Make quantized loading fast for both T5XXL and FLUX transformer.

.github/workflows/python-checks.yml

@@ -62,7 +62,7 @@ jobs:
 
       - name: install ruff
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: pip install ruff==0.6.0
+        run: pip install ruff
         shell: bash
 
       - name: ruff check

.github/workflows/python-tests.yml

@@ -60,7 +60,7 @@ jobs:
             extra-index-url: 'https://download.pytorch.org/whl/cpu'
             github-env: $GITHUB_ENV
           - platform: macos-default
-            os: macOS-14
+            os: macOS-12
             github-env: $GITHUB_ENV
           - platform: windows-cpu
             os: windows-2022

docker/README.md

@@ -1,22 +1,20 @@
 # Invoke in Docker
 
-First things first:
-
-- Ensure that Docker can use your [NVIDIA][nvidia docker docs] or [AMD][amd docker docs] GPU.
-- This document assumes a Linux system, but should work similarly under Windows with WSL2.
+- Ensure that Docker can use the GPU on your system
+- This documentation assumes Linux, but should work similarly under Windows with WSL2
 - We don't recommend running Invoke in Docker on macOS at this time. It works, but very slowly.
 
-## Quickstart
+## Quickstart :lightning:
 
-No `docker compose`, no persistence, single command, using the official images:
+No `docker compose`, no persistence, just a simple one-liner using the official images:
 
-**CUDA (NVIDIA GPU):**
+**CUDA:**
 
 ```bash
 docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
 ```
 
-**ROCm (AMD GPU):**
+**ROCm:**
 
 ```bash
 docker run --device /dev/kfd --device /dev/dri --publish 9090:9090 ghcr.io/invoke-ai/invokeai:main-rocm
@@ -24,20 +22,12 @@ docker run --device /dev/kfd --device /dev/dri --publish 9090:9090 ghcr.io/invok
 
 Open `http://localhost:9090` in your browser once the container finishes booting, install some models, and generate away!
 
-### Data persistence
-
-To persist your generated images and downloaded models outside of the container, add a `--volume/-v` flag to the above command, e.g.:
-
-```bash
-docker run --volume /some/local/path:/invokeai {...etc...}
-```
-
-`/some/local/path/invokeai` will contain all your data.
-It can *usually* be reused between different installs of Invoke. Tread with caution and read the release notes!
+> [!TIP]
+> To persist your data (including downloaded models) outside of the container, add a `--volume/-v` flag to the above command, e.g.: `docker run --volume /some/local/path:/invokeai <...the rest of the command>`
 
 ## Customize the container
 
-The included `run.sh` script is a convenience wrapper around `docker compose`. It can be helpful for passing additional build arguments to `docker compose`. Alternatively, the familiar `docker compose` commands work just as well.
+We ship the `run.sh` script, which is a convenient wrapper around `docker compose` for cases where custom image build args are needed. Alternatively, the familiar `docker compose` commands work just as well.
 
 ```bash
 cd docker
@@ -48,14 +38,11 @@ cp .env.sample .env
 
 It will take a few minutes to build the image the first time. Once the application starts up, open `http://localhost:9090` in your browser to invoke!
 
->[!TIP]
->When using the `run.sh` script, the container will continue running after Ctrl+C. To shut it down, use the `docker compose down` command.
-
 ## Docker setup in detail
 
 #### Linux
 
-1. Ensure buildkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
+1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
 2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository).
     - The deprecated `docker-compose` (hyphenated) CLI probably won't work. Update to a recent version.
 3. Ensure docker daemon is able to access the GPU.
@@ -111,7 +98,25 @@ GPU_DRIVER=cuda
 
 Any environment variables supported by InvokeAI can be set here. See the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
 
----
+## Even More Customizing!
 
-[nvidia docker docs]: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
-[amd docker docs]: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html
+See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
+
+### Reconfigure the runtime directory
+
+Can be used to download additional models from the supported model list
+
+In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
+
+```yaml
+command:
+  - invokeai-configure
+  - --yes
+```
+
+Or install models:
+
+```yaml
+command:
+  - invokeai-model-install
+```

installer/templates/invoke.sh.in

@@ -17,7 +17,7 @@
 set -eu
 
 # Ensure we're in the correct folder in case user's CWD is somewhere else
-scriptdir=$(dirname $(readlink -f "$0"))
+scriptdir=$(dirname "$0")
 cd "$scriptdir"
 
 . .venv/bin/activate

invokeai/app/api/dependencies.py

@@ -1,6 +1,5 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-import asyncio
 from logging import Logger
 
 import torch
@@ -32,8 +31,6 @@ from invokeai.app.services.session_processor.session_processor_default import (
 )
 from invokeai.app.services.session_queue.session_queue_sqlite import SqliteSessionQueue
 from invokeai.app.services.shared.sqlite.sqlite_util import init_db
-from invokeai.app.services.style_preset_images.style_preset_images_disk import StylePresetImageFileStorageDisk
-from invokeai.app.services.style_preset_records.style_preset_records_sqlite import SqliteStylePresetRecordsStorage
 from invokeai.app.services.urls.urls_default import LocalUrlService
 from invokeai.app.services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
@@ -66,12 +63,7 @@ class ApiDependencies:
     invoker: Invoker
 
     @staticmethod
-    def initialize(
-        config: InvokeAIAppConfig,
-        event_handler_id: int,
-        loop: asyncio.AbstractEventLoop,
-        logger: Logger = logger,
-    ) -> None:
+    def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger) -> None:
         logger.info(f"InvokeAI version {__version__}")
         logger.info(f"Root directory = {str(config.root_path)}")
 
@@ -82,7 +74,6 @@ class ApiDependencies:
         image_files = DiskImageFileStorage(f"{output_folder}/images")
 
         model_images_folder = config.models_path
-        style_presets_folder = config.style_presets_path
 
         db = init_db(config=config, logger=logger, image_files=image_files)
 
@@ -93,7 +84,7 @@ class ApiDependencies:
         board_images = BoardImagesService()
         board_records = SqliteBoardRecordStorage(db=db)
         boards = BoardService()
-        events = FastAPIEventService(event_handler_id, loop=loop)
+        events = FastAPIEventService(event_handler_id)
         bulk_download = BulkDownloadService()
         image_records = SqliteImageRecordStorage(db=db)
         images = ImageService()
@@ -118,8 +109,6 @@ class ApiDependencies:
         session_queue = SqliteSessionQueue(db=db)
         urls = LocalUrlService()
         workflow_records = SqliteWorkflowRecordsStorage(db=db)
-        style_preset_records = SqliteStylePresetRecordsStorage(db=db)
-        style_preset_image_files = StylePresetImageFileStorageDisk(style_presets_folder / "images")
 
         services = InvocationServices(
             board_image_records=board_image_records,
@@ -145,8 +134,6 @@ class ApiDependencies:
             workflow_records=workflow_records,
             tensors=tensors,
             conditioning=conditioning,
-            style_preset_records=style_preset_records,
-            style_preset_image_files=style_preset_image_files,
         )
 
         ApiDependencies.invoker = Invoker(services)

invokeai/app/api/routers/images.py

@@ -218,8 +218,9 @@ async def get_image_workflow(
         raise HTTPException(status_code=404)
 
 
-@images_router.get(
+@images_router.api_route(
     "/i/{image_name}/full",
+    methods=["GET", "HEAD"],
     operation_id="get_image_full",
     response_class=Response,
     responses={
@@ -230,18 +231,6 @@ async def get_image_workflow(
         404: {"description": "Image not found"},
     },
 )
-@images_router.head(
-    "/i/{image_name}/full",
-    operation_id="get_image_full_head",
-    response_class=Response,
-    responses={
-        200: {
-            "description": "Return the full-resolution image",
-            "content": {"image/png": {}},
-        },
-        404: {"description": "Image not found"},
-    },
-)
 async def get_image_full(
     image_name: str = Path(description="The name of full-resolution image file to get"),
 ) -> Response:
@@ -253,7 +242,6 @@ async def get_image_full(
             content = f.read()
         response = Response(content, media_type="image/png")
         response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
-        response.headers["Content-Disposition"] = f'inline; filename="{image_name}"'
         return response
     except Exception:
         raise HTTPException(status_code=404)

invokeai/app/api/routers/session_queue.py

@@ -11,7 +11,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByOriginResult,
     ClearResult,
     EnqueueBatchResult,
     PruneResult,
@@ -106,19 +105,6 @@ async def cancel_by_batch_ids(
     return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(queue_id=queue_id, batch_ids=batch_ids)
 
 
-@session_queue_router.put(
-    "/{queue_id}/cancel_by_origin",
-    operation_id="cancel_by_origin",
-    responses={200: {"model": CancelByBatchIDsResult}},
-)
-async def cancel_by_origin(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    origin: str = Query(description="The origin to cancel all queue items for"),
-) -> CancelByOriginResult:
-    """Immediately cancels all queue items with the given origin"""
-    return ApiDependencies.invoker.services.session_queue.cancel_by_origin(queue_id=queue_id, origin=origin)
-
-
 @session_queue_router.put(
     "/{queue_id}/clear",
     operation_id="clear",

invokeai/app/api/routers/style_presets.py

@@ -1,274 +0,0 @@
-import csv
-import io
-import json
-import traceback
-from typing import Optional
-
-import pydantic
-from fastapi import APIRouter, File, Form, HTTPException, Path, Response, UploadFile
-from fastapi.responses import FileResponse
-from PIL import Image
-from pydantic import BaseModel, Field
-
-from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.api.routers.model_manager import IMAGE_MAX_AGE
-from invokeai.app.services.style_preset_images.style_preset_images_common import StylePresetImageFileNotFoundException
-from invokeai.app.services.style_preset_records.style_preset_records_common import (
-    InvalidPresetImportDataError,
-    PresetData,
-    PresetType,
-    StylePresetChanges,
-    StylePresetNotFoundError,
-    StylePresetRecordWithImage,
-    StylePresetWithoutId,
-    UnsupportedFileTypeError,
-    parse_presets_from_file,
-)
-
-
-class StylePresetFormData(BaseModel):
-    name: str = Field(description="Preset name")
-    positive_prompt: str = Field(description="Positive prompt")
-    negative_prompt: str = Field(description="Negative prompt")
-    type: PresetType = Field(description="Preset type")
-
-
-style_presets_router = APIRouter(prefix="/v1/style_presets", tags=["style_presets"])
-
-
-@style_presets_router.get(
-    "/i/{style_preset_id}",
-    operation_id="get_style_preset",
-    responses={
-        200: {"model": StylePresetRecordWithImage},
-    },
-)
-async def get_style_preset(
-    style_preset_id: str = Path(description="The style preset to get"),
-) -> StylePresetRecordWithImage:
-    """Gets a style preset"""
-    try:
-        image = ApiDependencies.invoker.services.style_preset_image_files.get_url(style_preset_id)
-        style_preset = ApiDependencies.invoker.services.style_preset_records.get(style_preset_id)
-        return StylePresetRecordWithImage(image=image, **style_preset.model_dump())
-    except StylePresetNotFoundError:
-        raise HTTPException(status_code=404, detail="Style preset not found")
-
-
-@style_presets_router.patch(
-    "/i/{style_preset_id}",
-    operation_id="update_style_preset",
-    responses={
-        200: {"model": StylePresetRecordWithImage},
-    },
-)
-async def update_style_preset(
-    image: Optional[UploadFile] = File(description="The image file to upload", default=None),
-    style_preset_id: str = Path(description="The id of the style preset to update"),
-    data: str = Form(description="The data of the style preset to update"),
-) -> StylePresetRecordWithImage:
-    """Updates a style preset"""
-    if image is not None:
-        if not image.content_type or not image.content_type.startswith("image"):
-            raise HTTPException(status_code=415, detail="Not an image")
-
-        contents = await image.read()
-        try:
-            pil_image = Image.open(io.BytesIO(contents))
-
-        except Exception:
-            ApiDependencies.invoker.services.logger.error(traceback.format_exc())
-            raise HTTPException(status_code=415, detail="Failed to read image")
-
-        try:
-            ApiDependencies.invoker.services.style_preset_image_files.save(style_preset_id, pil_image)
-        except ValueError as e:
-            raise HTTPException(status_code=409, detail=str(e))
-    else:
-        try:
-            ApiDependencies.invoker.services.style_preset_image_files.delete(style_preset_id)
-        except StylePresetImageFileNotFoundException:
-            pass
-
-    try:
-        parsed_data = json.loads(data)
-        validated_data = StylePresetFormData(**parsed_data)
-
-        name = validated_data.name
-        type = validated_data.type
-        positive_prompt = validated_data.positive_prompt
-        negative_prompt = validated_data.negative_prompt
-
-    except pydantic.ValidationError:
-        raise HTTPException(status_code=400, detail="Invalid preset data")
-
-    preset_data = PresetData(positive_prompt=positive_prompt, negative_prompt=negative_prompt)
-    changes = StylePresetChanges(name=name, preset_data=preset_data, type=type)
-
-    style_preset_image = ApiDependencies.invoker.services.style_preset_image_files.get_url(style_preset_id)
-    style_preset = ApiDependencies.invoker.services.style_preset_records.update(
-        style_preset_id=style_preset_id, changes=changes
-    )
-    return StylePresetRecordWithImage(image=style_preset_image, **style_preset.model_dump())
-
-
-@style_presets_router.delete(
-    "/i/{style_preset_id}",
-    operation_id="delete_style_preset",
-)
-async def delete_style_preset(
-    style_preset_id: str = Path(description="The style preset to delete"),
-) -> None:
-    """Deletes a style preset"""
-    try:
-        ApiDependencies.invoker.services.style_preset_image_files.delete(style_preset_id)
-    except StylePresetImageFileNotFoundException:
-        pass
-
-    ApiDependencies.invoker.services.style_preset_records.delete(style_preset_id)
-
-
-@style_presets_router.post(
-    "/",
-    operation_id="create_style_preset",
-    responses={
-        200: {"model": StylePresetRecordWithImage},
-    },
-)
-async def create_style_preset(
-    image: Optional[UploadFile] = File(description="The image file to upload", default=None),
-    data: str = Form(description="The data of the style preset to create"),
-) -> StylePresetRecordWithImage:
-    """Creates a style preset"""
-
-    try:
-        parsed_data = json.loads(data)
-        validated_data = StylePresetFormData(**parsed_data)
-
-        name = validated_data.name
-        type = validated_data.type
-        positive_prompt = validated_data.positive_prompt
-        negative_prompt = validated_data.negative_prompt
-
-    except pydantic.ValidationError:
-        raise HTTPException(status_code=400, detail="Invalid preset data")
-
-    preset_data = PresetData(positive_prompt=positive_prompt, negative_prompt=negative_prompt)
-    style_preset = StylePresetWithoutId(name=name, preset_data=preset_data, type=type)
-    new_style_preset = ApiDependencies.invoker.services.style_preset_records.create(style_preset=style_preset)
-
-    if image is not None:
-        if not image.content_type or not image.content_type.startswith("image"):
-            raise HTTPException(status_code=415, detail="Not an image")
-
-        contents = await image.read()
-        try:
-            pil_image = Image.open(io.BytesIO(contents))
-
-        except Exception:
-            ApiDependencies.invoker.services.logger.error(traceback.format_exc())
-            raise HTTPException(status_code=415, detail="Failed to read image")
-
-        try:
-            ApiDependencies.invoker.services.style_preset_image_files.save(new_style_preset.id, pil_image)
-        except ValueError as e:
-            raise HTTPException(status_code=409, detail=str(e))
-
-    preset_image = ApiDependencies.invoker.services.style_preset_image_files.get_url(new_style_preset.id)
-    return StylePresetRecordWithImage(image=preset_image, **new_style_preset.model_dump())
-
-
-@style_presets_router.get(
-    "/",
-    operation_id="list_style_presets",
-    responses={
-        200: {"model": list[StylePresetRecordWithImage]},
-    },
-)
-async def list_style_presets() -> list[StylePresetRecordWithImage]:
-    """Gets a page of style presets"""
-    style_presets_with_image: list[StylePresetRecordWithImage] = []
-    style_presets = ApiDependencies.invoker.services.style_preset_records.get_many()
-    for preset in style_presets:
-        image = ApiDependencies.invoker.services.style_preset_image_files.get_url(preset.id)
-        style_preset_with_image = StylePresetRecordWithImage(image=image, **preset.model_dump())
-        style_presets_with_image.append(style_preset_with_image)
-
-    return style_presets_with_image
-
-
-@style_presets_router.get(
-    "/i/{style_preset_id}/image",
-    operation_id="get_style_preset_image",
-    responses={
-        200: {
-            "description": "The style preset image was fetched successfully",
-        },
-        400: {"description": "Bad request"},
-        404: {"description": "The style preset image could not be found"},
-    },
-    status_code=200,
-)
-async def get_style_preset_image(
-    style_preset_id: str = Path(description="The id of the style preset image to get"),
-) -> FileResponse:
-    """Gets an image file that previews the model"""
-
-    try:
-        path = ApiDependencies.invoker.services.style_preset_image_files.get_path(style_preset_id)
-
-        response = FileResponse(
-            path,
-            media_type="image/png",
-            filename=style_preset_id + ".png",
-            content_disposition_type="inline",
-        )
-        response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
-        return response
-    except Exception:
-        raise HTTPException(status_code=404)
-
-
-@style_presets_router.get(
-    "/export",
-    operation_id="export_style_presets",
-    responses={200: {"content": {"text/csv": {}}, "description": "A CSV file with the requested data."}},
-    status_code=200,
-)
-async def export_style_presets():
-    # Create an in-memory stream to store the CSV data
-    output = io.StringIO()
-    writer = csv.writer(output)
-
-    # Write the header
-    writer.writerow(["name", "prompt", "negative_prompt"])
-
-    style_presets = ApiDependencies.invoker.services.style_preset_records.get_many(type=PresetType.User)
-
-    for preset in style_presets:
-        writer.writerow([preset.name, preset.preset_data.positive_prompt, preset.preset_data.negative_prompt])
-
-    csv_data = output.getvalue()
-    output.close()
-
-    return Response(
-        content=csv_data,
-        media_type="text/csv",
-        headers={"Content-Disposition": "attachment; filename=prompt_templates.csv"},
-    )
-
-
-@style_presets_router.post(
-    "/import",
-    operation_id="import_style_presets",
-)
-async def import_style_presets(file: UploadFile = File(description="The file to import")):
-    try:
-        style_presets = await parse_presets_from_file(file)
-        ApiDependencies.invoker.services.style_preset_records.create_many(style_presets)
-    except InvalidPresetImportDataError as e:
-        ApiDependencies.invoker.services.logger.error(traceback.format_exc())
-        raise HTTPException(status_code=400, detail=str(e))
-    except UnsupportedFileTypeError as e:
-        ApiDependencies.invoker.services.logger.error(traceback.format_exc())
-        raise HTTPException(status_code=415, detail=str(e))

invokeai/app/api_app.py

@@ -30,7 +30,6 @@ from invokeai.app.api.routers import (
     images,
     model_manager,
     session_queue,
-    style_presets,
     utilities,
     workflows,
 )
@@ -56,13 +55,11 @@ mimetypes.add_type("text/css", ".css")
 torch_device_name = TorchDevice.get_torch_device_name()
 logger.info(f"Using torch device: {torch_device_name}")
 
-loop = asyncio.new_event_loop()
-
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     # Add startup event to load dependencies
-    ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, loop=loop, logger=logger)
+    ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
     yield
     # Shut down threads
     ApiDependencies.shutdown()
@@ -109,7 +106,6 @@ app.include_router(board_images.board_images_router, prefix="/api")
 app.include_router(app_info.app_router, prefix="/api")
 app.include_router(session_queue.session_queue_router, prefix="/api")
 app.include_router(workflows.workflows_router, prefix="/api")
-app.include_router(style_presets.style_presets_router, prefix="/api")
 
 app.openapi = get_openapi_func(app)
 
@@ -188,6 +184,8 @@ def invoke_api() -> None:
 
     check_cudnn(logger)
 
+    # Start our own event loop for eventing usage
+    loop = asyncio.new_event_loop()
     config = uvicorn.Config(
         app=app,
         host=app_config.host,

invokeai/app/invocations/controlnet_image_processors.py

@@ -21,8 +21,6 @@ from controlnet_aux import (
 from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
 from pydantic import BaseModel, Field, field_validator, model_validator
-from transformers import pipeline
-from transformers.pipelines import DepthEstimationPipeline
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
@@ -46,12 +44,13 @@ from invokeai.app.invocations.util import validate_begin_end_step, validate_weig
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, heuristic_resize
 from invokeai.backend.image_util.canny import get_canny_edges
-from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
+from invokeai.backend.image_util.depth_anything import DEPTH_ANYTHING_MODELS, DepthAnythingDetector
 from invokeai.backend.image_util.dw_openpose import DWPOSE_MODELS, DWOpenposeDetector
 from invokeai.backend.image_util.hed import HEDProcessor
 from invokeai.backend.image_util.lineart import LineartProcessor
 from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor
 from invokeai.backend.image_util.util import np_to_pil, pil_to_np
+from invokeai.backend.util.devices import TorchDevice
 
 
 class ControlField(BaseModel):
@@ -593,14 +592,7 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
         return color_map
 
 
-DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small", "small_v2"]
-# DepthAnything V2 Small model is licensed under Apache 2.0 but not the base and large models.
-DEPTH_ANYTHING_MODELS = {
-    "large": "LiheYoung/depth-anything-large-hf",
-    "base": "LiheYoung/depth-anything-base-hf",
-    "small": "LiheYoung/depth-anything-small-hf",
-    "small_v2": "depth-anything/Depth-Anything-V2-Small-hf",
-}
+DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
 
 
 @invocation(
@@ -608,33 +600,28 @@ DEPTH_ANYTHING_MODELS = {
     title="Depth Anything Processor",
     tags=["controlnet", "depth", "depth anything"],
     category="controlnet",
-    version="1.1.3",
+    version="1.1.2",
 )
 class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a depth map based on the Depth Anything algorithm"""
 
     model_size: DEPTH_ANYTHING_MODEL_SIZES = InputField(
-        default="small_v2", description="The size of the depth model to use"
+        default="small", description="The size of the depth model to use"
     )
     resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
 
     def run_processor(self, image: Image.Image) -> Image.Image:
-        def load_depth_anything(model_path: Path):
-            depth_anything_pipeline = pipeline(model=str(model_path), task="depth-estimation", local_files_only=True)
-            assert isinstance(depth_anything_pipeline, DepthEstimationPipeline)
-            return DepthAnythingPipeline(depth_anything_pipeline)
+        def loader(model_path: Path):
+            return DepthAnythingDetector.load_model(
+                model_path, model_size=self.model_size, device=TorchDevice.choose_torch_device()
+            )
 
         with self._context.models.load_remote_model(
-            source=DEPTH_ANYTHING_MODELS[self.model_size], loader=load_depth_anything
-        ) as depth_anything_detector:
-            assert isinstance(depth_anything_detector, DepthAnythingPipeline)
-            depth_map = depth_anything_detector.generate_depth(image)
-
-            # Resizing to user target specified size
-            new_height = int(image.size[1] * (self.resolution / image.size[0]))
-            depth_map = depth_map.resize((self.resolution, new_height))
-
-            return depth_map
+            source=DEPTH_ANYTHING_MODELS[self.model_size], loader=loader
+        ) as model:
+            depth_anything_detector = DepthAnythingDetector(model, TorchDevice.choose_torch_device())
+            processed_image = depth_anything_detector(image=image, resolution=self.resolution)
+            return processed_image
 
 
 @invocation(

invokeai/app/invocations/fields.py

@@ -40,7 +40,6 @@ class UIType(str, Enum, metaclass=MetaEnum):
 
     # region Model Field Types
     MainModel = "MainModelField"
-    FluxMainModel = "FluxMainModelField"
     SDXLMainModel = "SDXLMainModelField"
     SDXLRefinerModel = "SDXLRefinerModelField"
     ONNXModel = "ONNXModelField"
@@ -49,7 +48,6 @@ class UIType(str, Enum, metaclass=MetaEnum):
     ControlNetModel = "ControlNetModelField"
     IPAdapterModel = "IPAdapterModelField"
     T2IAdapterModel = "T2IAdapterModelField"
-    T5EncoderModel = "T5EncoderModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
     # endregion
 
@@ -127,16 +125,13 @@ class FieldDescriptions:
     negative_cond = "Negative conditioning tensor"
     noise = "Noise tensor"
     clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
-    t5_encoder = "T5 tokenizer and text encoder"
     unet = "UNet (scheduler, LoRAs)"
-    transformer = "Transformer"
     vae = "VAE"
     cond = "Conditioning tensor"
     controlnet_model = "ControlNet model to load"
     vae_model = "VAE model to load"
     lora_model = "LoRA model to load"
     main_model = "Main model (UNet, VAE, CLIP) to load"
-    flux_model = "Flux model (Transformer) to load"
     sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
@@ -236,12 +231,6 @@ class ColorField(BaseModel):
         return (self.r, self.g, self.b, self.a)
 
 
-class FluxConditioningField(BaseModel):
-    """A conditioning tensor primitive value"""
-
-    conditioning_name: str = Field(description="The name of conditioning tensor")
-
-
 class ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 

invokeai/app/invocations/flux_text_encoder.py

@@ -1,86 +0,0 @@
-from typing import Literal
-
-import torch
-from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
-
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
-from invokeai.app.invocations.model import CLIPField, T5EncoderField
-from invokeai.app.invocations.primitives import FluxConditioningOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.modules.conditioner import HFEncoder
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
-
-
-@invocation(
-    "flux_text_encoder",
-    title="FLUX Text Encoding",
-    tags=["prompt", "conditioning", "flux"],
-    category="conditioning",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class FluxTextEncoderInvocation(BaseInvocation):
-    """Encodes and preps a prompt for a flux image."""
-
-    clip: CLIPField = InputField(
-        title="CLIP",
-        description=FieldDescriptions.clip,
-        input=Input.Connection,
-    )
-    t5_encoder: T5EncoderField = InputField(
-        title="T5Encoder",
-        description=FieldDescriptions.t5_encoder,
-        input=Input.Connection,
-    )
-    t5_max_seq_len: Literal[256, 512] = InputField(
-        description="Max sequence length for the T5 encoder. Expected to be 256 for FLUX schnell models and 512 for FLUX dev models."
-    )
-    prompt: str = InputField(description="Text prompt to encode.")
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> FluxConditioningOutput:
-        t5_embeddings, clip_embeddings = self._encode_prompt(context)
-        conditioning_data = ConditioningFieldData(
-            conditionings=[FLUXConditioningInfo(clip_embeds=clip_embeddings, t5_embeds=t5_embeddings)]
-        )
-
-        conditioning_name = context.conditioning.save(conditioning_data)
-        return FluxConditioningOutput.build(conditioning_name)
-
-    def _encode_prompt(self, context: InvocationContext) -> tuple[torch.Tensor, torch.Tensor]:
-        # Load CLIP.
-        clip_tokenizer_info = context.models.load(self.clip.tokenizer)
-        clip_text_encoder_info = context.models.load(self.clip.text_encoder)
-
-        # Load T5.
-        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
-        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
-
-        prompt = [self.prompt]
-
-        with (
-            t5_text_encoder_info as t5_text_encoder,
-            t5_tokenizer_info as t5_tokenizer,
-        ):
-            assert isinstance(t5_text_encoder, T5EncoderModel)
-            assert isinstance(t5_tokenizer, T5Tokenizer)
-
-            t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
-
-            prompt_embeds = t5_encoder(prompt)
-
-        with (
-            clip_text_encoder_info as clip_text_encoder,
-            clip_tokenizer_info as clip_tokenizer,
-        ):
-            assert isinstance(clip_text_encoder, CLIPTextModel)
-            assert isinstance(clip_tokenizer, CLIPTokenizer)
-
-            clip_encoder = HFEncoder(clip_text_encoder, clip_tokenizer, True, 77)
-
-            pooled_prompt_embeds = clip_encoder(prompt)
-
-        assert isinstance(prompt_embeds, torch.Tensor)
-        assert isinstance(pooled_prompt_embeds, torch.Tensor)
-        return prompt_embeds, pooled_prompt_embeds

invokeai/app/invocations/flux_text_to_image.py

@@ -1,172 +1,278 @@
-import torch
-from einops import rearrange
-from PIL import Image
+from pathlib import Path
+from typing import Literal
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    FluxConditioningField,
-    Input,
-    InputField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.model import TransformerField, VAEField
+import torch
+from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler
+from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
+from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
+from optimum.quanto import qfloat8
+from PIL import Image
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
+from transformers.models.auto import AutoModelForTextEncoding
+
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.fields import InputField, WithBoard, WithMetadata
 from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.session_processor.session_processor_common import CanceledException
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.modules.autoencoder import AutoEncoder
-from invokeai.backend.flux.sampling import denoise, get_noise, get_schedule, prepare_latent_img_patches, unpack
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
+from invokeai.backend.quantization.fast_quantized_diffusion_model import FastQuantizedDiffusersModel
+from invokeai.backend.quantization.fast_quantized_transformers_model import FastQuantizedTransformersModel
 from invokeai.backend.util.devices import TorchDevice
 
+TFluxModelKeys = Literal["flux-schnell"]
+FLUX_MODELS: dict[TFluxModelKeys, str] = {"flux-schnell": "black-forest-labs/FLUX.1-schnell"}
+
+
+class QuantizedFluxTransformer2DModel(FastQuantizedDiffusersModel):
+    base_class = FluxTransformer2DModel
+
+
+class QuantizedModelForTextEncoding(FastQuantizedTransformersModel):
+    auto_class = AutoModelForTextEncoding
+
 
 @invocation(
     "flux_text_to_image",
     title="FLUX Text to Image",
-    tags=["image", "flux"],
+    tags=["image"],
     category="image",
     version="1.0.0",
-    classification=Classification.Prototype,
 )
 class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Text-to-image generation using a FLUX model."""
 
