fix(ui): ImageDTO mocks

Currently translated at 98.5% (2041 of 2071 strings)

translationBot(ui): update translation (Italian)

Currently translated at 98.6% (2039 of 2067 strings)

Co-authored-by: Riccardo Giovanetti <riccardo.giovanetti@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/it/
Translation: InvokeAI/Web UI

.github/workflows/build-container.yml

@@ -45,6 +45,9 @@ jobs:
     steps:
       - name: Free up more disk space on the runner
         # https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
+        # the /mnt dir has 70GBs of free space
+        # /dev/sda1        74G   28K   70G   1% /mnt
+        # According to some online posts the /mnt is not always there, so checking before setting docker to use it
         run: |
           echo "----- Free space before cleanup"
           df -h
@@ -52,6 +55,11 @@ jobs:
           sudo rm -rf "$AGENT_TOOLSDIRECTORY"
           sudo swapoff /mnt/swapfile
           sudo rm -rf /mnt/swapfile
+          if [ -d /mnt ]; then
+            sudo chmod -R 777 /mnt
+            echo '{"data-root": "/mnt/docker-root"}' | sudo tee /etc/docker/daemon.json
+            sudo systemctl restart docker
+          fi
           echo "----- Free space after cleanup"
           df -h
 

.github/workflows/typegen-checks.yml

@@ -39,6 +39,18 @@ jobs:
       - name: checkout
         uses: actions/checkout@v4
 
+      - name: Free up more disk space on the runner
+        # https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
+        run: |
+          echo "----- Free space before cleanup"
+          df -h
+          sudo rm -rf /usr/share/dotnet
+          sudo rm -rf "$AGENT_TOOLSDIRECTORY"
+          sudo swapoff /mnt/swapfile
+          sudo rm -rf /mnt/swapfile
+          echo "----- Free space after cleanup"
+          df -h
+
       - name: check for changed files
         if: ${{ inputs.always_run != true }}
         id: changed-files

docker/.env.sample

@@ -22,6 +22,10 @@
 ## GPU_DRIVER can be set to either `cuda` or `rocm` to enable GPU support in the container accordingly.
 # GPU_DRIVER=cuda #| rocm
 
+## If you are using ROCM, you will need to ensure that the render group within the container and the host system use the same group ID.
+## To obtain the group ID of the render group on the host system, run `getent group render` and grab the number.
+# RENDER_GROUP_ID=
+
 ## CONTAINER_UID can be set to the UID of the user on the host system that should own the files in the container.
 ## It is usually not necessary to change this. Use `id -u` on the host system to find the UID.
 # CONTAINER_UID=1000

docker/Dockerfile

@@ -43,7 +43,6 @@ ENV \
     UV_MANAGED_PYTHON=1 \
     UV_LINK_MODE=copy \
     UV_PROJECT_ENVIRONMENT=/opt/venv \
-    UV_INDEX="https://download.pytorch.org/whl/cu124" \
     INVOKEAI_ROOT=/invokeai \
     INVOKEAI_HOST=0.0.0.0 \
     INVOKEAI_PORT=9090 \
@@ -74,19 +73,17 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=uv.lock,target=uv.lock \
     # this is just to get the package manager to recognize that the project exists, without making changes to the docker layer
     --mount=type=bind,source=invokeai/version,target=invokeai/version \
-    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then UV_INDEX="https://download.pytorch.org/whl/cpu"; \
-    elif [ "$GPU_DRIVER" = "rocm" ]; then UV_INDEX="https://download.pytorch.org/whl/rocm6.2"; \
-    fi && \
-    uv sync --frozen
-
-# build patchmatch
-RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
-RUN python -c "from patchmatch import patch_match"
+    ulimit -n 30000 && \
+    uv sync --extra $GPU_DRIVER --frozen
 
 # Link amdgpu.ids for ROCm builds
 # contributed by https://github.com/Rubonnek
 RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
-    ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
+    ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids" && groupadd render
+
+# build patchmatch
+RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
+RUN python -c "from patchmatch import patch_match"
 
 RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
 
@@ -105,8 +102,6 @@ COPY invokeai ${INVOKEAI_SRC}/invokeai
 RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
     --mount=type=bind,source=uv.lock,target=uv.lock \
-    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then UV_INDEX="https://download.pytorch.org/whl/cpu"; \
-    elif [ "$GPU_DRIVER" = "rocm" ]; then UV_INDEX="https://download.pytorch.org/whl/rocm6.2"; \
-    fi && \
-    uv pip install -e .
+    ulimit -n 30000 && \
+    uv pip install -e .[$GPU_DRIVER]
 

docker/Dockerfile-rocm-full

@@ -0,0 +1,136 @@
+# syntax=docker/dockerfile:1.4
+
+#### Web UI ------------------------------------
+
+FROM docker.io/node:22-slim AS web-builder
+ENV PNPM_HOME="/pnpm"
+ENV PATH="$PNPM_HOME:$PATH"
+RUN corepack use pnpm@8.x
+RUN corepack enable
+
+WORKDIR /build
+COPY invokeai/frontend/web/ ./
+RUN --mount=type=cache,target=/pnpm/store \
+    pnpm install --frozen-lockfile
+RUN npx vite build
+
+## Backend ---------------------------------------
+
+FROM library/ubuntu:24.04
+
+ARG DEBIAN_FRONTEND=noninteractive
+RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
+RUN --mount=type=cache,target=/var/cache/apt \
+    --mount=type=cache,target=/var/lib/apt \
+    apt update && apt install -y --no-install-recommends \
+    ca-certificates \
+    git \
+    gosu \
+    libglib2.0-0 \
+    libgl1 \
+    libglx-mesa0 \
+    build-essential \
+    libopencv-dev \
+    libstdc++-10-dev \
+    wget
+
+ENV \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    VIRTUAL_ENV=/opt/venv \
+    INVOKEAI_SRC=/opt/invokeai \
+    PYTHON_VERSION=3.12 \
+    UV_PYTHON=3.12 \
+    UV_COMPILE_BYTECODE=1 \
+    UV_MANAGED_PYTHON=1 \
+    UV_LINK_MODE=copy \
+    UV_PROJECT_ENVIRONMENT=/opt/venv \
+    INVOKEAI_ROOT=/invokeai \
+    INVOKEAI_HOST=0.0.0.0 \
+    INVOKEAI_PORT=9090 \
+    PATH="/opt/venv/bin:$PATH" \
+    CONTAINER_UID=${CONTAINER_UID:-1000} \
+    CONTAINER_GID=${CONTAINER_GID:-1000}
+
+ARG GPU_DRIVER=cuda
+
+# Install `uv` for package management
+COPY --from=ghcr.io/astral-sh/uv:0.6.9 /uv /uvx /bin/
+
+# Install python & allow non-root user to use it by traversing the /root dir without read permissions
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv python install ${PYTHON_VERSION} && \
+    # chmod --recursive a+rX /root/.local/share/uv/python
+    chmod 711 /root
+
+WORKDIR ${INVOKEAI_SRC}
+
+# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
+# bind-mount instead of copy to defer adding sources to the image until next layer.
+#
+# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
+# x86_64/CUDA is the default
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
+    --mount=type=bind,source=uv.lock,target=uv.lock \
+    # this is just to get the package manager to recognize that the project exists, without making changes to the docker layer
+    --mount=type=bind,source=invokeai/version,target=invokeai/version \
+    ulimit -n 30000 && \
+    uv sync --extra $GPU_DRIVER --frozen
+
+RUN --mount=type=cache,target=/var/cache/apt \
+    --mount=type=cache,target=/var/lib/apt \
+    if [ "$GPU_DRIVER" = "rocm" ]; then \
+    wget -O /tmp/amdgpu-install.deb \
+    https://repo.radeon.com/amdgpu-install/6.3.4/ubuntu/noble/amdgpu-install_6.3.60304-1_all.deb && \
+    apt install -y /tmp/amdgpu-install.deb && \
+    apt update && \
+    amdgpu-install --usecase=rocm -y && \
+    apt-get autoclean && \
+    apt clean && \
+    rm -rf /tmp/* /var/tmp/* && \
+    usermod -a -G render ubuntu && \
+    usermod -a -G video ubuntu && \
+    echo "\\n/opt/rocm/lib\\n/opt/rocm/lib64" >> /etc/ld.so.conf.d/rocm.conf && \
+    ldconfig && \
+    update-alternatives --auto rocm; \
+    fi
+
+## Heathen711: Leaving this for review input, will remove before merge
+# RUN --mount=type=cache,target=/var/cache/apt \
+#     --mount=type=cache,target=/var/lib/apt \
+#     if [ "$GPU_DRIVER" = "rocm" ]; then \
+#     groupadd render && \
+#     usermod -a -G render ubuntu && \
+#     usermod -a -G video ubuntu; \
+#     fi
+
+## Link amdgpu.ids for ROCm builds
+## contributed by https://github.com/Rubonnek
+# RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
+#     ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
+
+# build patchmatch
+RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
+RUN python -c "from patchmatch import patch_match"
+
+RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
+
+COPY docker/docker-entrypoint.sh ./
+ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
+CMD ["invokeai-web"]
+
+# --link requires buldkit w/ dockerfile syntax 1.4, does not work with podman
+COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
+
+# add sources last to minimize image changes on code changes
+COPY invokeai ${INVOKEAI_SRC}/invokeai
+
+# this should not increase image size because we've already installed dependencies
+# in a previous layer
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
+    --mount=type=bind,source=uv.lock,target=uv.lock \
+    ulimit -n 30000 && \
+    uv pip install -e .[$GPU_DRIVER]
+

docker/docker-compose.yml

@@ -47,8 +47,9 @@ services:
 
   invokeai-rocm:
     <<: *invokeai
-    devices:
-      - /dev/kfd:/dev/kfd
-      - /dev/dri:/dev/dri
+    environment:
+      - AMD_VISIBLE_DEVICES=all
+      - RENDER_GROUP_ID=${RENDER_GROUP_ID}
+    runtime: amd
     profiles:
       - rocm

docker/docker-entrypoint.sh

@@ -21,6 +21,17 @@ _=$(id ${USER} 2>&1) || useradd -u ${USER_ID} ${USER}
 # ensure the UID is correct
 usermod -u ${USER_ID} ${USER} 1>/dev/null
 
+## ROCM specific configuration
+# render group within the container must match the host render group
+# otherwise the container will not be able to access the host GPU.
+if [[ -v "RENDER_GROUP_ID" ]] && [[ ! -z "${RENDER_GROUP_ID}" ]]; then
+  # ensure the render group exists
+  groupmod -g ${RENDER_GROUP_ID} render
+  usermod -a -G render ${USER}
+  usermod -a -G video ${USER}
+fi
+
+
 ### Set the $PUBLIC_KEY env var to enable SSH access.
 # We do not install openssh-server in the image by default to avoid bloat.
 # but it is useful to have the full SSH server e.g. on Runpod.

docker/run.sh

@@ -13,7 +13,7 @@ run() {
 
   # parse .env file for build args
   build_args=$(awk '$1 ~ /=[^$]/ && $0 !~ /^#/ {print "--build-arg " $0 " "}' .env) &&
-  profile="$(awk -F '=' '/GPU_DRIVER/ {print $2}' .env)"
+  profile="$(awk -F '=' '/GPU_DRIVER=/ {print $2}' .env)"
 
   # default to 'cuda' profile
   [[ -z "$profile" ]] && profile="cuda"
@@ -30,7 +30,7 @@ run() {
 
   printf "%s\n" "starting service $service_name"
   docker compose --profile "$profile" up -d "$service_name"
-  docker compose logs -f
+  docker compose --profile "$profile" logs -f
 }
 
 run

docs/contributing/MODEL_MANAGER.md

@@ -265,7 +265,7 @@ If the key is unrecognized, this call raises an
 
 #### exists(key) -> AnyModelConfig
 
-Returns True if a model with the given key exists in the databsae.
+Returns True if a model with the given key exists in the database.
 
 #### search_by_path(path) -> AnyModelConfig
 
@@ -718,7 +718,7 @@ When downloading remote models is implemented, additional
 configuration information, such as list of trigger terms, will be
 retrieved from the HuggingFace and Civitai model repositories.
 
-The probed values can be overriden by providing a dictionary in the
+The probed values can be overridden by providing a dictionary in the
 optional `config` argument passed to `import_model()`. You may provide
 overriding values for any of the model's configuration
 attributes. Here is an example of setting the
@@ -841,7 +841,7 @@ variable.
 
 #### installer.start(invoker)
 
-The `start` method is called by the API intialization routines when
+The `start` method is called by the API initialization routines when
 the API starts up. Its effect is to call `sync_to_config()` to
 synchronize the model record store database with what's currently on
 disk.

docs/contributing/contributors.md

@@ -16,7 +16,7 @@ We thank [all contributors](https://github.com/invoke-ai/InvokeAI/graphs/contrib
 - @psychedelicious (Spencer Mabrito) - Web Team Leader
 - @joshistoast (Josh Corbett) - Web Development
 - @cheerio (Mary Rogers) - Lead Engineer & Web App Development
-- @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
+- @ebr (Eugene Brodsky) - Cloud/DevOps/Software engineer; your friendly neighbourhood cluster-autoscaler
 - @sunija - Standalone version
 - @brandon (Brandon Rising) - Platform, Infrastructure, Backend Systems
 - @ryanjdick (Ryan Dick) - Machine Learning & Training

docs/installation/manual.md

@@ -69,34 +69,34 @@ The following commands vary depending on the version of Invoke being installed a
     - If you have an Nvidia 20xx series GPU or older, use `invokeai[xformers]`.
     - If you have an Nvidia 30xx series GPU or newer, or do not have an Nvidia GPU, use `invokeai`.
 
-7. Determine the `PyPI` index URL to use for installation, if any. This is necessary to get the right version of torch installed.
+7. Determine the torch backend to use for installation, if any. This is necessary to get the right version of torch installed. This is acheived by using [UV's built in torch support.](https://docs.astral.sh/uv/guides/integration/pytorch/#automatic-backend-selection)
 
     === "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.**
+        - If you are on Windows or Linux with an Nvidia GPU, use `--torch-backend=cu128`.
+        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
+        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm6.3`.
+        - **In all other cases, do not use a torch backend.**
 
     === "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`.
-        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.2.4`.
+        - If you are on Windows or Linux with an Nvidia GPU, use `--torch-backend=cu126`.
+        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
+        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm6.2.4`.
         - **In all other cases, do not use an index.**
 
     === "Invoke v5.0.0 to v5.9.1"
 
-        - If you are on Windows with an Nvidia GPU, use `https://download.pytorch.org/whl/cu124`.
-        - 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/rocm6.1`.
+        - If you are on Windows with an Nvidia GPU, use `--torch-backend=cu124`.
+        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
+        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm6.1`.
         - **In all other cases, do not use an index.**
 
     === "Invoke v4"
 
-        - If you are on Windows with an Nvidia GPU, use `https://download.pytorch.org/whl/cu124`.
-        - 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/rocm5.2`.
+        - If you are on Windows with an Nvidia GPU, use `--torch-backend=cu124`.
+        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
+        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm5.2`.
         - **In all other cases, do not use an index.**
 
 8. Install the `invokeai` package. Substitute the package specifier and version.
@@ -105,10 +105,10 @@ The following commands vary depending on the version of Invoke being installed a
     uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --force-reinstall
     ```
 
-    If you determined you needed to use a `PyPI` index URL in the previous step, you'll need to add `--index=<INDEX_URL>` like this:
+    If you determined you needed to use a torch backend in the previous step, you'll need to set the backend like this:
 
     ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --torch-backend=<VERSION> --force-reinstall
     ```
 
 9. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:

docs/nodes/NODES.md

@@ -41,7 +41,7 @@ Nodes have a "Use Cache" option in their footer. This allows for performance imp
 
 There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
 
-### Noise
+### Create Latent Noise
 
 An initial noise tensor is necessary for the latent diffusion process. As a result, the Denoising node requires a noise node input.  
 

invokeai/app/api/dependencies.py

@@ -5,11 +5,14 @@ from logging import Logger
 
 import torch
 
-from invokeai.app.services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
-from invokeai.app.services.board_images.board_images_default import BoardImagesService
+# from invokeai.app.services.board_image_records.board_image_records_sqlite import SqliteBoardImageRecordStorage
+# from invokeai.app.services.board_images.board_images_default import BoardImagesService
 from invokeai.app.services.board_records.board_records_sqlite import SqliteBoardRecordStorage
+from invokeai.app.services.board_resource_records.board_resource_records_sqlite import SqliteBoardResourceRecordStorage
+from invokeai.app.services.board_resources.board_resources_default import BoardResourcesService
 from invokeai.app.services.boards.boards_default import BoardService
 from invokeai.app.services.bulk_download.bulk_download_default import BulkDownloadService
+from invokeai.app.services.client_state_persistence.client_state_persistence_sqlite import ClientStatePersistenceSqlite
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
 from invokeai.app.services.download.download_default import DownloadQueueService
 from invokeai.app.services.events.events_fastapievents import FastAPIEventService
@@ -39,6 +42,8 @@ from invokeai.app.services.shared.sqlite.sqlite_util import init_db
 from invokeai.app.services.style_preset_images.style_preset_images_disk import StylePresetImageFileStorageDisk
 from invokeai.app.services.style_preset_records.style_preset_records_sqlite import SqliteStylePresetRecordsStorage
 from invokeai.app.services.urls.urls_default import LocalUrlService
+from invokeai.app.services.video_records.video_records_sqlite import SqliteVideoRecordStorage
+from invokeai.app.services.videos.videos_default import VideoService
 from invokeai.app.services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_disk import WorkflowThumbnailFileStorageDisk
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
@@ -102,14 +107,18 @@ class ApiDependencies:
         configuration = config
         logger = logger
 
-        board_image_records = SqliteBoardImageRecordStorage(db=db)
-        board_images = BoardImagesService()
+        # board_image_records = SqliteBoardImageRecordStorage(db=db)
+        # board_images = BoardImagesService()
+        board_resource_records = SqliteBoardResourceRecordStorage(db=db)
+        board_resources = BoardResourcesService()
         board_records = SqliteBoardRecordStorage(db=db)
         boards = BoardService()
         events = FastAPIEventService(event_handler_id, loop=loop)
         bulk_download = BulkDownloadService()
         image_records = SqliteImageRecordStorage(db=db)
         images = ImageService()
+        video_records = SqliteVideoRecordStorage(db=db)
+        videos = VideoService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         tensors = ObjectSerializerForwardCache(
             ObjectSerializerDisk[torch.Tensor](
@@ -151,10 +160,13 @@ class ApiDependencies:
         style_preset_records = SqliteStylePresetRecordsStorage(db=db)
         style_preset_image_files = StylePresetImageFileStorageDisk(style_presets_folder / "images")
         workflow_thumbnails = WorkflowThumbnailFileStorageDisk(workflow_thumbnails_folder)
+        client_state_persistence = ClientStatePersistenceSqlite(db=db)
 
         services = InvocationServices(
-            board_image_records=board_image_records,
-            board_images=board_images,
+            # board_image_records=board_image_records,
+            # board_images=board_images,
+            board_resource_records=board_resource_records,
+            board_resources=board_resources,
             board_records=board_records,
             boards=boards,
             bulk_download=bulk_download,
@@ -175,12 +187,15 @@ class ApiDependencies:
             session_processor=session_processor,
             session_queue=session_queue,
             urls=urls,
+            videos=videos,
+            video_records=video_records,
             workflow_records=workflow_records,
             tensors=tensors,
             conditioning=conditioning,
             style_preset_records=style_preset_records,
             style_preset_image_files=style_preset_image_files,
             workflow_thumbnails=workflow_thumbnails,
+            client_state_persistence=client_state_persistence,
         )
 
