chore: bump version to v6.1.0rc1

Make the staging area a virtualized list so it doesn't choke when there
are a large number (i.e. more than a few hundred) of queue items.

.github/CODEOWNERS

@@ -1,32 +1,31 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr @jazzhaiku
+/.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr @jazzhaiku @psychedelicious
 
 # documentation
 /docs/ @lstein @blessedcoolant @hipsterusername @psychedelicious
 /mkdocs.yml @lstein @blessedcoolant @hipsterusername @psychedelicious
 
 # nodes
-/invokeai/app/ @blessedcoolant @psychedelicious @brandonrising @hipsterusername @jazzhaiku
+/invokeai/app/ @blessedcoolant @psychedelicious @hipsterusername @jazzhaiku
 
 # installation and configuration
-/pyproject.toml  @lstein @blessedcoolant @hipsterusername
-/docker/  @lstein @blessedcoolant @hipsterusername @ebr
-/scripts/ @ebr @lstein @hipsterusername
-/installer/ @lstein @ebr @hipsterusername
-/invokeai/assets @lstein @ebr @hipsterusername
-/invokeai/configs @lstein @hipsterusername
-/invokeai/version @lstein @blessedcoolant @hipsterusername
+/pyproject.toml  @lstein @blessedcoolant @psychedelicious @hipsterusername
+/docker/  @lstein @blessedcoolant @psychedelicious @hipsterusername @ebr
+/scripts/ @ebr @lstein @psychedelicious @hipsterusername
+/installer/ @lstein @ebr @psychedelicious @hipsterusername
+/invokeai/assets @lstein @ebr @psychedelicious @hipsterusername
+/invokeai/configs @lstein @psychedelicious @hipsterusername
+/invokeai/version @lstein @blessedcoolant @psychedelicious @hipsterusername
 
 # web ui
 /invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
-/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
 
 # generation, model management, postprocessing
-/invokeai/backend  @lstein @blessedcoolant @brandonrising @hipsterusername @jazzhaiku
+/invokeai/backend  @lstein @blessedcoolant @hipsterusername @jazzhaiku @psychedelicious @maryhipp 
 
 # front ends
-/invokeai/frontend/CLI @lstein @hipsterusername
-/invokeai/frontend/install @lstein @ebr @hipsterusername
-/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
-/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/CLI @lstein @psychedelicious @hipsterusername
+/invokeai/frontend/install @lstein @ebr @psychedelicious @hipsterusername
+/invokeai/frontend/merge @lstein @blessedcoolant @psychedelicious @hipsterusername
+/invokeai/frontend/training @lstein @blessedcoolant @psychedelicious @hipsterusername
 /invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp @hipsterusername

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

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

.github/workflows/python-checks.yml

@@ -67,6 +67,10 @@ jobs:
           version: '0.6.10'
           enable-cache: true
 
+      - name: check pypi classifiers
+        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
+        run: uv run --no-project scripts/check_classifiers.py ./pyproject.toml
+
       - name: ruff check
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
         run: uv tool run ruff@0.11.2 check --output-format=github .

.gitignore

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

docs/contributing/frontend/workflows.md

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

docs/installation/manual.md

@@ -71,7 +71,14 @@ The following commands vary depending on the version of Invoke being installed a
 
 7. Determine the `PyPI` index URL to use for installation, if any. This is necessary to get the right version of torch installed.
 
-    === "Invoke v5.10.0 and later"
+    === "Invoke v5.12 and later"
+
+        - If you are on Windows or Linux with an Nvidia GPU, use `https://download.pytorch.org/whl/cu128`.
+        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
+        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.2.4`.
+        - **In all other cases, do not use an index.**
+
+    === "Invoke v5.10.0 to v5.11.0"
 
         - If you are on Windows or Linux with an Nvidia GPU, use `https://download.pytorch.org/whl/cu126`.
         - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.

docs/installation/quick_start.md

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

docs/nodes/communityNodes.md

@@ -13,6 +13,7 @@ If you'd prefer, you can also just download the whole node folder from the linke
 To use a community workflow, download the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor. 
 
 - Community Nodes
+    + [Anamorphic Tools](#anamorphic-tools)
     + [Adapters-Linked](#adapters-linked-nodes)
     + [Autostereogram](#autostereogram-nodes)
     + [Average Images](#average-images)
@@ -20,9 +21,12 @@ To use a community workflow, download the `.json` node graph file and load it in
     + [Close Color Mask](#close-color-mask) 
     + [Clothing Mask](#clothing-mask)
     + [Contrast Limited Adaptive Histogram Equalization](#contrast-limited-adaptive-histogram-equalization)
+    + [Curves](#curves)
     + [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
     + [Enhance Detail](#enhance-detail)
     + [Film Grain](#film-grain)
+    + [Flip Pose](#flip-pose)
+    + [Flux Ideal Size](#flux-ideal-size)
     + [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
     + [GPT2RandomPromptMaker](#gpt2randompromptmaker)
     + [Grid to Gif](#grid-to-gif)
@@ -61,6 +65,13 @@ To use a community workflow, download the `.json` node graph file and load it in
 - [Help](#help)
 
 
+--------------------------------
+### Anamorphic Tools
+
+**Description:** A set of nodes to perform anamorphic modifications to images, like lens blur, streaks, spherical distortion, and vignetting.
+
+**Node Link:** https://github.com/JPPhoto/anamorphic-tools
+
 --------------------------------
 ### Adapters Linked Nodes
 
@@ -132,6 +143,13 @@ Node Link: https://github.com/VeyDlin/clahe-node
 View:
 </br><img src="https://raw.githubusercontent.com/VeyDlin/clahe-node/master/.readme/node.png" width="500" />
 
+--------------------------------
+### Curves
+
+**Description:** Adjust an image's curve based on a user-defined string.
+
+**Node Link:** https://github.com/JPPhoto/curves-node
+
 --------------------------------
 ### Depth Map from Wavefront OBJ
 
@@ -162,6 +180,20 @@ To be imported, an .obj must use triangulated meshes, so make sure to enable tha
 
 **Node Link:** https://github.com/JPPhoto/film-grain-node
 
+--------------------------------
+### Flip Pose
+
+**Description:** This node will flip an openpose image horizontally, recoloring it to make sure that it isn't facing the wrong direction. Note that it does not work with openpose hands.
+
+**Node Link:** https://github.com/JPPhoto/flip-pose-node
+
+--------------------------------
+### Flux Ideal Size
+
+**Description:** This node returns an ideal size to use for the first stage of a Flux image generation pipeline. Generating at the right size helps limit duplication and odd subject placement.
+
+**Node Link:** https://github.com/JPPhoto/flux-ideal-size
+
 --------------------------------
 ### Generative Grammar-Based Prompt Nodes
 

invokeai/app/api/routers/app_info.py

@@ -1,8 +1,7 @@
 import typing
 from enum import Enum
-from importlib.metadata import PackageNotFoundError, version
+from importlib.metadata import distributions
 from pathlib import Path
-from platform import python_version
 from typing import Optional
 
 import torch
@@ -44,24 +43,6 @@ class AppVersion(BaseModel):
     highlights: Optional[list[str]] = Field(default=None, description="Highlights of release")
 
 
-class AppDependencyVersions(BaseModel):
-    """App depencency Versions Response"""
-
-    accelerate: str = Field(description="accelerate version")
-    compel: str = Field(description="compel version")
-    cuda: Optional[str] = Field(description="CUDA version")
-    diffusers: str = Field(description="diffusers version")
-    numpy: str = Field(description="Numpy version")
-    opencv: str = Field(description="OpenCV version")
-    onnx: str = Field(description="ONNX version")
-    pillow: str = Field(description="Pillow (PIL) version")
-    python: str = Field(description="Python version")
-    torch: str = Field(description="PyTorch version")
-    torchvision: str = Field(description="PyTorch Vision version")
-    transformers: str = Field(description="transformers version")
-    xformers: Optional[str] = Field(description="xformers version")
-
-
 class AppConfig(BaseModel):
     """App Config Response"""
 
@@ -76,27 +57,19 @@ async def get_version() -> AppVersion:
     return AppVersion(version=__version__)
 
 
-@app_router.get("/app_deps", operation_id="get_app_deps", status_code=200, response_model=AppDependencyVersions)
-async def get_app_deps() -> AppDependencyVersions:
+@app_router.get("/app_deps", operation_id="get_app_deps", status_code=200, response_model=dict[str, str])
+async def get_app_deps() -> dict[str, str]:
+    deps: dict[str, str] = {dist.metadata["Name"]: dist.version for dist in distributions()}
     try:
-        xformers = version("xformers")
-    except PackageNotFoundError:
-        xformers = None
-    return AppDependencyVersions(
-        accelerate=version("accelerate"),
-        compel=version("compel"),
-        cuda=torch.version.cuda,
-        diffusers=version("diffusers"),
-        numpy=version("numpy"),
-        opencv=version("opencv-python"),
-        onnx=version("onnx"),
-        pillow=version("pillow"),
-        python=python_version(),
-        torch=torch.version.__version__,
-        torchvision=version("torchvision"),
-        transformers=version("transformers"),
-        xformers=xformers,
-    )
+        cuda = torch.version.cuda or "N/A"
+    except Exception:
+        cuda = "N/A"
+
+    deps["CUDA"] = cuda
+
+    sorted_deps = dict(sorted(deps.items(), key=lambda item: item[0].lower()))
+
+    return sorted_deps
 
 
 @app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)

invokeai/app/api/routers/board_images.py

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

invokeai/app/api/routers/boards.py

@@ -146,7 +146,7 @@ async def list_boards(
     response_model=list[str],
 )
 async def list_all_board_image_names(
-    board_id: str = Path(description="The id of the board"),
+    board_id: str = Path(description="The id of the board or 'none' for uncategorized images"),
     categories: list[ImageCategory] | None = Query(default=None, description="The categories of image to include."),
     is_intermediate: bool | None = Query(default=None, description="Whether to list intermediate images."),
 ) -> list[str]:

invokeai/app/api/routers/images.py

@@ -1,24 +1,34 @@
 import io
+import json
 import traceback
-from typing import Optional
+from typing import ClassVar, Optional
 
 from fastapi import BackgroundTasks, Body, HTTPException, Path, Query, Request, Response, UploadFile
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
 from PIL import Image
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 
 from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.api.extract_metadata_from_image import extract_metadata_from_image
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
+    ImageNamesResult,
     ImageRecordChanges,
     ResourceOrigin,
 )
-from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
+from invokeai.app.services.images.images_common import (
+    DeleteImagesResult,
+    ImageDTO,
+    ImageUrlsDTO,
+    StarredImagesResult,
+    UnstarredImagesResult,
+)
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.util.controlnet_utils import heuristic_resize_fast
+from invokeai.backend.image_util.util import np_to_pil, pil_to_np
 
 images_router = APIRouter(prefix="/v1/images", tags=["images"])
 
@@ -27,6 +37,19 @@ images_router = APIRouter(prefix="/v1/images", tags=["images"])
 IMAGE_MAX_AGE = 31536000
 
 
+class ResizeToDimensions(BaseModel):
+    width: int = Field(..., gt=0)
+    height: int = Field(..., gt=0)
+
+    MAX_SIZE: ClassVar[int] = 4096 * 4096
+
+    @model_validator(mode="after")
+    def validate_total_output_size(self):
+        if self.width * self.height > self.MAX_SIZE:
+            raise ValueError(f"Max total output size for resizing is {self.MAX_SIZE} pixels")
+        return self
+
+
 @images_router.post(
     "/upload",
     operation_id="upload_image",
@@ -46,6 +69,11 @@ async def upload_image(
     board_id: Optional[str] = Query(default=None, description="The board to add this image to, if any"),
     session_id: Optional[str] = Query(default=None, description="The session ID associated with this upload, if any"),
     crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
+    resize_to: Optional[str] = Body(
+        default=None,
+        description=f"Dimensions to resize the image to, must be stringified tuple of 2 integers. Max total pixel count: {ResizeToDimensions.MAX_SIZE}",
+        examples=['"[1024,1024]"'],
+    ),
     metadata: Optional[str] = Body(
         default=None,
         description="The metadata to associate with the image, must be a stringified JSON dict",
@@ -59,13 +87,33 @@ async def upload_image(
     contents = await file.read()
     try:
         pil_image = Image.open(io.BytesIO(contents))
-        if crop_visible:
-            bbox = pil_image.getbbox()
-            pil_image = pil_image.crop(bbox)
     except Exception:
         ApiDependencies.invoker.services.logger.error(traceback.format_exc())
         raise HTTPException(status_code=415, detail="Failed to read image")
 
+    if crop_visible:
+        try:
+            bbox = pil_image.getbbox()
+            pil_image = pil_image.crop(bbox)
+        except Exception:
+            raise HTTPException(status_code=500, detail="Failed to crop image")
+
+    if resize_to:
+        try:
+            dims = json.loads(resize_to)
+            resize_dims = ResizeToDimensions(**dims)
+        except Exception:
+            raise HTTPException(status_code=400, detail="Invalid resize_to format or size")
+
+        try:
+            # heuristic_resize_fast expects an RGB or RGBA image
+            pil_rgba = pil_image.convert("RGBA")
+            np_image = pil_to_np(pil_rgba)
+            np_image = heuristic_resize_fast(np_image, (resize_dims.width, resize_dims.height))
+            pil_image = np_to_pil(np_image)
+        except Exception:
+            raise HTTPException(status_code=500, detail="Failed to resize image")
+
     extracted_metadata = extract_metadata_from_image(
         pil_image=pil_image,
         invokeai_metadata_override=metadata,
@@ -112,18 +160,30 @@ async def create_image_upload_entry(
     raise HTTPException(status_code=501, detail="Not implemented")
 
 
-@images_router.delete("/i/{image_name}", operation_id="delete_image")
+@images_router.delete("/i/{image_name}", operation_id="delete_image", response_model=DeleteImagesResult)
 async def delete_image(
     image_name: str = Path(description="The name of the image to delete"),
-) -> None:
+) -> DeleteImagesResult:
     """Deletes an image"""
 
+    deleted_images: set[str] = set()
+    affected_boards: set[str] = set()
+
     try:
+        image_dto = ApiDependencies.invoker.services.images.get_dto(image_name)
+        board_id = image_dto.board_id or "none"
         ApiDependencies.invoker.services.images.delete(image_name)
+        deleted_images.add(image_name)
+        affected_boards.add(board_id)
     except Exception:
         # TODO: Does this need any exception handling at all?
         pass
 
+    return DeleteImagesResult(
+        deleted_images=list(deleted_images),
+        affected_boards=list(affected_boards),
+    )
+
 
 @images_router.delete("/intermediates", operation_id="clear_intermediates")
 async def clear_intermediates() -> int:
@@ -335,23 +395,52 @@ async def list_image_dtos(
     return image_dtos
 
 
-class DeleteImagesFromListResult(BaseModel):
-    deleted_images: list[str]
-
-
-@images_router.post("/delete", operation_id="delete_images_from_list", response_model=DeleteImagesFromListResult)
+@images_router.post("/delete", operation_id="delete_images_from_list", response_model=DeleteImagesResult)
 async def delete_images_from_list(
     image_names: list[str] = Body(description="The list of names of images to delete", embed=True),
-) -> DeleteImagesFromListResult:
+) -> DeleteImagesResult:
     try:
-        deleted_images: list[str] = []
+        deleted_images: set[str] = set()
+        affected_boards: set[str] = set()
+        for image_name in image_names:
+            try:
+                image_dto = ApiDependencies.invoker.services.images.get_dto(image_name)
+                board_id = image_dto.board_id or "none"
+                ApiDependencies.invoker.services.images.delete(image_name)
+                deleted_images.add(image_name)
+                affected_boards.add(board_id)
+            except Exception:
+                pass
+        return DeleteImagesResult(
+            deleted_images=list(deleted_images),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to delete images")
+
+
+@images_router.delete("/uncategorized", operation_id="delete_uncategorized_images", response_model=DeleteImagesResult)
+async def delete_uncategorized_images() -> DeleteImagesResult:
+    """Deletes all images that are uncategorized"""
+
+    image_names = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
+        board_id="none", categories=None, is_intermediate=None
+    )
+
+    try:
+        deleted_images: set[str] = set()
+        affected_boards: set[str] = set()
         for image_name in image_names:
             try:
                 ApiDependencies.invoker.services.images.delete(image_name)
-                deleted_images.append(image_name)
+                deleted_images.add(image_name)
+                affected_boards.add("none")
             except Exception:
                 pass
-        return DeleteImagesFromListResult(deleted_images=deleted_images)
+        return DeleteImagesResult(
+            deleted_images=list(deleted_images),
+            affected_boards=list(affected_boards),
+        )
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to delete images")
 
@@ -360,36 +449,50 @@ class ImagesUpdatedFromListResult(BaseModel):
     updated_image_names: list[str] = Field(description="The image names that were updated")
 
 
-@images_router.post("/star", operation_id="star_images_in_list", response_model=ImagesUpdatedFromListResult)
+@images_router.post("/star", operation_id="star_images_in_list", response_model=StarredImagesResult)
 async def star_images_in_list(
     image_names: list[str] = Body(description="The list of names of images to star", embed=True),
-) -> ImagesUpdatedFromListResult:
+) -> StarredImagesResult:
     try:
-        updated_image_names: list[str] = []
+        starred_images: set[str] = set()
+        affected_boards: set[str] = set()
         for image_name in image_names:
             try:
-                ApiDependencies.invoker.services.images.update(image_name, changes=ImageRecordChanges(starred=True))
-                updated_image_names.append(image_name)
+                updated_image_dto = ApiDependencies.invoker.services.images.update(
+                    image_name, changes=ImageRecordChanges(starred=True)
+                )
+                starred_images.add(image_name)
+                affected_boards.add(updated_image_dto.board_id or "none")
             except Exception:
                 pass
-        return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
+        return StarredImagesResult(
+            starred_images=list(starred_images),
+            affected_boards=list(affected_boards),
+        )
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to star images")
 
 
-@images_router.post("/unstar", operation_id="unstar_images_in_list", response_model=ImagesUpdatedFromListResult)
+@images_router.post("/unstar", operation_id="unstar_images_in_list", response_model=UnstarredImagesResult)
 async def unstar_images_in_list(
     image_names: list[str] = Body(description="The list of names of images to unstar", embed=True),
-) -> ImagesUpdatedFromListResult:
+) -> UnstarredImagesResult:
     try:
-        updated_image_names: list[str] = []
+        unstarred_images: set[str] = set()
+        affected_boards: set[str] = set()
         for image_name in image_names:
             try:
-                ApiDependencies.invoker.services.images.update(image_name, changes=ImageRecordChanges(starred=False))
-                updated_image_names.append(image_name)
+                updated_image_dto = ApiDependencies.invoker.services.images.update(
+                    image_name, changes=ImageRecordChanges(starred=False)
+                )
+                unstarred_images.add(image_name)
+                affected_boards.add(updated_image_dto.board_id or "none")
             except Exception:
                 pass
-        return ImagesUpdatedFromListResult(updated_image_names=updated_image_names)
+        return UnstarredImagesResult(
+            unstarred_images=list(unstarred_images),
+            affected_boards=list(affected_boards),
+        )
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to unstar images")
 
@@ -460,3 +563,61 @@ async def get_bulk_download_item(
         return response
     except Exception:
         raise HTTPException(status_code=404)
+
+
+@images_router.get("/names", operation_id="get_image_names")
+async def get_image_names(
+    image_origin: Optional[ResourceOrigin] = Query(default=None, description="The origin of images to list."),
+    categories: Optional[list[ImageCategory]] = Query(default=None, description="The categories of image to include."),
+    is_intermediate: Optional[bool] = Query(default=None, description="Whether to list intermediate images."),
+    board_id: Optional[str] = Query(
+        default=None,
+        description="The board id to filter by. Use 'none' to find images without a board.",
+    ),
+    order_dir: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The order of sort"),
+    starred_first: bool = Query(default=True, description="Whether to sort by starred images first"),
+    search_term: Optional[str] = Query(default=None, description="The term to search for"),
+) -> ImageNamesResult:
+    """Gets ordered list of image names with metadata for optimistic updates"""
+
+    try:
+        result = ApiDependencies.invoker.services.images.get_image_names(
+            starred_first=starred_first,
+            order_dir=order_dir,
+            image_origin=image_origin,
+            categories=categories,
+            is_intermediate=is_intermediate,
+            board_id=board_id,
+            search_term=search_term,
+        )
+        return result
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to get image names")
+
+
+@images_router.post(
+    "/images_by_names",
+    operation_id="get_images_by_names",
+    responses={200: {"model": list[ImageDTO]}},
+)
+async def get_images_by_names(
+    image_names: list[str] = Body(embed=True, description="Object containing list of image names to fetch DTOs for"),
+) -> list[ImageDTO]:
+    """Gets image DTOs for the specified image names. Maintains order of input names."""
+
+    try:
+        image_service = ApiDependencies.invoker.services.images
+
+        # Fetch DTOs preserving the order of requested names
+        image_dtos: list[ImageDTO] = []
+        for name in image_names:
+            try:
+                dto = image_service.get_dto(name)
+                image_dtos.append(dto)
+            except Exception:
+                # Skip missing images - they may have been deleted between name fetch and DTO fetch
+                continue
+
+        return image_dtos
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to get image DTOs")

invokeai/app/api/routers/model_manager.py

@@ -41,6 +41,7 @@ from invokeai.backend.model_manager.starter_models import (
     STARTER_BUNDLES,
     STARTER_MODELS,
     StarterModel,
+    StarterModelBundle,
     StarterModelWithoutDependencies,
 )
 
@@ -291,7 +292,7 @@ async def get_hugging_face_models(
 )
 async def update_model_record(
     key: Annotated[str, Path(description="Unique key of model")],
-    changes: Annotated[ModelRecordChanges, Body(description="Model config", example=example_model_input)],
+    changes: Annotated[ModelRecordChanges, Body(description="Model config", examples=[example_model_input])],
 ) -> AnyModelConfig:
     """Update a model's config."""
     logger = ApiDependencies.invoker.services.logger
@@ -449,7 +450,7 @@ async def install_model(
     access_token: Optional[str] = Query(description="access token for the remote resource", default=None),
     config: ModelRecordChanges = Body(
         description="Object containing fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
-        example={"name": "string", "description": "string"},
+        examples=[{"name": "string", "description": "string"}],
     ),
 ) -> ModelInstallJob:
     """Install a model using a string identifier.
@@ -799,7 +800,7 @@ async def convert_model(
 
 class StarterModelResponse(BaseModel):
     starter_models: list[StarterModel]
-    starter_bundles: dict[str, list[StarterModel]]
+    starter_bundles: dict[str, StarterModelBundle]
 
 
 def get_is_installed(
@@ -833,7 +834,7 @@ async def get_starter_models() -> StarterModelResponse:
         model.dependencies = missing_deps
 
     for bundle in starter_bundles.values():
-        for model in bundle:
+        for model in bundle.models:
             model.is_installed = get_is_installed(model, installed_models)
             # Remove already-installed dependencies
             missing_deps: list[StarterModelWithoutDependencies] = []

invokeai/app/api/routers/session_queue.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from fastapi import Body, Path, Query
+from fastapi import Body, HTTPException, Path, Query
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
 
@@ -14,13 +14,15 @@ from invokeai.app.services.session_queue.session_queue_common import (
     CancelByBatchIDsResult,
     CancelByDestinationResult,
     ClearResult,
+    DeleteAllExceptCurrentResult,
+    DeleteByDestinationResult,
     EnqueueBatchResult,
     FieldIdentifier,
     PruneResult,
     RetryItemsResult,
     SessionQueueCountsByDestination,
     SessionQueueItem,
-    SessionQueueItemDTO,
+    SessionQueueItemNotFoundError,
     SessionQueueStatus,
 )
 from invokeai.app.services.shared.pagination import CursorPaginatedResults
@@ -58,17 +60,19 @@ async def enqueue_batch(
     ),
 ) -> EnqueueBatchResult:
     """Processes a batch and enqueues the output graphs for execution."""
-
-    return await ApiDependencies.invoker.services.session_queue.enqueue_batch(
-        queue_id=queue_id, batch=batch, prepend=prepend
-    )
+    try:
+        return await ApiDependencies.invoker.services.session_queue.enqueue_batch(
+            queue_id=queue_id, batch=batch, prepend=prepend
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while enqueuing batch: {e}")
 
 
 @session_queue_router.get(
     "/{queue_id}/list",
     operation_id="list_queue_items",
     responses={
-        200: {"model": CursorPaginatedResults[SessionQueueItemDTO]},
+        200: {"model": CursorPaginatedResults[SessionQueueItem]},
     },
 )
 async def list_queue_items(
@@ -77,12 +81,42 @@ async def list_queue_items(
     status: Optional[QUEUE_ITEM_STATUS] = Query(default=None, description="The status of items to fetch"),
     cursor: Optional[int] = Query(default=None, description="The pagination cursor"),
     priority: int = Query(default=0, description="The pagination cursor priority"),
-) -> CursorPaginatedResults[SessionQueueItemDTO]:
-    """Gets all queue items (without graphs)"""
+    destination: Optional[str] = Query(default=None, description="The destination of queue items to fetch"),
+) -> CursorPaginatedResults[SessionQueueItem]:
+    """Gets cursor-paginated queue items"""
 
-    return ApiDependencies.invoker.services.session_queue.list_queue_items(
-        queue_id=queue_id, limit=limit, status=status, cursor=cursor, priority=priority
-    )
+    try:
+        return ApiDependencies.invoker.services.session_queue.list_queue_items(
+            queue_id=queue_id,
+            limit=limit,
+            status=status,
+            cursor=cursor,
+            priority=priority,
+            destination=destination,
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while listing all items: {e}")
+
+
+@session_queue_router.get(
+    "/{queue_id}/list_all",
+    operation_id="list_all_queue_items",
+    responses={
+        200: {"model": list[SessionQueueItem]},
+    },
+)
+async def list_all_queue_items(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    destination: Optional[str] = Query(default=None, description="The destination of queue items to fetch"),
+) -> list[SessionQueueItem]:
+    """Gets all queue items"""
+    try:
+        return ApiDependencies.invoker.services.session_queue.list_all_queue_items(
+            queue_id=queue_id,
+            destination=destination,
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while listing all queue items: {e}")
 