-    transformer: TransformerField = InputField(
-        description=FieldDescriptions.flux_model,
-        input=Input.Connection,
-        title="Transformer",
-    )
-    vae: VAEField = InputField(
-        description=FieldDescriptions.vae,
-        input=Input.Connection,
-    )
-    positive_text_conditioning: FluxConditioningField = InputField(
-        description=FieldDescriptions.positive_cond, input=Input.Connection
+    model: TFluxModelKeys = InputField(description="The FLUX model to use for text-to-image generation.")
+    use_8bit: bool = InputField(
+        default=False, description="Whether to quantize the transformer model to 8-bit precision."
     )
+    positive_prompt: str = InputField(description="Positive prompt for text-to-image generation.")
     width: int = InputField(default=1024, multiple_of=16, description="Width of the generated image.")
     height: int = InputField(default=1024, multiple_of=16, description="Height of the generated image.")
-    num_steps: int = InputField(
-        default=4, description="Number of diffusion steps. Recommend values are schnell: 4, dev: 50."
-    )
+    num_steps: int = InputField(default=4, description="Number of diffusion steps.")
     guidance: float = InputField(
         default=4.0,
-        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images. FLUX dev only, ignored for schnell.",
+        description="The guidance strength. Higher values adhere more strictly to the prompt, and will produce less diverse images.",
     )
     seed: int = InputField(default=0, description="Randomness seed for reproducibility.")
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Load the conditioning data.
-        cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
-        assert len(cond_data.conditionings) == 1
-        flux_conditioning = cond_data.conditionings[0]
-        assert isinstance(flux_conditioning, FLUXConditioningInfo)
+        model_path = context.models.download_and_cache_model(FLUX_MODELS[self.model])
 
-        latents = self._run_diffusion(context, flux_conditioning.clip_embeds, flux_conditioning.t5_embeds)
-        image = self._run_vae_decoding(context, latents)
+        t5_embeddings, clip_embeddings = self._encode_prompt(context, model_path)
+        latents = self._run_diffusion(context, model_path, clip_embeddings, t5_embeddings)
+        image = self._run_vae_decoding(context, model_path, latents)
         image_dto = context.images.save(image=image)
         return ImageOutput.build(image_dto)
 
+    def _encode_prompt(self, context: InvocationContext, flux_model_dir: Path) -> tuple[torch.Tensor, torch.Tensor]:
+        # Determine the T5 max sequence length based on the model.
+        if self.model == "flux-schnell":
+            max_seq_len = 256
+        # elif self.model == "flux-dev":
+        #     max_seq_len = 512
+        else:
+            raise ValueError(f"Unknown model: {self.model}")
+
+        # Load the CLIP tokenizer.
+        clip_tokenizer_path = flux_model_dir / "tokenizer"
+        clip_tokenizer = CLIPTokenizer.from_pretrained(clip_tokenizer_path, local_files_only=True)
+        assert isinstance(clip_tokenizer, CLIPTokenizer)
+
+        # Load the T5 tokenizer.
+        t5_tokenizer_path = flux_model_dir / "tokenizer_2"
+        t5_tokenizer = T5TokenizerFast.from_pretrained(t5_tokenizer_path, local_files_only=True)
+        assert isinstance(t5_tokenizer, T5TokenizerFast)
+
+        clip_text_encoder_path = flux_model_dir / "text_encoder"
+        t5_text_encoder_path = flux_model_dir / "text_encoder_2"
+        with (
+            context.models.load_local_model(
+                model_path=clip_text_encoder_path, loader=self._load_flux_text_encoder
+            ) as clip_text_encoder,
+            context.models.load_local_model(
+                model_path=t5_text_encoder_path, loader=self._load_flux_text_encoder_2
+            ) as t5_text_encoder,
+        ):
+            assert isinstance(clip_text_encoder, CLIPTextModel)
+            assert isinstance(t5_text_encoder, T5EncoderModel)
+            pipeline = FluxPipeline(
+                scheduler=None,
+                vae=None,
+                text_encoder=clip_text_encoder,
+                tokenizer=clip_tokenizer,
+                text_encoder_2=t5_text_encoder,
+                tokenizer_2=t5_tokenizer,
+                transformer=None,
+            )
+
+            # prompt_embeds: T5 embeddings
+            # pooled_prompt_embeds: CLIP embeddings
+            prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(
+                prompt=self.positive_prompt,
+                prompt_2=self.positive_prompt,
+                device=TorchDevice.choose_torch_device(),
+                max_sequence_length=max_seq_len,
+            )
+
+        assert isinstance(prompt_embeds, torch.Tensor)
+        assert isinstance(pooled_prompt_embeds, torch.Tensor)
+        return prompt_embeds, pooled_prompt_embeds
+
     def _run_diffusion(
         self,
         context: InvocationContext,
+        flux_model_dir: Path,
         clip_embeddings: torch.Tensor,
         t5_embeddings: torch.Tensor,
     ):
-        transformer_info = context.models.load(self.transformer.transformer)
-        inference_dtype = torch.bfloat16
-
-        # Prepare input noise.
-        x = get_noise(
-            num_samples=1,
-            height=self.height,
-            width=self.width,
-            device=TorchDevice.choose_torch_device(),
-            dtype=inference_dtype,
-            seed=self.seed,
-        )
-
-        img, img_ids = prepare_latent_img_patches(x)
-
-        is_schnell = "schnell" in transformer_info.config.config_path
-
-        timesteps = get_schedule(
-            num_steps=self.num_steps,
-            image_seq_len=img.shape[1],
-            shift=not is_schnell,
-        )
-
-        bs, t5_seq_len, _ = t5_embeddings.shape
-        txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
+        scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(flux_model_dir / "scheduler", local_files_only=True)
 
         # HACK(ryand): Manually empty the cache. Currently we don't check the size of the model before loading it from
         # disk. Since the transformer model is large (24GB), there's a good chance that it will OOM on 32GB RAM systems
         # if the cache is not empty.
         context.models._services.model_manager.load.ram_cache.make_room(24 * 2**30)
 
-        with transformer_info as transformer:
-            assert isinstance(transformer, Flux)
+        transformer_path = flux_model_dir / "transformer"
+        with context.models.load_local_model(
+            model_path=transformer_path, loader=self._load_flux_transformer
+        ) as transformer:
+            assert isinstance(transformer, FluxTransformer2DModel)
 
-            def step_callback() -> None:
-                if context.util.is_canceled():
-                    raise CanceledException
-
-                # TODO: Make this look like the image before re-enabling
-                # latent_image = unpack(img.float(), self.height, self.width)
-                # latent_image = latent_image.squeeze()  # Remove unnecessary dimensions
-                # flattened_tensor = latent_image.reshape(-1)  # Flatten to shape [48*128*128]
-
-                # # Create a new tensor of the required shape [255, 255, 3]
-                # latent_image = flattened_tensor[: 255 * 255 * 3].reshape(255, 255, 3)  # Reshape to RGB format
-
-                # # Convert to a NumPy array and then to a PIL Image
-                # image = Image.fromarray(latent_image.cpu().numpy().astype(np.uint8))
-
-                # (width, height) = image.size
-                # width *= 8
-                # height *= 8
-
-                # dataURL = image_to_dataURL(image, image_format="JPEG")
-
-                # # TODO: move this whole function to invocation context to properly reference these variables
-                # context._services.events.emit_invocation_denoise_progress(
-                #     context._data.queue_item,
-                #     context._data.invocation,
-                #     state,
-                #     ProgressImage(dataURL=dataURL, width=width, height=height),
-                # )
-
-            x = denoise(
-                model=transformer,
-                img=img,
-                img_ids=img_ids,
-                txt=t5_embeddings,
-                txt_ids=txt_ids,
-                vec=clip_embeddings,
-                timesteps=timesteps,
-                step_callback=step_callback,
-                guidance=self.guidance,
+            flux_pipeline_with_transformer = FluxPipeline(
+                scheduler=scheduler,
+                vae=None,
+                text_encoder=None,
+                tokenizer=None,
+                text_encoder_2=None,
+                tokenizer_2=None,
+                transformer=transformer,
             )
 
-        x = unpack(x.float(), self.height, self.width)
+            t5_embeddings = t5_embeddings.to(dtype=transformer.dtype)
+            clip_embeddings = clip_embeddings.to(dtype=transformer.dtype)
 
-        return x
+            latents = flux_pipeline_with_transformer(
+                height=self.height,
+                width=self.width,
+                num_inference_steps=self.num_steps,
+                guidance_scale=self.guidance,
+                generator=torch.Generator().manual_seed(self.seed),
+                prompt_embeds=t5_embeddings,
+                pooled_prompt_embeds=clip_embeddings,
+                output_type="latent",
+                return_dict=False,
+            )[0]
+
+        assert isinstance(latents, torch.Tensor)
+        return latents
 
     def _run_vae_decoding(
         self,
         context: InvocationContext,
+        flux_model_dir: Path,
         latents: torch.Tensor,
     ) -> Image.Image:
-        vae_info = context.models.load(self.vae.vae)
-        with vae_info as vae:
-            assert isinstance(vae, AutoEncoder)
-            latents = latents.to(dtype=TorchDevice.choose_torch_dtype())
-            img = vae.decode(latents)
+        vae_path = flux_model_dir / "vae"
+        with context.models.load_local_model(model_path=vae_path, loader=self._load_flux_vae) as vae:
+            assert isinstance(vae, AutoencoderKL)
 
-        img = img.clamp(-1, 1)
-        img = rearrange(img[0], "c h w -> h w c")
-        img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
+            flux_pipeline_with_vae = FluxPipeline(
+                scheduler=None,
+                vae=vae,
+                text_encoder=None,
+                tokenizer=None,
+                text_encoder_2=None,
+                tokenizer_2=None,
+                transformer=None,
+            )
 
-        return img_pil
+            latents = flux_pipeline_with_vae._unpack_latents(
+                latents, self.height, self.width, flux_pipeline_with_vae.vae_scale_factor
+            )
+            latents = (
+                latents / flux_pipeline_with_vae.vae.config.scaling_factor
+            ) + flux_pipeline_with_vae.vae.config.shift_factor
+            latents = latents.to(dtype=vae.dtype)
+            image = flux_pipeline_with_vae.vae.decode(latents, return_dict=False)[0]
+            image = flux_pipeline_with_vae.image_processor.postprocess(image, output_type="pil")[0]
+
+        assert isinstance(image, Image.Image)
+        return image
+
+    @staticmethod
+    def _load_flux_text_encoder(path: Path) -> CLIPTextModel:
+        model = CLIPTextModel.from_pretrained(path, local_files_only=True)
+        assert isinstance(model, CLIPTextModel)
+        return model
+
+    def _load_flux_text_encoder_2(self, path: Path) -> T5EncoderModel:
+        if self.use_8bit:
+            model_8bit_path = path / "quantized"
+            if model_8bit_path.exists():
+                # The quantized model exists, load it.
+                # TODO(ryand): The requantize(...) operation in from_pretrained(...) is very slow. This seems like
+                # something that we should be able to make much faster.
+                q_model = QuantizedModelForTextEncoding.from_pretrained(model_8bit_path)
+
+                # Access the underlying wrapped model.
+                # We access the wrapped model, even though it is private, because it simplifies the type checking by
+                # always returning a T5EncoderModel from this function.
+                model = q_model._wrapped
+            else:
+                # The quantized model does not exist yet, quantize and save it.
+                # TODO(ryand): dtype?
+                model = T5EncoderModel.from_pretrained(path, local_files_only=True)
+                assert isinstance(model, T5EncoderModel)
+
+                q_model = QuantizedModelForTextEncoding.quantize(model, weights=qfloat8)
+
+                model_8bit_path.mkdir(parents=True, exist_ok=True)
+                q_model.save_pretrained(model_8bit_path)
+
+                # (See earlier comment about accessing the wrapped model.)
+                model = q_model._wrapped
+        else:
+            model = T5EncoderModel.from_pretrained(path, local_files_only=True)
+
+        assert isinstance(model, T5EncoderModel)
+        return model
+
+    def _load_flux_transformer(self, path: Path) -> FluxTransformer2DModel:
+        if self.use_8bit:
+            model_8bit_path = path / "quantized"
+            if model_8bit_path.exists():
+                # The quantized model exists, load it.
+                # TODO(ryand): The requantize(...) operation in from_pretrained(...) is very slow. This seems like
+                # something that we should be able to make much faster.
+                q_model = QuantizedFluxTransformer2DModel.from_pretrained(model_8bit_path)
+
+                # Access the underlying wrapped model.
+                # We access the wrapped model, even though it is private, because it simplifies the type checking by
+                # always returning a FluxTransformer2DModel from this function.
+                model = q_model._wrapped
+            else:
+                # The quantized model does not exist yet, quantize and save it.
+                # TODO(ryand): Loading in float16 and then quantizing seems to result in NaNs. In order to run this on
+                # GPUs that don't support bfloat16, we would need to host the quantized model instead of generating it
+                # here.
+                model = FluxTransformer2DModel.from_pretrained(path, local_files_only=True, torch_dtype=torch.bfloat16)
+                assert isinstance(model, FluxTransformer2DModel)
+
+                q_model = QuantizedFluxTransformer2DModel.quantize(model, weights=qfloat8)
+
+                model_8bit_path.mkdir(parents=True, exist_ok=True)
+                q_model.save_pretrained(model_8bit_path)
+
+                # (See earlier comment about accessing the wrapped model.)
+                model = q_model._wrapped
+        else:
+            model = FluxTransformer2DModel.from_pretrained(path, local_files_only=True, torch_dtype=torch.bfloat16)
+
+        assert isinstance(model, FluxTransformer2DModel)
+        return model
+
+    @staticmethod
+    def _load_flux_vae(path: Path) -> AutoencoderKL:
+        model = AutoencoderKL.from_pretrained(path, local_files_only=True)
+        assert isinstance(model, AutoencoderKL)
+        return model

invokeai/app/invocations/image.py

@@ -6,19 +6,13 @@ import cv2
 import numpy
 from PIL import Image, ImageChops, ImageFilter, ImageOps
 
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    Classification,
-    invocation,
-    invocation_output,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.constants import IMAGE_MODES
 from invokeai.app.invocations.fields import (
     ColorField,
     FieldDescriptions,
     ImageField,
     InputField,
-    OutputField,
     WithBoard,
     WithMetadata,
 )
@@ -1013,62 +1007,3 @@ class MaskFromIDInvocation(BaseInvocation, WithMetadata, WithBoard):
         image_dto = context.images.save(image=mask, image_category=ImageCategory.MASK)
 
         return ImageOutput.build(image_dto)
-
-
-@invocation_output("canvas_v2_mask_and_crop_output")
-class CanvasV2MaskAndCropOutput(ImageOutput):
-    offset_x: int = OutputField(description="The x offset of the image, after cropping")
-    offset_y: int = OutputField(description="The y offset of the image, after cropping")
-
-
-@invocation(
-    "canvas_v2_mask_and_crop",
-    title="Canvas V2 Mask and Crop",
-    tags=["image", "mask", "id"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class CanvasV2MaskAndCropInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Handles Canvas V2 image output masking and cropping"""
-
-    source_image: ImageField | None = InputField(
-        default=None,
-        description="The source image onto which the masked generated image is pasted. If omitted, the masked generated image is returned with transparency.",
-    )
-    generated_image: ImageField = InputField(description="The image to apply the mask to")
-    mask: ImageField = InputField(description="The mask to apply")
-    mask_blur: int = InputField(default=0, ge=0, description="The amount to blur the mask by")
-
-    def _prepare_mask(self, mask: Image.Image) -> Image.Image:
-        mask_array = numpy.array(mask)
-        kernel = numpy.ones((self.mask_blur, self.mask_blur), numpy.uint8)
-        dilated_mask_array = cv2.erode(mask_array, kernel, iterations=3)
-        dilated_mask = Image.fromarray(dilated_mask_array)
-        if self.mask_blur > 0:
-            mask = dilated_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
-        return ImageOps.invert(mask.convert("L"))
-
-    def invoke(self, context: InvocationContext) -> CanvasV2MaskAndCropOutput:
-        mask = self._prepare_mask(context.images.get_pil(self.mask.image_name))
-
-        if self.source_image:
-            generated_image = context.images.get_pil(self.generated_image.image_name)
-            source_image = context.images.get_pil(self.source_image.image_name)
-            source_image.paste(generated_image, (0, 0), mask)
-            image_dto = context.images.save(image=source_image)
-        else:
-            generated_image = context.images.get_pil(self.generated_image.image_name)
-            generated_image.putalpha(mask)
-            image_dto = context.images.save(image=generated_image)
-
-        # bbox = image.getbbox()
-        # image = image.crop(bbox)
-
-        return CanvasV2MaskAndCropOutput(
-            image=ImageField(image_name=image_dto.image_name),
-            offset_x=0,
-            offset_y=0,
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/model.py

@@ -1,5 +1,5 @@
 import copy
-from typing import List, Literal, Optional
+from typing import List, Optional
 
 from pydantic import BaseModel, Field
 
@@ -13,14 +13,7 @@ from invokeai.app.invocations.baseinvocation import (
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.flux.util import max_seq_lengths
-from invokeai.backend.model_manager.config import (
-    AnyModelConfig,
-    BaseModelType,
-    CheckpointConfigBase,
-    ModelType,
-    SubModelType,
-)
+from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType, SubModelType
 
 
 class ModelIdentifierField(BaseModel):
@@ -67,15 +60,6 @@ class CLIPField(BaseModel):
     loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
-class TransformerField(BaseModel):
-    transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
-
-
-class T5EncoderField(BaseModel):
-    tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
-    text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
-
-
 class VAEField(BaseModel):
     vae: ModelIdentifierField = Field(description="Info to load vae submodel")
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
@@ -138,112 +122,6 @@ class ModelIdentifierInvocation(BaseInvocation):
         return ModelIdentifierOutput(model=self.model)
 
 
-@invocation_output("flux_model_loader_output")
-class FluxModelLoaderOutput(BaseInvocationOutput):
-    """Flux base model loader output"""
-
-    transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
-    clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
-    t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
-    vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
-    max_seq_len: Literal[256, 512] = OutputField(
-        description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
-        title="Max Seq Length",
-    )
-
-
-@invocation(
-    "flux_model_loader",
-    title="Flux Main Model",
-    tags=["model", "flux"],
-    category="model",
-    version="1.0.3",
-    classification=Classification.Prototype,
-)
-class FluxModelLoaderInvocation(BaseInvocation):
-    """Loads a flux base model, outputting its submodels."""
-
-    model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.flux_model,
-        ui_type=UIType.FluxMainModel,
-        input=Input.Direct,
-    )
-
-    t5_encoder: ModelIdentifierField = InputField(
-        description=FieldDescriptions.t5_encoder,
-        ui_type=UIType.T5EncoderModel,
-        input=Input.Direct,
-    )
-
-    def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
-        model_key = self.model.key
-
-        if not context.models.exists(model_key):
-            raise ValueError(f"Unknown model: {model_key}")
-        transformer = self._get_model(context, SubModelType.Transformer)
-        tokenizer = self._get_model(context, SubModelType.Tokenizer)
-        tokenizer2 = self._get_model(context, SubModelType.Tokenizer2)
-        clip_encoder = self._get_model(context, SubModelType.TextEncoder)
-        t5_encoder = self._get_model(context, SubModelType.TextEncoder2)
-        vae = self._get_model(context, SubModelType.VAE)
-        transformer_config = context.models.get_config(transformer)
-        assert isinstance(transformer_config, CheckpointConfigBase)
-
-        return FluxModelLoaderOutput(
-            transformer=TransformerField(transformer=transformer),
-            clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
-            vae=VAEField(vae=vae),
-            max_seq_len=max_seq_lengths[transformer_config.config_path],
-        )
-
-    def _get_model(self, context: InvocationContext, submodel: SubModelType) -> ModelIdentifierField:
-        match submodel:
-            case SubModelType.Transformer:
-                return self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
-            case SubModelType.VAE:
-                return self._pull_model_from_mm(
-                    context,
-                    SubModelType.VAE,
-                    "FLUX.1-schnell_ae",
-                    ModelType.VAE,
-                    BaseModelType.Flux,
-                )
-            case submodel if submodel in [SubModelType.Tokenizer, SubModelType.TextEncoder]:
-                return self._pull_model_from_mm(
-                    context,
-                    submodel,
-                    "clip-vit-large-patch14",
-                    ModelType.CLIPEmbed,
-                    BaseModelType.Any,
-                )
-            case submodel if submodel in [SubModelType.Tokenizer2, SubModelType.TextEncoder2]:
-                return self._pull_model_from_mm(
-                    context,
-                    submodel,
-                    self.t5_encoder.name,
-                    ModelType.T5Encoder,
-                    BaseModelType.Any,
-                )
-            case _:
-                raise Exception(f"{submodel.value} is not a supported submodule for a flux model")
-
-    def _pull_model_from_mm(
-        self,
-        context: InvocationContext,
-        submodel: SubModelType,
-        name: str,
-        type: ModelType,
-        base: BaseModelType,
-    ):
-        if models := context.models.search_by_attrs(name=name, base=base, type=type):
-            if len(models) != 1:
-                raise Exception(f"Multiple models detected for selected model with name {name}")
-            return ModelIdentifierField.from_config(models[0]).model_copy(update={"submodel_type": submodel})
-        else:
-            raise ValueError(f"Please install the {base}:{type} model named {name} via starter models")
-
-
 @invocation(
     "main_model_loader",
     title="Main Model",

invokeai/app/invocations/primitives.py

@@ -12,7 +12,6 @@ from invokeai.app.invocations.fields import (
     ConditioningField,
     DenoiseMaskField,
     FieldDescriptions,
-    FluxConditioningField,
     ImageField,
     Input,
     InputField,
@@ -415,17 +414,6 @@ class MaskOutput(BaseInvocationOutput):
     height: int = OutputField(description="The height of the mask in pixels.")
 
 
-@invocation_output("flux_conditioning_output")
-class FluxConditioningOutput(BaseInvocationOutput):
-    """Base class for nodes that output a single conditioning tensor"""
-
-    conditioning: FluxConditioningField = OutputField(description=FieldDescriptions.cond)
-
-    @classmethod
-    def build(cls, conditioning_name: str) -> "FluxConditioningOutput":
-        return cls(conditioning=FluxConditioningField(conditioning_name=conditioning_name))
-
-
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""

invokeai/app/services/config/config_default.py

@@ -91,7 +91,6 @@ class InvokeAIAppConfig(BaseSettings):
         db_dir: Path to InvokeAI databases directory.
         outputs_dir: Path to directory for outputs.
         custom_nodes_dir: Path to directory for custom nodes.
-        style_presets_dir: Path to directory for style presets.
         log_handlers: Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".
         log_format: Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.<br>Valid values: `plain`, `color`, `syslog`, `legacy`
         log_level: Emit logging messages at this level or higher.<br>Valid values: `debug`, `info`, `warning`, `error`, `critical`
@@ -154,7 +153,6 @@ class InvokeAIAppConfig(BaseSettings):
     db_dir:                        Path = Field(default=Path("databases"),  description="Path to InvokeAI databases directory.")
     outputs_dir:                   Path = Field(default=Path("outputs"),    description="Path to directory for outputs.")
     custom_nodes_dir:              Path = Field(default=Path("nodes"),      description="Path to directory for custom nodes.")
-    style_presets_dir:      Path = Field(default=Path("style_presets"),      description="Path to directory for style presets.")
 
     # LOGGING
     log_handlers:             list[str] = Field(default=["console"],        description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".')
@@ -302,11 +300,6 @@ class InvokeAIAppConfig(BaseSettings):
         """Path to the models directory, resolved to an absolute path.."""
         return self._resolve(self.models_dir)
 
-    @property
-    def style_presets_path(self) -> Path:
-        """Path to the style presets directory, resolved to an absolute path.."""
-        return self._resolve(self.style_presets_dir)
-
     @property
     def convert_cache_path(self) -> Path:
         """Path to the converted cache models directory, resolved to an absolute path.."""

invokeai/app/services/events/events_common.py

@@ -88,7 +88,6 @@ class QueueItemEventBase(QueueEventBase):
 
     item_id: int = Field(description="The ID of the queue item")
     batch_id: str = Field(description="The ID of the queue batch")
-    origin: str | None = Field(default=None, description="The origin of the batch")
 
 
 class InvocationEventBase(QueueItemEventBase):
@@ -96,6 +95,8 @@ class InvocationEventBase(QueueItemEventBase):
 
     session_id: str = Field(description="The ID of the session (aka graph execution state)")
     queue_id: str = Field(description="The ID of the queue")
+    item_id: int = Field(description="The ID of the queue item")
+    batch_id: str = Field(description="The ID of the queue batch")
     session_id: str = Field(description="The ID of the session (aka graph execution state)")
     invocation: AnyInvocation = Field(description="The ID of the invocation")
     invocation_source_id: str = Field(description="The ID of the prepared invocation's source node")
@@ -113,7 +114,6 @@ class InvocationStartedEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -147,7 +147,6 @@ class InvocationDenoiseProgressEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -185,7 +184,6 @@ class InvocationCompleteEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -218,7 +216,6 @@ class InvocationErrorEvent(InvocationEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             invocation=invocation,
             invocation_source_id=queue_item.session.prepared_source_mapping[invocation.id],
@@ -256,7 +253,6 @@ class QueueItemStatusChangedEvent(QueueItemEventBase):
             queue_id=queue_item.queue_id,
             item_id=queue_item.item_id,
             batch_id=queue_item.batch_id,
-            origin=queue_item.origin,
             session_id=queue_item.session_id,
             status=queue_item.status,
             error_type=queue_item.error_type,
@@ -283,14 +279,12 @@ class BatchEnqueuedEvent(QueueEventBase):
         description="The number of invocations initially requested to be enqueued (may be less than enqueued if queue was full)"
     )
     priority: int = Field(description="The priority of the batch")
-    origin: str | None = Field(default=None, description="The origin of the batch")
 
     @classmethod
     def build(cls, enqueue_result: EnqueueBatchResult) -> "BatchEnqueuedEvent":
         return cls(
             queue_id=enqueue_result.queue_id,
             batch_id=enqueue_result.batch.batch_id,
-            origin=enqueue_result.batch.origin,
             enqueued=enqueue_result.enqueued,
             requested=enqueue_result.requested,
             priority=enqueue_result.priority,

invokeai/app/services/events/events_fastapievents.py

@@ -1,44 +1,46 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
 import asyncio
 import threading
+from queue import Empty, Queue
 
 from fastapi_events.dispatcher import dispatch
 
 from invokeai.app.services.events.events_base import EventServiceBase
-from invokeai.app.services.events.events_common import EventBase
+from invokeai.app.services.events.events_common import (
+    EventBase,
+)
 
 
 class FastAPIEventService(EventServiceBase):
-    def __init__(self, event_handler_id: int, loop: asyncio.AbstractEventLoop) -> None:
+    def __init__(self, event_handler_id: int) -> None:
         self.event_handler_id = event_handler_id
-        self._queue = asyncio.Queue[EventBase | None]()
+        self._queue = Queue[EventBase | None]()
         self._stop_event = threading.Event()
-        self._loop = loop
-
-        # We need to store a reference to the task so it doesn't get GC'd
-        # See: https://docs.python.org/3/library/asyncio-task.html#creating-tasks
-        self._background_tasks: set[asyncio.Task[None]] = set()
-        task = self._loop.create_task(self._dispatch_from_queue(stop_event=self._stop_event))
-        self._background_tasks.add(task)
-        task.add_done_callback(self._background_tasks.remove)
+        asyncio.create_task(self._dispatch_from_queue(stop_event=self._stop_event))
 
         super().__init__()
 
     def stop(self, *args, **kwargs):
         self._stop_event.set()
-        self._loop.call_soon_threadsafe(self._queue.put_nowait, None)
+        self._queue.put(None)
 
     def dispatch(self, event: EventBase) -> None:
-        self._loop.call_soon_threadsafe(self._queue.put_nowait, event)
+        self._queue.put(event)
 
     async def _dispatch_from_queue(self, stop_event: threading.Event):
         """Get events on from the queue and dispatch them, from the correct thread"""
         while not stop_event.is_set():
             try:
-                event = await self._queue.get()
+                event = self._queue.get(block=False)
                 if not event:  # Probably stopping
                     continue
                 # Leave the payloads as live pydantic models
                 dispatch(event, middleware_id=self.event_handler_id, payload_schema_dump=False)
 
+            except Empty:
+                await asyncio.sleep(0.1)
+                pass
+
             except asyncio.CancelledError as e:
                 raise e  # Raise a proper error

invokeai/app/services/invocation_services.py

@@ -4,8 +4,6 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 from invokeai.app.services.object_serializer.object_serializer_base import ObjectSerializerBase
-from invokeai.app.services.style_preset_images.style_preset_images_base import StylePresetImageFileStorageBase
-from invokeai.app.services.style_preset_records.style_preset_records_base import StylePresetRecordsStorageBase
 
 if TYPE_CHECKING:
     from logging import Logger
@@ -63,8 +61,6 @@ class InvocationServices:
         workflow_records: "WorkflowRecordsStorageBase",
         tensors: "ObjectSerializerBase[torch.Tensor]",
         conditioning: "ObjectSerializerBase[ConditioningFieldData]",
-        style_preset_records: "StylePresetRecordsStorageBase",
-        style_preset_image_files: "StylePresetImageFileStorageBase",
     ):
         self.board_images = board_images
         self.board_image_records = board_image_records
@@ -89,5 +85,3 @@ class InvocationServices:
         self.workflow_records = workflow_records
         self.tensors = tensors
         self.conditioning = conditioning
-        self.style_preset_records = style_preset_records
-        self.style_preset_image_files = style_preset_image_files

invokeai/app/services/model_install/model_install_default.py

@@ -783,9 +783,8 @@ class ModelInstallService(ModelInstallServiceBase):
         # So what we do is to synthesize a folder named "sdxl-turbo_vae" here.
         if subfolder:
             top = Path(remote_files[0].path.parts[0])  # e.g. "sdxl-turbo/"
-            path_to_remove = top / subfolder  # sdxl-turbo/vae/
-            subfolder_rename = subfolder.name.replace("/", "_").replace("\\", "_")
-            path_to_add = Path(f"{top}_{subfolder_rename}")
+            path_to_remove = top / subfolder.parts[-1]  # sdxl-turbo/vae/
+            path_to_add = Path(f"{top}_{subfolder}")
         else:
             path_to_remove = Path(".")
             path_to_add = Path(".")