         ApiDependencies.invoker = Invoker(services)

invokeai/app/api/routers/board_resources.py

@@ -0,0 +1,93 @@
+from fastapi import Body, HTTPException
+from fastapi.routing import APIRouter
+
+from invokeai.app.api.dependencies import ApiDependencies
+from invokeai.app.services.board_resources.board_resources_common import ResourceType
+from invokeai.app.services.resources.resources_common import AddResourcesToBoardResult, RemoveResourcesFromBoardResult
+
+board_resources_router = APIRouter(prefix="/v1/board_resources", tags=["boards"])
+
+
+@board_resources_router.post(
+    "/batch",
+    operation_id="add_resources_to_board",
+    responses={
+        201: {"description": "Resources were added to board successfully"},
+    },
+    status_code=201,
+    response_model=AddResourcesToBoardResult,  # For now, using same response model
+)
+async def add_resources_to_board(
+    board_id: str = Body(description="The id of the board to add to"),
+    resource_ids: list[str] = Body(description="The ids of the resources to add", embed=True),
+    resource_type: ResourceType = Body(description="The type of resources"),
+) -> AddResourcesToBoardResult:
+    """Adds a list of resources to a board"""
+    try:
+        added_resources: set[str] = set()
+        affected_boards: set[str] = set()
+        for resource_id in resource_ids:
+            try:
+                if resource_type == ResourceType.IMAGE:
+                    old_board_id = ApiDependencies.invoker.services.images.get_dto(resource_id).board_id or "none"
+                else:
+                    old_board_id = ApiDependencies.invoker.services.videos.get_dto(resource_id).board_id or "none"
+
+                ApiDependencies.invoker.services.board_resources.add_resource_to_board(
+                    board_id=board_id,
+                    resource_id=resource_id,
+                    resource_type=resource_type,
+                )
+                added_resources.add(resource_id)
+                affected_boards.add(board_id)
+                affected_boards.add(old_board_id)
+
+            except Exception:
+                pass
+        return AddResourcesToBoardResult(
+            added_resources=list(added_resources),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to add resources to board")
+
+
+@board_resources_router.post(
+    "/batch/delete",
+    operation_id="remove_resources_from_board",
+    responses={
+        201: {"description": "Resources were removed from board successfully"},
+    },
+    status_code=201,
+    response_model=RemoveResourcesFromBoardResult,  # For now, using same response model
+)
+async def remove_resources_from_board(
+    resource_ids: list[str] = Body(description="The ids of the resources to remove", embed=True),
+    resource_type: ResourceType = Body(description="The type of resources", embed=True),
+) -> RemoveResourcesFromBoardResult:
+    """Removes a list of resources from their board, if they had one"""
+    try:
+        removed_resources: set[str] = set()
+        affected_boards: set[str] = set()
+        for resource_id in resource_ids:
+            try:
+                if resource_type == ResourceType.IMAGE:
+                    old_board_id = ApiDependencies.invoker.services.images.get_dto(resource_id).board_id or "none"
+                else:
+                    # For videos, we'll need to implement this once video service exists
+                    old_board_id = ApiDependencies.invoker.services.videos.get_dto(resource_id).board_id or "none"
+
+                ApiDependencies.invoker.services.board_resources.remove_resource_from_board(
+                    resource_id=resource_id, resource_type=resource_type
+                )
+                removed_resources.add(resource_id)
+                affected_boards.add("none")
+                affected_boards.add(old_board_id)
+            except Exception:
+                pass
+        return RemoveResourcesFromBoardResult(
+            removed_resources=list(removed_resources),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to remove resources from board")

invokeai/app/api/routers/boards.py

@@ -8,6 +8,7 @@ from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecordOrderBy
 from invokeai.app.services.boards.boards_common import BoardDTO
 from invokeai.app.services.image_records.image_records_common import ImageCategory
+from invokeai.app.services.resources.resources_common import ResourceIdentifier, ResourceType
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
@@ -16,10 +17,10 @@ boards_router = APIRouter(prefix="/v1/boards", tags=["boards"])
 
 class DeleteBoardResult(BaseModel):
     board_id: str = Field(description="The id of the board that was deleted.")
-    deleted_board_images: list[str] = Field(
-        description="The image names of the board-images relationships that were deleted."
+    deleted_board_resources: list[str] = Field(
+        description="The resource ids of the board-resources relationships that were deleted."
     )
-    deleted_images: list[str] = Field(description="The names of the images that were deleted.")
+    deleted_resources: list[str] = Field(description="The names of the resources that were deleted.")
 
 
 @boards_router.post(
@@ -82,34 +83,34 @@ async def update_board(
 @boards_router.delete("/{board_id}", operation_id="delete_board", response_model=DeleteBoardResult)
 async def delete_board(
     board_id: str = Path(description="The id of board to delete"),
-    include_images: Optional[bool] = Query(description="Permanently delete all images on the board", default=False),
+    include_resources: Optional[bool] = Query(
+        description="Permanently delete all resources on the board", default=False
+    ),
 ) -> DeleteBoardResult:
     """Deletes a board"""
     try:
-        if include_images is True:
-            deleted_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
+        if include_resources is True:
+            deleted_resources = ApiDependencies.invoker.services.board_resources.get_all_board_resource_ids_for_board(
                 board_id=board_id,
-                categories=None,
-                is_intermediate=None,
             )
             ApiDependencies.invoker.services.images.delete_images_on_board(board_id=board_id)
             ApiDependencies.invoker.services.boards.delete(board_id=board_id)
             return DeleteBoardResult(
                 board_id=board_id,
-                deleted_board_images=[],
-                deleted_images=deleted_images,
+                deleted_board_resources=[],
+                deleted_resources=deleted_resources,
             )
         else:
-            deleted_board_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
-                board_id=board_id,
-                categories=None,
-                is_intermediate=None,
+            deleted_board_resources = (
+                ApiDependencies.invoker.services.board_resources.get_all_board_resource_ids_for_board(
+                    board_id=board_id,
+                )
             )
             ApiDependencies.invoker.services.boards.delete(board_id=board_id)
             return DeleteBoardResult(
                 board_id=board_id,
-                deleted_board_images=deleted_board_images,
-                deleted_images=[],
+                deleted_board_resources=deleted_board_resources,
+                deleted_resources=[],
             )
     except Exception:
         raise HTTPException(status_code=500, detail="Failed to delete board")
@@ -141,20 +142,22 @@ async def list_boards(
 
 
 @boards_router.get(
-    "/{board_id}/image_names",
-    operation_id="list_all_board_image_names",
-    response_model=list[str],
+    "/{board_id}/resource_ids",
+    operation_id="list_all_board_resource_ids",
+    response_model=list[ResourceIdentifier],
 )
-async def list_all_board_image_names(
+async def list_all_board_resource_ids(
     board_id: str = Path(description="The id of the board or 'none' for uncategorized images"),
+    resource_type: ResourceType = Query(default=ResourceType.IMAGE, description="The type of resource to include."),
     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]:
+) -> list[ResourceIdentifier]:
     """Gets a list of images for a board"""
 
-    image_names = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
+    resources = ApiDependencies.invoker.services.board_resources.get_all_board_resource_ids_for_board(
         board_id,
+        resource_type,
         categories,
         is_intermediate,
     )
-    return image_names
+    return resources

invokeai/app/api/routers/client_state.py

@@ -0,0 +1,58 @@
+from fastapi import Body, HTTPException, Path, Query
+from fastapi.routing import APIRouter
+
+from invokeai.app.api.dependencies import ApiDependencies
+from invokeai.backend.util.logging import logging
+
+client_state_router = APIRouter(prefix="/v1/client_state", tags=["client_state"])
+
+
+@client_state_router.get(
+    "/{queue_id}/get_by_key",
+    operation_id="get_client_state_by_key",
+    response_model=str | None,
+)
+async def get_client_state_by_key(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    key: str = Query(..., description="Key to get"),
+) -> str | None:
+    """Gets the client state"""
+    try:
+        return ApiDependencies.invoker.services.client_state_persistence.get_by_key(queue_id, key)
+    except Exception as e:
+        logging.error(f"Error getting client state: {e}")
+        raise HTTPException(status_code=500, detail="Error setting client state")
+
+
+@client_state_router.post(
+    "/{queue_id}/set_by_key",
+    operation_id="set_client_state",
+    response_model=str,
+)
+async def set_client_state(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    key: str = Query(..., description="Key to set"),
+    value: str = Body(..., description="Stringified value to set"),
+) -> str:
+    """Sets the client state"""
+    try:
+        return ApiDependencies.invoker.services.client_state_persistence.set_by_key(queue_id, key, value)
+    except Exception as e:
+        logging.error(f"Error setting client state: {e}")
+        raise HTTPException(status_code=500, detail="Error setting client state")
+
+
+@client_state_router.post(
+    "/{queue_id}/delete",
+    operation_id="delete_client_state",
+    responses={204: {"description": "Client state deleted"}},
+)
+async def delete_client_state(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+) -> None:
+    """Deletes the client state"""
+    try:
+        ApiDependencies.invoker.services.client_state_persistence.delete(queue_id)
+    except Exception as e:
+        logging.error(f"Error deleting client state: {e}")
+        raise HTTPException(status_code=500, detail="Error deleting client state")

invokeai/app/api/routers/images.py

@@ -19,13 +19,9 @@ from invokeai.app.services.image_records.image_records_common import (
     ResourceOrigin,
 )
 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
@@ -160,31 +156,6 @@ async def create_image_upload_entry(
     raise HTTPException(status_code=501, detail="Not implemented")
 
 
-@images_router.delete("/i/{image_name}", operation_id="delete_image", response_model=DeleteImagesResult)
-async def delete_image(
-    image_name: str = Path(description="The name of the image to delete"),
-) -> 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:
     """Clears all intermediates"""
@@ -367,136 +338,6 @@ async def get_image_urls(
         raise HTTPException(status_code=404)
 
 
-@images_router.get(
-    "/",
-    operation_id="list_image_dtos",
-    response_model=OffsetPaginatedResults[ImageDTO],
-)
-async def list_image_dtos(
-    image_origin: Optional[ResourceOrigin] = Query(default=None, description="The origin of images to list."),
-    categories: Optional[list[ImageCategory]] = Query(default=None, description="The categories of image to include."),
-    is_intermediate: Optional[bool] = Query(default=None, description="Whether to list intermediate images."),
-    board_id: Optional[str] = Query(
-        default=None,
-        description="The board id to filter by. Use 'none' to find images without a board.",
-    ),
-    offset: int = Query(default=0, description="The page offset"),
-    limit: int = Query(default=10, description="The number of images per page"),
-    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"),
-) -> OffsetPaginatedResults[ImageDTO]:
-    """Gets a list of image DTOs"""
-
-    image_dtos = ApiDependencies.invoker.services.images.get_many(
-        offset, limit, starred_first, order_dir, image_origin, categories, is_intermediate, board_id, search_term
-    )
-
-    return image_dtos
-
-
-@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),
-) -> DeleteImagesResult:
-    try:
-        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.add(image_name)
-                affected_boards.add("none")
-            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")
-
-
-class ImagesUpdatedFromListResult(BaseModel):
-    updated_image_names: list[str] = Field(description="The image names that were updated")
-
-
-@images_router.post("/star", operation_id="star_images_in_list", response_model=StarredImagesResult)
-async def star_images_in_list(
-    image_names: list[str] = Body(description="The list of names of images to star", embed=True),
-) -> StarredImagesResult:
-    try:
-        starred_images: set[str] = set()
-        affected_boards: set[str] = set()
-        for image_name in image_names:
-            try:
-                updated_image_dto = ApiDependencies.invoker.services.images.update(
-                    image_name, changes=ImageRecordChanges(starred=True)
-                )
-                starred_images.add(image_name)
-                affected_boards.add(updated_image_dto.board_id or "none")
-            except Exception:
-                pass
-        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=UnstarredImagesResult)
-async def unstar_images_in_list(
-    image_names: list[str] = Body(description="The list of names of images to unstar", embed=True),
-) -> UnstarredImagesResult:
-    try:
-        unstarred_images: set[str] = set()
-        affected_boards: set[str] = set()
-        for image_name in image_names:
-            try:
-                updated_image_dto = ApiDependencies.invoker.services.images.update(
-                    image_name, changes=ImageRecordChanges(starred=False)
-                )
-                unstarred_images.add(image_name)
-                affected_boards.add(updated_image_dto.board_id or "none")
-            except Exception:
-                pass
-        return UnstarredImagesResult(
-            unstarred_images=list(unstarred_images),
-            affected_boards=list(affected_boards),
-        )
-    except Exception:
-        raise HTTPException(status_code=500, detail="Failed to unstar images")
-
-
 class ImagesDownloaded(BaseModel):
     response: Optional[str] = Field(
         default=None, description="The message to display to the user when images begin downloading"

invokeai/app/api/routers/resources.py

@@ -0,0 +1,159 @@
+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.image_records.image_records_common import (
+    ImageRecordChanges,
+)
+from invokeai.app.services.resources.resources_common import (
+    DeleteResourcesResult,
+    ResourceIdentifier,
+    ResourceType,
+    StarredResourcesResult,
+    UnstarredResourcesResult,
+)
+from invokeai.app.services.video_records.video_records_common import VideoRecordChanges
+
+# routes that act on both images and videos, possibly together
+resources_router = APIRouter(prefix="/v1/resources", tags=["resources"])
+
+
+@resources_router.post("/delete", operation_id="delete_resources_from_list", response_model=DeleteResourcesResult)
+async def delete_resources_from_list(
+    resources: list[ResourceIdentifier] = Body(description="The list of resources to delete", embed=True),
+) -> DeleteResourcesResult:
+    try:
+        deleted_resources: set[ResourceIdentifier] = set()
+        affected_boards: set[str] = set()
+        for resource in resources:
+            if resource.resource_type == ResourceType.IMAGE:
+                try:
+                    image_dto = ApiDependencies.invoker.services.images.get_dto(resource.resource_id)
+                    board_id = image_dto.board_id or "none"
+                    ApiDependencies.invoker.services.images.delete(resource.resource_id)
+                    deleted_resources.add(resource)
+                    affected_boards.add(board_id)
+                except Exception:
+                    pass
+            elif resource.resource_type == ResourceType.VIDEO:
+                try:
+                    video_dto = ApiDependencies.invoker.services.videos.get_dto(resource.resource_id)
+                    board_id = video_dto.board_id or "none"
+                    ApiDependencies.invoker.services.videos.delete(resource.resource_id)
+                    deleted_resources.add(resource)
+                    affected_boards.add(board_id)
+                except Exception:
+                    pass
+        return DeleteResourcesResult(
+            deleted_resources=list(deleted_resources),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to delete images")
+
+
+@resources_router.delete(
+    "/uncategorized", operation_id="delete_uncategorized_resources", response_model=DeleteResourcesResult
+)
+async def delete_uncategorized_resources() -> DeleteResourcesResult:
+    """Deletes all resources that are uncategorized"""
+
+    resources = ApiDependencies.invoker.services.board_resources.get_all_board_resource_ids_for_board(board_id="none")
+
+    try:
+        deleted_resources: set[ResourceIdentifier] = set()
+        affected_boards: set[str] = set()
+        for resource in resources:
+            if resource.resource_type == ResourceType.IMAGE:
+                try:
+                    ApiDependencies.invoker.services.images.delete(resource.resource_id)
+                    deleted_resources.add(resource)
+                    affected_boards.add("none")
+                except Exception:
+                    pass
+            elif resource.resource_type == ResourceType.VIDEO:
+                try:
+                    ApiDependencies.invoker.services.videos.delete(resource.resource_id)
+                    deleted_resources.add(resource)
+                    affected_boards.add("none")
+                except Exception:
+                    pass
+        return DeleteResourcesResult(
+            deleted_resources=list(deleted_resources),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to delete images")
+
+
+class ResourcesUpdatedFromListResult(BaseModel):
+    updated_resource: list[ResourceIdentifier] = Field(description="The resource ids that were updated")
+
+
+@resources_router.post("/star", operation_id="star_resources_in_list", response_model=StarredResourcesResult)
+async def star_resources_in_list(
+    resources: list[ResourceIdentifier] = Body(description="The list of resources to star", embed=True),
+) -> StarredResourcesResult:
+    try:
+        starred_resources: set[ResourceIdentifier] = set()
+        affected_boards: set[str] = set()
+        for resource in resources:
+            if resource.resource_type == ResourceType.IMAGE:
+                try:
+                    updated_resource_dto = ApiDependencies.invoker.services.images.update(
+                        resource.resource_id, changes=ImageRecordChanges(starred=True)
+                    )
+                    starred_resources.add(resource)
+                    affected_boards.add(updated_resource_dto.board_id or "none")
+                except Exception:
+                    pass
+            elif resource.resource_type == ResourceType.VIDEO:
+                try:
+                    updated_resource_dto = ApiDependencies.invoker.services.videos.update(
+                        resource.resource_id, changes=VideoRecordChanges(starred=True)
+                    )
+                    starred_resources.add(resource)
+                    affected_boards.add(updated_resource_dto.board_id or "none")
+                except Exception:
+                    pass
+        return StarredResourcesResult(
+            starred_resources=list(starred_resources),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to star images")
+
+
+@resources_router.post("/unstar", operation_id="unstar_resources_in_list", response_model=UnstarredResourcesResult)
+async def unstar_resources_in_list(
+    resources: list[ResourceIdentifier] = Body(description="The list of resources to unstar", embed=True),
+) -> UnstarredResourcesResult:
+    try:
+        unstarred_resources: set[ResourceIdentifier] = set()
+        affected_boards: set[str] = set()
+        for resource in resources:
+            if resource.resource_type == ResourceType.IMAGE:
+                try:
+                    updated_resource_dto = ApiDependencies.invoker.services.images.update(
+                        resource.resource_id, changes=ImageRecordChanges(starred=False)
+                    )
+                    unstarred_resources.add(resource)
+                    affected_boards.add(updated_resource_dto.board_id or "none")
+                except Exception:
+                    pass
+            elif resource.resource_type == ResourceType.VIDEO:
+                try:
+                    updated_resource_dto = ApiDependencies.invoker.services.videos.update(
+                        resource.resource_id, changes=VideoRecordChanges(starred=False)
+                    )
+                    unstarred_resources.add(resource)
+                    affected_boards.add(updated_resource_dto.board_id or "none")
+                except Exception:
+                    pass
+        return UnstarredResourcesResult(
+            unstarred_resources=list(unstarred_resources),
+            affected_boards=list(affected_boards),
+        )
+    except Exception:
+        raise HTTPException(status_code=500, detail="Failed to unstar images")