 
 @session_queue_router.put(
@@ -94,7 +128,10 @@ async def resume(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> SessionProcessorStatus:
     """Resumes session processor"""
-    return ApiDependencies.invoker.services.session_processor.resume()
+    try:
+        return ApiDependencies.invoker.services.session_processor.resume()
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while resuming queue: {e}")
 
 
 @session_queue_router.put(
@@ -106,7 +143,10 @@ async def Pause(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> SessionProcessorStatus:
     """Pauses session processor"""
-    return ApiDependencies.invoker.services.session_processor.pause()
+    try:
+        return ApiDependencies.invoker.services.session_processor.pause()
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while pausing queue: {e}")
 
 
 @session_queue_router.put(
@@ -118,7 +158,25 @@ async def cancel_all_except_current(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> CancelAllExceptCurrentResult:
     """Immediately cancels all queue items except in-processing items"""
-    return ApiDependencies.invoker.services.session_queue.cancel_all_except_current(queue_id=queue_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.cancel_all_except_current(queue_id=queue_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while canceling all except current: {e}")
+
+
+@session_queue_router.put(
+    "/{queue_id}/delete_all_except_current",
+    operation_id="delete_all_except_current",
+    responses={200: {"model": DeleteAllExceptCurrentResult}},
+)
+async def delete_all_except_current(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> DeleteAllExceptCurrentResult:
+    """Immediately deletes all queue items except in-processing items"""
+    try:
+        return ApiDependencies.invoker.services.session_queue.delete_all_except_current(queue_id=queue_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while deleting all except current: {e}")
 
 
 @session_queue_router.put(
@@ -131,7 +189,12 @@ async def cancel_by_batch_ids(
     batch_ids: list[str] = Body(description="The list of batch_ids to cancel all queue items for", embed=True),
 ) -> CancelByBatchIDsResult:
     """Immediately cancels all queue items from the given batch ids"""
-    return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(queue_id=queue_id, batch_ids=batch_ids)
+    try:
+        return ApiDependencies.invoker.services.session_queue.cancel_by_batch_ids(
+            queue_id=queue_id, batch_ids=batch_ids
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while canceling by batch id: {e}")
 
 
 @session_queue_router.put(
@@ -144,9 +207,12 @@ async def cancel_by_destination(
     destination: str = Query(description="The destination to cancel all queue items for"),
 ) -> CancelByDestinationResult:
     """Immediately cancels all queue items with the given origin"""
-    return ApiDependencies.invoker.services.session_queue.cancel_by_destination(
-        queue_id=queue_id, destination=destination
-    )
+    try:
+        return ApiDependencies.invoker.services.session_queue.cancel_by_destination(
+            queue_id=queue_id, destination=destination
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while canceling by destination: {e}")
 
 
 @session_queue_router.put(
@@ -159,7 +225,10 @@ async def retry_items_by_id(
     item_ids: list[int] = Body(description="The queue item ids to retry"),
 ) -> RetryItemsResult:
     """Immediately cancels all queue items with the given origin"""
-    return ApiDependencies.invoker.services.session_queue.retry_items_by_id(queue_id=queue_id, item_ids=item_ids)
+    try:
+        return ApiDependencies.invoker.services.session_queue.retry_items_by_id(queue_id=queue_id, item_ids=item_ids)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while retrying queue items: {e}")
 
 
 @session_queue_router.put(
@@ -173,11 +242,14 @@ async def clear(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> ClearResult:
     """Clears the queue entirely, immediately canceling the currently-executing session"""
-    queue_item = ApiDependencies.invoker.services.session_queue.get_current(queue_id)
-    if queue_item is not None:
-        ApiDependencies.invoker.services.session_queue.cancel_queue_item(queue_item.item_id)
-    clear_result = ApiDependencies.invoker.services.session_queue.clear(queue_id)
-    return clear_result
+    try:
+        queue_item = ApiDependencies.invoker.services.session_queue.get_current(queue_id)
+        if queue_item is not None:
+            ApiDependencies.invoker.services.session_queue.cancel_queue_item(queue_item.item_id)
+        clear_result = ApiDependencies.invoker.services.session_queue.clear(queue_id)
+        return clear_result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while clearing queue: {e}")
 
 
 @session_queue_router.put(
@@ -191,7 +263,10 @@ async def prune(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> PruneResult:
     """Prunes all completed or errored queue items"""
-    return ApiDependencies.invoker.services.session_queue.prune(queue_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.prune(queue_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while pruning queue: {e}")
 
 
 @session_queue_router.get(
@@ -205,7 +280,10 @@ async def get_current_queue_item(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> Optional[SessionQueueItem]:
     """Gets the currently execution queue item"""
-    return ApiDependencies.invoker.services.session_queue.get_current(queue_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.get_current(queue_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while getting current queue item: {e}")
 
 
 @session_queue_router.get(
@@ -219,7 +297,10 @@ async def get_next_queue_item(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> Optional[SessionQueueItem]:
     """Gets the next queue item, without executing it"""
-    return ApiDependencies.invoker.services.session_queue.get_next(queue_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.get_next(queue_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while getting next queue item: {e}")
 
 
 @session_queue_router.get(
@@ -233,9 +314,12 @@ async def get_queue_status(
     queue_id: str = Path(description="The queue id to perform this operation on"),
 ) -> SessionQueueAndProcessorStatus:
     """Gets the status of the session queue"""
-    queue = ApiDependencies.invoker.services.session_queue.get_queue_status(queue_id)
-    processor = ApiDependencies.invoker.services.session_processor.get_status()
-    return SessionQueueAndProcessorStatus(queue=queue, processor=processor)
+    try:
+        queue = ApiDependencies.invoker.services.session_queue.get_queue_status(queue_id)
+        processor = ApiDependencies.invoker.services.session_processor.get_status()
+        return SessionQueueAndProcessorStatus(queue=queue, processor=processor)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while getting queue status: {e}")
 
 
 @session_queue_router.get(
@@ -250,7 +334,10 @@ async def get_batch_status(
     batch_id: str = Path(description="The batch to get the status of"),
 ) -> BatchStatus:
     """Gets the status of the session queue"""
-    return ApiDependencies.invoker.services.session_queue.get_batch_status(queue_id=queue_id, batch_id=batch_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.get_batch_status(queue_id=queue_id, batch_id=batch_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while getting batch status: {e}")
 
 
 @session_queue_router.get(
@@ -266,7 +353,27 @@ async def get_queue_item(
     item_id: int = Path(description="The queue item to get"),
 ) -> SessionQueueItem:
     """Gets a queue item"""
-    return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
+    except SessionQueueItemNotFoundError:
+        raise HTTPException(status_code=404, detail=f"Queue item with id {item_id} not found in queue {queue_id}")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while fetching queue item: {e}")
+
+
+@session_queue_router.delete(
+    "/{queue_id}/i/{item_id}",
+    operation_id="delete_queue_item",
+)
+async def delete_queue_item(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    item_id: int = Path(description="The queue item to delete"),
+) -> None:
+    """Deletes a queue item"""
+    try:
+        ApiDependencies.invoker.services.session_queue.delete_queue_item(item_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while deleting queue item: {e}")
 
 
 @session_queue_router.put(
@@ -281,8 +388,12 @@ async def cancel_queue_item(
     item_id: int = Path(description="The queue item to cancel"),
 ) -> SessionQueueItem:
     """Deletes a queue item"""
-
-    return ApiDependencies.invoker.services.session_queue.cancel_queue_item(item_id)
+    try:
+        return ApiDependencies.invoker.services.session_queue.cancel_queue_item(item_id)
+    except SessionQueueItemNotFoundError:
+        raise HTTPException(status_code=404, detail=f"Queue item with id {item_id} not found in queue {queue_id}")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while canceling queue item: {e}")
 
 
 @session_queue_router.get(
@@ -295,6 +406,27 @@ async def counts_by_destination(
     destination: str = Query(description="The destination to query"),
 ) -> SessionQueueCountsByDestination:
     """Gets the counts of queue items by destination"""
-    return ApiDependencies.invoker.services.session_queue.get_counts_by_destination(
-        queue_id=queue_id, destination=destination
-    )
+    try:
+        return ApiDependencies.invoker.services.session_queue.get_counts_by_destination(
+            queue_id=queue_id, destination=destination
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while fetching counts by destination: {e}")
+
+
+@session_queue_router.delete(
+    "/{queue_id}/d/{destination}",
+    operation_id="delete_by_destination",
+    responses={200: {"model": DeleteByDestinationResult}},
+)
+async def delete_by_destination(
+    queue_id: str = Path(description="The queue id to query"),
+    destination: str = Path(description="The destination to query"),
+) -> DeleteByDestinationResult:
+    """Deletes all items with the given destination"""
+    try:
+        return ApiDependencies.invoker.services.session_queue.delete_by_destination(
+            queue_id=queue_id, destination=destination
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while deleting by destination: {e}")

invokeai/app/api_app.py

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

invokeai/app/invocations/baseinvocation.py

@@ -499,7 +499,7 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
 
         ui_type = field.json_schema_extra.get("ui_type", None)
         if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
-            logger.warn(f'"UIType.{ui_type.split("_")[-1]}" is deprecated, ignoring')
+            logger.warning(f'"UIType.{ui_type.split("_")[-1]}" is deprecated, ignoring')
             field.json_schema_extra.pop("ui_type")
     return None
 
@@ -582,6 +582,8 @@ def invocation(
 
         fields: dict[str, tuple[Any, FieldInfo]] = {}
 
+        original_model_fields: dict[str, OriginalModelField] = {}
+
         for field_name, field_info in cls.model_fields.items():
             annotation = field_info.annotation
             assert annotation is not None, f"{field_name} on invocation {invocation_type} has no type annotation."
@@ -589,7 +591,7 @@ def invocation(
                 f"{field_name} on invocation {invocation_type} has a non-dict json_schema_extra, did you forget to use InputField?"
             )
 
-            cls._original_model_fields[field_name] = OriginalModelField(annotation=annotation, field_info=field_info)
+            original_model_fields[field_name] = OriginalModelField(annotation=annotation, field_info=field_info)
 
             validate_field_default(cls.__name__, field_name, invocation_type, annotation, field_info)
 
@@ -613,7 +615,7 @@ def invocation(
                 raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
             uiconfig["version"] = version
         else:
-            logger.warn(f'No version specified for node "{invocation_type}", using "1.0.0"')
+            logger.warning(f'No version specified for node "{invocation_type}", using "1.0.0"')
             uiconfig["version"] = "1.0.0"
 
         cls.UIConfig = UIConfigBase(**uiconfig)
@@ -643,6 +645,16 @@ def invocation(
 
         fields["type"] = (invocation_type_annotation, invocation_type_field_info)
 
+        # Invocation outputs must be registered using the @invocation_output decorator, but it is possible that the
+        # output is registered _after_ this invocation is registered. It depends on module import ordering.
+        #
+        # We can only confirm the output for an invocation is registered after all modules are imported. There's
+        # only really one good time to do that - during application startup, in `run_app.py`, after loading all
+        # custom nodes.
+        #
+        # We can still do some basic validation here - ensure the invoke method is defined and returns an instance
+        # of BaseInvocationOutput.
+
         # Validate the `invoke()` method is implemented
         if "invoke" in cls.__abstractmethods__:
             raise ValueError(f'Invocation "{invocation_type}" must implement the "invoke" method')
@@ -666,6 +678,7 @@ def invocation(
         docstring = cls.__doc__
         new_class = create_model(cls.__qualname__, __base__=cls, __module__=cls.__module__, **fields)  # type: ignore
         new_class.__doc__ = docstring
+        new_class._original_model_fields = original_model_fields
 
         InvocationRegistry.register_invocation(new_class)
 

invokeai/app/invocations/compel.py

@@ -1,7 +1,7 @@
 from typing import Iterator, List, Optional, Tuple, Union, cast
 
 import torch
-from compel import Compel, ReturnedEmbeddingsType
+from compel import Compel, ReturnedEmbeddingsType, SplitLongTextMode
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
@@ -104,6 +104,7 @@ class CompelInvocation(BaseInvocation):
                 dtype_for_device_getter=TorchDevice.choose_torch_dtype,
                 truncate_long_prompts=False,
                 device=TorchDevice.choose_torch_device(),
+                split_long_text_mode=SplitLongTextMode.SENTENCES,
             )
 
             conjunction = Compel.parse_prompt_string(self.prompt)
@@ -113,6 +114,13 @@ class CompelInvocation(BaseInvocation):
 
             c, _options = compel.build_conditioning_tensor_for_conjunction(conjunction)
 
+        del compel
+        del patched_tokenizer
+        del tokenizer
+        del ti_manager
+        del text_encoder
+        del text_encoder_info
+
         c = c.detach().to("cpu")
 
         conditioning_data = ConditioningFieldData(conditionings=[BasicConditioningInfo(embeds=c)])
@@ -205,6 +213,7 @@ class SDXLPromptInvocationBase:
                 returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,  # TODO: clip skip
                 requires_pooled=get_pooled,
                 device=TorchDevice.choose_torch_device(),
+                split_long_text_mode=SplitLongTextMode.SENTENCES,
             )
 
             conjunction = Compel.parse_prompt_string(prompt)
@@ -220,7 +229,10 @@ class SDXLPromptInvocationBase:
             else:
                 c_pooled = None
 
+        del compel
+        del patched_tokenizer
         del tokenizer
+        del ti_manager
         del text_encoder
         del text_encoder_info
 

invokeai/app/invocations/controlnet.py

@@ -22,7 +22,11 @@ from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
 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.app.util.controlnet_utils import (
+    CONTROLNET_MODE_VALUES,
+    CONTROLNET_RESIZE_VALUES,
+    heuristic_resize_fast,
+)
 from invokeai.backend.image_util.util import np_to_pil, pil_to_np
 
 
@@ -109,7 +113,7 @@ class ControlNetInvocation(BaseInvocation):
     title="Heuristic Resize",
     tags=["image, controlnet"],
     category="image",
-    version="1.0.1",
+    version="1.1.1",
     classification=Classification.Prototype,
 )
 class HeuristicResizeInvocation(BaseInvocation):
@@ -122,7 +126,7 @@ class HeuristicResizeInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.images.get_pil(self.image.image_name, "RGB")
         np_img = pil_to_np(image)
-        np_resized = heuristic_resize(np_img, (self.width, self.height))
+        np_resized = heuristic_resize_fast(np_img, (self.width, self.height))
         resized = np_to_pil(np_resized)
         image_dto = context.images.save(image=resized)
         return ImageOutput.build(image_dto)

invokeai/app/invocations/create_gradient_mask.py

@@ -1,12 +1,14 @@
 from typing import Literal, Optional
 
+import cv2
 import numpy as np
 import torch
 import torchvision.transforms as T
-from PIL import Image, ImageFilter
+from PIL import Image
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
     FieldDescriptions,
@@ -42,15 +44,13 @@ class GradientMaskOutput(BaseInvocationOutput):
     title="Create Gradient Mask",
     tags=["mask", "denoise"],
     category="latents",
-    version="1.2.1",
+    version="1.3.0",
 )
 class CreateGradientMaskInvocation(BaseInvocation):
-    """Creates mask for denoising model run."""
+    """Creates mask for denoising."""
 
     mask: ImageField = InputField(description="Image which will be masked", ui_order=1)
-    edge_radius: int = InputField(
-        default=16, ge=0, description="How far to blur/expand the edges of the mask", ui_order=2
-    )
+    edge_radius: int = InputField(default=16, ge=0, description="How far to expand the edges of the mask", ui_order=2)
     coherence_mode: Literal["Gaussian Blur", "Box Blur", "Staged"] = InputField(default="Gaussian Blur", ui_order=3)
     minimum_denoise: float = InputField(
         default=0.0, ge=0, le=1, description="Minimum denoise level for the coherence region", ui_order=4
@@ -81,45 +81,110 @@ class CreateGradientMaskInvocation(BaseInvocation):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> GradientMaskOutput:
         mask_image = context.images.get_pil(self.mask.image_name, mode="L")
+
+        # Resize the mask_image. Makes the filter 64x faster and doesn't hurt quality in latent scale anyway
+        mask_image = mask_image.resize(
+            (
+                mask_image.width // LATENT_SCALE_FACTOR,
+                mask_image.height // LATENT_SCALE_FACTOR,
+            ),
+            resample=Image.Resampling.BILINEAR,
+        )
+
+        mask_np_orig = np.array(mask_image, dtype=np.float32)
+
+        self.edge_radius = self.edge_radius // LATENT_SCALE_FACTOR  # scale the edge radius to match the mask size
+
         if self.edge_radius > 0:
+            mask_np = 255 - mask_np_orig  # invert so 0 is unmasked (higher values = higher denoise strength)
+            dilated_mask = mask_np.copy()
+
+            # Create kernel based on coherence mode
             if self.coherence_mode == "Box Blur":
-                blur_mask = mask_image.filter(ImageFilter.BoxBlur(self.edge_radius))
-            else:  # Gaussian Blur OR Staged
-                # Gaussian Blur uses standard deviation. 1/2 radius is a good approximation
-                blur_mask = mask_image.filter(ImageFilter.GaussianBlur(self.edge_radius / 2))
+                # Create a circular distance kernel that fades from center outward
+                kernel_size = self.edge_radius * 2 + 1
+                center = self.edge_radius
+                kernel = np.zeros((kernel_size, kernel_size), dtype=np.float32)
+                for i in range(kernel_size):
+                    for j in range(kernel_size):
+                        dist = np.sqrt((i - center) ** 2 + (j - center) ** 2)
+                        if dist <= self.edge_radius:
+                            kernel[i, j] = 1.0 - (dist / self.edge_radius)
+            else:  # Gaussian Blur or Staged
+                # Create a Gaussian kernel
+                kernel_size = self.edge_radius * 2 + 1
+                kernel = cv2.getGaussianKernel(
+                    kernel_size, self.edge_radius / 2.5
+                )  # 2.5 is a magic number (standard deviation capturing)
+                kernel = kernel * kernel.T  # Make 2D gaussian kernel
+                kernel = kernel / np.max(kernel)  # Normalize center to 1.0
 
-            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(blur_mask, normalize=False)
+                # Ensure values outside radius are 0
+                center = self.edge_radius
+                for i in range(kernel_size):
+                    for j in range(kernel_size):
+                        dist = np.sqrt((i - center) ** 2 + (j - center) ** 2)
+                        if dist > self.edge_radius:
+                            kernel[i, j] = 0
 
-            # redistribute blur so that the original edges are 0 and blur outwards to 1
-            blur_tensor = (blur_tensor - 0.5) * 2
-            blur_tensor[blur_tensor < 0] = 0.0
+            # 2D max filter
+            mask_tensor = torch.tensor(mask_np)
+            kernel_tensor = torch.tensor(kernel)
+            dilated_mask = 255 - self.max_filter2D_torch(mask_tensor, kernel_tensor).cpu()
+            dilated_mask = dilated_mask.numpy()
 
-            threshold = 1 - self.minimum_denoise
+            threshold = (1 - self.minimum_denoise) * 255
 
             if self.coherence_mode == "Staged":
-                # wherever the blur_tensor is less than fully masked, convert it to threshold
-                blur_tensor = torch.where((blur_tensor < 1) & (blur_tensor > 0), threshold, blur_tensor)
-            else:
-                # wherever the blur_tensor is above threshold but less than 1, drop it to threshold
-                blur_tensor = torch.where((blur_tensor > threshold) & (blur_tensor < 1), threshold, blur_tensor)
+                # wherever expanded mask is darker than the original mask but original was above threshhold, set it to the threshold
+                # makes any expansion areas drop to threshhold. Raising minimum across the image happen outside of this if
+                threshold_mask = (dilated_mask < mask_np_orig) & (mask_np_orig > threshold)
+                dilated_mask = np.where(threshold_mask, threshold, mask_np_orig)
+
+            # wherever expanded mask is less than 255 but greater than threshold, drop it to threshold (minimum denoise)
+            threshold_mask = (dilated_mask > threshold) & (dilated_mask < 255)
+            dilated_mask = np.where(threshold_mask, threshold, dilated_mask)
 
         else:
-            blur_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+            dilated_mask = mask_np_orig.copy()
 
-        mask_name = context.tensors.save(tensor=blur_tensor.unsqueeze(1))
+        # convert to tensor
+        dilated_mask = np.clip(dilated_mask, 0, 255).astype(np.uint8)
+        mask_tensor = torch.tensor(dilated_mask, device=torch.device("cpu"))
 
-        # compute a [0, 1] mask from the blur_tensor
-        expanded_mask = torch.where((blur_tensor < 1), 0, 1)
-        expanded_mask_image = Image.fromarray((expanded_mask.squeeze(0).numpy() * 255).astype(np.uint8), mode="L")
+        # binary mask for compositing
+        expanded_mask = np.where((dilated_mask < 255), 0, 255)
+        expanded_mask_image = Image.fromarray(expanded_mask.astype(np.uint8), mode="L")
+        expanded_mask_image = expanded_mask_image.resize(
+            (
+                mask_image.width * LATENT_SCALE_FACTOR,
+                mask_image.height * LATENT_SCALE_FACTOR,
+            ),
+            resample=Image.Resampling.NEAREST,
+        )
         expanded_image_dto = context.images.save(expanded_mask_image)
 
+        # restore the original mask size
+        dilated_mask = Image.fromarray(dilated_mask.astype(np.uint8))
+        dilated_mask = dilated_mask.resize(
+            (
+                mask_image.width * LATENT_SCALE_FACTOR,
+                mask_image.height * LATENT_SCALE_FACTOR,
+            ),
+            resample=Image.Resampling.NEAREST,
+        )
+
+        # stack the mask as a tensor, repeating 4 times on dimmension 1
+        dilated_mask_tensor = image_resized_to_grid_as_tensor(dilated_mask, normalize=False)
+        mask_name = context.tensors.save(tensor=dilated_mask_tensor.unsqueeze(0))
+
         masked_latents_name = None
         if self.unet is not None and self.vae is not None and self.image is not None:
             # all three fields must be present at the same time
             main_model_config = context.models.get_config(self.unet.unet.key)
             assert isinstance(main_model_config, MainConfigBase)
             if main_model_config.variant is ModelVariantType.Inpaint:
-                mask = blur_tensor
+                mask = dilated_mask_tensor
                 vae_info: LoadedModel = context.models.load(self.vae.vae)
                 image = context.images.get_pil(self.image.image_name)
                 image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
@@ -137,3 +202,29 @@ class CreateGradientMaskInvocation(BaseInvocation):
             denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=masked_latents_name, gradient=True),
             expanded_mask_area=ImageField(image_name=expanded_image_dto.image_name),
         )
+
+    def max_filter2D_torch(self, image: torch.Tensor, kernel: torch.Tensor) -> torch.Tensor:
+        """
+        This morphological operation is much faster in torch than numpy or opencv
+        For reasonable kernel sizes, the overhead of copying the data to the GPU is not worth it.
+        """
+        h, w = kernel.shape
+        pad_h, pad_w = h // 2, w // 2
+
+        padded = torch.nn.functional.pad(image, (pad_w, pad_w, pad_h, pad_h), mode="constant", value=0)
+        result = torch.zeros_like(image)
+
+        # This looks like it's inside out, but it does the same thing and is more efficient
+        for i in range(h):
+            for j in range(w):
+                weight = kernel[i, j]
+                if weight <= 0:
+                    continue
+
+                # Extract the region from padded tensor
+                region = padded[i : i + image.shape[0], j : j + image.shape[1]]
+
+                # Apply weight and update max
+                result = torch.maximum(result, region * weight)
+
+        return result

invokeai/app/invocations/fields.py

@@ -62,7 +62,9 @@ class UIType(str, Enum, metaclass=MetaEnum):
     FluxReduxModel = "FluxReduxModelField"
     LlavaOnevisionModel = "LLaVAModelField"
     Imagen3Model = "Imagen3ModelField"
+    Imagen4Model = "Imagen4ModelField"
     ChatGPT4oModel = "ChatGPT4oModelField"
+    FluxKontextModel = "FluxKontextModelField"
     # endregion
 
     # region Misc Field Types
@@ -213,6 +215,7 @@ class FieldDescriptions:
     flux_redux_conditioning = "FLUX Redux conditioning tensor"
     vllm_model = "The VLLM model to use"
     flux_fill_conditioning = "FLUX Fill conditioning tensor"
+    flux_kontext_conditioning = "FLUX Kontext conditioning (reference image)"
 
 
 class ImageField(BaseModel):
@@ -289,6 +292,12 @@ class FluxFillConditioningField(BaseModel):
     mask: TensorField = Field(description="The FLUX Fill inpaint mask.")
 