invokeai/app/services/model_records/model_records_base.py

@@ -77,7 +77,6 @@ class ModelRecordChanges(BaseModelExcludeNull):
     type: Optional[ModelType] = Field(description="Type of model", default=None)
     key: Optional[str] = Field(description="Database ID for this model", default=None)
     hash: Optional[str] = Field(description="hash of model file", default=None)
-    format: Optional[str] = Field(description="format of model file", default=None)
     trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
     default_settings: Optional[MainModelDefaultSettings | ControlAdapterDefaultSettings] = Field(
         description="Default settings for this model", default=None

invokeai/app/services/session_queue/session_queue_base.py

@@ -6,7 +6,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -96,11 +95,6 @@ class SessionQueueBase(ABC):
         """Cancels all queue items with matching batch IDs"""
         pass
 
-    @abstractmethod
-    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
-        """Cancels all queue items with the given batch origin"""
-        pass
-
     @abstractmethod
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         """Cancels all queue items with matching queue ID"""

invokeai/app/services/session_queue/session_queue_common.py

@@ -77,7 +77,6 @@ BatchDataCollection: TypeAlias = list[list[BatchDatum]]
 
 class Batch(BaseModel):
     batch_id: str = Field(default_factory=uuid_string, description="The ID of the batch")
-    origin: str | None = Field(default=None, description="The origin of this batch.")
     data: Optional[BatchDataCollection] = Field(default=None, description="The batch data collection.")
     graph: Graph = Field(description="The graph to initialize the session with")
     workflow: Optional[WorkflowWithoutID] = Field(
@@ -196,7 +195,6 @@ class SessionQueueItemWithoutGraph(BaseModel):
     status: QUEUE_ITEM_STATUS = Field(default="pending", description="The status of this queue item")
     priority: int = Field(default=0, description="The priority of this queue item")
     batch_id: str = Field(description="The ID of the batch associated with this queue item")
-    origin: str | None = Field(default=None, description="The origin of this queue item. ")
     session_id: str = Field(
         description="The ID of the session associated with this queue item. The session doesn't exist in graph_executions until the queue item is executed."
     )
@@ -296,7 +294,6 @@ class SessionQueueStatus(BaseModel):
 class BatchStatus(BaseModel):
     queue_id: str = Field(..., description="The ID of the queue")
     batch_id: str = Field(..., description="The ID of the batch")
-    origin: str | None = Field(..., description="The origin of the batch")
     pending: int = Field(..., description="Number of queue items with status 'pending'")
     in_progress: int = Field(..., description="Number of queue items with status 'in_progress'")
     completed: int = Field(..., description="Number of queue items with status 'complete'")
@@ -331,12 +328,6 @@ class CancelByBatchIDsResult(BaseModel):
     canceled: int = Field(..., description="Number of queue items canceled")
 
 
-class CancelByOriginResult(BaseModel):
-    """Result of canceling by list of batch ids"""
-
-    canceled: int = Field(..., description="Number of queue items canceled")
-
-
 class CancelByQueueIDResult(CancelByBatchIDsResult):
     """Result of canceling by queue id"""
 
@@ -442,7 +433,6 @@ class SessionQueueValueToInsert(NamedTuple):
     field_values: Optional[str]  # field_values json
     priority: int  # priority
     workflow: Optional[str]  # workflow json
-    origin: str | None
 
 
 ValuesToInsert: TypeAlias = list[SessionQueueValueToInsert]
@@ -463,7 +453,6 @@ def prepare_values_to_insert(queue_id: str, batch: Batch, priority: int, max_new
                 json.dumps(field_values, default=to_jsonable_python) if field_values else None,  # field_values (json)
                 priority,  # priority
                 json.dumps(workflow, default=to_jsonable_python) if workflow else None,  # workflow (json)
-                batch.origin,  # origin
             )
         )
     return values_to_insert

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -10,7 +10,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     Batch,
     BatchStatus,
     CancelByBatchIDsResult,
-    CancelByOriginResult,
     CancelByQueueIDResult,
     ClearResult,
     EnqueueBatchResult,
@@ -128,8 +127,8 @@ class SqliteSessionQueue(SessionQueueBase):
 
             self.__cursor.executemany(
                 """--sql
-                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin)
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow)
+                VALUES (?, ?, ?, ?, ?, ?, ?)
                 """,
                 values_to_insert,
             )
@@ -418,7 +417,11 @@ class SqliteSessionQueue(SessionQueueBase):
             )
             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")
+                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
@@ -426,46 +429,6 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.release()
         return CancelByBatchIDsResult(canceled=count)
 
-    def cancel_by_origin(self, queue_id: str, origin: str) -> CancelByOriginResult:
-        try:
-            current_queue_item = self.get_current(queue_id)
-            self.__lock.acquire()
-            where = """--sql
-                WHERE
-                  queue_id == ?
-                  AND origin == ?
-                  AND status != 'canceled'
-                  AND status != 'completed'
-                  AND status != 'failed'
-                """
-            params = (queue_id, origin)
-            self.__cursor.execute(
-                f"""--sql
-                SELECT COUNT(*)
-                FROM session_queue
-                {where};
-                """,
-                params,
-            )
-            count = self.__cursor.fetchone()[0]
-            self.__cursor.execute(
-                f"""--sql
-                UPDATE session_queue
-                SET status = 'canceled'
-                {where};
-                """,
-                params,
-            )
-            self.__conn.commit()
-            if current_queue_item is not None and current_queue_item.origin == origin:
-                self._set_queue_item_status(current_queue_item.item_id, "canceled")
-        except Exception:
-            self.__conn.rollback()
-            raise
-        finally:
-            self.__lock.release()
-        return CancelByOriginResult(canceled=count)
-
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         try:
             current_queue_item = self.get_current(queue_id)
@@ -578,8 +541,7 @@ class SqliteSessionQueue(SessionQueueBase):
                     started_at,
                     session_id,
                     batch_id,
-                    queue_id,
-                    origin
+                    queue_id
                 FROM session_queue
                 WHERE queue_id = ?
             """
@@ -659,7 +621,7 @@ class SqliteSessionQueue(SessionQueueBase):
             self.__lock.acquire()
             self.__cursor.execute(
                 """--sql
-                SELECT status, count(*), origin
+                SELECT status, count(*)
                 FROM session_queue
                 WHERE
                   queue_id = ?
@@ -671,7 +633,6 @@ class SqliteSessionQueue(SessionQueueBase):
             result = cast(list[sqlite3.Row], self.__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
         except Exception:
             self.__conn.rollback()
             raise
@@ -680,7 +641,6 @@ class SqliteSessionQueue(SessionQueueBase):
 
         return BatchStatus(
             batch_id=batch_id,
-            origin=origin,
             queue_id=queue_id,
             pending=counts.get("pending", 0),
             in_progress=counts.get("in_progress", 0),

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -16,8 +16,6 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_10 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_11 import build_migration_11
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_12 import build_migration_12
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_13 import build_migration_13
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_14 import build_migration_14
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_15 import build_migration_15
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -51,8 +49,6 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_11(app_config=config, logger=logger))
     migrator.register_migration(build_migration_12(app_config=config))
     migrator.register_migration(build_migration_13())
-    migrator.register_migration(build_migration_14())
-    migrator.register_migration(build_migration_15())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_14.py

@@ -1,61 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration14Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._create_style_presets(cursor)
-
-    def _create_style_presets(self, cursor: sqlite3.Cursor) -> None:
-        """Create the table used to store style presets."""
-        tables = [
-            """--sql
-            CREATE TABLE IF NOT EXISTS style_presets (
-                id TEXT NOT NULL PRIMARY KEY,
-                name TEXT NOT NULL,
-                preset_data TEXT NOT NULL,
-                type TEXT NOT NULL DEFAULT "user",
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW'))
-            );
-            """
-        ]
-
-        # Add trigger for `updated_at`.
-        triggers = [
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS style_presets
-            AFTER UPDATE
-            ON style_presets FOR EACH ROW
-            BEGIN
-                UPDATE style_presets SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                    WHERE id = old.id;
-            END;
-            """
-        ]
-
-        # Add indexes for searchable fields
-        indices = [
-            "CREATE INDEX IF NOT EXISTS idx_style_presets_name ON style_presets(name);",
-        ]
-
-        for stmt in tables + indices + triggers:
-            cursor.execute(stmt)
-
-
-def build_migration_14() -> Migration:
-    """
-    Build the migration from database version 13 to 14..
-
-    This migration does the following:
-    - Create the table used to store style presets.
-    """
-    migration_14 = Migration(
-        from_version=13,
-        to_version=14,
-        callback=Migration14Callback(),
-    )
-
-    return migration_14

invokeai/app/services/shared/sqlite_migrator/migrations/migration_15.py

@@ -1,31 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration15Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._add_origin_col(cursor)
-
-    def _add_origin_col(self, cursor: sqlite3.Cursor) -> None:
-        """
-        - Adds `origin` column to the session queue table.
-        """
-
-        cursor.execute("ALTER TABLE session_queue ADD COLUMN origin TEXT;")
-
-
-def build_migration_15() -> Migration:
-    """
-    Build the migration from database version 14 to 15.
-
-    This migration does the following:
-        - Adds `origin` column to the session queue table.
-    """
-    migration_15 = Migration(
-        from_version=14,
-        to_version=15,
-        callback=Migration15Callback(),
-    )
-
-    return migration_15

invokeai/app/services/style_preset_images/style_preset_images_base.py

@@ -1,33 +0,0 @@
-from abc import ABC, abstractmethod
-from pathlib import Path
-
-from PIL.Image import Image as PILImageType
-
-
-class StylePresetImageFileStorageBase(ABC):
-    """Low-level service responsible for storing and retrieving image files."""
-
-    @abstractmethod
-    def get(self, style_preset_id: str) -> PILImageType:
-        """Retrieves a style preset image as PIL Image."""
-        pass
-
-    @abstractmethod
-    def get_path(self, style_preset_id: str) -> Path:
-        """Gets the internal path to a style preset image."""
-        pass
-
-    @abstractmethod
-    def get_url(self, style_preset_id: str) -> str | None:
-        """Gets the URL to fetch a style preset image."""
-        pass
-
-    @abstractmethod
-    def save(self, style_preset_id: str, image: PILImageType) -> None:
-        """Saves a style preset image."""
-        pass
-
-    @abstractmethod
-    def delete(self, style_preset_id: str) -> None:
-        """Deletes a style preset image."""
-        pass

invokeai/app/services/style_preset_images/style_preset_images_common.py

@@ -1,19 +0,0 @@
-class StylePresetImageFileNotFoundException(Exception):
-    """Raised when an image file is not found in storage."""
-
-    def __init__(self, message: str = "Style preset image file not found"):
-        super().__init__(message)
-
-
-class StylePresetImageFileSaveException(Exception):
-    """Raised when an image cannot be saved."""
-
-    def __init__(self, message: str = "Style preset image file not saved"):
-        super().__init__(message)
-
-
-class StylePresetImageFileDeleteException(Exception):
-    """Raised when an image cannot be deleted."""
-
-    def __init__(self, message: str = "Style preset image file not deleted"):
-        super().__init__(message)

invokeai/app/services/style_preset_images/style_preset_images_disk.py

@@ -1,88 +0,0 @@
-from pathlib import Path
-
-from PIL import Image
-from PIL.Image import Image as PILImageType
-
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.style_preset_images.style_preset_images_base import StylePresetImageFileStorageBase
-from invokeai.app.services.style_preset_images.style_preset_images_common import (
-    StylePresetImageFileDeleteException,
-    StylePresetImageFileNotFoundException,
-    StylePresetImageFileSaveException,
-)
-from invokeai.app.services.style_preset_records.style_preset_records_common import PresetType
-from invokeai.app.util.misc import uuid_string
-from invokeai.app.util.thumbnails import make_thumbnail
-
-
-class StylePresetImageFileStorageDisk(StylePresetImageFileStorageBase):
-    """Stores images on disk"""
-
-    def __init__(self, style_preset_images_folder: Path):
-        self._style_preset_images_folder = style_preset_images_folder
-        self._validate_storage_folders()
-
-    def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-
-    def get(self, style_preset_id: str) -> PILImageType:
-        try:
-            path = self.get_path(style_preset_id)
-
-            return Image.open(path)
-        except FileNotFoundError as e:
-            raise StylePresetImageFileNotFoundException from e
-
-    def save(self, style_preset_id: str, image: PILImageType) -> None:
-        try:
-            self._validate_storage_folders()
-            image_path = self._style_preset_images_folder / (style_preset_id + ".webp")
-            thumbnail = make_thumbnail(image, 256)
-            thumbnail.save(image_path, format="webp")
-
-        except Exception as e:
-            raise StylePresetImageFileSaveException from e
-
-    def get_path(self, style_preset_id: str) -> Path:
-        style_preset = self._invoker.services.style_preset_records.get(style_preset_id)
-        if style_preset.type is PresetType.Default:
-            default_images_dir = Path(__file__).parent / Path("default_style_preset_images")
-            path = default_images_dir / (style_preset.name + ".png")
-        else:
-            path = self._style_preset_images_folder / (style_preset_id + ".webp")
-
-        return path
-
-    def get_url(self, style_preset_id: str) -> str | None:
-        path = self.get_path(style_preset_id)
-        if not self._validate_path(path):
-            return
-
-        url = self._invoker.services.urls.get_style_preset_image_url(style_preset_id)
-
-        # The image URL never changes, so we must add random query string to it to prevent caching
-        url += f"?{uuid_string()}"
-
-        return url
-
-    def delete(self, style_preset_id: str) -> None:
-        try:
-            path = self.get_path(style_preset_id)
-
-            if not self._validate_path(path):
-                raise StylePresetImageFileNotFoundException
-
-            path.unlink()
-
-        except StylePresetImageFileNotFoundException as e:
-            raise StylePresetImageFileNotFoundException from e
-        except Exception as e:
-            raise StylePresetImageFileDeleteException from e
-
-    def _validate_path(self, path: Path) -> bool:
-        """Validates the path given for an image."""
-        return path.exists()
-
-    def _validate_storage_folders(self) -> None:
-        """Checks if the required folders exist and create them if they don't"""
-        self._style_preset_images_folder.mkdir(parents=True, exist_ok=True)

invokeai/app/services/style_preset_records/default_style_presets.json

@@ -1,146 +0,0 @@
-[
-  {
-    "name": "Photography (General)",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt}. photography. f/2.8 macro photo, bokeh, photorealism",
-      "negative_prompt": "painting, digital art. sketch, blurry"
-    }
-  },
-  {
-    "name": "Photography (Studio Lighting)",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt}, photography. f/8 photo. centered subject, studio lighting.",
-      "negative_prompt": "painting, digital art. sketch, blurry"
-    }
-  },
-  {
-    "name": "Photography (Landscape)",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt}, landscape photograph, f/12, lifelike,  highly detailed.",
-      "negative_prompt": "painting, digital art. sketch, blurry"
-    }
-  },
-  {
-    "name": "Photography (Portrait)",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt}. photography. portraiture. catch light in eyes. one flash. rembrandt lighting. Soft box. dark shadows. High contrast. 80mm lens. F2.8.",
-      "negative_prompt": "painting, digital art. sketch, blurry"
-    }
-  },
-  {
-    "name": "Photography (Black and White)",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt} photography. natural light. 80mm lens. F1.4. strong contrast, hard light. dark contrast. blurred background. black and white",
-      "negative_prompt": "painting, digital art. sketch, colour+"
-    }
-  },
-  {
-    "name": "Architectural Visualization",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt}. architectural photography, f/12, luxury, aesthetically pleasing form and function.",
-      "negative_prompt": "painting, digital art. sketch, blurry"
-    }
-  },
-  {
-    "name": "Concept Art (Fantasy)",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "concept artwork of a {prompt}. (digital painterly art style)++, mythological, (textured 2d dry media brushpack)++, glazed brushstrokes, otherworldly. painting+, illustration+",
-      "negative_prompt": "photo. distorted, blurry, out of focus. sketch. (cgi, 3d.)++"
-    }
-  },
-  {
-    "name": "Concept Art (Sci-Fi)",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "(concept art)++, {prompt}, (sleek futurism)++, (textured 2d dry media)++, metallic highlights, digital painting style",
-      "negative_prompt": "photo. distorted, blurry, out of focus. sketch. (cgi, 3d.)++"
-    }
-  },
-  {
-    "name": "Concept Art (Character)",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "(character concept art)++, stylized painterly digital painting of {prompt}, (painterly, impasto. Dry brush.)++",
-      "negative_prompt": "photo. distorted, blurry, out of focus. sketch. (cgi, 3d.)++"
-    }
-  },
-  {
-    "name": "Concept Art (Painterly)",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt} oil painting. high contrast. impasto. sfumato. chiaroscuro. Palette knife.",
-      "negative_prompt": "photo. smooth. border. frame"
-    }
-  },
-  {
-    "name": "Environment Art",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt} environment artwork, hyper-realistic digital painting style with cinematic composition, atmospheric, depth and detail, voluminous. textured dry brush 2d media",
-      "negative_prompt": "photo, distorted, blurry, out of focus. sketch."
-    }
-  },
-  {
-    "name": "Interior Design (Visualization)",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt} interior design photo, gentle shadows, light mid-tones, dimension, mix of smooth and textured surfaces, focus on negative space and clean lines, focus",
-      "negative_prompt": "photo, distorted. sketch."
-    }
-  },
-  {
-    "name": "Product Rendering",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt} high quality product photography, 3d rendering with key lighting, shallow depth of field, simple plain background, studio lighting.",
-      "negative_prompt": "blurry, sketch, messy, dirty. unfinished."
-    }
-  },
-  {
-    "name": "Sketch",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt} black and white pencil drawing, off-center composition, cross-hatching for shadows, bold strokes, textured paper. sketch+++",
-      "negative_prompt": "blurry, photo, painting, color. messy, dirty. unfinished. frame, borders."
-    }
-  },
-  {
-    "name": "Line Art",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt} Line art. bold outline. simplistic. white background. 2d",
-      "negative_prompt": "photo. digital art. greyscale. solid black. painting"
-    }
-  },
-  {
-    "name": "Anime",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt} anime++, bold outline, cel-shaded coloring, shounen, seinen",
-      "negative_prompt": "(photo)+++. greyscale. solid black. painting"
-    }
-  },
-  {
-    "name": "Illustration",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "{prompt} illustration, bold linework, illustrative details, vector art style, flat coloring",
-      "negative_prompt": "(photo)+++. greyscale. painting, black and white."
-    }
-  },
-  {
-    "name": "Vehicles",
-    "type": "default",
-    "preset_data": {
-      "positive_prompt": "A weird futuristic normal auto, {prompt} elegant design, nice color, nice wheels",
-      "negative_prompt": "sketch. digital art. greyscale. painting"
-    }
-  }
-]

invokeai/app/services/style_preset_records/style_preset_records_base.py

@@ -1,42 +0,0 @@
-from abc import ABC, abstractmethod
-
-from invokeai.app.services.style_preset_records.style_preset_records_common import (
-    PresetType,
-    StylePresetChanges,
-    StylePresetRecordDTO,
-    StylePresetWithoutId,
-)
-
-
-class StylePresetRecordsStorageBase(ABC):
-    """Base class for style preset storage services."""
-
-    @abstractmethod
-    def get(self, style_preset_id: str) -> StylePresetRecordDTO:
-        """Get style preset by id."""
-        pass
-
-    @abstractmethod
-    def create(self, style_preset: StylePresetWithoutId) -> StylePresetRecordDTO:
-        """Creates a style preset."""
-        pass
-
-    @abstractmethod
-    def create_many(self, style_presets: list[StylePresetWithoutId]) -> None:
-        """Creates many style presets."""
-        pass
-
-    @abstractmethod
-    def update(self, style_preset_id: str, changes: StylePresetChanges) -> StylePresetRecordDTO:
-        """Updates a style preset."""
-        pass
-
-    @abstractmethod
-    def delete(self, style_preset_id: str) -> None:
-        """Deletes a style preset."""
-        pass
-
-    @abstractmethod
-    def get_many(self, type: PresetType | None = None) -> list[StylePresetRecordDTO]:
-        """Gets many workflows."""
-        pass

invokeai/app/services/style_preset_records/style_preset_records_common.py

@@ -1,139 +0,0 @@
-import codecs
-import csv
-import json
-from enum import Enum
-from typing import Any, Optional
-
-import pydantic
-from fastapi import UploadFile
-from pydantic import AliasChoices, BaseModel, ConfigDict, Field, TypeAdapter
-
-from invokeai.app.util.metaenum import MetaEnum
-
-
-class StylePresetNotFoundError(Exception):
-    """Raised when a style preset is not found"""
-
-
-class PresetData(BaseModel, extra="forbid"):
-    positive_prompt: str = Field(description="Positive prompt")
-    negative_prompt: str = Field(description="Negative prompt")
-
-
-PresetDataValidator = TypeAdapter(PresetData)
-
-
-class PresetType(str, Enum, metaclass=MetaEnum):
-    User = "user"
-    Default = "default"
-    Project = "project"
-
-
-class StylePresetChanges(BaseModel, extra="forbid"):
-    name: Optional[str] = Field(default=None, description="The style preset's new name.")
-    preset_data: Optional[PresetData] = Field(default=None, description="The updated data for style preset.")
-    type: Optional[PresetType] = Field(description="The updated type of the style preset")
-
-
-class StylePresetWithoutId(BaseModel):
-    name: str = Field(description="The name of the style preset.")
-    preset_data: PresetData = Field(description="The preset data")
-    type: PresetType = Field(description="The type of style preset")
-
-
-class StylePresetRecordDTO(StylePresetWithoutId):
-    id: str = Field(description="The style preset ID.")
-
-    @classmethod
-    def from_dict(cls, data: dict[str, Any]) -> "StylePresetRecordDTO":
-        data["preset_data"] = PresetDataValidator.validate_json(data.get("preset_data", ""))
-        return StylePresetRecordDTOValidator.validate_python(data)
-
-
-StylePresetRecordDTOValidator = TypeAdapter(StylePresetRecordDTO)
-
-
-class StylePresetRecordWithImage(StylePresetRecordDTO):
-    image: Optional[str] = Field(description="The path for image")
-
-
-class StylePresetImportRow(BaseModel):
-    name: str = Field(min_length=1, description="The name of the preset.")
-    positive_prompt: str = Field(
-        default="",
-        description="The positive prompt for the preset.",
-        validation_alias=AliasChoices("positive_prompt", "prompt"),
-    )
-    negative_prompt: str = Field(default="", description="The negative prompt for the preset.")
-
-    model_config = ConfigDict(str_strip_whitespace=True, extra="forbid")
-
-
-StylePresetImportList = list[StylePresetImportRow]
-StylePresetImportListTypeAdapter = TypeAdapter(StylePresetImportList)
-
-
-class UnsupportedFileTypeError(ValueError):
-    """Raised when an unsupported file type is encountered"""
-
-    pass
-
-
-class InvalidPresetImportDataError(ValueError):
-    """Raised when invalid preset import data is encountered"""
-
-    pass
-
-
-async def parse_presets_from_file(file: UploadFile) -> list[StylePresetWithoutId]:
-    """Parses style presets from a file. The file must be a CSV or JSON file.
-
-    If CSV, the file must have the following columns:
-    - name
-    - prompt (or positive_prompt)
-    - negative_prompt
-
-    If JSON, the file must be a list of objects with the following keys:
-    - name
-    - prompt (or positive_prompt)
-    - negative_prompt
-
-    Args:
-        file (UploadFile): The file to parse.
-
-    Returns:
-        list[StylePresetWithoutId]: The parsed style presets.
-
-    Raises:
-        UnsupportedFileTypeError: If the file type is not supported.
-        InvalidPresetImportDataError: If the data in the file is invalid.
-    """
-    if file.content_type not in ["text/csv", "application/json"]:
-        raise UnsupportedFileTypeError()
-
-    if file.content_type == "text/csv":
-        csv_reader = csv.DictReader(codecs.iterdecode(file.file, "utf-8"))
-        data = list(csv_reader)
-    else:  # file.content_type == "application/json":
-        json_data = await file.read()
-        data = json.loads(json_data)
-
-    try:
-        imported_presets = StylePresetImportListTypeAdapter.validate_python(data)
-
-        style_presets: list[StylePresetWithoutId] = []
-
-        for imported in imported_presets:
-            preset_data = PresetData(positive_prompt=imported.positive_prompt, negative_prompt=imported.negative_prompt)
-            style_preset = StylePresetWithoutId(name=imported.name, preset_data=preset_data, type=PresetType.User)
-            style_presets.append(style_preset)
-    except pydantic.ValidationError as e:
-        if file.content_type == "text/csv":
-            msg = "Invalid CSV format: must include columns 'name', 'prompt', and 'negative_prompt' and name cannot be blank"
-        else:  # file.content_type == "application/json":
-            msg = "Invalid JSON format: must be a list of objects with keys 'name', 'prompt', and 'negative_prompt' and name cannot be blank"
-        raise InvalidPresetImportDataError(msg) from e
-    finally:
-        file.file.close()
-
-    return style_presets

invokeai/app/services/style_preset_records/style_preset_records_sqlite.py

@@ -1,215 +0,0 @@
-import json
-from pathlib import Path
-
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
-from invokeai.app.services.style_preset_records.style_preset_records_base import StylePresetRecordsStorageBase
-from invokeai.app.services.style_preset_records.style_preset_records_common import (
-    PresetType,
-    StylePresetChanges,
-    StylePresetNotFoundError,
-    StylePresetRecordDTO,
-    StylePresetWithoutId,
-)
-from invokeai.app.util.misc import uuid_string
-
-
-class SqliteStylePresetRecordsStorage(StylePresetRecordsStorageBase):
-    def __init__(self, db: SqliteDatabase) -> None:
-        super().__init__()
-        self._lock = db.lock
-        self._conn = db.conn
-        self._cursor = self._conn.cursor()
-
-    def start(self, invoker: Invoker) -> None:
-        self._invoker = invoker
-        self._sync_default_style_presets()
-
-    def get(self, style_preset_id: str) -> StylePresetRecordDTO:
-        """Gets a style preset by ID."""
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                SELECT *
-                FROM style_presets
-                WHERE id = ?;
-                """,
-                (style_preset_id,),
-            )
-            row = self._cursor.fetchone()
-            if row is None:
-                raise StylePresetNotFoundError(f"Style preset with id {style_preset_id} not found")
-            return StylePresetRecordDTO.from_dict(dict(row))
-        except Exception:
-            self._conn.rollback()
-            raise
-        finally:
-            self._lock.release()
-
-    def create(self, style_preset: StylePresetWithoutId) -> StylePresetRecordDTO:
-        style_preset_id = uuid_string()
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                INSERT OR IGNORE INTO style_presets (
-                    id,
-                    name,
-                    preset_data,
-                    type
-                )
-                VALUES (?, ?, ?, ?);
-                """,
-                (
-                    style_preset_id,
-                    style_preset.name,
-                    style_preset.preset_data.model_dump_json(),
-                    style_preset.type,
-                ),
-            )
-            self._conn.commit()
-        except Exception:
-            self._conn.rollback()
-            raise
-        finally:
-            self._lock.release()
-        return self.get(style_preset_id)
-
-    def create_many(self, style_presets: list[StylePresetWithoutId]) -> None:
-        style_preset_ids = []
-        try:
-            self._lock.acquire()
-            for style_preset in style_presets:
-                style_preset_id = uuid_string()
-                style_preset_ids.append(style_preset_id)
-                self._cursor.execute(
-                    """--sql
-                    INSERT OR IGNORE INTO style_presets (
-                        id,
-                        name,
-                        preset_data,
-                        type
-                    )
-                    VALUES (?, ?, ?, ?);
-                    """,
-                    (
-                        style_preset_id,
-                        style_preset.name,
-                        style_preset.preset_data.model_dump_json(),
-                        style_preset.type,
-                    ),
-                )
-            self._conn.commit()
-        except Exception:
-            self._conn.rollback()
-            raise
-        finally:
-            self._lock.release()
-
-        return None
-
-    def update(self, style_preset_id: str, changes: StylePresetChanges) -> StylePresetRecordDTO:
-        try:
-            self._lock.acquire()
-            # Change the name of a style preset
-            if changes.name is not None:
-                self._cursor.execute(
-                    """--sql
-                    UPDATE style_presets
-                    SET name = ?
-                    WHERE id = ?;
-                    """,
-                    (changes.name, style_preset_id),
-                )
-
-            # Change the preset data for a style preset
-            if changes.preset_data is not None:
-                self._cursor.execute(
-                    """--sql
-                    UPDATE style_presets
-                    SET preset_data = ?
-                    WHERE id = ?;
-                    """,
-                    (changes.preset_data.model_dump_json(), style_preset_id),
-                )
-
-            self._conn.commit()
-        except Exception:
-            self._conn.rollback()
-            raise
-        finally:
-            self._lock.release()
-        return self.get(style_preset_id)
-
-    def delete(self, style_preset_id: str) -> None:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                DELETE from style_presets
-                WHERE id = ?;
-                """,
-                (style_preset_id,),
-            )
-            self._conn.commit()
-        except Exception:
-            self._conn.rollback()
-            raise
-        finally:
-            self._lock.release()
-        return None
-
-    def get_many(self, type: PresetType | None = None) -> list[StylePresetRecordDTO]:
-        try:
-            self._lock.acquire()
-            main_query = """
-                SELECT
-                    *
-                FROM style_presets
-                """
-
-            if type is not None:
-                main_query += "WHERE type = ? "
-
-            main_query += "ORDER BY LOWER(name) ASC"
-
-            if type is not None:
-                self._cursor.execute(main_query, (type,))
-            else:
-                self._cursor.execute(main_query)
-
-            rows = self._cursor.fetchall()
-            style_presets = [StylePresetRecordDTO.from_dict(dict(row)) for row in rows]
-
-            return style_presets
-        except Exception:
-            self._conn.rollback()
-            raise
-        finally:
-            self._lock.release()
-
-    def _sync_default_style_presets(self) -> None:
-        """Syncs default style presets to the database. Internal use only."""
-
-        # First delete all existing default style presets
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                DELETE FROM style_presets
-                WHERE type = "default";
-                """
-            )
-            self._conn.commit()
-        except Exception:
-            self._conn.rollback()
-            raise
-        finally:
-            self._lock.release()
-        # Next, parse and create the default style presets
-        with self._lock, open(Path(__file__).parent / Path("default_style_presets.json"), "r") as file:
-            presets = json.load(file)
-            for preset in presets:
-                style_preset = StylePresetWithoutId.model_validate(preset)
-                self.create(style_preset)

