improved JSON serialization/deserialization, and lots of bug fixs)
-# This preprocessor is licensed by CMU for non-commercial use only.
-
-
-import os
-
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-import warnings
-from typing import List, NamedTuple, Tuple, Union
-
-import cv2
-import numpy as np
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image
-
-from invokeai.backend.bria.controlnet_aux.open_pose import util
-from invokeai.backend.bria.controlnet_aux.open_pose.body import Body, BodyResult, Keypoint
-from invokeai.backend.bria.controlnet_aux.open_pose.face import Face
-from invokeai.backend.bria.controlnet_aux.open_pose.hand import Hand
-from invokeai.backend.bria.controlnet_aux.util import HWC3, resize_image
-
-HandResult = List[Keypoint]
-FaceResult = List[Keypoint]
-
-
-class PoseResult(NamedTuple):
-    body: BodyResult
-    left_hand: Union[HandResult, None]
-    right_hand: Union[HandResult, None]
-    face: Union[FaceResult, None]
-
-
-def draw_poses(poses: List[PoseResult], H, W, draw_body=True, draw_hand=True, draw_face=True):
-    """
-    Draw the detected poses on an empty canvas.
-
-    Args:
-        poses (List[PoseResult]): A list of PoseResult objects containing the detected poses.
-        H (int): The height of the canvas.
-        W (int): The width of the canvas.
-        draw_body (bool, optional): Whether to draw body keypoints. Defaults to True.
-        draw_hand (bool, optional): Whether to draw hand keypoints. Defaults to True.
-        draw_face (bool, optional): Whether to draw face keypoints. Defaults to True.
-
-    Returns:
-        numpy.ndarray: A 3D numpy array representing the canvas with the drawn poses.
-    """
-    canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
-
-    for pose in poses:
-        if draw_body:
-            canvas = util.draw_bodypose(canvas, pose.body.keypoints)
-
-        if draw_hand:
-            canvas = util.draw_handpose(canvas, pose.left_hand)
-            canvas = util.draw_handpose(canvas, pose.right_hand)
-
-        if draw_face:
-            canvas = util.draw_facepose(canvas, pose.face)
-
-    return canvas
-
-
-class OpenposeDetector:
-    """
-    A class for detecting human poses in images using the Openpose model.
-
-    Attributes:
-        model_dir (str): Path to the directory where the pose models are stored.
-    """
-
-    def __init__(self, body_estimation, hand_estimation=None, face_estimation=None):
-        self.body_estimation = body_estimation
-        self.hand_estimation = hand_estimation
-        self.face_estimation = face_estimation
-
-    @classmethod
-    def from_pretrained(
-        cls,
-        pretrained_model_or_path,
-        filename=None,
-        hand_filename=None,
-        face_filename=None,
-        cache_dir=None,
-        local_files_only=False,
-    ):
-        if pretrained_model_or_path == "lllyasviel/ControlNet":
-            filename = filename or "annotator/ckpts/body_pose_model.pth"
-            hand_filename = hand_filename or "annotator/ckpts/hand_pose_model.pth"
-            face_filename = face_filename or "facenet.pth"
-
-            face_pretrained_model_or_path = "lllyasviel/Annotators"
-        else:
-            filename = filename or "body_pose_model.pth"
-            hand_filename = hand_filename or "hand_pose_model.pth"
-            face_filename = face_filename or "facenet.pth"
-
-            face_pretrained_model_or_path = pretrained_model_or_path
-
-        if os.path.isdir(pretrained_model_or_path):
-            body_model_path = os.path.join(pretrained_model_or_path, filename)
-            hand_model_path = os.path.join(pretrained_model_or_path, hand_filename)
-            face_model_path = os.path.join(face_pretrained_model_or_path, face_filename)
-        else:
-            body_model_path = hf_hub_download(
-                pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only
-            )
-            hand_model_path = hf_hub_download(
-                pretrained_model_or_path, hand_filename, cache_dir=cache_dir, local_files_only=local_files_only
-            )
-            face_model_path = hf_hub_download(
-                face_pretrained_model_or_path, face_filename, cache_dir=cache_dir, local_files_only=local_files_only
-            )
-
-        body_estimation = Body(body_model_path)
-        hand_estimation = Hand(hand_model_path)
-        face_estimation = Face(face_model_path)
-
-        return cls(body_estimation, hand_estimation, face_estimation)
-
-    def to(self, device):
-        self.body_estimation.to(device)
-        self.hand_estimation.to(device)
-        self.face_estimation.to(device)
-        return self
-
-    def detect_hands(self, body: BodyResult, oriImg) -> Tuple[Union[HandResult, None], Union[HandResult, None]]:
-        left_hand = None
-        right_hand = None
-        H, W, _ = oriImg.shape
-        for x, y, w, is_left in util.handDetect(body, oriImg):
-            peaks = self.hand_estimation(oriImg[y : y + w, x : x + w, :]).astype(np.float32)
-            if peaks.ndim == 2 and peaks.shape[1] == 2:
-                peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
-                peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
-
-                hand_result = [Keypoint(x=peak[0], y=peak[1]) for peak in peaks]
-
-                if is_left:
-                    left_hand = hand_result
-                else:
-                    right_hand = hand_result
-
-        return left_hand, right_hand
-
-    def detect_face(self, body: BodyResult, oriImg) -> Union[FaceResult, None]:
-        face = util.faceDetect(body, oriImg)
-        if face is None:
-            return None
-
-        x, y, w = face
-        H, W, _ = oriImg.shape
-        heatmaps = self.face_estimation(oriImg[y : y + w, x : x + w, :])
-        peaks = self.face_estimation.compute_peaks_from_heatmaps(heatmaps).astype(np.float32)
-        if peaks.ndim == 2 and peaks.shape[1] == 2:
-            peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
-            peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
-            return [Keypoint(x=peak[0], y=peak[1]) for peak in peaks]
-
-        return None
-
-    def detect_poses(self, oriImg, include_hand=False, include_face=False) -> List[PoseResult]:
-        """
-        Detect poses in the given image.
-            Args:
-                oriImg (numpy.ndarray): The input image for pose detection.
-                include_hand (bool, optional): Whether to include hand detection. Defaults to False.
-                include_face (bool, optional): Whether to include face detection. Defaults to False.
-
-        Returns:
-            List[PoseResult]: A list of PoseResult objects containing the detected poses.
-        """
-        oriImg = oriImg[:, :, ::-1].copy()
-        H, W, C = oriImg.shape
-        with torch.no_grad():
-            candidate, subset = self.body_estimation(oriImg)
-            bodies = self.body_estimation.format_body_result(candidate, subset)
-
-            results = []
-            for body in bodies:
-                left_hand, right_hand, face = (None,) * 3
-                if include_hand:
-                    left_hand, right_hand = self.detect_hands(body, oriImg)
-                if include_face:
-                    face = self.detect_face(body, oriImg)
-
-                results.append(
-                    PoseResult(
-                        BodyResult(
-                            keypoints=[
-                                Keypoint(x=keypoint.x / float(W), y=keypoint.y / float(H))
-                                if keypoint is not None
-                                else None
-                                for keypoint in body.keypoints
-                            ],
-                            total_score=body.total_score,
-                            total_parts=body.total_parts,
-                        ),
-                        left_hand,
-                        right_hand,
-                        face,
-                    )
-                )
-
-            return results
-
-    def __call__(
-        self,
-        input_image,
-        detect_resolution=512,
-        image_resolution=512,
-        include_body=True,
-        include_hand=False,
-        include_face=False,
-        hand_and_face=None,
-        output_type="pil",
-        **kwargs,
-    ):
-        if hand_and_face is not None:
-            warnings.warn(
-                "hand_and_face is deprecated. Use include_hand and include_face instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            include_hand = hand_and_face
-            include_face = hand_and_face
-
-        if "return_pil" in kwargs:
-            warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning, stacklevel=2)
-            output_type = "pil" if kwargs["return_pil"] else "np"
-        if type(output_type) is bool:
-            warnings.warn(
-                "Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions",
-                stacklevel=2,
-            )
-            if output_type:
-                output_type = "pil"
-
-        if not isinstance(input_image, np.ndarray):
-            input_image = np.array(input_image, dtype=np.uint8)
-
-        input_image = HWC3(input_image)
-        input_image = resize_image(input_image, detect_resolution)
-        H, W, C = input_image.shape
-
-        poses = self.detect_poses(input_image, include_hand, include_face)
-        canvas = draw_poses(poses, H, W, draw_body=include_body, draw_hand=include_hand, draw_face=include_face)
-
-        detected_map = canvas
-        detected_map = HWC3(detected_map)
-
-        img = resize_image(input_image, image_resolution)
-        H, W, C = img.shape
-
-        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
-
-        if output_type == "pil":
-            detected_map = Image.fromarray(detected_map)
-
-        return detected_map

invokeai/backend/bria/controlnet_aux/open_pose/body.py

@@ -1,319 +0,0 @@
-import math
-from typing import List, NamedTuple, Union
-
-import numpy as np
-import torch
-from scipy.ndimage.filters import gaussian_filter
-
-from invokeai.backend.bria.controlnet_aux.open_pose import util
-from invokeai.backend.bria.controlnet_aux.open_pose.model import bodypose_model
-
-
-class Keypoint(NamedTuple):
-    x: float
-    y: float
-    score: float = 1.0
-    id: int = -1
-
-
-class BodyResult(NamedTuple):
-    # Note: Using `Union` instead of `|` operator as the ladder is a Python
-    # 3.10 feature.
-    # Annotator code should be Python 3.8 Compatible, as controlnet repo uses
-    # Python 3.8 environment.
-    # https://github.com/lllyasviel/ControlNet/blob/d3284fcd0972c510635a4f5abe2eeb71dc0de524/environment.yaml#L6
-    keypoints: List[Union[Keypoint, None]]
-    total_score: float
-    total_parts: int
-
-
-class Body(object):
-    def __init__(self, model_path):
-        self.model = bodypose_model()
-        model_dict = util.transfer(self.model, torch.load(model_path))
-        self.model.load_state_dict(model_dict)
-        self.model.eval()
-
-    def to(self, device):
-        self.model.to(device)
-        return self
-
-    def __call__(self, oriImg):
-        device = next(iter(self.model.parameters())).device
-        # scale_search = [0.5, 1.0, 1.5, 2.0]
-        scale_search = [0.5]
-        boxsize = 368
-        stride = 8
-        padValue = 128
-        thre1 = 0.1
-        thre2 = 0.05
-        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
-        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
-        paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
-
-        for m in range(len(multiplier)):
-            scale = multiplier[m]
-            imageToTest = util.smart_resize_k(oriImg, fx=scale, fy=scale)
-            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
-            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
-            im = np.ascontiguousarray(im)
-
-            data = torch.from_numpy(im).float()
-            data = data.to(device)
-            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
-            with torch.no_grad():
-                Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
-            Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
-            Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
-
-            # extract outputs, resize, and remove padding
-            # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0))  # output 1 is heatmaps
-            heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0))  # output 1 is heatmaps
-            heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride)
-            heatmap = heatmap[: imageToTest_padded.shape[0] - pad[2], : imageToTest_padded.shape[1] - pad[3], :]
-            heatmap = util.smart_resize(heatmap, (oriImg.shape[0], oriImg.shape[1]))
-
-            # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0))  # output 0 is PAFs
-            paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0))  # output 0 is PAFs
-            paf = util.smart_resize_k(paf, fx=stride, fy=stride)
-            paf = paf[: imageToTest_padded.shape[0] - pad[2], : imageToTest_padded.shape[1] - pad[3], :]
-            paf = util.smart_resize(paf, (oriImg.shape[0], oriImg.shape[1]))
-
-            heatmap_avg += heatmap_avg + heatmap / len(multiplier)
-            paf_avg += +paf / len(multiplier)
-
-        all_peaks = []
-        peak_counter = 0
-
-        for part in range(18):
-            map_ori = heatmap_avg[:, :, part]
-            one_heatmap = gaussian_filter(map_ori, sigma=3)
-
-            map_left = np.zeros(one_heatmap.shape)
-            map_left[1:, :] = one_heatmap[:-1, :]
-            map_right = np.zeros(one_heatmap.shape)
-            map_right[:-1, :] = one_heatmap[1:, :]
-            map_up = np.zeros(one_heatmap.shape)
-            map_up[:, 1:] = one_heatmap[:, :-1]
-            map_down = np.zeros(one_heatmap.shape)
-            map_down[:, :-1] = one_heatmap[:, 1:]
-
-            peaks_binary = np.logical_and.reduce(
-                (
-                    one_heatmap >= map_left,
-                    one_heatmap >= map_right,
-                    one_heatmap >= map_up,
-                    one_heatmap >= map_down,
-                    one_heatmap > thre1,
-                )
-            )
-            peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0], strict=False))  # note reverse
-            peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
-            peak_id = range(peak_counter, peak_counter + len(peaks))
-            peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))]
-
-            all_peaks.append(peaks_with_score_and_id)
-            peak_counter += len(peaks)
-
-        # find connection in the specified sequence, center 29 is in the position 15
-        limbSeq = [
-            [2, 3],
-            [2, 6],
-            [3, 4],
-            [4, 5],
-            [6, 7],
-            [7, 8],
-            [2, 9],
-            [9, 10],
-            [10, 11],
-            [2, 12],
-            [12, 13],
-            [13, 14],
-            [2, 1],
-            [1, 15],
-            [15, 17],
-            [1, 16],
-            [16, 18],
-            [3, 17],
-            [6, 18],
-        ]
-        # the middle joints heatmap correpondence
-        mapIdx = [
-            [31, 32],
-            [39, 40],
-            [33, 34],
-            [35, 36],
-            [41, 42],
-            [43, 44],
-            [19, 20],
-            [21, 22],
-            [23, 24],
-            [25, 26],
-            [27, 28],
-            [29, 30],
-            [47, 48],
-            [49, 50],
-            [53, 54],
-            [51, 52],
-            [55, 56],
-            [37, 38],
-            [45, 46],
-        ]
-
-        connection_all = []
-        special_k = []
-        mid_num = 10
-
-        for k in range(len(mapIdx)):
-            score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
-            candA = all_peaks[limbSeq[k][0] - 1]
-            candB = all_peaks[limbSeq[k][1] - 1]
-            nA = len(candA)
-            nB = len(candB)
-            indexA, indexB = limbSeq[k]
-            if nA != 0 and nB != 0:
-                connection_candidate = []
-                for i in range(nA):
-                    for j in range(nB):
-                        vec = np.subtract(candB[j][:2], candA[i][:2])
-                        norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
-                        norm = max(0.001, norm)
-                        vec = np.divide(vec, norm)
-
-                        startend = list(
-                            zip(
-                                np.linspace(candA[i][0], candB[j][0], num=mid_num),
-                                np.linspace(candA[i][1], candB[j][1], num=mid_num),
-                                strict=False,
-                            )
-                        )
-
-                        vec_x = np.array(
-                            [
-                                score_mid[int(round(startend[i][1])), int(round(startend[i][0])), 0]
-                                for i in range(len(startend))
-                            ]
-                        )
-                        vec_y = np.array(
-                            [
-                                score_mid[int(round(startend[i][1])), int(round(startend[i][0])), 1]
-                                for i in range(len(startend))
-                            ]
-                        )
-
-                        score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
-                        score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
-                            0.5 * oriImg.shape[0] / norm - 1, 0
-                        )
-                        criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts)
-                        criterion2 = score_with_dist_prior > 0
-                        if criterion1 and criterion2:
-                            connection_candidate.append(
-                                [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]]
-                            )
-
-                connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
-                connection = np.zeros((0, 5))
-                for c in range(len(connection_candidate)):
-                    i, j, s = connection_candidate[c][0:3]
-                    if i not in connection[:, 3] and j not in connection[:, 4]:
-                        connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
-                        if len(connection) >= min(nA, nB):
-                            break
-
-                connection_all.append(connection)
-            else:
-                special_k.append(k)
-                connection_all.append([])
-
-        # last number in each row is the total parts number of that person
-        # the second last number in each row is the score of the overall configuration
-        subset = -1 * np.ones((0, 20))
-        candidate = np.array([item for sublist in all_peaks for item in sublist])
-
-        for k in range(len(mapIdx)):
-            if k not in special_k:
-                partAs = connection_all[k][:, 0]
-                partBs = connection_all[k][:, 1]
-                indexA, indexB = np.array(limbSeq[k]) - 1
-
-                for i in range(len(connection_all[k])):  # = 1:size(temp,1)
-                    found = 0
-                    subset_idx = [-1, -1]
-                    for j in range(len(subset)):  # 1:size(subset,1):
-                        if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
-                            subset_idx[found] = j
-                            found += 1
-
-                    if found == 1:
-                        j = subset_idx[0]
-                        if subset[j][indexB] != partBs[i]:
-                            subset[j][indexB] = partBs[i]
-                            subset[j][-1] += 1
-                            subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
-                    elif found == 2:  # if found 2 and disjoint, merge them
-                        j1, j2 = subset_idx
-                        membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
-                        if len(np.nonzero(membership == 2)[0]) == 0:  # merge
-                            subset[j1][:-2] += subset[j2][:-2] + 1
-                            subset[j1][-2:] += subset[j2][-2:]
-                            subset[j1][-2] += connection_all[k][i][2]
-                            subset = np.delete(subset, j2, 0)
-                        else:  # as like found == 1
-                            subset[j1][indexB] = partBs[i]
-                            subset[j1][-1] += 1
-                            subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
-
-                    # if find no partA in the subset, create a new subset
-                    elif not found and k < 17:
-                        row = -1 * np.ones(20)
-                        row[indexA] = partAs[i]
-                        row[indexB] = partBs[i]
-                        row[-1] = 2
-                        row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
-                        subset = np.vstack([subset, row])
-        # delete some rows of subset which has few parts occur
-        deleteIdx = []
-        for i in range(len(subset)):
-            if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
-                deleteIdx.append(i)
-        subset = np.delete(subset, deleteIdx, axis=0)
-
-        # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
-        # candidate: x, y, score, id
-        return candidate, subset
-
-    @staticmethod
-    def format_body_result(candidate: np.ndarray, subset: np.ndarray) -> List[BodyResult]:
-        """
-        Format the body results from the candidate and subset arrays into a list of BodyResult objects.
-
-        Args:
-            candidate (np.ndarray): An array of candidates containing the x, y coordinates, score, and id
-                for each body part.
-            subset (np.ndarray): An array of subsets containing indices to the candidate array for each
-                person detected. The last two columns of each row hold the total score and total parts
-                of the person.
-
-        Returns:
-            List[BodyResult]: A list of BodyResult objects, where each object represents a person with
-                detected keypoints, total score, and total parts.
-        """
-        return [
-            BodyResult(
-                keypoints=[
-                    Keypoint(
-                        x=candidate[candidate_index][0],
-                        y=candidate[candidate_index][1],
-                        score=candidate[candidate_index][2],
-                        id=candidate[candidate_index][3],
-                    )
-                    if candidate_index != -1
-                    else None
-                    for candidate_index in person[:18].astype(int)
-                ],
-                total_score=person[18],
-                total_parts=person[19],
-            )
-            for person in subset
-        ]