 
+class FluxKontextConditioningField(BaseModel):
+    """A conditioning field for FLUX Kontext (reference image)."""
+
+    image: ImageField = Field(description="The Kontext reference image.")
+
+
 class SD3ConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 
@@ -436,7 +445,7 @@ class WithWorkflow:
     workflow = None
 
     def __init_subclass__(cls) -> None:
-        logger.warn(
+        logger.warning(
             f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
         )
         super().__init_subclass__()
@@ -577,7 +586,7 @@ def InputField(
 
     if default_factory is not _Unset and default_factory is not None:
         default = default_factory()
-        logger.warn('"default_factory" is not supported, calling it now to set "default"')
+        logger.warning('"default_factory" is not supported, calling it now to set "default"')
 
     # These are the args we may wish pass to the pydantic `Field()` function
     field_args = {

invokeai/app/invocations/flux_denoise.py

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

invokeai/app/invocations/flux_kontext.py

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

invokeai/app/invocations/flux_text_encoder.py

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

invokeai/app/invocations/image.py

@@ -1218,12 +1218,15 @@ class ApplyMaskToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Add Image Noise",
     tags=["image", "noise"],
     category="image",
-    version="1.0.1",
+    version="1.1.0",
 )
 class ImageNoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Add noise to an image"""
 
     image: ImageField = InputField(description="The image to add noise to")
+    mask: Optional[ImageField] = InputField(
+        default=None, description="Optional mask determining where to apply noise (black=noise, white=no noise)"
+    )
     seed: int = InputField(
         default=0,
         ge=0,
@@ -1267,12 +1270,27 @@ class ImageNoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         noise = Image.fromarray(noise.astype(numpy.uint8), mode="RGB").resize(
             (image.width, image.height), Image.Resampling.NEAREST
         )
+
+        # Create a noisy version of the input image
         noisy_image = Image.blend(image.convert("RGB"), noise, self.amount).convert("RGBA")
 
-        # Paste back the alpha channel
-        noisy_image.putalpha(alpha)
+        # Apply mask if provided
+        if self.mask is not None:
+            mask_image = context.images.get_pil(self.mask.image_name, mode="L")
 
-        image_dto = context.images.save(image=noisy_image)
+            if mask_image.size != image.size:
+                mask_image = mask_image.resize(image.size, Image.Resampling.LANCZOS)
+
+            result_image = image.copy()
+            mask_image = ImageOps.invert(mask_image)
+            result_image.paste(noisy_image, (0, 0), mask=mask_image)
+        else:
+            result_image = noisy_image
+
+        # Paste back the alpha channel from the original image
+        result_image.putalpha(alpha)
+
+        image_dto = context.images.save(image=result_image)
 
         return ImageOutput.build(image_dto)
 

invokeai/app/invocations/metadata.py

@@ -42,7 +42,9 @@ class IPAdapterMetadataField(BaseModel):
     image: ImageField = Field(description="The IP-Adapter image prompt.")
     ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model.")
     clip_vision_model: Literal["ViT-L", "ViT-H", "ViT-G"] = Field(description="The CLIP Vision model")
-    method: Literal["full", "style", "composition"] = Field(description="Method to apply IP Weights with")
+    method: Literal["full", "style", "composition", "style_strong", "style_precise"] = Field(
+        description="Method to apply IP Weights with"
+    )
     weight: Union[float, list[float]] = Field(description="The weight given to the IP-Adapter")
     begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
     end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")

invokeai/app/invocations/segment_anything.py

@@ -6,7 +6,7 @@ import numpy as np
 import torch
 from PIL import Image
 from pydantic import BaseModel, Field
-from transformers import AutoModelForMaskGeneration, AutoProcessor
+from transformers import AutoProcessor
 from transformers.models.sam import SamModel
 from transformers.models.sam.processing_sam import SamProcessor
 
@@ -104,14 +104,13 @@ class SegmentAnythingInvocation(BaseInvocation):
 
     @staticmethod
     def _load_sam_model(model_path: Path):
-        sam_model = AutoModelForMaskGeneration.from_pretrained(
+        sam_model = SamModel.from_pretrained(
             model_path,
             local_files_only=True,
             # TODO(ryand): Setting the torch_dtype here doesn't work. Investigate whether fp16 is supported by the
             # model, and figure out how to make it work in the pipeline.
             # torch_dtype=TorchDevice.choose_torch_dtype(),
         )
-        assert isinstance(sam_model, SamModel)
 
         sam_processor = AutoProcessor.from_pretrained(model_path, local_files_only=True)
         assert isinstance(sam_processor, SamProcessor)

invokeai/app/run_app.py

@@ -1,12 +1,3 @@
-import uvicorn
-
-from invokeai.app.invocations.load_custom_nodes import load_custom_nodes
-from invokeai.app.services.config.config_default import get_config
-from invokeai.app.util.torch_cuda_allocator import configure_torch_cuda_allocator
-from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.frontend.cli.arg_parser import InvokeAIArgs
-
-
 def get_app():
     """Import the app and event loop. We wrap this in a function to more explicitly control when it happens, because
     importing from api_app does a bunch of stuff - it's more like calling a function than importing a module.
@@ -18,9 +9,18 @@ def get_app():
 
 def run_app() -> None:
     """The main entrypoint for the app."""
-    # Parse the CLI arguments.
+    from invokeai.frontend.cli.arg_parser import InvokeAIArgs
+
+    # Parse the CLI arguments before doing anything else, which ensures CLI args correctly override settings from other
+    # sources like `invokeai.yaml` or env vars.
     InvokeAIArgs.parse_args()
 
+    import uvicorn
+
+    from invokeai.app.services.config.config_default import get_config
+    from invokeai.app.util.torch_cuda_allocator import configure_torch_cuda_allocator
+    from invokeai.backend.util.logging import InvokeAILogger
+
     # Load config.
     app_config = get_config()
 
@@ -32,6 +32,8 @@ def run_app() -> None:
         configure_torch_cuda_allocator(app_config.pytorch_cuda_alloc_conf, logger)
 
     # This import must happen after configure_torch_cuda_allocator() is called, because the module imports torch.
+    from invokeai.app.invocations.baseinvocation import InvocationRegistry
+    from invokeai.app.invocations.load_custom_nodes import load_custom_nodes
     from invokeai.backend.util.devices import TorchDevice
 
     torch_device_name = TorchDevice.get_torch_device_name()
@@ -66,6 +68,15 @@ def run_app() -> None:
     # core nodes have been imported so that we can catch when a custom node clobbers a core node.
     load_custom_nodes(custom_nodes_path=app_config.custom_nodes_path, logger=logger)
 
+    # Check all invocations and ensure their outputs are registered.
+    for invocation in InvocationRegistry.get_invocation_classes():
+        invocation_type = invocation.get_type()
+        output_annotation = invocation.get_output_annotation()
+        if output_annotation not in InvocationRegistry.get_output_classes():
+            logger.warning(
+                f'Invocation "{invocation_type}" has unregistered output class "{output_annotation.__name__}"'
+            )
+
     if app_config.dev_reload:
         # load_custom_nodes seems to bypass jurrigged's import sniffer, so be sure to call it *after* they're already
         # imported.

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -98,9 +98,18 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
                 FROM images
                 LEFT JOIN board_images ON board_images.image_name = images.image_name
                 WHERE 1=1
+                """
+
+        # Handle board_id filter
+        if board_id == "none":
+            stmt += """--sql
+                AND board_images.board_id IS NULL
+                """
+        else:
+            stmt += """--sql
                 AND board_images.board_id = ?
                 """
-        params.append(board_id)
+            params.append(board_id)
 
         # Add the category filter
         if categories is not None:

invokeai/app/services/config/config_default.py

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

invokeai/app/services/image_records/image_records_base.py

@@ -5,6 +5,7 @@ from typing import Optional
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
+    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ResourceOrigin,
@@ -97,3 +98,17 @@ class ImageRecordStorageBase(ABC):
     def get_most_recent_image_for_board(self, board_id: str) -> Optional[ImageRecord]:
         """Gets the most recent image for a board."""
         pass
+
+    @abstractmethod
+    def get_image_names(
+        self,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        image_origin: Optional[ResourceOrigin] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> ImageNamesResult:
+        """Gets ordered list of image names with metadata for optimistic updates."""
+        pass

invokeai/app/services/image_records/image_records_common.py

@@ -3,7 +3,7 @@ import datetime
 from enum import Enum
 from typing import Optional, Union
 
-from pydantic import Field, StrictBool, StrictStr
+from pydantic import BaseModel, Field, StrictBool, StrictStr
 
 from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import get_iso_timestamp
@@ -207,3 +207,16 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
         starred=starred,
         has_workflow=has_workflow,
     )
+
+
+class ImageCollectionCounts(BaseModel):
+    starred_count: int = Field(description="The number of starred images in the collection.")
+    unstarred_count: int = Field(description="The number of unstarred images in the collection.")
+
+
+class ImageNamesResult(BaseModel):
+    """Response containing ordered image names with metadata for optimistic updates."""
+
+    image_names: list[str] = Field(description="Ordered list of image names")
+    starred_count: int = Field(description="Number of starred images (when starred_first=True)")
+    total_count: int = Field(description="Total number of images matching the query")

invokeai/app/services/image_records/image_records_sqlite.py

@@ -7,6 +7,7 @@ from invokeai.app.services.image_records.image_records_base import ImageRecordSt
 from invokeai.app.services.image_records.image_records_common import (
     IMAGE_DTO_COLS,
     ImageCategory,
+    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ImageRecordDeleteException,
@@ -196,9 +197,13 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
         # Search term condition
         if search_term:
             query_conditions += """--sql
-            AND images.metadata LIKE ?
+            AND (
+                images.metadata LIKE ?
+                OR images.created_at LIKE ?
+            )
             """
             query_params.append(f"%{search_term.lower()}%")
+            query_params.append(f"%{search_term.lower()}%")
 
         if starred_first:
             query_pagination = f"""--sql
@@ -382,3 +387,96 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
             return None
 
         return deserialize_image_record(dict(result))
+
+    def get_image_names(
+        self,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        image_origin: Optional[ResourceOrigin] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> ImageNamesResult:
+        cursor = self._conn.cursor()
+
+        # Build query conditions (reused for both starred count and image names queries)
+        query_conditions = ""
+        query_params: list[Union[int, str, bool]] = []
+
+        if image_origin is not None:
+            query_conditions += """--sql
+            AND images.image_origin = ?
+            """
+            query_params.append(image_origin.value)
+
+        if categories is not None:
+            category_strings = [c.value for c in set(categories)]
+            placeholders = ",".join("?" * len(category_strings))
+            query_conditions += f"""--sql
+            AND images.image_category IN ( {placeholders} )
+            """
+            for c in category_strings:
+                query_params.append(c)
+
+        if is_intermediate is not None:
+            query_conditions += """--sql
+            AND images.is_intermediate = ?
+            """
+            query_params.append(is_intermediate)
+
+        if board_id == "none":
+            query_conditions += """--sql
+            AND board_images.board_id IS NULL
+            """
+        elif board_id is not None:
+            query_conditions += """--sql
+            AND board_images.board_id = ?
+            """
+            query_params.append(board_id)
+
+        if search_term:
+            query_conditions += """--sql
+            AND (
+                images.metadata LIKE ?
+                OR images.created_at LIKE ?
+            )
+            """
+            query_params.append(f"%{search_term.lower()}%")
+            query_params.append(f"%{search_term.lower()}%")
+
+        # Get starred count if starred_first is enabled
+        starred_count = 0
+        if starred_first:
+            starred_count_query = f"""--sql
+            SELECT COUNT(*)
+            FROM images
+            LEFT JOIN board_images ON board_images.image_name = images.image_name
+            WHERE images.starred = TRUE AND (1=1{query_conditions})
+            """
+            cursor.execute(starred_count_query, query_params)
+            starred_count = cast(int, cursor.fetchone()[0])
+
+        # Get all image names with proper ordering
+        if starred_first:
+            names_query = f"""--sql
+            SELECT images.image_name
+            FROM images
+            LEFT JOIN board_images ON board_images.image_name = images.image_name
+            WHERE 1=1{query_conditions}
+            ORDER BY images.starred DESC, images.created_at {order_dir.value}
+            """
+        else:
+            names_query = f"""--sql
+            SELECT images.image_name
+            FROM images
+            LEFT JOIN board_images ON board_images.image_name = images.image_name
+            WHERE 1=1{query_conditions}
+            ORDER BY images.created_at {order_dir.value}
+            """
+
+        cursor.execute(names_query, query_params)
+        result = cast(list[sqlite3.Row], cursor.fetchall())
+        image_names = [row[0] for row in result]
+
+        return ImageNamesResult(image_names=image_names, starred_count=starred_count, total_count=len(image_names))

invokeai/app/services/images/images_base.py

@@ -6,6 +6,7 @@ from PIL.Image import Image as PILImageType
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
+    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ResourceOrigin,
@@ -125,7 +126,7 @@ class ImageServiceABC(ABC):
         board_id: Optional[str] = None,
         search_term: Optional[str] = None,
     ) -> OffsetPaginatedResults[ImageDTO]:
-        """Gets a paginated list of image DTOs."""
+        """Gets a paginated list of image DTOs with starred images first when starred_first=True."""
         pass
 
     @abstractmethod
@@ -147,3 +148,17 @@ class ImageServiceABC(ABC):
     def delete_images_on_board(self, board_id: str):
         """Deletes all images on a board."""
         pass
+
+    @abstractmethod
+    def get_image_names(
+        self,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        image_origin: Optional[ResourceOrigin] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> ImageNamesResult:
+        """Gets ordered list of image names with metadata for optimistic updates."""
+        pass

invokeai/app/services/images/images_common.py

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

invokeai/app/services/images/images_default.py

@@ -10,6 +10,7 @@ from invokeai.app.services.image_files.image_files_common import (
 )
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
+    ImageNamesResult,
     ImageRecord,
     ImageRecordChanges,
     ImageRecordDeleteException,
@@ -78,7 +79,7 @@ class ImageService(ImageServiceABC):
                         board_id=board_id, image_name=image_name
                     )
                 except Exception as e:
-                    self.__invoker.services.logger.warn(f"Failed to add image to board {board_id}: {str(e)}")
+                    self.__invoker.services.logger.warning(f"Failed to add image to board {board_id}: {str(e)}")
             self.__invoker.services.image_files.save(
                 image_name=image_name, image=image, metadata=metadata, workflow=workflow, graph=graph
             )
@@ -309,3 +310,27 @@ class ImageService(ImageServiceABC):
         except Exception as e:
             self.__invoker.services.logger.error("Problem getting intermediates count")
             raise e
+
+    def get_image_names(
+        self,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        image_origin: Optional[ResourceOrigin] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> ImageNamesResult:
+        try:
+            return self.__invoker.services.image_records.get_image_names(
+                starred_first=starred_first,
+                order_dir=order_dir,
+                image_origin=image_origin,
+                categories=categories,
+                is_intermediate=is_intermediate,
+                board_id=board_id,
+                search_term=search_term,
+            )
+        except Exception as e:
+            self.__invoker.services.logger.error("Problem getting image names")
+            raise e

invokeai/app/services/model_install/model_install_default.py

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

invokeai/app/services/session_processor/session_processor_default.py

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

invokeai/app/services/session_queue/session_queue_base.py

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

invokeai/app/services/session_queue/session_queue_common.py

@@ -205,9 +205,10 @@ class FieldIdentifier(BaseModel):
     kind: Literal["input", "output"] = Field(description="The kind of field")
     node_id: str = Field(description="The ID of the node")
     field_name: str = Field(description="The name of the field")
+    user_label: str | None = Field(description="The user label of the field, if any")
 
 
-class SessionQueueItemWithoutGraph(BaseModel):
+class SessionQueueItem(BaseModel):
     """Session queue item without the full graph. Used for serialization."""
 
     item_id: int = Field(description="The identifier of the session queue item")
@@ -251,42 +252,7 @@ class SessionQueueItemWithoutGraph(BaseModel):
         default=None,
         description="The ID of the published workflow associated with this queue item",
     )
-    api_input_fields: Optional[list[FieldIdentifier]] = Field(
-        default=None, description="The fields that were used as input to the API"
-    )
-    api_output_fields: Optional[list[FieldIdentifier]] = Field(
-        default=None, description="The nodes that were used as output from the API"
-    )
     credits: Optional[float] = Field(default=None, description="The total credits used for this queue item")
-
-    @classmethod
-    def queue_item_dto_from_dict(cls, queue_item_dict: dict) -> "SessionQueueItemDTO":
-        # must parse these manually
-        queue_item_dict["field_values"] = get_field_values(queue_item_dict)
-        return SessionQueueItemDTO(**queue_item_dict)
-
-    model_config = ConfigDict(
-        json_schema_extra={
-            "required": [
-                "item_id",
-                "status",
-                "batch_id",
-                "queue_id",
-                "session_id",
-                "priority",
-                "session_id",
-                "created_at",
-                "updated_at",
-            ]
-        }
-    )
-
-
-class SessionQueueItemDTO(SessionQueueItemWithoutGraph):
-    pass
-
-
-class SessionQueueItem(SessionQueueItemWithoutGraph):
     session: GraphExecutionState = Field(description="The fully-populated session to be executed")
     workflow: Optional[WorkflowWithoutID] = Field(
         default=None, description="The workflow associated with this queue item"
@@ -366,6 +332,7 @@ class EnqueueBatchResult(BaseModel):
     requested: int = Field(description="The total number of queue items requested to be enqueued")
     batch: Batch = Field(description="The batch that was enqueued")
     priority: int = Field(description="The priority of the enqueued batch")
+    item_ids: list[int] = Field(description="The IDs of the queue items that were enqueued")
 
 
 class RetryItemsResult(BaseModel):
@@ -397,6 +364,18 @@ class CancelByDestinationResult(CancelByBatchIDsResult):
     pass
 
 
+class DeleteByDestinationResult(BaseModel):
+    """Result of deleting by a destination"""
+
+    deleted: int = Field(..., description="Number of queue items deleted")
+
+
+class DeleteAllExceptCurrentResult(DeleteByDestinationResult):
+    """Result of deleting all except current"""
+
+    pass
+
+
 class CancelByQueueIDResult(CancelByBatchIDsResult):
     """Result of canceling by queue id"""
 

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -17,6 +17,8 @@ from invokeai.app.services.session_queue.session_queue_common import (
     CancelByDestinationResult,
     CancelByQueueIDResult,
     ClearResult,
+    DeleteAllExceptCurrentResult,
+    DeleteByDestinationResult,
     EnqueueBatchResult,
     IsEmptyResult,
     IsFullResult,
@@ -24,7 +26,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     RetryItemsResult,
     SessionQueueCountsByDestination,
     SessionQueueItem,
-    SessionQueueItemDTO,
     SessionQueueItemNotFoundError,
     SessionQueueStatus,
     ValueToInsertTuple,
@@ -46,10 +47,6 @@ class SqliteSessionQueue(SessionQueueBase):
             clear_result = self.clear(DEFAULT_QUEUE_ID)
             if clear_result.deleted > 0:
                 self.__invoker.services.logger.info(f"Cleared all {clear_result.deleted} queue items")
-        else:
-            prune_result = self.prune(DEFAULT_QUEUE_ID)
-            if prune_result.deleted > 0:
-                self.__invoker.services.logger.info(f"Pruned {prune_result.deleted} finished queue items")
 
     def __init__(self, db: SqliteDatabase) -> None:
         super().__init__()
@@ -104,11 +101,7 @@ class SqliteSessionQueue(SessionQueueBase):
         return cast(Union[int, None], cursor.fetchone()[0]) or 0
 
     async def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
-        return await asyncio.to_thread(self._enqueue_batch, queue_id, batch, prepend)
-
-    def _enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
         try:
-            cursor = self._conn.cursor()
             # TODO: how does this work in a multi-user scenario?
             current_queue_size = self._get_current_queue_size(queue_id)
             max_queue_size = self.__invoker.services.configuration.max_queue_size
@@ -118,8 +111,12 @@ class SqliteSessionQueue(SessionQueueBase):
             if prepend:
                 priority = self._get_highest_priority(queue_id) + 1
 
-            requested_count = calc_session_count(batch)
-            values_to_insert = prepare_values_to_insert(
+            requested_count = await asyncio.to_thread(
+                calc_session_count,
+                batch=batch,
+            )
+            values_to_insert = await asyncio.to_thread(
+                prepare_values_to_insert,
                 queue_id=queue_id,
                 batch=batch,
                 priority=priority,
@@ -127,19 +124,28 @@ class SqliteSessionQueue(SessionQueueBase):
             )
             enqueued_count = len(values_to_insert)
 
-            if requested_count > enqueued_count:
-                values_to_insert = values_to_insert[:max_new_queue_items]
-
-            cursor.executemany(
-                """--sql
-                INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin, destination, retried_from_item_id)
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
-                """,
-                values_to_insert,
-            )
-            self._conn.commit()
+            with self._conn:
+                cursor = self._conn.cursor()
+                cursor.executemany(
+                    """--sql
+                    INSERT INTO session_queue (queue_id, session, session_id, batch_id, field_values, priority, workflow, origin, destination, retried_from_item_id)
+                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                    """,
+                    values_to_insert,
+                )
+            with self._conn:
+                cursor = self._conn.cursor()
+                cursor.execute(
+                    """--sql
+                    SELECT item_id
+                    FROM session_queue
+                    WHERE batch_id = ?
+                    ORDER BY item_id DESC;
+                    """,
+                    (batch.batch_id,),
+                )
+                item_ids = [row[0] for row in cursor.fetchall()]
         except Exception:
-            self._conn.rollback()
             raise
         enqueue_result = EnqueueBatchResult(
             queue_id=queue_id,
@@ -147,6 +153,7 @@ class SqliteSessionQueue(SessionQueueBase):
             enqueued=enqueued_count,
             batch=batch,
             priority=priority,
+            item_ids=item_ids,
         )
         self.__invoker.services.events.emit_batch_enqueued(enqueue_result)
         return enqueue_result
@@ -220,6 +227,19 @@ class SqliteSessionQueue(SessionQueueBase):
     ) -> SessionQueueItem:
         try:
             cursor = self._conn.cursor()
+            cursor.execute(
+                """--sql
+                SELECT status FROM session_queue WHERE item_id = ?
+                """,
+                (item_id,),
+            )
+            row = cursor.fetchone()
+            if row is None:
+                raise SessionQueueItemNotFoundError(f"No queue item with id {item_id}")
+            current_status = row[0]
+            # Only update if not already finished (completed, failed or canceled)
+            if current_status in ("completed", "failed", "canceled"):
+                return self.get_queue_item(item_id)
             cursor.execute(
                 """--sql
                 UPDATE session_queue
@@ -331,6 +351,27 @@ class SqliteSessionQueue(SessionQueueBase):
         queue_item = self._set_queue_item_status(item_id=item_id, status="canceled")
         return queue_item
 
+    def delete_queue_item(self, item_id: int) -> None:
+        """Deletes a session queue item"""
+        try:
+            self.cancel_queue_item(item_id)
+        except SessionQueueItemNotFoundError:
+            pass
+        try:
+            cursor = self._conn.cursor()
+            cursor.execute(
+                """--sql
+                DELETE
+                FROM session_queue
+                WHERE item_id = ?
+                """,
+                (item_id,),
+            )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
+
     def complete_queue_item(self, item_id: int) -> SessionQueueItem:
         queue_item = self._set_queue_item_status(item_id=item_id, status="completed")
         return queue_item
@@ -363,6 +404,8 @@ class SqliteSessionQueue(SessionQueueBase):
                   AND status != 'canceled'
                   AND status != 'completed'
                   AND status != 'failed'
+                  -- We will cancel the current item separately below - skip it here
+                  AND status != 'in_progress'
                 """
             params = [queue_id] + batch_ids
             cursor.execute(
@@ -401,6 +444,8 @@ class SqliteSessionQueue(SessionQueueBase):
                   AND status != 'canceled'
                   AND status != 'completed'
                   AND status != 'failed'
+                  -- We will cancel the current item separately below - skip it here
+                  AND status != 'in_progress'
                 """
             params = (queue_id, destination)
             cursor.execute(
@@ -428,6 +473,71 @@ class SqliteSessionQueue(SessionQueueBase):
             raise
         return CancelByDestinationResult(canceled=count)
 
+    def delete_by_destination(self, queue_id: str, destination: str) -> DeleteByDestinationResult:
+        try:
+            cursor = self._conn.cursor()
+            current_queue_item = self.get_current(queue_id)
+            if current_queue_item is not None and current_queue_item.destination == destination:
+                self.cancel_queue_item(current_queue_item.item_id)
+            params = (queue_id, destination)
+            cursor.execute(
+                """--sql
+                SELECT COUNT(*)
+                FROM session_queue
+                WHERE
+                  queue_id = ?
+                  AND destination = ?;
+                """,
+                params,
+            )
+            count = cursor.fetchone()[0]
+            cursor.execute(
+                """--sql
+                DELETE
+                FROM session_queue
+                WHERE
+                  queue_id = ?
+                  AND destination = ?;
+                """,
+                params,
+            )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
+        return DeleteByDestinationResult(deleted=count)
+
+    def delete_all_except_current(self, queue_id: str) -> DeleteAllExceptCurrentResult:
+        try:
+            cursor = self._conn.cursor()
+            where = """--sql
+                WHERE
+                  queue_id == ?
+                  AND status == 'pending'
+                """
+            cursor.execute(
+                f"""--sql
+                SELECT COUNT(*)
+                FROM session_queue
+                {where};
+                """,
+                (queue_id,),
+            )
+            count = cursor.fetchone()[0]
+            cursor.execute(
+                f"""--sql
+                DELETE
+                FROM session_queue
+                {where};
+                """,
+                (queue_id,),
+            )
+            self._conn.commit()
+        except Exception:
+            self._conn.rollback()
+            raise
+        return DeleteAllExceptCurrentResult(deleted=count)
+
     def cancel_by_queue_id(self, queue_id: str) -> CancelByQueueIDResult:
         try:
             cursor = self._conn.cursor()
@@ -438,6 +548,8 @@ class SqliteSessionQueue(SessionQueueBase):
                   AND status != 'canceled'
                   AND status != 'completed'
                   AND status != 'failed'
+                  -- We will cancel the current item separately below - skip it here
+                  AND status != 'in_progress'
                 """
             params = [queue_id]
             cursor.execute(
@@ -458,12 +570,9 @@ class SqliteSessionQueue(SessionQueueBase):
                 tuple(params),
             )
             self._conn.commit()
+
             if current_queue_item is not None and current_queue_item.queue_id == queue_id:
-                batch_status = self.get_batch_status(queue_id=queue_id, batch_id=current_queue_item.batch_id)
-                queue_status = self.get_queue_status(queue_id=queue_id)
-                self.__invoker.services.events.emit_queue_item_status_changed(
-                    current_queue_item, batch_status, queue_status
-                )
+                self._set_queue_item_status(current_queue_item.item_id, "canceled")
         except Exception:
             self._conn.rollback()
             raise
@@ -543,26 +652,12 @@ class SqliteSessionQueue(SessionQueueBase):
         priority: int,
         cursor: Optional[int] = None,
         status: Optional[QUEUE_ITEM_STATUS] = None,
-    ) -> CursorPaginatedResults[SessionQueueItemDTO]:
+        destination: Optional[str] = None,
+    ) -> CursorPaginatedResults[SessionQueueItem]:
         cursor_ = self._conn.cursor()
         item_id = cursor
         query = """--sql
-            SELECT item_id,
-                status,
-                priority,
-                field_values,
-                error_type,
-                error_message,
-                error_traceback,
-                created_at,
-                updated_at,
-                completed_at,
-                started_at,
-                session_id,
-                batch_id,
-                queue_id,
-                origin,
-                destination
+            SELECT *
             FROM session_queue
             WHERE queue_id = ?
         """
@@ -574,6 +669,12 @@ class SqliteSessionQueue(SessionQueueBase):
                 """
             params.append(status)
 