invokeai/app/services/urls/urls_base.py

@@ -13,8 +13,3 @@ class UrlServiceBase(ABC):
     def get_model_image_url(self, model_key: str) -> str:
         """Gets the URL for a model image"""
         pass
-
-    @abstractmethod
-    def get_style_preset_image_url(self, style_preset_id: str) -> str:
-        """Gets the URL for a style preset image"""
-        pass

invokeai/app/services/urls/urls_default.py

@@ -19,6 +19,3 @@ class LocalUrlService(UrlServiceBase):
 
     def get_model_image_url(self, model_key: str) -> str:
         return f"{self._base_url_v2}/models/i/{model_key}/image"
-
-    def get_style_preset_image_url(self, style_preset_id: str) -> str:
-        return f"{self._base_url}/style_presets/i/{style_preset_id}/image"

invokeai/app/services/workflow_records/default_workflows/Flux Text to Image.json

@@ -1,266 +0,0 @@
-{
-  "name": "FLUX Text to Image",
-  "author": "InvokeAI",
-  "description": "A simple text-to-image workflow using FLUX dev or schnell models. Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
-  "version": "1.0.0",
-  "contact": "",
-  "tags": "text2image, flux",
-  "notes": "Prerequisite model downloads: T5 Encoder, CLIP-L Encoder, and FLUX VAE. Quantized and un-quantized versions can be found in the starter models tab within your Model Manager. We recommend 4 steps for FLUX schnell models and 30 steps for FLUX dev models.",
-  "exposedFields": [
-    {
-      "nodeId": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "fieldName": "model"
-    },
-    {
-      "nodeId": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "fieldName": "prompt"
-    },
-    {
-      "nodeId": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "fieldName": "num_steps"
-    },
-    {
-      "nodeId": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "fieldName": "t5_encoder"
-    }
-  ],
-  "meta": {
-    "version": "3.0.0",
-    "category": "default"
-  },
-  "nodes": [
-    {
-      "id": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "type": "invocation",
-      "data": {
-        "id": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-        "type": "flux_model_loader",
-        "version": "1.0.3",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": false,
-        "inputs": {
-          "model": {
-            "name": "model",
-            "label": "Model (Starter Models can be found in Model Manager)",
-            "value": {
-              "key": "f04a7a2f-c74d-4538-8d5e-879a53501662",
-              "hash": "random:4875da7a9508444ffa706f61961c260d0c6729f6181a86b31fad06df1277b850",
-              "name": "FLUX Dev (Quantized)",
-              "base": "flux",
-              "type": "main"
-            }
-          },
-          "t5_encoder": {
-            "name": "t5_encoder",
-            "label": "T 5 Encoder (Starter Models can be found in Model Manager)",
-            "value": {
-              "key": "20dcd9ec-5fbb-4012-8401-049e707da5e5",
-              "hash": "random:f986be43ff3502169e4adbdcee158afb0e0a65a1edc4cab16ae59963630cfd8f",
-              "name": "t5_bnb_int8_quantized_encoder",
-              "base": "any",
-              "type": "t5_encoder"
-            }
-          }
-        }
-      },
-      "position": {
-        "x": 337.09365228062825,
-        "y": 40.63469521079861
-      }
-    },
-    {
-      "id": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "type": "invocation",
-      "data": {
-        "id": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-        "type": "flux_text_encoder",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": true,
-        "inputs": {
-          "clip": {
-            "name": "clip",
-            "label": ""
-          },
-          "t5_encoder": {
-            "name": "t5_encoder",
-            "label": ""
-          },
-          "t5_max_seq_len": {
-            "name": "t5_max_seq_len",
-            "label": "T5 Max Seq Len",
-            "value": 256
-          },
-          "prompt": {
-            "name": "prompt",
-            "label": "",
-            "value": "a cat"
-          }
-        }
-      },
-      "position": {
-        "x": 824.1970602278849,
-        "y": 146.98251001061735
-      }
-    },
-    {
-      "id": "4754c534-a5f3-4ad0-9382-7887985e668c",
-      "type": "invocation",
-      "data": {
-        "id": "4754c534-a5f3-4ad0-9382-7887985e668c",
-        "type": "rand_int",
-        "version": "1.0.1",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": true,
-        "useCache": false,
-        "inputs": {
-          "low": {
-            "name": "low",
-            "label": "",
-            "value": 0
-          },
-          "high": {
-            "name": "high",
-            "label": "",
-            "value": 2147483647
-          }
-        }
-      },
-      "position": {
-        "x": 822.9899179655476,
-        "y": 360.9657214885052
-      }
-    },
-    {
-      "id": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "type": "invocation",
-      "data": {
-        "id": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-        "type": "flux_text_to_image",
-        "version": "1.0.0",
-        "label": "",
-        "notes": "",
-        "isOpen": true,
-        "isIntermediate": false,
-        "useCache": true,
-        "inputs": {
-          "board": {
-            "name": "board",
-            "label": ""
-          },
-          "metadata": {
-            "name": "metadata",
-            "label": ""
-          },
-          "transformer": {
-            "name": "transformer",
-            "label": ""
-          },
-          "vae": {
-            "name": "vae",
-            "label": ""
-          },
-          "positive_text_conditioning": {
-            "name": "positive_text_conditioning",
-            "label": ""
-          },
-          "width": {
-            "name": "width",
-            "label": "",
-            "value": 1024
-          },
-          "height": {
-            "name": "height",
-            "label": "",
-            "value": 1024
-          },
-          "num_steps": {
-            "name": "num_steps",
-            "label": "Steps (Recommend 30 for Dev, 4 for Schnell)",
-            "value": 30
-          },
-          "guidance": {
-            "name": "guidance",
-            "label": "",
-            "value": 4
-          },
-          "seed": {
-            "name": "seed",
-            "label": "",
-            "value": 0
-          }
-        }
-      },
-      "position": {
-        "x": 1216.3900791301849,
-        "y": 5.500841807102248
-      }
-    }
-  ],
-  "edges": [
-    {
-      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33amax_seq_len-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_max_seq_len",
-      "type": "default",
-      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "sourceHandle": "max_seq_len",
-      "targetHandle": "t5_max_seq_len"
-    },
-    {
-      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33avae-159bdf1b-79e7-4174-b86e-d40e646964c8vae",
-      "type": "default",
-      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "sourceHandle": "vae",
-      "targetHandle": "vae"
-    },
-    {
-      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33atransformer-159bdf1b-79e7-4174-b86e-d40e646964c8transformer",
-      "type": "default",
-      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "sourceHandle": "transformer",
-      "targetHandle": "transformer"
-    },
-    {
-      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33at5_encoder-01f674f8-b3d1-4df1-acac-6cb8e0bfb63ct5_encoder",
-      "type": "default",
-      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "sourceHandle": "t5_encoder",
-      "targetHandle": "t5_encoder"
-    },
-    {
-      "id": "reactflow__edge-4f0207c2-ff40-41fd-b047-ad33fbb1c33aclip-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cclip",
-      "type": "default",
-      "source": "4f0207c2-ff40-41fd-b047-ad33fbb1c33a",
-      "target": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "sourceHandle": "clip",
-      "targetHandle": "clip"
-    },
-    {
-      "id": "reactflow__edge-01f674f8-b3d1-4df1-acac-6cb8e0bfb63cconditioning-159bdf1b-79e7-4174-b86e-d40e646964c8positive_text_conditioning",
-      "type": "default",
-      "source": "01f674f8-b3d1-4df1-acac-6cb8e0bfb63c",
-      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "sourceHandle": "conditioning",
-      "targetHandle": "positive_text_conditioning"
-    },
-    {
-      "id": "reactflow__edge-4754c534-a5f3-4ad0-9382-7887985e668cvalue-159bdf1b-79e7-4174-b86e-d40e646964c8seed",
-      "type": "default",
-      "source": "4754c534-a5f3-4ad0-9382-7887985e668c",
-      "target": "159bdf1b-79e7-4174-b86e-d40e646964c8",
-      "sourceHandle": "value",
-      "targetHandle": "seed"
-    }
-  ]
-}

invokeai/app/util/custom_openapi.py

@@ -81,7 +81,7 @@ def get_openapi_func(
         # Add the output map to the schema
         openapi_schema["components"]["schemas"]["InvocationOutputMap"] = {
             "type": "object",
-            "properties": dict(sorted(invocation_output_map_properties.items())),
+            "properties": invocation_output_map_properties,
             "required": invocation_output_map_required,
         }
 

invokeai/backend/flux/math.py

@@ -1,32 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-import torch
-from einops import rearrange
-from torch import Tensor
-
-
-def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
-    q, k = apply_rope(q, k, pe)
-
-    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
-    x = rearrange(x, "B H L D -> B L (H D)")
-
-    return x
-
-
-def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
-    assert dim % 2 == 0
-    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
-    omega = 1.0 / (theta**scale)
-    out = torch.einsum("...n,d->...nd", pos, omega)
-    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
-    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
-    return out.float()
-
-
-def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
-    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
-    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
-    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
-    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
-    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)

invokeai/backend/flux/model.py

@@ -1,117 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-from dataclasses import dataclass
-
-import torch
-from torch import Tensor, nn
-
-from invokeai.backend.flux.modules.layers import (
-    DoubleStreamBlock,
-    EmbedND,
-    LastLayer,
-    MLPEmbedder,
-    SingleStreamBlock,
-    timestep_embedding,
-)
-
-
-@dataclass
-class FluxParams:
-    in_channels: int
-    vec_in_dim: int
-    context_in_dim: int
-    hidden_size: int
-    mlp_ratio: float
-    num_heads: int
-    depth: int
-    depth_single_blocks: int
-    axes_dim: list[int]
-    theta: int
-    qkv_bias: bool
-    guidance_embed: bool
-
-
-class Flux(nn.Module):
-    """
-    Transformer model for flow matching on sequences.
-    """
-
-    def __init__(self, params: FluxParams):
-        super().__init__()
-
-        self.params = params
-        self.in_channels = params.in_channels
-        self.out_channels = self.in_channels
-        if params.hidden_size % params.num_heads != 0:
-            raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
-        pe_dim = params.hidden_size // params.num_heads
-        if sum(params.axes_dim) != pe_dim:
-            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
-        self.hidden_size = params.hidden_size
-        self.num_heads = params.num_heads
-        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
-        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
-        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
-        self.guidance_in = (
-            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
-        )
-        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
-
-        self.double_blocks = nn.ModuleList(
-            [
-                DoubleStreamBlock(
-                    self.hidden_size,
-                    self.num_heads,
-                    mlp_ratio=params.mlp_ratio,
-                    qkv_bias=params.qkv_bias,
-                )
-                for _ in range(params.depth)
-            ]
-        )
-
-        self.single_blocks = nn.ModuleList(
-            [
-                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
-                for _ in range(params.depth_single_blocks)
-            ]
-        )
-
-        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
-
-    def forward(
-        self,
-        img: Tensor,
-        img_ids: Tensor,
-        txt: Tensor,
-        txt_ids: Tensor,
-        timesteps: Tensor,
-        y: Tensor,
-        guidance: Tensor | None = None,
-    ) -> Tensor:
-        if img.ndim != 3 or txt.ndim != 3:
-            raise ValueError("Input img and txt tensors must have 3 dimensions.")
-
-        # running on sequences img
-        img = self.img_in(img)
-        vec = self.time_in(timestep_embedding(timesteps, 256))
-        if self.params.guidance_embed:
-            if guidance is None:
-                raise ValueError("Didn't get guidance strength for guidance distilled model.")
-            vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
-        vec = vec + self.vector_in(y)
-        txt = self.txt_in(txt)
-
-        ids = torch.cat((txt_ids, img_ids), dim=1)
-        pe = self.pe_embedder(ids)
-
-        for block in self.double_blocks:
-            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
-
-        img = torch.cat((txt, img), 1)
-        for block in self.single_blocks:
-            img = block(img, vec=vec, pe=pe)
-        img = img[:, txt.shape[1] :, ...]
-
-        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
-        return img

invokeai/backend/flux/modules/autoencoder.py

@@ -1,310 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-from dataclasses import dataclass
-
-import torch
-from einops import rearrange
-from torch import Tensor, nn
-
-
-@dataclass
-class AutoEncoderParams:
-    resolution: int
-    in_channels: int
-    ch: int
-    out_ch: int
-    ch_mult: list[int]
-    num_res_blocks: int
-    z_channels: int
-    scale_factor: float
-    shift_factor: float
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels: int):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-        self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1)
-        self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1)
-        self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1)
-        self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1)
-
-    def attention(self, h_: Tensor) -> Tensor:
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        b, c, h, w = q.shape
-        q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous()
-        k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous()
-        v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous()
-        h_ = nn.functional.scaled_dot_product_attention(q, k, v)
-
-        return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
-
-    def forward(self, x: Tensor) -> Tensor:
-        return x + self.proj_out(self.attention(x))
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, in_channels: int, out_channels: int):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-
-        self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
-        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        if self.in_channels != self.out_channels:
-            self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, x):
-        h = x
-        h = self.norm1(h)
-        h = torch.nn.functional.silu(h)
-        h = self.conv1(h)
-
-        h = self.norm2(h)
-        h = torch.nn.functional.silu(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            x = self.nin_shortcut(x)
-
-        return x + h
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels: int):
-        super().__init__()
-        # no asymmetric padding in torch conv, must do it ourselves
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
-
-    def forward(self, x: Tensor):
-        pad = (0, 1, 0, 1)
-        x = nn.functional.pad(x, pad, mode="constant", value=0)
-        x = self.conv(x)
-        return x
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels: int):
-        super().__init__()
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x: Tensor):
-        x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        x = self.conv(x)
-        return x
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        resolution: int,
-        in_channels: int,
-        ch: int,
-        ch_mult: list[int],
-        num_res_blocks: int,
-        z_channels: int,
-    ):
-        super().__init__()
-        self.ch = ch
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        # downsampling
-        self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
-
-        curr_res = resolution
-        in_ch_mult = (1,) + tuple(ch_mult)
-        self.in_ch_mult = in_ch_mult
-        self.down = nn.ModuleList()
-        block_in = self.ch
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch * in_ch_mult[i_level]
-            block_out = ch * ch_mult[i_level]
-            for _ in range(self.num_res_blocks):
-                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
-                block_in = block_out
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions - 1:
-                down.downsample = Downsample(block_in)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-
-        # end
-        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
-        self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x: Tensor) -> Tensor:
-        # downsampling
-        hs = [self.conv_in(x)]
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](hs[-1])
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-                hs.append(h)
-            if i_level != self.num_resolutions - 1:
-                hs.append(self.down[i_level].downsample(hs[-1]))
-
-        # middle
-        h = hs[-1]
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-        # end
-        h = self.norm_out(h)
-        h = torch.nn.functional.silu(h)
-        h = self.conv_out(h)
-        return h
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        ch: int,
-        out_ch: int,
-        ch_mult: list[int],
-        num_res_blocks: int,
-        in_channels: int,
-        resolution: int,
-        z_channels: int,
-    ):
-        super().__init__()
-        self.ch = ch
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.ffactor = 2 ** (self.num_resolutions - 1)
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        block_in = ch * ch_mult[self.num_resolutions - 1]
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.z_shape = (1, z_channels, curr_res, curr_res)
-
-        # z to block_in
-        self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
-
-        # middle
-        self.mid = nn.Module()
-        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-        self.mid.attn_1 = AttnBlock(block_in)
-        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch * ch_mult[i_level]
-            for _ in range(self.num_res_blocks + 1):
-                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
-                block_in = block_out
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in)
-                curr_res = curr_res * 2
-            self.up.insert(0, up)  # prepend to get consistent order
-
-        # end
-        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
-        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, z: Tensor) -> Tensor:
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        h = self.mid.block_1(h)
-        h = self.mid.attn_1(h)
-        h = self.mid.block_2(h)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.up[i_level].block[i_block](h)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        h = self.norm_out(h)
-        h = torch.nn.functional.silu(h)
-        h = self.conv_out(h)
-        return h
-
-
-class DiagonalGaussian(nn.Module):
-    def __init__(self, sample: bool = True, chunk_dim: int = 1):
-        super().__init__()
-        self.sample = sample
-        self.chunk_dim = chunk_dim
-
-    def forward(self, z: Tensor) -> Tensor:
-        mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim)
-        if self.sample:
-            std = torch.exp(0.5 * logvar)
-            return mean + std * torch.randn_like(mean)
-        else:
-            return mean
-
-
-class AutoEncoder(nn.Module):
-    def __init__(self, params: AutoEncoderParams):
-        super().__init__()
-        self.encoder = Encoder(
-            resolution=params.resolution,
-            in_channels=params.in_channels,
-            ch=params.ch,
-            ch_mult=params.ch_mult,
-            num_res_blocks=params.num_res_blocks,
-            z_channels=params.z_channels,
-        )
-        self.decoder = Decoder(
-            resolution=params.resolution,
-            in_channels=params.in_channels,
-            ch=params.ch,
-            out_ch=params.out_ch,
-            ch_mult=params.ch_mult,
-            num_res_blocks=params.num_res_blocks,
-            z_channels=params.z_channels,
-        )
-        self.reg = DiagonalGaussian()
-
-        self.scale_factor = params.scale_factor
-        self.shift_factor = params.shift_factor
-
-    def encode(self, x: Tensor) -> Tensor:
-        z = self.reg(self.encoder(x))
-        z = self.scale_factor * (z - self.shift_factor)
-        return z
-
-    def decode(self, z: Tensor) -> Tensor:
-        z = z / self.scale_factor + self.shift_factor
-        return self.decoder(z)
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.decode(self.encode(x))

invokeai/backend/flux/modules/conditioner.py

@@ -1,33 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-from torch import Tensor, nn
-from transformers import PreTrainedModel, PreTrainedTokenizer
-
-
-class HFEncoder(nn.Module):
-    def __init__(self, encoder: PreTrainedModel, tokenizer: PreTrainedTokenizer, is_clip: bool, max_length: int):
-        super().__init__()
-        self.max_length = max_length
-        self.is_clip = is_clip
-        self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"
-        self.tokenizer = tokenizer
-        self.hf_module = encoder
-        self.hf_module = self.hf_module.eval().requires_grad_(False)
-
-    def forward(self, text: list[str]) -> Tensor:
-        batch_encoding = self.tokenizer(
-            text,
-            truncation=True,
-            max_length=self.max_length,
-            return_length=False,
-            return_overflowing_tokens=False,
-            padding="max_length",
-            return_tensors="pt",
-        )
-
-        outputs = self.hf_module(
-            input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
-            attention_mask=None,
-            output_hidden_states=False,
-        )
-        return outputs[self.output_key]

invokeai/backend/flux/modules/layers.py

@@ -1,253 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-import math
-from dataclasses import dataclass
-
-import torch
-from einops import rearrange
-from torch import Tensor, nn
-
-from invokeai.backend.flux.math import attention, rope
-
-
-class EmbedND(nn.Module):
-    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
-        super().__init__()
-        self.dim = dim
-        self.theta = theta
-        self.axes_dim = axes_dim
-
-    def forward(self, ids: Tensor) -> Tensor:
-        n_axes = ids.shape[-1]
-        emb = torch.cat(
-            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
-            dim=-3,
-        )
-
-        return emb.unsqueeze(1)
-
-
-def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
-    """
-    Create sinusoidal timestep embeddings.
-    :param t: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an (N, D) Tensor of positional embeddings.
-    """
-    t = time_factor * t
-    half = dim // 2
-    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(t.device)
-
-    args = t[:, None].float() * freqs[None]
-    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-    if dim % 2:
-        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    if torch.is_floating_point(t):
-        embedding = embedding.to(t)
-    return embedding
-
-
-class MLPEmbedder(nn.Module):
-    def __init__(self, in_dim: int, hidden_dim: int):
-        super().__init__()
-        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
-        self.silu = nn.SiLU()
-        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.out_layer(self.silu(self.in_layer(x)))
-
-
-class RMSNorm(torch.nn.Module):
-    def __init__(self, dim: int):
-        super().__init__()
-        self.scale = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x: Tensor):
-        x_dtype = x.dtype
-        x = x.float()
-        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
-        return (x * rrms).to(dtype=x_dtype) * self.scale
-
-
-class QKNorm(torch.nn.Module):
-    def __init__(self, dim: int):
-        super().__init__()
-        self.query_norm = RMSNorm(dim)
-        self.key_norm = RMSNorm(dim)
-
-    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
-        q = self.query_norm(q)
-        k = self.key_norm(k)
-        return q.to(v), k.to(v)
-
-
-class SelfAttention(nn.Module):
-    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.norm = QKNorm(head_dim)
-        self.proj = nn.Linear(dim, dim)
-
-    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
-        qkv = self.qkv(x)
-        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        q, k = self.norm(q, k, v)
-        x = attention(q, k, v, pe=pe)
-        x = self.proj(x)
-        return x
-
-
-@dataclass
-class ModulationOut:
-    shift: Tensor
-    scale: Tensor
-    gate: Tensor
-
-
-class Modulation(nn.Module):
-    def __init__(self, dim: int, double: bool):
-        super().__init__()
-        self.is_double = double
-        self.multiplier = 6 if double else 3
-        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
-
-    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
-        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
-
-        return (
-            ModulationOut(*out[:3]),
-            ModulationOut(*out[3:]) if self.is_double else None,
-        )
-
-
-class DoubleStreamBlock(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False):
-        super().__init__()
-
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        self.num_heads = num_heads
-        self.hidden_size = hidden_size
-        self.img_mod = Modulation(hidden_size, double=True)
-        self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
-
-        self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.img_mlp = nn.Sequential(
-            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
-            nn.GELU(approximate="tanh"),
-            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
-        )
-
-        self.txt_mod = Modulation(hidden_size, double=True)
-        self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
-
-        self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.txt_mlp = nn.Sequential(
-            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
-            nn.GELU(approximate="tanh"),
-            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
-        )
-
-    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
-        img_mod1, img_mod2 = self.img_mod(vec)
-        txt_mod1, txt_mod2 = self.txt_mod(vec)
-
-        # prepare image for attention
-        img_modulated = self.img_norm1(img)
-        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
-        img_qkv = self.img_attn.qkv(img_modulated)
-        img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
-
-        # prepare txt for attention
-        txt_modulated = self.txt_norm1(txt)
-        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
-        txt_qkv = self.txt_attn.qkv(txt_modulated)
-        txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
-
-        # run actual attention
-        q = torch.cat((txt_q, img_q), dim=2)
-        k = torch.cat((txt_k, img_k), dim=2)
-        v = torch.cat((txt_v, img_v), dim=2)
-
-        attn = attention(q, k, v, pe=pe)
-        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
-
-        # calculate the img bloks
-        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
-
-        # calculate the txt bloks
-        txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
-        txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
-        return img, txt
-
-
-class SingleStreamBlock(nn.Module):
-    """
-    A DiT block with parallel linear layers as described in
-    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        mlp_ratio: float = 4.0,
-        qk_scale: float | None = None,
-    ):
-        super().__init__()
-        self.hidden_dim = hidden_size
-        self.num_heads = num_heads
-        head_dim = hidden_size // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        # qkv and mlp_in
-        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
-        # proj and mlp_out
-        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
-
-        self.norm = QKNorm(head_dim)
-
-        self.hidden_size = hidden_size
-        self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-
-        self.mlp_act = nn.GELU(approximate="tanh")
-        self.modulation = Modulation(hidden_size, double=False)
-
-    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
-        mod, _ = self.modulation(vec)
-        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
-        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
-
-        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        q, k = self.norm(q, k, v)
-
-        # compute attention
-        attn = attention(q, k, v, pe=pe)
-        # compute activation in mlp stream, cat again and run second linear layer
-        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
-        return x + mod.gate * output
-
-
-class LastLayer(nn.Module):
-    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
-        super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
-
-    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
-        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
-        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
-        x = self.linear(x)
-        return x

invokeai/backend/flux/sampling.py

@@ -1,176 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-import math
-from typing import Callable
-
-import torch
-from einops import rearrange, repeat
-from torch import Tensor
-from tqdm import tqdm
-
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.modules.conditioner import HFEncoder
-
-
-def get_noise(
-    num_samples: int,
-    height: int,
-    width: int,
-    device: torch.device,
-    dtype: torch.dtype,
-    seed: int,
-):
-    # We always generate noise on the same device and dtype then cast to ensure consistency across devices/dtypes.
-    rand_device = "cpu"
-    rand_dtype = torch.float16
-    return torch.randn(
-        num_samples,
-        16,
-        # allow for packing
-        2 * math.ceil(height / 16),
-        2 * math.ceil(width / 16),
-        device=rand_device,
-        dtype=rand_dtype,
-        generator=torch.Generator(device=rand_device).manual_seed(seed),
-    ).to(device=device, dtype=dtype)
-
-
-def prepare(t5: HFEncoder, clip: HFEncoder, img: Tensor, prompt: str | list[str]) -> dict[str, Tensor]:
-    bs, c, h, w = img.shape
-    if bs == 1 and not isinstance(prompt, str):
-        bs = len(prompt)
-
-    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
-    if img.shape[0] == 1 and bs > 1:
-        img = repeat(img, "1 ... -> bs ...", bs=bs)
-
-    img_ids = torch.zeros(h // 2, w // 2, 3)
-    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
-    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
-    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
-
-    if isinstance(prompt, str):
-        prompt = [prompt]
-    txt = t5(prompt)
-    if txt.shape[0] == 1 and bs > 1:
-        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
-    txt_ids = torch.zeros(bs, txt.shape[1], 3)
-
-    vec = clip(prompt)
-    if vec.shape[0] == 1 and bs > 1:
-        vec = repeat(vec, "1 ... -> bs ...", bs=bs)
-
-    return {
-        "img": img,
-        "img_ids": img_ids.to(img.device),
-        "txt": txt.to(img.device),
-        "txt_ids": txt_ids.to(img.device),
-        "vec": vec.to(img.device),
-    }
-
-
-def time_shift(mu: float, sigma: float, t: Tensor):
-    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
-
-
-def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
-    m = (y2 - y1) / (x2 - x1)
-    b = y1 - m * x1
-    return lambda x: m * x + b
-
-
-def get_schedule(
-    num_steps: int,
-    image_seq_len: int,
-    base_shift: float = 0.5,
-    max_shift: float = 1.15,
-    shift: bool = True,
-) -> list[float]:
-    # extra step for zero
-    timesteps = torch.linspace(1, 0, num_steps + 1)
-
-    # shifting the schedule to favor high timesteps for higher signal images
-    if shift:
-        # eastimate mu based on linear estimation between two points
-        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
-        timesteps = time_shift(mu, 1.0, timesteps)
-
-    return timesteps.tolist()
-
-
-def denoise(
-    model: Flux,
-    # model input
-    img: Tensor,
-    img_ids: Tensor,
-    txt: Tensor,
-    txt_ids: Tensor,
-    vec: Tensor,
-    # sampling parameters
-    timesteps: list[float],
-    step_callback: Callable[[], None],
-    guidance: float = 4.0,
-):
-    dtype = model.txt_in.bias.dtype
-
-    # TODO(ryand): This shouldn't be necessary if we manage the dtypes properly in the caller.
-    img = img.to(dtype=dtype)
-    img_ids = img_ids.to(dtype=dtype)
-    txt = txt.to(dtype=dtype)
-    txt_ids = txt_ids.to(dtype=dtype)
-    vec = vec.to(dtype=dtype)
-
-    # this is ignored for schnell
-    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
-    for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
-        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
-        pred = model(
-            img=img,
-            img_ids=img_ids,
-            txt=txt,
-            txt_ids=txt_ids,
-            y=vec,
-            timesteps=t_vec,
-            guidance=guidance_vec,
-        )
-
-        img = img + (t_prev - t_curr) * pred
-        step_callback()
-
-    return img
-
-
-def unpack(x: Tensor, height: int, width: int) -> Tensor:
-    return rearrange(
-        x,
-        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
-        h=math.ceil(height / 16),
-        w=math.ceil(width / 16),
-        ph=2,
-        pw=2,
-    )
-
-
-def prepare_latent_img_patches(latent_img: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-    """Convert an input image in latent space to patches for diffusion.
-
-    This implementation was extracted from:
-    https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/sampling.py#L32
-
-    Returns:
-        tuple[Tensor, Tensor]: (img, img_ids), as defined in the original flux repo.
-    """
-    bs, c, h, w = latent_img.shape
-
-    # Pixel unshuffle with a scale of 2, and flatten the height/width dimensions to get an array of patches.
-    img = rearrange(latent_img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
-    if img.shape[0] == 1 and bs > 1:
-        img = repeat(img, "1 ... -> bs ...", bs=bs)
-
-    # Generate patch position ids.
-    img_ids = torch.zeros(h // 2, w // 2, 3, device=img.device)
-    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=img.device)[:, None]
-    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=img.device)[None, :]
-    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
-
-    return img, img_ids