invokeai/backend/bria/controlnet_aux/open_pose/face.py

@@ -1,307 +0,0 @@
-import logging
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch.nn import Conv2d, MaxPool2d, Module, ReLU, init
-from torchvision.transforms import ToPILImage, ToTensor
-
-from invokeai.backend.bria.controlnet_aux.open_pose import util
-
-
-class FaceNet(Module):
-    """Model the cascading heatmaps."""
-
-    def __init__(self):
-        super(FaceNet, self).__init__()
-        # cnn to make feature map
-        self.relu = ReLU()
-        self.max_pooling_2d = MaxPool2d(kernel_size=2, stride=2)
-        self.conv1_1 = Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1)
-        self.conv1_2 = Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)
-        self.conv2_1 = Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
-        self.conv2_2 = Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1)
-        self.conv3_1 = Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1)
-        self.conv3_2 = Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1)
-        self.conv3_3 = Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1)
-        self.conv3_4 = Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1)
-        self.conv4_1 = Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1)
-        self.conv4_2 = Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)
-        self.conv4_3 = Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)
-        self.conv4_4 = Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)
-        self.conv5_1 = Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)
-        self.conv5_2 = Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)
-        self.conv5_3_CPM = Conv2d(in_channels=512, out_channels=128, kernel_size=3, stride=1, padding=1)
-
-        # stage1
-        self.conv6_1_CPM = Conv2d(in_channels=128, out_channels=512, kernel_size=1, stride=1, padding=0)
-        self.conv6_2_CPM = Conv2d(in_channels=512, out_channels=71, kernel_size=1, stride=1, padding=0)
-
-        # stage2
-        self.Mconv1_stage2 = Conv2d(in_channels=199, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv2_stage2 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv3_stage2 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv4_stage2 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv5_stage2 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv6_stage2 = Conv2d(in_channels=128, out_channels=128, kernel_size=1, stride=1, padding=0)
-        self.Mconv7_stage2 = Conv2d(in_channels=128, out_channels=71, kernel_size=1, stride=1, padding=0)
-
-        # stage3
-        self.Mconv1_stage3 = Conv2d(in_channels=199, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv2_stage3 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv3_stage3 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv4_stage3 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv5_stage3 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv6_stage3 = Conv2d(in_channels=128, out_channels=128, kernel_size=1, stride=1, padding=0)
-        self.Mconv7_stage3 = Conv2d(in_channels=128, out_channels=71, kernel_size=1, stride=1, padding=0)
-
-        # stage4
-        self.Mconv1_stage4 = Conv2d(in_channels=199, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv2_stage4 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv3_stage4 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv4_stage4 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv5_stage4 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv6_stage4 = Conv2d(in_channels=128, out_channels=128, kernel_size=1, stride=1, padding=0)
-        self.Mconv7_stage4 = Conv2d(in_channels=128, out_channels=71, kernel_size=1, stride=1, padding=0)
-
-        # stage5
-        self.Mconv1_stage5 = Conv2d(in_channels=199, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv2_stage5 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv3_stage5 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv4_stage5 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv5_stage5 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv6_stage5 = Conv2d(in_channels=128, out_channels=128, kernel_size=1, stride=1, padding=0)
-        self.Mconv7_stage5 = Conv2d(in_channels=128, out_channels=71, kernel_size=1, stride=1, padding=0)
-
-        # stage6
-        self.Mconv1_stage6 = Conv2d(in_channels=199, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv2_stage6 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv3_stage6 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv4_stage6 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv5_stage6 = Conv2d(in_channels=128, out_channels=128, kernel_size=7, stride=1, padding=3)
-        self.Mconv6_stage6 = Conv2d(in_channels=128, out_channels=128, kernel_size=1, stride=1, padding=0)
-        self.Mconv7_stage6 = Conv2d(in_channels=128, out_channels=71, kernel_size=1, stride=1, padding=0)
-
-        for m in self.modules():
-            if isinstance(m, Conv2d):
-                init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        """Return a list of heatmaps."""
-        heatmaps = []
-
-        h = self.relu(self.conv1_1(x))
-        h = self.relu(self.conv1_2(h))
-        h = self.max_pooling_2d(h)
-        h = self.relu(self.conv2_1(h))
-        h = self.relu(self.conv2_2(h))
-        h = self.max_pooling_2d(h)
-        h = self.relu(self.conv3_1(h))
-        h = self.relu(self.conv3_2(h))
-        h = self.relu(self.conv3_3(h))
-        h = self.relu(self.conv3_4(h))
-        h = self.max_pooling_2d(h)
-        h = self.relu(self.conv4_1(h))
-        h = self.relu(self.conv4_2(h))
-        h = self.relu(self.conv4_3(h))
-        h = self.relu(self.conv4_4(h))
-        h = self.relu(self.conv5_1(h))
-        h = self.relu(self.conv5_2(h))
-        h = self.relu(self.conv5_3_CPM(h))
-        feature_map = h
-
-        # stage1
-        h = self.relu(self.conv6_1_CPM(h))
-        h = self.conv6_2_CPM(h)
-        heatmaps.append(h)
-
-        # stage2
-        h = torch.cat([h, feature_map], dim=1)  # channel concat
-        h = self.relu(self.Mconv1_stage2(h))
-        h = self.relu(self.Mconv2_stage2(h))
-        h = self.relu(self.Mconv3_stage2(h))
-        h = self.relu(self.Mconv4_stage2(h))
-        h = self.relu(self.Mconv5_stage2(h))
-        h = self.relu(self.Mconv6_stage2(h))
-        h = self.Mconv7_stage2(h)
-        heatmaps.append(h)
-
-        # stage3
-        h = torch.cat([h, feature_map], dim=1)  # channel concat
-        h = self.relu(self.Mconv1_stage3(h))
-        h = self.relu(self.Mconv2_stage3(h))
-        h = self.relu(self.Mconv3_stage3(h))
-        h = self.relu(self.Mconv4_stage3(h))
-        h = self.relu(self.Mconv5_stage3(h))
-        h = self.relu(self.Mconv6_stage3(h))
-        h = self.Mconv7_stage3(h)
-        heatmaps.append(h)
-
-        # stage4
-        h = torch.cat([h, feature_map], dim=1)  # channel concat
-        h = self.relu(self.Mconv1_stage4(h))
-        h = self.relu(self.Mconv2_stage4(h))
-        h = self.relu(self.Mconv3_stage4(h))
-        h = self.relu(self.Mconv4_stage4(h))
-        h = self.relu(self.Mconv5_stage4(h))
-        h = self.relu(self.Mconv6_stage4(h))
-        h = self.Mconv7_stage4(h)
-        heatmaps.append(h)
-
-        # stage5
-        h = torch.cat([h, feature_map], dim=1)  # channel concat
-        h = self.relu(self.Mconv1_stage5(h))
-        h = self.relu(self.Mconv2_stage5(h))
-        h = self.relu(self.Mconv3_stage5(h))
-        h = self.relu(self.Mconv4_stage5(h))
-        h = self.relu(self.Mconv5_stage5(h))
-        h = self.relu(self.Mconv6_stage5(h))
-        h = self.Mconv7_stage5(h)
-        heatmaps.append(h)
-
-        # stage6
-        h = torch.cat([h, feature_map], dim=1)  # channel concat
-        h = self.relu(self.Mconv1_stage6(h))
-        h = self.relu(self.Mconv2_stage6(h))
-        h = self.relu(self.Mconv3_stage6(h))
-        h = self.relu(self.Mconv4_stage6(h))
-        h = self.relu(self.Mconv5_stage6(h))
-        h = self.relu(self.Mconv6_stage6(h))
-        h = self.Mconv7_stage6(h)
-        heatmaps.append(h)
-
-        return heatmaps
-
-
-LOG = logging.getLogger(__name__)
-TOTEN = ToTensor()
-TOPIL = ToPILImage()
-
-
-params = {
-    "gaussian_sigma": 2.5,
-    "inference_img_size": 736,  # 368, 736, 1312
-    "heatmap_peak_thresh": 0.1,
-    "crop_scale": 1.5,
-    "line_indices": [
-        [0, 1],
-        [1, 2],
-        [2, 3],
-        [3, 4],
-        [4, 5],
-        [5, 6],
-        [6, 7],
-        [7, 8],
-        [8, 9],
-        [9, 10],
-        [10, 11],
-        [11, 12],
-        [12, 13],
-        [13, 14],
-        [14, 15],
-        [15, 16],
-        [17, 18],
-        [18, 19],
-        [19, 20],
-        [20, 21],
-        [22, 23],
-        [23, 24],
-        [24, 25],
-        [25, 26],
-        [27, 28],
-        [28, 29],
-        [29, 30],
-        [31, 32],
-        [32, 33],
-        [33, 34],
-        [34, 35],
-        [36, 37],
-        [37, 38],
-        [38, 39],
-        [39, 40],
-        [40, 41],
-        [41, 36],
-        [42, 43],
-        [43, 44],
-        [44, 45],
-        [45, 46],
-        [46, 47],
-        [47, 42],
-        [48, 49],
-        [49, 50],
-        [50, 51],
-        [51, 52],
-        [52, 53],
-        [53, 54],
-        [54, 55],
-        [55, 56],
-        [56, 57],
-        [57, 58],
-        [58, 59],
-        [59, 48],
-        [60, 61],
-        [61, 62],
-        [62, 63],
-        [63, 64],
-        [64, 65],
-        [65, 66],
-        [66, 67],
-        [67, 60],
-    ],
-}
-
-
-class Face(object):
-    """
-    The OpenPose face landmark detector model.
-
-    Args:
-        inference_size: set the size of the inference image size, suggested:
-            368, 736, 1312, default 736
-        gaussian_sigma: blur the heatmaps, default 2.5
-        heatmap_peak_thresh: return landmark if over threshold, default 0.1
-
-    """
-
-    def __init__(self, face_model_path, inference_size=None, gaussian_sigma=None, heatmap_peak_thresh=None):
-        self.inference_size = inference_size or params["inference_img_size"]
-        self.sigma = gaussian_sigma or params["gaussian_sigma"]
-        self.threshold = heatmap_peak_thresh or params["heatmap_peak_thresh"]
-        self.model = FaceNet()
-        self.model.load_state_dict(torch.load(face_model_path))
-        self.model.eval()
-
-    def to(self, device):
-        self.model.to(device)
-        return self
-
-    def __call__(self, face_img):
-        device = next(iter(self.model.parameters())).device
-        H, W, C = face_img.shape
-
-        w_size = 384
-        x_data = torch.from_numpy(util.smart_resize(face_img, (w_size, w_size))).permute([2, 0, 1]) / 256.0 - 0.5
-
-        x_data = x_data.to(device)
-
-        with torch.no_grad():
-            hs = self.model(x_data[None, ...])
-            heatmaps = F.interpolate(hs[-1], (H, W), mode="bilinear", align_corners=True).cpu().numpy()[0]
-        return heatmaps
-
-    def compute_peaks_from_heatmaps(self, heatmaps):
-        all_peaks = []
-        for part in range(heatmaps.shape[0]):
-            map_ori = heatmaps[part].copy()
-            binary = np.ascontiguousarray(map_ori > 0.05, dtype=np.uint8)
-
-            if np.sum(binary) == 0:
-                continue
-
-            positions = np.where(binary > 0.5)
-            intensities = map_ori[positions]
-            mi = np.argmax(intensities)
-            y, x = positions[0][mi], positions[1][mi]
-            all_peaks.append([x, y])
-
-        return np.array(all_peaks)

invokeai/backend/bria/controlnet_aux/open_pose/hand.py

@@ -1,91 +0,0 @@
-import cv2
-import numpy as np
-import torch
-from scipy.ndimage.filters import gaussian_filter
-from skimage.measure import label
-
-from invokeai.backend.bria.controlnet_aux.open_pose import util
-from invokeai.backend.bria.controlnet_aux.open_pose.model import handpose_model
-
-
-class Hand(object):
-    def __init__(self, model_path):
-        self.model = handpose_model()
-        model_dict = util.transfer(self.model, torch.load(model_path))
-        self.model.load_state_dict(model_dict)
-        self.model.eval()
-
-    def to(self, device):
-        self.model.to(device)
-        return self
-
-    def __call__(self, oriImgRaw):
-        device = next(iter(self.model.parameters())).device
-        scale_search = [0.5, 1.0, 1.5, 2.0]
-        # scale_search = [0.5]
-        boxsize = 368
-        stride = 8
-        padValue = 128
-        thre = 0.05
-        multiplier = [x * boxsize for x in scale_search]
-
-        wsize = 128
-        heatmap_avg = np.zeros((wsize, wsize, 22))
-
-        Hr, Wr, Cr = oriImgRaw.shape
-
-        oriImg = cv2.GaussianBlur(oriImgRaw, (0, 0), 0.8)
-
-        for m in range(len(multiplier)):
-            scale = multiplier[m]
-            imageToTest = util.smart_resize(oriImg, (scale, scale))
-
-            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
-            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
-            im = np.ascontiguousarray(im)
-
-            data = torch.from_numpy(im).float()
-            data = data.to(device)
-
-            with torch.no_grad():
-                output = self.model(data).cpu().numpy()
-
-            # extract outputs, resize, and remove padding
-            heatmap = np.transpose(np.squeeze(output), (1, 2, 0))  # output 1 is heatmaps
-            heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride)
-            heatmap = heatmap[: imageToTest_padded.shape[0] - pad[2], : imageToTest_padded.shape[1] - pad[3], :]
-            heatmap = util.smart_resize(heatmap, (wsize, wsize))
-
-            heatmap_avg += heatmap / len(multiplier)
-
-        all_peaks = []
-        for part in range(21):
-            map_ori = heatmap_avg[:, :, part]
-            one_heatmap = gaussian_filter(map_ori, sigma=3)
-            binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8)
-
-            if np.sum(binary) == 0:
-                all_peaks.append([0, 0])
-                continue
-            label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim)
-            max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1
-            label_img[label_img != max_index] = 0
-            map_ori[label_img == 0] = 0
-
-            y, x = util.npmax(map_ori)
-            y = int(float(y) * float(Hr) / float(wsize))
-            x = int(float(x) * float(Wr) / float(wsize))
-            all_peaks.append([x, y])
-        return np.array(all_peaks)
-
-
-if __name__ == "__main__":
-    hand_estimation = Hand("../model/hand_pose_model.pth")
-
-    # test_image = '../images/hand.jpg'
-    test_image = "../images/hand.jpg"
-    oriImg = cv2.imread(test_image)  # B,G,R order
-    peaks = hand_estimation(oriImg)
-    canvas = util.draw_handpose(oriImg, peaks, True)
-    cv2.imshow("", canvas)
-    cv2.waitKey(0)