+        if destination is not None:
+            query += """---sql
+                AND destination = ?
+            """
+            params.append(destination)
+
         if item_id is not None:
             query += """--sql
                 AND (priority < ?) OR (priority = ? AND item_id > ?)
@@ -589,7 +690,7 @@ class SqliteSessionQueue(SessionQueueBase):
         params.append(limit + 1)
         cursor_.execute(query, params)
         results = cast(list[sqlite3.Row], cursor_.fetchall())
-        items = [SessionQueueItemDTO.queue_item_dto_from_dict(dict(result)) for result in results]
+        items = [SessionQueueItem.queue_item_from_dict(dict(result)) for result in results]
         has_more = False
         if len(items) > limit:
             # remove the extra item
@@ -597,6 +698,37 @@ class SqliteSessionQueue(SessionQueueBase):
             has_more = True
         return CursorPaginatedResults(items=items, limit=limit, has_more=has_more)
 
+    def list_all_queue_items(
+        self,
+        queue_id: str,
+        destination: Optional[str] = None,
+    ) -> list[SessionQueueItem]:
+        """Gets all queue items that match the given parameters"""
+        cursor_ = self._conn.cursor()
+        query = """--sql
+            SELECT *
+            FROM session_queue
+            WHERE queue_id = ?
+        """
+        params: list[Union[str, int]] = [queue_id]
+
+        if destination is not None:
+            query += """---sql
+                AND destination = ?
+            """
+            params.append(destination)
+
+        query += """--sql
+            ORDER BY
+                priority DESC,
+                item_id ASC
+            ;
+            """
+        cursor_.execute(query, params)
+        results = cast(list[sqlite3.Row], cursor_.fetchall())
+        items = [SessionQueueItem.queue_item_from_dict(dict(result)) for result in results]
+        return items
+
     def get_queue_status(self, queue_id: str) -> SessionQueueStatus:
         cursor = self._conn.cursor()
         cursor.execute(
@@ -611,7 +743,7 @@ class SqliteSessionQueue(SessionQueueBase):
         counts_result = cast(list[sqlite3.Row], cursor.fetchall())
 
         current_item = self.get_current(queue_id=queue_id)
-        total = sum(row[1] for row in counts_result)
+        total = sum(row[1] or 0 for row in counts_result)
         counts: dict[str, int] = {row[0]: row[1] for row in counts_result}
         return SessionQueueStatus(
             queue_id=queue_id,
@@ -640,7 +772,7 @@ class SqliteSessionQueue(SessionQueueBase):
             (queue_id, batch_id),
         )
         result = cast(list[sqlite3.Row], cursor.fetchall())
-        total = sum(row[1] for row in result)
+        total = sum(row[1] or 0 for row in result)
         counts: dict[str, int] = {row[0]: row[1] for row in result}
         origin = result[0]["origin"] if result else None
         destination = result[0]["destination"] if result else None
@@ -672,7 +804,7 @@ class SqliteSessionQueue(SessionQueueBase):
         )
         counts_result = cast(list[sqlite3.Row], cursor.fetchall())
 
-        total = sum(row[1] for row in counts_result)
+        total = sum(row[1] or 0 for row in counts_result)
         counts: dict[str, int] = {row[0]: row[1] for row in counts_result}
 
         return SessionQueueCountsByDestination(

invokeai/app/services/shared/graph.py

@@ -2,11 +2,12 @@
 
 import copy
 import itertools
-from typing import Any, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
+from typing import Any, Optional, TypeVar, Union, get_args, get_origin
 
 import networkx as nx
 from pydantic import (
     BaseModel,
+    ConfigDict,
     GetCoreSchemaHandler,
     GetJsonSchemaHandler,
     ValidationError,
@@ -57,17 +58,32 @@ class Edge(BaseModel):
 
 
 def get_output_field_type(node: BaseInvocation, field: str) -> Any:
-    node_type = type(node)
-    node_outputs = get_type_hints(node_type.get_output_annotation())
-    node_output_field = node_outputs.get(field) or None
-    return node_output_field
+    # TODO(psyche): This is awkward - if field_info is None, it means the field is not defined in the output, which
+    # really should raise. The consumers of this utility expect it to never raise, and return None instead. Fixing this
+    # would require some fairly significant changes and I don't want risk breaking anything.
+    try:
+        invocation_class = type(node)
+        invocation_output_class = invocation_class.get_output_annotation()
+        field_info = invocation_output_class.model_fields.get(field)
+        assert field_info is not None, f"Output field '{field}' not found in {invocation_output_class.get_type()}"
+        output_field_type = field_info.annotation
+        return output_field_type
+    except Exception:
+        return None
 
 
 def get_input_field_type(node: BaseInvocation, field: str) -> Any:
-    node_type = type(node)
-    node_inputs = get_type_hints(node_type)
-    node_input_field = node_inputs.get(field) or None
-    return node_input_field
+    # TODO(psyche): This is awkward - if field_info is None, it means the field is not defined in the output, which
+    # really should raise. The consumers of this utility expect it to never raise, and return None instead. Fixing this
+    # would require some fairly significant changes and I don't want risk breaking anything.
+    try:
+        invocation_class = type(node)
+        field_info = invocation_class.model_fields.get(field)
+        assert field_info is not None, f"Input field '{field}' not found in {invocation_class.get_type()}"
+        input_field_type = field_info.annotation
+        return input_field_type
+    except Exception:
+        return None
 
 
 def is_union_subtype(t1, t2):
@@ -787,6 +803,22 @@ class GraphExecutionState(BaseModel):
         default_factory=dict,
     )
 
+    model_config = ConfigDict(
+        json_schema_extra={
+            "required": [
+                "id",
+                "graph",
+                "execution_graph",
+                "executed",
+                "executed_history",
+                "results",
+                "errors",
+                "prepared_source_mapping",
+                "source_prepared_mapping",
+            ]
+        }
+    )
+
     @field_validator("graph")
     def graph_is_valid(cls, v: Graph):
         """Validates that the graph is valid"""
@@ -975,10 +1007,11 @@ class GraphExecutionState(BaseModel):
         new_node_ids = []
         if isinstance(next_node, CollectInvocation):
             # Collapse all iterator input mappings and create a single execution node for the collect invocation
-            all_iteration_mappings = list(
-                itertools.chain(*(((s, p) for p in self.source_prepared_mapping[s]) for s in next_node_parents))
-            )
-            # all_iteration_mappings = list(set(itertools.chain(*prepared_parent_mappings)))
+            all_iteration_mappings = []
+            for source_node_id in next_node_parents:
+                prepared_nodes = self.source_prepared_mapping[source_node_id]
+                all_iteration_mappings.extend([(source_node_id, p) for p in prepared_nodes])
+
             create_results = self._create_execution_node(next_node_id, all_iteration_mappings)
             if create_results is not None:
                 new_node_ids.extend(create_results)

invokeai/app/util/controlnet_utils.py

@@ -230,6 +230,86 @@ def heuristic_resize(np_img: np.ndarray[Any, Any], size: tuple[int, int]) -> np.
     return resized
 
 
+# precompute common kernels
+_KERNEL3 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
+# directional masks for NMS
+_DIRS = [
+    np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], np.uint8),
+    np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], np.uint8),
+    np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], np.uint8),
+    np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], np.uint8),
+]
+
+
+def heuristic_resize_fast(np_img: np.ndarray, size: tuple[int, int]) -> np.ndarray:
+    h, w = np_img.shape[:2]
+    # early exit
+    if (w, h) == size:
+        return np_img
+
+    # separate alpha channel
+    img = np_img
+    alpha = None
+    if img.ndim == 3 and img.shape[2] == 4:
+        alpha, img = img[:, :, 3], img[:, :, :3]
+
+    # build small sample for unique‐color & binary detection
+    flat = img.reshape(-1, img.shape[-1])
+    N = flat.shape[0]
+    # include four corners to avoid missing extreme values
+    corners = np.vstack([img[0, 0], img[0, w - 1], img[h - 1, 0], img[h - 1, w - 1]])
+    cnt = min(N, 100_000)
+    samp = np.vstack([corners, flat[np.random.choice(N, cnt, replace=False)]])
+    uc = np.unique(samp, axis=0).shape[0]
+    vmin, vmax = samp.min(), samp.max()
+
+    # detect binary edge map & one‐pixel‐edge case
+    is_binary = uc == 2 and vmin < 16 and vmax > 240
+    one_pixel_edge = False
+    if is_binary:
+        # single gray conversion
+        gray0 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        grad = cv2.morphologyEx(gray0, cv2.MORPH_GRADIENT, _KERNEL3)
+        cnt_edge = cv2.countNonZero(grad)
+        cnt_all = cv2.countNonZero((gray0 > 127).astype(np.uint8))
+        one_pixel_edge = (2 * cnt_edge) > cnt_all
+
+    # choose interp for color/seg/grayscale
+    area_new, area_old = size[0] * size[1], w * h
+    if 2 < uc < 200:  # segmentation map
+        interp = cv2.INTER_NEAREST
+    elif area_new < area_old:
+        interp = cv2.INTER_AREA
+    else:
+        interp = cv2.INTER_CUBIC
+
+    # single resize pass on RGB
+    resized = cv2.resize(img, size, interpolation=interp)
+
+    if is_binary:
+        # convert to gray & apply NMS via C++ dilate
+        gray_r = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
+        nms = np.zeros_like(gray_r)
+        for K in _DIRS:
+            d = cv2.dilate(gray_r, K)
+            mask = d == gray_r
+            nms[mask] = gray_r[mask]
+
+        # threshold + thinning if needed
+        _, bw = cv2.threshold(nms, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+        out_bin = cv2.ximgproc.thinning(bw) if one_pixel_edge else bw
+        # restore 3 channels
+        resized = np.stack([out_bin] * 3, axis=2)
+
+    # restore alpha with same interp as RGB for consistency
+    if alpha is not None:
+        am = cv2.resize(alpha, size, interpolation=interp)
+        am = (am > 127).astype(np.uint8) * 255
+        resized = np.dstack((resized, am))
+
+    return resized
+
+
 ###########################################################################
 # Copied from detectmap_proc method in scripts/detectmap_proc.py in Mikubill/sd-webui-controlnet
 #    modified for InvokeAI
@@ -244,7 +324,7 @@ def np_img_resize(
     np_img = normalize_image_channel_count(np_img)
 
     if resize_mode == "just_resize":  # RESIZE
-        np_img = heuristic_resize(np_img, (w, h))
+        np_img = heuristic_resize_fast(np_img, (w, h))
         np_img = clone_contiguous(np_img)
         return np_img_to_torch(np_img, device), np_img
 
@@ -265,7 +345,7 @@ def np_img_resize(
             # Inpaint hijack
             high_quality_border_color[3] = 255
         high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
-        np_img = heuristic_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
+        np_img = heuristic_resize_fast(np_img, (safeint(old_w * k), safeint(old_h * k)))
         new_h, new_w, _ = np_img.shape
         pad_h = max(0, (h - new_h) // 2)
         pad_w = max(0, (w - new_w) // 2)
@@ -275,7 +355,7 @@ def np_img_resize(
         return np_img_to_torch(np_img, device), np_img
     else:  # resize_mode == "crop_resize"  (INNER_FIT)
         k = max(k0, k1)
-        np_img = heuristic_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
+        np_img = heuristic_resize_fast(np_img, (safeint(old_w * k), safeint(old_h * k)))
         new_h, new_w, _ = np_img.shape
         pad_h = max(0, (new_h - h) // 2)
         pad_w = max(0, (new_w - w) // 2)

invokeai/app/util/custom_openapi.py

@@ -12,6 +12,9 @@ from invokeai.app.invocations.fields import InputFieldJSONSchemaExtra, OutputFie
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.services.events.events_common import EventBase
 from invokeai.app.services.session_processor.session_processor_common import ProgressImage
+from invokeai.backend.util.logging import InvokeAILogger
+
+logger = InvokeAILogger.get_logger()
 
 
 def move_defs_to_top_level(openapi_schema: dict[str, Any], component_schema: dict[str, Any]) -> None:

invokeai/app/util/step_callback.py

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

invokeai/backend/flux/denoise.py

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

invokeai/backend/flux/extensions/kontext_extension.py

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

invokeai/backend/flux/sampling_utils.py

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

invokeai/backend/flux/util.py

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

invokeai/backend/ip_adapter/README.md

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

invokeai/backend/model_manager/config.py

@@ -37,6 +37,7 @@ from invokeai.app.util.misc import uuid_string
 from invokeai.backend.model_hash.hash_validator import validate_hash
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS
 from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
+from invokeai.backend.model_manager.omi import flux_dev_1_lora, stable_diffusion_xl_1_lora
 from invokeai.backend.model_manager.taxonomy import (
     AnyVariant,
     BaseModelType,
@@ -296,7 +297,7 @@ class LoRAConfigBase(ABC, BaseModel):
         from invokeai.backend.patches.lora_conversions.formats import flux_format_from_state_dict
 
         sd = mod.load_state_dict(mod.path)
-        value = flux_format_from_state_dict(sd)
+        value = flux_format_from_state_dict(sd, mod.metadata())
         mod.cache[key] = value
         return value
 
@@ -334,6 +335,36 @@ class T5EncoderBnbQuantizedLlmInt8bConfig(T5EncoderConfigBase, LegacyProbeMixin,
     format: Literal[ModelFormat.BnbQuantizedLlmInt8b] = ModelFormat.BnbQuantizedLlmInt8b
 
 
+class LoRAOmiConfig(LoRAConfigBase, ModelConfigBase):
+    format: Literal[ModelFormat.OMI] = ModelFormat.OMI
+
+    @classmethod
+    def matches(cls, mod: ModelOnDisk) -> bool:
+        if mod.path.is_dir():
+            return False
+
+        metadata = mod.metadata()
+        return (
+            metadata.get("modelspec.sai_model_spec")
+            and metadata.get("ot_branch") == "omi_format"
+            and metadata["modelspec.architecture"].split("/")[1].lower() == "lora"
+        )
+
+    @classmethod
+    def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
+        metadata = mod.metadata()
+        architecture = metadata["modelspec.architecture"]
+
+        if architecture == stable_diffusion_xl_1_lora:
+            base = BaseModelType.StableDiffusionXL
+        elif architecture == flux_dev_1_lora:
+            base = BaseModelType.Flux
+        else:
+            raise InvalidModelConfigException(f"Unrecognised/unsupported architecture for OMI LoRA: {architecture}")
+
+        return {"base": base}
+
+
 class LoRALyCORISConfig(LoRAConfigBase, ModelConfigBase):
     """Model config for LoRA/Lycoris models."""
 
@@ -350,7 +381,7 @@ class LoRALyCORISConfig(LoRAConfigBase, ModelConfigBase):
 
         state_dict = mod.load_state_dict()
         for key in state_dict.keys():
-            if type(key) is int:
+            if isinstance(key, int):
                 continue
 
             if key.startswith(("lora_te_", "lora_unet_", "lora_te1_", "lora_te2_", "lora_transformer_")):
@@ -668,6 +699,7 @@ AnyModelConfig = Annotated[
         Annotated[ControlNetDiffusersConfig, ControlNetDiffusersConfig.get_tag()],
         Annotated[ControlNetCheckpointConfig, ControlNetCheckpointConfig.get_tag()],
         Annotated[LoRALyCORISConfig, LoRALyCORISConfig.get_tag()],
+        Annotated[LoRAOmiConfig, LoRAOmiConfig.get_tag()],
         Annotated[ControlLoRALyCORISConfig, ControlLoRALyCORISConfig.get_tag()],
         Annotated[ControlLoRADiffusersConfig, ControlLoRADiffusersConfig.get_tag()],
         Annotated[LoRADiffusersConfig, LoRADiffusersConfig.get_tag()],

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

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

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

@@ -13,6 +13,7 @@ from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.omi.omi import convert_from_omi
 from invokeai.backend.model_manager.taxonomy import (
     AnyModel,
     BaseModelType,
@@ -20,6 +21,10 @@ from invokeai.backend.model_manager.taxonomy import (
     ModelType,
     SubModelType,
 )
+from invokeai.backend.patches.lora_conversions.flux_aitoolkit_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_aitoolkit_format,
+    lora_model_from_flux_aitoolkit_state_dict,
+)
 from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import (
     is_state_dict_likely_flux_control,
     lora_model_from_flux_control_state_dict,
@@ -39,6 +44,8 @@ from invokeai.backend.patches.lora_conversions.sd_lora_conversion_utils import l
 from invokeai.backend.patches.lora_conversions.sdxl_lora_conversion_utils import convert_sdxl_keys_to_diffusers_format
 
 
+@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.LoRA, format=ModelFormat.OMI)
+@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.LoRA, format=ModelFormat.OMI)
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.Diffusers)
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.LyCORIS)
 @ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.ControlLoRa, format=ModelFormat.LyCORIS)
@@ -73,12 +80,23 @@ class LoRALoader(ModelLoader):
         else:
             state_dict = torch.load(model_path, map_location="cpu")
 
+        # Strip 'bundle_emb' keys - these are unused and currently cause downstream errors.
+        # To revisit later to determine if they're needed/useful.
+        state_dict = {k: v for k, v in state_dict.items() if not k.startswith("bundle_emb")}
+
+        # At the time of writing, we support the OMI standard for base models Flux and SDXL
+        if config.format == ModelFormat.OMI and self._model_base in [
+            BaseModelType.StableDiffusionXL,
+            BaseModelType.Flux,
+        ]:
+            state_dict = convert_from_omi(state_dict, config.base)  # type: ignore
+
         # Apply state_dict key conversions, if necessary.
         if self._model_base == BaseModelType.StableDiffusionXL:
             state_dict = convert_sdxl_keys_to_diffusers_format(state_dict)
             model = lora_model_from_sd_state_dict(state_dict=state_dict)
         elif self._model_base == BaseModelType.Flux:
-            if config.format == ModelFormat.Diffusers:
+            if config.format in [ModelFormat.Diffusers, ModelFormat.OMI]:
                 # HACK(ryand): We set alpha=None for diffusers PEFT format models. These models are typically
                 # distributed as a single file without the associated metadata containing the alpha value. We chose
                 # alpha=None, because this is treated as alpha=rank internally in `LoRALayerBase.scale()`. alpha=rank
@@ -92,8 +110,10 @@ class LoRALoader(ModelLoader):
                     model = lora_model_from_flux_onetrainer_state_dict(state_dict=state_dict)
                 elif is_state_dict_likely_flux_control(state_dict=state_dict):
                     model = lora_model_from_flux_control_state_dict(state_dict=state_dict)
+                elif is_state_dict_likely_in_flux_aitoolkit_format(state_dict=state_dict):
+                    model = lora_model_from_flux_aitoolkit_state_dict(state_dict=state_dict)
                 else:
-                    raise ValueError(f"LoRA model is in unsupported FLUX format: {config.format}")
+                    raise ValueError("LoRA model is in unsupported FLUX format")
             else:
                 raise ValueError(f"LoRA model is in unsupported FLUX format: {config.format}")
         elif self._model_base in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:

invokeai/backend/model_manager/metadata/fetch/huggingface.py

@@ -62,11 +62,14 @@ class HuggingFaceMetadataFetch(ModelMetadataFetchBase):
         # If this too fails, raise exception.
 
         model_info = None
+
+        # Handling for our special syntax - we only want the base HF `org/repo` here.
+        repo_id = id.split("::")[0] or id
         while not model_info:
             try:
-                model_info = HfApi().model_info(repo_id=id, files_metadata=True, revision=variant)
+                model_info = HfApi().model_info(repo_id=repo_id, files_metadata=True, revision=variant)
             except RepositoryNotFoundError as excp:
-                raise UnknownMetadataException(f"'{id}' not found. See trace for details.") from excp
+                raise UnknownMetadataException(f"'{repo_id}' not found. See trace for details.") from excp
             except RevisionNotFoundError:
                 if variant is None:
                     raise
@@ -75,14 +78,14 @@ class HuggingFaceMetadataFetch(ModelMetadataFetchBase):
 
         files: list[RemoteModelFile] = []
 
-        _, name = id.split("/")
+        _, name = repo_id.split("/")
 
         for s in model_info.siblings or []:
             assert s.rfilename is not None
             assert s.size is not None
             files.append(
                 RemoteModelFile(
-                    url=hf_hub_url(id, s.rfilename, revision=variant or "main"),
+                    url=hf_hub_url(repo_id, s.rfilename, revision=variant or "main"),
                     path=Path(name, s.rfilename),
                     size=s.size,
                     sha256=s.lfs.get("sha256") if s.lfs else None,

invokeai/backend/model_manager/omi/__init__.py

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

invokeai/backend/model_manager/omi/omi.py

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

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

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

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

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

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

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

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

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

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

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

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

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

invokeai/backend/model_manager/starter_models.py

@@ -23,7 +23,7 @@ class StarterModel(StarterModelWithoutDependencies):
     dependencies: Optional[list[StarterModelWithoutDependencies]] = None
 
 
-class StarterModelBundles(BaseModel):
+class StarterModelBundle(BaseModel):
     name: str
     models: list[StarterModel]
 
@@ -109,7 +109,7 @@ flux_vae = StarterModel(
 
 # region: Main
 flux_schnell_quantized = StarterModel(
-    name="FLUX Schnell (Quantized)",
+    name="FLUX.1 schnell (quantized)",
     base=BaseModelType.Flux,
     source="InvokeAI/flux_schnell::transformer/bnb_nf4/flux1-schnell-bnb_nf4.safetensors",
     description="FLUX schnell transformer quantized to bitsandbytes NF4 format. Total size with dependencies: ~12GB",
@@ -117,7 +117,7 @@ flux_schnell_quantized = StarterModel(
     dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
 )
 flux_dev_quantized = StarterModel(
-    name="FLUX Dev (Quantized)",
+    name="FLUX.1 dev (quantized)",
     base=BaseModelType.Flux,
     source="InvokeAI/flux_dev::transformer/bnb_nf4/flux1-dev-bnb_nf4.safetensors",
     description="FLUX dev transformer quantized to bitsandbytes NF4 format. Total size with dependencies: ~12GB",
@@ -125,7 +125,7 @@ flux_dev_quantized = StarterModel(
     dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
 )
 flux_schnell = StarterModel(
-    name="FLUX Schnell",
+    name="FLUX.1 schnell",
     base=BaseModelType.Flux,
     source="InvokeAI/flux_schnell::transformer/base/flux1-schnell.safetensors",
     description="FLUX schnell transformer in bfloat16. Total size with dependencies: ~33GB",
@@ -133,13 +133,29 @@ flux_schnell = StarterModel(
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
 flux_dev = StarterModel(
-    name="FLUX Dev",
+    name="FLUX.1 dev",
     base=BaseModelType.Flux,
     source="InvokeAI/flux_dev::transformer/base/flux1-dev.safetensors",
     description="FLUX dev transformer in bfloat16. Total size with dependencies: ~33GB",
     type=ModelType.Main,
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
+flux_kontext = StarterModel(
+    name="FLUX.1 Kontext dev",
+    base=BaseModelType.Flux,
+    source="https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev/resolve/main/flux1-kontext-dev.safetensors",
+    description="FLUX.1 Kontext dev transformer in bfloat16. Total size with dependencies: ~33GB",
+    type=ModelType.Main,
+    dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
+)
+flux_kontext_quantized = StarterModel(
+    name="FLUX.1 Kontext dev (Quantized)",
+    base=BaseModelType.Flux,
+    source="https://huggingface.co/unsloth/FLUX.1-Kontext-dev-GGUF/resolve/main/flux1-kontext-dev-Q4_K_M.gguf",
+    description="FLUX.1 Kontext dev quantized (q4_k_m). Total size with dependencies: ~14GB",
+    type=ModelType.Main,
+    dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
+)
 sd35_medium = StarterModel(
     name="SD3.5 Medium",
     base=BaseModelType.StableDiffusion3,
@@ -297,6 +313,15 @@ ip_adapter_sdxl = StarterModel(
     dependencies=[ip_adapter_sdxl_image_encoder],
     previous_names=["IP Adapter SDXL"],
 )
+ip_adapter_plus_sdxl = StarterModel(
+    name="Precise Reference (IP Adapter Plus ViT-H)",
+    base=BaseModelType.StableDiffusionXL,
+    source="https://huggingface.co/InvokeAI/ip-adapter-plus_sdxl_vit-h/resolve/main/ip-adapter-plus_sdxl_vit-h.safetensors",
+    description="References images with a higher degree of precision.",
+    type=ModelType.IPAdapter,
+    dependencies=[ip_adapter_sdxl_image_encoder],
+    previous_names=["IP Adapter Plus SDXL"],
+)
 ip_adapter_flux = StarterModel(
     name="Standard Reference (XLabs FLUX IP-Adapter v2)",
     base=BaseModelType.Flux,
@@ -647,6 +672,7 @@ flux_fill = StarterModel(
 # List of starter models, displayed on the frontend.
 # The order/sort of this list is not changed by the frontend - set it how you want it here.
 STARTER_MODELS: list[StarterModel] = [
+    flux_kontext_quantized,
     flux_schnell_quantized,
     flux_dev_quantized,
     flux_schnell,
@@ -672,6 +698,7 @@ STARTER_MODELS: list[StarterModel] = [
     ip_adapter_plus_sd1,
     ip_adapter_plus_face_sd1,
     ip_adapter_sdxl,
+    ip_adapter_plus_sdxl,
     ip_adapter_flux,
     qr_code_cnet_sd1,
     qr_code_cnet_sdxl,
@@ -744,6 +771,7 @@ sdxl_bundle: list[StarterModel] = [
     juggernaut_sdxl,
     sdxl_fp16_vae_fix,
     ip_adapter_sdxl,
+    ip_adapter_plus_sdxl,
     canny_sdxl,
     depth_sdxl,
     softedge_sdxl,
@@ -765,12 +793,13 @@ flux_bundle: list[StarterModel] = [
     flux_depth_control_lora,
     flux_redux,
     flux_fill,
+    flux_kontext_quantized,
 ]
 
-STARTER_BUNDLES: dict[str, list[StarterModel]] = {
-    BaseModelType.StableDiffusion1: sd1_bundle,
-    BaseModelType.StableDiffusionXL: sdxl_bundle,
-    BaseModelType.Flux: flux_bundle,
+STARTER_BUNDLES: dict[str, StarterModelBundle] = {
+    BaseModelType.StableDiffusion1: StarterModelBundle(name="Stable Diffusion 1.5", models=sd1_bundle),
+    BaseModelType.StableDiffusionXL: StarterModelBundle(name="SDXL", models=sdxl_bundle),
+    BaseModelType.Flux: StarterModelBundle(name="FLUX.1 dev", models=flux_bundle),
 }
 
 assert len(STARTER_MODELS) == len({m.source for m in STARTER_MODELS}), "Duplicate starter models"

invokeai/backend/model_manager/taxonomy.py

@@ -27,7 +27,9 @@ class BaseModelType(str, Enum):
     Flux = "flux"
     CogView4 = "cogview4"
     Imagen3 = "imagen3"
+    Imagen4 = "imagen4"
     ChatGPT4o = "chatgpt-4o"
+    FluxKontext = "flux-kontext"
 
 
 class ModelType(str, Enum):
@@ -87,6 +89,7 @@ class ModelVariantType(str, Enum):
 class ModelFormat(str, Enum):
     """Storage format of model."""
 