invokeai/backend/flux/util.py

@@ -1,71 +0,0 @@
-# Initially pulled from https://github.com/black-forest-labs/flux
-
-from dataclasses import dataclass
-from typing import Dict, Literal
-
-from invokeai.backend.flux.model import FluxParams
-from invokeai.backend.flux.modules.autoencoder import AutoEncoderParams
-
-
-@dataclass
-class ModelSpec:
-    params: FluxParams
-    ae_params: AutoEncoderParams
-    ckpt_path: str | None
-    ae_path: str | None
-    repo_id: str | None
-    repo_flow: str | None
-    repo_ae: str | None
-
-
-max_seq_lengths: Dict[str, Literal[256, 512]] = {
-    "flux-dev": 512,
-    "flux-schnell": 256,
-}
-
-
-ae_params = {
-    "flux": AutoEncoderParams(
-        resolution=256,
-        in_channels=3,
-        ch=128,
-        out_ch=3,
-        ch_mult=[1, 2, 4, 4],
-        num_res_blocks=2,
-        z_channels=16,
-        scale_factor=0.3611,
-        shift_factor=0.1159,
-    )
-}
-
-
-params = {
-    "flux-dev": FluxParams(
-        in_channels=64,
-        vec_in_dim=768,
-        context_in_dim=4096,
-        hidden_size=3072,
-        mlp_ratio=4.0,
-        num_heads=24,
-        depth=19,
-        depth_single_blocks=38,
-        axes_dim=[16, 56, 56],
-        theta=10_000,
-        qkv_bias=True,
-        guidance_embed=True,
-    ),
-    "flux-schnell": FluxParams(
-        in_channels=64,
-        vec_in_dim=768,
-        context_in_dim=4096,
-        hidden_size=3072,
-        mlp_ratio=4.0,
-        num_heads=24,
-        depth=19,
-        depth_single_blocks=38,
-        axes_dim=[16, 56, 56],
-        theta=10_000,
-        qkv_bias=True,
-        guidance_embed=False,
-    ),
-}

invokeai/backend/image_util/depth_anything/__init__.py

@@ -0,0 +1,90 @@
+from pathlib import Path
+from typing import Literal
+
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+from einops import repeat
+from PIL import Image
+from torchvision.transforms import Compose
+
+from invokeai.app.services.config.config_default import get_config
+from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
+from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
+from invokeai.backend.util.logging import InvokeAILogger
+
+config = get_config()
+logger = InvokeAILogger.get_logger(config=config)
+
+DEPTH_ANYTHING_MODELS = {
+    "large": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth?download=true",
+    "base": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitb14.pth?download=true",
+    "small": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vits14.pth?download=true",
+}
+
+
+transform = Compose(
+    [
+        Resize(
+            width=518,
+            height=518,
+            resize_target=False,
+            keep_aspect_ratio=True,
+            ensure_multiple_of=14,
+            resize_method="lower_bound",
+            image_interpolation_method=cv2.INTER_CUBIC,
+        ),
+        NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        PrepareForNet(),
+    ]
+)
+
+
+class DepthAnythingDetector:
+    def __init__(self, model: DPT_DINOv2, device: torch.device) -> None:
+        self.model = model
+        self.device = device
+
+    @staticmethod
+    def load_model(
+        model_path: Path, device: torch.device, model_size: Literal["large", "base", "small"] = "small"
+    ) -> DPT_DINOv2:
+        match model_size:
+            case "small":
+                model = DPT_DINOv2(encoder="vits", features=64, out_channels=[48, 96, 192, 384])
+            case "base":
+                model = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768])
+            case "large":
+                model = DPT_DINOv2(encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024])
+
+        model.load_state_dict(torch.load(model_path.as_posix(), map_location="cpu"))
+        model.eval()
+
+        model.to(device)
+        return model
+
+    def __call__(self, image: Image.Image, resolution: int = 512) -> Image.Image:
+        if not self.model:
+            logger.warn("DepthAnything model was not loaded. Returning original image")
+            return image
+
+        np_image = np.array(image, dtype=np.uint8)
+        np_image = np_image[:, :, ::-1] / 255.0
+
+        image_height, image_width = np_image.shape[:2]
+        np_image = transform({"image": np_image})["image"]
+        tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(self.device)
+
+        with torch.no_grad():
+            depth = self.model(tensor_image)
+            depth = F.interpolate(depth[None], (image_height, image_width), mode="bilinear", align_corners=False)[0, 0]
+            depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+
+        depth_map = repeat(depth, "h w -> h w 3").cpu().numpy().astype(np.uint8)
+        depth_map = Image.fromarray(depth_map)
+
+        new_height = int(image_height * (resolution / image_width))
+        depth_map = depth_map.resize((resolution, new_height))
+
+        return depth_map

invokeai/backend/image_util/depth_anything/depth_anything_pipeline.py

@@ -1,31 +0,0 @@
-from typing import Optional
-
-import torch
-from PIL import Image
-from transformers.pipelines import DepthEstimationPipeline
-
-from invokeai.backend.raw_model import RawModel
-
-
-class DepthAnythingPipeline(RawModel):
-    """Custom wrapper for the Depth Estimation pipeline from transformers adding compatibility
-    for Invoke's Model Management System"""
-
-    def __init__(self, pipeline: DepthEstimationPipeline) -> None:
-        self._pipeline = pipeline
-
-    def generate_depth(self, image: Image.Image) -> Image.Image:
-        depth_map = self._pipeline(image)["depth"]
-        assert isinstance(depth_map, Image.Image)
-        return depth_map
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None):
-        if device is not None and device.type not in {"cpu", "cuda"}:
-            device = None
-        self._pipeline.model.to(device=device, dtype=dtype)
-        self._pipeline.device = self._pipeline.model.device
-
-    def calc_size(self) -> int:
-        from invokeai.backend.model_manager.load.model_util import calc_module_size
-
-        return calc_module_size(self._pipeline.model)

invokeai/backend/image_util/depth_anything/model/blocks.py

@@ -0,0 +1,145 @@
+import torch.nn as nn
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    if len(in_shape) >= 4:
+        out_shape4 = out_shape
+
+    if expand:
+        out_shape1 = out_shape
+        out_shape2 = out_shape * 2
+        out_shape3 = out_shape * 4
+        if len(in_shape) >= 4:
+            out_shape4 = out_shape * 8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    if len(in_shape) >= 4:
+        scratch.layer4_rn = nn.Conv2d(
+            in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+        )
+
+    return scratch
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module."""
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups = 1
+
+        self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
+
+        self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
+
+        if self.bn:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn:
+            out = self.bn1(out)
+
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block."""
+
+    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, size=None):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups = 1
+
+        self.expand = expand
+        out_features = features
+        if self.expand:
+            out_features = features // 2
+
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+        self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+        self.size = size
+
+    def forward(self, *xs, size=None):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+
+        output = self.resConfUnit2(output)
+
+        if (size is None) and (self.size is None):
+            modifier = {"scale_factor": 2}
+        elif size is None:
+            modifier = {"size": self.size}
+        else:
+            modifier = {"size": size}
+
+        output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners)
+
+        output = self.out_conv(output)
+
+        return output

invokeai/backend/image_util/depth_anything/model/dpt.py

@@ -0,0 +1,183 @@
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from invokeai.backend.image_util.depth_anything.model.blocks import FeatureFusionBlock, _make_scratch
+
+torchhub_path = Path(__file__).parent.parent / "torchhub"
+
+
+def _make_fusion_block(features, use_bn, size=None):
+    return FeatureFusionBlock(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+        size=size,
+    )
+
+
+class DPTHead(nn.Module):
+    def __init__(self, nclass, in_channels, features, out_channels, use_bn=False, use_clstoken=False):
+        super(DPTHead, self).__init__()
+
+        self.nclass = nclass
+        self.use_clstoken = use_clstoken
+
+        self.projects = nn.ModuleList(
+            [
+                nn.Conv2d(
+                    in_channels=in_channels,
+                    out_channels=out_channel,
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                )
+                for out_channel in out_channels
+            ]
+        )
+
+        self.resize_layers = nn.ModuleList(
+            [
+                nn.ConvTranspose2d(
+                    in_channels=out_channels[0], out_channels=out_channels[0], kernel_size=4, stride=4, padding=0
+                ),
+                nn.ConvTranspose2d(
+                    in_channels=out_channels[1], out_channels=out_channels[1], kernel_size=2, stride=2, padding=0
+                ),
+                nn.Identity(),
+                nn.Conv2d(
+                    in_channels=out_channels[3], out_channels=out_channels[3], kernel_size=3, stride=2, padding=1
+                ),
+            ]
+        )
+
+        if use_clstoken:
+            self.readout_projects = nn.ModuleList()
+            for _ in range(len(self.projects)):
+                self.readout_projects.append(nn.Sequential(nn.Linear(2 * in_channels, in_channels), nn.GELU()))
+
+        self.scratch = _make_scratch(
+            out_channels,
+            features,
+            groups=1,
+            expand=False,
+        )
+
+        self.scratch.stem_transpose = None
+
+        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+
+        head_features_1 = features
+        head_features_2 = 32
+
+        if nclass > 1:
+            self.scratch.output_conv = nn.Sequential(
+                nn.Conv2d(head_features_1, head_features_1, kernel_size=3, stride=1, padding=1),
+                nn.ReLU(True),
+                nn.Conv2d(head_features_1, nclass, kernel_size=1, stride=1, padding=0),
+            )
+        else:
+            self.scratch.output_conv1 = nn.Conv2d(
+                head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1
+            )
+
+            self.scratch.output_conv2 = nn.Sequential(
+                nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
+                nn.ReLU(True),
+                nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0),
+                nn.ReLU(True),
+                nn.Identity(),
+            )
+
+    def forward(self, out_features, patch_h, patch_w):
+        out = []
+        for i, x in enumerate(out_features):
+            if self.use_clstoken:
+                x, cls_token = x[0], x[1]
+                readout = cls_token.unsqueeze(1).expand_as(x)
+                x = self.readout_projects[i](torch.cat((x, readout), -1))
+            else:
+                x = x[0]
+
+            x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))
+
+            x = self.projects[i](x)
+            x = self.resize_layers[i](x)
+
+            out.append(x)
+
+        layer_1, layer_2, layer_3, layer_4 = out
+
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+        path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+        out = self.scratch.output_conv1(path_1)
+        out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
+        out = self.scratch.output_conv2(out)
+
+        return out
+
+
+class DPT_DINOv2(nn.Module):
+    def __init__(
+        self,
+        features,
+        out_channels,
+        encoder="vitl",
+        use_bn=False,
+        use_clstoken=False,
+    ):
+        super(DPT_DINOv2, self).__init__()
+
+        assert encoder in ["vits", "vitb", "vitl"]
+
+        # # in case the Internet connection is not stable, please load the DINOv2 locally
+        # if use_local:
+        #     self.pretrained = torch.hub.load(
+        #         torchhub_path / "facebookresearch_dinov2_main",
+        #         "dinov2_{:}14".format(encoder),
+        #         source="local",
+        #         pretrained=False,
+        #     )
+        # else:
+        #     self.pretrained = torch.hub.load(
+        #         "facebookresearch/dinov2",
+        #         "dinov2_{:}14".format(encoder),
+        #     )
+
+        self.pretrained = torch.hub.load(
+            "facebookresearch/dinov2",
+            "dinov2_{:}14".format(encoder),
+        )
+
+        dim = self.pretrained.blocks[0].attn.qkv.in_features
+
+        self.depth_head = DPTHead(1, dim, features, out_channels=out_channels, use_bn=use_bn, use_clstoken=use_clstoken)
+
+    def forward(self, x):
+        h, w = x.shape[-2:]
+
+        features = self.pretrained.get_intermediate_layers(x, 4, return_class_token=True)
+
+        patch_h, patch_w = h // 14, w // 14
+
+        depth = self.depth_head(features, patch_h, patch_w)
+        depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True)
+        depth = F.relu(depth)
+
+        return depth.squeeze(1)

invokeai/backend/image_util/depth_anything/utilities/util.py

@@ -0,0 +1,227 @@
+import math
+
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+    """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+    Args:
+        sample (dict): sample
+        size (tuple): image size
+
+    Returns:
+        tuple: new size
+    """
+    shape = list(sample["disparity"].shape)
+
+    if shape[0] >= size[0] and shape[1] >= size[1]:
+        return sample
+
+    scale = [0, 0]
+    scale[0] = size[0] / shape[0]
+    scale[1] = size[1] / shape[1]
+
+    scale = max(scale)
+
+    shape[0] = math.ceil(scale * shape[0])
+    shape[1] = math.ceil(scale * shape[1])
+
+    # resize
+    sample["image"] = cv2.resize(sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method)
+
+    sample["disparity"] = cv2.resize(sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST)
+    sample["mask"] = cv2.resize(
+        sample["mask"].astype(np.float32),
+        tuple(shape[::-1]),
+        interpolation=cv2.INTER_NEAREST,
+    )
+    sample["mask"] = sample["mask"].astype(bool)
+
+    return tuple(shape)
+
+
+class Resize(object):
+    """Resize sample to given size (width, height)."""
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller
+                    than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(scale_height * height, min_val=self.__height)
+            new_width = self.constrain_to_multiple_of(scale_width * width, min_val=self.__width)
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(scale_height * height, max_val=self.__height)
+            new_width = self.constrain_to_multiple_of(scale_width * width, max_val=self.__width)
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(sample["image"].shape[1], sample["image"].shape[0])
+
+        # resize sample
+        sample["image"] = cv2.resize(
+            sample["image"],
+            (width, height),
+            interpolation=self.__image_interpolation_method,
+        )
+
+        if self.__resize_target:
+            if "disparity" in sample:
+                sample["disparity"] = cv2.resize(
+                    sample["disparity"],
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST)
+
+            if "semseg_mask" in sample:
+                # sample["semseg_mask"] = cv2.resize(
+                #     sample["semseg_mask"], (width, height), interpolation=cv2.INTER_NEAREST
+                # )
+                sample["semseg_mask"] = F.interpolate(
+                    torch.from_numpy(sample["semseg_mask"]).float()[None, None, ...], (height, width), mode="nearest"
+                ).numpy()[0, 0]
+
+            if "mask" in sample:
+                sample["mask"] = cv2.resize(
+                    sample["mask"].astype(np.float32),
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+                # sample["mask"] = sample["mask"].astype(bool)
+
+        # print(sample['image'].shape, sample['depth'].shape)
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std."""
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input."""
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+
+        if "semseg_mask" in sample:
+            sample["semseg_mask"] = sample["semseg_mask"].astype(np.float32)
+            sample["semseg_mask"] = np.ascontiguousarray(sample["semseg_mask"])
+
+        return sample

invokeai/backend/load_flux_model.py

@@ -0,0 +1,129 @@
+import json
+import os
+import time
+from pathlib import Path
+from typing import Union
+
+import torch
+from diffusers.models.model_loading_utils import load_state_dict
+from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
+from diffusers.utils import (
+    CONFIG_NAME,
+    SAFE_WEIGHTS_INDEX_NAME,
+    SAFETENSORS_WEIGHTS_NAME,
+    _get_checkpoint_shard_files,
+    is_accelerate_available,
+)
+from optimum.quanto import qfloat8
+from optimum.quanto.models import QuantizedDiffusersModel
+from optimum.quanto.models.shared_dict import ShardedStateDict
+
+from invokeai.backend.requantize import requantize
+
+
+class QuantizedFluxTransformer2DModel(QuantizedDiffusersModel):
+    base_class = FluxTransformer2DModel
+
+    @classmethod
+    def from_pretrained(cls, model_name_or_path: Union[str, os.PathLike]):
+        if cls.base_class is None:
+            raise ValueError("The `base_class` attribute needs to be configured.")
+
+        if not is_accelerate_available():
+            raise ValueError("Reloading a quantized diffusers model requires the accelerate library.")
+        from accelerate import init_empty_weights
+
+        if os.path.isdir(model_name_or_path):
+            # Look for a quantization map
+            qmap_path = os.path.join(model_name_or_path, cls._qmap_name())
+            if not os.path.exists(qmap_path):
+                raise ValueError(f"No quantization map found in {model_name_or_path}: is this a quantized model ?")
+
+            # Look for original model config file.
+            model_config_path = os.path.join(model_name_or_path, CONFIG_NAME)
+            if not os.path.exists(model_config_path):
+                raise ValueError(f"{CONFIG_NAME} not found in {model_name_or_path}.")
+
+            with open(qmap_path, "r", encoding="utf-8") as f:
+                qmap = json.load(f)
+
+            with open(model_config_path, "r", encoding="utf-8") as f:
+                original_model_cls_name = json.load(f)["_class_name"]
+            configured_cls_name = cls.base_class.__name__
+            if configured_cls_name != original_model_cls_name:
+                raise ValueError(
+                    f"Configured base class ({configured_cls_name}) differs from what was derived from the provided configuration ({original_model_cls_name})."
+                )
+
+            # Create an empty model
+            config = cls.base_class.load_config(model_name_or_path)
+            with init_empty_weights():
+                model = cls.base_class.from_config(config)
+
+            # Look for the index of a sharded checkpoint
+            checkpoint_file = os.path.join(model_name_or_path, SAFE_WEIGHTS_INDEX_NAME)
+            if os.path.exists(checkpoint_file):
+                # Convert the checkpoint path to a list of shards
+                _, sharded_metadata = _get_checkpoint_shard_files(model_name_or_path, checkpoint_file)
+                # Create a mapping for the sharded safetensor files
+                state_dict = ShardedStateDict(model_name_or_path, sharded_metadata["weight_map"])
+            else:
+                # Look for a single checkpoint file
+                checkpoint_file = os.path.join(model_name_or_path, SAFETENSORS_WEIGHTS_NAME)
+                if not os.path.exists(checkpoint_file):
+                    raise ValueError(f"No safetensor weights found in {model_name_or_path}.")
+                # Get state_dict from model checkpoint
+                state_dict = load_state_dict(checkpoint_file)
+
+            # Requantize and load quantized weights from state_dict
+            requantize(model, state_dict=state_dict, quantization_map=qmap)
+            model.eval()
+            return cls(model)
+        else:
+            raise NotImplementedError("Reloading quantized models directly from the hub is not supported yet.")
+
+
+def load_flux_transformer(path: Path) -> FluxTransformer2DModel:
+    # model = FluxTransformer2DModel.from_pretrained(path, local_files_only=True, torch_dtype=torch.bfloat16)
+    model_8bit_path = path / "quantized"
+    if model_8bit_path.exists():
+        # The quantized model exists, load it.
+        # TODO(ryand): The requantize(...) operation in from_pretrained(...) is very slow. This seems like
+        # something that we should be able to make much faster.
+        q_model = QuantizedFluxTransformer2DModel.from_pretrained(model_8bit_path)
+
+        # Access the underlying wrapped model.
+        # We access the wrapped model, even though it is private, because it simplifies the type checking by
+        # always returning a FluxTransformer2DModel from this function.
+        model = q_model._wrapped
+    else:
+        # The quantized model does not exist yet, quantize and save it.
+        # TODO(ryand): Loading in float16 and then quantizing seems to result in NaNs. In order to run this on
+        # GPUs that don't support bfloat16, we would need to host the quantized model instead of generating it
+        # here.
+        model = FluxTransformer2DModel.from_pretrained(path, local_files_only=True, torch_dtype=torch.bfloat16)
+        assert isinstance(model, FluxTransformer2DModel)
+
+        q_model = QuantizedFluxTransformer2DModel.quantize(model, weights=qfloat8)
+
+        model_8bit_path.mkdir(parents=True, exist_ok=True)
+        q_model.save_pretrained(model_8bit_path)
+
+        # (See earlier comment about accessing the wrapped model.)
+        model = q_model._wrapped
+
+    assert isinstance(model, FluxTransformer2DModel)
+    return model
+
+
+def main():
+    start = time.time()
+    model = load_flux_transformer(
+        Path("/data/invokeai/models/.download_cache/black-forest-labs_flux.1-schnell/FLUX.1-schnell/transformer/")
+    )
+    print(f"Time to load: {time.time() - start}s")
+    print("hi")
+
+
+if __name__ == "__main__":
+    main()

invokeai/backend/lora.py

@@ -220,17 +220,11 @@ class LoKRLayer(LoRALayerBase):
         if self.w1 is None:
             self.w1_a = values["lokr_w1_a"]
             self.w1_b = values["lokr_w1_b"]
-        else:
-            self.w1_b = None
-            self.w1_a = None
 
         self.w2 = values.get("lokr_w2", None)
         if self.w2 is None:
             self.w2_a = values["lokr_w2_a"]
             self.w2_b = values["lokr_w2_b"]
-        else:
-            self.w2_a = None
-            self.w2_b = None
 
         self.t2 = values.get("lokr_t2", None)
 
@@ -378,39 +372,7 @@ class IA3Layer(LoRALayerBase):
         self.on_input = self.on_input.to(device=device, dtype=dtype)
 
 
-class NormLayer(LoRALayerBase):
-    # bias handled in LoRALayerBase(calc_size, to)
-    # weight: torch.Tensor
-    # bias: Optional[torch.Tensor]
-
-    def __init__(
-        self,
-        layer_key: str,
-        values: Dict[str, torch.Tensor],
-    ):
-        super().__init__(layer_key, values)
-
-        self.weight = values["w_norm"]
-        self.bias = values.get("b_norm", None)
-
-        self.rank = None  # unscaled
-        self.check_keys(values, {"w_norm", "b_norm"})
-
-    def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
-        return self.weight
-
-    def calc_size(self) -> int:
-        model_size = super().calc_size()
-        model_size += self.weight.nelement() * self.weight.element_size()
-        return model_size
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
-        super().to(device=device, dtype=dtype)
-
-        self.weight = self.weight.to(device=device, dtype=dtype)
-
-
-AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer, NormLayer]
+AnyLoRALayer = Union[LoRALayer, LoHALayer, LoKRLayer, FullLayer, IA3Layer]
 
 
 class LoRAModelRaw(RawModel):  # (torch.nn.Module):
@@ -551,10 +513,6 @@ class LoRAModelRaw(RawModel):  # (torch.nn.Module):
             elif "on_input" in values:
                 layer = IA3Layer(layer_key, values)
 
-            # norms
-            elif "w_norm" in values:
-                layer = NormLayer(layer_key, values)
-
             else:
                 print(f">> Encountered unknown lora layer module in {model.name}: {layer_key} - {list(values.keys())}")
                 raise Exception("Unknown lora format!")

invokeai/backend/model_manager/config.py

@@ -52,7 +52,6 @@ class BaseModelType(str, Enum):
     StableDiffusion2 = "sd-2"
     StableDiffusionXL = "sdxl"
     StableDiffusionXLRefiner = "sdxl-refiner"
-    Flux = "flux"
     # Kandinsky2_1 = "kandinsky-2.1"
 
 
@@ -67,9 +66,7 @@ class ModelType(str, Enum):
     TextualInversion = "embedding"
     IPAdapter = "ip_adapter"
     CLIPVision = "clip_vision"
-    CLIPEmbed = "clip_embed"
     T2IAdapter = "t2i_adapter"
-    T5Encoder = "t5_encoder"
     SpandrelImageToImage = "spandrel_image_to_image"
 
 
@@ -77,7 +74,6 @@ class SubModelType(str, Enum):
     """Submodel type."""
 
     UNet = "unet"
-    Transformer = "transformer"
     TextEncoder = "text_encoder"
     TextEncoder2 = "text_encoder_2"
     Tokenizer = "tokenizer"
@@ -108,9 +104,6 @@ class ModelFormat(str, Enum):
     EmbeddingFile = "embedding_file"
     EmbeddingFolder = "embedding_folder"
     InvokeAI = "invokeai"
-    T5Encoder = "t5_encoder"
-    BnbQuantizedLlmInt8b = "bnb_quantized_int8b"
-    BnbQuantizednf4b = "bnb_quantized_nf4b"
 
 
 class SchedulerPredictionType(str, Enum):
@@ -193,9 +186,7 @@ class ModelConfigBase(BaseModel):
 class CheckpointConfigBase(ModelConfigBase):
     """Model config for checkpoint-style models."""
 
-    format: Literal[ModelFormat.Checkpoint, ModelFormat.BnbQuantizednf4b] = Field(
-        description="Format of the provided checkpoint model", default=ModelFormat.Checkpoint
-    )
+    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
     config_path: str = Field(description="path to the checkpoint model config file")
     converted_at: Optional[float] = Field(
         description="When this model was last converted to diffusers", default_factory=time.time
@@ -214,26 +205,6 @@ class LoRAConfigBase(ModelConfigBase):
     trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
 
 
-class T5EncoderConfigBase(ModelConfigBase):
-    type: Literal[ModelType.T5Encoder] = ModelType.T5Encoder
-
-
-class T5EncoderConfig(T5EncoderConfigBase):
-    format: Literal[ModelFormat.T5Encoder] = ModelFormat.T5Encoder
-
-    @staticmethod
-    def get_tag() -> Tag:
-        return Tag(f"{ModelType.T5Encoder.value}.{ModelFormat.T5Encoder.value}")
-
-
-class T5EncoderBnbQuantizedLlmInt8bConfig(T5EncoderConfigBase):
-    format: Literal[ModelFormat.BnbQuantizedLlmInt8b] = ModelFormat.BnbQuantizedLlmInt8b
-
-    @staticmethod
-    def get_tag() -> Tag:
-        return Tag(f"{ModelType.T5Encoder.value}.{ModelFormat.BnbQuantizedLlmInt8b.value}")
-
-
 class LoRALyCORISConfig(LoRAConfigBase):
     """Model config for LoRA/Lycoris models."""
 
@@ -258,6 +229,7 @@ class VAECheckpointConfig(CheckpointConfigBase):
     """Model config for standalone VAE models."""
 
     type: Literal[ModelType.VAE] = ModelType.VAE
+    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
 
     @staticmethod
     def get_tag() -> Tag:
@@ -296,6 +268,7 @@ class ControlNetCheckpointConfig(CheckpointConfigBase, ControlAdapterConfigBase)
     """Model config for ControlNet models (diffusers version)."""
 
     type: Literal[ModelType.ControlNet] = ModelType.ControlNet
+    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
 
     @staticmethod
     def get_tag() -> Tag:
@@ -344,21 +317,6 @@ class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase):
         return Tag(f"{ModelType.Main.value}.{ModelFormat.Checkpoint.value}")
 
 
-class MainBnbQuantized4bCheckpointConfig(CheckpointConfigBase, MainConfigBase):
-    """Model config for main checkpoint models."""
-
-    prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
-    upcast_attention: bool = False
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.format = ModelFormat.BnbQuantizednf4b
-
-    @staticmethod
-    def get_tag() -> Tag:
-        return Tag(f"{ModelType.Main.value}.{ModelFormat.BnbQuantizednf4b.value}")
-
-
 class MainDiffusersConfig(DiffusersConfigBase, MainConfigBase):
     """Model config for main diffusers models."""
 
@@ -392,17 +350,6 @@ class IPAdapterCheckpointConfig(IPAdapterBaseConfig):
         return Tag(f"{ModelType.IPAdapter.value}.{ModelFormat.Checkpoint.value}")
 
 
-class CLIPEmbedDiffusersConfig(DiffusersConfigBase):
-    """Model config for Clip Embeddings."""
-
-    type: Literal[ModelType.CLIPEmbed] = ModelType.CLIPEmbed
-    format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
-
-    @staticmethod
-    def get_tag() -> Tag:
-        return Tag(f"{ModelType.CLIPEmbed.value}.{ModelFormat.Diffusers.value}")
-
-
 class CLIPVisionDiffusersConfig(DiffusersConfigBase):
     """Model config for CLIPVision."""
 