invokeai/app/api/routers/videos.py

@@ -0,0 +1,116 @@
+from typing import Optional
+
+from fastapi import Body, Path, Query, Response
+from fastapi.routing import APIRouter
+
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.video_records.video_records_common import (
+    VideoNamesResult,
+    VideoRecordChanges,
+)
+from invokeai.app.services.videos.videos_common import (
+    VideoDTO,
+)
+
+videos_router = APIRouter(prefix="/v1/videos", tags=["videos"])
+
+
+# videos are immutable; set a high max-age
+VIDEO_MAX_AGE = 31536000
+
+
+@videos_router.get(
+    "/i/{video_id}",
+    operation_id="get_video_dto",
+    response_model=VideoDTO,
+)
+async def get_video_dto(
+    video_id: str = Path(description="The id of the video to get"),
+) -> VideoDTO:
+    """Gets a video's DTO"""
+
+    raise NotImplementedError("Not implemented")
+
+
+@videos_router.get(
+    "/",
+    operation_id="list_video_dtos",
+    response_model=OffsetPaginatedResults[VideoDTO],
+)
+async def list_video_dtos(
+    board_id: Optional[str] = Query(
+        default=None,
+        description="The board id to filter by. Use 'none' to find videos without a board.",
+    ),
+    offset: int = Query(default=0, description="The page offset"),
+    limit: int = Query(default=10, description="The number of videos per page"),
+    order_dir: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The order of sort"),
+    starred_first: bool = Query(default=True, description="Whether to sort by starred videos first"),
+    search_term: Optional[str] = Query(default=None, description="The term to search for"),
+) -> OffsetPaginatedResults[VideoDTO]:
+    """Gets a list of video DTOs"""
+
+    raise NotImplementedError("Not implemented")
+
+
+@videos_router.get("/ids", operation_id="get_video_ids")
+async def get_video_ids(
+    board_id: Optional[str] = Query(
+        default=None,
+        description="The board id to filter by. Use 'none' to find videos 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 videos first"),
+    search_term: Optional[str] = Query(default=None, description="The term to search for"),
+) -> VideoNamesResult:
+    """Gets ordered list of video names with metadata for optimistic updates"""
+
+    raise NotImplementedError("Not implemented")
+
+
+@videos_router.post(
+    "/videos_by_ids",
+    operation_id="get_videos_by_ids",
+    responses={200: {"model": list[VideoDTO]}},
+)
+async def get_videos_by_ids(
+    video_ids: list[str] = Body(embed=True, description="Object containing list of video ids to fetch DTOs for"),
+) -> list[VideoDTO]:
+    """Gets video DTOs for the specified video ids. Maintains order of input ids."""
+
+    raise NotImplementedError("Not implemented")
+
+
+@videos_router.patch(
+    "/i/{video_id}",
+    operation_id="update_video",
+    response_model=VideoDTO,
+)
+async def update_video(
+    video_id: str = Path(description="The id of the video to update"),
+    video_changes: VideoRecordChanges = Body(description="The changes to apply to the video"),
+) -> VideoDTO:
+    """Updates a video"""
+
+    raise NotImplementedError("Not implemented")
+
+
+@videos_router.get(
+    "/i/{video_id}/thumbnail",
+    operation_id="get_video_thumbnail",
+    response_class=Response,
+    responses={
+        200: {
+            "description": "Return the video thumbnail",
+            "content": {"image/webp": {}},
+        },
+        404: {"description": "Video not found"},
+    },
+)
+async def get_video_thumbnail(
+    video_id: str = Path(description="The id of video to get thumbnail for"),
+) -> Response:
+    """Gets a video thumbnail file"""
+
+    raise NotImplementedError("Not implemented")

invokeai/app/api_app.py

@@ -17,15 +17,19 @@ from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.app.api.routers import (
     app_info,
-    board_images,
+    # board_images,
+    board_resources,
     boards,
+    client_state,
     download_queue,
     images,
     model_manager,
     model_relationships,
+    resources,
     session_queue,
     style_presets,
     utilities,
+    videos,
     workflows,
 )
 from invokeai.app.api.sockets import SocketIO
@@ -124,13 +128,16 @@ app.include_router(utilities.utilities_router, prefix="/api")
 app.include_router(model_manager.model_manager_router, prefix="/api")
 app.include_router(download_queue.download_queue_router, prefix="/api")
 app.include_router(images.images_router, prefix="/api")
+app.include_router(videos.videos_router, prefix="/api")
+app.include_router(resources.resources_router, prefix="/api")
 app.include_router(boards.boards_router, prefix="/api")
-app.include_router(board_images.board_images_router, prefix="/api")
+app.include_router(board_resources.board_resources_router, prefix="/api")
 app.include_router(model_relationships.model_relationships_router, prefix="/api")
 app.include_router(app_info.app_router, prefix="/api")
 app.include_router(session_queue.session_queue_router, prefix="/api")
 app.include_router(workflows.workflows_router, prefix="/api")
 app.include_router(style_presets.style_presets_router, prefix="/api")
+app.include_router(client_state.client_state_router, prefix="/api")
 
 app.openapi = get_openapi_func(app)
 
@@ -155,6 +162,12 @@ def overridden_redoc() -> HTMLResponse:
 
 web_root_path = Path(list(web_dir.__path__)[0])
 
+if app_config.unsafe_disable_picklescan:
+    logger.warning(
+        "The unsafe_disable_picklescan option is enabled. This disables malware scanning while installing and"
+        "loading models, which may allow malicious code to be executed. Use at your own risk."
+    )
+
 try:
     app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
 except RuntimeError:

invokeai/app/invocations/cogview4_image_to_latents.py

@@ -36,9 +36,19 @@ class CogView4ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
     image: ImageField = InputField(description="The image to encode.")
     vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
 
+    def _estimate_working_memory(self, image_tensor: torch.Tensor, vae: AutoencoderKL) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        # Encode operations use approximately 50% of the memory required for decode operations
+        h = image_tensor.shape[-2]
+        w = image_tensor.shape[-1]
+        element_size = next(vae.parameters()).element_size()
+        scaling_constant = 1100  # 50% of decode scaling constant (2200)
+        working_memory = h * w * element_size * scaling_constant
+        return int(working_memory)
+
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
-        with vae_info as vae:
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor, estimated_working_memory: int) -> torch.Tensor:
+        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, AutoencoderKL)
 
             vae.disable_tiling()
@@ -62,7 +72,12 @@ class CogView4ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
         vae_info = context.models.load(self.vae.vae)
-        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
+        assert isinstance(vae_info.model, AutoencoderKL)
+
+        estimated_working_memory = self._estimate_working_memory(image_tensor, vae_info.model)
+        latents = self.vae_encode(
+            vae_info=vae_info, image_tensor=image_tensor, estimated_working_memory=estimated_working_memory
+        )
 
         latents = latents.to("cpu")
         name = context.tensors.save(tensor=latents)

invokeai/app/invocations/flux_denoise.py

@@ -63,7 +63,7 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="4.0.0",
+    version="4.1.0",
 )
 class FluxDenoiseInvocation(BaseInvocation):
     """Run denoising process with a FLUX transformer model."""
@@ -153,7 +153,7 @@ class FluxDenoiseInvocation(BaseInvocation):
         description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection
     )
 
-    kontext_conditioning: Optional[FluxKontextConditioningField] = InputField(
+    kontext_conditioning: FluxKontextConditioningField | list[FluxKontextConditioningField] | None = InputField(
         default=None,
         description="FLUX Kontext conditioning (reference image).",
         input=Input.Connection,
@@ -328,6 +328,21 @@ class FluxDenoiseInvocation(BaseInvocation):
             cfg_scale_end_step=self.cfg_scale_end_step,
         )
 
+        kontext_extension = None
+        if self.kontext_conditioning:
+            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
+                if isinstance(self.kontext_conditioning, list)
+                else [self.kontext_conditioning],
+                vae_field=self.controlnet_vae,
+                device=TorchDevice.choose_torch_device(),
+                dtype=inference_dtype,
+            )
+
         with ExitStack() as exit_stack:
             # Prepare ControlNet extensions.
             # Note: We do this before loading the transformer model to minimize peak memory (see implementation).
@@ -385,19 +400,6 @@ class FluxDenoiseInvocation(BaseInvocation):
                 dtype=inference_dtype,
             )
 
-            kontext_extension = None
-            if self.kontext_conditioning is not None:
-                if not self.controlnet_vae:
-                    raise ValueError("A VAE (e.g., controlnet_vae) must be provided to use Kontext conditioning.")
-
-                kontext_extension = KontextExtension(
-                    context=context,
-                    kontext_conditioning=self.kontext_conditioning,
-                    vae_field=self.controlnet_vae,
-                    device=TorchDevice.choose_torch_device(),
-                    dtype=inference_dtype,
-                )
-
             # Prepare Kontext conditioning if provided
             img_cond_seq = None
             img_cond_seq_ids = None

invokeai/app/invocations/flux_vae_encode.py

@@ -35,14 +35,24 @@ class FluxVaeEncodeInvocation(BaseInvocation):
         input=Input.Connection,
     )
 
+    def _estimate_working_memory(self, image_tensor: torch.Tensor, vae: AutoEncoder) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        # Encode operations use approximately 50% of the memory required for decode operations
+        h = image_tensor.shape[-2]
+        w = image_tensor.shape[-1]
+        element_size = next(vae.parameters()).element_size()
+        scaling_constant = 1100  # 50% of decode scaling constant (2200)
+        working_memory = h * w * element_size * scaling_constant
+        return int(working_memory)
+
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor, estimated_working_memory: int) -> torch.Tensor:
         # TODO(ryand): Expose seed parameter at the invocation level.
         # TODO(ryand): Write a util function for generating random tensors that is consistent across devices / dtypes.
         # There's a starting point in get_noise(...), but it needs to be extracted and generalized. This function
         # should be used for VAE encode sampling.
         generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
-        with vae_info as vae:
+        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, AutoEncoder)
             vae_dtype = next(iter(vae.parameters())).dtype
             image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
@@ -60,7 +70,10 @@ class FluxVaeEncodeInvocation(BaseInvocation):
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
         context.util.signal_progress("Running VAE")
-        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
+        estimated_working_memory = self._estimate_working_memory(image_tensor, vae_info.model)
+        latents = self.vae_encode(
+            vae_info=vae_info, image_tensor=image_tensor, estimated_working_memory=estimated_working_memory
+        )
 
         latents = latents.to("cpu")
         name = context.tensors.save(tensor=latents)

invokeai/app/invocations/image.py

@@ -1347,3 +1347,96 @@ class PasteImageIntoBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoar
 
         image_dto = context.images.save(image=target_image)
         return ImageOutput.build(image_dto)
+
+
+@invocation(
+    "flux_kontext_image_prep",
+    title="FLUX Kontext Image Prep",
+    tags=["image", "concatenate", "flux", "kontext"],
+    category="image",
+    version="1.0.0",
+)
+class FluxKontextConcatenateImagesInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Prepares an image or images for use with FLUX Kontext. The first/single image is resized to the nearest
+    preferred Kontext resolution. All other images are concatenated horizontally, maintaining their aspect ratio."""
+
+    images: list[ImageField] = InputField(
+        description="The images to concatenate",
+        min_length=1,
+        max_length=10,
+    )
+
+    use_preferred_resolution: bool = InputField(
+        default=True, description="Use FLUX preferred resolutions for the first image"
+    )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        from invokeai.backend.flux.util import PREFERED_KONTEXT_RESOLUTIONS
+
+        # Step 1: Load all images
+        pil_images = []
+        for image_field in self.images:
+            image = context.images.get_pil(image_field.image_name, mode="RGBA")
+            pil_images.append(image)
+
+        # Step 2: Determine target resolution for the first image
+        first_image = pil_images[0]
+        width, height = first_image.size
+
+        if self.use_preferred_resolution:
+            aspect_ratio = width / height
+
+            # Find the closest preferred resolution for the first image
+            _, 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
+            scaled_height = 2 * int(target_height / 16)
+            final_height = 8 * scaled_height  # This will be consistent for all images
+            scaled_width = 2 * int(target_width / 16)
+            first_width = 8 * scaled_width
+        else:
+            # Use original dimensions of first image, ensuring divisibility by 16
+            final_height = 16 * (height // 16)
+            first_width = 16 * (width // 16)
+            # Ensure minimum dimensions
+            if final_height < 16:
+                final_height = 16
+            if first_width < 16:
+                first_width = 16
+
+        # Step 3: Process and resize all images with consistent height
+        processed_images = []
+        total_width = 0
+
+        for i, image in enumerate(pil_images):
+            if i == 0:
+                # First image uses the calculated dimensions
+                final_width = first_width
+            else:
+                # Subsequent images maintain aspect ratio with the same height
+                img_aspect_ratio = image.width / image.height
+                # Calculate width that maintains aspect ratio at the target height
+                calculated_width = int(final_height * img_aspect_ratio)
+                # Ensure width is divisible by 16 for proper VAE encoding
+                final_width = 16 * (calculated_width // 16)
+                # Ensure minimum width
+                if final_width < 16:
+                    final_width = 16
+
+            # Resize image to calculated dimensions
+            resized_image = image.resize((final_width, final_height), Image.Resampling.LANCZOS)
+            processed_images.append(resized_image)
+            total_width += final_width
+
+        # Step 4: Concatenate images horizontally
+        concatenated_image = Image.new("RGB", (total_width, final_height))
+        x_offset = 0
+        for img in processed_images:
+            concatenated_image.paste(img, (x_offset, 0))
+            x_offset += img.width
+
+        # Save the concatenated image
+        image_dto = context.images.save(image=concatenated_image)
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/image_to_latents.py

@@ -52,11 +52,48 @@ class ImageToLatentsInvocation(BaseInvocation):
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
     fp32: bool = InputField(default=False, description=FieldDescriptions.fp32)
 
+    def _estimate_working_memory(
+        self, image_tensor: torch.Tensor, use_tiling: bool, vae: AutoencoderKL | AutoencoderTiny
+    ) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        # Encode operations use approximately 50% of the memory required for decode operations
+        element_size = 4 if self.fp32 else 2
+        scaling_constant = 1100  # 50% of decode scaling constant (2200)
+
+        if use_tiling:
+            tile_size = self.tile_size
+            if tile_size == 0:
+                tile_size = vae.tile_sample_min_size
+                assert isinstance(tile_size, int)
+            h = tile_size
+            w = tile_size
+            working_memory = h * w * element_size * scaling_constant
+
+            # We add 25% to the working memory estimate when tiling is enabled to account for factors like tile overlap
+            # and number of tiles. We could make this more precise in the future, but this should be good enough for
+            # most use cases.
+            working_memory = working_memory * 1.25
+        else:
+            h = image_tensor.shape[-2]
+            w = image_tensor.shape[-1]
+            working_memory = h * w * element_size * scaling_constant
+
+        if self.fp32:
+            # If we are running in FP32, then we should account for the likely increase in model size (~250MB).
+            working_memory += 250 * 2**20
+
+        return int(working_memory)
+
     @staticmethod
     def vae_encode(
-        vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor, tile_size: int = 0
+        vae_info: LoadedModel,
+        upcast: bool,
+        tiled: bool,
+        image_tensor: torch.Tensor,
+        tile_size: int = 0,
+        estimated_working_memory: int = 0,
     ) -> torch.Tensor:
-        with vae_info as vae:
+        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             orig_dtype = vae.dtype
             if upcast:
@@ -113,14 +150,23 @@ class ImageToLatentsInvocation(BaseInvocation):
         image = context.images.get_pil(self.image.image_name)
 
         vae_info = context.models.load(self.vae.vae)
+        assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
+        use_tiling = self.tiled or context.config.get().force_tiled_decode
+        estimated_working_memory = self._estimate_working_memory(image_tensor, use_tiling, vae_info.model)
+
         context.util.signal_progress("Running VAE encoder")
         latents = self.vae_encode(
-            vae_info=vae_info, upcast=self.fp32, tiled=self.tiled, image_tensor=image_tensor, tile_size=self.tile_size
+            vae_info=vae_info,
+            upcast=self.fp32,
+            tiled=self.tiled,
+            image_tensor=image_tensor,
+            tile_size=self.tile_size,
+            estimated_working_memory=estimated_working_memory,
         )
 
         latents = latents.to("cpu")

invokeai/app/invocations/sd3_image_to_latents.py

@@ -32,9 +32,19 @@ class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
     image: ImageField = InputField(description="The image to encode")
     vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
 