+    OMI = "omi"
     Diffusers = "diffusers"
     Checkpoint = "checkpoint"
     LyCORIS = "lycoris"
@@ -136,6 +139,7 @@ class FluxLoRAFormat(str, Enum):
     Kohya = "flux.kohya"
     OneTrainer = "flux.onetrainer"
     Control = "flux.control"
+    AIToolkit = "flux.aitoolkit"
 
 
 AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]

invokeai/backend/model_patcher.py

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

invokeai/backend/patches/layer_patcher.py

@@ -1,3 +1,4 @@
+import re
 from contextlib import contextmanager
 from typing import Dict, Iterable, Optional, Tuple
 
@@ -7,6 +8,7 @@ from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
 from invokeai.backend.patches.layers.flux_control_lora_layer import FluxControlLoRALayer
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.patches.pad_with_zeros import pad_with_zeros
+from invokeai.backend.util import InvokeAILogger
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.original_weights_storage import OriginalWeightsStorage
 
@@ -23,6 +25,7 @@ class LayerPatcher:
         cached_weights: Optional[Dict[str, torch.Tensor]] = None,
         force_direct_patching: bool = False,
         force_sidecar_patching: bool = False,
+        suppress_warning_layers: Optional[re.Pattern] = None,
     ):
         """Apply 'smart' model patching that chooses whether to use direct patching or a sidecar wrapper for each
         module.
@@ -44,6 +47,7 @@ class LayerPatcher:
                     dtype=dtype,
                     force_direct_patching=force_direct_patching,
                     force_sidecar_patching=force_sidecar_patching,
+                    suppress_warning_layers=suppress_warning_layers,
                 )
 
             yield
@@ -70,6 +74,7 @@ class LayerPatcher:
         dtype: torch.dtype,
         force_direct_patching: bool,
         force_sidecar_patching: bool,
+        suppress_warning_layers: Optional[re.Pattern] = None,
     ):
         """Apply a single LoRA patch to a model using the 'smart' patching strategy that chooses whether to use direct
         patching or a sidecar wrapper for each module.
@@ -89,9 +94,17 @@ class LayerPatcher:
             if not layer_key.startswith(prefix):
                 continue
 
-            module_key, module = LayerPatcher._get_submodule(
-                model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
-            )
+            try:
+                module_key, module = LayerPatcher._get_submodule(
+                    model, layer_key[prefix_len:], layer_key_is_flattened=layer_keys_are_flattened
+                )
+            except AttributeError:
+                if suppress_warning_layers and suppress_warning_layers.search(layer_key):
+                    pass
+                else:
+                    logger = InvokeAILogger.get_logger(LayerPatcher.__name__)
+                    logger.warning("Failed to find module for LoRA layer key: %s", layer_key)
+                continue
 
             # Decide whether to use direct patching or a sidecar patch.
             # Direct patching is preferred, because it results in better runtime speed.

invokeai/backend/patches/lora_conversions/flux_aitoolkit_lora_conversion_utils.py

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

invokeai/backend/patches/lora_conversions/formats.py

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

invokeai/backend/quantization/gguf/utils.py

@@ -5,7 +5,8 @@ from typing import Callable, Optional, Union
 import gguf
 import torch
 
-TORCH_COMPATIBLE_QTYPES = {None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}
+# should not be a Set until this is resolved: https://github.com/pytorch/pytorch/issues/145761
+TORCH_COMPATIBLE_QTYPES = [None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16]
 
 # K Quants #
 QK_K = 256

invokeai/backend/rectified_flow/rectified_flow_inpaint_extension.py

@@ -30,18 +30,13 @@ class RectifiedFlowInpaintExtension:
     def _apply_mask_gradient_adjustment(self, t_prev: float) -> torch.Tensor:
         """Applies inpaint mask gradient adjustment and returns the inpaint mask to be used at the current timestep."""
         # As we progress through the denoising process, we promote gradient regions of the mask to have a full weight of
-        # 1.0. This helps to produce more coherent seams around the inpainted region. We experimented with a (small)
-        # number of promotion strategies (e.g. gradual promotion based on timestep), but found that a simple cutoff
-        # threshold worked well.
+        # 1.0. This helps to produce more coherent seams around the inpainted region.
+
         # We use a small epsilon to avoid any potential issues with floating point precision.
         eps = 1e-4
-        mask_gradient_t_cutoff = 0.5
-        if t_prev > mask_gradient_t_cutoff:
-            # Early in the denoising process, use the inpaint mask as-is.
-            return self._inpaint_mask
-        else:
-            # After the cut-off, promote all non-zero mask values to 1.0.
-            mask = self._inpaint_mask.where(self._inpaint_mask <= (0.0 + eps), 1.0)
+        mask = torch.where(self._inpaint_mask >= t_prev + eps, 1.0, 0.0).to(
+            dtype=self._inpaint_mask.dtype, device=self._inpaint_mask.device
+        )
 
         return mask
 

invokeai/frontend/web/.eslintrc.js

@@ -9,13 +9,25 @@ module.exports = {
     // https://github.com/qdanik/eslint-plugin-path
     'path/no-relative-imports': ['error', { maxDepth: 0 }],
     // https://github.com/edvardchen/eslint-plugin-i18next/blob/HEAD/docs/rules/no-literal-string.md
-    'i18next/no-literal-string': 'error',
+    // TODO: ENABLE THIS RULE BEFORE v6.0.0
+    // 'i18next/no-literal-string': 'error',
     // https://eslint.org/docs/latest/rules/no-console
-    'no-console': 'error',
+    'no-console': 'warn',
     // https://eslint.org/docs/latest/rules/no-promise-executor-return
     'no-promise-executor-return': 'error',
     // https://eslint.org/docs/latest/rules/require-await
     'require-await': 'error',
+    // Restrict setActiveTab calls to only use-navigation-api.tsx
+    'no-restricted-syntax': [
+      'error',
+      {
+        selector: 'CallExpression[callee.name="setActiveTab"]',
+        message:
+          'setActiveTab() can only be called from use-navigation-api.tsx. Use navigationApi.switchToTab() instead.',
+      },
+    ],
+    // TODO: ENABLE THIS RULE BEFORE v6.0.0
+    'react/display-name': 'off',
     'no-restricted-properties': [
       'error',
       {
@@ -30,8 +42,38 @@ module.exports = {
           'The Clipboard API is not available by default in Firefox. Use the `useClipboard` hook instead, which wraps clipboard access to prevent errors.',
       },
     ],
+    'no-restricted-imports': [
+      'error',
+      {
+        paths: [
+          {
+            name: 'lodash-es',
+            importNames: ['isEqual'],
+            message: 'Please use objectEquals from @observ33r/object-equals instead.',
+          },
+          {
+            name: 'lodash-es',
+            message: 'Please use es-toolkit instead.',
+          },
+          {
+            name: 'es-toolkit',
+            importNames: ['isEqual'],
+            message: 'Please use objectEquals from @observ33r/object-equals instead.',
+          },
+        ],
+      },
+    ],
   },
   overrides: [
+    /**
+     * Allow setActiveTab calls only in use-navigation-api.tsx
+     */
+    {
+      files: ['**/use-navigation-api.tsx'],
+      rules: {
+        'no-restricted-syntax': 'off',
+      },
+    },
     /**
      * Overrides for stories
      */

invokeai/frontend/web/knip.ts

@@ -12,8 +12,8 @@ const config: KnipConfig = {
     'src/features/parameters/types/parameterSchemas.ts',
     // TODO(psyche): maybe we can clean up these utils after canvas v2 release
     'src/features/controlLayers/konva/util.ts',
-    // TODO(psyche): restore HRF functionality?
-    'src/features/hrf/**',
+    // Will be using this
+    'src/common/hooks/useAsyncState.ts',
   ],
   ignoreBinaries: ['only-allow'],
   paths: {

invokeai/frontend/web/package.json

@@ -38,70 +38,60 @@
     "test:ui": "vitest --coverage --ui",
     "test:no-watch": "vitest --no-watch"
   },
-  "madge": {
-    "excludeRegExp": [
-      "^index.ts$"
-    ],
-    "detectiveOptions": {
-      "ts": {
-        "skipTypeImports": true
-      },
-      "tsx": {
-        "skipTypeImports": true
-      }
-    }
-  },
   "dependencies": {
-    "@atlaskit/pragmatic-drag-and-drop": "^1.5.3",
-    "@atlaskit/pragmatic-drag-and-drop-auto-scroll": "^2.1.0",
-    "@atlaskit/pragmatic-drag-and-drop-hitbox": "^1.0.3",
-    "@dagrejs/dagre": "^1.1.4",
+    "@atlaskit/pragmatic-drag-and-drop": "^1.7.4",
+    "@atlaskit/pragmatic-drag-and-drop-auto-scroll": "^2.1.1",
+    "@atlaskit/pragmatic-drag-and-drop-hitbox": "^1.1.0",
+    "@dagrejs/dagre": "^1.1.5",
     "@dagrejs/graphlib": "^2.2.4",
-    "@fontsource-variable/inter": "^5.2.5",
+    "@fontsource-variable/inter": "^5.2.6",
     "@invoke-ai/ui-library": "^0.0.46",
     "@nanostores/react": "^1.0.0",
-    "@reduxjs/toolkit": "2.7.0",
+    "@observ33r/object-equals": "^1.1.4",
+    "@reduxjs/toolkit": "2.8.2",
     "@roarr/browser-log-writer": "^1.3.0",
-    "@xyflow/react": "^12.6.0",
+    "@xyflow/react": "^12.7.1",
+    "ag-psd": "^28.2.1",
     "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.2",
     "cmdk": "^1.1.1",
     "compare-versions": "^6.1.1",
+    "dockview": "^4.4.0",
+    "es-toolkit": "^1.39.5",
     "filesize": "^10.1.6",
-    "fracturedjsonjs": "^4.0.2",
+    "fracturedjsonjs": "^4.1.0",
     "framer-motion": "^11.10.0",
-    "i18next": "^25.0.1",
+    "i18next": "^25.2.1",
     "i18next-http-backend": "^3.0.2",
-    "idb-keyval": "^6.2.1",
+    "idb-keyval": "^6.2.2",
     "jsondiffpatch": "^0.7.3",
     "konva": "^9.3.20",
-    "linkify-react": "^4.2.0",
-    "linkifyjs": "^4.2.0",
-    "lodash-es": "^4.17.21",
+    "linkify-react": "^4.3.1",
+    "linkifyjs": "^4.3.1",
     "lru-cache": "^11.1.0",
     "mtwist": "^1.0.2",
     "nanoid": "^5.1.5",
     "nanostores": "^1.0.1",
     "new-github-issue-url": "^1.1.0",
-    "overlayscrollbars": "^2.11.1",
+    "overlayscrollbars": "^2.11.4",
     "overlayscrollbars-react": "^0.5.6",
     "perfect-freehand": "^1.2.2",
-    "query-string": "^9.1.1",
+    "query-string": "^9.2.1",
     "raf-throttle": "^2.0.6",
     "react": "^18.3.1",
     "react-colorful": "^5.6.1",
     "react-dom": "^18.3.1",
     "react-dropzone": "^14.3.8",
     "react-error-boundary": "^5.0.0",
-    "react-hook-form": "^7.56.1",
+    "react-hook-form": "^7.58.1",
     "react-hotkeys-hook": "4.5.0",
-    "react-i18next": "^15.5.1",
+    "react-i18next": "^15.5.3",
     "react-icons": "^5.5.0",
     "react-redux": "9.2.0",
-    "react-resizable-panels": "^2.1.8",
+    "react-resizable-panels": "^3.0.3",
     "react-textarea-autosize": "^8.5.9",
     "react-use": "^17.6.0",
-    "react-virtuoso": "^4.12.6",
+    "react-virtuoso": "^4.13.0",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^5.2.0",
     "redux-undo": "^1.1.0",
@@ -109,12 +99,12 @@
     "roarr": "^7.21.1",
     "serialize-error": "^12.0.0",
     "socket.io-client": "^4.8.1",
-    "stable-hash": "^0.0.5",
-    "use-debounce": "^10.0.4",
+    "stable-hash": "^0.0.6",
+    "use-debounce": "^10.0.5",
     "use-device-pixel-ratio": "^1.1.2",
     "uuid": "^11.1.0",
-    "zod": "^3.24.3",
-    "zod-validation-error": "^3.4.0"
+    "zod": "^3.25.67",
+    "zod-validation-error": "^3.5.2"
   },
   "peerDependencies": {
     "react": "^18.2.0",
@@ -131,7 +121,6 @@
     "@storybook/react": "^8.6.12",
     "@storybook/react-vite": "^8.6.12",
     "@storybook/theming": "^8.6.12",
-    "@types/lodash-es": "^4.17.12",
     "@types/node": "^22.15.1",
     "@types/react": "^18.3.11",
     "@types/react-dom": "^18.3.0",
@@ -145,7 +134,7 @@
     "eslint": "^8.57.1",
     "eslint-plugin-i18next": "^6.1.1",
     "eslint-plugin-path": "^1.3.0",
-    "knip": "^5.50.5",
+    "knip": "^5.61.3",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^7.6.1",
     "prettier": "^3.5.3",
@@ -154,7 +143,7 @@
     "tsafe": "^1.8.5",
     "type-fest": "^4.40.0",
     "typescript": "^5.8.3",
-    "vite": "^6.3.3",
+    "vite": "^7.0.2",
     "vite-plugin-css-injected-by-js": "^3.5.2",
     "vite-plugin-dts": "^4.5.3",
     "vite-plugin-eslint": "^1.8.1",
@@ -162,7 +151,7 @@
     "vitest": "^3.1.2"
   },
   "engines": {
-    "pnpm": "8"
+    "pnpm": "10"
   },
-  "packageManager": "pnpm@8.15.9+sha512.499434c9d8fdd1a2794ebf4552b3b25c0a633abcee5bb15e7b5de90f32f47b513aca98cd5cfd001c31f0db454bc3804edccd578501e4ca293a6816166bbd9f81"
+  "packageManager": "pnpm@10.12.4"
 }