invokeai/backend/bria/controlnet_aux/open_pose/model.py

@@ -1,240 +0,0 @@
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-
-
-def make_layers(block, no_relu_layers):
-    layers = []
-    for layer_name, v in block.items():
-        if "pool" in layer_name:
-            layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
-            layers.append((layer_name, layer))
-        else:
-            conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1], kernel_size=v[2], stride=v[3], padding=v[4])
-            layers.append((layer_name, conv2d))
-            if layer_name not in no_relu_layers:
-                layers.append(("relu_" + layer_name, nn.ReLU(inplace=True)))
-
-    return nn.Sequential(OrderedDict(layers))
-
-
-class bodypose_model(nn.Module):
-    def __init__(self):
-        super(bodypose_model, self).__init__()
-
-        # these layers have no relu layer
-        no_relu_layers = [
-            "conv5_5_CPM_L1",
-            "conv5_5_CPM_L2",
-            "Mconv7_stage2_L1",
-            "Mconv7_stage2_L2",
-            "Mconv7_stage3_L1",
-            "Mconv7_stage3_L2",
-            "Mconv7_stage4_L1",
-            "Mconv7_stage4_L2",
-            "Mconv7_stage5_L1",
-            "Mconv7_stage5_L2",
-            "Mconv7_stage6_L1",
-            "Mconv7_stage6_L1",
-        ]
-        blocks = {}
-        block0 = OrderedDict(
-            [
-                ("conv1_1", [3, 64, 3, 1, 1]),
-                ("conv1_2", [64, 64, 3, 1, 1]),
-                ("pool1_stage1", [2, 2, 0]),
-                ("conv2_1", [64, 128, 3, 1, 1]),
-                ("conv2_2", [128, 128, 3, 1, 1]),
-                ("pool2_stage1", [2, 2, 0]),
-                ("conv3_1", [128, 256, 3, 1, 1]),
-                ("conv3_2", [256, 256, 3, 1, 1]),
-                ("conv3_3", [256, 256, 3, 1, 1]),
-                ("conv3_4", [256, 256, 3, 1, 1]),
-                ("pool3_stage1", [2, 2, 0]),
-                ("conv4_1", [256, 512, 3, 1, 1]),
-                ("conv4_2", [512, 512, 3, 1, 1]),
-                ("conv4_3_CPM", [512, 256, 3, 1, 1]),
-                ("conv4_4_CPM", [256, 128, 3, 1, 1]),
-            ]
-        )
-
-        # Stage 1
-        block1_1 = OrderedDict(
-            [
-                ("conv5_1_CPM_L1", [128, 128, 3, 1, 1]),
-                ("conv5_2_CPM_L1", [128, 128, 3, 1, 1]),
-                ("conv5_3_CPM_L1", [128, 128, 3, 1, 1]),
-                ("conv5_4_CPM_L1", [128, 512, 1, 1, 0]),
-                ("conv5_5_CPM_L1", [512, 38, 1, 1, 0]),
-            ]
-        )
-
-        block1_2 = OrderedDict(
-            [
-                ("conv5_1_CPM_L2", [128, 128, 3, 1, 1]),
-                ("conv5_2_CPM_L2", [128, 128, 3, 1, 1]),
-                ("conv5_3_CPM_L2", [128, 128, 3, 1, 1]),
-                ("conv5_4_CPM_L2", [128, 512, 1, 1, 0]),
-                ("conv5_5_CPM_L2", [512, 19, 1, 1, 0]),
-            ]
-        )
-        blocks["block1_1"] = block1_1
-        blocks["block1_2"] = block1_2
-
-        self.model0 = make_layers(block0, no_relu_layers)
-
-        # Stages 2 - 6
-        for i in range(2, 7):
-            blocks["block%d_1" % i] = OrderedDict(
-                [
-                    ("Mconv1_stage%d_L1" % i, [185, 128, 7, 1, 3]),
-                    ("Mconv2_stage%d_L1" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv3_stage%d_L1" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv4_stage%d_L1" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv5_stage%d_L1" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv6_stage%d_L1" % i, [128, 128, 1, 1, 0]),
-                    ("Mconv7_stage%d_L1" % i, [128, 38, 1, 1, 0]),
-                ]
-            )
-
-            blocks["block%d_2" % i] = OrderedDict(
-                [
-                    ("Mconv1_stage%d_L2" % i, [185, 128, 7, 1, 3]),
-                    ("Mconv2_stage%d_L2" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv3_stage%d_L2" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv4_stage%d_L2" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv5_stage%d_L2" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv6_stage%d_L2" % i, [128, 128, 1, 1, 0]),
-                    ("Mconv7_stage%d_L2" % i, [128, 19, 1, 1, 0]),
-                ]
-            )
-
-        for k in blocks.keys():
-            blocks[k] = make_layers(blocks[k], no_relu_layers)
-
-        self.model1_1 = blocks["block1_1"]
-        self.model2_1 = blocks["block2_1"]
-        self.model3_1 = blocks["block3_1"]
-        self.model4_1 = blocks["block4_1"]
-        self.model5_1 = blocks["block5_1"]
-        self.model6_1 = blocks["block6_1"]
-
-        self.model1_2 = blocks["block1_2"]
-        self.model2_2 = blocks["block2_2"]
-        self.model3_2 = blocks["block3_2"]
-        self.model4_2 = blocks["block4_2"]
-        self.model5_2 = blocks["block5_2"]
-        self.model6_2 = blocks["block6_2"]
-
-    def forward(self, x):
-        out1 = self.model0(x)
-
-        out1_1 = self.model1_1(out1)
-        out1_2 = self.model1_2(out1)
-        out2 = torch.cat([out1_1, out1_2, out1], 1)
-
-        out2_1 = self.model2_1(out2)
-        out2_2 = self.model2_2(out2)
-        out3 = torch.cat([out2_1, out2_2, out1], 1)
-
-        out3_1 = self.model3_1(out3)
-        out3_2 = self.model3_2(out3)
-        out4 = torch.cat([out3_1, out3_2, out1], 1)
-
-        out4_1 = self.model4_1(out4)
-        out4_2 = self.model4_2(out4)
-        out5 = torch.cat([out4_1, out4_2, out1], 1)
-
-        out5_1 = self.model5_1(out5)
-        out5_2 = self.model5_2(out5)
-        out6 = torch.cat([out5_1, out5_2, out1], 1)
-
-        out6_1 = self.model6_1(out6)
-        out6_2 = self.model6_2(out6)
-
-        return out6_1, out6_2
-
-
-class handpose_model(nn.Module):
-    def __init__(self):
-        super(handpose_model, self).__init__()
-
-        # these layers have no relu layer
-        no_relu_layers = [
-            "conv6_2_CPM",
-            "Mconv7_stage2",
-            "Mconv7_stage3",
-            "Mconv7_stage4",
-            "Mconv7_stage5",
-            "Mconv7_stage6",
-        ]
-        # stage 1
-        block1_0 = OrderedDict(
-            [
-                ("conv1_1", [3, 64, 3, 1, 1]),
-                ("conv1_2", [64, 64, 3, 1, 1]),
-                ("pool1_stage1", [2, 2, 0]),
-                ("conv2_1", [64, 128, 3, 1, 1]),
-                ("conv2_2", [128, 128, 3, 1, 1]),
-                ("pool2_stage1", [2, 2, 0]),
-                ("conv3_1", [128, 256, 3, 1, 1]),
-                ("conv3_2", [256, 256, 3, 1, 1]),
-                ("conv3_3", [256, 256, 3, 1, 1]),
-                ("conv3_4", [256, 256, 3, 1, 1]),
-                ("pool3_stage1", [2, 2, 0]),
-                ("conv4_1", [256, 512, 3, 1, 1]),
-                ("conv4_2", [512, 512, 3, 1, 1]),
-                ("conv4_3", [512, 512, 3, 1, 1]),
-                ("conv4_4", [512, 512, 3, 1, 1]),
-                ("conv5_1", [512, 512, 3, 1, 1]),
-                ("conv5_2", [512, 512, 3, 1, 1]),
-                ("conv5_3_CPM", [512, 128, 3, 1, 1]),
-            ]
-        )
-
-        block1_1 = OrderedDict([("conv6_1_CPM", [128, 512, 1, 1, 0]), ("conv6_2_CPM", [512, 22, 1, 1, 0])])
-
-        blocks = {}
-        blocks["block1_0"] = block1_0
-        blocks["block1_1"] = block1_1
-
-        # stage 2-6
-        for i in range(2, 7):
-            blocks["block%d" % i] = OrderedDict(
-                [
-                    ("Mconv1_stage%d" % i, [150, 128, 7, 1, 3]),
-                    ("Mconv2_stage%d" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv3_stage%d" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv4_stage%d" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv5_stage%d" % i, [128, 128, 7, 1, 3]),
-                    ("Mconv6_stage%d" % i, [128, 128, 1, 1, 0]),
-                    ("Mconv7_stage%d" % i, [128, 22, 1, 1, 0]),
-                ]
-            )
-
-        for k in blocks.keys():
-            blocks[k] = make_layers(blocks[k], no_relu_layers)
-
-        self.model1_0 = blocks["block1_0"]
-        self.model1_1 = blocks["block1_1"]
-        self.model2 = blocks["block2"]
-        self.model3 = blocks["block3"]
-        self.model4 = blocks["block4"]
-        self.model5 = blocks["block5"]
-        self.model6 = blocks["block6"]
-
-    def forward(self, x):
-        out1_0 = self.model1_0(x)
-        out1_1 = self.model1_1(out1_0)
-        concat_stage2 = torch.cat([out1_1, out1_0], 1)
-        out_stage2 = self.model2(concat_stage2)
-        concat_stage3 = torch.cat([out_stage2, out1_0], 1)
-        out_stage3 = self.model3(concat_stage3)
-        concat_stage4 = torch.cat([out_stage3, out1_0], 1)
-        out_stage4 = self.model4(concat_stage4)
-        concat_stage5 = torch.cat([out_stage4, out1_0], 1)
-        out_stage5 = self.model5(concat_stage5)
-        concat_stage6 = torch.cat([out_stage5, out1_0], 1)
-        out_stage6 = self.model6(concat_stage6)
-        return out_stage6

invokeai/backend/bria/controlnet_aux/open_pose/util.py

@@ -1,436 +0,0 @@
-import math
-from typing import List, Tuple, Union
-
-import cv2
-import numpy as np
-
-from invokeai.backend.bria.controlnet_aux.open_pose.body import BodyResult, Keypoint
-
-eps = 0.01
-
-
-def smart_resize(x, s):
-    Ht, Wt = s
-    if x.ndim == 2:
-        Ho, Wo = x.shape
-        Co = 1
-    else:
-        Ho, Wo, Co = x.shape
-    if Co == 3 or Co == 1:
-        k = float(Ht + Wt) / float(Ho + Wo)
-        return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
-    else:
-        return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2)
-
-
-def smart_resize_k(x, fx, fy):
-    if x.ndim == 2:
-        Ho, Wo = x.shape
-        Co = 1
-    else:
-        Ho, Wo, Co = x.shape
-    Ht, Wt = Ho * fy, Wo * fx
-    if Co == 3 or Co == 1:
-        k = float(Ht + Wt) / float(Ho + Wo)
-        return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
-    else:
-        return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2)
-
-
-def padRightDownCorner(img, stride, padValue):
-    h = img.shape[0]
-    w = img.shape[1]
-
-    pad = 4 * [None]
-    pad[0] = 0  # up
-    pad[1] = 0  # left
-    pad[2] = 0 if (h % stride == 0) else stride - (h % stride)  # down
-    pad[3] = 0 if (w % stride == 0) else stride - (w % stride)  # right
-
-    img_padded = img
-    pad_up = np.tile(img_padded[0:1, :, :] * 0 + padValue, (pad[0], 1, 1))
-    img_padded = np.concatenate((pad_up, img_padded), axis=0)
-    pad_left = np.tile(img_padded[:, 0:1, :] * 0 + padValue, (1, pad[1], 1))
-    img_padded = np.concatenate((pad_left, img_padded), axis=1)
-    pad_down = np.tile(img_padded[-2:-1, :, :] * 0 + padValue, (pad[2], 1, 1))
-    img_padded = np.concatenate((img_padded, pad_down), axis=0)
-    pad_right = np.tile(img_padded[:, -2:-1, :] * 0 + padValue, (1, pad[3], 1))
-    img_padded = np.concatenate((img_padded, pad_right), axis=1)
-
-    return img_padded, pad
-
-
-def transfer(model, model_weights):
-    transfered_model_weights = {}
-    for weights_name in model.state_dict().keys():
-        transfered_model_weights[weights_name] = model_weights[".".join(weights_name.split(".")[1:])]
-    return transfered_model_weights
-
-
-def draw_bodypose(canvas: np.ndarray, keypoints: List[Keypoint]) -> np.ndarray:
-    """
-    Draw keypoints and limbs representing body pose on a given canvas.
-
-    Args:
-        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the body pose.
-        keypoints (List[Keypoint]): A list of Keypoint objects representing the body keypoints to be drawn.
-
-    Returns:
-        np.ndarray: A 3D numpy array representing the modified canvas with the drawn body pose.
-
-    Note:
-        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
-    """
-    H, W, C = canvas.shape
-    stickwidth = 4
-
-    limbSeq = [
-        [2, 3],
-        [2, 6],
-        [3, 4],
-        [4, 5],
-        [6, 7],
-        [7, 8],
-        [2, 9],
-        [9, 10],
-        [10, 11],
-        [2, 12],
-        [12, 13],
-        [13, 14],
-        [2, 1],
-        [1, 15],
-        [15, 17],
-        [1, 16],
-        [16, 18],
-    ]
-
-    colors = [
-        [255, 0, 0],
-        [255, 85, 0],
-        [255, 170, 0],
-        [255, 255, 0],
-        [170, 255, 0],
-        [85, 255, 0],
-        [0, 255, 0],
-        [0, 255, 85],
-        [0, 255, 170],
-        [0, 255, 255],
-        [0, 170, 255],
-        [0, 85, 255],
-        [0, 0, 255],
-        [85, 0, 255],
-        [170, 0, 255],
-        [255, 0, 255],
-        [255, 0, 170],
-        [255, 0, 85],
-    ]
-
-    for (k1_index, k2_index), color in zip(limbSeq, colors, strict=False):
-        keypoint1 = keypoints[k1_index - 1]
-        keypoint2 = keypoints[k2_index - 1]
-
-        if keypoint1 is None or keypoint2 is None:
-            continue
-
-        Y = np.array([keypoint1.x, keypoint2.x]) * float(W)
-        X = np.array([keypoint1.y, keypoint2.y]) * float(H)
-        mX = np.mean(X)
-        mY = np.mean(Y)
-        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
-        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
-        polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
-        cv2.fillConvexPoly(canvas, polygon, [int(float(c) * 0.6) for c in color])
-
-    for keypoint, color in zip(keypoints, colors, strict=False):
-        if keypoint is None:
-            continue
-
-        x, y = keypoint.x, keypoint.y
-        x = int(x * W)
-        y = int(y * H)
-        cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1)
-
-    return canvas
-
-
-def draw_handpose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray:
-    import matplotlib
-
-    """
-    Draw keypoints and connections representing hand pose on a given canvas.
-
-    Args:
-        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose.
-        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn
-                                          or None if no keypoints are present.
-
-    Returns:
-        np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose.
-
-    Note:
-        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
-    """
-    if not keypoints:
-        return canvas
-
-    H, W, C = canvas.shape
-
-    edges = [
-        [0, 1],
-        [1, 2],
-        [2, 3],
-        [3, 4],
-        [0, 5],
-        [5, 6],
-        [6, 7],
-        [7, 8],
-        [0, 9],
-        [9, 10],
-        [10, 11],
-        [11, 12],
-        [0, 13],
-        [13, 14],
-        [14, 15],
-        [15, 16],
-        [0, 17],
-        [17, 18],
-        [18, 19],
-        [19, 20],
-    ]
-
-    for ie, (e1, e2) in enumerate(edges):
-        k1 = keypoints[e1]
-        k2 = keypoints[e2]
-        if k1 is None or k2 is None:
-            continue
-
-        x1 = int(k1.x * W)
-        y1 = int(k1.y * H)
-        x2 = int(k2.x * W)
-        y2 = int(k2.y * H)
-        if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
-            cv2.line(
-                canvas,
-                (x1, y1),
-                (x2, y2),
-                matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255,
-                thickness=2,
-            )
-
-    for keypoint in keypoints:
-        x, y = keypoint.x, keypoint.y
-        x = int(x * W)
-        y = int(y * H)
-        if x > eps and y > eps:
-            cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
-    return canvas
-
-
-def draw_facepose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray:
-    """
-    Draw keypoints representing face pose on a given canvas.
-
-    Args:
-        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the face pose.
-        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the face keypoints to be drawn
-                                          or None if no keypoints are present.
-
-    Returns:
-        np.ndarray: A 3D numpy array representing the modified canvas with the drawn face pose.
-
-    Note:
-        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
-    """
-    if not keypoints:
-        return canvas
-
-    H, W, C = canvas.shape
-    for keypoint in keypoints:
-        x, y = keypoint.x, keypoint.y
-        x = int(x * W)
-        y = int(y * H)
-        if x > eps and y > eps:
-            cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1)
-    return canvas
-
-
-# detect hand according to body pose keypoints
-# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
-def handDetect(body: BodyResult, oriImg) -> List[Tuple[int, int, int, bool]]:
-    """
-    Detect hands in the input body pose keypoints and calculate the bounding box for each hand.
-
-    Args:
-        body (BodyResult): A BodyResult object containing the detected body pose keypoints.
-        oriImg (numpy.ndarray): A 3D numpy array representing the original input image.
-
-    Returns:
-        List[Tuple[int, int, int, bool]]: A list of tuples, each containing the coordinates (x, y) of the top-left
-                                          corner of the bounding box, the width (height) of the bounding box, and
-                                          a boolean flag indicating whether the hand is a left hand (True) or a
-                                          right hand (False).
-
-    Notes:
-        - The width and height of the bounding boxes are equal since the network requires squared input.
-        - The minimum bounding box size is 20 pixels.
-    """
-    ratioWristElbow = 0.33
-    detect_result = []
-    image_height, image_width = oriImg.shape[0:2]
-
-    keypoints = body.keypoints
-    # right hand: wrist 4, elbow 3, shoulder 2
-    # left hand: wrist 7, elbow 6, shoulder 5
-    left_shoulder = keypoints[5]
-    left_elbow = keypoints[6]
-    left_wrist = keypoints[7]
-    right_shoulder = keypoints[2]
-    right_elbow = keypoints[3]
-    right_wrist = keypoints[4]
-
-    # if any of three not detected
-    has_left = all(keypoint is not None for keypoint in (left_shoulder, left_elbow, left_wrist))
-    has_right = all(keypoint is not None for keypoint in (right_shoulder, right_elbow, right_wrist))
-    if not (has_left or has_right):
-        return []
-
-    hands = []
-    # left hand
-    if has_left:
-        hands.append([left_shoulder.x, left_shoulder.y, left_elbow.x, left_elbow.y, left_wrist.x, left_wrist.y, True])
-    # right hand
-    if has_right:
-        hands.append(
-            [right_shoulder.x, right_shoulder.y, right_elbow.x, right_elbow.y, right_wrist.x, right_wrist.y, False]
-        )
-
-    for x1, y1, x2, y2, x3, y3, is_left in hands:
-        # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
-        # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
-        # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
-        # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
-        # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
-        # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
-        x = x3 + ratioWristElbow * (x3 - x2)
-        y = y3 + ratioWristElbow * (y3 - y2)
-        distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
-        distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
-        width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
-        # x-y refers to the center --> offset to topLeft point
-        # handRectangle.x -= handRectangle.width / 2.f;
-        # handRectangle.y -= handRectangle.height / 2.f;
-        x -= width / 2
-        y -= width / 2  # width = height
-        # overflow the image
-        if x < 0:
-            x = 0
-        if y < 0:
-            y = 0
-        width1 = width
-        width2 = width
-        if x + width > image_width:
-            width1 = image_width - x
-        if y + width > image_height:
-            width2 = image_height - y
-        width = min(width1, width2)
-        # the max hand box value is 20 pixels
-        if width >= 20:
-            detect_result.append((int(x), int(y), int(width), is_left))
-
-    """
-    return value: [[x, y, w, True if left hand else False]].
-    width=height since the network require squared input.
-    x, y is the coordinate of top left.
-    """
-    return detect_result
-
-
-# Written by Lvmin
-def faceDetect(body: BodyResult, oriImg) -> Union[Tuple[int, int, int], None]:
-    """
-    Detect the face in the input body pose keypoints and calculate the bounding box for the face.
-
-    Args:
-        body (BodyResult): A BodyResult object containing the detected body pose keypoints.
-        oriImg (numpy.ndarray): A 3D numpy array representing the original input image.
-
-    Returns:
-        Tuple[int, int, int] | None: A tuple containing the coordinates (x, y) of the top-left corner of the
-                                   bounding box and the width (height) of the bounding box, or None if the
-                                   face is not detected or the bounding box width is less than 20 pixels.
-
-    Notes:
-        - The width and height of the bounding box are equal.
-        - The minimum bounding box size is 20 pixels.
-    """
-    # left right eye ear 14 15 16 17
-    image_height, image_width = oriImg.shape[0:2]
-
-    keypoints = body.keypoints
-    head = keypoints[0]
-    left_eye = keypoints[14]
-    right_eye = keypoints[15]
-    left_ear = keypoints[16]
-    right_ear = keypoints[17]
-
-    if head is None or all(keypoint is None for keypoint in (left_eye, right_eye, left_ear, right_ear)):
-        return None
-
-    width = 0.0
-    x0, y0 = head.x, head.y
-
-    if left_eye is not None:
-        x1, y1 = left_eye.x, left_eye.y
-        d = max(abs(x0 - x1), abs(y0 - y1))
-        width = max(width, d * 3.0)
-
-    if right_eye is not None:
-        x1, y1 = right_eye.x, right_eye.y
-        d = max(abs(x0 - x1), abs(y0 - y1))
-        width = max(width, d * 3.0)
-
-    if left_ear is not None:
-        x1, y1 = left_ear.x, left_ear.y
-        d = max(abs(x0 - x1), abs(y0 - y1))
-        width = max(width, d * 1.5)
-
-    if right_ear is not None:
-        x1, y1 = right_ear.x, right_ear.y
-        d = max(abs(x0 - x1), abs(y0 - y1))
-        width = max(width, d * 1.5)
-
-    x, y = x0, y0
-
-    x -= width
-    y -= width
-
-    if x < 0:
-        x = 0
-
-    if y < 0:
-        y = 0
-
-    width1 = width * 2
-    width2 = width * 2
-
-    if x + width > image_width:
-        width1 = image_width - x
-
-    if y + width > image_height:
-        width2 = image_height - y
-
-    width = min(width1, width2)
-
-    if width >= 20:
-        return int(x), int(y), int(width)
-    else:
-        return None
-
-
-# get max index of 2d array
-def npmax(array):
-    arrayindex = array.argmax(1)
-    arrayvalue = array.max(1)
-    i = arrayvalue.argmax()
-    j = arrayindex[i]
-    return i, j