@@ -461,15 +408,12 @@ AnyModelConfig = Annotated[
     Union[
         Annotated[MainDiffusersConfig, MainDiffusersConfig.get_tag()],
         Annotated[MainCheckpointConfig, MainCheckpointConfig.get_tag()],
-        Annotated[MainBnbQuantized4bCheckpointConfig, MainBnbQuantized4bCheckpointConfig.get_tag()],
         Annotated[VAEDiffusersConfig, VAEDiffusersConfig.get_tag()],
         Annotated[VAECheckpointConfig, VAECheckpointConfig.get_tag()],
         Annotated[ControlNetDiffusersConfig, ControlNetDiffusersConfig.get_tag()],
         Annotated[ControlNetCheckpointConfig, ControlNetCheckpointConfig.get_tag()],
         Annotated[LoRALyCORISConfig, LoRALyCORISConfig.get_tag()],
         Annotated[LoRADiffusersConfig, LoRADiffusersConfig.get_tag()],
-        Annotated[T5EncoderConfig, T5EncoderConfig.get_tag()],
-        Annotated[T5EncoderBnbQuantizedLlmInt8bConfig, T5EncoderBnbQuantizedLlmInt8bConfig.get_tag()],
         Annotated[TextualInversionFileConfig, TextualInversionFileConfig.get_tag()],
         Annotated[TextualInversionFolderConfig, TextualInversionFolderConfig.get_tag()],
         Annotated[IPAdapterInvokeAIConfig, IPAdapterInvokeAIConfig.get_tag()],
@@ -477,7 +421,6 @@ AnyModelConfig = Annotated[
         Annotated[T2IAdapterConfig, T2IAdapterConfig.get_tag()],
         Annotated[SpandrelImageToImageConfig, SpandrelImageToImageConfig.get_tag()],
         Annotated[CLIPVisionDiffusersConfig, CLIPVisionDiffusersConfig.get_tag()],
-        Annotated[CLIPEmbedDiffusersConfig, CLIPEmbedDiffusersConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]

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

@@ -1,234 +0,0 @@
-# Copyright (c) 2024, Brandon W. Rising and the InvokeAI Development Team
-"""Class for Flux model loading in InvokeAI."""
-
-from pathlib import Path
-from typing import Optional
-
-import accelerate
-import torch
-from safetensors.torch import load_file
-from transformers import AutoConfig, AutoModelForTextEncoding, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
-
-from invokeai.app.services.config.config_default import get_config
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.modules.autoencoder import AutoEncoder
-from invokeai.backend.flux.util import ae_params, params
-from invokeai.backend.model_manager import (
-    AnyModel,
-    AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
-)
-from invokeai.backend.model_manager.config import (
-    CheckpointConfigBase,
-    CLIPEmbedDiffusersConfig,
-    MainBnbQuantized4bCheckpointConfig,
-    MainCheckpointConfig,
-    T5EncoderBnbQuantizedLlmInt8bConfig,
-    T5EncoderConfig,
-    VAECheckpointConfig,
-)
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.util.silence_warnings import SilenceWarnings
-
-try:
-    from invokeai.backend.quantization.bnb_llm_int8 import quantize_model_llm_int8
-    from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4
-
-    bnb_available = True
-except ImportError:
-    bnb_available = False
-
-app_config = get_config()
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.VAE, format=ModelFormat.Checkpoint)
-class FluxVAELoader(ModelLoader):
-    """Class to load VAE models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, VAECheckpointConfig):
-            raise ValueError("Only VAECheckpointConfig models are currently supported here.")
-        model_path = Path(config.path)
-
-        with SilenceWarnings():
-            model = AutoEncoder(ae_params[config.config_path])
-            sd = load_file(model_path)
-            model.load_state_dict(sd, assign=True)
-            model.to(dtype=self._torch_dtype)
-
-        return model
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPEmbed, format=ModelFormat.Diffusers)
-class ClipCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, CLIPEmbedDiffusersConfig):
-            raise ValueError("Only CLIPEmbedDiffusersConfig models are currently supported here.")
-
-        match submodel_type:
-            case SubModelType.Tokenizer:
-                return CLIPTokenizer.from_pretrained(Path(config.path) / "tokenizer")
-            case SubModelType.TextEncoder:
-                return CLIPTextModel.from_pretrained(Path(config.path) / "text_encoder")
-
-        raise ValueError(
-            f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.BnbQuantizedLlmInt8b)
-class BnbQuantizedLlmInt8bCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, T5EncoderBnbQuantizedLlmInt8bConfig):
-            raise ValueError("Only T5EncoderBnbQuantizedLlmInt8bConfig models are currently supported here.")
-        if not bnb_available:
-            raise ImportError(
-                "The bnb modules are not available. Please install bitsandbytes if available on your platform."
-            )
-        match submodel_type:
-            case SubModelType.Tokenizer2:
-                return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2:
-                te2_model_path = Path(config.path) / "text_encoder_2"
-                model_config = AutoConfig.from_pretrained(te2_model_path)
-                with accelerate.init_empty_weights():
-                    model = AutoModelForTextEncoding.from_config(model_config)
-                    model = quantize_model_llm_int8(model, modules_to_not_convert=set())
-
-                state_dict_path = te2_model_path / "bnb_llm_int8_model.safetensors"
-                state_dict = load_file(state_dict_path)
-                self._load_state_dict_into_t5(model, state_dict)
-
-                return model
-
-        raise ValueError(
-            f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-    @classmethod
-    def _load_state_dict_into_t5(cls, model: T5EncoderModel, state_dict: dict[str, torch.Tensor]):
-        # There is a shared reference to a single weight tensor in the model.
-        # Both "encoder.embed_tokens.weight" and "shared.weight" refer to the same tensor, so only the latter should
-        # be present in the state_dict.
-        missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False, assign=True)
-        assert len(unexpected_keys) == 0
-        assert set(missing_keys) == {"encoder.embed_tokens.weight"}
-        # Assert that the layers we expect to be shared are actually shared.
-        assert model.encoder.embed_tokens.weight is model.shared.weight
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.T5Encoder)
-class T5EncoderCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, T5EncoderConfig):
-            raise ValueError("Only T5EncoderConfig models are currently supported here.")
-
-        match submodel_type:
-            case SubModelType.Tokenizer2:
-                return T5Tokenizer.from_pretrained(Path(config.path) / "tokenizer_2", max_length=512)
-            case SubModelType.TextEncoder2:
-                return T5EncoderModel.from_pretrained(Path(config.path) / "text_encoder_2")
-
-        raise ValueError(
-            f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.Main, format=ModelFormat.Checkpoint)
-class FluxCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, CheckpointConfigBase):
-            raise ValueError("Only CheckpointConfigBase models are currently supported here.")
-
-        match submodel_type:
-            case SubModelType.Transformer:
-                return self._load_from_singlefile(config)
-
-        raise ValueError(
-            f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-    def _load_from_singlefile(
-        self,
-        config: AnyModelConfig,
-    ) -> AnyModel:
-        assert isinstance(config, MainCheckpointConfig)
-        model_path = Path(config.path)
-
-        with SilenceWarnings():
-            model = Flux(params[config.config_path])
-            sd = load_file(model_path)
-            model.load_state_dict(sd, assign=True)
-        return model
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.Main, format=ModelFormat.BnbQuantizednf4b)
-class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
-    """Class to load main models."""
-
-    def _load_model(
-        self,
-        config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        if not isinstance(config, CheckpointConfigBase):
-            raise ValueError("Only CheckpointConfigBase models are currently supported here.")
-
-        match submodel_type:
-            case SubModelType.Transformer:
-                return self._load_from_singlefile(config)
-
-        raise ValueError(
-            f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
-        )
-
-    def _load_from_singlefile(
-        self,
-        config: AnyModelConfig,
-    ) -> AnyModel:
-        assert isinstance(config, MainBnbQuantized4bCheckpointConfig)
-        if not bnb_available:
-            raise ImportError(
-                "The bnb modules are not available. Please install bitsandbytes if available on your platform."
-            )
-        model_path = Path(config.path)
-
-        with SilenceWarnings():
-            with accelerate.init_empty_weights():
-                model = Flux(params[config.config_path])
-                model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=torch.bfloat16)
-            sd = load_file(model_path)
-            model.load_state_dict(sd, assign=True)
-        return model

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

@@ -78,12 +78,7 @@ class GenericDiffusersLoader(ModelLoader):
 
     # TO DO: Add exception handling
     def _hf_definition_to_type(self, module: str, class_name: str) -> ModelMixin:  # fix with correct type
-        if module in [
-            "diffusers",
-            "transformers",
-            "invokeai.backend.quantization.fast_quantized_transformers_model",
-            "invokeai.backend.quantization.fast_quantized_diffusion_model",
-        ]:
+        if module in ["diffusers", "transformers"]:
             res_type = sys.modules[module]
         else:
             res_type = sys.modules["diffusers"].pipelines

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

@@ -36,18 +36,8 @@ VARIANT_TO_IN_CHANNEL_MAP = {
 }
 
 
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(
-    base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Diffusers
-)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Checkpoint)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Checkpoint)
-@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Checkpoint)
-@ModelLoaderRegistry.register(
-    base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Checkpoint
-)
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Diffusers)
+@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Checkpoint)
 class StableDiffusionDiffusersModel(GenericDiffusersLoader):
     """Class to load main models."""
 

invokeai/backend/model_manager/load/model_util.py

@@ -9,9 +9,8 @@ from typing import Optional
 import torch
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
-from transformers import CLIPTokenizer, T5Tokenizer, T5TokenizerFast
+from transformers import CLIPTokenizer
 
-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
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
@@ -46,21 +45,9 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
             SpandrelImageToImageModel,
             GroundingDinoPipeline,
             SegmentAnythingPipeline,
-            DepthAnythingPipeline,
         ),
     ):
         return model.calc_size()
-    elif isinstance(
-        model,
-        (
-            T5TokenizerFast,
-            T5Tokenizer,
-        ),
-    ):
-        # HACK(ryand): len(model) just returns the vocabulary size, so this is blatantly wrong. It should be small
-        # relative to the text encoder that it's used with, so shouldn't matter too much, but we should fix this at some
-        # point.
-        return len(model)
     else:
         # TODO(ryand): Promote this from a log to an exception once we are confident that we are handling all of the
         # supported model types.

invokeai/backend/model_manager/probe.py

@@ -95,7 +95,6 @@ class ModelProbe(object):
     }
 
     CLASS2TYPE = {
-        "FluxPipeline": ModelType.Main,
         "StableDiffusionPipeline": ModelType.Main,
         "StableDiffusionInpaintPipeline": ModelType.Main,
         "StableDiffusionXLPipeline": ModelType.Main,
@@ -107,7 +106,6 @@ class ModelProbe(object):
         "ControlNetModel": ModelType.ControlNet,
         "CLIPVisionModelWithProjection": ModelType.CLIPVision,
         "T2IAdapter": ModelType.T2IAdapter,
-        "CLIPModel": ModelType.CLIPEmbed,
     }
 
     @classmethod
@@ -163,7 +161,7 @@ class ModelProbe(object):
         fields["description"] = (
             fields.get("description") or f"{fields['base'].value} {model_type.value} model {fields['name']}"
         )
-        fields["format"] = ModelFormat(fields.get("format")) if "format" in fields else probe.get_format()
+        fields["format"] = fields.get("format") or probe.get_format()
         fields["hash"] = fields.get("hash") or ModelHash(algorithm=hash_algo).hash(model_path)
 
         fields["default_settings"] = fields.get("default_settings")
@@ -178,10 +176,10 @@ class ModelProbe(object):
             fields["repo_variant"] = fields.get("repo_variant") or probe.get_repo_variant()
 
         # additional fields needed for main and controlnet models
-        if fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE] and fields["format"] in [
-            ModelFormat.Checkpoint,
-            ModelFormat.BnbQuantizednf4b,
-        ]:
+        if (
+            fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE]
+            and fields["format"] is ModelFormat.Checkpoint
+        ):
             ckpt_config_path = cls._get_checkpoint_config_path(
                 model_path,
                 model_type=fields["type"],
@@ -224,8 +222,7 @@ class ModelProbe(object):
         ckpt = ckpt.get("state_dict", ckpt)
 
         for key in [str(k) for k in ckpt.keys()]:
-            if key.startswith(("cond_stage_model.", "first_stage_model.", "model.diffusion_model.", "double_blocks.")):
-                # Keys starting with double_blocks are associated with Flux models
+            if key.startswith(("cond_stage_model.", "first_stage_model.", "model.diffusion_model.")):
                 return ModelType.Main
             elif key.startswith(("encoder.conv_in", "decoder.conv_in")):
                 return ModelType.VAE
@@ -324,27 +321,10 @@ class ModelProbe(object):
             return possible_conf.absolute()
 
         if model_type is ModelType.Main:
-            if base_type == BaseModelType.Flux:
-                # TODO: Decide between dev/schnell
-                checkpoint = ModelProbe._scan_and_load_checkpoint(model_path)
-                state_dict = checkpoint.get("state_dict") or checkpoint
-                if "guidance_in.out_layer.weight" in state_dict:
-                    # For flux, this is a key in invokeai.backend.flux.util.params
-                    #   Due to model type and format being the descriminator for model configs this
-                    #   is used rather than attempting to support flux with separate model types and format
-                    #   If changed in the future, please fix me
-                    config_file = "flux-dev"
-                else:
-                    # For flux, this is a key in invokeai.backend.flux.util.params
-                    #   Due to model type and format being the descriminator for model configs this
-                    #   is used rather than attempting to support flux with separate model types and format
-                    #   If changed in the future, please fix me
-                    config_file = "flux-schnell"
-            else:
-                config_file = LEGACY_CONFIGS[base_type][variant_type]
-                if isinstance(config_file, dict):  # need another tier for sd-2.x models
-                    config_file = config_file[prediction_type]
-                config_file = f"stable-diffusion/{config_file}"
+            config_file = LEGACY_CONFIGS[base_type][variant_type]
+            if isinstance(config_file, dict):  # need another tier for sd-2.x models
+                config_file = config_file[prediction_type]
+            config_file = f"stable-diffusion/{config_file}"
         elif model_type is ModelType.ControlNet:
             config_file = (
                 "controlnet/cldm_v15.yaml"
@@ -353,13 +333,7 @@ class ModelProbe(object):
             )
         elif model_type is ModelType.VAE:
             config_file = (
-                # For flux, this is a key in invokeai.backend.flux.util.ae_params
-                #   Due to model type and format being the descriminator for model configs this
-                #   is used rather than attempting to support flux with separate model types and format
-                #   If changed in the future, please fix me
-                "flux"
-                if base_type is BaseModelType.Flux
-                else "stable-diffusion/v1-inference.yaml"
+                "stable-diffusion/v1-inference.yaml"
                 if base_type is BaseModelType.StableDiffusion1
                 else "stable-diffusion/sd_xl_base.yaml"
                 if base_type is BaseModelType.StableDiffusionXL
@@ -442,15 +416,11 @@ class CheckpointProbeBase(ProbeBase):
         self.checkpoint = ModelProbe._scan_and_load_checkpoint(model_path)
 
     def get_format(self) -> ModelFormat:
-        state_dict = self.checkpoint.get("state_dict") or self.checkpoint
-        if "double_blocks.0.img_attn.proj.weight.quant_state.bitsandbytes__nf4" in state_dict:
-            return ModelFormat.BnbQuantizednf4b
         return ModelFormat("checkpoint")
 
     def get_variant_type(self) -> ModelVariantType:
         model_type = ModelProbe.get_model_type_from_checkpoint(self.model_path, self.checkpoint)
-        base_type = self.get_base_type()
-        if model_type != ModelType.Main or base_type == BaseModelType.Flux:
+        if model_type != ModelType.Main:
             return ModelVariantType.Normal
         state_dict = self.checkpoint.get("state_dict") or self.checkpoint
         in_channels = state_dict["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
@@ -470,8 +440,6 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
     def get_base_type(self) -> BaseModelType:
         checkpoint = self.checkpoint
         state_dict = self.checkpoint.get("state_dict") or checkpoint
-        if "double_blocks.0.img_attn.norm.key_norm.scale" in state_dict:
-            return BaseModelType.Flux
         key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
         if key_name in state_dict and state_dict[key_name].shape[-1] == 768:
             return BaseModelType.StableDiffusion1
@@ -514,7 +482,6 @@ class VaeCheckpointProbe(CheckpointProbeBase):
             (r"xl", BaseModelType.StableDiffusionXL),
             (r"sd2", BaseModelType.StableDiffusion2),
             (r"vae", BaseModelType.StableDiffusion1),
-            (r"FLUX.1-schnell_ae", BaseModelType.Flux),
         ]:
             if re.search(regexp, self.model_path.name, re.IGNORECASE):
                 return basetype
@@ -746,11 +713,6 @@ class TextualInversionFolderProbe(FolderProbeBase):
         return TextualInversionCheckpointProbe(path).get_base_type()
 
 
-class T5EncoderFolderProbe(FolderProbeBase):
-    def get_format(self) -> ModelFormat:
-        return ModelFormat.T5Encoder
-
-
 class ONNXFolderProbe(PipelineFolderProbe):
     def get_base_type(self) -> BaseModelType:
         # Due to the way the installer is set up, the configuration file for safetensors
@@ -843,11 +805,6 @@ class CLIPVisionFolderProbe(FolderProbeBase):
         return BaseModelType.Any
 
 
-class CLIPEmbedFolderProbe(FolderProbeBase):
-    def get_base_type(self) -> BaseModelType:
-        return BaseModelType.Any
-
-
 class SpandrelImageToImageFolderProbe(FolderProbeBase):
     def get_base_type(self) -> BaseModelType:
         raise NotImplementedError()
@@ -878,10 +835,8 @@ ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.VAE, VaeFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.LoRA, LoRAFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.T5Encoder, T5EncoderFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.IPAdapter, IPAdapterFolderProbe)
-ModelProbe.register_probe("diffusers", ModelType.CLIPEmbed, CLIPEmbedFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.CLIPVision, CLIPVisionFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.T2IAdapter, T2IAdapterFolderProbe)
 ModelProbe.register_probe("diffusers", ModelType.SpandrelImageToImage, SpandrelImageToImageFolderProbe)

invokeai/backend/model_manager/starter_models.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from pydantic import BaseModel
 
-from invokeai.backend.model_manager.config import BaseModelType, ModelFormat, ModelType
+from invokeai.backend.model_manager.config import BaseModelType, ModelType
 
 
 class StarterModelWithoutDependencies(BaseModel):
@@ -11,7 +11,6 @@ class StarterModelWithoutDependencies(BaseModel):
     name: str
     base: BaseModelType
     type: ModelType
-    format: Optional[ModelFormat] = None
     is_installed: bool = False
 
 
@@ -52,76 +51,10 @@ cyberrealistic_negative = StarterModel(
     type=ModelType.TextualInversion,
 )
 
-t5_base_encoder = StarterModel(
-    name="t5_base_encoder",
-    base=BaseModelType.Any,
-    source="InvokeAI/t5-v1_1-xxl::bfloat16",
-    description="T5-XXL text encoder (used in FLUX pipelines). ~8GB",
-    type=ModelType.T5Encoder,
-)
-
-t5_8b_quantized_encoder = StarterModel(
-    name="t5_bnb_int8_quantized_encoder",
-    base=BaseModelType.Any,
-    source="InvokeAI/t5-v1_1-xxl::bnb_llm_int8",
-    description="T5-XXL text encoder with bitsandbytes LLM.int8() quantization (used in FLUX pipelines). ~5GB",
-    type=ModelType.T5Encoder,
-    format=ModelFormat.BnbQuantizedLlmInt8b,
-)
-
-clip_l_encoder = StarterModel(
-    name="clip-vit-large-patch14",
-    base=BaseModelType.Any,
-    source="InvokeAI/clip-vit-large-patch14-text-encoder::bfloat16",
-    description="CLIP-L text encoder (used in FLUX pipelines). ~250MB",
-    type=ModelType.CLIPEmbed,
-)
-
-flux_vae = StarterModel(
-    name="FLUX.1-schnell_ae",
-    base=BaseModelType.Flux,
-    source="black-forest-labs/FLUX.1-schnell::ae.safetensors",
-    description="FLUX VAE compatible with both schnell and dev variants.",
-    type=ModelType.VAE,
-)
-
-
 # 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] = [
     # region: Main
-    StarterModel(
-        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",
-        type=ModelType.Main,
-        dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
-    ),
-    StarterModel(
-        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",
-        type=ModelType.Main,
-        dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
-    ),
-    StarterModel(
-        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",
-        type=ModelType.Main,
-        dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
-    ),
-    StarterModel(
-        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],
-    ),
     StarterModel(
         name="CyberRealistic v4.1",
         base=BaseModelType.StableDiffusion1,
@@ -192,7 +125,6 @@ STARTER_MODELS: list[StarterModel] = [
     # endregion
     # region VAE
     sdxl_fp16_vae_fix,
-    flux_vae,
     # endregion
     # region LoRA
     StarterModel(
@@ -518,11 +450,6 @@ STARTER_MODELS: list[StarterModel] = [
         type=ModelType.SpandrelImageToImage,
     ),
     # endregion
-    # region TextEncoders
-    t5_base_encoder,
-    t5_8b_quantized_encoder,
-    clip_l_encoder,
-    # endregion
 ]
 
 assert len(STARTER_MODELS) == len({m.source for m in STARTER_MODELS}), "Duplicate starter models"

invokeai/backend/model_manager/util/select_hf_files.py

@@ -54,7 +54,7 @@ def filter_files(
                 "lora_weights.safetensors",
                 "weights.pb",
                 "onnx_data",
-                "spiece.model",  # Added for `black-forest-labs/FLUX.1-schnell`.
+                "spiece.model", # Added for `black-forest-labs/FLUX.1-schnell`.
             )
         ):
             paths.append(file)
@@ -69,7 +69,7 @@ def filter_files(
     # limit search to subfolder if requested
     if subfolder:
         subfolder = root / subfolder
-        paths = [x for x in paths if Path(subfolder) in x.parents]
+        paths = [x for x in paths if x.parent == Path(subfolder)]
 
     # _filter_by_variant uniquifies the paths and returns a set
     return sorted(_filter_by_variant(paths, variant))

invokeai/backend/quantization/bnb_llm_int8.py

@@ -1,125 +0,0 @@
-import bitsandbytes as bnb
-import torch
-
-# This file contains utils for working with models that use bitsandbytes LLM.int8() quantization.
-# The utils in this file are partially inspired by:
-# https://github.com/Lightning-AI/pytorch-lightning/blob/1551a16b94f5234a4a78801098f64d0732ef5cb5/src/lightning/fabric/plugins/precision/bitsandbytes.py
-
-
-# NOTE(ryand): All of the custom state_dict manipulation logic in this file is pretty hacky. This could be made much
-# cleaner by re-implementing bnb.nn.Linear8bitLt with proper use of buffers and less magic. But, for now, we try to
-# stick close to the bitsandbytes classes to make interoperability easier with other models that might use bitsandbytes.
-
-
-class InvokeInt8Params(bnb.nn.Int8Params):
-    """We override cuda() to avoid re-quantizing the weights in the following cases:
-    - We loaded quantized weights from a state_dict on the cpu, and then moved the model to the gpu.
-    - We are moving the model back-and-forth between the cpu and gpu.
-    """
-
-    def cuda(self, device):
-        if self.has_fp16_weights:
-            return super().cuda(device)
-        elif self.CB is not None and self.SCB is not None:
-            self.data = self.data.cuda()
-            self.CB = self.data
-            self.SCB = self.SCB.cuda()
-        else:
-            # we store the 8-bit rows-major weight
-            # we convert this weight to the turning/ampere weight during the first inference pass
-            B = self.data.contiguous().half().cuda(device)
-            CB, CBt, SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B)
-            del CBt
-            del SCBt
-            self.data = CB
-            self.CB = CB
-            self.SCB = SCB
-
-        return self
-
-
-class InvokeLinear8bitLt(bnb.nn.Linear8bitLt):
-    def _load_from_state_dict(
-        self,
-        state_dict: dict[str, torch.Tensor],
-        prefix: str,
-        local_metadata,
-        strict,
-        missing_keys,
-        unexpected_keys,
-        error_msgs,
-    ):
-        weight = state_dict.pop(prefix + "weight")
-        bias = state_dict.pop(prefix + "bias", None)
-
-        # See `bnb.nn.Linear8bitLt._save_to_state_dict()` for the serialization logic of SCB and weight_format.
-        scb = state_dict.pop(prefix + "SCB", None)
-        # weight_format is unused, but we pop it so we can validate that there are no unexpected keys.
-        _weight_format = state_dict.pop(prefix + "weight_format", None)
-
-        # TODO(ryand): Technically, we should be using `strict`, `missing_keys`, `unexpected_keys`, and `error_msgs`
-        # rather than raising an exception to correctly implement this API.
-        assert len(state_dict) == 0
-
-        if scb is not None:
-            # We are loading a pre-quantized state dict.
-            self.weight = InvokeInt8Params(
-                data=weight,
-                requires_grad=self.weight.requires_grad,
-                has_fp16_weights=False,
-                # Note: After quantization, CB is the same as weight.
-                CB=weight,
-                SCB=scb,
-            )
-            self.bias = bias if bias is None else torch.nn.Parameter(bias)
-        else:
-            # We are loading a non-quantized state dict.
-
-            # We could simply call the `super()._load_from_state_dict()` method here, but then we wouldn't be able to
-            # load from a state_dict into a model on the "meta" device. Attempting to load into a model on the "meta"
-            # device requires setting `assign=True`, doing this with the default `super()._load_from_state_dict()`
-            # implementation causes `Params4Bit` to be replaced by a `torch.nn.Parameter`. By initializing a new
-            # `Params4bit` object, we work around this issue. It's a bit hacky, but it gets the job done.
-            self.weight = InvokeInt8Params(
-                data=weight,
-                requires_grad=self.weight.requires_grad,
-                has_fp16_weights=False,
-                CB=None,
-                SCB=None,
-            )
-            self.bias = bias if bias is None else torch.nn.Parameter(bias)
-
-
-def _convert_linear_layers_to_llm_8bit(
-    module: torch.nn.Module, ignore_modules: set[str], outlier_threshold: float, prefix: str = ""
-) -> None:
-    """Convert all linear layers in the module to bnb.nn.Linear8bitLt layers."""
-    for name, child in module.named_children():
-        fullname = f"{prefix}.{name}" if prefix else name
-        if isinstance(child, torch.nn.Linear) and not any(fullname.startswith(s) for s in ignore_modules):
-            has_bias = child.bias is not None
-            replacement = InvokeLinear8bitLt(
-                child.in_features,
-                child.out_features,
-                bias=has_bias,
-                has_fp16_weights=False,
-                threshold=outlier_threshold,
-            )
-            replacement.weight.data = child.weight.data
-            if has_bias:
-                replacement.bias.data = child.bias.data
-            replacement.requires_grad_(False)
-            module.__setattr__(name, replacement)
-        else:
-            _convert_linear_layers_to_llm_8bit(
-                child, ignore_modules, outlier_threshold=outlier_threshold, prefix=fullname
-            )
-
-
-def quantize_model_llm_int8(model: torch.nn.Module, modules_to_not_convert: set[str], outlier_threshold: float = 6.0):
-    """Apply bitsandbytes LLM.8bit() quantization to the model."""
-    _convert_linear_layers_to_llm_8bit(
-        module=model, ignore_modules=modules_to_not_convert, outlier_threshold=outlier_threshold
-    )
-
-    return model

invokeai/backend/quantization/bnb_nf4.py

@@ -1,156 +0,0 @@
-import bitsandbytes as bnb
-import torch
-
-# This file contains utils for working with models that use bitsandbytes NF4 quantization.
-# The utils in this file are partially inspired by:
-# https://github.com/Lightning-AI/pytorch-lightning/blob/1551a16b94f5234a4a78801098f64d0732ef5cb5/src/lightning/fabric/plugins/precision/bitsandbytes.py
-
-# NOTE(ryand): All of the custom state_dict manipulation logic in this file is pretty hacky. This could be made much
-# cleaner by re-implementing bnb.nn.LinearNF4 with proper use of buffers and less magic. But, for now, we try to stick
-# close to the bitsandbytes classes to make interoperability easier with other models that might use bitsandbytes.
-
-
-class InvokeLinearNF4(bnb.nn.LinearNF4):
-    """A class that extends `bnb.nn.LinearNF4` to add the following functionality:
-    - Ability to load Linear NF4 layers from a pre-quantized state_dict.
-    - Ability to load Linear NF4 layers from a state_dict when the model is on the "meta" device.
-    """
-
-    def _load_from_state_dict(
-        self,
-        state_dict: dict[str, torch.Tensor],
-        prefix: str,
-        local_metadata,
-        strict,
-        missing_keys,
-        unexpected_keys,
-        error_msgs,
-    ):
-        """This method is based on the logic in the bitsandbytes serialization unit tests for `Linear4bit`:
-        https://github.com/bitsandbytes-foundation/bitsandbytes/blob/6d714a5cce3db5bd7f577bc447becc7a92d5ccc7/tests/test_linear4bit.py#L52-L71
-        """
-        weight = state_dict.pop(prefix + "weight")
-        bias = state_dict.pop(prefix + "bias", None)
-        # We expect the remaining keys to be quant_state keys.
-        quant_state_sd = state_dict
-
-        # During serialization, the quant_state is stored as subkeys of "weight." (See
-        # `bnb.nn.LinearNF4._save_to_state_dict()`). We validate that they at least have the correct prefix.
-        # TODO(ryand): Technically, we should be using `strict`, `missing_keys`, `unexpected_keys`, and `error_msgs`
-        # rather than raising an exception to correctly implement this API.
-        assert all(k.startswith(prefix + "weight.") for k in quant_state_sd.keys())
-
-        if len(quant_state_sd) > 0:
-            # We are loading a pre-quantized state dict.
-            self.weight = bnb.nn.Params4bit.from_prequantized(
-                data=weight, quantized_stats=quant_state_sd, device=weight.device
-            )
-            self.bias = bias if bias is None else torch.nn.Parameter(bias, requires_grad=False)
-        else:
-            # We are loading a non-quantized state dict.
-
-            # We could simply call the `super()._load_from_state_dict()` method here, but then we wouldn't be able to
-            # load from a state_dict into a model on the "meta" device. Attempting to load into a model on the "meta"
-            # device requires setting `assign=True`, doing this with the default `super()._load_from_state_dict()`
-            # implementation causes `Params4Bit` to be replaced by a `torch.nn.Parameter`. By initializing a new
-            # `Params4bit` object, we work around this issue. It's a bit hacky, but it gets the job done.
-            self.weight = bnb.nn.Params4bit(
-                data=weight,
-                requires_grad=self.weight.requires_grad,
-                compress_statistics=self.weight.compress_statistics,
-                quant_type=self.weight.quant_type,
-                quant_storage=self.weight.quant_storage,
-                module=self,
-            )
-            self.bias = bias if bias is None else torch.nn.Parameter(bias)
-
-
-def _replace_param(
-    param: torch.nn.Parameter | bnb.nn.Params4bit,
-    data: torch.Tensor,
-) -> torch.nn.Parameter:
-    """A helper function to replace the data of a model parameter with new data in a way that allows replacing params on
-    the "meta" device.
-
-    Supports both `torch.nn.Parameter` and `bnb.nn.Params4bit` parameters.
-    """
-    if param.device.type == "meta":
-        # Doing `param.data = data` raises a RuntimeError if param.data was on the "meta" device, so we need to
-        # re-create the param instead of overwriting the data.
-        if isinstance(param, bnb.nn.Params4bit):
-            return bnb.nn.Params4bit(
-                data,
-                requires_grad=data.requires_grad,
-                quant_state=param.quant_state,
-                compress_statistics=param.compress_statistics,
-                quant_type=param.quant_type,
-            )
-        return torch.nn.Parameter(data, requires_grad=data.requires_grad)
-
-    param.data = data
-    return param
-
-
-def _convert_linear_layers_to_nf4(
-    module: torch.nn.Module,
-    ignore_modules: set[str],
-    compute_dtype: torch.dtype,
-    compress_statistics: bool = False,
-    prefix: str = "",
-) -> None:
-    """Convert all linear layers in the model to NF4 quantized linear layers.
-
-    Args:
-        module: All linear layers in this module will be converted.
-        ignore_modules: A set of module prefixes to ignore when converting linear layers.
-        compute_dtype: The dtype to use for computation in the quantized linear layers.
-        compress_statistics: Whether to enable nested quantization (aka double quantization) where the quantization
-           constants from the first quantization are quantized again.
-        prefix: The prefix of the current module in the model. Used to call this function recursively.
-    """
-    for name, child in module.named_children():
-        fullname = f"{prefix}.{name}" if prefix else name
-        if isinstance(child, torch.nn.Linear) and not any(fullname.startswith(s) for s in ignore_modules):
-            has_bias = child.bias is not None
-            replacement = InvokeLinearNF4(
-                child.in_features,
-                child.out_features,
-                bias=has_bias,
-                compute_dtype=compute_dtype,
-                compress_statistics=compress_statistics,
-            )
-            if has_bias:
-                replacement.bias = _replace_param(replacement.bias, child.bias.data)
-            replacement.weight = _replace_param(replacement.weight, child.weight.data)
-            replacement.requires_grad_(False)
-            module.__setattr__(name, replacement)
-        else:
-            _convert_linear_layers_to_nf4(child, ignore_modules, compute_dtype=compute_dtype, prefix=fullname)
-
-
-def quantize_model_nf4(model: torch.nn.Module, modules_to_not_convert: set[str], compute_dtype: torch.dtype):
-    """Apply bitsandbytes nf4 quantization to the model.
-
-    You likely want to call this function inside a `accelerate.init_empty_weights()` context.
-
-    Example usage:
-    ```
-    # Initialize the model from a config on the meta device.
-    with accelerate.init_empty_weights():
-        model = ModelClass.from_config(...)
-
-    # Add NF4 quantization linear layers to the model - still on the meta device.
-    with accelerate.init_empty_weights():
-        model = quantize_model_nf4(model, modules_to_not_convert=set(), compute_dtype=torch.float16)
-
-    # Load a state_dict into the model. (Could be either a prequantized or non-quantized state_dict.)
-    model.load_state_dict(state_dict, strict=True, assign=True)
-
-    # Move the model to the "cuda" device. If the model was non-quantized, this is where the weight quantization takes
-    # place.
-    model.to("cuda")
-    ```
-    """
-    _convert_linear_layers_to_nf4(module=model, ignore_modules=modules_to_not_convert, compute_dtype=compute_dtype)
-
-    return model