+    def _estimate_working_memory(self, image_tensor: torch.Tensor, vae: AutoencoderKL) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        # Encode operations use approximately 50% of the memory required for decode operations
+        h = image_tensor.shape[-2]
+        w = image_tensor.shape[-1]
+        element_size = next(vae.parameters()).element_size()
+        scaling_constant = 1100  # 50% of decode scaling constant (2200)
+        working_memory = h * w * element_size * scaling_constant
+        return int(working_memory)
+
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
-        with vae_info as vae:
+    def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor, estimated_working_memory: int) -> torch.Tensor:
+        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, AutoencoderKL)
 
             vae.disable_tiling()
@@ -58,7 +68,12 @@ class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
             image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
 
         vae_info = context.models.load(self.vae.vae)
-        latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
+        assert isinstance(vae_info.model, AutoencoderKL)
+
+        estimated_working_memory = self._estimate_working_memory(image_tensor, vae_info.model)
+        latents = self.vae_encode(
+            vae_info=vae_info, image_tensor=image_tensor, estimated_working_memory=estimated_working_memory
+        )
 
         latents = latents.to("cpu")
         name = context.tensors.save(tensor=latents)

invokeai/app/services/board_resource_records/board_resource_records_base.py

@@ -0,0 +1,57 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from invokeai.app.services.image_records.image_records_common import ImageCategory
+from invokeai.app.services.resources.resources_common import ResourceIdentifier, ResourceType
+
+
+class BoardResourceRecordStorageBase(ABC):
+    """Abstract base class for the one-to-many board-resource relationship record storage."""
+
+    @abstractmethod
+    def add_resource_to_board(
+        self,
+        board_id: str,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> None:
+        """Adds a resource to a board."""
+        pass
+
+    @abstractmethod
+    def remove_resource_from_board(
+        self,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> None:
+        """Removes a resource from a board."""
+        pass
+
+    @abstractmethod
+    def get_all_board_resource_ids_for_board(
+        self,
+        board_id: str,
+        resource_type: Optional[ResourceType] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+    ) -> list[ResourceIdentifier]:
+        """Gets all board resources for a board, as a list of resource IDs."""
+        pass
+
+    @abstractmethod
+    def get_board_for_resource(
+        self,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> Optional[str]:
+        """Gets a resource's board id, if it has one."""
+        pass
+
+    @abstractmethod
+    def get_resource_count_for_board(
+        self,
+        board_id: str,
+        resource_type: Optional[ResourceType] = None,
+    ) -> int:
+        """Gets the number of resources for a board."""
+        pass

invokeai/app/services/board_resource_records/board_resource_records_sqlite.py

@@ -0,0 +1,153 @@
+import sqlite3
+from typing import Optional, cast
+
+from invokeai.app.services.board_resource_records.board_resource_records_base import BoardResourceRecordStorageBase
+from invokeai.app.services.image_records.image_records_common import ImageCategory
+from invokeai.app.services.resources.resources_common import ResourceIdentifier, ResourceType
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+
+
+class SqliteBoardResourceRecordStorage(BoardResourceRecordStorageBase):
+    def __init__(self, db: SqliteDatabase) -> None:
+        super().__init__()
+        self._db = db
+
+    def add_resource_to_board(
+        self,
+        board_id: str,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> None:
+        with self._db.transaction() as cursor:
+            if resource_type == ResourceType.IMAGE:
+                cursor.execute(
+                    """--sql
+                    INSERT INTO board_images (board_id, image_name)
+                    VALUES (?, ?)
+                    ON CONFLICT (image_name) DO UPDATE SET board_id = ?;
+                    """,
+                    (board_id, resource_id, board_id),
+                )
+            elif resource_type == ResourceType.VIDEO:
+                raise NotImplementedError("Video resource type is not supported in OSS")
+
+    def remove_resource_from_board(
+        self,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> None:
+        with self._db.transaction() as cursor:
+            if resource_type == ResourceType.IMAGE:
+                cursor.execute(
+                    """--sql
+                    DELETE FROM board_images
+                    WHERE image_name = ?;
+                    """,
+                    (resource_id,),
+                )
+            elif resource_type == ResourceType.VIDEO:
+                raise NotImplementedError("Video resource type is not supported in OSS")
+
+    def get_all_board_resource_ids_for_board(
+        self,
+        board_id: str,
+        resource_type: Optional[ResourceType] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+    ) -> list[ResourceIdentifier]:
+        image_name_results = []
+
+        with self._db.transaction() as cursor:
+            if resource_type == ResourceType.IMAGE or resource_type is None:
+                params: list[str | bool] = []
+
+                # Base query is a join between images and board_images
+                stmt = """
+                        SELECT images.image_name
+                        FROM images
+                        LEFT JOIN board_images ON board_images.image_name = images.image_name
+                        WHERE 1=1
+                        """
+
+                # Handle board_id filter
+                if board_id == "none":
+                    stmt += """--sql
+                        AND board_images.board_id IS NULL
+                        """
+                else:
+                    stmt += """--sql
+                        AND board_images.board_id = ?
+                        """
+                    params.append(board_id)
+
+                # Add the category filter
+                if categories is not None:
+                    # Convert the enum values to unique list of strings
+                    category_strings = [c.value for c in set(categories)]
+                    # Create the correct length of placeholders
+                    placeholders = ",".join("?" * len(category_strings))
+                    stmt += f"""--sql
+                        AND images.image_category IN ( {placeholders} )
+                        """
+
+                    # Unpack the included categories into the query params
+                    for c in category_strings:
+                        params.append(c)
+
+                # Add the is_intermediate filter
+                if is_intermediate is not None:
+                    stmt += """--sql
+                        AND images.is_intermediate = ?
+                        """
+                    params.append(is_intermediate)
+
+                # Put a ring on it
+                stmt += ";"
+
+                cursor.execute(stmt, params)
+
+                image_name_results = cast(list[sqlite3.Row], cursor.fetchall())
+
+            if resource_type == ResourceType.VIDEO or resource_type is None:
+                #    this is not actually a valid code path for OSS, just demonstrating that it could be
+                raise NotImplementedError("Video resource type is not supported in OSS")
+
+            return [
+                ResourceIdentifier(resource_id=image_name, resource_type=ResourceType.IMAGE)
+                for image_name in image_name_results
+            ]
+
+    def get_board_for_resource(
+        self,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> Optional[str]:
+        with self._db.transaction() as cursor:
+            cursor.execute(
+                """--sql
+                    SELECT board_id
+                    FROM board_images
+                    WHERE image_name = ?;
+                    """,
+                (resource_id,),
+            )
+            result = cursor.fetchone()
+        if result is None:
+            return None
+        return cast(str, result[0])
+
+    def get_resource_count_for_board(self, board_id: str, resource_type: Optional[ResourceType] = None) -> int:
+        with self._db.transaction() as cursor:
+            # only images are supported in OSS
+            cursor.execute(
+                """--sql
+                    SELECT COUNT(*)
+                    FROM board_images
+                    INNER JOIN images ON board_images.image_name = images.image_name
+                    WHERE images.is_intermediate = FALSE
+                    AND board_images.board_id = ?;
+                    """,
+                (board_id,),
+            )
+            count = cast(int, cursor.fetchone()[0])
+        return count

invokeai/app/services/board_resources/board_resources_base.py

@@ -0,0 +1,48 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from invokeai.app.services.image_records.image_records_common import ImageCategory
+from invokeai.app.services.resources.resources_common import ResourceIdentifier, ResourceType
+
+
+class BoardResourcesServiceABC(ABC):
+    """High-level service for board-resource relationship management."""
+
+    @abstractmethod
+    def add_resource_to_board(
+        self,
+        board_id: str,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> None:
+        """Adds a resource (image or video) to a board."""
+        pass
+
+    @abstractmethod
+    def remove_resource_from_board(
+        self,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> None:
+        """Removes a resource (image or video) from a board."""
+        pass
+
+    @abstractmethod
+    def get_all_board_resource_ids_for_board(
+        self,
+        board_id: str,
+        resource_type: Optional[ResourceType] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+    ) -> list[ResourceIdentifier]:
+        """Gets all board resources for a board, as a list of resource IDs."""
+        pass
+
+    @abstractmethod
+    def get_board_for_resource(
+        self,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> Optional[str]:
+        """Gets a resource's board id, if it has one."""
+        pass

invokeai/app/services/board_resources/board_resources_common.py

@@ -0,0 +1,12 @@
+from pydantic import Field
+
+from invokeai.app.services.resources.resources_common import ResourceType
+from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
+
+
+class BoardResource(BaseModelExcludeNull):
+    """Represents a resource (image or video) associated with a board."""
+
+    board_id: str = Field(description="The id of the board")
+    resource_id: str = Field(description="The id of the resource (image_name or video_id)")
+    resource_type: ResourceType = Field(description="The type of resource")

invokeai/app/services/board_resources/board_resources_default.py

@@ -0,0 +1,49 @@
+from typing import Optional
+
+from invokeai.app.services.board_resources.board_resources_base import BoardResourcesServiceABC
+from invokeai.app.services.image_records.image_records_common import ImageCategory
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.resources.resources_common import ResourceIdentifier, ResourceType
+
+
+class BoardResourcesService(BoardResourcesServiceABC):
+    __invoker: Invoker
+
+    def start(self, invoker: Invoker) -> None:
+        self.__invoker = invoker
+
+    def add_resource_to_board(
+        self,
+        board_id: str,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> None:
+        self.__invoker.services.board_resource_records.add_resource_to_board(board_id, resource_id, resource_type)
+
+    def remove_resource_from_board(
+        self,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> None:
+        self.__invoker.services.board_resource_records.remove_resource_from_board(resource_id, resource_type)
+
+    def get_all_board_resource_ids_for_board(
+        self,
+        board_id: str,
+        resource_type: Optional[ResourceType] = None,
+        categories: Optional[list[ImageCategory]] = None,
+        is_intermediate: Optional[bool] = None,
+    ) -> list[ResourceIdentifier]:
+        return self.__invoker.services.board_resource_records.get_all_board_resource_ids_for_board(
+            board_id,
+            resource_type,
+            categories,
+            is_intermediate,
+        )
+
+    def get_board_for_resource(
+        self,
+        resource_id: str,
+        resource_type: ResourceType,
+    ) -> Optional[str]:
+        return self.__invoker.services.board_resource_records.get_board_for_resource(resource_id, resource_type)

invokeai/app/services/boards/boards_common.py

@@ -12,12 +12,17 @@ class BoardDTO(BoardRecord):
     """The URL of the thumbnail of the most recent image in the board."""
     image_count: int = Field(description="The number of images in the board.")
     """The number of images in the board."""
+    video_count: int = Field(description="The number of videos in the board.")
+    """The number of videos in the board."""
 
 
-def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
+def board_record_to_dto(
+    board_record: BoardRecord, cover_image_name: Optional[str], image_count: int, video_count: int
+) -> BoardDTO:
     """Converts a board record to a board DTO."""
     return BoardDTO(
         **board_record.model_dump(exclude={"cover_image_name"}),
         cover_image_name=cover_image_name,
         image_count=image_count,
+        video_count=video_count,
     )

invokeai/app/services/boards/boards_default.py

@@ -2,6 +2,7 @@ from invokeai.app.services.board_records.board_records_common import BoardChange
 from invokeai.app.services.boards.boards_base import BoardServiceABC
 from invokeai.app.services.boards.boards_common import BoardDTO, board_record_to_dto
 from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.resources.resources_common import ResourceType
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
@@ -17,7 +18,7 @@ class BoardService(BoardServiceABC):
         board_name: str,
     ) -> BoardDTO:
         board_record = self.__invoker.services.board_records.save(board_name)
-        return board_record_to_dto(board_record, None, 0)
+        return board_record_to_dto(board_record, None, 0, 0)
 
     def get_dto(self, board_id: str) -> BoardDTO:
         board_record = self.__invoker.services.board_records.get(board_id)
@@ -26,8 +27,13 @@ class BoardService(BoardServiceABC):
             cover_image_name = cover_image.image_name
         else:
             cover_image_name = None
-        image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
-        return board_record_to_dto(board_record, cover_image_name, image_count)
+        image_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
+            board_id, ResourceType.IMAGE
+        )
+        video_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
+            board_id, ResourceType.VIDEO
+        )
+        return board_record_to_dto(board_record, cover_image_name, image_count, video_count)
 
     def update(
         self,
@@ -41,8 +47,13 @@ class BoardService(BoardServiceABC):
         else:
             cover_image_name = None
 
-        image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
-        return board_record_to_dto(board_record, cover_image_name, image_count)
+        image_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
+            board_id, ResourceType.IMAGE
+        )
+        video_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
+            board_id, ResourceType.VIDEO
+        )
+        return board_record_to_dto(board_record, cover_image_name, image_count, video_count)
 
     def delete(self, board_id: str) -> None:
         self.__invoker.services.board_records.delete(board_id)
@@ -58,7 +69,7 @@ class BoardService(BoardServiceABC):
         board_records = self.__invoker.services.board_records.get_many(
             order_by, direction, offset, limit, include_archived
         )
-        board_dtos = []
+        board_dtos: list[BoardDTO] = []
         for r in board_records.items:
             cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
             if cover_image:
@@ -66,8 +77,13 @@ class BoardService(BoardServiceABC):
             else:
                 cover_image_name = None
 
-            image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
-            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
+            image_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
+                r.board_id, ResourceType.IMAGE
+            )
+            video_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
+                r.board_id, ResourceType.VIDEO
+            )
+            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count, video_count))
 
         return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
 
@@ -75,7 +91,7 @@ class BoardService(BoardServiceABC):
         self, order_by: BoardRecordOrderBy, direction: SQLiteDirection, include_archived: bool = False
     ) -> list[BoardDTO]:
         board_records = self.__invoker.services.board_records.get_all(order_by, direction, include_archived)
-        board_dtos = []
+        board_dtos: list[BoardDTO] = []
         for r in board_records:
             cover_image = self.__invoker.services.image_records.get_most_recent_image_for_board(r.board_id)
             if cover_image:
@@ -83,7 +99,12 @@ class BoardService(BoardServiceABC):
             else:
                 cover_image_name = None
 
-            image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
-            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
+            image_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
+                r.board_id, ResourceType.IMAGE
+            )
+            video_count = self.__invoker.services.board_resource_records.get_resource_count_for_board(
+                r.board_id, ResourceType.VIDEO
+            )
+            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count, video_count))
 
         return board_dtos

invokeai/app/services/bulk_download/bulk_download_default.py

@@ -14,6 +14,7 @@ from invokeai.app.services.bulk_download.bulk_download_common import (
 from invokeai.app.services.image_records.image_records_common import ImageRecordNotFoundException
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.resources.resources_common import ResourceType
 from invokeai.app.util.misc import uuid_string
 
 
@@ -63,12 +64,11 @@ class BulkDownloadService(BulkDownloadBase):
         return [self._invoker.services.images.get_dto(image_name) for image_name in image_names]
 
     def _board_handler(self, board_id: str) -> list[ImageDTO]:
-        image_names = self._invoker.services.board_image_records.get_all_board_image_names_for_board(
+        resource_ids = self._invoker.services.board_resource_records.get_all_board_resource_ids_for_board(
             board_id,
-            categories=None,
-            is_intermediate=None,
+            resource_type=ResourceType.IMAGE,
         )
-        return self._image_handler(image_names)
+        return self._image_handler([resource_id.resource_id for resource_id in resource_ids])
 
     def generate_item_id(self, board_id: Optional[str]) -> str:
         return uuid_string() if board_id is None else self._get_clean_board_name(board_id) + "_" + uuid_string()

invokeai/app/services/client_state_persistence/client_state_persistence_base.py

@@ -0,0 +1,42 @@
+from abc import ABC, abstractmethod
+
+
+class ClientStatePersistenceABC(ABC):
+    """
+    Base class for client persistence implementations.
+    This class defines the interface for persisting client data.
+    """
+
+    @abstractmethod
+    def set_by_key(self, queue_id: str, key: str, value: str) -> str:
+        """
+        Set a key-value pair for the client.
+
+        Args:
+            key (str): The key to set.
+            value (str): The value to set for the key.
+
+        Returns:
+            str: The value that was set.
+        """
+        pass
+
+    @abstractmethod
+    def get_by_key(self, queue_id: str, key: str) -> str | None:
+        """
+        Get the value for a specific key of the client.
+
+        Args:
+            key (str): The key to retrieve the value for.
+
+        Returns:
+            str | None: The value associated with the key, or None if the key does not exist.
+        """
+        pass
+
+    @abstractmethod
+    def delete(self, queue_id: str) -> None:
+        """
+        Delete all client state.
+        """
+        pass

invokeai/app/services/client_state_persistence/client_state_persistence_sqlite.py

@@ -0,0 +1,65 @@
+import json
+
+from invokeai.app.services.client_state_persistence.client_state_persistence_base import ClientStatePersistenceABC
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+
+
+class ClientStatePersistenceSqlite(ClientStatePersistenceABC):
+    """
+    Base class for client persistence implementations.
+    This class defines the interface for persisting client data.
+    """
+
+    def __init__(self, db: SqliteDatabase) -> None:
+        super().__init__()
+        self._db = db
+        self._default_row_id = 1
+
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+
+    def _get(self) -> dict[str, str] | None:
+        with self._db.transaction() as cursor:
+            cursor.execute(
+                f"""
+                SELECT data FROM client_state
+                WHERE id = {self._default_row_id}
+                """
+            )
+            row = cursor.fetchone()
+            if row is None:
+                return None
+            return json.loads(row[0])
+
+    def set_by_key(self, queue_id: str, key: str, value: str) -> str:
+        state = self._get() or {}
+        state.update({key: value})
+
+        with self._db.transaction() as cursor:
+            cursor.execute(
+                f"""
+                INSERT INTO client_state (id, data)
+                VALUES ({self._default_row_id}, ?)
+                ON CONFLICT(id) DO UPDATE
+                  SET data = excluded.data;
+                """,
+                (json.dumps(state),),
+            )
+
+        return value
+
+    def get_by_key(self, queue_id: str, key: str) -> str | None:
+        state = self._get()
+        if state is None:
+            return None
+        return state.get(key, None)
+
+    def delete(self, queue_id: str) -> None:
+        with self._db.transaction() as cursor:
+            cursor.execute(
+                f"""
+                DELETE FROM client_state
+                WHERE id = {self._default_row_id}
+                """
+            )

invokeai/app/services/config/config_default.py

@@ -107,6 +107,7 @@ class InvokeAIAppConfig(BaseSettings):
         hashing_algorithm: Model hashing algorthim for model installs. 'blake3_multi' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.<br>Valid values: `blake3_multi`, `blake3_single`, `random`, `md5`, `sha1`, `sha224`, `sha256`, `sha384`, `sha512`, `blake2b`, `blake2s`, `sha3_224`, `sha3_256`, `sha3_384`, `sha3_512`, `shake_128`, `shake_256`
         remote_api_tokens: List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.
         scan_models_on_startup: Scan the models directory on startup, registering orphaned models. This is typically only used in conjunction with `use_memory_db` for testing purposes.
+        unsafe_disable_picklescan: UNSAFE. Disable the picklescan security check during model installation. Recommended only for development and testing purposes. This will allow arbitrary code execution during model installation, so should never be used in production.
     """
 
     _root: Optional[Path] = PrivateAttr(default=None)
@@ -196,6 +197,7 @@ class InvokeAIAppConfig(BaseSettings):
     hashing_algorithm: HASHING_ALGORITHMS = Field(default="blake3_single",  description="Model hashing algorthim for model installs. 'blake3_multi' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.")
     remote_api_tokens: Optional[list[URLRegexTokenPair]] = Field(default=None, description="List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.")
     scan_models_on_startup:        bool = Field(default=False,              description="Scan the models directory on startup, registering orphaned models. This is typically only used in conjunction with `use_memory_db` for testing purposes.")
+    unsafe_disable_picklescan:     bool = Field(default=False,              description="UNSAFE. Disable the picklescan security check during model installation. Recommended only for development and testing purposes. This will allow arbitrary code execution during model installation, so should never be used in production.")
 