invokeai/backend/bria/controlnet_aux/util.py

@@ -1,260 +0,0 @@
-import os
-import random
-
-import cv2
-import numpy as np
-import torch
-
-annotator_ckpts_path = os.path.join(os.path.dirname(__file__), "ckpts")
-
-
-def HWC3(x):
-    assert x.dtype == np.uint8
-    if x.ndim == 2:
-        x = x[:, :, None]
-    assert x.ndim == 3
-    H, W, C = x.shape
-    assert C == 1 or C == 3 or C == 4
-    if C == 3:
-        return x
-    if C == 1:
-        return np.concatenate([x, x, x], axis=2)
-    if C == 4:
-        color = x[:, :, 0:3].astype(np.float32)
-        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
-        y = color * alpha + 255.0 * (1.0 - alpha)
-        y = y.clip(0, 255).astype(np.uint8)
-        return y
-
-
-def make_noise_disk(H, W, C, F):
-    noise = np.random.uniform(low=0, high=1, size=((H // F) + 2, (W // F) + 2, C))
-    noise = cv2.resize(noise, (W + 2 * F, H + 2 * F), interpolation=cv2.INTER_CUBIC)
-    noise = noise[F : F + H, F : F + W]
-    noise -= np.min(noise)
-    noise /= np.max(noise)
-    if C == 1:
-        noise = noise[:, :, None]
-    return noise
-
-
-def nms(x, t, s):
-    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
-
-    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
-    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
-    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
-    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
-
-    y = np.zeros_like(x)
-
-    for f in [f1, f2, f3, f4]:
-        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
-
-    z = np.zeros_like(y, dtype=np.uint8)
-    z[y > t] = 255
-    return z
-
-
-def min_max_norm(x):
-    x -= np.min(x)
-    x /= np.maximum(np.max(x), 1e-5)
-    return x
-
-
-def safe_step(x, step=2):
-    y = x.astype(np.float32) * float(step + 1)
-    y = y.astype(np.int32).astype(np.float32) / float(step)
-    return y
-
-
-def img2mask(img, H, W, low=10, high=90):
-    assert img.ndim == 3 or img.ndim == 2
-    assert img.dtype == np.uint8
-
-    if img.ndim == 3:
-        y = img[:, :, random.randrange(0, img.shape[2])]
-    else:
-        y = img
-
-    y = cv2.resize(y, (W, H), interpolation=cv2.INTER_CUBIC)
-
-    if random.uniform(0, 1) < 0.5:
-        y = 255 - y
-
-    return y < np.percentile(y, random.randrange(low, high))
-
-
-def resize_image(input_image, resolution):
-    H, W, C = input_image.shape
-    H = float(H)
-    W = float(W)
-    k = float(resolution) / min(H, W)
-    H *= k
-    W *= k
-    H = int(np.round(H / 64.0)) * 64
-    W = int(np.round(W / 64.0)) * 64
-    img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
-    return img
-
-
-def torch_gc():
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        torch.cuda.ipc_collect()
-
-
-def ade_palette():
-    """ADE20K palette that maps each class to RGB values."""
-    return [
-        [120, 120, 120],
-        [180, 120, 120],
-        [6, 230, 230],
-        [80, 50, 50],
-        [4, 200, 3],
-        [120, 120, 80],
-        [140, 140, 140],
-        [204, 5, 255],
-        [230, 230, 230],
-        [4, 250, 7],
-        [224, 5, 255],
-        [235, 255, 7],
-        [150, 5, 61],
-        [120, 120, 70],
-        [8, 255, 51],
-        [255, 6, 82],
-        [143, 255, 140],
-        [204, 255, 4],
-        [255, 51, 7],
-        [204, 70, 3],
-        [0, 102, 200],
-        [61, 230, 250],
-        [255, 6, 51],
-        [11, 102, 255],
-        [255, 7, 71],
-        [255, 9, 224],
-        [9, 7, 230],
-        [220, 220, 220],
-        [255, 9, 92],
-        [112, 9, 255],
-        [8, 255, 214],
-        [7, 255, 224],
-        [255, 184, 6],
-        [10, 255, 71],
-        [255, 41, 10],
-        [7, 255, 255],
-        [224, 255, 8],
-        [102, 8, 255],
-        [255, 61, 6],
-        [255, 194, 7],
-        [255, 122, 8],
-        [0, 255, 20],
-        [255, 8, 41],
-        [255, 5, 153],
-        [6, 51, 255],
-        [235, 12, 255],
-        [160, 150, 20],
-        [0, 163, 255],
-        [140, 140, 140],
-        [250, 10, 15],
-        [20, 255, 0],
-        [31, 255, 0],
-        [255, 31, 0],
-        [255, 224, 0],
-        [153, 255, 0],
-        [0, 0, 255],
-        [255, 71, 0],
-        [0, 235, 255],
-        [0, 173, 255],
-        [31, 0, 255],
-        [11, 200, 200],
-        [255, 82, 0],
-        [0, 255, 245],
-        [0, 61, 255],
-        [0, 255, 112],
-        [0, 255, 133],
-        [255, 0, 0],
-        [255, 163, 0],
-        [255, 102, 0],
-        [194, 255, 0],
-        [0, 143, 255],
-        [51, 255, 0],
-        [0, 82, 255],
-        [0, 255, 41],
-        [0, 255, 173],
-        [10, 0, 255],
-        [173, 255, 0],
-        [0, 255, 153],
-        [255, 92, 0],
-        [255, 0, 255],
-        [255, 0, 245],
-        [255, 0, 102],
-        [255, 173, 0],
-        [255, 0, 20],
-        [255, 184, 184],
-        [0, 31, 255],
-        [0, 255, 61],
-        [0, 71, 255],
-        [255, 0, 204],
-        [0, 255, 194],
-        [0, 255, 82],
-        [0, 10, 255],
-        [0, 112, 255],
-        [51, 0, 255],
-        [0, 194, 255],
-        [0, 122, 255],
-        [0, 255, 163],
-        [255, 153, 0],
-        [0, 255, 10],
-        [255, 112, 0],
-        [143, 255, 0],
-        [82, 0, 255],
-        [163, 255, 0],
-        [255, 235, 0],
-        [8, 184, 170],
-        [133, 0, 255],
-        [0, 255, 92],
-        [184, 0, 255],
-        [255, 0, 31],
-        [0, 184, 255],
-        [0, 214, 255],
-        [255, 0, 112],
-        [92, 255, 0],
-        [0, 224, 255],
-        [112, 224, 255],
-        [70, 184, 160],
-        [163, 0, 255],
-        [153, 0, 255],
-        [71, 255, 0],
-        [255, 0, 163],
-        [255, 204, 0],
-        [255, 0, 143],
-        [0, 255, 235],
-        [133, 255, 0],
-        [255, 0, 235],
-        [245, 0, 255],
-        [255, 0, 122],
-        [255, 245, 0],
-        [10, 190, 212],
-        [214, 255, 0],
-        [0, 204, 255],
-        [20, 0, 255],
-        [255, 255, 0],
-        [0, 153, 255],
-        [0, 41, 255],
-        [0, 255, 204],
-        [41, 0, 255],
-        [41, 255, 0],
-        [173, 0, 255],
-        [0, 245, 255],
-        [71, 0, 255],
-        [122, 0, 255],
-        [0, 255, 184],
-        [0, 92, 255],
-        [184, 255, 0],
-        [0, 133, 255],
-        [255, 214, 0],
-        [25, 194, 194],
-        [102, 255, 0],
-        [92, 0, 255],
-    ]