invokeai/backend/quantization/fast_quantized_diffusion_model.py

@@ -0,0 +1,77 @@
+import json
+import os
+from typing import Union
+
+from diffusers.models.model_loading_utils import load_state_dict
+from diffusers.utils import (
+    CONFIG_NAME,
+    SAFE_WEIGHTS_INDEX_NAME,
+    SAFETENSORS_WEIGHTS_NAME,
+    _get_checkpoint_shard_files,
+    is_accelerate_available,
+)
+from optimum.quanto.models import QuantizedDiffusersModel
+from optimum.quanto.models.shared_dict import ShardedStateDict
+
+from invokeai.backend.requantize import requantize
+
+
+class FastQuantizedDiffusersModel(QuantizedDiffusersModel):
+    @classmethod
+    def from_pretrained(cls, model_name_or_path: Union[str, os.PathLike]):
+        """We override the `from_pretrained()` method in order to use our custom `requantize()` implementation."""
+        if cls.base_class is None:
+            raise ValueError("The `base_class` attribute needs to be configured.")
+
+        if not is_accelerate_available():
+            raise ValueError("Reloading a quantized diffusers model requires the accelerate library.")
+        from accelerate import init_empty_weights
+
+        if os.path.isdir(model_name_or_path):
+            # Look for a quantization map
+            qmap_path = os.path.join(model_name_or_path, cls._qmap_name())
+            if not os.path.exists(qmap_path):
+                raise ValueError(f"No quantization map found in {model_name_or_path}: is this a quantized model ?")
+
+            # Look for original model config file.
+            model_config_path = os.path.join(model_name_or_path, CONFIG_NAME)
+            if not os.path.exists(model_config_path):
+                raise ValueError(f"{CONFIG_NAME} not found in {model_name_or_path}.")
+
+            with open(qmap_path, "r", encoding="utf-8") as f:
+                qmap = json.load(f)
+
+            with open(model_config_path, "r", encoding="utf-8") as f:
+                original_model_cls_name = json.load(f)["_class_name"]
+            configured_cls_name = cls.base_class.__name__
+            if configured_cls_name != original_model_cls_name:
+                raise ValueError(
+                    f"Configured base class ({configured_cls_name}) differs from what was derived from the provided configuration ({original_model_cls_name})."
+                )
+
+            # Create an empty model
+            config = cls.base_class.load_config(model_name_or_path)
+            with init_empty_weights():
+                model = cls.base_class.from_config(config)
+
+            # Look for the index of a sharded checkpoint
+            checkpoint_file = os.path.join(model_name_or_path, SAFE_WEIGHTS_INDEX_NAME)
+            if os.path.exists(checkpoint_file):
+                # Convert the checkpoint path to a list of shards
+                _, sharded_metadata = _get_checkpoint_shard_files(model_name_or_path, checkpoint_file)
+                # Create a mapping for the sharded safetensor files
+                state_dict = ShardedStateDict(model_name_or_path, sharded_metadata["weight_map"])
+            else:
+                # Look for a single checkpoint file
+                checkpoint_file = os.path.join(model_name_or_path, SAFETENSORS_WEIGHTS_NAME)
+                if not os.path.exists(checkpoint_file):
+                    raise ValueError(f"No safetensor weights found in {model_name_or_path}.")
+                # Get state_dict from model checkpoint
+                state_dict = load_state_dict(checkpoint_file)
+
+            # Requantize and load quantized weights from state_dict
+            requantize(model, state_dict=state_dict, quantization_map=qmap)
+            model.eval()
+            return cls(model)
+        else:
+            raise NotImplementedError("Reloading quantized models directly from the hub is not supported yet.")

invokeai/backend/quantization/fast_quantized_transformers_model.py

@@ -0,0 +1,61 @@
+import json
+import os
+from typing import Union
+
+from optimum.quanto.models import QuantizedTransformersModel
+from optimum.quanto.models.shared_dict import ShardedStateDict
+from transformers import AutoConfig
+from transformers.modeling_utils import get_checkpoint_shard_files, load_state_dict
+from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, is_accelerate_available
+
+from invokeai.backend.requantize import requantize
+
+
+class FastQuantizedTransformersModel(QuantizedTransformersModel):
+    @classmethod
+    def from_pretrained(cls, model_name_or_path: Union[str, os.PathLike]):
+        """We override the `from_pretrained()` method in order to use our custom `requantize()` implementation."""
+        if cls.auto_class is None:
+            raise ValueError(
+                "Quantized models cannot be reloaded using {cls}: use a specialized quantized class such as QuantizedModelForCausalLM instead."
+            )
+        if not is_accelerate_available():
+            raise ValueError("Reloading a quantized transformers model requires the accelerate library.")
+        from accelerate import init_empty_weights
+
+        if os.path.isdir(model_name_or_path):
+            # Look for a quantization map
+            qmap_path = os.path.join(model_name_or_path, cls._qmap_name())
+            if not os.path.exists(qmap_path):
+                raise ValueError(f"No quantization map found in {model_name_or_path}: is this a quantized model ?")
+            with open(qmap_path, "r", encoding="utf-8") as f:
+                qmap = json.load(f)
+            # Create an empty model
+            config = AutoConfig.from_pretrained(model_name_or_path)
+            with init_empty_weights():
+                model = cls.auto_class.from_config(config)
+            # Look for the index of a sharded checkpoint
+            checkpoint_file = os.path.join(model_name_or_path, SAFE_WEIGHTS_INDEX_NAME)
+            if os.path.exists(checkpoint_file):
+                # Convert the checkpoint path to a list of shards
+                checkpoint_file, sharded_metadata = get_checkpoint_shard_files(model_name_or_path, checkpoint_file)
+                # Create a mapping for the sharded safetensor files
+                state_dict = ShardedStateDict(model_name_or_path, sharded_metadata["weight_map"])
+            else:
+                # Look for a single checkpoint file
+                checkpoint_file = os.path.join(model_name_or_path, SAFE_WEIGHTS_NAME)
+                if not os.path.exists(checkpoint_file):
+                    raise ValueError(f"No safetensor weights found in {model_name_or_path}.")
+                # Get state_dict from model checkpoint
+                state_dict = load_state_dict(checkpoint_file)
+            # Requantize and load quantized weights from state_dict
+            requantize(model, state_dict=state_dict, quantization_map=qmap)
+            if getattr(model.config, "tie_word_embeddings", True):
+                # Tie output weight embeddings to input weight embeddings
+                # Note that if they were quantized they would NOT be tied
+                model.tie_weights()
+            # Set model in evaluation mode as it is done in transformers
+            model.eval()
+            return cls(model)
+        else:
+            raise NotImplementedError("Reloading quantized models directly from the hub is not supported yet.")

invokeai/backend/quantization/scripts/load_flux_model_bnb_llm_int8.py

@@ -1,79 +0,0 @@
-from pathlib import Path
-
-import accelerate
-from safetensors.torch import load_file, save_file
-
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.util import params
-from invokeai.backend.quantization.bnb_llm_int8 import quantize_model_llm_int8
-from invokeai.backend.quantization.scripts.load_flux_model_bnb_nf4 import log_time
-
-
-def main():
-    """A script for quantizing a FLUX transformer model using the bitsandbytes LLM.int8() quantization method.
-
-    This script is primarily intended for reference. The script params (e.g. the model_path, modules_to_not_convert,
-    etc.) are hardcoded and would need to be modified for other use cases.
-    """
-    # Load the FLUX transformer model onto the meta device.
-    model_path = Path(
-        "/data/invokeai/models/.download_cache/https__huggingface.co_black-forest-labs_flux.1-schnell_resolve_main_flux1-schnell.safetensors/flux1-schnell.safetensors"
-    )
-
-    with log_time("Intialize FLUX transformer on meta device"):
-        # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
-        p = params["flux-schnell"]
-
-        # Initialize the model on the "meta" device.
-        with accelerate.init_empty_weights():
-            model = Flux(p)
-
-    # TODO(ryand): We may want to add some modules to not quantize here (e.g. the proj_out layer). See the accelerate
-    # `get_keys_to_not_convert(...)` function for a heuristic to determine which modules to not quantize.
-    modules_to_not_convert: set[str] = set()
-
-    model_int8_path = model_path.parent / "bnb_llm_int8.safetensors"
-    if model_int8_path.exists():
-        # The quantized model already exists, load it and return it.
-        print(f"A pre-quantized model already exists at '{model_int8_path}'. Attempting to load it...")
-
-        # Replace the linear layers with LLM.int8() quantized linear layers (still on the meta device).
-        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
-            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
-
-        with log_time("Load state dict into model"):
-            sd = load_file(model_int8_path)
-            model.load_state_dict(sd, strict=True, assign=True)
-
-        with log_time("Move model to cuda"):
-            model = model.to("cuda")
-
-        print(f"Successfully loaded pre-quantized model from '{model_int8_path}'.")
-
-    else:
-        # The quantized model does not exist, quantize the model and save it.
-        print(f"No pre-quantized model found at '{model_int8_path}'. Quantizing the model...")
-
-        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
-            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
-
-        with log_time("Load state dict into model"):
-            state_dict = load_file(model_path)
-            # TODO(ryand): Cast the state_dict to the appropriate dtype?
-            model.load_state_dict(state_dict, strict=True, assign=True)
-
-        with log_time("Move model to cuda and quantize"):
-            model = model.to("cuda")
-
-        with log_time("Save quantized model"):
-            model_int8_path.parent.mkdir(parents=True, exist_ok=True)
-            save_file(model.state_dict(), model_int8_path)
-
-        print(f"Successfully quantized and saved model to '{model_int8_path}'.")
-
-    assert isinstance(model, Flux)
-    return model
-
-
-if __name__ == "__main__":
-    main()

invokeai/backend/quantization/scripts/load_flux_model_bnb_nf4.py

@@ -1,96 +0,0 @@
-import time
-from contextlib import contextmanager
-from pathlib import Path
-
-import accelerate
-import torch
-from safetensors.torch import load_file, save_file
-
-from invokeai.backend.flux.model import Flux
-from invokeai.backend.flux.util import params
-from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4
-
-
-@contextmanager
-def log_time(name: str):
-    """Helper context manager to log the time taken by a block of code."""
-    start = time.time()
-    try:
-        yield None
-    finally:
-        end = time.time()
-        print(f"'{name}' took {end - start:.4f} secs")
-
-
-def main():
-    """A script for quantizing a FLUX transformer model using the bitsandbytes NF4 quantization method.
-
-    This script is primarily intended for reference. The script params (e.g. the model_path, modules_to_not_convert,
-    etc.) are hardcoded and would need to be modified for other use cases.
-    """
-    model_path = Path(
-        "/data/invokeai/models/.download_cache/https__huggingface.co_black-forest-labs_flux.1-schnell_resolve_main_flux1-schnell.safetensors/flux1-schnell.safetensors"
-    )
-
-    # inference_dtype = torch.bfloat16
-    with log_time("Intialize FLUX transformer on meta device"):
-        # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
-        p = params["flux-schnell"]
-
-        # Initialize the model on the "meta" device.
-        with accelerate.init_empty_weights():
-            model = Flux(p)
-
-    # TODO(ryand): We may want to add some modules to not quantize here (e.g. the proj_out layer). See the accelerate
-    # `get_keys_to_not_convert(...)` function for a heuristic to determine which modules to not quantize.
-    modules_to_not_convert: set[str] = set()
-
-    model_nf4_path = model_path.parent / "bnb_nf4.safetensors"
-    if model_nf4_path.exists():
-        # The quantized model already exists, load it and return it.
-        print(f"A pre-quantized model already exists at '{model_nf4_path}'. Attempting to load it...")
-
-        # Replace the linear layers with NF4 quantized linear layers (still on the meta device).
-        with log_time("Replace linear layers with NF4 layers"), accelerate.init_empty_weights():
-            model = quantize_model_nf4(
-                model, modules_to_not_convert=modules_to_not_convert, compute_dtype=torch.bfloat16
-            )
-
-        with log_time("Load state dict into model"):
-            state_dict = load_file(model_nf4_path)
-            model.load_state_dict(state_dict, strict=True, assign=True)
-
-        with log_time("Move model to cuda"):
-            model = model.to("cuda")
-
-        print(f"Successfully loaded pre-quantized model from '{model_nf4_path}'.")
-
-    else:
-        # The quantized model does not exist, quantize the model and save it.
-        print(f"No pre-quantized model found at '{model_nf4_path}'. Quantizing the model...")
-
-        with log_time("Replace linear layers with NF4 layers"), accelerate.init_empty_weights():
-            model = quantize_model_nf4(
-                model, modules_to_not_convert=modules_to_not_convert, compute_dtype=torch.bfloat16
-            )
-
-        with log_time("Load state dict into model"):
-            state_dict = load_file(model_path)
-            # TODO(ryand): Cast the state_dict to the appropriate dtype?
-            model.load_state_dict(state_dict, strict=True, assign=True)
-
-        with log_time("Move model to cuda and quantize"):
-            model = model.to("cuda")
-
-        with log_time("Save quantized model"):
-            model_nf4_path.parent.mkdir(parents=True, exist_ok=True)
-            save_file(model.state_dict(), model_nf4_path)
-
-        print(f"Successfully quantized and saved model to '{model_nf4_path}'.")
-
-    assert isinstance(model, Flux)
-    return model
-
-
-if __name__ == "__main__":
-    main()

invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py

@@ -1,92 +0,0 @@
-from pathlib import Path
-
-import accelerate
-from safetensors.torch import load_file, save_file
-from transformers import AutoConfig, AutoModelForTextEncoding, T5EncoderModel
-
-from invokeai.backend.quantization.bnb_llm_int8 import quantize_model_llm_int8
-from invokeai.backend.quantization.scripts.load_flux_model_bnb_nf4 import log_time
-
-
-def load_state_dict_into_t5(model: T5EncoderModel, state_dict: dict):
-    # There is a shared reference to a single weight tensor in the model.
-    # Both "encoder.embed_tokens.weight" and "shared.weight" refer to the same tensor, so only the latter should
-    # be present in the state_dict.
-    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False, assign=True)
-    assert len(unexpected_keys) == 0
-    assert set(missing_keys) == {"encoder.embed_tokens.weight"}
-    # Assert that the layers we expect to be shared are actually shared.
-    assert model.encoder.embed_tokens.weight is model.shared.weight
-
-
-def main():
-    """A script for quantizing a T5 text encoder model using the bitsandbytes LLM.int8() quantization method.
-
-    This script is primarily intended for reference. The script params (e.g. the model_path, modules_to_not_convert,
-    etc.) are hardcoded and would need to be modified for other use cases.
-    """
-    model_path = Path("/data/misc/text_encoder_2")
-
-    with log_time("Intialize T5 on meta device"):
-        model_config = AutoConfig.from_pretrained(model_path)
-        with accelerate.init_empty_weights():
-            model = AutoModelForTextEncoding.from_config(model_config)
-
-    # TODO(ryand): We may want to add some modules to not quantize here (e.g. the proj_out layer). See the accelerate
-    # `get_keys_to_not_convert(...)` function for a heuristic to determine which modules to not quantize.
-    modules_to_not_convert: set[str] = set()
-
-    model_int8_path = model_path / "bnb_llm_int8.safetensors"
-    if model_int8_path.exists():
-        # The quantized model already exists, load it and return it.
-        print(f"A pre-quantized model already exists at '{model_int8_path}'. Attempting to load it...")
-
-        # Replace the linear layers with LLM.int8() quantized linear layers (still on the meta device).
-        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
-            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
-
-        with log_time("Load state dict into model"):
-            sd = load_file(model_int8_path)
-            load_state_dict_into_t5(model, sd)
-
-        with log_time("Move model to cuda"):
-            model = model.to("cuda")
-
-        print(f"Successfully loaded pre-quantized model from '{model_int8_path}'.")
-
-    else:
-        # The quantized model does not exist, quantize the model and save it.
-        print(f"No pre-quantized model found at '{model_int8_path}'. Quantizing the model...")
-
-        with log_time("Replace linear layers with LLM.int8() layers"), accelerate.init_empty_weights():
-            model = quantize_model_llm_int8(model, modules_to_not_convert=modules_to_not_convert)
-
-        with log_time("Load state dict into model"):
-            # Load sharded state dict.
-            files = list(model_path.glob("*.safetensors"))
-            state_dict = {}
-            for file in files:
-                sd = load_file(file)
-                state_dict.update(sd)
-            load_state_dict_into_t5(model, state_dict)
-
-        with log_time("Move model to cuda and quantize"):
-            model = model.to("cuda")
-
-        with log_time("Save quantized model"):
-            model_int8_path.parent.mkdir(parents=True, exist_ok=True)
-            state_dict = model.state_dict()
-            state_dict.pop("encoder.embed_tokens.weight")
-            save_file(state_dict, model_int8_path)
-            # This handling of shared weights could also be achieved with save_model(...), but then we'd lose control
-            # over which keys are kept. And, the corresponding load_model(...) function does not support assign=True.
-            # save_model(model, model_int8_path)
-
-        print(f"Successfully quantized and saved model to '{model_int8_path}'.")
-
-    assert isinstance(model, T5EncoderModel)
-    return model
-
-
-if __name__ == "__main__":
-    main()

invokeai/backend/requantize.py

@@ -0,0 +1,53 @@
+from typing import Any, Dict
+
+import torch
+from optimum.quanto.quantize import _quantize_submodule
+
+# def custom_freeze(model: torch.nn.Module):
+#     for name, m in model.named_modules():
+#         if isinstance(m, QModuleMixin):
+#             m.weight =
+#             m.freeze()
+
+
+def requantize(
+    model: torch.nn.Module,
+    state_dict: Dict[str, Any],
+    quantization_map: Dict[str, Dict[str, str]],
+    device: torch.device = None,
+):
+    if device is None:
+        device = next(model.parameters()).device
+        if device.type == "meta":
+            device = torch.device("cpu")
+
+    # Quantize the model with parameters from the quantization map
+    for name, m in model.named_modules():
+        qconfig = quantization_map.get(name, None)
+        if qconfig is not None:
+            weights = qconfig["weights"]
+            if weights == "none":
+                weights = None
+            activations = qconfig["activations"]
+            if activations == "none":
+                activations = None
+            _quantize_submodule(model, name, m, weights=weights, activations=activations)
+
+    # Move model parameters and buffers to CPU before materializing quantized weights
+    for name, m in model.named_modules():
+
+        def move_tensor(t, device):
+            if t.device.type == "meta":
+                return torch.empty_like(t, device=device)
+            return t.to(device)
+
+        for name, param in m.named_parameters(recurse=False):
+            setattr(m, name, torch.nn.Parameter(move_tensor(param, "cpu")))
+        for name, param in m.named_buffers(recurse=False):
+            setattr(m, name, move_tensor(param, "cpu"))
+    # Freeze model and move to target device
+    # freeze(model)
+    # model.to(device)
+
+    # Load the quantized model weights
+    model.load_state_dict(state_dict, strict=False)

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -25,6 +25,11 @@ class BasicConditioningInfo:
         return self
 
 
+@dataclass
+class ConditioningFieldData:
+    conditionings: List[BasicConditioningInfo]
+
+
 @dataclass
 class SDXLConditioningInfo(BasicConditioningInfo):
     """SDXL text conditioning information produced by Compel."""
@@ -38,17 +43,6 @@ class SDXLConditioningInfo(BasicConditioningInfo):
         return super().to(device=device, dtype=dtype)
 
 
-@dataclass
-class FLUXConditioningInfo:
-    clip_embeds: torch.Tensor
-    t5_embeds: torch.Tensor
-
-
-@dataclass
-class ConditioningFieldData:
-    conditionings: List[BasicConditioningInfo] | List[SDXLConditioningInfo] | List[FLUXConditioningInfo]
-
-
 @dataclass
 class IPAdapterConditioningInfo:
     cond_image_prompt_embeds: torch.Tensor

invokeai/backend/util/hotfixes.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union
 import diffusers
 import torch
 from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.loaders import FromOriginalControlNetMixin
+from diffusers.loaders.single_file_model import FromOriginalModelMixin
 from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
 from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
 from diffusers.models.embeddings import (
@@ -32,7 +32,7 @@ from invokeai.backend.util.logging import InvokeAILogger
 logger = InvokeAILogger.get_logger(__name__)
 
 
-class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlNetMixin):
+class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     """
     A ControlNet model.
 

invokeai/frontend/web/.eslintrc.js

@@ -12,10 +12,6 @@ module.exports = {
     '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',
   },
   overrides: [
     /**

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

@@ -1,5 +1,5 @@
 import { PropsWithChildren, memo, useEffect } from 'react';
-import { modelChanged } from '../src/features/controlLayers/store/paramsSlice';
+import { modelChanged } from '../src/features/parameters/store/generationSlice';
 import { useAppDispatch } from '../src/app/store/storeHooks';
 import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
 /**
@@ -10,9 +10,7 @@ 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(modelChanged({ key: 'test_model', hash: 'some_hash', name: 'some name', base: 'sd-1', type: 'main' }));
   }, []);
 
   return props.children;

invokeai/frontend/web/knip.ts

@@ -9,8 +9,6 @@ const config: KnipConfig = {
     'src/services/api/schema.ts',
     'src/features/nodes/types/v1/**',
     'src/features/nodes/types/v2/**',
-    // TODO(psyche): maybe we can clean up these utils after canvas v2 release
-    'src/features/controlLayers/konva/util.ts',
   ],
   ignoreBinaries: ['only-allow'],
   paths: {

invokeai/frontend/web/package.json

@@ -24,7 +24,7 @@
     "build": "pnpm run lint && vite build",
     "typegen": "node scripts/typegen.js",
     "preview": "vite preview",
-    "lint:knip": "knip --tags=-knipignore",
+    "lint:knip": "knip",
     "lint:dpdm": "dpdm --no-warning --no-tree --transform --exit-code circular:1 src/main.tsx",
     "lint:eslint": "eslint --max-warnings=0 .",
     "lint:prettier": "prettier --check .",
@@ -52,65 +52,65 @@
     }
   },
   "dependencies": {
-    "@dagrejs/dagre": "^1.1.3",
-    "@dagrejs/graphlib": "^2.2.3",
+    "@chakra-ui/react-use-size": "^2.1.0",
+    "@dagrejs/dagre": "^1.1.2",
+    "@dagrejs/graphlib": "^2.2.2",
     "@dnd-kit/core": "^6.1.0",
     "@dnd-kit/sortable": "^8.0.0",
     "@dnd-kit/utilities": "^3.2.2",
-    "@fontsource-variable/inter": "^5.0.20",
-    "@invoke-ai/ui-library": "^0.0.32",
-    "@nanostores/react": "^0.7.3",
+    "@fontsource-variable/inter": "^5.0.18",
+    "@invoke-ai/ui-library": "^0.0.25",
+    "@nanostores/react": "^0.7.2",
     "@reduxjs/toolkit": "2.2.3",
     "@roarr/browser-log-writer": "^1.3.0",
-    "async-mutex": "^0.5.0",
-    "chakra-react-select": "^4.9.1",
-    "compare-versions": "^6.1.1",
+    "chakra-react-select": "^4.7.6",
+    "compare-versions": "^6.1.0",
     "dateformat": "^5.0.3",
-    "fracturedjsonjs": "^4.0.2",
-    "framer-motion": "^11.3.24",
-    "i18next": "^23.12.2",
-    "i18next-http-backend": "^2.5.2",
+    "fracturedjsonjs": "^4.0.1",
+    "framer-motion": "^11.1.8",
+    "i18next": "^23.11.3",
+    "i18next-http-backend": "^2.5.1",
     "idb-keyval": "^6.2.1",
     "jsondiffpatch": "^0.6.0",
-    "konva": "^9.3.14",
+    "konva": "^9.3.6",
     "lodash-es": "^4.17.21",
-    "lru-cache": "^11.0.0",
-    "nanoid": "^5.0.7",
-    "nanostores": "^0.11.2",
+    "nanostores": "^0.10.3",
     "new-github-issue-url": "^1.0.0",
-    "overlayscrollbars": "^2.10.0",
+    "overlayscrollbars": "^2.7.3",
     "overlayscrollbars-react": "^0.5.6",
-    "query-string": "^9.1.0",
+    "query-string": "^9.0.0",
     "react": "^18.3.1",
     "react-colorful": "^5.6.1",
     "react-dom": "^18.3.1",
     "react-dropzone": "^14.2.3",
     "react-error-boundary": "^4.0.13",
-    "react-hook-form": "^7.52.2",
+    "react-hook-form": "^7.51.4",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^14.1.3",
-    "react-icons": "^5.2.1",
+    "react-i18next": "^14.1.1",
+    "react-icons": "^5.2.0",
+    "react-konva": "^18.2.10",
     "react-redux": "9.1.2",
-    "react-resizable-panels": "^2.0.23",
+    "react-resizable-panels": "^2.0.19",
     "react-select": "5.8.0",
-    "react-use": "^17.5.1",
-    "react-virtuoso": "^4.9.0",
-    "reactflow": "^11.11.4",
+    "react-use": "^17.5.0",
+    "react-virtuoso": "^4.7.10",
+    "reactflow": "^11.11.3",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.1.0",
     "redux-undo": "^1.1.0",
-    "rfdc": "^1.4.1",
+    "rfdc": "^1.3.1",
     "roarr": "^7.21.1",
     "serialize-error": "^11.0.3",
     "socket.io-client": "^4.7.5",
-    "stable-hash": "^0.0.4",
-    "use-debounce": "^10.0.2",
+    "use-debounce": "^10.0.0",
     "use-device-pixel-ratio": "^1.1.2",
-    "uuid": "^10.0.0",
-    "zod": "^3.23.8",
-    "zod-validation-error": "^3.3.1"
+    "use-image": "^1.1.1",
+    "uuid": "^9.0.1",
+    "zod": "^3.23.6",
+    "zod-validation-error": "^3.2.0"
   },
   "peerDependencies": {
+    "@chakra-ui/react": "^2.8.2",
     "react": "^18.2.0",
     "react-dom": "^18.2.0",
     "ts-toolbelt": "^9.6.0"
@@ -118,38 +118,38 @@
   "devDependencies": {
     "@invoke-ai/eslint-config-react": "^0.0.14",
     "@invoke-ai/prettier-config-react": "^0.0.7",
-    "@storybook/addon-essentials": "^8.2.8",
-    "@storybook/addon-interactions": "^8.2.8",
-    "@storybook/addon-links": "^8.2.8",
-    "@storybook/addon-storysource": "^8.2.8",
-    "@storybook/manager-api": "^8.2.8",
-    "@storybook/react": "^8.2.8",
-    "@storybook/react-vite": "^8.2.8",
-    "@storybook/theming": "^8.2.8",
+    "@storybook/addon-essentials": "^8.0.10",
+    "@storybook/addon-interactions": "^8.0.10",
+    "@storybook/addon-links": "^8.0.10",
+    "@storybook/addon-storysource": "^8.0.10",
+    "@storybook/manager-api": "^8.0.10",
+    "@storybook/react": "^8.0.10",
+    "@storybook/react-vite": "^8.0.10",
+    "@storybook/theming": "^8.0.10",
     "@types/dateformat": "^5.0.2",
     "@types/lodash-es": "^4.17.12",
-    "@types/node": "^20.14.15",
-    "@types/react": "^18.3.3",
+    "@types/node": "^20.12.10",
+    "@types/react": "^18.3.1",
     "@types/react-dom": "^18.3.0",
-    "@types/uuid": "^10.0.0",
-    "@vitejs/plugin-react-swc": "^3.7.0",
+    "@types/uuid": "^9.0.8",
+    "@vitejs/plugin-react-swc": "^3.6.0",
     "@vitest/coverage-v8": "^1.5.0",
     "@vitest/ui": "^1.5.0",
     "concurrently": "^8.2.2",
     "dpdm": "^3.14.0",
     "eslint": "^8.57.0",
-    "eslint-plugin-i18next": "^6.0.9",
+    "eslint-plugin-i18next": "^6.0.3",
     "eslint-plugin-path": "^1.3.0",
-    "knip": "^5.27.2",
+    "knip": "^5.12.3",
     "openapi-types": "^12.1.3",
-    "openapi-typescript": "^7.3.0",
-    "prettier": "^3.3.3",
+    "openapi-typescript": "^6.7.5",
+    "prettier": "^3.2.5",
     "rollup-plugin-visualizer": "^5.12.0",
-    "storybook": "^8.2.8",
+    "storybook": "^8.0.10",
     "ts-toolbelt": "^9.6.0",
-    "tsafe": "^1.7.2",
-    "typescript": "^5.5.4",
-    "vite": "^5.4.0",
+    "tsafe": "^1.6.6",
+    "typescript": "^5.4.5",
+    "vite": "^5.2.11",
     "vite-plugin-css-injected-by-js": "^3.5.1",
     "vite-plugin-dts": "^3.9.1",
     "vite-plugin-eslint": "^1.8.1",