     # fmt: on
 

invokeai/app/services/images/images_common.py

@@ -1,6 +1,6 @@
-from typing import Optional
+from typing import Any, Literal, Optional
 
-from pydantic import BaseModel, Field
+from pydantic import ConfigDict, Field
 
 from invokeai.app.services.image_records.image_records_common import ImageRecord
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
@@ -17,14 +17,24 @@ class ImageUrlsDTO(BaseModelExcludeNull):
     """The URL of the image's thumbnail."""
 
 
+def make_type_required(s: dict[str, Any]):
+    if "required" in s:
+        s["required"].append("type")
+    else:
+        s["required"] = ["type"]
+
+
 class ImageDTO(ImageRecord, ImageUrlsDTO):
     """Deserialized image record, enriched for the frontend."""
 
+    type: Literal["image"] = Field(default="image")
     board_id: Optional[str] = Field(
         default=None, description="The id of the board the image belongs to, if one exists."
     )
     """The id of the board the image belongs to, if one exists."""
 
+    model_config = ConfigDict(json_schema_extra=make_type_required)
+
 
 def image_record_to_dto(
     image_record: ImageRecord,
@@ -39,27 +49,3 @@ def image_record_to_dto(
         thumbnail_url=thumbnail_url,
         board_id=board_id,
     )
-
-
-class ResultWithAffectedBoards(BaseModel):
-    affected_boards: list[str] = Field(description="The ids of boards affected by the delete operation")
-
-
-class DeleteImagesResult(ResultWithAffectedBoards):
-    deleted_images: list[str] = Field(description="The names of the images that were deleted")
-
-
-class StarredImagesResult(ResultWithAffectedBoards):
-    starred_images: list[str] = Field(description="The names of the images that were starred")
-
-
-class UnstarredImagesResult(ResultWithAffectedBoards):
-    unstarred_images: list[str] = Field(description="The names of the images that were unstarred")
-
-
-class AddImagesToBoardResult(ResultWithAffectedBoards):
-    added_images: list[str] = Field(description="The image names that were added to the board")
-
-
-class RemoveImagesFromBoardResult(ResultWithAffectedBoards):
-    removed_images: list[str] = Field(description="The image names that were removed from their board")

invokeai/app/services/images/images_default.py

@@ -23,6 +23,7 @@ from invokeai.app.services.image_records.image_records_common import (
 from invokeai.app.services.images.images_base import ImageServiceABC
 from invokeai.app.services.images.images_common import ImageDTO, image_record_to_dto
 from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.resources.resources_common import ResourceType
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
@@ -75,8 +76,8 @@ class ImageService(ImageServiceABC):
             )
             if board_id is not None:
                 try:
-                    self.__invoker.services.board_image_records.add_image_to_board(
-                        board_id=board_id, image_name=image_name
+                    self.__invoker.services.board_resource_records.add_resource_to_board(
+                        board_id=board_id, resource_id=image_name, resource_type=ResourceType.IMAGE
                     )
                 except Exception as e:
                     self.__invoker.services.logger.warning(f"Failed to add image to board {board_id}: {str(e)}")
@@ -142,7 +143,9 @@ class ImageService(ImageServiceABC):
                 image_record=image_record,
                 image_url=self.__invoker.services.urls.get_image_url(image_name),
                 thumbnail_url=self.__invoker.services.urls.get_image_url(image_name, True),
-                board_id=self.__invoker.services.board_image_records.get_board_for_image(image_name),
+                board_id=self.__invoker.services.board_resource_records.get_board_for_resource(
+                    image_name, ResourceType.IMAGE
+                ),
             )
 
             return image_dto
@@ -234,7 +237,9 @@ class ImageService(ImageServiceABC):
                     image_record=r,
                     image_url=self.__invoker.services.urls.get_image_url(r.image_name),
                     thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
-                    board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
+                    board_id=self.__invoker.services.board_resource_records.get_board_for_resource(
+                        r.image_name, ResourceType.IMAGE
+                    ),
                 )
                 for r in results.items
             ]
@@ -266,10 +271,9 @@ class ImageService(ImageServiceABC):
 
     def delete_images_on_board(self, board_id: str):
         try:
-            image_names = self.__invoker.services.board_image_records.get_all_board_image_names_for_board(
+            image_names = self.__invoker.services.board_resource_records.get_all_board_resource_ids_for_board(
                 board_id,
-                categories=None,
-                is_intermediate=None,
+                resource_type=ResourceType.IMAGE,
             )
             for image_name in image_names:
                 self.__invoker.services.image_files.delete(image_name)

invokeai/app/services/invocation_services.py

@@ -12,11 +12,14 @@ if TYPE_CHECKING:
 
     import torch
 
-    from invokeai.app.services.board_image_records.board_image_records_base import BoardImageRecordStorageBase
-    from invokeai.app.services.board_images.board_images_base import BoardImagesServiceABC
+    # from invokeai.app.services.board_image_records.board_image_records_base import BoardImageRecordStorageBase
+    # from invokeai.app.services.board_images.board_images_base import BoardImagesServiceABC
     from invokeai.app.services.board_records.board_records_base import BoardRecordStorageBase
+    from invokeai.app.services.board_resource_records.board_resource_records_base import BoardResourceRecordStorageBase
+    from invokeai.app.services.board_resources.board_resources_base import BoardResourcesServiceABC
     from invokeai.app.services.boards.boards_base import BoardServiceABC
     from invokeai.app.services.bulk_download.bulk_download_base import BulkDownloadBase
+    from invokeai.app.services.client_state_persistence.client_state_persistence_base import ClientStatePersistenceABC
     from invokeai.app.services.config import InvokeAIAppConfig
     from invokeai.app.services.download import DownloadQueueServiceBase
     from invokeai.app.services.events.events_base import EventServiceBase
@@ -35,6 +38,8 @@ if TYPE_CHECKING:
     from invokeai.app.services.session_processor.session_processor_base import SessionProcessorBase
     from invokeai.app.services.session_queue.session_queue_base import SessionQueueBase
     from invokeai.app.services.urls.urls_base import UrlServiceBase
+    from invokeai.app.services.video_records.video_records_base import VideoRecordStorageBase
+    from invokeai.app.services.videos.videos_base import VideoServiceABC
     from invokeai.app.services.workflow_records.workflow_records_base import WorkflowRecordsStorageBase
     from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_base import WorkflowThumbnailServiceBase
     from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
@@ -45,8 +50,10 @@ class InvocationServices:
 
     def __init__(
         self,
-        board_images: "BoardImagesServiceABC",
-        board_image_records: "BoardImageRecordStorageBase",
+        # board_images: "BoardImagesServiceABC",
+        # board_image_records: "BoardImageRecordStorageBase",
+        board_resources: "BoardResourcesServiceABC",
+        board_resource_records: "BoardResourceRecordStorageBase",
         boards: "BoardServiceABC",
         board_records: "BoardRecordStorageBase",
         bulk_download: "BulkDownloadBase",
@@ -67,15 +74,20 @@ class InvocationServices:
         invocation_cache: "InvocationCacheBase",
         names: "NameServiceBase",
         urls: "UrlServiceBase",
+        videos: "VideoServiceABC",
+        video_records: "VideoRecordStorageBase",
         workflow_records: "WorkflowRecordsStorageBase",
         tensors: "ObjectSerializerBase[torch.Tensor]",
         conditioning: "ObjectSerializerBase[ConditioningFieldData]",
         style_preset_records: "StylePresetRecordsStorageBase",
         style_preset_image_files: "StylePresetImageFileStorageBase",
         workflow_thumbnails: "WorkflowThumbnailServiceBase",
+        client_state_persistence: "ClientStatePersistenceABC",
     ):
-        self.board_images = board_images
-        self.board_image_records = board_image_records
+        # self.board_images = board_images
+        # self.board_image_records = board_image_records
+        self.board_resources = board_resources
+        self.board_resource_records = board_resource_records
         self.boards = boards
         self.board_records = board_records
         self.bulk_download = bulk_download
@@ -96,9 +108,12 @@ class InvocationServices:
         self.invocation_cache = invocation_cache
         self.names = names
         self.urls = urls
+        self.videos = videos
+        self.video_records = video_records
         self.workflow_records = workflow_records
         self.tensors = tensors
         self.conditioning = conditioning
         self.style_preset_records = style_preset_records
         self.style_preset_image_files = style_preset_image_files
         self.workflow_thumbnails = workflow_thumbnails
+        self.client_state_persistence = client_state_persistence

invokeai/app/services/model_install/model_install_default.py

@@ -7,7 +7,7 @@ import threading
 import time
 from pathlib import Path
 from queue import Empty, Queue
-from shutil import copyfile, copytree, move, rmtree
+from shutil import move, rmtree
 from tempfile import mkdtemp
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union
 
@@ -186,13 +186,15 @@ class ModelInstallService(ModelInstallServiceBase):
         info: AnyModelConfig = self._probe(Path(model_path), config)  # type: ignore
 
         if preferred_name := config.name:
-            preferred_name = Path(preferred_name).with_suffix(model_path.suffix)
+            if Path(model_path).is_file():
+                # Careful! Don't use pathlib.Path(...).with_suffix - it can will strip everything after the first dot.
+                preferred_name = f"{preferred_name}{model_path.suffix}"
 
         dest_path = (
             self.app_config.models_path / info.base.value / info.type.value / (preferred_name or model_path.name)
         )
         try:
-            new_path = self._copy_model(model_path, dest_path)
+            new_path = self._move_model(model_path, dest_path)
         except FileExistsError as excp:
             raise DuplicateModelException(
                 f"A model named {model_path.name} is already installed at {dest_path.as_posix()}"
@@ -617,30 +619,17 @@ class ModelInstallService(ModelInstallServiceBase):
         self.record_store.update_model(key, ModelRecordChanges(path=model.path))
         return model
 
-    def _copy_model(self, old_path: Path, new_path: Path) -> Path:
-        if old_path == new_path:
-            return old_path
-        new_path.parent.mkdir(parents=True, exist_ok=True)
-        if old_path.is_dir():
-            copytree(old_path, new_path)
-        else:
-            copyfile(old_path, new_path)
-        return new_path
-
     def _move_model(self, old_path: Path, new_path: Path) -> Path:
         if old_path == new_path:
             return old_path
 
+        if new_path.exists():
+            raise FileExistsError(f"Cannot move {old_path} to {new_path}: destination already exists")
+
         new_path.parent.mkdir(parents=True, exist_ok=True)
 
-        # if path already exists then we jigger the name to make it unique
-        counter: int = 1
-        while new_path.exists():
-            path = new_path.with_stem(new_path.stem + f"_{counter:02d}")
-            if not path.exists():
-                new_path = path
-            counter += 1
         move(old_path, new_path)
+
         return new_path
 
     def _probe(self, model_path: Path, config: Optional[ModelRecordChanges] = None):

invokeai/app/services/model_load/model_load_default.py

@@ -87,9 +87,21 @@ class ModelLoadService(ModelLoadServiceBase):
         def torch_load_file(checkpoint: Path) -> AnyModel:
             scan_result = scan_file_path(checkpoint)
             if scan_result.infected_files != 0:
-                raise Exception(f"The model at {checkpoint} is potentially infected by malware. Aborting load.")
+                if self._app_config.unsafe_disable_picklescan:
+                    self._logger.warning(
+                        f"Model at {checkpoint} is potentially infected by malware, but picklescan is disabled. "
+                        "Proceeding with caution."
+                    )
+                else:
+                    raise Exception(f"The model at {checkpoint} is potentially infected by malware. Aborting load.")
             if scan_result.scan_err:
-                raise Exception(f"Error scanning model at {checkpoint} for malware. Aborting load.")
+                if self._app_config.unsafe_disable_picklescan:
+                    self._logger.warning(
+                        f"Error scanning model at {checkpoint} for malware, but picklescan is disabled. "
+                        "Proceeding with caution."
+                    )
+                else:
+                    raise Exception(f"Error scanning model at {checkpoint} for malware. Aborting load.")
 
             result = torch_load(checkpoint, map_location="cpu")
             return result

invokeai/app/services/names/names_common.py

@@ -1,8 +0,0 @@
-from enum import Enum, EnumMeta
-
-
-class ResourceType(str, Enum, metaclass=EnumMeta):
-    """Enum for resource types."""
-
-    IMAGE = "image"
-    LATENT = "latent"

invokeai/app/services/resources/resources_common.py

@@ -0,0 +1,39 @@
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+
+class ResourceType(str, Enum):
+    """The type of resource that can be associated with a board."""
+
+    IMAGE = "image"
+    VIDEO = "video"
+
+
+class ResourceIdentifier(BaseModel):
+    resource_id: str = Field(description="The id of the resource to delete")
+    resource_type: ResourceType = Field(description="The type of the resource to delete")
+
+
+class ResultWithAffectedBoards(BaseModel):
+    affected_boards: list[str] = Field(description="The ids of boards affected by the delete operation")
+
+
+class DeleteResourcesResult(ResultWithAffectedBoards):
+    deleted_resources: list[ResourceIdentifier] = Field(description="The ids of the resources that were deleted")
+
+
+class StarredResourcesResult(ResultWithAffectedBoards):
+    starred_resources: list[ResourceIdentifier] = Field(description="The resources that were starred")
+
+
+class UnstarredResourcesResult(ResultWithAffectedBoards):
+    unstarred_resources: list[ResourceIdentifier] = Field(description="The resources that were unstarred")
+
+
+class AddResourcesToBoardResult(ResultWithAffectedBoards):
+    added_resources: list[ResourceIdentifier] = Field(description="The resources that were added to the board")
+
+
+class RemoveResourcesFromBoardResult(ResultWithAffectedBoards):
+    removed_resources: list[ResourceIdentifier] = Field(description="The resources that were removed from their board")

invokeai/app/services/shared/invocation_context.py

@@ -16,6 +16,7 @@ from invokeai.app.services.image_records.image_records_common import ImageCatego
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.invocation_services import InvocationServices
 from invokeai.app.services.model_records.model_records_base import UnknownModelException
+from invokeai.app.services.resources.resources_common import ResourceType
 from invokeai.app.services.session_processor.session_processor_common import ProgressImage
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 from invokeai.app.util.step_callback import diffusion_step_callback
@@ -112,7 +113,7 @@ class BoardsInterface(InvocationContextInterface):
             board_id: The ID of the board to add the image to.
             image_name: The name of the image to add to the board.
         """
-        return self._services.board_images.add_image_to_board(board_id, image_name)
+        return self._services.board_resources.add_resource_to_board(board_id, image_name, ResourceType.IMAGE)
 
     def get_all_image_names_for_board(self, board_id: str) -> list[str]:
         """Gets all image names for a board.
@@ -123,11 +124,11 @@ class BoardsInterface(InvocationContextInterface):
         Returns:
             A list of all image names for the board.
         """
-        return self._services.board_images.get_all_board_image_names_for_board(
+        resource_ids = self._services.board_resources.get_all_board_resource_ids_for_board(
             board_id,
-            categories=None,
-            is_intermediate=None,
+            resource_type=ResourceType.IMAGE,
         )
+        return [resource_id.resource_id for resource_id in resource_ids]
 
 
 class LoggerInterface(InvocationContextInterface):

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

@@ -23,6 +23,7 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_17 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_18 import build_migration_18
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_19 import build_migration_19
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_20 import build_migration_20
+from invokeai.app.services.shared.sqlite_migrator.migrations.migration_21 import build_migration_21
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -63,6 +64,7 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_18())
     migrator.register_migration(build_migration_19(app_config=config))
     migrator.register_migration(build_migration_20())
+    migrator.register_migration(build_migration_21())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_21.py

@@ -0,0 +1,40 @@
+import sqlite3
+
+from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
+
+
+class Migration21Callback:
+    def __call__(self, cursor: sqlite3.Cursor) -> None:
+        cursor.execute(
+            """
+            CREATE TABLE client_state (
+              id          INTEGER PRIMARY KEY CHECK(id = 1),
+              data        TEXT    NOT NULL, -- Frontend will handle the shape of this data
+              updated_at  DATETIME NOT NULL DEFAULT (CURRENT_TIMESTAMP)
+            );
+            """
+        )
+        cursor.execute(
+            """
+            CREATE TRIGGER tg_client_state_updated_at
+            AFTER UPDATE ON client_state
+            FOR EACH ROW
+            BEGIN
+              UPDATE client_state
+                SET updated_at = CURRENT_TIMESTAMP
+              WHERE id = OLD.id;
+            END;
+            """
+        )
+
+
+def build_migration_21() -> Migration:
+    """Builds the migration object for migrating from version 20 to version 21. This includes:
+    - Creating the `client_state` table.
+    - Adding a trigger to update the `updated_at` field on updates.
+    """
+    return Migration(
+        from_version=20,
+        to_version=21,
+        callback=Migration21Callback(),
+    )

invokeai/app/services/video_records/video_records_base.py

@@ -0,0 +1,105 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.video_records.video_records_common import (
+    VideoNamesResult,
+    VideoRecord,
+    VideoRecordChanges,
+)
+
+
+class VideoRecordStorageBase(ABC):
+    """Low-level service responsible for interfacing with the video record store."""
+
+    @abstractmethod
+    def get(self, video_id: str) -> VideoRecord:
+        """Gets a video record."""
+        pass
+
+    @abstractmethod
+    def get_metadata(self, video_id: str) -> Optional[MetadataField]:
+        """Gets a video's metadata."""
+        pass
+
+    @abstractmethod
+    def update(
+        self,
+        video_id: str,
+        changes: VideoRecordChanges,
+    ) -> None:
+        """Updates a video record."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> OffsetPaginatedResults[VideoRecord]:
+        """Gets a page of video records."""
+        pass
+
+    @abstractmethod
+    def delete(self, video_id: str) -> None:
+        """Deletes a video record."""
+        pass
+
+    @abstractmethod
+    def delete_many(self, video_ids: list[str]) -> None:
+        """Deletes many video records."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+        video_id: str,
+        width: int,
+        height: int,
+        duration: Optional[float] = None,
+        frame_rate: Optional[float] = None,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+        metadata: Optional[str] = None,
+        workflow: Optional[str] = None,
+        graph: Optional[str] = None,
+    ) -> VideoRecord:
+        """Saves a video record."""
+        pass
+
+    @abstractmethod
+    def get_workflow(self, video_id: str) -> Optional[str]:
+        """Gets a video's workflow."""
+        pass
+
+    @abstractmethod
+    def get_graph(self, video_id: str) -> Optional[str]:
+        """Gets a video's graph."""
+        pass
+
+    @abstractmethod
+    def get_video_names(
+        self,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> VideoNamesResult:
+        """Gets video names with metadata for optimistic updates."""
+        pass
+
+    @abstractmethod
+    def get_intermediates_count(self) -> int:
+        """Gets the count of intermediate videos."""
+        pass
+
+    @abstractmethod
+    def delete_intermediates(self) -> int:
+        """Deletes all intermediate videos and returns the count of deleted videos."""
+        pass