invokeai/backend/bria/controlnet_bria.py

@@ -1,559 +0,0 @@
-# type: ignore
-# Copyright 2024 Black Forest Labs, The HuggingFace Team and The InstantX Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass
-from enum import Enum
-from typing import Any, Dict, List, Literal, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import PeftAdapterMixin
-from diffusers.models.attention_processor import AttentionProcessor
-from diffusers.models.controlnet import zero_module
-from diffusers.models.modeling_outputs import Transformer2DModelOutput
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
-from diffusers.utils.outputs import BaseOutput
-
-from invokeai.backend.bria.transformer_bria import (
-    EmbedND,
-    FluxSingleTransformerBlock,
-    FluxTransformerBlock,
-    TimestepProjEmbeddings,
-)
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-BRIA_CONTROL_MODES = Literal["depth", "canny", "colorgrid", "recolor", "tile", "pose"]
-
-
-class BriaControlModes(Enum):
-    depth = 0
-    canny = 1
-    colorgrid = 2
-    recolor = 3
-    tile = 4
-    pose = 5
-
-
-@dataclass
-class BriaControlNetOutput(BaseOutput):
-    controlnet_block_samples: Tuple[torch.Tensor]
-    controlnet_single_block_samples: Tuple[torch.Tensor]
-
-
-class BriaControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        patch_size: int = 1,
-        in_channels: int = 64,
-        num_layers: int = 19,
-        num_single_layers: int = 38,
-        attention_head_dim: int = 128,
-        num_attention_heads: int = 24,
-        joint_attention_dim: int = 4096,
-        pooled_projection_dim: int = 768,
-        guidance_embeds: bool = False,
-        axes_dims_rope: Optional[List[int]] = None,
-        num_mode: int = None,
-        rope_theta: int = 10000,
-        time_theta: int = 10000,
-    ):
-        super().__init__()
-        self.out_channels = in_channels
-        self.inner_dim = num_attention_heads * attention_head_dim
-
-        # self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
-        axes_dims_rope = [16, 56, 56] if axes_dims_rope is None else axes_dims_rope
-        self.pos_embed = EmbedND(theta=rope_theta, axes_dim=axes_dims_rope)
-
-        # text_time_guidance_cls = (
-        # CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
-        # )
-        # self.time_text_embed = text_time_guidance_cls(
-        # embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
-        # )
-        self.time_embed = TimestepProjEmbeddings(embedding_dim=self.inner_dim, time_theta=time_theta)
-
-        self.context_embedder = nn.Linear(joint_attention_dim, self.inner_dim)
-        self.x_embedder = torch.nn.Linear(in_channels, self.inner_dim)
-
-        self.transformer_blocks = nn.ModuleList(
-            [
-                FluxTransformerBlock(
-                    dim=self.inner_dim,
-                    num_attention_heads=num_attention_heads,
-                    attention_head_dim=attention_head_dim,
-                )
-                for i in range(num_layers)
-            ]
-        )
-
-        self.single_transformer_blocks = nn.ModuleList(
-            [
-                FluxSingleTransformerBlock(
-                    dim=self.inner_dim,
-                    num_attention_heads=num_attention_heads,
-                    attention_head_dim=attention_head_dim,
-                )
-                for i in range(num_single_layers)
-            ]
-        )
-
-        # controlnet_blocks
-        self.controlnet_blocks = nn.ModuleList([])
-        for _ in range(len(self.transformer_blocks)):
-            self.controlnet_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
-
-        self.controlnet_single_blocks = nn.ModuleList([])
-        for _ in range(len(self.single_transformer_blocks)):
-            self.controlnet_single_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
-
-        self.union = num_mode is not None and num_mode > 0
-        if self.union:
-            self.controlnet_mode_embedder = nn.Embedding(num_mode, self.inner_dim)
-
-        self.controlnet_x_embedder = zero_module(torch.nn.Linear(in_channels, self.inner_dim))
-
-        self.gradient_checkpointing = False
-
-    @property
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
-    def attn_processors(self):
-        r"""
-        Returns:
-            `dict` of attention processors: A dictionary containing all attention processors used in the model with
-            indexed by its weight name.
-        """
-        # set recursively
-        processors = {}
-
-        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
-            if hasattr(module, "get_processor"):
-                processors[f"{name}.processor"] = module.get_processor()
-
-            for sub_name, child in module.named_children():
-                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
-
-            return processors
-
-        for name, module in self.named_children():
-            fn_recursive_add_processors(name, module, processors)
-
-        return processors
-
-    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
-    def set_attn_processor(self, processor):
-        r"""
-        Sets the attention processor to use to compute attention.
-        Parameters:
-            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
-                The instantiated processor class or a dictionary of processor classes that will be set as the processor
-                for **all** `Attention` layers.
-                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
-                processor. This is strongly recommended when setting trainable attention processors.
-        """
-        count = len(self.attn_processors.keys())
-
-        if isinstance(processor, dict) and len(processor) != count:
-            raise ValueError(
-                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
-                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
-            )
-
-        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
-            if hasattr(module, "set_processor"):
-                if not isinstance(processor, dict):
-                    module.set_processor(processor)
-                else:
-                    module.set_processor(processor.pop(f"{name}.processor"))
-
-            for sub_name, child in module.named_children():
-                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
-
-        for name, module in self.named_children():
-            fn_recursive_attn_processor(name, module, processor)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
-    @classmethod
-    def from_transformer(
-        cls,
-        transformer,
-        num_layers: int = 4,
-        num_single_layers: int = 10,
-        attention_head_dim: int = 128,
-        num_attention_heads: int = 24,
-        load_weights_from_transformer=True,
-    ):
-        config = transformer.config
-        config["num_layers"] = num_layers
-        config["num_single_layers"] = num_single_layers
-        config["attention_head_dim"] = attention_head_dim
-        config["num_attention_heads"] = num_attention_heads
-
-        controlnet = cls(**config)
-
-        if load_weights_from_transformer:
-            controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
-            controlnet.time_text_embed.load_state_dict(transformer.time_text_embed.state_dict())
-            controlnet.context_embedder.load_state_dict(transformer.context_embedder.state_dict())
-            controlnet.x_embedder.load_state_dict(transformer.x_embedder.state_dict())
-            controlnet.transformer_blocks.load_state_dict(transformer.transformer_blocks.state_dict(), strict=False)
-            controlnet.single_transformer_blocks.load_state_dict(
-                transformer.single_transformer_blocks.state_dict(), strict=False
-            )
-
-            controlnet.controlnet_x_embedder = zero_module(controlnet.controlnet_x_embedder)
-
-        return controlnet
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        controlnet_cond: torch.Tensor,
-        controlnet_mode: torch.Tensor = None,
-        conditioning_scale: float = 1.0,
-        encoder_hidden_states: torch.Tensor = None,
-        pooled_projections: torch.Tensor = None,
-        timestep: torch.LongTensor = None,
-        img_ids: torch.Tensor = None,
-        txt_ids: torch.Tensor = None,
-        guidance: torch.Tensor = None,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
-    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
-        """
-        The [`FluxTransformer2DModel`] forward method.
-        Args:
-            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
-                Input `hidden_states`.
-            controlnet_cond (`torch.Tensor`):
-                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
-            controlnet_mode (`torch.Tensor`):
-                The mode tensor of shape `(batch_size, 1)`.
-            conditioning_scale (`float`, defaults to `1.0`):
-                The scale factor for ControlNet outputs.
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
-                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
-            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
-                from the embeddings of input conditions.
-            timestep ( `torch.LongTensor`):
-                Used to indicate denoising step.
-            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
-                A list of tensors that if specified are added to the residuals of transformer blocks.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
-                tuple.
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
-        if guidance is not None:
-            print("guidance is not supported in BriaControlNetModel")
-        if pooled_projections is not None:
-            print("pooled_projections is not supported in BriaControlNetModel")
-        if joint_attention_kwargs is not None:
-            joint_attention_kwargs = joint_attention_kwargs.copy()
-            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-        hidden_states = self.x_embedder(hidden_states)
-
-        # Convert controlnet_cond to the same dtype as the model weights
-        controlnet_cond = controlnet_cond.to(dtype=self.controlnet_x_embedder.weight.dtype)
-
-        # add
-        hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_cond)
-
-        timestep = timestep.to(hidden_states.dtype)  # Original code was * 1000
-        if guidance is not None:
-            guidance = guidance.to(hidden_states.dtype)  # Original code was * 1000
-        else:
-            guidance = None
-
-        temb = self.time_embed(timestep, dtype=hidden_states.dtype)
-
-        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
-
-        if txt_ids.ndim == 3:
-            logger.warning(
-                "Passing `txt_ids` 3d torch.Tensor is deprecated."
-                "Please remove the batch dimension and pass it as a 2d torch Tensor"
-            )
-            txt_ids = txt_ids[0]
-        if img_ids.ndim == 3:
-            logger.warning(
-                "Passing `img_ids` 3d torch.Tensor is deprecated."
-                "Please remove the batch dimension and pass it as a 2d torch Tensor"
-            )
-            img_ids = img_ids[0]
-
-        if self.union:
-            # union mode
-            if controlnet_mode is None:
-                raise ValueError("`controlnet_mode` cannot be `None` when applying ControlNet-Union")
-
-            # Validate controlnet_mode values are within the valid range
-            if torch.any(controlnet_mode < 0) or torch.any(controlnet_mode >= self.num_mode):
-                raise ValueError(
-                    f"`controlnet_mode` values must be in range [0, {self.num_mode - 1}], but got values outside this range"
-                )
-
-            # union mode emb
-            controlnet_mode_emb = self.controlnet_mode_embedder(controlnet_mode)
-            if controlnet_mode_emb.shape[0] < encoder_hidden_states.shape[0]:  # duplicate mode emb for each batch
-                controlnet_mode_emb = controlnet_mode_emb.expand(
-                    encoder_hidden_states.shape[0], 1, encoder_hidden_states.shape[2]
-                )
-            encoder_hidden_states = torch.cat([controlnet_mode_emb, encoder_hidden_states], dim=1)
-
-        txt_ids = torch.cat((txt_ids[0:1, :], txt_ids), dim=0)
-        ids = torch.cat((txt_ids, img_ids), dim=0)
-        image_rotary_emb = self.pos_embed(ids)
-
-        block_samples = ()
-        for _, block in enumerate(self.transformer_blocks):
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    encoder_hidden_states,
-                    temb,
-                    image_rotary_emb,
-                    **ckpt_kwargs,
-                )
-
-            else:
-                encoder_hidden_states, hidden_states = block(
-                    hidden_states=hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    temb=temb,
-                    image_rotary_emb=image_rotary_emb,
-                )
-            block_samples = block_samples + (hidden_states,)
-
-        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
-        single_block_samples = ()
-        for _, block in enumerate(self.single_transformer_blocks):
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    temb,
-                    image_rotary_emb,
-                    **ckpt_kwargs,
-                )
-
-            else:
-                hidden_states = block(
-                    hidden_states=hidden_states,
-                    temb=temb,
-                    image_rotary_emb=image_rotary_emb,
-                )
-            single_block_samples = single_block_samples + (hidden_states[:, encoder_hidden_states.shape[1] :],)
-
-        # controlnet block
-        controlnet_block_samples = ()
-        for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks, strict=False):
-            block_sample = controlnet_block(block_sample)
-            controlnet_block_samples = controlnet_block_samples + (block_sample,)
-
-        controlnet_single_block_samples = ()
-        for single_block_sample, controlnet_block in zip(
-            single_block_samples, self.controlnet_single_blocks, strict=False
-        ):
-            single_block_sample = controlnet_block(single_block_sample)
-            controlnet_single_block_samples = controlnet_single_block_samples + (single_block_sample,)
-
-        # scaling
-        controlnet_block_samples = [sample * conditioning_scale for sample in controlnet_block_samples]
-        controlnet_single_block_samples = [sample * conditioning_scale for sample in controlnet_single_block_samples]
-
-        controlnet_block_samples = None if len(controlnet_block_samples) == 0 else controlnet_block_samples
-        controlnet_single_block_samples = (
-            None if len(controlnet_single_block_samples) == 0 else controlnet_single_block_samples
-        )
-
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
-        if not return_dict:
-            return (controlnet_block_samples, controlnet_single_block_samples)
-
-        return BriaControlNetOutput(
-            controlnet_block_samples=controlnet_block_samples,
-            controlnet_single_block_samples=controlnet_single_block_samples,
-        )
-
-
-class BriaMultiControlNetModel(ModelMixin):
-    r"""
-    `BriaMultiControlNetModel` wrapper class for Multi-BriaControlNetModel
-    This module is a wrapper for multiple instances of the `BriaControlNetModel`. The `forward()` API is designed to be
-    compatible with `BriaControlNetModel`.
-    Args:
-        controlnets (`List[BriaControlNetModel]`):
-            Provides additional conditioning to the unet during the denoising process. You must set multiple
-            `BriaControlNetModel` as a list.
-    """
-
-    def __init__(self, controlnets):
-        super().__init__()
-        self.nets = nn.ModuleList(controlnets)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        controlnet_cond: List[torch.tensor],
-        controlnet_mode: List[torch.tensor],
-        conditioning_scale: List[float],
-        encoder_hidden_states: torch.Tensor = None,
-        pooled_projections: torch.Tensor = None,
-        timestep: torch.LongTensor = None,
-        img_ids: torch.Tensor = None,
-        txt_ids: torch.Tensor = None,
-        guidance: torch.Tensor = None,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
-    ) -> Union[BriaControlNetOutput, Tuple]:
-        # ControlNet-Union with multiple conditions
-        # only load one ControlNet for saving memories
-        if len(self.nets) == 1 and self.nets[0].union:
-            controlnet = self.nets[0]
-
-            for i, (image, mode, scale) in enumerate(
-                zip(controlnet_cond, controlnet_mode, conditioning_scale, strict=False)
-            ):
-                block_samples, single_block_samples = controlnet(
-                    hidden_states=hidden_states,
-                    controlnet_cond=image,
-                    controlnet_mode=mode[:, None],
-                    conditioning_scale=scale,
-                    timestep=timestep,
-                    guidance=guidance,
-                    pooled_projections=pooled_projections,
-                    encoder_hidden_states=encoder_hidden_states,
-                    txt_ids=txt_ids,
-                    img_ids=img_ids,
-                    joint_attention_kwargs=joint_attention_kwargs,
-                    return_dict=return_dict,
-                )
-
-                # merge samples
-                if i == 0:
-                    control_block_samples = block_samples
-                    control_single_block_samples = single_block_samples
-                else:
-                    control_block_samples = [
-                        control_block_sample + block_sample
-                        for control_block_sample, block_sample in zip(
-                            control_block_samples, block_samples, strict=False
-                        )
-                    ]
-
-                    control_single_block_samples = [
-                        control_single_block_sample + block_sample
-                        for control_single_block_sample, block_sample in zip(
-                            control_single_block_samples, single_block_samples, strict=False
-                        )
-                    ]
-
-        # Regular Multi-ControlNets
-        # load all ControlNets into memories
-        else:
-            for i, (image, mode, scale, controlnet) in enumerate(
-                zip(controlnet_cond, controlnet_mode, conditioning_scale, self.nets, strict=False)
-            ):
-                block_samples, single_block_samples = controlnet(
-                    hidden_states=hidden_states,
-                    controlnet_cond=image,
-                    controlnet_mode=mode[:, None],
-                    conditioning_scale=scale,
-                    timestep=timestep,
-                    guidance=guidance,
-                    pooled_projections=pooled_projections,
-                    encoder_hidden_states=encoder_hidden_states,
-                    txt_ids=txt_ids,
-                    img_ids=img_ids,
-                    joint_attention_kwargs=joint_attention_kwargs,
-                    return_dict=return_dict,
-                )
-
-                # merge samples
-                if i == 0:
-                    control_block_samples = block_samples
-                    control_single_block_samples = single_block_samples
-                else:
-                    if block_samples is not None and control_block_samples is not None:
-                        control_block_samples = [
-                            control_block_sample + block_sample
-                            for control_block_sample, block_sample in zip(
-                                control_block_samples, block_samples, strict=False
-                            )
-                        ]
-                    if single_block_samples is not None and control_single_block_samples is not None:
-                        control_single_block_samples = [
-                            control_single_block_sample + block_sample
-                            for control_single_block_sample, block_sample in zip(
-                                control_single_block_samples, single_block_samples, strict=False
-                            )
-                        ]
-
-        return control_block_samples, control_single_block_samples

invokeai/backend/bria/controlnet_utils.py

@@ -1,68 +0,0 @@
-from typing import List, Tuple
-
-import torch
-from diffusers.image_processor import VaeImageProcessor
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from PIL import Image
-
-
-@torch.no_grad()
-def prepare_control_images(
-    vae: AutoencoderKL,
-    control_images: list[Image.Image],
-    control_modes: list[int],
-    width: int,
-    height: int,
-    device: torch.device,
-) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
-    tensored_control_images = []
-    tensored_control_modes = []
-    for idx, control_image_ in enumerate(control_images):
-        tensored_control_image = _prepare_image(
-            image=control_image_,
-            width=width,
-            height=height,
-            device=device,
-            dtype=vae.dtype,
-        )
-        height, width = tensored_control_image.shape[-2:]
-
-        # vae encode
-        tensored_control_image = vae.encode(tensored_control_image).latent_dist.sample()
-        tensored_control_image = (tensored_control_image) * vae.config.scaling_factor
-
-        # pack
-        height_control_image, width_control_image = tensored_control_image.shape[2:]
-        tensored_control_image = _pack_latents(
-            tensored_control_image,
-            height_control_image,
-            width_control_image,
-        )
-        tensored_control_images.append(tensored_control_image)
-        tensored_control_modes.append(
-            torch.tensor(control_modes[idx]).expand(tensored_control_image.shape[0]).to(device, dtype=torch.long)
-        )
-
-    return tensored_control_images, tensored_control_modes
-
-
-def _prepare_image(
-    image: Image.Image,
-    width: int,
-    height: int,
-    device: torch.device,
-    dtype: torch.dtype,
-) -> torch.Tensor:
-    image = image.convert("RGB")
-    image = VaeImageProcessor(vae_scale_factor=16).preprocess(image, height=height, width=width)
-    image = image.repeat_interleave(1, dim=0)
-    image = image.to(device=device, dtype=dtype)
-    return image
-
-
-def _pack_latents(latents, height, width):
-    latents = latents.view(1, 4, height // 2, 2, width // 2, 2)
-    latents = latents.permute(0, 2, 4, 1, 3, 5)
-    latents = latents.reshape(1, (height // 2) * (width // 2), 16)
-
-    return latents