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

@@ -80,7 +80,6 @@
         "aboutDesc": "Using Invoke for work? Check out:",
         "aboutHeading": "Own Your Creative Power",
         "accept": "Accept",
-        "apply": "Apply",
         "add": "Add",
         "advanced": "Advanced",
         "ai": "ai",
@@ -116,7 +115,6 @@
         "githubLabel": "Github",
         "goTo": "Go to",
         "hotkeysLabel": "Hotkeys",
-        "loadingImage": "Loading Image",
         "imageFailedToLoad": "Unable to Load Image",
         "img2img": "Image To Image",
         "inpaint": "inpaint",
@@ -202,7 +200,6 @@
         "delete": "Delete",
         "depthAnything": "Depth Anything",
         "depthAnythingDescription": "Depth map generation using the Depth Anything technique",
-        "depthAnythingSmallV2": "Small V2",
         "depthMidas": "Depth (Midas)",
         "depthMidasDescription": "Depth map generation using Midas",
         "depthZoe": "Depth (Zoe)",
@@ -327,10 +324,6 @@
         "canceled": "Canceled",
         "completedIn": "Completed in",
         "batch": "Batch",
-        "origin": "Origin",
-        "originCanvas": "Canvas",
-        "originWorkflows": "Workflows",
-        "originOther": "Other",
         "batchFieldValues": "Batch Field Values",
         "item": "Item",
         "session": "Session",
@@ -790,7 +783,6 @@
         "simpleModelPlaceholder": "URL or path to a local file or diffusers folder",
         "source": "Source",
         "starterModels": "Starter Models",
-        "starterModelsInModelManager": "Starter Models can be found in Model Manager",
         "syncModels": "Sync Models",
         "textualInversions": "Textual Inversions",
         "triggerPhrases": "Trigger Phrases",
@@ -1102,6 +1094,7 @@
         "confirmOnDelete": "Confirm On Delete",
         "developer": "Developer",
         "displayInProgress": "Display Progress Images",
+        "enableImageDebugging": "Enable Image Debugging",
         "enableInformationalPopovers": "Enable Informational Popovers",
         "informationalPopoversDisabled": "Informational Popovers Disabled",
         "informationalPopoversDisabledDesc": "Informational popovers have been disabled. Enable them in Settings.",
@@ -1147,8 +1140,6 @@
         "imageSavingFailed": "Image Saving Failed",
         "imageUploaded": "Image Uploaded",
         "imageUploadFailed": "Image Upload Failed",
-        "importFailed": "Import Failed",
-        "importSuccessful": "Import Successful",
         "invalidUpload": "Invalid Upload",
         "loadedWithWarnings": "Workflow Loaded with Warnings",
         "maskSavedAssets": "Mask Saved to Assets",
@@ -1568,7 +1559,7 @@
         "copyToClipboard": "Copy to Clipboard",
         "cursorPosition": "Cursor Position",
         "darkenOutsideSelection": "Darken Outside Selection",
-        "discardAll": "Discard All & Cancel Pending Generations",
+        "discardAll": "Discard All",
         "discardCurrent": "Discard Current",
         "downloadAsImage": "Download As Image",
         "enableMask": "Enable Mask",
@@ -1646,124 +1637,41 @@
         "storeNotInitialized": "Store is not initialized"
     },
     "controlLayers": {
-        "clearHistory": "Clear History",
-        "generateMode": "Generate",
-        "generateModeDesc": "Create individual images. Generated images are added directly to the gallery.",
-        "composeMode": "Compose",
-        "composeModeDesc": "Compose your work iterative. Generated images are added back to the canvas.",
-        "autoSave": "Auto-save to Gallery",
-        "resetCanvas": "Reset Canvas",
-        "resetAll": "Reset All",
-        "clearCaches": "Clear Caches",
-        "recalculateRects": "Recalculate Rects",
-        "clipToBbox": "Clip Strokes to Bbox",
+        "deleteAll": "Delete All",
         "addLayer": "Add Layer",
-        "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
         "moveToBack": "Move to Back",
         "moveForward": "Move Forward",
         "moveBackward": "Move Backward",
         "brushSize": "Brush Size",
-        "width": "Width",
-        "zoom": "Zoom",
-        "resetView": "Reset View",
         "controlLayers": "Control Layers",
         "globalMaskOpacity": "Global Mask Opacity",
         "autoNegative": "Auto Negative",
-        "enableAutoNegative": "Enable Auto Negative",
-        "disableAutoNegative": "Disable Auto Negative",
         "deletePrompt": "Delete Prompt",
         "resetRegion": "Reset Region",
         "debugLayers": "Debug Layers",
         "rectangle": "Rectangle",
-        "maskFill": "Mask Fill",
+        "maskPreviewColor": "Mask Preview Color",
         "addPositivePrompt": "Add $t(common.positivePrompt)",
         "addNegativePrompt": "Add $t(common.negativePrompt)",
         "addIPAdapter": "Add $t(common.ipAdapter)",
+        "regionalGuidance": "Regional Guidance",
         "regionalGuidanceLayer": "$t(controlLayers.regionalGuidance) $t(unifiedCanvas.layer)",
-        "raster": "Raster",
-        "rasterLayer_one": "Raster Layer",
-        "controlLayer_one": "Control Layer",
-        "inpaintMask_one": "Inpaint Mask",
-        "regionalGuidance_one": "Regional Guidance",
-        "ipAdapter_one": "IP Adapter",
-        "rasterLayer_other": "Raster Layers",
-        "controlLayer_other": "Control Layers",
-        "inpaintMask_other": "Inpaint Masks",
-        "regionalGuidance_other": "Regional Guidance",
-        "ipAdapter_other": "IP Adapters",
         "opacity": "Opacity",
-        "regionalGuidance_withCount_hidden": "Regional Guidance ({{count}} hidden)",
-        "controlAdapters_withCount_hidden": "Control Adapters ({{count}} hidden)",
-        "controlLayers_withCount_hidden": "Control Layers ({{count}} hidden)",
-        "rasterLayers_withCount_hidden": "Raster Layers ({{count}} hidden)",
-        "ipAdapters_withCount_hidden": "IP Adapters ({{count}} hidden)",
-        "inpaintMasks_withCount_hidden": "Inpaint Masks ({{count}} hidden)",
-        "regionalGuidance_withCount_visible": "Regional Guidance ({{count}})",
-        "controlAdapters_withCount_visible": "Control Adapters ({{count}})",
-        "controlLayers_withCount_visible": "Control Layers ({{count}})",
-        "rasterLayers_withCount_visible": "Raster Layers ({{count}})",
-        "ipAdapters_withCount_visible": "IP Adapters ({{count}})",
-        "inpaintMasks_withCount_visible": "Inpaint Masks ({{count}})",
         "globalControlAdapter": "Global $t(controlnet.controlAdapter_one)",
         "globalControlAdapterLayer": "Global $t(controlnet.controlAdapter_one) $t(unifiedCanvas.layer)",
         "globalIPAdapter": "Global $t(common.ipAdapter)",
         "globalIPAdapterLayer": "Global $t(common.ipAdapter) $t(unifiedCanvas.layer)",
         "globalInitialImage": "Global Initial Image",
         "globalInitialImageLayer": "$t(controlLayers.globalInitialImage) $t(unifiedCanvas.layer)",
-        "layer": "Layer",
         "opacityFilter": "Opacity Filter",
         "clearProcessor": "Clear Processor",
         "resetProcessor": "Reset Processor to Defaults",
         "noLayersAdded": "No Layers Added",
         "layers_one": "Layer",
-        "layers_other": "Layers",
-        "objects_zero": "empty",
-        "objects_one": "{{count}} object",
-        "objects_other": "{{count}} objects",
-        "convertToControlLayer": "Convert to Control Layer",
-        "convertToRasterLayer": "Convert to Raster Layer",
-        "transparency": "Transparency",
-        "enableTransparencyEffect": "Enable Transparency Effect",
-        "disableTransparencyEffect": "Disable Transparency Effect",
-        "hidingType": "Hiding {{type}}",
-        "showingType": "Showing {{type}}",
-        "dynamicGrid": "Dynamic Grid",
-        "logDebugInfo": "Log Debug Info",
-        "locked": "Locked",
-        "unlocked": "Unlocked",
-        "deleteSelected": "Delete Selected",
-        "deleteAll": "Delete All",
-        "fill": {
-            "fillStyle": "Fill Style",
-            "solid": "Solid",
-            "grid": "Grid",
-            "crosshatch": "Crosshatch",
-            "vertical": "Vertical",
-            "horizontal": "Horizontal",
-            "diagonal": "Diagonal"
-        },
-        "tool": {
-            "brush": "Brush",
-            "eraser": "Eraser",
-            "rectangle": "Rectangle",
-            "bbox": "Bbox",
-            "move": "Move",
-            "view": "View",
-            "transform": "Transform",
-            "colorPicker": "Color Picker"
-        },
-        "filter": {
-            "filter": "Filter",
-            "filters": "Filters",
-            "filterType": "Filter Type",
-            "preview": "Preview",
-            "apply": "Apply",
-            "cancel": "Cancel"
-        }
+        "layers_other": "Layers"
     },
     "upscaling": {
-        "upscale": "Upscale",
         "creativity": "Creativity",
         "exceedsMaxSize": "Upscale settings exceed max size limit",
         "exceedsMaxSizeDetails": "Max upscale limit is {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixels. Please try a smaller image or decrease your scale selection.",
@@ -1780,53 +1688,6 @@
         "missingUpscaleModel": "Missing upscale model",
         "missingTileControlNetModel": "No valid tile ControlNet models installed"
     },
-    "stylePresets": {
-        "active": "Active",
-        "choosePromptTemplate": "Choose Prompt Template",
-        "clearTemplateSelection": "Clear Template Selection",
-        "copyTemplate": "Copy Template",
-        "createPromptTemplate": "Create Prompt Template",
-        "defaultTemplates": "Default Templates",
-        "deleteImage": "Delete Image",
-        "deleteTemplate": "Delete Template",
-        "deleteTemplate2": "Are you sure you want to delete this template? This cannot be undone.",
-        "exportPromptTemplates": "Export My Prompt Templates (CSV)",
-        "editTemplate": "Edit Template",
-        "exportDownloaded": "Export Downloaded",
-        "exportFailed": "Unable to generate and download CSV",
-        "flatten": "Flatten selected template into current prompt",
-        "importTemplates": "Import Prompt Templates (CSV/JSON)",
-        "acceptedColumnsKeys": "Accepted columns/keys:",
-        "nameColumn": "'name'",
-        "positivePromptColumn": "'prompt' or 'positive_prompt'",
-        "negativePromptColumn": "'negative_prompt'",
-        "insertPlaceholder": "Insert placeholder",
-        "myTemplates": "My Templates",
-        "name": "Name",
-        "negativePrompt": "Negative Prompt",
-        "noTemplates": "No templates",
-        "noMatchingTemplates": "No matching templates",
-        "promptTemplatesDesc1": "Prompt templates add text to the prompts you write in the prompt box.",
-        "promptTemplatesDesc2": "Use the placeholder string <Pre>{{placeholder}}</Pre> to specify where your prompt should be included in the template.",
-        "promptTemplatesDesc3": "If you omit the placeholder, the template will be appended to the end of your prompt.",
-        "positivePrompt": "Positive Prompt",
-        "preview": "Preview",
-        "private": "Private",
-        "promptTemplateCleared": "Prompt Template Cleared",
-        "searchByName": "Search by name",
-        "shared": "Shared",
-        "sharedTemplates": "Shared Templates",
-        "templateActions": "Template Actions",
-        "templateDeleted": "Prompt template deleted",
-        "toggleViewMode": "Toggle View Mode",
-        "type": "Type",
-        "unableToDeleteTemplate": "Unable to delete prompt template",
-        "updatePromptTemplate": "Update Prompt Template",
-        "uploadImage": "Upload Image",
-        "useForTemplate": "Use For Prompt Template",
-        "viewList": "View Template List",
-        "viewModeTooltip": "This is how your prompt will look with your currently selected template. To edit your prompt, click anywhere in the text box."
-    },
     "upsell": {
         "inviteTeammates": "Invite Teammates",
         "professional": "Professional",
@@ -1848,30 +1709,5 @@
             "upscaling": "Upscaling",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
         }
-    },
-    "system": {
-        "enableLogging": "Enable Logging",
-        "logLevel": {
-            "logLevel": "Log Level",
-            "trace": "Trace",
-            "debug": "Debug",
-            "info": "Info",
-            "warn": "Warn",
-            "error": "Error",
-            "fatal": "Fatal"
-        },
-        "logNamespaces": {
-            "logNamespaces": "Log Namespaces",
-            "gallery": "Gallery",
-            "models": "Models",
-            "config": "Config",
-            "canvas": "Canvas",
-            "generation": "Generation",
-            "workflows": "Workflows",
-            "system": "System",
-            "events": "Events",
-            "queue": "Queue",
-            "metadata": "Metadata"
-        }
     }
 }

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

@@ -90,7 +90,7 @@
         "disabled": "Disabilitato",
         "comparingDesc": "Confronta due immagini",
         "comparing": "Confronta",
-        "dontShowMeThese": "Non mostrare più"
+        "dontShowMeThese": "Non mostrarmi questi"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -701,9 +701,7 @@
         "baseModelChanged": "Modello base modificato",
         "sessionRef": "Sessione: {{sessionId}}",
         "somethingWentWrong": "Qualcosa è andato storto",
-        "outOfMemoryErrorDesc": "Le impostazioni della generazione attuale superano la capacità del sistema. Modifica le impostazioni e riprova.",
-        "importFailed": "Importazione non riuscita",
-        "importSuccessful": "Importazione riuscita"
+        "outOfMemoryErrorDesc": "Le impostazioni della generazione attuale superano la capacità del sistema. Modifica le impostazioni e riprova."
     },
     "tooltip": {
         "feature": {
@@ -929,7 +927,7 @@
         "missingInvocationTemplate": "Modello di invocazione mancante",
         "missingFieldTemplate": "Modello di campo mancante",
         "singleFieldType": "{{name}} (Singola)",
-        "imageAccessError": "Impossibile trovare l'immagine {{image_name}}, ripristino ai valori predefiniti",
+        "imageAccessError": "Impossibile trovare l'immagine {{image_name}}, ripristino delle impostazioni predefinite",
         "boardAccessError": "Impossibile trovare la bacheca {{board_id}}, ripristino ai valori predefiniti",
         "modelAccessError": "Impossibile trovare il modello {{key}}, ripristino ai valori predefiniti"
     },
@@ -1528,7 +1526,7 @@
         },
         "upscaleModel": {
             "paragraphs": [
-                "Il modello di ampliamento (Upscale), scala l'immagine alle dimensioni di uscita prima di aggiungere i dettagli. È possibile utilizzare qualsiasi modello di ampliamento supportato, ma alcuni sono specializzati per diversi tipi di immagini, come foto o disegni al tratto."
+                "Il modello di ampliamento ridimensiona l'immagine alle dimensioni di uscita prima che vengano aggiunti i dettagli. È possibile utilizzare qualsiasi modello di ampliamento supportato, ma alcuni sono specializzati per diversi tipi di immagini, come foto o disegni al tratto."
             ],
             "heading": "Modello di ampliamento"
         },
@@ -1737,58 +1735,12 @@
         "missingUpscaleModel": "Modello per l’ampliamento mancante",
         "missingTileControlNetModel": "Nessun modello ControlNet Tile valido installato",
         "postProcessingModel": "Modello di post-elaborazione",
-        "postProcessingMissingModelWarning": "Visita <LinkComponent>Gestione modelli</LinkComponent> per installare un modello di post-elaborazione (da immagine a immagine).",
-        "exceedsMaxSize": "Le impostazioni di ampliamento superano il limite massimo delle dimensioni",
-        "exceedsMaxSizeDetails": "Il limite massimo di ampliamento è {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixel. Prova un'immagine più piccola o diminuisci la scala selezionata."
+        "postProcessingMissingModelWarning": "Visita <LinkComponent>Gestione modelli</LinkComponent> per installare un modello di post-elaborazione (da immagine a immagine)."
     },
     "upsell": {
         "inviteTeammates": "Invita collaboratori",
         "shareAccess": "Condividi l'accesso",
         "professional": "Professionale",
         "professionalUpsell": "Disponibile nell'edizione Professional di Invoke. Fai clic qui o visita invoke.com/pricing per ulteriori dettagli."
-    },
-    "stylePresets": {
-        "active": "Attivo",
-        "choosePromptTemplate": "Scegli un modello di prompt",
-        "clearTemplateSelection": "Cancella selezione modello",
-        "copyTemplate": "Copia modello",
-        "createPromptTemplate": "Crea modello di prompt",
-        "defaultTemplates": "Modelli predefiniti",
-        "deleteImage": "Elimina immagine",
-        "deleteTemplate": "Elimina modello",
-        "editTemplate": "Modifica modello",
-        "flatten": "Unisci il modello selezionato al prompt corrente",
-        "insertPlaceholder": "Inserisci segnaposto",
-        "myTemplates": "I miei modelli",
-        "name": "Nome",
-        "negativePrompt": "Prompt Negativo",
-        "noMatchingTemplates": "Nessun modello corrispondente",
-        "promptTemplatesDesc1": "I modelli di prompt aggiungono testo ai prompt che scrivi nelle caselle dei prompt.",
-        "promptTemplatesDesc3": "Se si omette il segnaposto, il modello verrà aggiunto alla fine del prompt.",
-        "positivePrompt": "Prompt Positivo",
-        "preview": "Anteprima",
-        "private": "Privato",
-        "searchByName": "Cerca per nome",
-        "shared": "Condiviso",
-        "sharedTemplates": "Modelli condivisi",
-        "templateDeleted": "Modello di prompt eliminato",
-        "toggleViewMode": "Attiva/disattiva visualizzazione",
-        "uploadImage": "Carica immagine",
-        "useForTemplate": "Usa per modello di prompt",
-        "viewList": "Visualizza l'elenco dei modelli",
-        "viewModeTooltip": "Ecco come apparirà il tuo prompt con il modello attualmente selezionato. Per modificare il tuo prompt, clicca in un punto qualsiasi della casella di testo.",
-        "deleteTemplate2": "Vuoi davvero eliminare questo modello? Questa operazione non può essere annullata.",
-        "unableToDeleteTemplate": "Impossibile eliminare il modello di prompt",
-        "updatePromptTemplate": "Aggiorna il modello di prompt",
-        "type": "Tipo",
-        "promptTemplatesDesc2": "Utilizza la stringa segnaposto <Pre>{{placeholder}}</Pre> per specificare dove inserire il tuo prompt nel modello.",
-        "importTemplates": "Importa modelli di prompt (CSV/JSON)",
-        "exportDownloaded": "Esportazione completata",
-        "exportFailed": "Impossibile generare e scaricare il file CSV",
-        "exportPromptTemplates": "Esporta i miei modelli di prompt (CSV)",
-        "positivePromptColumn": "'prompt' o 'positive_prompt'",
-        "noTemplates": "Nessun modello",
-        "acceptedColumnsKeys": "Colonne/chiavi accettate:",
-        "templateActions": "Azioni modello"
     }
 }

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

@@ -91,8 +91,7 @@
         "enabled": "Включено",
         "disabled": "Отключено",
         "comparingDesc": "Сравнение двух изображений",
-        "comparing": "Сравнение",
-        "dontShowMeThese": "Не показывай мне это"
+        "comparing": "Сравнение"
     },
     "gallery": {
         "galleryImageSize": "Размер изображений",
@@ -154,11 +153,7 @@
         "showArchivedBoards": "Показать архивированные доски",
         "searchImages": "Поиск по метаданным",
         "displayBoardSearch": "Отобразить поиск досок",
-        "displaySearch": "Отобразить поиск",
-        "exitBoardSearch": "Выйти из поиска досок",
-        "go": "Перейти",
-        "exitSearch": "Выйти из поиска",
-        "jump": "Пыгнуть"
+        "displaySearch": "Отобразить поиск"
     },
     "hotkeys": {
         "keyboardShortcuts": "Горячие клавиши",
@@ -381,10 +376,6 @@
         "toggleViewer": {
             "title": "Переключить просмотр изображений",
             "desc": "Переключение между средством просмотра изображений и рабочей областью для текущей вкладки."
-        },
-        "postProcess": {
-            "desc": "Обработайте текущее изображение с помощью выбранной модели постобработки",
-            "title": "Обработать изображение"
         }
     },
     "modelManager": {
@@ -598,10 +589,7 @@
         "infillColorValue": "Цвет заливки",
         "globalSettings": "Глобальные настройки",
         "globalNegativePromptPlaceholder": "Глобальный негативный запрос",
-        "globalPositivePromptPlaceholder": "Глобальный запрос",
-        "postProcessing": "Постобработка (Shift + U)",
-        "processImage": "Обработка изображения",
-        "sendToUpscale": "Отправить на увеличение"
+        "globalPositivePromptPlaceholder": "Глобальный запрос"
     },
     "settings": {
         "models": "Модели",
@@ -635,9 +623,7 @@
         "intermediatesCleared_many": "Очищено {{count}} промежуточных",
         "clearIntermediatesDesc1": "Очистка промежуточных элементов приведет к сбросу состояния Canvas и ControlNet.",
         "intermediatesClearedFailed": "Проблема очистки промежуточных",
-        "reloadingIn": "Перезагрузка через",
-        "informationalPopoversDisabled": "Информационные всплывающие окна отключены",
-        "informationalPopoversDisabledDesc": "Информационные всплывающие окна были отключены. Включите их в Настройках."
+        "reloadingIn": "Перезагрузка через"
     },
     "toast": {
         "uploadFailed": "Загрузка не удалась",
@@ -708,9 +694,7 @@
         "sessionRef": "Сессия: {{sessionId}}",
         "outOfMemoryError": "Ошибка нехватки памяти",
         "outOfMemoryErrorDesc": "Ваши текущие настройки генерации превышают возможности системы. Пожалуйста, измените настройки и повторите попытку.",
-        "somethingWentWrong": "Что-то пошло не так",
-        "importFailed": "Импорт неудачен",
-        "importSuccessful": "Импорт успешен"
+        "somethingWentWrong": "Что-то пошло не так"
     },
     "tooltip": {
         "feature": {
@@ -1033,8 +1017,7 @@
         "composition": "Только композиция",
         "hed": "HED",
         "beginEndStepPercentShort": "Начало/конец %",
-        "setControlImageDimensionsForce": "Скопируйте размер в Ш/В (игнорируйте модель)",
-        "depthAnythingSmallV2": "Small V2"
+        "setControlImageDimensionsForce": "Скопируйте размер в Ш/В (игнорируйте модель)"
     },
     "boards": {
         "autoAddBoard": "Авто добавление Доски",
@@ -1059,7 +1042,7 @@
         "downloadBoard": "Скачать доску",
         "deleteBoard": "Удалить доску",
         "deleteBoardAndImages": "Удалить доску и изображения",
-        "deletedBoardsCannotbeRestored": "Удаленные доски не могут быть восстановлены. Выбор «Удалить только доску» переведет изображения в состояние без категории.",
+        "deletedBoardsCannotbeRestored": "Удаленные доски не подлежат восстановлению",
         "assetsWithCount_one": "{{count}} ассет",
         "assetsWithCount_few": "{{count}} ассета",
         "assetsWithCount_many": "{{count}} ассетов",
@@ -1074,11 +1057,7 @@
         "boards": "Доски",
         "addPrivateBoard": "Добавить личную доску",
         "private": "Личные доски",
-        "shared": "Общие доски",
-        "hideBoards": "Скрыть доски",
-        "viewBoards": "Просмотреть доски",
-        "noBoards": "Нет досок {{boardType}}",
-        "deletedPrivateBoardsCannotbeRestored": "Удаленные доски не могут быть восстановлены. Выбор «Удалить только доску» переведет изображения в приватное состояние без категории для создателя изображения."
+        "shared": "Общие доски"
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -1438,30 +1417,6 @@
             "paragraphs": [
                 "Метод, с помощью которого применяется текущий IP-адаптер."
             ]
-        },
-        "structure": {
-            "paragraphs": [
-                "Структура контролирует, насколько точно выходное изображение будет соответствовать макету оригинала. Низкая структура допускает значительные изменения, в то время как высокая структура строго сохраняет исходную композицию и макет."
-            ],
-            "heading": "Структура"
-        },
-        "scale": {
-            "paragraphs": [
-                "Масштаб управляет размером выходного изображения и основывается на кратном разрешении входного изображения. Например, при увеличении в 2 раза изображения 1024x1024 на выходе получится 2048 x 2048."
-            ],
-            "heading": "Масштаб"
-        },
-        "creativity": {
-            "paragraphs": [
-                "Креативность контролирует степень свободы, предоставляемой модели при добавлении деталей. При низкой креативности модель остается близкой к оригинальному изображению, в то время как высокая креативность позволяет вносить больше изменений. При использовании подсказки высокая креативность увеличивает влияние подсказки."
-            ],
-            "heading": "Креативность"
-        },
-        "upscaleModel": {
-            "heading": "Модель увеличения",
-            "paragraphs": [
-                "Модель увеличения масштаба масштабирует изображение до выходного размера перед добавлением деталей. Можно использовать любую поддерживаемую модель масштабирования, но некоторые из них специализированы для различных видов изображений, например фотографий или линейных рисунков."
-            ]
         }
     },
     "metadata": {
@@ -1738,78 +1693,7 @@
             "canvasTab": "$t(ui.tabs.canvas) $t(common.tab)",
             "queueTab": "$t(ui.tabs.queue) $t(common.tab)",
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
-            "queue": "Очередь",
-            "upscaling": "Увеличение",
-            "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)"
+            "queue": "Очередь"
         }
-    },
-    "upscaling": {
-        "exceedsMaxSize": "Параметры масштабирования превышают максимальный размер",
-        "exceedsMaxSizeDetails": "Максимальный предел масштабирования составляет {{maxUpscaleDimension}}x{{maxUpscaleDimension}} пикселей. Пожалуйста, попробуйте использовать меньшее изображение или уменьшите масштаб.",
-        "structure": "Структура",
-        "missingTileControlNetModel": "Не установлены подходящие модели ControlNet",
-        "missingUpscaleInitialImage": "Отсутствует увеличиваемое изображение",
-        "missingUpscaleModel": "Отсутствует увеличивающая модель",
-        "creativity": "Креативность",
-        "upscaleModel": "Модель увеличения",
-        "scale": "Масштаб",
-        "mainModelDesc": "Основная модель (архитектура SD1.5 или SDXL)",
-        "upscaleModelDesc": "Модель увеличения (img2img)",
-        "postProcessingModel": "Модель постобработки",
-        "tileControlNetModelDesc": "Модель ControlNet для выбранной архитектуры основной модели",
-        "missingModelsWarning": "Зайдите в <LinkComponent>Менеджер моделей</LinkComponent> чтоб установить необходимые модели:",
-        "postProcessingMissingModelWarning": "Посетите <LinkComponent>Менеджер моделей</LinkComponent>, чтобы установить модель постобработки (img2img)."
-    },
-    "stylePresets": {
-        "noMatchingTemplates": "Нет подходящих шаблонов",
-        "promptTemplatesDesc1": "Шаблоны подсказок добавляют текст к подсказкам, которые вы пишете в окне подсказок.",
-        "sharedTemplates": "Общие шаблоны",
-        "templateDeleted": "Шаблон запроса удален",
-        "toggleViewMode": "Переключить режим просмотра",
-        "type": "Тип",
-        "unableToDeleteTemplate": "Не получилось удалить шаблон запроса",
-        "viewModeTooltip": "Вот как будет выглядеть ваш запрос с выбранным шаблоном. Чтобы его отредактировать, щелкните в любом месте текстового поля.",
-        "viewList": "Просмотреть список шаблонов",
-        "active": "Активно",
-        "choosePromptTemplate": "Выберите шаблон запроса",
-        "defaultTemplates": "Стандартные шаблоны",
-        "deleteImage": "Удалить изображение",
-        "deleteTemplate": "Удалить шаблон",
-        "deleteTemplate2": "Вы уверены, что хотите удалить этот шаблон? Это нельзя отменить.",
-        "editTemplate": "Редактировать шаблон",
-        "exportPromptTemplates": "Экспорт моих шаблонов запроса (CSV)",
-        "exportDownloaded": "Экспорт скачан",
-        "exportFailed": "Невозможно сгенерировать и загрузить CSV",
-        "flatten": "Объединить выбранный шаблон с текущим запросом",
-        "acceptedColumnsKeys": "Принимаемые столбцы/ключи:",
-        "positivePromptColumn": "'prompt' или 'positive_prompt'",
-        "insertPlaceholder": "Вставить заполнитель",
-        "name": "Имя",
-        "negativePrompt": "Негативный запрос",
-        "promptTemplatesDesc3": "Если вы не используете заполнитель, шаблон будет добавлен в конец запроса.",
-        "positivePrompt": "Позитивный запрос",
-        "preview": "Предпросмотр",
-        "private": "Приватный",
-        "templateActions": "Действия с шаблоном",
-        "updatePromptTemplate": "Обновить шаблон запроса",
-        "uploadImage": "Загрузить изображение",
-        "useForTemplate": "Использовать для шаблона запроса",
-        "clearTemplateSelection": "Очистить выбор шаблона",
-        "copyTemplate": "Копировать шаблон",
-        "createPromptTemplate": "Создать шаблон запроса",
-        "importTemplates": "Импортировать шаблоны запроса (CSV/JSON)",
-        "nameColumn": "'name'",
-        "negativePromptColumn": "'negative_prompt'",
-        "myTemplates": "Мои шаблоны",
-        "noTemplates": "Нет шаблонов",
-        "promptTemplatesDesc2": "Используйте строку-заполнитель <Pre>{{placeholder}}</Pre>, чтобы указать место, куда должен быть включен ваш запрос в шаблоне.",
-        "searchByName": "Поиск по имени",
-        "shared": "Общий"
-    },
-    "upsell": {
-        "inviteTeammates": "Пригласите членов команды",
-        "professional": "Профессионал",
-        "professionalUpsell": "Доступно в профессиональной версии Invoke. Нажмите здесь или посетите invoke.com/pricing для получения более подробной информации.",
-        "shareAccess": "Поделиться доступом"
     }
 }