invokeai/app/services/video_records/video_records_common.py

@@ -0,0 +1,52 @@
+import datetime
+from typing import Optional, Union
+
+from pydantic import BaseModel, Field
+
+from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
+
+VIDEO_DTO_COLS = ", ".join(
+    [
+        "videos." + c
+        for c in [
+            "id",
+            "width",
+            "height",
+            "created_at",
+            "updated_at",
+        ]
+    ]
+)
+
+
+class VideoRecord(BaseModelExcludeNull):
+    """Deserialized video record without metadata."""
+
+    id: str = Field(description="The unique id of the video.")
+    """The unique id of the video."""
+    width: int = Field(description="The width of the video in px.")
+    """The actual width of the video in px."""
+    height: int = Field(description="The height of the video in px.")
+    """The actual height of the video in px."""
+    created_at: Union[datetime.datetime, str] = Field(description="The created timestamp of the video.")
+    """The created timestamp of the video."""
+    updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the video.")
+    """The updated timestamp of the video."""
+
+
+class VideoRecordChanges(BaseModelExcludeNull):
+    """
+    A set of changes to apply to a video record.
+
+    Only limited changes are allowed:
+    - `starred` - Whether the video is starred.
+    """
+
+    starred: Optional[bool] = Field(default=None, description="Whether the video is starred.")
+    """The video's new `starred` state."""
+
+
+class VideoNamesResult(BaseModel):
+    """Result of fetching video names."""
+
+    video_ids: list[str] = Field(description="The video IDs")

invokeai/app/services/video_records/video_records_sqlite.py

@@ -0,0 +1,86 @@
+from typing import Optional
+
+from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
+from invokeai.app.services.video_records.video_records_base import VideoRecordStorageBase
+from invokeai.app.services.video_records.video_records_common import (
+    VideoNamesResult,
+    VideoRecord,
+    VideoRecordChanges,
+)
+
+
+class SqliteVideoRecordStorage(VideoRecordStorageBase):
+    def __init__(self, db: SqliteDatabase) -> None:
+        super().__init__()
+        self._db = db
+
+    def get(self, video_id: str) -> VideoRecord:
+        # For now, this is a placeholder that raises NotImplementedError
+        # In a real implementation, this would query the videos table
+        raise NotImplementedError("Video record storage not yet implemented")
+
+    def get_metadata(self, video_id: str) -> Optional[MetadataField]:
+        raise NotImplementedError("Video record storage not yet implemented")
+
+    def update(
+        self,
+        video_id: str,
+        changes: VideoRecordChanges,
+    ) -> None:
+        raise NotImplementedError("Video record storage not yet implemented")
+
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> OffsetPaginatedResults[VideoRecord]:
+        raise NotImplementedError("Video record storage not yet implemented")
+
+    def delete(self, video_id: str) -> None:
+        raise NotImplementedError("Video record storage not yet implemented")
+
+    def delete_many(self, video_ids: list[str]) -> None:
+        raise NotImplementedError("Video record storage not yet implemented")
+
+    def save(
+        self,
+        video_id: str,
+        width: int,
+        height: int,
+        duration: Optional[float] = None,
+        frame_rate: Optional[float] = None,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+        metadata: Optional[str] = None,
+        workflow: Optional[str] = None,
+        graph: Optional[str] = None,
+    ) -> VideoRecord:
+        raise NotImplementedError("Video record storage not yet implemented")
+
+    def get_workflow(self, video_id: str) -> Optional[str]:
+        raise NotImplementedError("Video record storage not yet implemented")
+
+    def get_graph(self, video_id: str) -> Optional[str]:
+        raise NotImplementedError("Video record storage not yet implemented")
+
+    def get_video_names(
+        self,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> VideoNamesResult:
+        raise NotImplementedError("Video record storage not yet implemented")
+
+    def get_intermediates_count(self) -> int:
+        return 0  # Placeholder implementation
+
+    def delete_intermediates(self) -> int:
+        return 0  # Placeholder implementation

invokeai/app/services/videos/videos_base.py

@@ -0,0 +1,151 @@
+from abc import ABC, abstractmethod
+from typing import Callable, Optional
+
+from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.video_records.video_records_common import (
+    VideoNamesResult,
+    VideoRecord,
+    VideoRecordChanges,
+)
+from invokeai.app.services.videos.videos_common import VideoDTO
+
+
+class VideoServiceABC(ABC):
+    """High-level service for video management."""
+
+    _on_changed_callbacks: list[Callable[[VideoDTO], None]]
+    _on_deleted_callbacks: list[Callable[[str], None]]
+
+    def __init__(self) -> None:
+        self._on_changed_callbacks = []
+        self._on_deleted_callbacks = []
+
+    def on_changed(self, on_changed: Callable[[VideoDTO], None]) -> None:
+        """Register a callback for when a video is changed."""
+        self._on_changed_callbacks.append(on_changed)
+
+    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
+        """Register a callback for when a video is deleted."""
+        self._on_deleted_callbacks.append(on_deleted)
+
+    def _on_changed(self, item: VideoDTO) -> None:
+        for callback in self._on_changed_callbacks:
+            callback(item)
+
+    def _on_deleted(self, item_id: str) -> None:
+        for callback in self._on_deleted_callbacks:
+            callback(item_id)
+
+    @abstractmethod
+    def create(
+        self,
+        video_id: str,
+        width: int,
+        height: int,
+        duration: Optional[float] = None,
+        frame_rate: Optional[float] = None,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+        board_id: Optional[str] = None,
+        is_intermediate: Optional[bool] = False,
+        metadata: Optional[str] = None,
+        workflow: Optional[str] = None,
+        graph: Optional[str] = None,
+    ) -> VideoDTO:
+        """Creates a video record and returns its DTO."""
+        pass
+
+    @abstractmethod
+    def update(
+        self,
+        video_id: str,
+        changes: VideoRecordChanges,
+    ) -> VideoDTO:
+        """Updates a video record and returns its DTO."""
+        pass
+
+    @abstractmethod
+    def get_record(self, video_id: str) -> VideoRecord:
+        """Gets a video record."""
+        pass
+
+    @abstractmethod
+    def get_dto(self, video_id: str) -> VideoDTO:
+        """Gets a video DTO."""
+        pass
+
+    @abstractmethod
+    def get_metadata(self, video_id: str) -> Optional[MetadataField]:
+        """Gets a video's metadata."""
+        pass
+
+    @abstractmethod
+    def get_workflow(self, video_id: str) -> Optional[str]:
+        """Gets a video's workflow."""
+        pass
+
+    @abstractmethod
+    def get_graph(self, video_id: str) -> Optional[str]:
+        """Gets a video's graph."""
+        pass
+
+    @abstractmethod
+    def get_path(self, video_id: str, thumbnail: bool = False) -> str:
+        """Gets a video's path on disk."""
+        pass
+
+    @abstractmethod
+    def validate_path(self, path: str) -> bool:
+        """Validates a video path."""
+        pass
+
+    @abstractmethod
+    def get_url(self, video_id: str, thumbnail: bool = False) -> str:
+        """Gets a video's URL."""
+        pass
+
+    @abstractmethod
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> OffsetPaginatedResults[VideoDTO]:
+        """Gets a page of video DTOs."""
+        pass
+
+    @abstractmethod
+    def delete(self, video_id: str):
+        """Deletes a video."""
+        pass
+
+    @abstractmethod
+    def delete_intermediates(self) -> int:
+        """Deletes all intermediate videos and returns the count."""
+        pass
+
+    @abstractmethod
+    def get_intermediates_count(self) -> int:
+        """Gets the count of intermediate videos."""
+        pass
+
+    @abstractmethod
+    def delete_videos_on_board(self, board_id: str):
+        """Deletes all videos on a board."""
+        pass
+
+    @abstractmethod
+    def get_video_names(
+        self,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> VideoNamesResult:
+        """Gets video names with metadata for optimistic updates."""
+        pass

invokeai/app/services/videos/videos_common.py

@@ -0,0 +1,38 @@
+from typing import Any, Literal, Optional
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from invokeai.app.services.video_records.video_records_common import VideoRecord
+
+
+def make_type_required(s: dict[str, Any]):
+    if "required" in s:
+        s["required"].append("type")
+    else:
+        s["required"] = ["type"]
+
+
+class VideoDTO(BaseModel):
+    """Deserialized video record, enriched for the frontend."""
+
+    type: Literal["video"] = Field(default="video")
+    video_id: str = Field(description="The id of the board the video belongs to, if one exists.")
+    """The id of the board the video belongs to, if one exists."""
+    width: int = Field(description="The width of the video.")
+    height: int = Field(description="The height of the video.")
+    board_id: Optional[str] = Field(
+        default=None, description="The id of the board the video belongs to, if one exists."
+    )
+
+    model_config = ConfigDict(json_schema_extra=make_type_required)
+
+
+def video_record_to_dto(
+    video_record: VideoRecord,
+    board_id: Optional[str],
+) -> VideoDTO:
+    """Converts a VideoRecord to a VideoDTO."""
+    return VideoDTO(
+        **video_record.model_dump(),
+        board_id=board_id,
+    )

invokeai/app/services/videos/videos_default.py

@@ -0,0 +1,103 @@
+from typing import Optional
+
+from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.services.invoker import Invoker
+from invokeai.app.services.shared.pagination import OffsetPaginatedResults
+from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.video_records.video_records_common import (
+    VideoNamesResult,
+    VideoRecord,
+    VideoRecordChanges,
+)
+from invokeai.app.services.videos.videos_base import VideoServiceABC
+from invokeai.app.services.videos.videos_common import VideoDTO
+
+
+class VideoService(VideoServiceABC):
+    __invoker: Invoker
+
+    def start(self, invoker: Invoker) -> None:
+        self.__invoker = invoker
+
+    def create(
+        self,
+        video_id: str,
+        width: int,
+        height: int,
+        duration: Optional[float] = None,
+        frame_rate: Optional[float] = None,
+        node_id: Optional[str] = None,
+        session_id: Optional[str] = None,
+        board_id: Optional[str] = None,
+        is_intermediate: Optional[bool] = False,
+        metadata: Optional[str] = None,
+        workflow: Optional[str] = None,
+        graph: Optional[str] = None,
+    ) -> VideoDTO:
+        # For now, this is a placeholder implementation
+        raise NotImplementedError("Video service not yet implemented")
+
+    def update(
+        self,
+        video_id: str,
+        changes: VideoRecordChanges,
+    ) -> VideoDTO:
+        raise NotImplementedError("Video service not yet implemented")
+
+    def get_record(self, video_id: str) -> VideoRecord:
+        raise NotImplementedError("Video service not yet implemented")
+
+    def get_dto(self, video_id: str) -> VideoDTO:
+        raise NotImplementedError("Video service not yet implemented")
+
+    def get_metadata(self, video_id: str) -> Optional[MetadataField]:
+        raise NotImplementedError("Video service not yet implemented")
+
+    def get_workflow(self, video_id: str) -> Optional[str]:
+        raise NotImplementedError("Video service not yet implemented")
+
+    def get_graph(self, video_id: str) -> Optional[str]:
+        raise NotImplementedError("Video service not yet implemented")
+
+    def get_path(self, video_id: str, thumbnail: bool = False) -> str:
+        raise NotImplementedError("Video service not yet implemented")
+
+    def validate_path(self, path: str) -> bool:
+        raise NotImplementedError("Video service not yet implemented")
+
+    def get_url(self, video_id: str, thumbnail: bool = False) -> str:
+        raise NotImplementedError("Video service not yet implemented")
+
+    def get_many(
+        self,
+        offset: int = 0,
+        limit: int = 10,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> OffsetPaginatedResults[VideoDTO]:
+        # Return empty results for now
+        return OffsetPaginatedResults(items=[], offset=offset, limit=limit, total=0, has_more=False)
+
+    def delete(self, video_id: str):
+        raise NotImplementedError("Video service not yet implemented")
+
+    def delete_intermediates(self) -> int:
+        return 0  # Placeholder
+
+    def get_intermediates_count(self) -> int:
+        return 0  # Placeholder
+
+    def delete_videos_on_board(self, board_id: str):
+        raise NotImplementedError("Video service not yet implemented")
+
+    def get_video_names(
+        self,
+        starred_first: bool = True,
+        order_dir: SQLiteDirection = SQLiteDirection.Descending,
+        board_id: Optional[str] = None,
+        search_term: Optional[str] = None,
+    ) -> VideoNamesResult:
+        # Return empty results for now
+        return VideoNamesResult(video_ids=[])

invokeai/backend/flux/denoise.py

@@ -112,7 +112,7 @@ def denoise(
         )
 
         # Slice prediction to only include the main image tokens
-        if img_input_ids is not None:
+        if img_cond_seq is not None:
             pred = pred[:, :original_seq_len]
 
         step_cfg_scale = cfg_scale[step_index]
@@ -125,9 +125,26 @@ def denoise(
             if neg_regional_prompting_extension is None:
                 raise ValueError("Negative text conditioning is required when cfg_scale is not 1.0.")
 
+            # For negative prediction with Kontext, we need to include the reference images
+            # to maintain consistency between positive and negative passes. Without this,
+            # CFG would create artifacts as the attention mechanism would see different
+            # spatial structures in each pass
+            neg_img_input = img
+            neg_img_input_ids = img_ids
+
+            # Add channel-wise conditioning for negative pass if present
+            if img_cond is not None:
+                neg_img_input = torch.cat((neg_img_input, img_cond), dim=-1)
+
+            # Add sequence-wise conditioning (Kontext) for negative pass
+            # This ensures reference images are processed consistently
+            if img_cond_seq is not None:
+                neg_img_input = torch.cat((neg_img_input, img_cond_seq), dim=1)
+                neg_img_input_ids = torch.cat((neg_img_input_ids, img_cond_seq_ids), dim=1)
+
             neg_pred = model(
-                img=img,
-                img_ids=img_ids,
+                img=neg_img_input,
+                img_ids=neg_img_input_ids,
                 txt=neg_regional_prompting_extension.regional_text_conditioning.t5_embeddings,
                 txt_ids=neg_regional_prompting_extension.regional_text_conditioning.t5_txt_ids,
                 y=neg_regional_prompting_extension.regional_text_conditioning.clip_embeddings,
@@ -140,6 +157,10 @@ def denoise(
                 ip_adapter_extensions=neg_ip_adapter_extensions,
                 regional_prompting_extension=neg_regional_prompting_extension,
             )
+
+            # Slice negative prediction to match main image tokens
+            if img_cond_seq is not None:
+                neg_pred = neg_pred[:, :original_seq_len]
             pred = neg_pred + step_cfg_scale * (pred - neg_pred)
 
         preview_img = img - t_curr * pred

invokeai/backend/flux/extensions/kontext_extension.py

@@ -1,15 +1,14 @@
-import einops
-import numpy as np
 import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
 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.modules.autoencoder import AutoEncoder
 from invokeai.backend.flux.sampling_utils import pack
-from invokeai.backend.flux.util import PREFERED_KONTEXT_RESOLUTIONS
+from invokeai.backend.util.devices import TorchDevice
 
 
 def generate_img_ids_with_offset(
@@ -19,8 +18,10 @@ def generate_img_ids_with_offset(
     device: torch.device,
     dtype: torch.dtype,
     idx_offset: int = 0,
+    h_offset: int = 0,
+    w_offset: int = 0,
 ) -> torch.Tensor:
-    """Generate tensor of image position ids with an optional offset.
+    """Generate tensor of image position ids with optional index and spatial offsets.
 
     Args:
         latent_height (int): Height of image in latent space (after packing, this becomes h//2).
@@ -28,7 +29,9 @@ def generate_img_ids_with_offset(
         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.
+        idx_offset (int): Offset to add to the first dimension of the image ids (default: 0).
+        h_offset (int): Spatial offset for height/y-coordinates in latent space (default: 0).
+        w_offset (int): Spatial offset for width/x-coordinates in latent space (default: 0).
 
     Returns:
         torch.Tensor: Image position ids with shape [batch_size, (latent_height//2 * latent_width//2), 3].
@@ -42,6 +45,10 @@ def generate_img_ids_with_offset(
     packed_height = latent_height // 2
     packed_width = latent_width // 2
 
+    # Convert spatial offsets from latent space to packed space
+    packed_h_offset = h_offset // 2
+    packed_w_offset = w_offset // 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)
@@ -49,13 +56,13 @@ def generate_img_ids_with_offset(
     # 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)
+    # Create y-coordinate indices (vertical positions) with spatial offset
+    y_indices = torch.arange(packed_height, device=device, dtype=dtype) + packed_h_offset
     # 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)
+    # Create x-coordinate indices (horizontal positions) with spatial offset
+    x_indices = torch.arange(packed_width, device=device, dtype=dtype) + packed_w_offset
     # Broadcast x_indices to match the spatial dimensions [1, packed_width]
     img_ids[..., 2] = x_indices[None, :]
 
@@ -73,14 +80,14 @@ class KontextExtension:
 
     def __init__(
         self,
-        kontext_conditioning: FluxKontextConditioningField,
+        kontext_conditioning: list[FluxKontextConditioningField],
         context: InvocationContext,
         vae_field: VAEField,
         device: torch.device,
         dtype: torch.dtype,
     ):
         """
-        Initializes the KontextExtension, pre-processing the reference image
+        Initializes the KontextExtension, pre-processing the reference images
         into latents and positional IDs.
         """
         self._context = context
@@ -93,54 +100,116 @@ class KontextExtension:
         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)
+        """Encodes the reference images and prepares their concatenated latents and IDs with spatial tiling."""
+        all_latents = []
+        all_ids = []
 
-        # Calculate aspect ratio of input image
-        width, height = image.size
-        aspect_ratio = width / height
+        # Track cumulative dimensions for spatial tiling
+        # These track the running extent of the virtual canvas in latent space
+        canvas_h = 0  # Running canvas height
+        canvas_w = 0  # Running canvas width
 
-        # 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
+        for idx, kontext_field in enumerate(self.kontext_conditioning):
+            image = self._context.images.get_pil(kontext_field.image.image_name)
 
-        # 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,
-        )
+            # Convert to RGB
+            image = image.convert("RGB")
 