invokeai/backend/bria/pipeline_bria.py

@@ -1,636 +0,0 @@
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import diffusers
-import numpy as np
-import torch
-from diffusers import AutoencoderKL, DDIMScheduler, EulerAncestralDiscreteScheduler
-from diffusers.image_processor import VaeImageProcessor
-from diffusers.loaders import FluxLoraLoaderMixin
-from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
-from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
-from diffusers.utils import (
-    USE_PEFT_BACKEND,
-    logging,
-    replace_example_docstring,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
-from diffusers.utils.torch_utils import randn_tensor
-from transformers import (
-    T5EncoderModel,
-    T5TokenizerFast,
-)
-
-from invokeai.backend.bria.bria_utils import get_original_sigmas, get_t5_prompt_embeds, is_ng_none
-from invokeai.backend.bria.transformer_bria import BriaTransformer2DModel
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import StableDiffusion3Pipeline
-
-        >>> pipe = StableDiffusion3Pipeline.from_pretrained(
-        ...     "stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16
-        ... )
-        >>> pipe.to("cuda")
-        >>> prompt = "A cat holding a sign that says hello world"
-        >>> image = pipe(prompt).images[0]
-        >>> image.save("sd3.png")
-        ```
-"""
-
-T5_PRECISION = torch.float16
-
-"""
-Based on FluxPipeline with several changes:
-- no pooled embeddings
-- We use zero padding for prompts
-- No guidance embedding since this is not a distilled version
-"""
-
-
-class BriaPipeline(FluxPipeline):
-    r"""
-    Args:
-        transformer ([`SD3Transformer2DModel`]):
-            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
-        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`T5EncoderModel`]):
-            Frozen text-encoder. Stable Diffusion 3 uses
-            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
-            [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
-        tokenizer (`T5TokenizerFast`):
-            Tokenizer of class
-            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
-    """
-
-    def __init__(
-        self,
-        transformer: BriaTransformer2DModel,
-        scheduler: Union[FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers],
-        vae: AutoencoderKL,
-        text_encoder: T5EncoderModel,
-        tokenizer: T5TokenizerFast,
-    ):
-        self.register_modules(
-            vae=vae,
-            transformer=transformer,
-            scheduler=scheduler,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-        )
-
-        # TODO - why different than offical flux (-1)
-        self.vae_scale_factor = (
-            2 ** (len(self.vae.config.block_out_channels)) if hasattr(self, "vae") and self.vae is not None else 16
-        )
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.default_sample_size = 64  # due to patchify=> 128,128 => res of 1k,1k
-
-        # T5 is senstive to precision so we use the precision used for precompute and cast as needed
-
-        if self.vae.config.shift_factor is None:
-            self.vae.config.shift_factor = 0
-            self.vae.to(dtype=torch.float32)
-
-    def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        device: Optional[torch.device] = None,
-        num_images_per_prompt: int = 1,
-        do_classifier_free_guidance: bool = True,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        max_sequence_length: int = 128,
-        lora_scale: Optional[float] = None,
-    ):
-        r"""
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            do_classifier_free_guidance (`bool`):
-                whether to use classifier free guidance or not
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-        """
-        device = device or self._execution_device
-
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-            # dynamically adjust the LoRA scale
-            if self.text_encoder is not None and USE_PEFT_BACKEND:
-                scale_lora_layers(self.text_encoder, lora_scale)
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-        if prompt is not None:
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-            prompt_embeds = get_t5_prompt_embeds(
-                self.tokenizer,
-                self.text_encoder,
-                prompt=prompt,
-                num_images_per_prompt=num_images_per_prompt,
-                max_sequence_length=max_sequence_length,
-                device=device,
-            ).to(dtype=self.transformer.dtype)
-
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            if not is_ng_none(negative_prompt):
-                negative_prompt = (
-                    batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-                )
-
-                if prompt is not None and type(prompt) is not type(negative_prompt):
-                    raise TypeError(
-                        f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                        f" {type(prompt)}."
-                    )
-                elif batch_size != len(negative_prompt):
-                    raise ValueError(
-                        f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                        f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                        " the batch size of `prompt`."
-                    )
-
-                negative_prompt_embeds = get_t5_prompt_embeds(
-                    self.tokenizer,
-                    self.text_encoder,
-                    prompt=negative_prompt,
-                    num_images_per_prompt=num_images_per_prompt,
-                    max_sequence_length=max_sequence_length,
-                    device=device,
-                ).to(dtype=self.transformer.dtype)
-            else:
-                negative_prompt_embeds = torch.zeros_like(prompt_embeds)
-
-        if self.text_encoder is not None:
-            if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
-                # Retrieve the original scale by scaling back the LoRA layers
-                unscale_lora_layers(self.text_encoder, lora_scale)
-
-        dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
-        text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
-        text_ids = text_ids.repeat(num_images_per_prompt, 1, 1)
-
-        return prompt_embeds, negative_prompt_embeds, text_ids
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-    # corresponds to doing no classifier free guidance.
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
-
-    @property
-    def joint_attention_kwargs(self):
-        return self._joint_attention_kwargs
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
-    @property
-    def interrupt(self):
-        return self._interrupt
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 30,
-        timesteps: List[int] = None,
-        guidance_scale: float = 5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
-        callback_on_step_end_tensor_inputs: Optional[List[str]] = None,
-        max_sequence_length: int = 128,
-        clip_value: Union[None, float] = None,
-        normalize: bool = False,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image. This is set to 1024 by default for the best results.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image. This is set to 1024 by default for the best results.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
-                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
-                passed will be used. Must be in descending order.
-            guidance_scale (`float`, *optional*, defaults to 5.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
-                of a plain tuple.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            callback_on_step_end (`Callable`, *optional*):
-                A function that calls at the end of each denoising steps during the inference. The function is called
-                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
-                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
-                `callback_on_step_end_tensor_inputs`.
-            callback_on_step_end_tensor_inputs (`List`, *optional*):
-                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
-                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeline class.
-            max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
-
-        Examples:
-
-          Returns:
-            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
-            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
-            images.
-        """
-
-        height = height or self.default_sample_size * self.vae_scale_factor
-        width = width or self.default_sample_size * self.vae_scale_factor
-
-        # 1. Check inputs. Raise error if not correct
-        callback_on_step_end_tensor_inputs = (
-            ["latents"] if callback_on_step_end_tensor_inputs is None else callback_on_step_end_tensor_inputs
-        )
-        self.check_inputs(
-            prompt=prompt,
-            height=height,
-            width=width,
-            prompt_embeds=prompt_embeds,
-            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
-            max_sequence_length=max_sequence_length,
-        )
-
-        self._guidance_scale = guidance_scale
-        self._joint_attention_kwargs = joint_attention_kwargs
-        self._interrupt = False
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-
-        lora_scale = self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
-
-        (prompt_embeds, negative_prompt_embeds, text_ids) = self.encode_prompt(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            do_classifier_free_guidance=self.do_classifier_free_guidance,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            max_sequence_length=max_sequence_length,
-            lora_scale=lora_scale,
-        )
-
-        if self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.transformer.config.in_channels // 4  # due to patch=2, we devide by 4
-        latents, latent_image_ids = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        if (
-            isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler)
-            and self.scheduler.config["use_dynamic_shifting"]
-        ):
-            sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
-            image_seq_len = latents.shape[1]  # Shift by height - Why just height?
-            print(f"Using dynamic shift in pipeline with sequence length {image_seq_len}")
-
-            mu = calculate_shift(
-                image_seq_len,
-                self.scheduler.config.base_image_seq_len,
-                self.scheduler.config.max_image_seq_len,
-                self.scheduler.config.base_shift,
-                self.scheduler.config.max_shift,
-            )
-            timesteps, num_inference_steps = retrieve_timesteps(
-                self.scheduler,
-                num_inference_steps,
-                device,
-                timesteps,
-                sigmas,
-                mu=mu,
-            )
-        else:
-            # 4. Prepare timesteps
-            # Sample from training sigmas
-            if isinstance(self.scheduler, DDIMScheduler) or isinstance(self.scheduler, EulerAncestralDiscreteScheduler):
-                timesteps, num_inference_steps = retrieve_timesteps(
-                    self.scheduler, num_inference_steps, device, None, None
-                )
-            else:
-                sigmas = get_original_sigmas(
-                    num_train_timesteps=self.scheduler.config.num_train_timesteps,
-                    num_inference_steps=num_inference_steps,
-                )
-                timesteps, num_inference_steps = retrieve_timesteps(
-                    self.scheduler, num_inference_steps, device, timesteps, sigmas=sigmas
-                )
-
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        self._num_timesteps = len(timesteps)
-
-        # Supprot different diffusers versions
-        if diffusers.__version__ >= "0.32.0":
-            latent_image_ids = latent_image_ids[0]
-            text_ids = text_ids[0]
-
-        # 6. Denoising loop
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
-                if not isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
-                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latent_model_input.shape[0])
-
-                # This is predicts "v" from flow-matching or eps from diffusion
-                noise_pred = self.transformer(
-                    hidden_states=latent_model_input,
-                    timestep=timestep,
-                    encoder_hidden_states=prompt_embeds,
-                    joint_attention_kwargs=self.joint_attention_kwargs,
-                    return_dict=False,
-                    txt_ids=text_ids,
-                    img_ids=latent_image_ids,
-                )[0]
-
-                # perform guidance
-                if self.do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    cfg_noise_pred_text = noise_pred_text.std()
-                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                if normalize:
-                    noise_pred = noise_pred * (0.7 * (cfg_noise_pred_text / noise_pred.std())) + 0.3 * noise_pred
-
-                if clip_value:
-                    assert clip_value > 0
-                    noise_pred = noise_pred.clip(-clip_value, clip_value)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents_dtype = latents.dtype
-                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-
-                if latents.dtype != latents_dtype:
-                    if torch.backends.mps.is_available():
-                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
-                        latents = latents.to(latents_dtype)
-
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-        if output_type == "latent":
-            image = latents
-
-        else:
-            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
-            latents = (latents.to(dtype=torch.float32) / self.vae.config.scaling_factor) + self.vae.config.shift_factor
-            image = self.vae.decode(latents.to(dtype=self.vae.dtype), return_dict=False)[0]
-            image = self.image_processor.postprocess(image, output_type=output_type)
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (image,)
-
-        return FluxPipelineOutput(images=image)
-
-    def check_inputs(
-        self,
-        prompt,
-        height,
-        width,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        callback_on_step_end_tensor_inputs=None,
-        max_sequence_length=None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if callback_on_step_end_tensor_inputs is not None and not all(
-            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
-        ):
-            raise ValueError(
-                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if max_sequence_length is not None and max_sequence_length > 512:
-            raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
-
-    def to(self, *args, **kwargs):
-        DiffusionPipeline.to(self, *args, **kwargs)
-        # T5 is senstive to precision so we use the precision used for precompute and cast as needed
-        self.text_encoder = self.text_encoder.to(dtype=T5_PRECISION)
-        for block in self.text_encoder.encoder.block:
-            block.layer[-1].DenseReluDense.wo.to(dtype=torch.float32)
-
-        if self.vae.config.shift_factor == 0 and self.vae.dtype != torch.float32:
-            self.vae.to(dtype=torch.float32)
-
-        return self
-
-    def prepare_latents(
-        self,
-        batch_size,
-        num_channels_latents,
-        height,
-        width,
-        dtype,
-        device,
-        generator,
-        latents=None,
-    ):
-        # VAE applies 8x compression on images but we must also account for packing which requires
-        # latent height and width to be divisible by 2.
-        height = 2 * (int(height) // self.vae_scale_factor)
-        width = 2 * (int(width) // self.vae_scale_factor)
-
-        shape = (batch_size, num_channels_latents, height, width)
-
-        if latents is not None:
-            latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
-            return latents.to(device=device, dtype=dtype), latent_image_ids
-
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
-
-        latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
-
-        return latents, latent_image_ids
-
-    @staticmethod
-    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
-        latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
-        latents = latents.permute(0, 2, 4, 1, 3, 5)
-        latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
-
-        return latents
-
-    @staticmethod
-    def _unpack_latents(latents, height, width, vae_scale_factor):
-        batch_size, num_patches, channels = latents.shape
-
-        height = height // vae_scale_factor
-        width = width // vae_scale_factor
-
-        latents = latents.view(batch_size, height, width, channels // 4, 2, 2)
-        latents = latents.permute(0, 3, 1, 4, 2, 5)
-
-        latents = latents.reshape(batch_size, channels // (2 * 2), height * 2, width * 2)
-
-        return latents
-
-    @staticmethod
-    def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
-        latent_image_ids = torch.zeros(height, width, 3)
-        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
-        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
-
-        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
-
-        latent_image_ids = latent_image_ids.repeat(batch_size, 1, 1, 1)
-        latent_image_ids = latent_image_ids.reshape(
-            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
-        )
-
-        return latent_image_ids.to(device=device, dtype=dtype)

invokeai/backend/bria/pipeline_bria_controlnet.py

@@ -1,671 +0,0 @@
-# Copyright 2024 Stability AI and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import diffusers
-import numpy as np
-import torch
-from diffusers import AutoencoderKL  # Waiting for diffusers udpdate
-from diffusers.image_processor import PipelineImageInput
-from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps
-from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
-from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
-from diffusers.utils import USE_PEFT_BACKEND, logging
-from diffusers.utils.peft_utils import scale_lora_layers, unscale_lora_layers
-from diffusers.utils.torch_utils import randn_tensor
-from transformers import (
-    T5EncoderModel,
-    T5TokenizerFast,
-)
-
-from invokeai.backend.bria.bria_utils import get_original_sigmas, get_t5_prompt_embeds, is_ng_none
-from invokeai.backend.bria.controlnet_bria import BriaControlNetModel
-from invokeai.backend.bria.pipeline_bria import BriaPipeline
-from invokeai.backend.bria.transformer_bria import BriaTransformer2DModel
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class BriaControlNetPipeline(BriaPipeline):
-    r"""
-    Args:
-        transformer ([`SD3Transformer2DModel`]):
-            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
-        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`T5EncoderModel`]):
-            Frozen text-encoder. Stable Diffusion 3 uses
-            [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
-            [t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
-        tokenizer (`T5TokenizerFast`):
-            Tokenizer of class
-            [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
-    """
-
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder->transformer->vae"
-    _optional_components = []
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
-
-    def __init__(  # EYAL - removed clip text encoder + tokenizer
-        self,
-        transformer: BriaTransformer2DModel,
-        scheduler: Union[FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers],
-        vae: AutoencoderKL,
-        text_encoder: T5EncoderModel,
-        tokenizer: T5TokenizerFast,
-        controlnet: BriaControlNetModel,
-    ):
-        super().__init__(
-            transformer=transformer, scheduler=scheduler, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer
-        )
-        self.register_modules(controlnet=controlnet)
-
-    def prepare_image(
-        self,
-        image,
-        width,
-        height,
-        batch_size,
-        num_images_per_prompt,
-        device,
-        dtype,
-        do_classifier_free_guidance=False,
-        guess_mode=False,
-    ):
-        if isinstance(image, torch.Tensor):
-            pass
-        else:
-            image = self.image_processor.preprocess(image, height=height, width=width)
-
-        image_batch_size = image.shape[0]
-
-        if image_batch_size == 1:
-            repeat_by = batch_size
-        else:
-            # image batch size is the same as prompt batch size
-            repeat_by = num_images_per_prompt
-
-        image = image.repeat_interleave(repeat_by, dim=0)
-
-        image = image.to(device=device, dtype=dtype)
-
-        if do_classifier_free_guidance and not guess_mode:
-            image = torch.cat([image] * 2)
-
-        return image
-
-    def prepare_control(self, control_image, width, height, batch_size, num_images_per_prompt, device, control_mode):
-        num_channels_latents = self.transformer.config.in_channels // 4
-        control_image = self.prepare_image(
-            image=control_image,
-            width=width,
-            height=height,
-            batch_size=batch_size * num_images_per_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            device=device,
-            dtype=self.vae.dtype,
-        )
-        height, width = control_image.shape[-2:]
-
-        # vae encode
-        control_image = self.vae.encode(control_image).latent_dist.sample()
-        control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-
-        # pack
-        height_control_image, width_control_image = control_image.shape[2:]
-        control_image = self._pack_latents(
-            control_image,
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height_control_image,
-            width_control_image,
-        )
-
-        # Here we ensure that `control_mode` has the same length as the control_image.
-        if control_mode is not None:
-            if not isinstance(control_mode, int):
-                raise ValueError(" For `BriaControlNet`, `control_mode` should be an `int` or `None`")
-            control_mode = torch.tensor(control_mode).to(device, dtype=torch.long)
-            control_mode = control_mode.view(-1, 1).expand(control_image.shape[0], 1)
-
-        return control_image, control_mode
-
-    def prepare_multi_control(
-        self, control_image, width, height, batch_size, num_images_per_prompt, device, control_mode
-    ):
-        num_channels_latents = self.transformer.config.in_channels // 4
-        control_images = []
-        for _, control_image_ in enumerate(control_image):
-            control_image_ = self.prepare_image(
-                image=control_image_,
-                width=width,
-                height=height,
-                batch_size=batch_size * num_images_per_prompt,
-                num_images_per_prompt=num_images_per_prompt,