invokeai/frontend/web/pnpm-workspace.yaml

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

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

@@ -24,15 +24,18 @@
         "autoAddBoard": "Auto-Add Board",
         "boards": "Boards",
         "selectedForAutoAdd": "Selected for Auto-Add",
-        "bottomMessage": "Deleting this board and its images will reset any features currently using them.",
+        "bottomMessage": "Deleting images will reset any features currently using them.",
         "cancel": "Cancel",
         "changeBoard": "Change Board",
         "clearSearch": "Clear Search",
         "deleteBoard": "Delete Board",
         "deleteBoardAndImages": "Delete Board and Images",
         "deleteBoardOnly": "Delete Board Only",
-        "deletedBoardsCannotbeRestored": "Deleted boards cannot be restored. Selecting 'Delete Board Only' will move images to an uncategorized state.",
-        "deletedPrivateBoardsCannotbeRestored": "Deleted boards cannot be restored. Selecting 'Delete Board Only' will move images to a private uncategorized state for the image's creator.",
+        "deletedBoardsCannotbeRestored": "Deleted boards and images cannot be restored. Selecting 'Delete Board Only' will move images to an uncategorized state.",
+        "deletedPrivateBoardsCannotbeRestored": "Deleted boards and images cannot be restored. Selecting 'Delete Board Only' will move images to a private uncategorized state for the image's creator.",
+        "uncategorizedImages": "Uncategorized Images",
+        "deleteAllUncategorizedImages": "Delete All Uncategorized Images",
+        "deletedImagesCannotBeRestored": "Deleted images cannot be restored.",
         "hideBoards": "Hide Boards",
         "loading": "Loading...",
         "menuItemAutoAdd": "Auto-add to this Board",
@@ -46,7 +49,7 @@
         "searchBoard": "Search Boards...",
         "selectBoard": "Select a Board",
         "shared": "Shared Boards",
-        "topMessage": "This board contains images used in the following features:",
+        "topMessage": "This selection contains images used in the following features:",
         "unarchiveBoard": "Unarchive Board",
         "uncategorized": "Uncategorized",
         "viewBoards": "View Boards",
@@ -222,7 +225,16 @@
     "prompt": {
         "addPromptTrigger": "Add Prompt Trigger",
         "compatibleEmbeddings": "Compatible Embeddings",
-        "noMatchingTriggers": "No matching triggers"
+        "noMatchingTriggers": "No matching triggers",
+        "generateFromImage": "Generate prompt from image",
+        "expandCurrentPrompt": "Expand Current Prompt",
+        "uploadImageForPromptGeneration": "Upload Image for Prompt Generation",
+        "expandingPrompt": "Expanding prompt...",
+        "resultTitle": "Prompt Expansion Complete",
+        "resultSubtitle": "Choose how to handle the expanded prompt:",
+        "replace": "Replace",
+        "insert": "Insert",
+        "discard": "Discard"
     },
     "queue": {
         "queue": "Queue",
@@ -332,14 +344,14 @@
         "images": "Images",
         "assets": "Assets",
         "alwaysShowImageSizeBadge": "Always Show Image Size Badge",
-        "assetsTab": "Files you’ve uploaded for use in your projects.",
+        "assetsTab": "Files you've uploaded for use in your projects.",
         "autoAssignBoardOnClick": "Auto-Assign Board on Click",
         "autoSwitchNewImages": "Auto-Switch to New Images",
         "boardsSettings": "Boards Settings",
         "copy": "Copy",
         "currentlyInUse": "This image is currently in use in the following features:",
         "drop": "Drop",
-        "dropOrUpload": "$t(gallery.drop) or Upload",
+        "dropOrUpload": "Drop or Upload",
         "dropToUpload": "$t(gallery.drop) to Upload",
         "deleteImage_one": "Delete Image",
         "deleteImage_other": "Delete {{count}} Images",
@@ -354,7 +366,7 @@
         "gallerySettings": "Gallery Settings",
         "go": "Go",
         "image": "image",
-        "imagesTab": "Images you’ve created and saved within Invoke.",
+        "imagesTab": "Images you've created and saved within Invoke.",
         "imagesSettings": "Gallery Images Settings",
         "jump": "Jump",
         "loading": "Loading",
@@ -393,7 +405,8 @@
         "compareHelp4": "Press <Kbd>Z</Kbd> or <Kbd>Esc</Kbd> to exit.",
         "openViewer": "Open Viewer",
         "closeViewer": "Close Viewer",
-        "move": "Move"
+        "move": "Move",
+        "useForPromptGeneration": "Use for Prompt Generation"
     },
     "hotkeys": {
         "hotkeys": "Hotkeys",
@@ -576,6 +589,16 @@
             "cancelTransform": {
                 "title": "Cancel Transform",
                 "desc": "Cancel the pending transform."
+            },
+            "settings": {
+                "behavior": "Behavior",
+                "display": "Display",
+                "grid": "Grid",
+                "debug": "Debug"
+            },
+            "toggleNonRasterLayers": {
+                "title": "Toggle Non-Raster Layers",
+                "desc": "Show or hide all non-raster layer categories (Control Layers, Inpaint Masks, Regional Guidance)."
             }
         },
         "workflows": {
@@ -739,7 +762,7 @@
         "vae": "VAE",
         "width": "Width",
         "workflow": "Workflow",
-        "canvasV2Metadata": "Canvas"
+        "canvasV2Metadata": "Canvas Layers"
     },
     "modelManager": {
         "active": "active",
@@ -760,7 +783,7 @@
         "convertToDiffusers": "Convert To Diffusers",
         "convertToDiffusersHelpText1": "This model will be converted to the 🧨 Diffusers format.",
         "convertToDiffusersHelpText2": "This process will replace your Model Manager entry with the Diffusers version of the same model.",
-        "convertToDiffusersHelpText3": "Your checkpoint file on disk WILL be deleted if it is in InvokeAI root folder. If it is in a custom location, then it WILL NOT be deleted.",
+        "convertToDiffusersHelpText3": "Your checkpoint file on disk WILL be deleted if it is in the InvokeAI root folder. If it is in a custom location, then it WILL NOT be deleted.",
         "convertToDiffusersHelpText4": "This is a one time process only. It might take around 30s-60s depending on the specifications of your computer.",
         "convertToDiffusersHelpText5": "Please make sure you have enough disk space. Models generally vary between 2GB-7GB in size.",
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
@@ -803,7 +826,11 @@
         "urlUnauthorizedErrorMessage": "You may need to configure an API token to access this model.",
         "urlUnauthorizedErrorMessage2": "Learn how here.",
         "imageEncoderModelId": "Image Encoder Model ID",
-        "includesNModels": "Includes {{n}} models and their dependencies",
+        "installedModelsCount": "{{installed}} of {{total}} models installed.",
+        "includesNModels": "Includes {{n}} models and their dependencies.",
+        "allNModelsInstalled": "All {{count}} models installed",
+        "nToInstall": "{{count}} to install",
+        "nAlreadyInstalled": "{{count}} already installed",
         "installQueue": "Install Queue",
         "inplaceInstall": "In-place install",
         "inplaceInstallDesc": "Install models without copying the files. When using the model, it will be loaded from its this location. If disabled, the model file(s) will be copied into the Invoke-managed models directory during installation.",
@@ -866,6 +893,25 @@
         "starterBundleHelpText": "Easily install all models needed to get started with a base model, including a main model, controlnets, IP adapters, and more. Selecting a bundle will skip any models that you already have installed.",
         "starterModels": "Starter Models",
         "starterModelsInModelManager": "Starter Models can be found in Model Manager",
+        "bundleAlreadyInstalled": "Bundle already installed",
+        "bundleAlreadyInstalledDesc": "All models in the {{bundleName}} bundle are already installed.",
+        "launchpadTab": "Launchpad",
+        "launchpad": {
+            "welcome": "Welcome to Model Management",
+            "description": "Invoke requires models to be installed to utilize most features of the platform. Choose from manual installation options or explore curated starter models.",
+            "manualInstall": "Manual Installation",
+            "urlDescription": "Install models from a URL or local file path. Perfect for specific models you want to add.",
+            "huggingFaceDescription": "Browse and install models directly from HuggingFace repositories.",
+            "scanFolderDescription": "Scan a local folder to automatically detect and install models.",
+            "recommendedModels": "Recommended Models",
+            "exploreStarter": "Or browse all available starter models",
+            "quickStart": "Quick Start Bundles",
+            "bundleDescription": "Each bundle includes essential models for each model family and curated base models to get started.",
+            "browseAll": "Or browse all available models:",
+            "stableDiffusion15": "Stable Diffusion 1.5",
+            "sdxl": "SDXL",
+            "fluxDev": "FLUX.1 dev"
+        },
         "controlLora": "Control LoRA",
         "llavaOnevision": "LLaVA OneVision",
         "syncModels": "Sync Models",
@@ -902,7 +948,8 @@
         "selectModel": "Select a Model",
         "noLoRAsInstalled": "No LoRAs installed",
         "noRefinerModelsInstalled": "No SDXL Refiner models installed",
-        "defaultVAE": "Default VAE"
+        "defaultVAE": "Default VAE",
+        "noCompatibleLoRAs": "No Compatible LoRAs"
     },
     "nodes": {
         "arithmeticSequence": "Arithmetic Sequence",
@@ -1144,6 +1191,7 @@
             "modelIncompatibleScaledBboxWidth": "Scaled bbox width is {{width}} but {{model}} requires multiple of {{multiple}}",
             "modelIncompatibleScaledBboxHeight": "Scaled bbox height is {{height}} but {{model}} requires multiple of {{multiple}}",
             "fluxModelMultipleControlLoRAs": "Can only use 1 Control LoRA at a time",
+            "fluxKontextMultipleReferenceImages": "Can only use 1 Reference Image at a time with Flux Kontext",
             "canvasIsFiltering": "Canvas is busy (filtering)",
             "canvasIsTransforming": "Canvas is busy (transforming)",
             "canvasIsRasterizing": "Canvas is busy (rasterizing)",
@@ -1151,7 +1199,9 @@
             "canvasIsSelectingObject": "Canvas is busy (selecting object)",
             "noPrompts": "No prompts generated",
             "noNodesInGraph": "No nodes in graph",
-            "systemDisconnected": "System disconnected"
+            "systemDisconnected": "System disconnected",
+            "promptExpansionPending": "Prompt expansion in progress",
+            "promptExpansionResultPending": "Please accept or discard your prompt expansion result"
         },
         "maskBlur": "Mask Blur",
         "negativePromptPlaceholder": "Negative Prompt",
@@ -1309,6 +1359,21 @@
         "problemCopyingLayer": "Unable to Copy Layer",
         "problemSavingLayer": "Unable to Save Layer",
         "problemDownloadingImage": "Unable to Download Image",
+        "noRasterLayers": "No Raster Layers Found",
+        "noRasterLayersDesc": "Create at least one raster layer to export to PSD",
+        "noActiveRasterLayers": "No Active Raster Layers",
+        "noActiveRasterLayersDesc": "Enable at least one raster layer to export to PSD",
+        "noVisibleRasterLayers": "No Visible Raster Layers",
+        "noVisibleRasterLayersDesc": "Enable at least one raster layer to export to PSD",
+        "invalidCanvasDimensions": "Invalid Canvas Dimensions",
+        "canvasTooLarge": "Canvas Too Large",
+        "canvasTooLargeDesc": "Canvas dimensions exceed the maximum allowed size for PSD export. Reduce the total width and height of the canvas of the canvas and try again.",
+        "failedToProcessLayers": "Failed to Process Layers",
+        "psdExportSuccess": "PSD Export Complete",
+        "psdExportSuccessDesc": "Successfully exported {{count}} layers to PSD file",
+        "problemExportingPSD": "Problem Exporting PSD",
+        "canvasManagerNotAvailable": "Canvas Manager Not Available",
+        "noValidLayerAdapters": "No Valid Layer Adapters Found",
         "pasteSuccess": "Pasted to {{destination}}",
         "pasteFailed": "Paste Failed",
         "prunedQueue": "Pruned Queue",
@@ -1332,11 +1397,17 @@
         "unableToCopyDesc": "Your browser does not support clipboard access. Firefox users may be able to fix this by following ",
         "unableToCopyDesc_theseSteps": "these steps",
         "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fill is not compatible with Text to Image or Image to Image. Use other FLUX models for these tasks.",
-        "imagen3IncompatibleGenerationMode": "Google Imagen3 supports Text to Image only. Use other models for Image to Image, Inpainting and Outpainting tasks.",
+        "imagenIncompatibleGenerationMode": "Google {{model}} supports Text to Image only. Use other models for Image to Image, Inpainting and Outpainting tasks.",
         "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4o supports Text to Image and Image to Image only. Use other models Inpainting and Outpainting tasks.",
+        "fluxKontextIncompatibleGenerationMode": "FLUX Kontext does not support generation from images placed on the canvas. Re-try using the Reference Image section and disable any Raster Layers.",
         "problemUnpublishingWorkflow": "Problem Unpublishing Workflow",
         "problemUnpublishingWorkflowDescription": "There was a problem unpublishing the workflow. Please try again.",
-        "workflowUnpublished": "Workflow Unpublished"
+        "workflowUnpublished": "Workflow Unpublished",
+        "sentToCanvas": "Sent to Canvas",
+        "sentToUpscale": "Sent to Upscale",
+        "promptGenerationStarted": "Prompt generation started",
+        "uploadAndPromptGenerationFailed": "Failed to upload image and generate prompt",
+        "promptExpansionFailed": "We ran into an issue. Please try prompt expansion again."
     },
     "popovers": {
         "clipSkip": {
@@ -1859,6 +1930,7 @@
         "saveCanvasToGallery": "Save Canvas to Gallery",
         "saveBboxToGallery": "Save Bbox to Gallery",
         "saveLayerToAssets": "Save Layer to Assets",
+        "exportCanvasToPSD": "Export Canvas to PSD",
         "cropLayerToBbox": "Crop Layer to Bbox",
         "savedToGalleryOk": "Saved to Gallery",
         "savedToGalleryError": "Error saving to gallery",
@@ -1884,11 +1956,13 @@
         "mergingLayers": "Merging layers",
         "clearHistory": "Clear History",
         "bboxOverlay": "Show Bbox Overlay",
+        "ruleOfThirds": "Show Rule of Thirds",
         "newSession": "New Session",
         "clearCaches": "Clear Caches",
         "recalculateRects": "Recalculate Rects",
         "clipToBbox": "Clip Strokes to Bbox",
         "outputOnlyMaskedRegions": "Output Only Generated Regions",
+        "saveAllImagesToGallery": "Save All Images to Gallery",
         "addLayer": "Add Layer",
         "duplicate": "Duplicate",
         "moveToFront": "Move to Front",
@@ -1907,11 +1981,13 @@
         "addPositivePrompt": "Add $t(controlLayers.prompt)",
         "addNegativePrompt": "Add $t(controlLayers.negativePrompt)",
         "addReferenceImage": "Add $t(controlLayers.referenceImage)",
+        "addImageNoise": "Add $t(controlLayers.imageNoise)",
         "addRasterLayer": "Add $t(controlLayers.rasterLayer)",
         "addControlLayer": "Add $t(controlLayers.controlLayer)",
         "addInpaintMask": "Add $t(controlLayers.inpaintMask)",
         "addRegionalGuidance": "Add $t(controlLayers.regionalGuidance)",
         "addGlobalReferenceImage": "Add $t(controlLayers.globalReferenceImage)",
+        "addDenoiseLimit": "Add $t(controlLayers.denoiseLimit)",
         "rasterLayer": "Raster Layer",
         "controlLayer": "Control Layer",
         "inpaintMask": "Inpaint Mask",
@@ -1987,6 +2063,8 @@
         "disableTransparencyEffect": "Disable Transparency Effect",
         "hidingType": "Hiding {{type}}",
         "showingType": "Showing {{type}}",
+        "showNonRasterLayers": "Show Non-Raster Layers (Shift+H)",
+        "hideNonRasterLayers": "Hide Non-Raster Layers (Shift+H)",
         "dynamicGrid": "Dynamic Grid",
         "logDebugInfo": "Log Debug Info",
         "locked": "Locked",
@@ -2009,8 +2087,12 @@
         "resetCanvasLayers": "Reset Canvas Layers",
         "resetGenerationSettings": "Reset Generation Settings",
         "replaceCurrent": "Replace Current",
-        "controlLayerEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer, or draw on the canvas to get started.",
-        "referenceImageEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer, or <PullBboxButton>pull the bounding box into this layer</PullBboxButton> to get started.",
+        "controlLayerEmptyState": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this layer, <PullBboxButton>pull the bounding box into this layer</PullBboxButton>, or draw on the canvas to get started.",
+        "referenceImageEmptyStateWithCanvasOptions": "<UploadButton>Upload an image</UploadButton>, drag an image from the <GalleryButton>gallery</GalleryButton> onto this Reference Image or <PullBboxButton>pull the bounding box into this Reference Image</PullBboxButton> to get started.",
+        "referenceImageEmptyState": "<UploadButton>Upload an image</UploadButton> or drag an image from the <GalleryButton>gallery</GalleryButton> onto this Reference Image to get started.",
+        "uploadOrDragAnImage": "Drag an image from the gallery or <UploadButton>upload an image</UploadButton>.",
+        "imageNoise": "Image Noise",
+        "denoiseLimit": "Denoise Limit",
         "warnings": {
             "problemsFound": "Problems found",
             "unsupportedModel": "layer not supported for selected base model",
@@ -2249,6 +2331,9 @@
                 "label": "Preserve Masked Region",
                 "alert": "Preserving Masked Region"
             },
+            "saveAllImagesToGallery": {
+                "alert": "Saving All Images to Gallery"
+            },
             "isolatedStagingPreview": "Isolated Staging Preview",
             "isolatedPreview": "Isolated Preview",
             "isolatedLayerPreview": "Isolated Layer Preview",
@@ -2277,6 +2362,7 @@
             "newGlobalReferenceImage": "New Global Reference Image",
             "newRegionalReferenceImage": "New Regional Reference Image",
             "newControlLayer": "New Control Layer",
+            "newResizedControlLayer": "New Resized Control Layer",
             "newRasterLayer": "New Raster Layer",
             "newInpaintMask": "New Inpaint Mask",
             "newRegionalGuidance": "New Regional Guidance",
@@ -2294,6 +2380,11 @@
             "saveToGallery": "Save To Gallery",
             "showResultsOn": "Showing Results",
             "showResultsOff": "Hiding Results"
+        },
+        "autoSwitch": {
+            "off": "Off",
+            "switchOnStart": "On Start",
+            "switchOnFinish": "On Finish"
         }
     },
     "upscaling": {
@@ -2360,7 +2451,8 @@
         "uploadImage": "Upload Image",
         "useForTemplate": "Use For Prompt Template",
         "viewList": "View Template List",
-        "viewModeTooltip": "This is how your prompt will look with your currently selected template. To edit your prompt, click anywhere in the text box."
+        "viewModeTooltip": "This is how your prompt will look with your currently selected template. To edit your prompt, click anywhere in the text box.",
+        "togglePromptPreviews": "Toggle Prompt Previews"
     },
     "upsell": {
         "inviteTeammates": "Invite Teammates",
@@ -2380,6 +2472,55 @@
             "upscaling": "Upscaling",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
             "gallery": "Gallery"
+        },
+        "launchpad": {
+            "workflowsTitle": "Go deep with Workflows.",
+            "upscalingTitle": "Upscale and add detail.",
+            "canvasTitle": "Edit and refine on Canvas.",
+            "generateTitle": "Generate images from text prompts.",
+            "modelGuideText": "Want to learn what prompts work best for each model?",
+            "modelGuideLink": "Check out our Model Guide.",
+            "workflows": {
+                "description": "Workflows are reusable templates that automate image generation tasks, allowing you to quickly perform complex operations and get consistent results.",
+                "learnMoreLink": "Learn more about creating workflows",
+                "browseTemplates": {
+                    "title": "Browse Workflow Templates",
+                    "description": "Choose from pre-built workflows for common tasks"
+                },
+                "createNew": {
+                    "title": "Create a new Workflow",
+                    "description": "Start a new workflow from scratch"
+                },
+                "loadFromFile": {
+                    "title": "Load workflow from file",
+                    "description": "Upload a workflow to start with an existing setup"
+                }
+            },
+            "upscaling": {
+                "uploadImage": {
+                    "title": "Upload Image to Upscale",
+                    "description": "Click or drag an image to upscale (JPG, PNG, WebP up to 100MB)"
+                },
+                "replaceImage": {
+                    "title": "Replace Current Image",
+                    "description": "Click or drag a new image to replace the current one"
+                },
+                "imageReady": {
+                    "title": "Image Ready",
+                    "description": "Press Invoke to begin upscaling"
+                },
+                "readyToUpscale": {
+                    "title": "Ready to upscale!",
+                    "description": "Configure your settings below, then click the Invoke button to begin upscaling your image."
+                },
+                "upscaleModel": "Upscale Model",
+                "model": "Model",
+                "scale": "Scale",
+                "helpText": {
+                    "promptAdvice": "When upscaling, use a prompt that describes the medium and style. Avoid describing specific content details in the image.",
+                    "styleAdvice": "Upscaling works best with the general style of your image."
+                }
+            }
         }
     },
     "system": {
@@ -2419,8 +2560,9 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "Nvidia 50xx GPUs: Invoke uses PyTorch 2.7.0, which is required for these GPUs.",
-            "Model Relationships: Link LoRAs to main models, and the LoRAs will show up first in the list."
+            "Generate images faster with new Launchpads and a simplified Generate tab.",
+            "Edit with prompts using Flux Kontext Dev.",
+            "Export to PSD, bulk-hide overlays, organize models & images — all in a reimagined interface built for control."
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",
@@ -2429,62 +2571,16 @@
     "supportVideos": {
         "supportVideos": "Support Videos",
         "gettingStarted": "Getting Started",
-        "controlCanvas": "Control Canvas",
         "watch": "Watch",
-        "studioSessionsDesc1": "Check out the <StudioSessionsPlaylistLink /> for Invoke deep dives.",
-        "studioSessionsDesc2": "Join our <DiscordLink /> to participate in the live sessions and ask questions. Sessions are uploaded to the playlist the following week.",
+        "studioSessionsDesc": "Join our <DiscordLink /> to participate in the live sessions and ask questions. Sessions are uploaded to the playlist the following week.",
         "videos": {
-            "creatingYourFirstImage": {
-                "title": "Creating Your First Image",
-                "description": "Introduction to creating an image from scratch using Invoke's tools."
+            "gettingStarted": {
+                "title": "Getting Started with Invoke",
+                "description": "Complete video series covering everything you need to know to get started with Invoke, from creating your first image to advanced techniques."
             },
-            "usingControlLayersAndReferenceGuides": {
-                "title": "Using Control Layers and Reference Guides",
-                "description": "Learn how to guide your image creation with control layers and reference images."
-            },
-            "understandingImageToImageAndDenoising": {
-                "title": "Understanding Image-to-Image and Denoising",
-                "description": "Overview of image-to-image transformations and denoising in Invoke."
-            },
-            "exploringAIModelsAndConceptAdapters": {
-                "title": "Exploring AI Models and Concept Adapters",
-                "description": "Dive into AI models and how to use concept adapters for creative control."
-            },
-            "creatingAndComposingOnInvokesControlCanvas": {
-                "title": "Creating and Composing on Invoke's Control Canvas",
-                "description": "Learn to compose images using Invoke's control canvas."
-            },
-            "upscaling": {
-                "title": "Upscaling",
-                "description": "How to upscale images with Invoke's tools to enhance resolution."
-            },
-            "howDoIGenerateAndSaveToTheGallery": {
-                "title": "How Do I Generate and Save to the Gallery?",
-                "description": "Steps to generate and save images to the gallery."
-            },
-            "howDoIEditOnTheCanvas": {
-                "title": "How Do I Edit on the Canvas?",
-                "description": "Guide to editing images directly on the canvas."
-            },
-            "howDoIDoImageToImageTransformation": {
-                "title": "How Do I Do Image-to-Image Transformation?",
-                "description": "Tutorial on performing image-to-image transformations in Invoke."
-            },
-            "howDoIUseControlNetsAndControlLayers": {
-                "title": "How Do I Use Control Nets and Control Layers?",
-                "description": "Learn to apply control layers and controlnets to your images."
-            },
-            "howDoIUseGlobalIPAdaptersAndReferenceImages": {
-                "title": "How Do I Use Global IP Adapters and Reference Images?",
-                "description": "Introduction to adding reference images and global IP adapters."
-            },
-            "howDoIUseInpaintMasks": {
-                "title": "How Do I Use Inpaint Masks?",
-                "description": "How to apply inpaint masks for image correction and variation."
-            },
-            "howDoIOutpaint": {
-                "title": "How Do I Outpaint?",
-                "description": "Guide to outpainting beyond the original image borders."
+            "studioSessions": {
+                "title": "Studio Sessions",
+                "description": "Deep dive sessions exploring advanced Invoke features, creative workflows, and community discussions."
             }
         }
     }

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

@@ -883,8 +883,8 @@
         "problemUnpublishingWorkflow": "Problema durante l'annullamento della pubblicazione del flusso di lavoro",
         "problemUnpublishingWorkflowDescription": "Si è verificato un problema durante l'annullamento della pubblicazione del flusso di lavoro. Riprova.",
         "workflowUnpublished": "Flusso di lavoro non pubblicato",
-        "imagen3IncompatibleGenerationMode": "Google Imagen3 supporta solo la conversione da testo a immagine. Utilizza altri modelli per le attività di conversione da immagine a immagine, inpainting e outpainting.",
-        "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4o supporta solo la conversione da testo a immagine e da immagine a immagine. Utilizza altri modelli per le attività di Inpainting e Outpainting."
+        "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4o supporta solo la conversione da testo a immagine e da immagine a immagine. Utilizza altri modelli per le attività di Inpainting e Outpainting.",
+        "imagenIncompatibleGenerationMode": "Google {{model}} supporta solo la generazione da testo a immagine. Utilizza altri modelli per le attività di conversione da immagine a immagine, inpainting e outpainting."
     },
     "accessibility": {
         "invokeProgressBar": "Barra di avanzamento generazione",
@@ -1086,11 +1086,11 @@
         "menuItemAutoAdd": "Aggiungi automaticamente a questa bacheca",
         "cancel": "Annulla",
         "addBoard": "Aggiungi Bacheca",
-        "bottomMessage": "L'eliminazione di questa bacheca e delle sue immagini ripristinerà tutte le funzionalità che le stanno attualmente utilizzando.",
+        "bottomMessage": "L'eliminazione delle immagini reimposterà tutte le funzionalità che le stanno utilizzando.",
         "changeBoard": "Cambia Bacheca",
         "loading": "Caricamento in corso ...",
         "clearSearch": "Cancella Ricerca",
-        "topMessage": "Questa bacheca contiene immagini utilizzate nelle seguenti funzionalità:",
+        "topMessage": "Questa selezione contiene immagini utilizzate nelle seguenti funzionalità:",
         "move": "Sposta",
         "myBoard": "Bacheca",
         "searchBoard": "Cerca bacheche ...",
@@ -1101,7 +1101,7 @@
         "deleteBoardOnly": "solo la Bacheca",
         "deleteBoard": "Elimina Bacheca",
         "deleteBoardAndImages": "Bacheca e Immagini",
-        "deletedBoardsCannotbeRestored": "Le bacheche eliminate non possono essere ripristinate. Selezionando \"Elimina solo bacheca\" le immagini verranno spostate nella bacheca \"Non categorizzato\".",
+        "deletedBoardsCannotbeRestored": "Le bacheche e le immagini eliminate non possono essere ripristinate. Selezionando \"Elimina solo bacheca\" le immagini verranno spostate in uno stato non categorizzato.",
         "movingImagesToBoard_one": "Spostare {{count}} immagine nella bacheca:",
         "movingImagesToBoard_many": "Spostare {{count}} immagini nella bacheca:",
         "movingImagesToBoard_other": "Spostare {{count}} immagini nella bacheca:",
@@ -1123,8 +1123,11 @@
         "noBoards": "Nessuna bacheca {{boardType}}",
         "hideBoards": "Nascondi bacheche",
         "viewBoards": "Visualizza bacheche",
-        "deletedPrivateBoardsCannotbeRestored": "Le bacheche cancellate non possono essere ripristinate. Selezionando 'Cancella solo bacheca', le immagini verranno spostate nella bacheca \"Non categorizzato\" privata dell'autore dell'immagine.",
-        "updateBoardError": "Errore durante l'aggiornamento della bacheca"
+        "deletedPrivateBoardsCannotbeRestored": "Le bacheche e le immagini eliminate non possono essere ripristinate. Selezionando \"Elimina solo bacheca\", le immagini verranno spostate in uno stato privato e non categorizzato per l'autore dell'immagine.",
+        "updateBoardError": "Errore durante l'aggiornamento della bacheca",
+        "uncategorizedImages": "Immagini non categorizzate",
+        "deleteAllUncategorizedImages": "Elimina tutte le immagini non categorizzate",
+        "deletedImagesCannotBeRestored": "Le immagini eliminate non possono essere ripristinate."
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
@@ -2005,11 +2008,11 @@
         "stagingOnCanvas": "Genera immagini nella",
         "ipAdapterMethod": {
             "full": "Stile e Composizione",
-            "style": "Solo Stile",
+            "style": "Stile (semplice)",
             "composition": "Solo Composizione",
             "ipAdapterMethod": "Modalità",
             "fullDesc": "Applica lo stile visivo (colori, texture) e la composizione (disposizione, struttura).",
-            "styleDesc": "Applica lo stile visivo (colori, texture) senza considerare la disposizione.",
+            "styleDesc": "Applica lo stile visivo (colori, texture) senza considerare la disposizione. Precedentemente chiamato \"Solo stile\".",
             "compositionDesc": "Replica disposizione e struttura ignorando lo stile di riferimento.",
             "styleStrong": "Stile (forte)",
             "styleStrongDesc": "Applica uno stile visivo forte, con un'influenza sulla composizione leggermente ridotta.",
@@ -2296,7 +2299,7 @@
         "replaceCurrent": "Sostituisci corrente",
         "mergeDown": "Unire in basso",
         "mergingLayers": "Unione dei livelli",
-        "controlLayerEmptyState": "<UploadButton>Carica un'immagine</UploadButton>, trascina un'immagine dalla <GalleryButton>galleria</GalleryButton> su questo livello oppure disegna sulla tela per iniziare.",
+        "controlLayerEmptyState": "<UploadButton>Carica un'immagine</UploadButton>, trascina un'immagine dalla <GalleryButton>galleria</GalleryButton> su questo livello, <PullBboxButton>trascina il riquadro di delimitazione in questo livello</PullBboxButton> oppure disegna sulla tela per iniziare.",
         "useImage": "Usa immagine",
         "resetGenerationSettings": "Ripristina impostazioni di generazione",
         "referenceImageEmptyState": "Per iniziare, <UploadButton>carica un'immagine</UploadButton>, trascina un'immagine dalla <GalleryButton>galleria</GalleryButton>, oppure <PullBboxButton>trascina il riquadro di delimitazione in questo livello</PullBboxButton> su questo livello.",
@@ -2345,7 +2348,11 @@
             "lowest": "Il più basso",
             "medium": "Medio",
             "highest": "La più alta"
-        }
+        },
+        "denoiseLimit": "Limite di riduzione del rumore",
+        "addImageNoise": "Aggiungi $t(controlLayers.imageNoise)",
+        "addDenoiseLimit": "Aggiungi $t(controlLayers.denoiseLimit)",
+        "imageNoise": "Rumore dell'immagine"
     },
     "ui": {
         "tabs": {
@@ -2445,8 +2452,8 @@
         "watchRecentReleaseVideos": "Guarda i video su questa versione",
         "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
         "items": [
-            "CogView4: supporto per i modelli CogView4 in Tela e Flussi di lavoro.",
-            "Dipendenze aggiornate: Invoke ora funziona con l'ultima versione delle sue dipendenze, tra cui Python 3.12 e Pytorch 2.6.0."
+            "Inpainting: livelli di rumore per maschera e limiti di denoise.",
+            "Canvas: proporzioni più intelligenti per SDXL e scorrimento e zoom migliorati."
         ]
     },
     "system": {