-        return kontext_latents_packed, kontext_ids
+            # Convert to tensor using torchvision transforms for consistency
+            transformation = T.Compose(
+                [
+                    T.ToTensor(),  # Converts PIL image to tensor and scales to [0, 1]
+                ]
+            )
+            image_tensor = transformation(image)
+            # Convert from [0, 1] to [-1, 1] range expected by VAE
+            image_tensor = image_tensor * 2.0 - 1.0
+            image_tensor = image_tensor.unsqueeze(0)  # Add batch dimension
+            image_tensor = image_tensor.to(self._device)
+
+            # Continue with VAE encoding
+            # Don't sample from the distribution for reference images - use the mean (matching ComfyUI)
+            # Estimate working memory for encode operation (50% of decode memory requirements)
+            img_h = image_tensor.shape[-2]
+            img_w = image_tensor.shape[-1]
+            element_size = next(vae_info.model.parameters()).element_size()
+            scaling_constant = 1100  # 50% of decode scaling constant (2200)
+            estimated_working_memory = int(img_h * img_w * element_size * scaling_constant)
+
+            with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+                assert isinstance(vae, AutoEncoder)
+                vae_dtype = next(iter(vae.parameters())).dtype
+                image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
+                # Use sample=False to get the distribution mean without noise
+                kontext_latents_unpacked = vae.encode(image_tensor, sample=False)
+                TorchDevice.empty_cache()
+
+            # Extract tensor dimensions
+            batch_size, _, latent_height, latent_width = kontext_latents_unpacked.shape
+
+            # Pad latents to be compatible with patch_size=2
+            # This ensures dimensions are even for the pack() function
+            pad_h = (2 - latent_height % 2) % 2
+            pad_w = (2 - latent_width % 2) % 2
+            if pad_h > 0 or pad_w > 0:
+                kontext_latents_unpacked = F.pad(kontext_latents_unpacked, (0, pad_w, 0, pad_h), mode="circular")
+                # Update dimensions after padding
+                _, _, latent_height, latent_width = kontext_latents_unpacked.shape
+
+            # Pack the latents
+            kontext_latents_packed = pack(kontext_latents_unpacked).to(self._device, self._dtype)
+
+            # Determine spatial offsets for this reference image
+            h_offset = 0
+            w_offset = 0
+
+            if idx > 0:  # First image starts at (0, 0)
+                # Calculate potential canvas dimensions for each tiling option
+                # Option 1: Tile vertically (below existing content)
+                potential_h_vertical = canvas_h + latent_height
+
+                # Option 2: Tile horizontally (to the right of existing content)
+                potential_w_horizontal = canvas_w + latent_width
+
+                # Choose arrangement that minimizes the maximum dimension
+                # This keeps the canvas closer to square, optimizing attention computation
+                if potential_h_vertical > potential_w_horizontal:
+                    # Tile horizontally (to the right of existing images)
+                    w_offset = canvas_w
+                    canvas_w = canvas_w + latent_width
+                    canvas_h = max(canvas_h, latent_height)
+                else:
+                    # Tile vertically (below existing images)
+                    h_offset = canvas_h
+                    canvas_h = canvas_h + latent_height
+                    canvas_w = max(canvas_w, latent_width)
+            else:
+                # First image - just set canvas dimensions
+                canvas_h = latent_height
+                canvas_w = latent_width
+
+            # Generate IDs with both index offset and spatial offsets
+            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,  # All reference images use index=1 (matching ComfyUI implementation)
+                h_offset=h_offset,
+                w_offset=w_offset,
+            )
+
+            all_latents.append(kontext_latents_packed)
+            all_ids.append(kontext_ids)
+
+        # Concatenate all latents and IDs along the sequence dimension
+        concatenated_latents = torch.cat(all_latents, dim=1)  # Concatenate along sequence dimension
+        concatenated_ids = torch.cat(all_ids, dim=1)  # Concatenate along sequence dimension
+
+        return concatenated_latents, concatenated_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."""

invokeai/backend/model_manager/legacy_probe.py

@@ -9,6 +9,7 @@ import spandrel
 import torch
 
 import invokeai.backend.util.logging as logger
+from invokeai.app.services.config.config_default import get_config
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.flux.controlnet.state_dict_utils import (
     is_state_dict_instantx_controlnet,
@@ -493,9 +494,21 @@ class ModelProbe(object):
         # scan model
         scan_result = pscan.scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
-            raise Exception(f"The model {model_name} is potentially infected by malware. Aborting import.")
+            if get_config().unsafe_disable_picklescan:
+                logger.warning(
+                    f"The model {model_name} is potentially infected by malware, but picklescan is disabled. "
+                    "Proceeding with caution."
+                )
+            else:
+                raise RuntimeError(f"The model {model_name} is potentially infected by malware. Aborting import.")
         if scan_result.scan_err:
-            raise Exception(f"Error scanning model {model_name} for malware. Aborting import.")
+            if get_config().unsafe_disable_picklescan:
+                logger.warning(
+                    f"Error scanning the model at {model_name} for malware, but picklescan is disabled. "
+                    "Proceeding with caution."
+                )
+            else:
+                raise RuntimeError(f"Error scanning the model at {model_name} for malware. Aborting import.")
 
 
 # Probing utilities

invokeai/backend/model_manager/model_on_disk.py

@@ -6,13 +6,17 @@ import torch
 from picklescan.scanner import scan_file_path
 from safetensors import safe_open
 
+from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
+from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 StateDict: TypeAlias = dict[str | int, Any]  # When are the keys int?
 
+logger = InvokeAILogger.get_logger()
+
 
 class ModelOnDisk:
     """A utility class representing a model stored on disk."""
@@ -79,8 +83,24 @@ class ModelOnDisk:
         with SilenceWarnings():
             if path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin")):
                 scan_result = scan_file_path(path)
-                if scan_result.infected_files != 0 or scan_result.scan_err:
-                    raise RuntimeError(f"The model {path.stem} is potentially infected by malware. Aborting import.")
+                if scan_result.infected_files != 0:
+                    if get_config().unsafe_disable_picklescan:
+                        logger.warning(
+                            f"The model {path.stem} is potentially infected by malware, but picklescan is disabled. "
+                            "Proceeding with caution."
+                        )
+                    else:
+                        raise RuntimeError(
+                            f"The model {path.stem} is potentially infected by malware. Aborting import."
+                        )
+                if scan_result.scan_err:
+                    if get_config().unsafe_disable_picklescan:
+                        logger.warning(
+                            f"Error scanning the model at {path.stem} for malware, but picklescan is disabled. "
+                            "Proceeding with caution."
+                        )
+                    else:
+                        raise RuntimeError(f"Error scanning the model at {path.stem} for malware. Aborting import.")
                 checkpoint = torch.load(path, map_location="cpu")
                 assert isinstance(checkpoint, dict)
             elif path.suffix.endswith(".gguf"):

invokeai/backend/model_manager/starter_models.py

@@ -149,13 +149,29 @@ flux_kontext = StarterModel(
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
 flux_kontext_quantized = StarterModel(
-    name="FLUX.1 Kontext dev (Quantized)",
+    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],
 )
+flux_krea = StarterModel(
+    name="FLUX.1 Krea dev",
+    base=BaseModelType.Flux,
+    source="https://huggingface.co/InvokeAI/FLUX.1-Krea-dev/resolve/main/flux1-krea-dev.safetensors",
+    description="FLUX.1 Krea dev. Total size with dependencies: ~33GB",
+    type=ModelType.Main,
+    dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
+)
+flux_krea_quantized = StarterModel(
+    name="FLUX.1 Krea dev (quantized)",
+    base=BaseModelType.Flux,
+    source="https://huggingface.co/InvokeAI/FLUX.1-Krea-dev-GGUF/resolve/main/flux1-krea-dev-Q4_K_M.gguf",
+    description="FLUX.1 Krea 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,
@@ -580,13 +596,14 @@ t2i_sketch_sdxl = StarterModel(
 )
 # endregion
 # region SpandrelImageToImage
-realesrgan_anime = StarterModel(
-    name="RealESRGAN_x4plus_anime_6B",
+animesharp_v4_rcan = StarterModel(
+    name="2x-AnimeSharpV4_RCAN",
     base=BaseModelType.Any,
-    source="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
-    description="A Real-ESRGAN 4x upscaling model (optimized for anime images).",
+    source="https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV4/2x-AnimeSharpV4_RCAN.safetensors",
+    description="A 2x upscaling model (optimized for anime images).",
     type=ModelType.SpandrelImageToImage,
 )
+
 realesrgan_x4 = StarterModel(
     name="RealESRGAN_x4plus",
     base=BaseModelType.Any,
@@ -732,7 +749,7 @@ STARTER_MODELS: list[StarterModel] = [
     t2i_lineart_sdxl,
     t2i_sketch_sdxl,
     realesrgan_x4,
-    realesrgan_anime,
+    animesharp_v4_rcan,
     realesrgan_x2,
     swinir,
     t5_base_encoder,
@@ -743,6 +760,8 @@ STARTER_MODELS: list[StarterModel] = [
     llava_onevision,
     flux_fill,
     cogview4,
+    flux_krea,
+    flux_krea_quantized,
 ]
 
 sd1_bundle: list[StarterModel] = [
@@ -794,6 +813,7 @@ flux_bundle: list[StarterModel] = [
     flux_redux,
     flux_fill,
     flux_kontext_quantized,
+    flux_krea_quantized,
 ]
 
 STARTER_BUNDLES: dict[str, StarterModelBundle] = {

invokeai/backend/model_manager/util/model_util.py

@@ -8,8 +8,12 @@ import picklescan.scanner as pscan
 import safetensors
 import torch
 
+from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.model_manager.taxonomy import ClipVariantType
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
+from invokeai.backend.util.logging import InvokeAILogger
+
+logger = InvokeAILogger.get_logger()
 
 
 def _fast_safetensors_reader(path: str) -> Dict[str, torch.Tensor]:
@@ -59,9 +63,21 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = True) -> Dict[str,
         if scan:
             scan_result = pscan.scan_file_path(path)
             if scan_result.infected_files != 0:
-                raise Exception(f"The model at {path} is potentially infected by malware. Aborting import.")
+                if get_config().unsafe_disable_picklescan:
+                    logger.warning(
+                        f"The model {path} is potentially infected by malware, but picklescan is disabled. "
+                        "Proceeding with caution."
+                    )
+                else:
+                    raise RuntimeError(f"The model {path} is potentially infected by malware. Aborting import.")
             if scan_result.scan_err:
-                raise Exception(f"Error scanning model at {path} for malware. Aborting import.")
+                if get_config().unsafe_disable_picklescan:
+                    logger.warning(
+                        f"Error scanning the model at {path} for malware, but picklescan is disabled. "
+                        "Proceeding with caution."
+                    )
+                else:
+                    raise RuntimeError(f"Error scanning the model at {path} for malware. Aborting import.")
 
         checkpoint = torch.load(path, map_location=torch.device("meta"))
     return checkpoint

invokeai/backend/quantization/scripts/load_flux_model_bnb_llm_int8.py

@@ -20,7 +20,7 @@ def main():
         "/data/invokeai/models/.download_cache/https__huggingface.co_black-forest-labs_flux.1-schnell_resolve_main_flux1-schnell.safetensors/flux1-schnell.safetensors"
     )
 
-    with log_time("Intialize FLUX transformer on meta device"):
+    with log_time("Initialize FLUX transformer on meta device"):
         # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
         p = params["flux-schnell"]
 

invokeai/backend/quantization/scripts/load_flux_model_bnb_nf4.py

@@ -33,7 +33,7 @@ def main():
     )
 
     # inference_dtype = torch.bfloat16
-    with log_time("Intialize FLUX transformer on meta device"):
+    with log_time("Initialize FLUX transformer on meta device"):
         # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
         p = params["flux-schnell"]
 

invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py

@@ -27,7 +27,7 @@ def main():
     """
     model_path = Path("/data/misc/text_encoder_2")
 
-    with log_time("Intialize T5 on meta device"):
+    with log_time("Initialize T5 on meta device"):
         model_config = AutoConfig.from_pretrained(model_path)
         with accelerate.init_empty_weights():
             model = AutoModelForTextEncoding.from_config(model_config)

invokeai/frontend/web/.gitignore

@@ -44,4 +44,5 @@ yalc.lock
 
 # vitest
 tsconfig.vitest-temp.json
-coverage/
+coverage/
+*.tgz

invokeai/frontend/web/.storybook/preview.tsx

@@ -26,7 +26,7 @@ i18n.use(initReactI18next).init({
   returnNull: false,
 });
 
-const store = createStore(undefined, false);
+const store = createStore();
 $store.set(store);
 $baseUrl.set('http://localhost:9090');
 

invokeai/frontend/web/eslint.config.mjs

@@ -197,6 +197,10 @@ export default [
               importNames: ['isEqual'],
               message: 'Please use objectEquals from @observ33r/object-equals instead.',
             },
+            {
+              name: 'zod/v3',
+              message: 'Import from zod instead.',
+            },
           ],
         },
       ],

invokeai/frontend/web/knip.ts

@@ -17,6 +17,7 @@ const config: KnipConfig = {
     'src/app/store/use-debounced-app-selector.ts',
   ],
   ignoreBinaries: ['only-allow'],
+  ignoreDependencies: ['magic-string'],
   paths: {
     'public/*': ['public/*'],
   },

invokeai/frontend/web/package.json

@@ -63,7 +63,7 @@
     "framer-motion": "^11.10.0",
     "i18next": "^25.3.2",
     "i18next-http-backend": "^3.0.2",
-    "idb-keyval": "6.2.2",
+    "idb-keyval": "6.2.1",
     "jsondiffpatch": "^0.7.3",
     "konva": "^9.3.22",
     "linkify-react": "^4.3.1",
@@ -103,7 +103,7 @@
     "use-debounce": "^10.0.5",
     "use-device-pixel-ratio": "^1.1.2",
     "uuid": "^11.1.0",
-    "zod": "^4.0.5",
+    "zod": "^4.0.10",
     "zod-validation-error": "^3.5.2"
   },
   "peerDependencies": {
@@ -139,6 +139,7 @@
     "eslint-plugin-unused-imports": "^4.1.4",
     "globals": "^16.3.0",
     "knip": "^5.61.3",
+    "magic-string": "^0.30.17",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^7.6.1",
     "prettier": "^3.5.3",

invokeai/frontend/web/pnpm-lock.yaml

@@ -81,8 +81,8 @@ importers:
         specifier: ^3.0.2
         version: 3.0.2
       idb-keyval:
-        specifier: 6.2.2
-        version: 6.2.2
+        specifier: 6.2.1
+        version: 6.2.1
       jsondiffpatch:
         specifier: ^0.7.3
         version: 0.7.3
@@ -201,11 +201,11 @@ importers:
         specifier: ^11.1.0
         version: 11.1.0
       zod:
-        specifier: ^4.0.5
-        version: 4.0.5
+        specifier: ^4.0.10
+        version: 4.0.10
       zod-validation-error:
         specifier: ^3.5.2
-        version: 3.5.3(zod@4.0.5)
+        version: 3.5.3(zod@4.0.10)
     devDependencies:
       '@eslint/js':
         specifier: ^9.31.0
@@ -291,6 +291,9 @@ importers:
       knip:
         specifier: ^5.61.3
         version: 5.61.3(@types/node@22.16.0)(typescript@5.8.3)
+      magic-string:
+        specifier: ^0.30.17
+        version: 0.30.17
       openapi-types:
         specifier: ^12.1.3
         version: 12.1.3
@@ -411,6 +414,10 @@ packages:
     resolution: {integrity: sha512-vbavdySgbTTrmFE+EsiqUTzlOr5bzlnJtUv9PynGCAKvfQqjIXbvFdumPM/GxMDfyuGMJaJAU6TO4zc1Jf1i8Q==}
     engines: {node: '>=6.9.0'}
 
+  '@babel/runtime@7.28.2':
+    resolution: {integrity: sha512-KHp2IflsnGywDjBWDkR9iEqiWSpc8GIi0lgTT3mOElT0PP1tG26P4tmFI2YvAdzgq9RGyoHZQEIEdZy6Ec5xCA==}
+    engines: {node: '>=6.9.0'}
+
   '@babel/template@7.27.2':
     resolution: {integrity: sha512-LPDZ85aEJyYSd18/DkjNh4/y1ntkE5KwUHWTiqgRxruuZL2F1yuHligVHLvcHY2vMHXttKFpJn6LwfI7cw7ODw==}
     engines: {node: '>=6.9.0'}
@@ -2771,8 +2778,8 @@ packages:
       typescript:
         optional: true
 
-  idb-keyval@6.2.2:
-    resolution: {integrity: sha512-yjD9nARJ/jb1g+CvD0tlhUHOrJ9Sy0P8T9MF3YaLlHnSRpwPfpTX0XIvpmw3gAJUmEu3FiICLBDPXVwyEvrleg==}
+  idb-keyval@6.2.1:
+    resolution: {integrity: sha512-8Sb3veuYCyrZL+VBt9LJfZjLUPWVvqn8tG28VqYNFCo43KHcKuq+b4EiXGeuaLAQWL2YmyDgMp2aSpH9JHsEQg==}
 
   ieee754@1.2.1:
     resolution: {integrity: sha512-dcyqhDvX1C46lXZcVqCpK+FtMRQVdIMN6/Df5js2zouUsqG7I6sFxitIC+7KYK29KdXOLHdu9zL4sFnoVQnqaA==}
@@ -4511,8 +4518,8 @@ packages:
   zod@3.25.76:
     resolution: {integrity: sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ==}
 
-  zod@4.0.5:
-    resolution: {integrity: sha512-/5UuuRPStvHXu7RS+gmvRf4NXrNxpSllGwDnCBcJZtQsKrviYXm54yDGV2KYNLT5kq0lHGcl7lqWJLgSaG+tgA==}
+  zod@4.0.10:
+    resolution: {integrity: sha512-3vB+UU3/VmLL2lvwcY/4RV2i9z/YU0DTV/tDuYjrwmx5WeJ7hwy+rGEEx8glHp6Yxw7ibRbKSaIFBgReRPe5KA==}
 
   zustand@4.5.7:
     resolution: {integrity: sha512-CHOUy7mu3lbD6o6LJLfllpjkzhHXSBlX8B9+qPddUsIfeF5S/UZ5q0kmCsnRqT1UHFQZchNFDDzMbQsuesHWlw==}
@@ -4633,6 +4640,8 @@ snapshots:
 
   '@babel/runtime@7.27.6': {}
 
+  '@babel/runtime@7.28.2': {}
+
   '@babel/template@7.27.2':
     dependencies:
       '@babel/code-frame': 7.27.1
@@ -5736,7 +5745,7 @@ snapshots:
   '@testing-library/dom@10.4.0':
     dependencies:
       '@babel/code-frame': 7.27.1
-      '@babel/runtime': 7.27.6
+      '@babel/runtime': 7.28.2
       '@types/aria-query': 5.0.4
       aria-query: 5.3.0
       chalk: 4.1.2
@@ -7266,7 +7275,7 @@ snapshots:
     optionalDependencies:
       typescript: 5.8.3
 