-                device=device,
-                dtype=self.vae.dtype,
-            )
-            height, width = control_image_.shape[-2:]
-
-            # vae encode
-            control_image_ = self.vae.encode(control_image_).latent_dist.sample()
-            control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-
-            # pack
-            height_control_image, width_control_image = control_image_.shape[2:]
-            control_image_ = self._pack_latents(
-                control_image_,
-                batch_size * num_images_per_prompt,
-                num_channels_latents,
-                height_control_image,
-                width_control_image,
-            )
-            control_images.append(control_image_)
-
-        control_image = control_images
-
-        # Here we ensure that `control_mode` has the same length as the control_image.
-        if isinstance(control_mode, list) and len(control_mode) != len(control_image):
-            raise ValueError(
-                "For Multi-ControlNet, `control_mode` must be a list of the same "
-                + " length as the number of controlnets (control images) specified"
-            )
-        if not isinstance(control_mode, list):
-            control_mode = [control_mode] * len(control_image)
-        # set control mode
-        control_modes = []
-        for cmode in control_mode:
-            if cmode is None:
-                cmode = -1
-            control_mode = torch.tensor(cmode).expand(control_images[0].shape[0]).to(device, dtype=torch.long)
-            control_modes.append(control_mode)
-        control_mode = control_modes
-
-        return control_image, control_mode
-
-    def get_controlnet_keep(self, timesteps, control_guidance_start, control_guidance_end):
-        controlnet_keep = []
-        for i in range(len(timesteps)):
-            keeps = [
-                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
-                for s, e in zip(control_guidance_start, control_guidance_end, strict=False)
-            ]
-            controlnet_keep.append(keeps[0] if isinstance(self.controlnet, BriaControlNetModel) else keeps)
-        return controlnet_keep
-
-    def get_control_start_end(self, control_guidance_start, control_guidance_end):
-        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
-            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
-        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
-            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
-        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
-            mult = 1  # TODO - why is this 1?
-            control_guidance_start, control_guidance_end = (
-                mult * [control_guidance_start],
-                mult * [control_guidance_end],
-            )
-
-        return control_guidance_start, control_guidance_end
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 30,
-        timesteps: List[int] = None,
-        guidance_scale: float = 3.5,
-        control_guidance_start: Union[float, List[float]] = 0.0,
-        control_guidance_end: Union[float, List[float]] = 1.0,
-        control_image: Optional[PipelineImageInput] = None,
-        control_mode: Optional[Union[int, List[int]]] = None,
-        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        latent_image_ids: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        text_ids: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
-        callback_on_step_end_tensor_inputs: Optional[List[str]] = None,
-        max_sequence_length: int = 128,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image. This is set to 1024 by default for the best results.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image. This is set to 1024 by default for the best results.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            timesteps (`List[int]`, *optional*):
-                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
-                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
-                passed will be used. Must be in descending order.
-            guidance_scale (`float`, *optional*, defaults to 5.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
-                of a plain tuple.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            callback_on_step_end (`Callable`, *optional*):
-                A function that calls at the end of each denoising steps during the inference. The function is called
-                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
-                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
-                `callback_on_step_end_tensor_inputs`.
-            callback_on_step_end_tensor_inputs (`List`, *optional*):
-                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
-                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeline class.
-            max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
-        Examples:
-        Returns:
-            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
-            `tuple`. When returning a tuple, the first element is a list with the generated images.
-        """
-
-        height = height or self.default_sample_size * self.vae_scale_factor
-        width = width or self.default_sample_size * self.vae_scale_factor
-        control_guidance_start, control_guidance_end = self.get_control_start_end(
-            control_guidance_start=control_guidance_start, control_guidance_end=control_guidance_end
-        )
-
-        # 1. Check inputs. Raise error if not correct
-        callback_on_step_end_tensor_inputs = (
-            ["latents"] if callback_on_step_end_tensor_inputs is None else callback_on_step_end_tensor_inputs
-        )
-        self.check_inputs(
-            prompt,
-            height,
-            width,
-            negative_prompt=negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
-            max_sequence_length=max_sequence_length,
-        )
-
-        self._guidance_scale = guidance_scale
-        self._joint_attention_kwargs = joint_attention_kwargs
-        self._interrupt = False
-
-        device = self._execution_device
-
-        # 4. Prepare timesteps
-        if (
-            isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler)
-            and self.scheduler.config["use_dynamic_shifting"]
-        ):
-            sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
-
-            # Determine image sequence length
-            if control_image is not None:
-                if isinstance(control_image, list):
-                    image_seq_len = control_image[0].shape[1]
-                else:
-                    image_seq_len = control_image.shape[1]
-            else:
-                # Use latents sequence length when no control image is provided
-                image_seq_len = latents.shape[1]
-
-            print(f"Using dynamic shift in pipeline with sequence length {image_seq_len}")
-
-            mu = calculate_shift(
-                image_seq_len,
-                self.scheduler.config.base_image_seq_len,
-                self.scheduler.config.max_image_seq_len,
-                self.scheduler.config.base_shift,
-                self.scheduler.config.max_shift,
-            )
-            timesteps, num_inference_steps = retrieve_timesteps(
-                self.scheduler,
-                num_inference_steps,
-                device,
-                timesteps=None,
-                sigmas=sigmas,
-                mu=mu,
-            )
-        else:
-            # 5. Prepare timesteps
-            sigmas = get_original_sigmas(
-                num_train_timesteps=self.scheduler.config.num_train_timesteps, num_inference_steps=num_inference_steps
-            )
-            timesteps, num_inference_steps = retrieve_timesteps(
-                self.scheduler, num_inference_steps, device, timesteps, sigmas=sigmas
-            )
-
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-        self._num_timesteps = len(timesteps)
-
-        # 6. Create tensor stating which controlnets to keep
-        if control_image is not None:
-            controlnet_keep = self.get_controlnet_keep(
-                timesteps=timesteps,
-                control_guidance_start=control_guidance_start,
-                control_guidance_end=control_guidance_end,
-            )
-
-        if diffusers.__version__ >= "0.32.0":
-            latent_image_ids = latent_image_ids[0]
-            text_ids = text_ids[0]
-
-        if self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-
-        # EYAL - added the CFG loop
-        # 7. Denoising loop
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
-                # if type(self.scheduler) != FlowMatchEulerDiscreteScheduler:
-                if not isinstance(self.scheduler, FlowMatchEulerDiscreteScheduler):
-                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                timestep = t.expand(latent_model_input.shape[0])
-
-                # Handling ControlNet
-                if control_image is not None:
-                    if isinstance(controlnet_keep[i], list):
-                        if isinstance(controlnet_conditioning_scale, list):
-                            cond_scale = controlnet_conditioning_scale
-                        else:
-                            cond_scale = [
-                                c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i], strict=False)
-                            ]
-                    else:
-                        controlnet_cond_scale = controlnet_conditioning_scale
-                        if isinstance(controlnet_cond_scale, list):
-                            controlnet_cond_scale = controlnet_cond_scale[0]
-                        cond_scale = controlnet_cond_scale * controlnet_keep[i]
-
-                    controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
-                        hidden_states=latents,
-                        controlnet_cond=control_image,
-                        controlnet_mode=control_mode,
-                        conditioning_scale=cond_scale,
-                        timestep=timestep,
-                        # guidance=guidance,
-                        # pooled_projections=pooled_prompt_embeds,
-                        encoder_hidden_states=prompt_embeds,
-                        txt_ids=text_ids,
-                        img_ids=latent_image_ids,
-                        joint_attention_kwargs=self.joint_attention_kwargs,
-                        return_dict=False,
-                    )
-                else:
-                    controlnet_block_samples, controlnet_single_block_samples = None, None
-
-                # This is predicts "v" from flow-matching
-                noise_pred = self.transformer(
-                    hidden_states=latent_model_input,
-                    timestep=timestep,
-                    encoder_hidden_states=prompt_embeds,
-                    joint_attention_kwargs=self.joint_attention_kwargs,
-                    return_dict=False,
-                    txt_ids=text_ids,
-                    img_ids=latent_image_ids,
-                    controlnet_block_samples=controlnet_block_samples,
-                    controlnet_single_block_samples=controlnet_single_block_samples,
-                )[0]
-
-                # perform guidance
-                if self.do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents_dtype = latents.dtype
-                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-
-                if latents.dtype != latents_dtype:
-                    if torch.backends.mps.is_available():
-                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
-                        latents = latents.to(latents_dtype)
-
-                if callback_on_step_end is not None:
-                    callback_kwargs = {}
-                    for k in callback_on_step_end_tensor_inputs:
-                        callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-        if output_type == "latent":
-            image = latents
-
-        else:
-            latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
-            latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
-            image = self.vae.decode(latents.to(dtype=self.vae.dtype), return_dict=False)[0]
-            image = self.image_processor.postprocess(image, output_type=output_type)
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (image,)
-
-        return FluxPipelineOutput(images=image)
-
-
-def encode_prompt(
-    prompt: Union[str, List[str]],
-    tokenizer: T5TokenizerFast,
-    text_encoder: T5EncoderModel,
-    device: Optional[torch.device] = None,
-    num_images_per_prompt: int = 1,
-    negative_prompt: Optional[Union[str, List[str]]] = None,
-    prompt_embeds: Optional[torch.FloatTensor] = None,
-    negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-    max_sequence_length: int = 128,
-    lora_scale: Optional[float] = None,
-):
-    r"""
-
-    Args:
-        prompt (`str` or `List[str]`, *optional*):
-            prompt to be encoded
-        device: (`torch.device`):
-            torch device
-        num_images_per_prompt (`int`):
-            number of images that should be generated per prompt
-        do_classifier_free_guidance (`bool`):
-            whether to use classifier free guidance or not
-        negative_prompt (`str` or `List[str]`, *optional*):
-            The prompt or prompts not to guide the image generation. If not defined, one has to pass
-            `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-            less than `1`).
-        prompt_embeds (`torch.FloatTensor`, *optional*):
-            Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-            provided, text embeddings will be generated from `prompt` input argument.
-        negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-            Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-            weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-            argument.
-    """
-    device = device or torch.device("cuda")
-
-    # set lora scale so that monkey patched LoRA
-    # function of text encoder can correctly access it
-    # dynamically adjust the LoRA scale
-    if text_encoder is not None and USE_PEFT_BACKEND:
-        scale_lora_layers(text_encoder, lora_scale)
-
-    prompt = [prompt] if isinstance(prompt, str) else prompt
-    if prompt is not None:
-        batch_size = len(prompt)
-    else:
-        batch_size = prompt_embeds.shape[0]
-
-    dtype = text_encoder.dtype if text_encoder is not None else torch.float32
-    if prompt_embeds is None:
-        prompt_embeds = get_t5_prompt_embeds(
-            tokenizer,
-            text_encoder,
-            prompt=prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            max_sequence_length=max_sequence_length,
-            device=device,
-        ).to(dtype=dtype)
-
-    if negative_prompt_embeds is None:
-        if not is_ng_none(negative_prompt):
-            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-
-            if prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-
-            negative_prompt_embeds = get_t5_prompt_embeds(
-                tokenizer,
-                text_encoder,
-                prompt=negative_prompt,
-                num_images_per_prompt=num_images_per_prompt,
-                max_sequence_length=max_sequence_length,
-                device=device,
-            ).to(dtype=dtype)
-        else:
-            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
-
-    if text_encoder is not None:
-        if USE_PEFT_BACKEND:
-            # Retrieve the original scale by scaling back the LoRA layers
-            unscale_lora_layers(text_encoder, lora_scale)
-
-    text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
-    text_ids = text_ids.repeat(num_images_per_prompt, 1, 1)
-
-    return prompt_embeds, negative_prompt_embeds, text_ids
-
-
-def prepare_latents(
-    batch_size: int,
-    num_channels_latents: int,
-    height: int,
-    width: int,
-    dtype: torch.dtype,
-    device: torch.device,
-    generator: torch.Generator,
-    latents: Optional[torch.FloatTensor] = None,
-):
-    # VAE applies 8x compression on images but we must also account for packing which requires
-    # latent height and width to be divisible by 2.
-    vae_scale_factor = 16
-    height = 2 * (int(height) // vae_scale_factor)
-    width = 2 * (int(width) // vae_scale_factor)
-
-    shape = (batch_size, num_channels_latents, height, width)
-
-    if latents is not None:
-        latent_image_ids = _prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
-        return latents.to(device=device, dtype=dtype), latent_image_ids
-
-    if isinstance(generator, list) and len(generator) != batch_size:
-        raise ValueError(
-            f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-            f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-        )
-
-    latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-    latents = _pack_latents(latents, batch_size, num_channels_latents, height, width)
-
-    latent_image_ids = _prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
-
-    return latents, latent_image_ids
-
-
-def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
-    latent_image_ids = torch.zeros(height, width, 3)
-    latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
-    latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
-
-    latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
-
-    latent_image_ids = latent_image_ids.repeat(batch_size, 1, 1, 1)
-    latent_image_ids = latent_image_ids.reshape(
-        batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
-    )
-
-    return latent_image_ids.to(device=device, dtype=dtype)
-
-
-def _pack_latents(latents, batch_size, num_channels_latents, height, width):
-    latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
-    latents = latents.permute(0, 2, 4, 1, 3, 5)
-    latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
-
-    return latents

invokeai/backend/bria/transformer_bria.py

@@ -1,322 +0,0 @@
-from typing import Any, Dict, List, Optional, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
-from diffusers.models.embeddings import TimestepEmbedding, get_timestep_embedding
-from diffusers.models.modeling_outputs import Transformer2DModelOutput
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.models.normalization import AdaLayerNormContinuous
-from diffusers.models.transformers.transformer_flux import FluxSingleTransformerBlock, FluxTransformerBlock
-from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
-
-from invokeai.backend.bria.bria_utils import FluxPosEmbed as EmbedND
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class Timesteps(nn.Module):
-    def __init__(
-        self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, scale: int = 1, time_theta=10000
-    ):
-        super().__init__()
-        self.num_channels = num_channels
-        self.flip_sin_to_cos = flip_sin_to_cos
-        self.downscale_freq_shift = downscale_freq_shift
-        self.scale = scale
-        self.time_theta = time_theta
-
-    def forward(self, timesteps):
-        t_emb = get_timestep_embedding(
-            timesteps,
-            self.num_channels,
-            flip_sin_to_cos=self.flip_sin_to_cos,
-            downscale_freq_shift=self.downscale_freq_shift,
-            scale=self.scale,
-            max_period=self.time_theta,
-        )
-        return t_emb
-
-
-class TimestepProjEmbeddings(nn.Module):
-    def __init__(self, embedding_dim, time_theta):
-        super().__init__()
-
-        self.time_proj = Timesteps(
-            num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, time_theta=time_theta
-        )
-        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
-
-    def forward(self, timestep, dtype):
-        timesteps_proj = self.time_proj(timestep)
-        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=dtype))  # (N, D)
-        return timesteps_emb
-
-
-"""
-Based on FluxPipeline with several changes:
-- no pooled embeddings
-- We use zero padding for prompts
-- No guidance embedding since this is not a distilled version
-"""
-
-
-class BriaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
-    """
-    The Transformer model introduced in Flux.
-
-    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
-
-    Parameters:
-        patch_size (`int`): Patch size to turn the input data into small patches.
-        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
-        num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
-        num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
-        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
-        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
-        joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
-        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
-        guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
-    """
-
-    _supports_gradient_checkpointing = True
-
-    @register_to_config
-    def __init__(
-        self,
-        patch_size: int = 1,
-        in_channels: int = 64,
-        num_layers: int = 19,
-        num_single_layers: int = 38,
-        attention_head_dim: int = 128,
-        num_attention_heads: int = 24,
-        joint_attention_dim: int = 4096,
-        pooled_projection_dim: int = None,
-        guidance_embeds: bool = False,
-        axes_dims_rope: Optional[List[int]] = None,
-        rope_theta=10000,
-        time_theta=10000,
-    ):
-        super().__init__()
-        self.out_channels = in_channels
-        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
-
-        axes_dims_rope = [16, 56, 56] if axes_dims_rope is None else axes_dims_rope
-        self.pos_embed = EmbedND(theta=rope_theta, axes_dim=axes_dims_rope)
-
-        self.time_embed = TimestepProjEmbeddings(embedding_dim=self.inner_dim, time_theta=time_theta)
-
-        # if pooled_projection_dim:
-        #     self.pooled_text_embed = PixArtAlphaTextProjection(pooled_projection_dim, embedding_dim=self.inner_dim, act_fn="silu")
-
-        if guidance_embeds:
-            self.guidance_embed = TimestepProjEmbeddings(embedding_dim=self.inner_dim)
-
-        self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.inner_dim)
-        self.x_embedder = torch.nn.Linear(self.config.in_channels, self.inner_dim)
-
-        self.transformer_blocks = nn.ModuleList(
-            [
-                FluxTransformerBlock(
-                    dim=self.inner_dim,
-                    num_attention_heads=self.config.num_attention_heads,
-                    attention_head_dim=self.config.attention_head_dim,
-                )
-                for i in range(self.config.num_layers)
-            ]
-        )
-
-        self.single_transformer_blocks = nn.ModuleList(
-            [
-                FluxSingleTransformerBlock(
-                    dim=self.inner_dim,
-                    num_attention_heads=self.config.num_attention_heads,
-                    attention_head_dim=self.config.attention_head_dim,
-                )
-                for i in range(self.config.num_single_layers)
-            ]
-        )
-
-        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
-        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
-
-        self.gradient_checkpointing = False