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

@@ -392,7 +392,7 @@
                 "title": "全選択"
             },
             "addNode": {
-                "desc": "ノード追加メニューを開く．",
+                "desc": "ノード追加メニューを開く。",
                 "title": "ノードを追加"
             },
             "pasteSelectionWithEdges": {
@@ -652,7 +652,9 @@
         "filterModels": "フィルターモデル",
         "modelPickerFallbackNoModelsInstalled": "モデルがインストールされていません.",
         "manageModels": "モデル管理",
-        "hfTokenReset": "ハギングフェイストークンリセット"
+        "hfTokenReset": "ハギングフェイストークンリセット",
+        "relatedModels": "関連のあるモデル",
+        "showOnlyRelatedModels": "関連している"
     },
     "parameters": {
         "images": "画像",
@@ -872,8 +874,8 @@
         "problemDeletingWorkflow": "ワークフローが削除された問題",
         "imageNotLoadedDesc": "画像を見つけられません",
         "parameterNotSetDesc": "{{parameter}}を呼び出せません",
-        "imagen3IncompatibleGenerationMode": "Google Imagen3 はテキストから画像への生成のみをサポートしています.画像から画像,インペインティング,アウトペインティングのタスクには他のモデルをご利用ください.",
-        "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4oは,テキストから画像への生成と画像から画像への生成のみをサポートしています.インペインティングおよび,アウトペインティングタスクには他のモデルを使用してください."
+        "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4oは,テキストから画像への生成と画像から画像への生成のみをサポートしています.インペインティングおよび,アウトペインティングタスクには他のモデルを使用してください.",
+        "imagenIncompatibleGenerationMode": "Google {{model}} はテキストから画像への変換のみをサポートしています. 画像から画像への変換, インペインティング,アウトペインティングのタスクには他のモデルを使用してください."
     },
     "accessibility": {
         "invokeProgressBar": "進捗バー",
@@ -1154,11 +1156,11 @@
         "unknownField": "不明なフィールド",
         "unexpectedField_withName": "予期しないフィールド\"{{name}}\"",
         "loadingTemplates": "読み込み中 {{name}}",
-        "validateConnectionsHelp": "無効な接続が行われたり,無効なグラフが呼び出されたりしないようにします.",
+        "validateConnectionsHelp": "無効な接続が行われたり,無効なグラフが呼び出されたりしないようにします",
         "validateConnections": "接続とグラフを確認する",
         "saveToGallery": "ギャラリーに保存",
         "newWorkflowDesc": "新しいワークフローを作りますか？",
-        "unknownFieldType": "$t(nodes.unknownField)型:{type}}",
+        "unknownFieldType": "$t(nodes.unknownField)型: {{type}}",
         "unsupportedArrayItemType": "サポートされていない配列項目型です \"{{type}}\"",
         "unableToLoadWorkflow": "ワークフローが読み込めません",
         "unableToValidateWorkflow": "ワークフローを確認できません",
@@ -1201,13 +1203,13 @@
         "downloadBoard": "ボードをダウンロード",
         "changeBoard": "ボードを変更",
         "loading": "ロード中...",
-        "topMessage": "このボードには、以下の機能で使用されている画像が含まれています：",
-        "bottomMessage": "このボードおよび画像を削除すると、現在これらを利用している機能はリセットされます。",
+        "topMessage": "この選択には、次の機能で使用される画像が含まれています:",
+        "bottomMessage": "この画像を削除すると、現在利用している機能はリセットされます。",
         "clearSearch": "検索をクリア",
         "deleteBoard": "ボードの削除",
         "deleteBoardAndImages": "ボードと画像の削除",
         "deleteBoardOnly": "ボードのみ削除",
-        "deletedBoardsCannotbeRestored": "削除されたボードは復元できません。\"ボードのみ削除\"を選択すると画像は未分類に移動されます。",
+        "deletedBoardsCannotbeRestored": "削除したボードと画像は復元できません。「ボードのみ削除」を選択すると、画像は未分類の状態になります。",
         "movingImagesToBoard_other": "{{count}} の画像をボードに移動:",
         "hideBoards": "ボードを隠す",
         "assetsWithCount_other": "{{count}} のアセット",
@@ -1222,9 +1224,12 @@
         "imagesWithCount_other": "{{count}} の画像",
         "updateBoardError": "ボード更新エラー",
         "selectedForAutoAdd": "自動追加に選択済み",
-        "deletedPrivateBoardsCannotbeRestored": "削除されたボードは復元できません。\"ボードのみ削除\"を選択すると画像はその作成者のプライベートな未分類に移動されます。",
+        "deletedPrivateBoardsCannotbeRestored": "削除されたボードと画像は復元できません。「ボードのみ削除」を選択すると、画像は作成者に対して非公開の未分類状態になります。",
         "noBoards": "{{boardType}} ボードがありません",
-        "viewBoards": "ボードを表示"
+        "viewBoards": "ボードを表示",
+        "uncategorizedImages": "分類されていない画像",
+        "deleteAllUncategorizedImages": "分類されていないすべての画像を削除",
+        "deletedImagesCannotBeRestored": "削除した画像は復元できません."
     },
     "invocationCache": {
         "invocationCache": "呼び出しキャッシュ",
@@ -1247,7 +1252,8 @@
         "paramRatio": {
             "heading": "縦横比",
             "paragraphs": [
-                "生成された画像の縦横比。"
+                "生成された画像の縦横比。",
+                "SD1.5 モデルの場合は 512x512 に相当する画像サイズ (ピクセル数) が推奨され, SDXL モデルの場合は 1024x1024 に相当するサイズが推奨されます."
             ]
         },
         "regionalGuidanceAndReferenceImage": {
@@ -1289,25 +1295,49 @@
             ]
         },
         "paramUpscaleMethod": {
-            "heading": "アップスケール手法"
+            "heading": "アップスケール手法",
+            "paragraphs": [
+                "高解像度修正のために画像を拡大するために使用される方法。"
+            ]
         },
         "upscaleModel": {
-            "heading": "アップスケールモデル"
+            "heading": "アップスケールモデル",
+            "paragraphs": [
+                "アップスケールモデルは、ディテールを追加する前に画像を出力サイズに合わせて拡大縮小します。サポートされているアップスケールモデルであればどれでも使用できますが、写真や線画など、特定の種類の画像に特化したモデルもあります。"
+            ]
         },
         "paramAspect": {
-            "heading": "縦横比"
+            "heading": "縦横比",
+            "paragraphs": [
+                "生成される画像のアスペクト比。比率を変更すると、幅と高さもそれに応じて更新されます。",
+                "「最適化」は、選択したモデルの幅と高さを最適な寸法に設定します。"
+            ]
         },
         "refinerSteps": {
-            "heading": "ステップ"
+            "heading": "ステップ",
+            "paragraphs": [
+                "生成プロセスのリファイナー部分で実行されるステップの数。",
+                "生成ステップと似ています。"
+            ]
         },
         "paramVAE": {
-            "heading": "VAE"
+            "heading": "VAE",
+            "paragraphs": [
+                "AI 出力を最終画像に変換するために使用されるモデル。"
+            ]
         },
         "scale": {
-            "heading": "スケール"
+            "heading": "スケール",
+            "paragraphs": [
+                "スケールは出力画像のサイズを制御し、入力画像の解像度の倍数に基づいて決定されます。例えば、1024x1024の画像を2倍に拡大すると、2048x2048の出力が生成されます。"
+            ]
         },
         "refinerScheduler": {
-            "heading": "スケジューラー"
+            "heading": "スケジューラー",
+            "paragraphs": [
+                "生成プロセスのリファイナー部分で使用されるスケジューラ。",
+                "生成スケジューラに似ています。"
+            ]
         },
         "compositingCoherenceMode": {
             "heading": "モード",
@@ -1316,13 +1346,23 @@
             ]
         },
         "paramModel": {
-            "heading": "モデル"
+            "heading": "モデル",
+            "paragraphs": [
+                "生成に使用されるモデル。異なるモデルは、異なる美的結果とコンテンツを生成するように特化するようにトレーニングされています。"
+            ]
         },
         "paramHeight": {
-            "heading": "高さ"
+            "heading": "高さ",
+            "paragraphs": [
+                "生成される画像の高さ。8の倍数にする必要があります。"
+            ]
         },
         "paramSteps": {
-            "heading": "ステップ"
+            "heading": "ステップ",
+            "paragraphs": [
+                "各生成で実行されるステップの数.",
+                "通常, ステップ数が多いほど, より高品質な画像が作成されますが生成時間も長くなります."
+            ]
         },
         "ipAdapterMethod": {
             "heading": "モード",
@@ -1331,10 +1371,18 @@
             ]
         },
         "paramSeed": {
-            "heading": "シード"
+            "heading": "シード",
+            "paragraphs": [
+                "生成に使用する始動ノイズを制御します.",
+                "同じ生成設定で同一の結果を生成するには, 「ランダム」オプションを無効にします."
+            ]
         },
         "paramIterations": {
-            "heading": "生成回数"
+            "heading": "生成回数",
+            "paragraphs": [
+                "生成する画像の数。",
+                "動的プロンプトが有効になっている場合、各プロンプトはこの回数生成されます。"
+            ]
         },
         "controlNet": {
             "heading": "ControlNet",
@@ -1343,16 +1391,29 @@
             ]
         },
         "paramWidth": {
-            "heading": "幅"
+            "heading": "幅",
+            "paragraphs": [
+                "生成される画像の幅。8の倍数にする必要があります。"
+            ]
         },
         "lora": {
-            "heading": "LoRA"
+            "heading": "LoRA",
+            "paragraphs": [
+                "ベースモデルと組み合わせて使用する軽量モデル."
+            ]
         },
         "loraWeight": {
-            "heading": "重み"
+            "heading": "重み",
+            "paragraphs": [
+                "LoRA の重み. 重みを大きくすると, 最終的な画像への影響が大きくなります."
+            ]
         },
         "patchmatchDownScaleSize": {
-            "heading": "Downscale"
+            "heading": "Downscale",
+            "paragraphs": [
+                "埋め込む前にどの程度のダウンスケーリングが行われるか。",
+                "ダウンスケーリングを大きくするとパフォーマンスは向上しますが、品質は低下します。"
+            ]
         },
         "controlNetWeight": {
             "heading": "重み",
@@ -1438,7 +1499,8 @@
             "heading": "ダイナミックプロンプト",
             "paragraphs": [
                 "ダイナミック プロンプトは,単一のプロンプトを複数のプロンプトに解析します.",
-                "基本的な構文は「{赤|緑|青}のボール」です.これにより,「赤いボール」「緑のボール」「青いボール」という3つのプロンプトが生成されます."
+                "基本的な構文は「{赤|緑|青}のボール」です.これにより,「赤いボール」「緑のボール」「青いボール」という3つのプロンプトが生成されます.",
+                "1 つのプロンプト内で構文を何度でも使用できますが, 生成されるプロンプトの数を Max Prompts 設定で制限するようにしてください."
             ]
         },
         "controlNetResizeMode": {
@@ -1458,6 +1520,159 @@
             "paragraphs": [
                 "プロンプトまたは コントロールネットのいずれかを重視します."
             ]
+        },
+        "noiseUseCPU": {
+            "paragraphs": [
+                "CPU または GPU でノイズを生成するかどうかを制御します.",
+                "CPU ノイズを有効にすると, 特定のシードによってどのマシンでも同じ画像が生成されます.",
+                "CPU ノイズを有効にしてもパフォーマンスに影響はありません."
+            ],
+            "heading": "CPUノイズを使用する"
+        },
+        "dynamicPromptsMaxPrompts": {
+            "heading": "最大プロンプト",
+            "paragraphs": [
+                "ダイナミック プロンプトによって生成できるプロンプトの数を制限します."
+            ]
+        },
+        "dynamicPromptsSeedBehaviour": {
+            "paragraphs": [
+                "プロンプトを生成するときにシードがどのように使用されるかを制御します.",
+                "反復ごとに固有のシードを使用します. 単一のシードでプロンプトのバリエーションを試す場合に使用します.",
+                "たとえば, プロンプトが 5 つある場合, 各画像は同じシードを使用します.",
+                "「画像ごと」では, 画像ごとに固有のシード値が使用されます. これにより、より多くのバリエーションが得られます."
+            ],
+            "heading": "シード行動"
+        },
+        "imageFit": {
+            "paragraphs": [
+                "初期画像の幅と高さを出力画像に合わせてサイズ変更します. 有効にすることをお勧めします."
+            ],
+            "heading": "初期画像を出力サイズに合わせる"
+        },
+        "infillMethod": {
+            "heading": "充填方法",
+            "paragraphs": [
+                "アウトペインティングまたはインペインティングのプロセス中に埋め込む方法."
+            ]
+        },
+        "paramGuidance": {
+            "paragraphs": [
+                "プロンプトが生成プロセスにどの程度影響するかを制御します。",
+                "ガイダンス値が高すぎると過飽和状態になる可能性があり、ガイダンス値が高すぎるか低すぎると生成結果に歪みが生じる可能性があります。ガイダンスはFLUX DEVモデルにのみ適用されます。"
+            ],
+            "heading": "ガイダンス"
+        },
+        "paramDenoisingStrength": {
+            "paragraphs": [
+                "生成されたイメージがラスター レイヤーとどの程度異なるかを制御します。",
+                "強度が低いほど、結合された表示ラスターレイヤーに近くなります。強度が高いほど、グローバルプロンプトに大きく依存します。",
+                "表示されるコンテンツを持つラスター レイヤーがない場合、この設定は無視されます。"
+            ],
+            "heading": "ディノイジングストレングス"
+        },
+        "refinerStart": {
+            "heading": "リファイナースタート",
+            "paragraphs": [
+                "生成プロセスのどの時点でリファイナーが使用され始めるか。",
+                "0 はリファイナーが生成プロセス全体で使用されることを意味し、0.8 は、リファイナーが生成プロセスの最後の 20% で使用されることを意味します。"
+            ]
+        },
+        "optimizedDenoising": {
+            "heading": "イメージtoイメージの最適化",
+            "paragraphs": [
+                "「イメージtoイメージを最適化」を有効にすると、Fluxモデルを用いた画像間変換およびインペインティング変換において、より段階的なノイズ除去強度スケールが適用されます。この設定により、画像に適用される変化量を制御する能力が向上しますが、標準のノイズ除去強度スケールを使用したい場合はオフにすることができます。この設定は現在調整中で、ベータ版です。"
+            ]
+        },
+        "refinerPositiveAestheticScore": {
+            "heading": "ポジティブ美的スコア",
+            "paragraphs": [
+                "トレーニング データに基づいて、美的スコアの高い画像に類似するように生成を重み付けします。"
+            ]
+        },
+        "paramCFGScale": {
+            "paragraphs": [
+                "プロンプトが生成プロセスにどの程度影響するかを制御します。",
+                "CFG スケールの値が高すぎると、飽和しすぎて生成結果が歪む可能性があります。 "
+            ],
+            "heading": "CFGスケール"
+        },
+        "paramVAEPrecision": {
+            "paragraphs": [
+                "VAE エンコードおよびデコード時に使用される精度。",
+                "Fp16/Half 精度は、画像のわずかな変化を犠牲にして、より効率的です。"
+            ],
+            "heading": "VAE精度"
+        },
+        "refinerModel": {
+            "heading": "リファイナーモデル",
+            "paragraphs": [
+                "生成プロセスの精製部分で使用されるモデル。",
+                "世代モデルに似ています。"
+            ]
+        },
+        "refinerCfgScale": {
+            "heading": "CFGスケール",
+            "paragraphs": [
+                "プロンプトが生成プロセスに与える影響を制御する。",
+                "生成CFG スケールに似ています。"
+            ]
+        },
+        "seamlessTilingYAxis": {
+            "heading": "シームレスタイリングY軸",
+            "paragraphs": [
+                "画像を垂直軸に沿ってシームレスに並べます。"
+            ]
+        },
+        "scaleBeforeProcessing": {
+            "heading": "プロセス前のスケール値",
+            "paragraphs": [
+                "「自動」は、画像生成プロセスの前に、選択した領域をモデルに最適なサイズに拡大縮小します。",
+                "「手動」では、画像生成プロセスの前に、選択した領域を拡大縮小する幅と高さを選択できます。"
+            ]
+        },
+        "creativity": {
+            "heading": "クリエイティビティ",
+            "paragraphs": [
+                "クリエイティビティは、ディテールを追加する際のモデルに与えられる自由度を制御します。クリエイティビティが低いと元のイメージに近いままになり、クリエイティビティが高いとより多くの変化を加えることができます。プロンプトを使用する場合、クリエイティビティが高いとプロンプトの影響が増します。"
+            ]
+        },
+        "paramHrf": {
+            "heading": "高解像度修正を有効にする",
+            "paragraphs": [
+                "モデルに最適な解像度よりも高い解像度で、高品質な画像を生成します。通常、生成された画像内の重複を防ぐために使用されます。"
+            ]
+        },
+        "seamlessTilingXAxis": {
+            "heading": "シームレスタイリングX軸",
+            "paragraphs": [
+                "画像を水平軸に沿ってシームレスに並べます。"
+            ]
+        },
+        "paramCFGRescaleMultiplier": {
+            "paragraphs": [
+                "ゼロ端末 SNR (ztsnr) を使用してトレーニングされたモデルに使用される、CFG ガイダンスのリスケールマルチプライヤー。",
+                "これらのモデルの場合、推奨値は 0.7 です。"
+            ],
+            "heading": "CFG リスケールマルチプライヤー"
+        },
+        "structure": {
+            "heading": "ストラクチャ",
+            "paragraphs": [
+                "ストラクチャは、出力画像が元のレイアウトにどれだけ忠実に従うかを制御します。低いストラクチャでは大幅な変更が可能ですが、高いストラクチャでは元の構成とレイアウトが厳密に維持されます。"
+            ]
+        },
+        "refinerNegativeAestheticScore": {
+            "paragraphs": [
+                "トレーニング データに基づいて、美観スコアが低い画像に類似するように生成に重み付けします。"
+            ],
+            "heading": "ネガティブ美的スコア"
+        },
+        "fluxDevLicense": {
+            "heading": "非商用ライセンス",
+            "paragraphs": [
+                "FLUX.1 [dev]モデルは、FLUX [dev]非商用ライセンスに基づいてライセンスされています。Invokeでこのモデルタイプを商用目的で使用する場合は、当社のウェブサイトをご覧ください。"
+            ]
         }
     },
     "accordions": {
@@ -1630,7 +1845,106 @@
         "workflows": "ワークフロー",
         "ascending": "昇順",
         "name": "名前",
-        "descending": "降順"
+        "descending": "降順",
+        "searchPlaceholder": "名前、説明、タグで検索",
+        "projectWorkflows": "プロジェクトワークフロー",
+        "searchWorkflows": "ワークフローを検索",
+        "updated": "アップデート",
+        "published": "公表",
+        "builder": {
+            "label": "ラベル",
+            "containerPlaceholder": "空のコンテナ",
+            "showDescription": "説明を表示",
+            "emptyRootPlaceholderEditMode": "開始するには、フォーム要素またはノード フィールドをここにドラッグします。",
+            "divider": "仕切り",
+            "deleteAllElements": "すべてのフォーム要素を削除",
+            "heading": "見出し",
+            "nodeField": "ノードフィールド",
+            "zoomToNode": "ノードにズーム",
+            "dropdown": "ドロップダウン",
+            "resetOptions": "オプションをリセット",
+            "both": "両方",
+            "builder": "フォームビルダー",
+            "text": "テキスト",
+            "row": "行",
+            "multiLine": "マルチライン",
+            "resetAllNodeFields": "すべてのノードフィールドをリセット",
+            "slider": "スライダー",
+            "layout": "レイアウト",
+            "addToForm": "フォームに追加",
+            "headingPlaceholder": "空の見出し",
+            "nodeFieldTooltip": "ノード フィールドを追加するには、ワークフロー エディターのフィールドにある小さなプラス記号ボタンをクリックするか、フィールド名をフォームにドラッグします。",
+            "workflowBuilderAlphaWarning": "ワークフロービルダーは現在アルファ版です。安定版リリースまでに互換性に影響する変更が発生する可能性があります。",
+            "component": "コンポーネント",
+            "textPlaceholder": "空のテキスト",
+            "emptyRootPlaceholderViewMode": "このワークフローのフォームの作成を開始するには、[編集] をクリックします。",
+            "addOption": "オプションを追加",
+            "singleLine": "単線",
+            "numberInput": "数値入力",
+            "column": "列",
+            "container": "コンテナ",
+            "containerRowLayout": "コンテナ（行レイアウト）",
+            "containerColumnLayout": "コンテナ（列レイアウト）",
+            "maximum": "最大",
+            "published": "公開済み",
+            "publishedWorkflowOutputs": "アウトプット",
+            "minimum": "最小",
+            "publish": "公開",
+            "unpublish": "非公開",
+            "publishedWorkflowInputs": "インプット"
+        },
+        "chooseWorkflowFromLibrary": "ライブラリからワークフローを選択",
+        "unnamedWorkflow": "名前のないワークフロー",
+        "download": "ダウンロード",
+        "savingWorkflow": "ワークフローを保存しています...",
+        "problemSavingWorkflow": "ワークフローの保存に関する問題",
+        "convertGraph": "グラフを変換",
+        "downloadWorkflow": "ファイルに保存",
+        "saveWorkflow": "ワークフローを保存",
+        "userWorkflows": "ユーザーワークフロー",
+        "yourWorkflows": "あなたのワークフロー",
+        "edit": "編集",
+        "workflowLibrary": "ワークフローライブラリ",
+        "workflowSaved": "ワークフローが保存されました",
+        "clearWorkflowSearchFilter": "ワークフロー検索フィルタをクリア",
+        "workflowCleared": "ワークフローが作成されました",
+        "autoLayout": "オートレイアウト",
+        "view": "ビュー",
+        "saveChanges": "変更を保存",
+        "noDescription": "説明なし",
+        "recommended": "あなたへのおすすめ",
+        "noRecentWorkflows": "最近のワークフローがありません",
+        "problemLoading": "ワークフローのローディングに関する問題",
+        "newWorkflowCreated": "新しいワークフローが作成されました",
+        "noWorkflows": "ワークフローがありません",
+        "copyShareLink": "共有リンクをコピー",
+        "copyShareLinkForWorkflow": "ワークフローの共有リンクをコピー",
+        "workflowThumbnail": "ワークフローサムネイル",
+        "loadWorkflow": "$t(common.load) ワークフロー",
+        "shared": "共有",
+        "openWorkflow": "ワークフローを開く",
+        "emptyStringPlaceholder": "<空の文字列>",
+        "browseWorkflows": "ワークフローを閲覧する",
+        "saveWorkflowAs": "ワークフローとして保存",
+        "private": "プライベート",
+        "deselectAll": "すべて選択解除",
+        "delete": "削除",
+        "openLibrary": "ライブラリを開く",
+        "loadMore": "もっと読み込む",
+        "saveWorkflowToProject": "ワークフローをプロジェクトに保存",
+        "created": "作成されました",
+        "workflowEditorMenu": "ワークフローエディターメニュー",
+        "defaultWorkflows": "デフォルトワークフロー",
+        "allLoaded": "すべてのワークフローが読み込まれました",
+        "filterByTags": "タグでフィルター",
+        "recentlyOpened": "最近開いた",
+        "opened": "オープン",
+        "deleteWorkflow": "ワークフローを削除",
+        "deleteWorkflow2": "このワークフローを削除してもよろしいですか? 元に戻すことはできません。",
+        "loadFromGraph": "グラフからワークフローをロード",
+        "workflowName": "ワークフロー名",
+        "loading": "ワークフローをロードしています",
+        "uploadWorkflow": "ファイルからロードする"
     },
     "system": {
         "logNamespaces": {

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

@@ -30,7 +30,7 @@
         "boards": "Bảng",
         "selectedForAutoAdd": "Đã Chọn Để Tự động thêm",
         "myBoard": "Bảng Của Tôi",
-        "deletedPrivateBoardsCannotbeRestored": "Bảng đã xoá sẽ không thể khôi phục lại. Chọn 'Chỉ Xoá Bảng' sẽ dời ảnh vào trạng thái chưa phân loại riêng cho chủ ảnh.",
+        "deletedPrivateBoardsCannotbeRestored": "Bảng và ảnh đã xoá sẽ không thể khôi phục lại. Chọn 'Chỉ Xoá Bảng' sẽ dời ảnh vào trạng thái chưa phân loại riêng cho chủ ảnh.",
         "changeBoard": "Thay Đổi Bảng",
         "clearSearch": "Làm Sạch Thanh Tìm Kiếm",
         "updateBoardError": "Lỗi khi cập nhật Bảng",
@@ -41,18 +41,21 @@
         "deleteBoard": "Xoá Bảng",
         "deleteBoardAndImages": "Xoá Bảng Lẫn Hình ảnh",
         "deleteBoardOnly": "Chỉ Xoá Bảng",
-        "deletedBoardsCannotbeRestored": "Bảng đã xoá sẽ không thể khôi phục lại. Chọn 'Chỉ Xoá Bảng' sẽ dời ảnh vào trạng thái chưa phân loại.",
-        "bottomMessage": "Xoá bảng này lẫn ảnh của nó sẽ khởi động lại mọi tính năng đang sử dụng chúng.",
+        "deletedBoardsCannotbeRestored": "Bảng và ảnh đã xoá sẽ không thể khôi phục lại. Chọn 'Chỉ Xoá Bảng' sẽ dời ảnh vào trạng thái chưa phân loại.",
+        "bottomMessage": "Việc xóa ảnh sẽ khởi động lại mọi tính năng đang sử dụng chúng.",
         "menuItemAutoAdd": "Tự động thêm cho Bảng này",
         "move": "Di Chuyển",
-        "topMessage": "Bảng này chứa ảnh được dùng với những tính năng sau:",
+        "topMessage": "Lựa chọn này chứa ảnh được dùng với những tính năng sau:",
         "uncategorized": "Chưa Sắp Xếp",
         "archived": "Được Lưu Trữ",
         "loading": "Đang Tải...",
         "selectBoard": "Chọn Bảng",
         "archiveBoard": "Lưu trữ Bảng",
         "unarchiveBoard": "Ngừng Lưu Trữ Bảng",
-        "assetsWithCount_other": "{{count}} tài nguyên"
+        "assetsWithCount_other": "{{count}} tài nguyên",
+        "uncategorizedImages": "Ảnh Chưa Sắp Xếp",
+        "deleteAllUncategorizedImages": "Xoá Tất Cả Ảnh Chưa Sắp Xếp",
+        "deletedImagesCannotBeRestored": "Ảnh đã xoá không thể phục hồi lại."
     },
     "gallery": {
         "swapImages": "Đổi Hình Ảnh",
@@ -789,7 +792,9 @@
         "modelPickerFallbackNoModelsInstalled2": "Nhấp vào <LinkComponent>Trình Quản Lý Model</LinkComponent> để tải.",
         "modelPickerFallbackNoModelsInstalled": "Không Có Sẵn Model.",
         "manageModels": "Quản Lý Model",
-        "hfTokenReset": "Làm Mới HF Token"
+        "hfTokenReset": "Làm Mới HF Token",
+        "relatedModels": "Model Liên Quan",
+        "showOnlyRelatedModels": "Liên Quan"
     },
     "metadata": {
         "guidance": "Hướng Dẫn",
@@ -1715,12 +1720,16 @@
         "fitBboxToLayers": "Xếp Vừa Hộp Giới Hạn Vào Layer",
         "ipAdapterMethod": {
             "full": "Phong Cách Và Thành Phần",
-            "style": "Chỉ Lấy Phong Cách",
+            "style": "Phong Cách (Đơn Giản)",
             "composition": "Chỉ Lấy Thành Phần",
             "ipAdapterMethod": "Cách Thức",
             "compositionDesc": "Áp dụng cách trình bày và bỏ qua phong cách mẫu.",
             "fullDesc": "Áp dụng phong cách trực quan (màu, cấu tạo) & thành phần (cách trình bày).",
-            "styleDesc": "Áp dụng phong cách trực quan (màu, cấu tạo) và bỏ qua cách trình bày."
+            "styleDesc": "Áp dụng phong cách trực quan (màu, cấu tạo) và bỏ qua cách trình bày. Tên trước đây là Chỉ Lấy Phong Cách.",
+            "styleStrong": "Phong Cách (Mạnh Mẽ)",
+            "styleStrongDesc": "Áp dụng cách trình bày mạnh mẽ, với một chút giảm nhẹ ảnh hưởng lên thành phần.",
+            "stylePrecise": "Phong Cách (Chính Xác)",
+            "stylePreciseDesc": "Áp dụng cách trình bày chính xác, loại bỏ các chủ thể ảnh hưởng."
         },
         "deletePrompt": "Xoá Lệnh",
         "rasterLayer": "Layer Dạng Raster",
@@ -2053,7 +2062,7 @@
             "colorPicker": "Chọn Màu"
         },
         "mergingLayers": "Đang gộp layer",
-        "controlLayerEmptyState": "<UploadButton>Tải lên ảnh</UploadButton>, kéo thả ảnh từ <GalleryButton>thư viện</GalleryButton> vào layer này, hoặc vẽ trên canvas để bắt đầu.",
+        "controlLayerEmptyState": "<UploadButton>Tải lên ảnh</UploadButton>, kéo thả ảnh từ <GalleryButton>thư viện</GalleryButton> vào layer này, <PullBboxButton>kéo hộp giới hạn vào layer này</PullBboxButton>, hoặc vẽ trên canvas để bắt đầu.",
         "referenceImageEmptyState": "<UploadButton>Tải lên hình ảnh</UploadButton>, kéo ảnh từ <GalleryButton>thư viện ảnh</GalleryButton> vào layer này, hoặc <PullBboxButton>kéo hộp giới hạn vào layer này</PullBboxButton> để bắt đầu.",
         "useImage": "Dùng Hình Ảnh",
         "resetCanvasLayers": "Khởi Động Lại Layer Canvas",
@@ -2102,7 +2111,11 @@
             "imageInfluence": "Ảnh Chi Phối",
             "medium": "Vừa",
             "highest": "Cao Nhất"
-        }
+        },
+        "addDenoiseLimit": "Thêm $t(controlLayers.denoiseLimit)",
+        "imageNoise": "Độ Nhiễu Hình Ảnh",
+        "denoiseLimit": "Giới Hạn Khử Nhiễu",
+        "addImageNoise": "Thêm $t(controlLayers.imageNoise)"
     },
     "stylePresets": {
         "negativePrompt": "Lệnh Tiêu Cực",
@@ -2243,8 +2256,8 @@
         "problemUnpublishingWorkflowDescription": "Có vấn đề khi ngừng đăng tải workflow. Vui lòng thử lại sau.",
         "workflowUnpublished": "Workflow Đã Được Ngừng Đăng Tải",
         "problemUnpublishingWorkflow": "Có Vấn Đề Khi Ngừng Đăng Tải Workflow",
-        "imagen3IncompatibleGenerationMode": "Google Imagen3 chỉ hỗ trợ Từ Ngữ Sang Hình Ảnh. Hãy dùng model khác cho các tác vụ Hình Ảnh Sang Hình Ảnh, Inpaint và Outpaint.",
-        "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4o chỉ hỗ trợ Từ Ngữ Sang Hình Ảnh và Hình Ảnh Sang Hình Ảnh. Hãy dùng model khác cho các tác vụ Inpaint và Outpaint."
+        "chatGPT4oIncompatibleGenerationMode": "ChatGPT 4o chỉ hỗ trợ Từ Ngữ Sang Hình Ảnh và Hình Ảnh Sang Hình Ảnh. Hãy dùng model khác cho các tác vụ Inpaint và Outpaint.",
+        "imagenIncompatibleGenerationMode": "Google {{model}} chỉ hỗ trợ Từ Ngữ Sang Hình Ảnh. Dùng các model khác cho Hình Ảnh Sang Hình Ảnh, Inpaint và Outpaint."
     },
     "ui": {
         "tabs": {
@@ -2426,8 +2439,8 @@
         "watchRecentReleaseVideos": "Xem Video Phát Hành Mới Nhất",
         "watchUiUpdatesOverview": "Xem Tổng Quan Về Những Cập Nhật Cho Giao Diện Người Dùng",
         "items": [
-            "CogView4: Hỗ trợ model CogView4 ở Canvas và Workflow.",
-            "Cập nhật Dependency: Invoke bây giờ sẽ chạy trên phiên bản mới nhất của các dependency của nó, bao gồm Python 3.12 và Pytorch 2.6.0"
+            "Nvidia 50xx GPUs: Invoke sử dụng PyTorch 2.7.0, thứ tối quan trọng cho những GPU trên.",
+            "Mối Quan Hệ Model: Kết nối LoRA với model chính, và LoRA đó sẽ được hiển thị đầu danh sách."
         ]
     },
     "upsell": {

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

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

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

@@ -1,4 +1,5 @@
 import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
+import { setupListeners } from '@reduxjs/toolkit/query';
 import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
 import { useStudioInitAction } from 'app/hooks/useStudioInitAction';
 import { useSyncQueueStatus } from 'app/hooks/useSyncQueueStatus';
@@ -8,19 +9,24 @@ import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/ap
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import { useFocusRegionWatcher } from 'common/hooks/focus';
+import { useCloseChakraTooltipsOnDragFix } from 'common/hooks/useCloseChakraTooltipsOnDragFix';
 import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
+import { useDndMonitor } from 'features/dnd/useDndMonitor';
 import { useDynamicPromptsWatcher } from 'features/dynamicPrompts/hooks/useDynamicPromptsWatcher';
 import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
 import { useWorkflowBuilderWatcher } from 'features/nodes/components/sidePanel/workflow/IsolatedWorkflowBuilderWatcher';
 import { useReadinessWatcher } from 'features/queue/store/readiness';
 import { configChanged } from 'features/system/store/configSlice';
 import { selectLanguage } from 'features/system/store/systemSelectors';
+import { useNavigationApi } from 'features/ui/layouts/use-navigation-api';
 import i18n from 'i18n';
-import { size } from 'lodash-es';
 import { memo, useEffect } from 'react';
 import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
+import { useGetQueueCountsByDestinationQuery } from 'services/api/endpoints/queue';
 import { useSocketIO } from 'services/events/useSocketIO';
 
+const queueCountArg = { destination: 'canvas' };
+
 /**
  * GlobalHookIsolator is a logical component that runs global hooks in an isolated component, so that they do not
  * cause needless re-renders of any other components.
@@ -38,22 +44,31 @@ export const GlobalHookIsolator = memo(
     useGlobalHotkeys();
     useGetOpenAPISchemaQuery();
     useSyncLoggingConfig();
+    useCloseChakraTooltipsOnDragFix();
+    useNavigationApi();
+    useDndMonitor();
+
+    // Persistent subscription to the queue counts query - canvas relies on this to know if there are pending
+    // and/or in progress canvas sessions.
+    useGetQueueCountsByDestinationQuery(queueCountArg);
 
     useEffect(() => {
       i18n.changeLanguage(language);
     }, [language]);
 
     useEffect(() => {
-      if (size(config)) {
-        logger.info({ config }, 'Received config');
-        dispatch(configChanged(config));
-      }
+      logger.info({ config }, 'Received config');
+      dispatch(configChanged(config));
     }, [dispatch, config, logger]);
 
     useEffect(() => {
       dispatch(appStarted());
     }, [dispatch]);
 
+    useEffect(() => {
+      return setupListeners(dispatch);
+    }, [dispatch]);
+
     useStudioInitAction(studioInitAction);
     useStarterModelsToast();
     useSyncQueueStatus();

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

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

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

@@ -6,7 +6,7 @@ import {
   NewGallerySessionDialog,
 } from 'features/controlLayers/components/NewSessionConfirmationAlertDialog';
 import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
+import { DeleteImageModal } from 'features/deleteImageModal/components/DeleteImageModal';
 import { FullscreenDropzone } from 'features/dnd/FullscreenDropzone';
 import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';
 import DeleteBoardModal from 'features/gallery/components/Boards/DeleteBoardModal';
@@ -15,6 +15,7 @@ import { ShareWorkflowModal } from 'features/nodes/components/sidePanel/workflow
 import { WorkflowLibraryModal } from 'features/nodes/components/sidePanel/workflow/WorkflowLibrary/WorkflowLibraryModal';
 import { CancelAllExceptCurrentQueueItemConfirmationAlertDialog } from 'features/queue/components/CancelAllExceptCurrentQueueItemConfirmationAlertDialog';
 import { ClearQueueConfirmationsAlertDialog } from 'features/queue/components/ClearQueueConfirmationAlertDialog';
+import { DeleteAllExceptCurrentQueueItemConfirmationAlertDialog } from 'features/queue/components/DeleteAllExceptCurrentQueueItemConfirmationAlertDialog';
 import { DeleteStylePresetDialog } from 'features/stylePresets/components/DeleteStylePresetDialog';
 import { StylePresetModal } from 'features/stylePresets/components/StylePresetForm/StylePresetModal';
 import RefreshAfterResetModal from 'features/system/components/SettingsModal/RefreshAfterResetModal';
@@ -39,6 +40,7 @@ export const GlobalModalIsolator = memo(() => {
       <StylePresetModal />
       <WorkflowLibraryModal />
       <CancelAllExceptCurrentQueueItemConfirmationAlertDialog />
+      <DeleteAllExceptCurrentQueueItemConfirmationAlertDialog />
       <ClearQueueConfirmationsAlertDialog />
       <NewWorkflowConfirmationAlertDialog />
       <LoadWorkflowConfirmationAlertDialog />

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

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

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

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

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

@@ -3,13 +3,12 @@ import { useAppStore } from 'app/store/storeHooks';
 import { useAssertSingleton } from 'common/hooks/useAssertSingleton';
 import { withResultAsync } from 'common/util/result';
 import { canvasReset } from 'features/controlLayers/store/actions';
-import { settingsSendToCanvasChanged } from 'features/controlLayers/store/canvasSettingsSlice';
 import { rasterLayerAdded } from 'features/controlLayers/store/canvasSlice';
+import { paramsReset } from 'features/controlLayers/store/paramsSlice';
 import type { CanvasRasterLayerState } from 'features/controlLayers/store/types';
 import { imageDTOToImageObject } from 'features/controlLayers/store/util';
-import { $imageViewer } from 'features/gallery/components/ImageViewer/useImageViewer';
 import { sentImageToCanvas } from 'features/gallery/store/actions';
-import { parseAndRecallAllMetadata } from 'features/metadata/util/handlers';
+import { MetadataUtils } from 'features/metadata/parsing';
 import { $hasTemplates } from 'features/nodes/store/nodesSlice';
 import { $isWorkflowLibraryModalOpen } from 'features/nodes/store/workflowLibraryModal';
 import {
@@ -20,7 +19,9 @@ import {
 } from 'features/nodes/store/workflowLibrarySlice';
 import { $isStylePresetsMenuOpen, activeStylePresetIdChanged } from 'features/stylePresets/store/stylePresetSlice';
 import { toast } from 'features/toast/toast';
-import { activeTabCanvasRightPanelChanged, setActiveTab } from 'features/ui/store/uiSlice';
+import { navigationApi } from 'features/ui/layouts/navigation-api';
+import { LAUNCHPAD_PANEL_ID, WORKSPACE_PANEL_ID } from 'features/ui/layouts/shared';
+import { activeTabCanvasRightPanelChanged } from 'features/ui/store/uiSlice';
 import { useLoadWorkflowWithDialog } from 'features/workflowLibrary/components/LoadWorkflowConfirmationAlertDialog';
 import { atom } from 'nanostores';
 import { useCallback, useEffect } from 'react';
@@ -91,12 +92,10 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
       const overrides: Partial<CanvasRasterLayerState> = {
         objects: [imageObject],
       };
+      await navigationApi.focusPanel('canvas', WORKSPACE_PANEL_ID);
       store.dispatch(canvasReset());
       store.dispatch(rasterLayerAdded({ overrides, isSelected: true }));
-      store.dispatch(settingsSendToCanvasChanged(true));
-      store.dispatch(setActiveTab('canvas'));
       store.dispatch(sentImageToCanvas());
-      $imageViewer.set(false);
       toast({
         title: t('toast.sentToCanvas'),
         status: 'info',
@@ -118,25 +117,25 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
         return;
       }
       const metadata = getImageMetadataResult.value;
+      store.dispatch(canvasReset());
       // This shows a toast
-      await parseAndRecallAllMetadata(metadata, true);
-      store.dispatch(setActiveTab('canvas'));
+      await MetadataUtils.recallAll(metadata, store);
     },
     [store, t]
   );
 
   const handleLoadWorkflow = useCallback(
-    async (workflowId: string) => {
+    (workflowId: string) => {
       // This shows a toast
-      await loadWorkflowWithDialog({
+      loadWorkflowWithDialog({
         type: 'library',
         data: workflowId,
         onSuccess: () => {
-          store.dispatch(setActiveTab('workflows'));
+          navigationApi.switchToTab('workflows');
         },
       });
     },
-    [loadWorkflowWithDialog, store]
+    [loadWorkflowWithDialog]
   );
 
   const handleSelectStylePreset = useCallback(
@@ -150,7 +149,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
         return;
       }
       store.dispatch(activeStylePresetIdChanged(stylePresetId));
-      store.dispatch(setActiveTab('canvas'));
+      navigationApi.switchToTab('canvas');
       toast({
         title: t('toast.stylePresetLoaded'),
         status: 'info',
@@ -160,37 +159,34 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
   );
 
   const handleGoToDestination = useCallback(
-    (destination: StudioDestinationAction['data']['destination']) => {
+    async (destination: StudioDestinationAction['data']['destination']) => {
       switch (destination) {
         case 'generation':
-          // Go to the canvas tab, open the image viewer, and enable send-to-gallery mode
-          store.dispatch(setActiveTab('canvas'));
+          // Go to the generate tab, open the launchpad
+          await navigationApi.focusPanel('generate', LAUNCHPAD_PANEL_ID);
+          store.dispatch(paramsReset());
           store.dispatch(activeTabCanvasRightPanelChanged('gallery'));
-          store.dispatch(settingsSendToCanvasChanged(false));
-          $imageViewer.set(true);
           break;
         case 'canvas':
-          // Go to the canvas tab, close the image viewer, and disable send-to-gallery mode
-          store.dispatch(setActiveTab('canvas'));
-          store.dispatch(settingsSendToCanvasChanged(true));
-          $imageViewer.set(false);
+          // Go to the canvas tab, open the launchpad
+          await navigationApi.focusPanel('canvas', WORKSPACE_PANEL_ID);
           break;
         case 'workflows':
           // Go to the workflows tab
-          store.dispatch(setActiveTab('workflows'));
+          navigationApi.switchToTab('workflows');
           break;
         case 'upscaling':
           // Go to the upscaling tab
-          store.dispatch(setActiveTab('upscaling'));
+          navigationApi.switchToTab('upscaling');
           break;
         case 'viewAllWorkflows':
           // Go to the workflows tab and open the workflow library modal
-          store.dispatch(setActiveTab('workflows'));
+          navigationApi.switchToTab('workflows');
           $isWorkflowLibraryModalOpen.set(true);
           break;
         case 'viewAllWorkflowsRecommended':
           // Go to the workflows tab and open the workflow library modal with the recommended workflows view
-          store.dispatch(setActiveTab('workflows'));
+          navigationApi.switchToTab('workflows');
           $isWorkflowLibraryModalOpen.set(true);
           store.dispatch(workflowLibraryViewChanged('defaults'));
           store.dispatch(workflowLibraryTagsReset());
@@ -202,7 +198,7 @@ export const useStudioInitAction = (action?: StudioInitAction) => {
           break;
         case 'viewAllStylePresets':
           // Go to the canvas tab and open the style presets menu
-          store.dispatch(setActiveTab('canvas'));
+          navigationApi.switchToTab('canvas');
           $isStylePresetsMenuOpen.set(true);
           break;
       }

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

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

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

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

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

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

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

@@ -1,7 +1,6 @@
 import type { TypedStartListening } from '@reduxjs/toolkit';
 import { addListener, createListenerMiddleware } from '@reduxjs/toolkit';
 import { addAdHocPostProcessingRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/addAdHocPostProcessingRequestedListener';
-import { addStagingListeners } from 'app/store/middleware/listenerMiddleware/listeners/addCommitStagingAreaImageListener';
 import { addAnyEnqueuedListener } from 'app/store/middleware/listenerMiddleware/listeners/anyEnqueued';
 import { addAppConfigReceivedListener } from 'app/store/middleware/listenerMiddleware/listeners/appConfigReceived';
 import { addAppStartedListener } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
@@ -9,16 +8,9 @@ import { addBatchEnqueuedListener } from 'app/store/middleware/listenerMiddlewar
 import { addDeleteBoardAndImagesFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted';
 import { addBoardIdSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/boardIdSelected';
 import { addBulkDownloadListeners } from 'app/store/middleware/listenerMiddleware/listeners/bulkDownload';
-import { addEnqueueRequestedLinear } from 'app/store/middleware/listenerMiddleware/listeners/enqueueRequestedLinear';
-import { addGalleryImageClickedListener } from 'app/store/middleware/listenerMiddleware/listeners/galleryImageClicked';
-import { addGalleryOffsetChangedListener } from 'app/store/middleware/listenerMiddleware/listeners/galleryOffsetChanged';
 import { addGetOpenAPISchemaListener } from 'app/store/middleware/listenerMiddleware/listeners/getOpenAPISchema';
 import { addImageAddedToBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageAddedToBoard';
-import { addImageDeletionListeners } from 'app/store/middleware/listenerMiddleware/listeners/imageDeletionListeners';
 import { addImageRemovedFromBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageRemovedFromBoard';
-import { addImagesStarredListener } from 'app/store/middleware/listenerMiddleware/listeners/imagesStarred';
-import { addImagesUnstarredListener } from 'app/store/middleware/listenerMiddleware/listeners/imagesUnstarred';
-import { addImageToDeleteSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/imageToDeleteSelected';
 import { addImageUploadedFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageUploaded';
 import { addModelSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelSelected';
 import { addModelsLoadedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelsLoaded';
@@ -27,7 +19,6 @@ import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMi
 import type { AppDispatch, RootState } from 'app/store/store';
 
 import { addArchivedOrDeletedBoardListener } from './listeners/addArchivedOrDeletedBoardListener';
-import { addEnqueueRequestedUpscale } from './listeners/enqueueRequestedUpscale';
 
 export const listenerMiddleware = createListenerMiddleware();
 
@@ -47,27 +38,12 @@ export const addAppListener = addListener.withTypes<RootState, AppDispatch>();
 addImageUploadedFulfilledListener(startAppListening);
 
 // Image deleted
-addImageDeletionListeners(startAppListening);
 addDeleteBoardAndImagesFulfilledListener(startAppListening);
-addImageToDeleteSelectedListener(startAppListening);
-
-// Image starred
-addImagesStarredListener(startAppListening);
-addImagesUnstarredListener(startAppListening);
-
-// Gallery
-addGalleryImageClickedListener(startAppListening);
-addGalleryOffsetChangedListener(startAppListening);
 
 // User Invoked
-addEnqueueRequestedLinear(startAppListening);
-addEnqueueRequestedUpscale(startAppListening);
 addAnyEnqueuedListener(startAppListening);
 addBatchEnqueuedListener(startAppListening);
 
-// Canvas actions
-addStagingListeners(startAppListening);
-
 // Socket.IO
 addSocketConnectedEventListener(startAppListening);
 

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

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

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

@@ -1,46 +0,0 @@
-import { isAnyOf } from '@reduxjs/toolkit';
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { canvasReset, newSessionRequested } from 'features/controlLayers/store/actions';
-import { stagingAreaReset } from 'features/controlLayers/store/canvasStagingAreaSlice';
-import { toast } from 'features/toast/toast';
-import { t } from 'i18next';
-import { queueApi } from 'services/api/endpoints/queue';
-
-const log = logger('canvas');
-
-const matchCanvasOrStagingAreaReset = isAnyOf(stagingAreaReset, canvasReset, newSessionRequested);
-
-export const addStagingListeners = (startAppListening: AppStartListening) => {
-  startAppListening({
-    matcher: matchCanvasOrStagingAreaReset,
-    effect: async (_, { dispatch }) => {
-      try {
-        const req = dispatch(
-          queueApi.endpoints.cancelByBatchDestination.initiate(
-            { destination: 'canvas' },
-            { fixedCacheKey: 'cancelByBatchOrigin' }
-          )
-        );
-        const { canceled } = await req.unwrap();
-        req.reset();
-
-        if (canceled > 0) {
-          log.debug(`Canceled ${canceled} canvas batches`);
-          toast({
-            id: 'CANCEL_BATCH_SUCCEEDED',
-            title: t('queue.cancelBatchSucceeded'),
-            status: 'success',
-          });
-        }
-      } catch {
-        log.error('Failed to cancel canvas batches');
-        toast({
-          id: 'CANCEL_BATCH_FAILED',
-          title: t('queue.cancelBatchFailed'),
-          status: 'error',
-        });
-      }
-    },
-  });
-};

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

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

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

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

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

@@ -1,6 +1,7 @@
 import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { selectRefImagesSlice } from 'features/controlLayers/store/refImagesSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
-import { getImageUsage } from 'features/deleteImageModal/store/selectors';
+import { getImageUsage } from 'features/deleteImageModal/store/state';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
 import { selectNodesSlice } from 'features/nodes/store/selectors';
 import { selectUpscaleSlice } from 'features/parameters/store/upscaleSlice';
@@ -20,9 +21,10 @@ export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppS
       const nodes = selectNodesSlice(state);
       const canvas = selectCanvasSlice(state);
       const upscale = selectUpscaleSlice(state);
+      const refImages = selectRefImagesSlice(state);
 
       deleted_images.forEach((image_name) => {
-        const imageUsage = getImageUsage(nodes, canvas, upscale, image_name);
+        const imageUsage = getImageUsage(nodes, canvas, upscale, refImages, image_name);
 
         if (imageUsage.isNodesImage && !wasNodeEditorReset) {
           dispatch(nodeEditorReset());

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

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

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

@@ -1,120 +0,0 @@
-import type { AlertStatus } from '@invoke-ai/ui-library';
-import { createAction } from '@reduxjs/toolkit';
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { extractMessageFromAssertionError } from 'common/util/extractMessageFromAssertionError';
-import { withResult, withResultAsync } from 'common/util/result';
-import { parseify } from 'common/util/serialize';
-import { $canvasManager } from 'features/controlLayers/store/ephemeral';
-import { prepareLinearUIBatch } from 'features/nodes/util/graph/buildLinearBatchConfig';
-import { buildChatGPT4oGraph } from 'features/nodes/util/graph/generation/buildChatGPT4oGraph';
-import { buildCogView4Graph } from 'features/nodes/util/graph/generation/buildCogView4Graph';
-import { buildFLUXGraph } from 'features/nodes/util/graph/generation/buildFLUXGraph';
-import { buildImagen3Graph } from 'features/nodes/util/graph/generation/buildImagen3Graph';
-import { buildSD1Graph } from 'features/nodes/util/graph/generation/buildSD1Graph';
-import { buildSD3Graph } from 'features/nodes/util/graph/generation/buildSD3Graph';
-import { buildSDXLGraph } from 'features/nodes/util/graph/generation/buildSDXLGraph';
-import { UnsupportedGenerationModeError } from 'features/nodes/util/graph/types';
-import { toast } from 'features/toast/toast';
-import { serializeError } from 'serialize-error';
-import { enqueueMutationFixedCacheKeyOptions, queueApi } from 'services/api/endpoints/queue';
-import { assert, AssertionError } from 'tsafe';
-
-const log = logger('generation');
-
-export const enqueueRequestedCanvas = createAction<{ prepend: boolean }>('app/enqueueRequestedCanvas');
-
-export const addEnqueueRequestedLinear = (startAppListening: AppStartListening) => {
-  startAppListening({
-    actionCreator: enqueueRequestedCanvas,
-    effect: async (action, { getState, dispatch }) => {
-      log.debug('Enqueue requested');
-      const state = getState();
-      const { prepend } = action.payload;
-
-      const manager = $canvasManager.get();
-      assert(manager, 'No canvas manager');
-
-      const model = state.params.model;
-      assert(model, 'No model found in state');
-      const base = model.base;
-
-      const buildGraphResult = await withResultAsync(async () => {
-        switch (base) {
-          case 'sdxl':
-            return await buildSDXLGraph(state, manager);
-          case 'sd-1':
-          case `sd-2`:
-            return await buildSD1Graph(state, manager);
-          case `sd-3`:
-            return await buildSD3Graph(state, manager);
-          case `flux`:
-            return await buildFLUXGraph(state, manager);
-          case 'cogview4':
-            return await buildCogView4Graph(state, manager);
-          case 'imagen3':
-            return await buildImagen3Graph(state, manager);
-          case 'chatgpt-4o':
-            return await buildChatGPT4oGraph(state, manager);
-          default:
-            assert(false, `No graph builders for base ${base}`);
-        }
-      });
-
-      if (buildGraphResult.isErr()) {
-        let title = 'Failed to build graph';
-        let status: AlertStatus = 'error';
-        let description: string | null = null;
-        if (buildGraphResult.error instanceof AssertionError) {
-          description = extractMessageFromAssertionError(buildGraphResult.error);
-        } else if (buildGraphResult.error instanceof UnsupportedGenerationModeError) {
-          title = 'Unsupported generation mode';
-          description = buildGraphResult.error.message;
-          status = 'warning';
-        }
-        const error = serializeError(buildGraphResult.error);
-        log.error({ error }, 'Failed to build graph');
-        toast({
-          status,
-          title,
-          description,
-        });
-        return;
-      }
-
-      const { g, seedFieldIdentifier, positivePromptFieldIdentifier } = buildGraphResult.value;
-
-      const destination = state.canvasSettings.sendToCanvas ? 'canvas' : 'gallery';
-
-      const prepareBatchResult = withResult(() =>
-        prepareLinearUIBatch({
-          state,
-          g,
-          prepend,
-          seedFieldIdentifier,
-          positivePromptFieldIdentifier,
-          origin: 'canvas',
-          destination,
-        })
-      );
-
-      if (prepareBatchResult.isErr()) {
-        log.error({ error: serializeError(prepareBatchResult.error) }, 'Failed to prepare batch');
-        return;
-      }
-
-      const req = dispatch(
-        queueApi.endpoints.enqueueBatch.initiate(prepareBatchResult.value, enqueueMutationFixedCacheKeyOptions)
-      );
-
-      try {
-        await req.unwrap();
-        log.debug(parseify({ batchConfig: prepareBatchResult.value }), 'Enqueued batch');
-      } catch (error) {
-        log.error({ error: serializeError(error as Error) }, 'Failed to enqueue batch');
-      } finally {
-        req.reset();
-      }
-    },
-  });
-};