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if hasattr(module, "gradient_checkpointing"):
-            module.gradient_checkpointing = value
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor = None,
-        pooled_projections: torch.Tensor = None,
-        timestep: torch.LongTensor = None,
-        img_ids: torch.Tensor = None,
-        txt_ids: torch.Tensor = None,
-        guidance: torch.Tensor = None,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        return_dict: bool = True,
-        controlnet_block_samples=None,
-        controlnet_single_block_samples=None,
-    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
-        """
-        The [`FluxTransformer2DModel`] forward method.
-
-        Args:
-            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
-                Input `hidden_states`.
-            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
-                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
-            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
-                from the embeddings of input conditions.
-            timestep ( `torch.LongTensor`):
-                Used to indicate denoising step.
-            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
-                A list of tensors that if specified are added to the residuals of transformer blocks.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
-                tuple.
-
-        Returns:
-            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
-            `tuple` where the first element is the sample tensor.
-        """
-        if joint_attention_kwargs is not None:
-            joint_attention_kwargs = joint_attention_kwargs.copy()
-            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-        hidden_states = self.x_embedder(hidden_states)
-
-        timestep = timestep.to(hidden_states.dtype)
-        if guidance is not None:
-            guidance = guidance.to(hidden_states.dtype)
-        else:
-            guidance = None
-
-        # temb = (
-        #     self.time_text_embed(timestep, pooled_projections)
-        #     if guidance is None
-        #     else self.time_text_embed(timestep, guidance, pooled_projections)
-        # )
-
-        temb = self.time_embed(timestep, dtype=hidden_states.dtype)
-
-        # if pooled_projections:
-        #     temb+=self.pooled_text_embed(pooled_projections)
-
-        if guidance:
-            temb += self.guidance_embed(guidance, dtype=hidden_states.dtype)
-
-        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
-
-        if len(txt_ids.shape) == 2:
-            ids = torch.cat((txt_ids, img_ids), dim=0)
-        else:
-            ids = torch.cat((txt_ids, img_ids), dim=1)
-        image_rotary_emb = self.pos_embed(ids)
-
-        for index_block, block in enumerate(self.transformer_blocks):
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    encoder_hidden_states,
-                    temb,
-                    image_rotary_emb,
-                    **ckpt_kwargs,
-                )
-
-            else:
-                encoder_hidden_states, hidden_states = block(
-                    hidden_states=hidden_states,
-                    encoder_hidden_states=encoder_hidden_states,
-                    temb=temb,
-                    image_rotary_emb=image_rotary_emb,
-                )
-
-            # controlnet residual
-            if controlnet_block_samples is not None:
-                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
-                interval_control = int(np.ceil(interval_control))
-                hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
-
-        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
-        for index_block, block in enumerate(self.single_transformer_blocks):
-            if self.training and self.gradient_checkpointing:
-
-                def create_custom_forward(module, return_dict=None):
-                    def custom_forward(*inputs):
-                        if return_dict is not None:
-                            return module(*inputs, return_dict=return_dict)
-                        else:
-                            return module(*inputs)
-
-                    return custom_forward
-
-                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
-                hidden_states = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(block),
-                    hidden_states,
-                    temb,
-                    image_rotary_emb,
-                    **ckpt_kwargs,
-                )
-
-            else:
-                hidden_states = block(
-                    hidden_states=hidden_states,
-                    temb=temb,
-                    image_rotary_emb=image_rotary_emb,
-                )
-
-            # controlnet residual
-            if controlnet_single_block_samples is not None:
-                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
-                interval_control = int(np.ceil(interval_control))
-                hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
-                    hidden_states[:, encoder_hidden_states.shape[1] :, ...]
-                    + controlnet_single_block_samples[index_block // interval_control]
-                )
-
-        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
-
-        hidden_states = self.norm_out(hidden_states, temb)
-        output = self.proj_out(hidden_states)
-
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
-        if not return_dict:
-            return (output,)
-
-        return Transformer2DModelOutput(sample=output)

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/ip_adapter/README.md

@@ -42,5 +42,4 @@ IP-Adapters:
 - [InvokeAI/ip_adapter_plus_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_sd15)
 - [InvokeAI/ip_adapter_plus_face_sd15](https://huggingface.co/InvokeAI/ip_adapter_plus_face_sd15)
 - [InvokeAI/ip_adapter_sdxl](https://huggingface.co/InvokeAI/ip_adapter_sdxl)
-- [InvokeAI/ip_adapter_sdxl_vit_h](https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h)
-- [InvokeAI/ip-adapter-plus_sdxl_vit-h](https://huggingface.co/InvokeAI/ip-adapter-plus_sdxl_vit-h)
+- [InvokeAI/ip_adapter_sdxl_vit_h](https://huggingface.co/InvokeAI/ip_adapter_sdxl_vit_h)

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,
@@ -187,7 +186,7 @@ class ModelConfigBase(ABC, BaseModel):
             else:
                 return config_cls.from_model_on_disk(mod, **overrides)
 
-        raise InvalidModelConfigException("Unable to determine model type")
+        raise InvalidModelConfigException("No valid config found")
 
     @classmethod
     def get_tag(cls) -> Tag:
@@ -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/legacy_probe.py

@@ -125,8 +125,6 @@ class ModelProbe(object):
     }
 
     CLASS2TYPE = {
-        "BriaPipeline": ModelType.Main,
-        "BriaTransformer2DModel": ModelType.ControlNet,
         "FluxPipeline": ModelType.Main,
         "StableDiffusionPipeline": ModelType.Main,
         "StableDiffusionInpaintPipeline": ModelType.Main,
@@ -863,8 +861,6 @@ class PipelineFolderProbe(FolderProbeBase):
                 return BaseModelType.StableDiffusion3
             elif transformer_conf["_class_name"] == "CogView4Transformer2DModel":
                 return BaseModelType.CogView4
-            elif transformer_conf["_class_name"] == "BriaTransformer2DModel":
-                return BaseModelType.Bria
             else:
                 raise InvalidModelConfigException(f"Unknown base model for {self.model_path}")
 
@@ -1014,9 +1010,6 @@ class ControlNetFolderProbe(FolderProbeBase):
         if config.get("_class_name", None) == "FluxControlNetModel":
             return BaseModelType.Flux
 
-        if config.get("_class_name", None) == "BriaTransformer2DModel":
-            return BaseModelType.Bria
-
         # no obvious way to distinguish between sd2-base and sd2-768
         dimension = config["cross_attention_dim"]
         if dimension == 768:

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

@@ -1,96 +0,0 @@
-from pathlib import Path
-from typing import Optional
-
-from invokeai.backend.model_manager.config import (
-    AnyModelConfig,
-    CheckpointConfigBase,
-    ControlNetCheckpointConfig,
-    ControlNetDiffusersConfig,
-    DiffusersConfigBase,
-)
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
-from invokeai.backend.model_manager.taxonomy import (
-    AnyModel,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
-)
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Bria, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
-class BriaControlNetDiffusersModel(GenericDiffusersLoader):
-    """Class to load Bria control net models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if isinstance(config, ControlNetCheckpointConfig):
-            raise NotImplementedError("CheckpointConfigBase is not implemented for Bria models.")
-
-        model_path = Path(config.path)
-        load_class = self.get_hf_load_class(model_path)
-        repo_variant = config.repo_variant if isinstance(config, ControlNetDiffusersConfig) else None
-        variant = repo_variant.value if repo_variant else None
-        model_path = model_path
-
-        dtype = self._torch_dtype
-
-        try:
-            result: AnyModel = load_class.from_pretrained(
-                model_path,
-                torch_dtype=dtype,
-                variant=variant,
-                use_safetensors=False,
-            )
-        except OSError as e:
-            if variant and "no file named" in str(
-                e
-            ):  # try without the variant, just in case user's preferences changed
-                result = load_class.from_pretrained(model_path, torch_dtype=dtype)
-            else:
-                raise e
-
-        return result
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Bria, type=ModelType.Main, format=ModelFormat.Diffusers)
-class BriaDiffusersModel(GenericDiffusersLoader):
-    """Class to load Bria main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if isinstance(config, CheckpointConfigBase):
-            raise NotImplementedError("CheckpointConfigBase is not implemented for Bria models.")
-
-        if submodel_type is None:
-            raise Exception("A submodel type must be provided when loading main pipelines.")
-
-        model_path = Path(config.path)
-        load_class = self.get_hf_load_class(model_path, submodel_type)
-        repo_variant = config.repo_variant if isinstance(config, DiffusersConfigBase) else None
-        variant = repo_variant.value if repo_variant else None
-        model_path = model_path / submodel_type.value
-
-        dtype = self._torch_dtype
-        try:
-            result: AnyModel = load_class.from_pretrained(
-                model_path,
-                torch_dtype=dtype,
-                variant=variant,
-            )
-        except OSError as e:
-            if variant and "no file named" in str(
-                e
-            ):  # try without the variant, just in case user's preferences changed
-                result = load_class.from_pretrained(model_path, torch_dtype=dtype)
-            else:
-                raise e
-
-        return result

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/generic_diffusers.py

@@ -80,13 +80,7 @@ class GenericDiffusersLoader(ModelLoader):
             "transformers",
             "invokeai.backend.quantization.fast_quantized_transformers_model",
             "invokeai.backend.quantization.fast_quantized_diffusion_model",
-            "transformer_bria",
         ]:
-            if module == "transformer_bria":
-                module = "invokeai.backend.bria.transformer_bria"
-            elif class_name == "BriaTransformer2DModel":
-                class_name = "BriaControlNetModel"
-                module = "invokeai.backend.bria.controlnet_bria"
             res_type = sys.modules[module]
         else:
             res_type = sys.modules["diffusers"].pipelines

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/load/model_util.py

@@ -12,9 +12,6 @@ from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
 from transformers import CLIPTokenizer, T5Tokenizer, T5TokenizerFast
 
-from invokeai.backend.bria.controlnet_aux.open_pose.body import Body
-from invokeai.backend.bria.controlnet_aux.open_pose.face import Face
-from invokeai.backend.bria.controlnet_aux.open_pose.hand import Hand
 from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
@@ -65,8 +62,6 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
         else:
             # If neither is available, return 0
             return 0
-    elif isinstance(model, (Body, Hand, Face)):
-        return calc_module_size(model.model)
     elif isinstance(
         model,
         (

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,
@@ -313,15 +297,6 @@ ip_adapter_sdxl = StarterModel(
     dependencies=[ip_adapter_sdxl_image_encoder],
     previous_names=["IP Adapter SDXL"],
 )
-ip_adapter_plus_sdxl = StarterModel(
-    name="Precise Reference (IP Adapter Plus ViT-H)",
-    base=BaseModelType.StableDiffusionXL,
-    source="https://huggingface.co/InvokeAI/ip-adapter-plus_sdxl_vit-h/resolve/main/ip-adapter-plus_sdxl_vit-h.safetensors",
-    description="References images with a higher degree of precision.",
-    type=ModelType.IPAdapter,
-    dependencies=[ip_adapter_sdxl_image_encoder],
-    previous_names=["IP Adapter Plus SDXL"],
-)
 ip_adapter_flux = StarterModel(
     name="Standard Reference (XLabs FLUX IP-Adapter v2)",
     base=BaseModelType.Flux,
@@ -672,7 +647,6 @@ 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,
@@ -698,7 +672,6 @@ STARTER_MODELS: list[StarterModel] = [
     ip_adapter_plus_sd1,
     ip_adapter_plus_face_sd1,
     ip_adapter_sdxl,
-    ip_adapter_plus_sdxl,
     ip_adapter_flux,
     qr_code_cnet_sd1,
     qr_code_cnet_sdxl,
@@ -771,7 +744,6 @@ sdxl_bundle: list[StarterModel] = [
     juggernaut_sdxl,
     sdxl_fp16_vae_fix,
     ip_adapter_sdxl,
-    ip_adapter_plus_sdxl,
     canny_sdxl,
     depth_sdxl,
     softedge_sdxl,
@@ -793,13 +765,12 @@ 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

@@ -30,7 +30,6 @@ class BaseModelType(str, Enum):
     Imagen4 = "imagen4"
     ChatGPT4o = "chatgpt-4o"
     FluxKontext = "flux-kontext"
-    Bria = "bria"
 
 
 class ModelType(str, Enum):
@@ -90,7 +89,6 @@ class ModelVariantType(str, Enum):
 class ModelFormat(str, Enum):
     """Storage format of model."""
 
-    OMI = "omi"
     Diffusers = "diffusers"
     Checkpoint = "checkpoint"
     LyCORIS = "lycoris"
@@ -140,7 +138,6 @@ 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_manager/util/lora_metadata_extractor.py

@@ -1,145 +0,0 @@
-"""Utility functions for extracting metadata from LoRA model files."""
-
-import json
-import logging
-from pathlib import Path
-from typing import Any, Dict, Optional, Set, Tuple
-
-from PIL import Image
-
-from invokeai.app.util.thumbnails import make_thumbnail
-from invokeai.backend.model_manager.config import AnyModelConfig, ModelType
-
-logger = logging.getLogger(__name__)
-
-
-def extract_lora_metadata(
-    model_path: Path, model_key: str, model_images_path: Path
-) -> Tuple[Optional[str], Optional[Set[str]]]:
-    """
-    Extract metadata for a LoRA model from associated JSON and image files.
-
-    Args:
-        model_path: Path to the LoRA model file
-        model_key: Unique key for the model
-        model_images_path: Path to the model images directory
-
-    Returns:
-        Tuple of (description, trigger_phrases)
-    """
-    model_stem = model_path.stem
-    model_dir = model_path.parent
-
-    # Find and process preview image
-    _process_preview_image(model_stem, model_dir, model_key, model_images_path)
-
-    # Extract metadata from JSON
-    description, trigger_phrases = _extract_json_metadata(model_stem, model_dir)
-
-    return description, trigger_phrases
-
-
-def _process_preview_image(model_stem: str, model_dir: Path, model_key: str, model_images_path: Path) -> bool:
-    """Find and process a preview image for the model, saving it to the model images store."""
-    image_extensions = [".png", ".jpg", ".jpeg", ".webp"]
-
-    for ext in image_extensions:
-        image_path = model_dir / f"{model_stem}{ext}"
-        if image_path.exists():
-            try:
-                # Open the image
-                with Image.open(image_path) as img:
-                    # Create thumbnail and save to model images directory
-                    thumbnail = make_thumbnail(img, 256)
-                    thumbnail_path = model_images_path / f"{model_key}.webp"
-                    thumbnail.save(thumbnail_path, format="webp")
-
-                logger.info(f"Processed preview image {image_path.name} for model {model_key}")
-                return True
-
-            except Exception as e:
-                logger.warning(f"Failed to process preview image {image_path.name}: {e}")
-                return False
-
-    return False
-
-
-def _extract_json_metadata(model_stem: str, model_dir: Path) -> Tuple[Optional[str], Optional[Set[str]]]:
-    """Extract metadata from a JSON file with the same name as the model."""
-    json_path = model_dir / f"{model_stem}.json"
-
-    if not json_path.exists():
-        return None, None
-
-    try:
-        with open(json_path, "r", encoding="utf-8") as f:
-            metadata = json.load(f)
-
-        # Extract description
-        description = _build_description(metadata)
-
-        # Extract trigger phrases
-        trigger_phrases = _extract_trigger_phrases(metadata)
-
-        if description or trigger_phrases:
-            logger.info(f"Applied metadata from {json_path.name}")
-
-        return description, trigger_phrases
-
-    except (json.JSONDecodeError, IOError, Exception) as e:
-        logger.warning(f"Failed to read metadata from {json_path}: {e}")
-        return None, None
-
-
-def _build_description(metadata: Dict[str, Any]) -> Optional[str]:
-    """Build a description from metadata fields."""
-    description_parts = []
-
-    if description := metadata.get("description"):
-        description_parts.append(str(description).strip())
-
-    if notes := metadata.get("notes"):
-        description_parts.append(str(notes).strip())
-
-    return " | ".join(description_parts) if description_parts else None
-
-
-def _extract_trigger_phrases(metadata: Dict[str, Any]) -> Optional[Set[str]]:
-    """Extract trigger phrases from metadata."""
-    if not (activation_text := metadata.get("activation text")):
-        return None
-
-    activation_text = str(activation_text).strip()
-    if not activation_text:
-        return None
-
-    # Split on commas and clean up each phrase
-    phrases = [phrase.strip() for phrase in activation_text.split(",") if phrase.strip()]
-
-    return set(phrases) if phrases else None
-
-
-def apply_lora_metadata(info: AnyModelConfig, model_path: Path, model_images_path: Path) -> None:
-    """
-    Apply extracted metadata to a LoRA model configuration.
-
-    Args:
-        info: The model configuration to update
-        model_path: Path to the LoRA model file
-        model_images_path: Path to the model images directory
-    """
-    # Only process LoRA models
-    if info.type != ModelType.LoRA:
-        return
-
-    # Extract and apply metadata
-    description, trigger_phrases = extract_lora_metadata(model_path, info.key, model_images_path)
-
-    # We don't set cover_image path in the config anymore since images are stored
-    # separately in the model images store by model key
-
-    if description:
-        info.description = description
-
-    if trigger_phrases:
-        info.trigger_phrases = trigger_phrases

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/.eslintignore

@@ -0,0 +1,10 @@
+dist/
+static/
+.husky/
+node_modules/
+patches/
+stats.html
+index.html
+.yarn/
+*.scss
+src/services/api/schema.ts

invokeai/frontend/web/.eslintrc.js

@@ -0,0 +1,46 @@
+module.exports = {
+  extends: ['@invoke-ai/eslint-config-react'],
+  plugins: ['path', 'i18next'],
+  rules: {
+    // TODO(psyche): Enable this rule. Requires no default exports in components - many changes.
+    'react-refresh/only-export-components': 'off',
+    // TODO(psyche): Enable this rule. Requires a lot of eslint-disable-next-line comments.
+    '@typescript-eslint/consistent-type-assertions': 'off',
+    // https://github.com/qdanik/eslint-plugin-path
+    'path/no-relative-imports': ['error', { maxDepth: 0 }],
+    // https://github.com/edvardchen/eslint-plugin-i18next/blob/HEAD/docs/rules/no-literal-string.md
+    'i18next/no-literal-string': 'error',
+    // https://eslint.org/docs/latest/rules/no-console
+    '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',
+    'no-restricted-properties': [
+      'error',
+      {
+        object: 'crypto',
+        property: 'randomUUID',
+        message: 'Use of crypto.randomUUID is not allowed as it is not available in all browsers.',
+      },
+      {
+        object: 'navigator',
+        property: 'clipboard',
+        message:
+          'The Clipboard API is not available by default in Firefox. Use the `useClipboard` hook instead, which wraps clipboard access to prevent errors.',
+      },
+    ],
+  },
+  overrides: [
+    /**
+     * Overrides for stories
+     */
+    {
+      files: ['*.stories.tsx'],
+      rules: {
+        // We may not have i18n available in stories.
+        'i18next/no-literal-string': 'off',
+      },
+    },
+  ],
+};

invokeai/frontend/web/.prettierignore

@@ -14,4 +14,3 @@ static/
 src/theme/css/overlayscrollbars.css
 src/theme_/css/overlayscrollbars.css
 pnpm-lock.yaml
-.claude

invokeai/frontend/web/.prettierrc.js

@@ -0,0 +1,11 @@
+module.exports = {
+  ...require('@invoke-ai/prettier-config-react'),
+  overrides: [
+    {
+      files: ['public/locales/*.json'],
+      options: {
+        tabWidth: 4,
+      },
+    },
+  ],
+};

invokeai/frontend/web/.prettierrc.json

@@ -1,17 +0,0 @@
-{
-  "$schema": "http://json.schemastore.org/prettierrc",
-  "trailingComma": "es5",
-  "printWidth": 120,
-  "tabWidth": 2,
-  "semi": true,
-  "singleQuote": true,
-  "endOfLine": "auto",
-  "overrides": [
-    {
-      "files": ["public/locales/*.json"],
-      "options": {
-        "tabWidth": 4
-      }
-    }
-  ]
-}

invokeai/frontend/web/.storybook/ReduxInit.tsx

@@ -1,23 +1,21 @@
-import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
-import type { PropsWithChildren } from 'react';
-import { memo, useEffect } from 'react';
-
-import { useAppDispatch } from '../src/app/store/storeHooks';
+import { PropsWithChildren, memo, useEffect } from 'react';
 import { modelChanged } from '../src/features/controlLayers/store/paramsSlice';
+import { useAppDispatch } from '../src/app/store/storeHooks';
+import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
 /**
  * Initializes some state for storybook. Must be in a different component
  * so that it is run inside the redux context.
  */
-export const ReduxInit = memo(({ children }: PropsWithChildren) => {
+export const ReduxInit = memo((props: PropsWithChildren) => {
   const dispatch = useAppDispatch();
   useGlobalModifiersInit();
   useEffect(() => {
     dispatch(
       modelChanged({ model: { key: 'test_model', hash: 'some_hash', name: 'some name', base: 'sd-1', type: 'main' } })
     );
-  }, [dispatch]);
+  }, []);
 
-  return children;
+  return props.children;
 });
 
 ReduxInit.displayName = 'ReduxInit';