-  idb-keyval@6.2.2: {}
+  idb-keyval@6.2.1: {}
 
   ieee754@1.2.1: {}
 
@@ -9062,13 +9071,13 @@ snapshots:
     dependencies:
       zod: 3.25.76
 
-  zod-validation-error@3.5.3(zod@4.0.5):
+  zod-validation-error@3.5.3(zod@4.0.10):
     dependencies:
-      zod: 4.0.5
+      zod: 4.0.10
 
   zod@3.25.76: {}
 
-  zod@4.0.5: {}
+  zod@4.0.10: {}
 
   zustand@4.5.7(@types/react@18.3.23)(immer@10.1.1)(react@18.3.1):
     dependencies:

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

@@ -1470,7 +1470,6 @@
     "ui": {
         "tabs": {
             "queue": "Warteschlange",
-            "generation": "Erzeugung",
             "gallery": "Galerie",
             "models": "Modelle",
             "upscaling": "Hochskalierung",

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

@@ -114,6 +114,9 @@
         "t2iAdapter": "T2I Adapter",
         "positivePrompt": "Positive Prompt",
         "negativePrompt": "Negative Prompt",
+        "removeNegativePrompt": "Remove Negative Prompt",
+        "addNegativePrompt": "Add Negative Prompt",
+        "selectYourModel": "Select Your Model",
         "discordLabel": "Discord",
         "dontAskMeAgain": "Don't ask me again",
         "dontShowMeThese": "Don't show me these",
@@ -610,6 +613,10 @@
                 "title": "Toggle Non-Raster Layers",
                 "desc": "Show or hide all non-raster layer categories (Control Layers, Inpaint Masks, Regional Guidance)."
             },
+            "fitBboxToLayers": {
+                "title": "Fit Bbox To Layers",
+                "desc": "Automatically adjust the generation bounding box to fit visible layers"
+            },
             "fitBboxToMasks": {
                 "title": "Fit Bbox To Masks",
                 "desc": "Automatically adjust the generation bounding box to fit visible inpaint masks"
@@ -763,6 +770,7 @@
         "allPrompts": "All Prompts",
         "cfgScale": "CFG scale",
         "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
+        "clipSkip": "$t(parameters.clipSkip)",
         "createdBy": "Created By",
         "generationMode": "Generation Mode",
         "guidance": "Guidance",
@@ -1235,7 +1243,6 @@
             "modelIncompatibleScaledBboxWidth": "Scaled bbox width is {{width}} but {{model}} requires multiple of {{multiple}}",
             "modelIncompatibleScaledBboxHeight": "Scaled bbox height is {{height}} but {{model}} requires multiple of {{multiple}}",
             "fluxModelMultipleControlLoRAs": "Can only use 1 Control LoRA at a time",
-            "fluxKontextMultipleReferenceImages": "Can only use 1 Reference Image at a time with Flux Kontext",
             "canvasIsFiltering": "Canvas is busy (filtering)",
             "canvasIsTransforming": "Canvas is busy (transforming)",
             "canvasIsRasterizing": "Canvas is busy (rasterizing)",
@@ -1283,6 +1290,7 @@
         "remixImage": "Remix Image",
         "usePrompt": "Use Prompt",
         "useSeed": "Use Seed",
+        "useClipSkip": "Use CLIP Skip",
         "width": "Width",
         "gaussianBlur": "Gaussian Blur",
         "boxBlur": "Box Blur",
@@ -2066,6 +2074,8 @@
         "asControlLayer": "As $t(controlLayers.controlLayer)",
         "asControlLayerResize": "As $t(controlLayers.controlLayer) (Resize)",
         "referenceImage": "Reference Image",
+        "maxRefImages": "Max Ref Images",
+        "useAsReferenceImage": "Use as Reference Image",
         "regionalReferenceImage": "Regional Reference Image",
         "globalReferenceImage": "Global Reference Image",
         "sendingToCanvas": "Staging Generations on Canvas",
@@ -2174,7 +2184,8 @@
             "rgReferenceImagesNotSupported": "regional Reference Images not supported for selected base model",
             "rgAutoNegativeNotSupported": "Auto-Negative not supported for selected base model",
             "rgNoRegion": "no region drawn",
-            "fluxFillIncompatibleWithControlLoRA": "Control LoRA is not compatible with FLUX Fill"
+            "fluxFillIncompatibleWithControlLoRA": "Control LoRA is not compatible with FLUX Fill",
+            "bboxHidden": "Bounding box is hidden (shift+o to toggle)"
         },
         "errors": {
             "unableToFindImage": "Unable to find image",
@@ -2533,7 +2544,7 @@
     },
     "ui": {
         "tabs": {
-            "generation": "Generation",
+            "generate": "Generate",
             "canvas": "Canvas",
             "workflows": "Workflows",
             "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
@@ -2542,8 +2553,16 @@
             "queue": "Queue",
             "upscaling": "Upscaling",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
+            "video": "Video",
             "gallery": "Gallery"
         },
+        "panels": {
+            "launchpad": "Launchpad",
+            "workflowEditor": "Workflow Editor",
+            "imageViewer": "Image Viewer",
+            "canvas": "Canvas",
+            "video": "Video"
+        },
         "launchpad": {
             "workflowsTitle": "Go deep with Workflows.",
             "upscalingTitle": "Upscale and add detail.",
@@ -2551,6 +2570,28 @@
             "generateTitle": "Generate images from text prompts.",
             "modelGuideText": "Want to learn what prompts work best for each model?",
             "modelGuideLink": "Check out our Model Guide.",
+            "createNewWorkflowFromScratch": "Create a new Workflow from scratch",
+            "browseAndLoadWorkflows": "Browse and load existing workflows",
+            "addStyleRef": {
+                "title": "Add a Style Reference",
+                "description": "Add an image to transfer its look."
+            },
+            "editImage": {
+                "title": "Edit Image",
+                "description": "Add an image to refine."
+            },
+            "generateFromText": {
+                "title": "Generate from Text",
+                "description": "Enter a prompt and Invoke."
+            },
+            "useALayoutImage": {
+                "title": "Use a Layout Image",
+                "description": "Add an image to control composition."
+            },
+            "generate": {
+                "canvasCalloutTitle": "Looking to get more control, edit, and iterate on your images?",
+                "canvasCalloutLink": "Navigate to Canvas for more capabilities."
+            },
             "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",
@@ -2587,6 +2628,13 @@
                 "upscaleModel": "Upscale Model",
                 "model": "Model",
                 "scale": "Scale",
+                "creativityAndStructure": {
+                    "title": "Creativity & Structure Defaults",
+                    "conservative": "Conservative",
+                    "balanced": "Balanced",
+                    "creative": "Creative",
+                    "artistic": "Artistic"
+                },
                 "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."
@@ -2631,10 +2679,8 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "New setting to send all Canvas generations directly to the Gallery.",
-            "New Invert Mask (Shift+V) and Fit BBox to Mask (Shift+B) capabilities.",
-            "Expanded support for Model Thumbnails and configurations.",
-            "Various other quality of life updates and fixes"
+            "Studio state is saved to the server, allowing you to continue your work on any device.",
+            "Support for multiple reference images for FLUX Kontext (local model only)."
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",

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

@@ -399,7 +399,6 @@
     "ui": {
         "tabs": {
             "canvas": "Lienzo",
-            "generation": "Generación",
             "queue": "Cola",
             "workflows": "Flujos de trabajo",
             "models": "Modelos",

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

@@ -1820,7 +1820,6 @@
             "upscaling": "Agrandissement",
             "gallery": "Galerie",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
-            "generation": "Génération",
             "workflows": "Workflows",
             "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
             "models": "Modèles",

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

@@ -128,7 +128,9 @@
         "search": "Cerca",
         "clear": "Cancella",
         "compactView": "Vista compatta",
-        "fullView": "Vista completa"
+        "fullView": "Vista completa",
+        "removeNegativePrompt": "Rimuovi prompt negativo",
+        "addNegativePrompt": "Aggiungi prompt negativo"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -254,8 +256,8 @@
                 "desc": "Attiva/disattiva il pannello destro."
             },
             "resetPanelLayout": {
-                "title": "Ripristina il layout del pannello",
-                "desc": "Ripristina le dimensioni e il layout predefiniti dei pannelli sinistro e destro."
+                "title": "Ripristina lo schema del pannello",
+                "desc": "Ripristina le dimensioni e lo schema predefiniti dei pannelli sinistro e destro."
             },
             "togglePanels": {
                 "title": "Attiva/disattiva i pannelli",
@@ -410,6 +412,10 @@
             "cancelSegmentAnything": {
                 "title": "Annulla Segment Anything",
                 "desc": "Annulla l'operazione Segment Anything corrente."
+            },
+            "fitBboxToLayers": {
+                "title": "Adatta il riquadro di delimitazione ai livelli",
+                "desc": "Regola automaticamente il riquadro di delimitazione della generazione per adattarlo ai livelli visibili"
             }
         },
         "workflows": {
@@ -539,6 +545,10 @@
             "galleryNavUpAlt": {
                 "desc": "Uguale a Naviga verso l'alto, ma seleziona l'immagine da confrontare, aprendo la modalità di confronto se non è già aperta.",
                 "title": "Naviga verso l'alto (Confronta immagine)"
+            },
+            "starImage": {
+                "desc": "Aggiungi/Rimuovi contrassegno all'immagine selezionata.",
+                "title": "Aggiungi / Rimuovi contrassegno immagine"
             }
         }
     },
@@ -794,7 +804,7 @@
             "modelIncompatibleScaledBboxWidth": "La larghezza scalata del riquadro è {{width}} ma {{model}} richiede multipli di {{multiple}}",
             "modelIncompatibleScaledBboxHeight": "L'altezza scalata del riquadro è {{height}} ma {{model}} richiede multipli di {{multiple}}",
             "modelDisabledForTrial": "La generazione con {{modelName}} non è disponibile per gli account di prova. Accedi alle impostazioni del tuo account per effettuare l'upgrade.",
-            "fluxKontextMultipleReferenceImages": "È possibile utilizzare solo 1 immagine di riferimento alla volta con Flux Kontext",
+            "fluxKontextMultipleReferenceImages": "È possibile utilizzare solo 1 immagine di riferimento alla volta con FLUX Kontext tramite BFL API",
             "promptExpansionResultPending": "Accetta o ignora il risultato dell'espansione del prompt",
             "promptExpansionPending": "Espansione del prompt in corso"
         },
@@ -1162,7 +1172,19 @@
         "unexpectedField_withName": "Campo \"{{name}}\" inaspettato",
         "missingSourceOrTargetHandle": "Identificatore del nodo sorgente o di destinazione mancante",
         "layout": {
-            "alignmentDR": "In basso a destra"
+            "alignmentDR": "In basso a destra",
+            "autoLayout": "Schema automatico",
+            "nodeSpacing": "Spaziatura nodi",
+            "layerSpacing": "Spaziatura livelli",
+            "layeringStrategy": "Strategia livelli",
+            "longestPath": "Percorso più lungo",
+            "layoutDirection": "Direzione schema",
+            "layoutDirectionRight": "A destra",
+            "layoutDirectionDown": "In basso",
+            "alignment": "Allineamento nodi",
+            "alignmentUL": "In alto a sinistra",
+            "alignmentDL": "In basso a sinistra",
+            "alignmentUR": "In alto a destra"
         }
     },
     "boards": {
@@ -1240,7 +1262,7 @@
         "batchQueuedDesc_other": "Aggiunte {{count}} sessioni a {{direction}} della coda",
         "graphQueued": "Grafico in coda",
         "batch": "Lotto",
-        "clearQueueAlertDialog": "Lo svuotamento della coda annulla immediatamente tutti gli elementi in elaborazione e cancella completamente la coda. I filtri in sospeso verranno annullati.",
+        "clearQueueAlertDialog": "La cancellazione della coda annulla immediatamente tutti gli elementi in elaborazione e cancella completamente la coda. I filtri in sospeso verranno annullati e l'area di lavoro della Tela verrà reimpostata.",
         "pending": "In attesa",
         "completedIn": "Completato in",
         "resumeFailed": "Problema nel riavvio dell'elaborazione",
@@ -1296,7 +1318,8 @@
         "retrySucceeded": "Elemento rieseguito",
         "retryItem": "Riesegui elemento",
         "retryFailed": "Problema riesecuzione elemento",
-        "credits": "Crediti"
+        "credits": "Crediti",
+        "cancelAllExceptCurrent": "Annulla tutto tranne quello corrente"
     },
     "models": {
         "noMatchingModels": "Nessun modello corrispondente",
@@ -1711,7 +1734,7 @@
         "structure": {
             "heading": "Struttura",
             "paragraphs": [
-                "La struttura determina quanto l'immagine finale rispecchierà il layout dell'originale. Una struttura bassa permette cambiamenti significativi, mentre una struttura alta conserva la composizione e il layout originali."
+                "La struttura determina quanto l'immagine finale rispecchierà lo schema dell'originale. Un valore struttura basso permette cambiamenti significativi, mentre un valore struttura alto conserva la composizione e lo schema originali."
             ]
         },
         "fluxDevLicense": {
@@ -1877,7 +1900,7 @@
         "opened": "Aperto",
         "convertGraph": "Converti grafico",
         "loadWorkflow": "$t(common.load) Flusso di lavoro",
-        "autoLayout": "Disposizione automatica",
+        "autoLayout": "Schema automatico",
         "loadFromGraph": "Carica il flusso di lavoro dal grafico",
         "userWorkflows": "Flussi di lavoro utente",
         "projectWorkflows": "Flussi di lavoro del progetto",
@@ -2478,11 +2501,12 @@
             "off": "Spento"
         },
         "invertMask": "Inverti maschera",
-        "fitBboxToMasks": "Adatta il riquadro di delimitazione alle maschere"
+        "fitBboxToMasks": "Adatta il riquadro di delimitazione alle maschere",
+        "maxRefImages": "Max Immagini di rif.to",
+        "useAsReferenceImage": "Usa come immagine di riferimento"
     },
     "ui": {
         "tabs": {
-            "generation": "Generazione",
             "canvas": "Tela",
             "workflows": "Flussi di lavoro",
             "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
@@ -2491,7 +2515,8 @@
             "queue": "Coda",
             "upscaling": "Amplia",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
-            "gallery": "Galleria"
+            "gallery": "Galleria",
+            "generate": "Genera"
         },
         "launchpad": {
             "workflowsTitle": "Approfondisci i flussi di lavoro.",
@@ -2539,8 +2564,43 @@
                 "helpText": {
                     "promptAdvice": "Durante l'ampliamento, utilizza un prompt che descriva il mezzo e lo stile. Evita di descrivere dettagli specifici del contenuto dell'immagine.",
                     "styleAdvice": "L'ampliamento funziona meglio con lo stile generale dell'immagine."
+                },
+                "creativityAndStructure": {
+                    "title": "Creatività e struttura predefinite",
+                    "conservative": "Conservativo",
+                    "balanced": "Bilanciato",
+                    "creative": "Creativo",
+                    "artistic": "Artistico"
                 }
+            },
+            "createNewWorkflowFromScratch": "Crea un nuovo flusso di lavoro da zero",
+            "browseAndLoadWorkflows": "Sfoglia e carica i flussi di lavoro esistenti",
+            "addStyleRef": {
+                "title": "Aggiungi un riferimento di stile",
+                "description": "Aggiungi un'immagine per trasferirne l'aspetto."
+            },
+            "editImage": {
+                "title": "Modifica immagine",
+                "description": "Aggiungi un'immagine da perfezionare."
+            },
+            "generateFromText": {
+                "title": "Genera da testo",
+                "description": "Inserisci un prompt e genera."
+            },
+            "useALayoutImage": {
+                "description": "Aggiungi un'immagine per controllare la composizione.",
+                "title": "Usa una immagine guida"
+            },
+            "generate": {
+                "canvasCalloutTitle": "Vuoi avere più controllo, modificare e affinare le tue immagini?",
+                "canvasCalloutLink": "Per ulteriori funzionalità, vai su Tela."
             }
+        },
+        "panels": {
+            "launchpad": "Rampa di lancio",
+            "workflowEditor": "Editor del flusso di lavoro",
+            "imageViewer": "Visualizzatore immagini",
+            "canvas": "Tela"
         }
     },
     "upscaling": {
@@ -2631,9 +2691,8 @@
         "watchRecentReleaseVideos": "Guarda i video su questa versione",
         "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
         "items": [
-            "Genera immagini più velocemente con le nuove Rampe di lancio e una scheda Genera semplificata.",
-            "Modifica con prompt utilizzando Flux Kontext Dev.",
-            "Esporta in PSD, nascondi sovrapposizioni in blocco, organizza modelli e immagini: il tutto in un'interfaccia riprogettata e pensata per il controllo."
+            "Lo stato dello studio viene salvato sul server, consentendoti di continuare a lavorare su qualsiasi dispositivo.",
+            "Supporto per più immagini di riferimento per FLUX Kontext (solo modello locale)."
         ]
     },
     "system": {

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

@@ -1783,7 +1783,6 @@
             "workflows": "ワークフロー",
             "models": "モデル",
             "gallery": "ギャラリー",
-            "generation": "生成",
             "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
             "modelsTab": "$t(ui.tabs.models) $t(common.tab)",
             "upscaling": "アップスケーリング",

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

@@ -1931,7 +1931,6 @@
     },
     "ui": {
         "tabs": {
-            "generation": "Генерация",
             "canvas": "Холст",
             "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
             "models": "Модели",

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

@@ -299,7 +299,7 @@
         "pruneTooltip": "Cắt bớt {{item_count}} mục đã hoàn tất",
         "pruneSucceeded": "Đã cắt bớt {{item_count}} mục đã hoàn tất khỏi hàng",
         "clearTooltip": "Huỷ Và Dọn Dẹp Tất Cả Mục",
-        "clearQueueAlertDialog": "Dọn dẹp hàng đợi sẽ ngay lập tức huỷ tất cả mục đang xử lý và làm sạch hàng hoàn toàn. Bộ lọc đang chờ xử lý sẽ bị huỷ bỏ.",
+        "clearQueueAlertDialog": "Dọn dẹp hàng đợi sẽ ngay lập tức huỷ tất cả mục đang xử lý và làm sạch hàng hoàn toàn. Bộ lọc đang chờ xử lý sẽ bị huỷ bỏ và Vùng Dựng Canva sẽ được khởi động lại.",
         "session": "Phiên",
         "item": "Mục",
         "resumeFailed": "Có Vấn Đề Khi Tiếp Tục Bộ Xử Lý",
@@ -343,13 +343,14 @@
         "retrySucceeded": "Mục Đã Thử Lại",
         "retryFailed": "Có Vấn Đề Khi Thử Lại Mục",
         "retryItem": "Thử Lại Mục",
-        "credits": "Nguồn"
+        "credits": "Nguồn",
+        "cancelAllExceptCurrent": "Huỷ Bỏ Tất Cả Ngoại Trừ Mục Hiện Tại"
     },
     "hotkeys": {
         "canvas": {
             "fitLayersToCanvas": {
                 "title": "Xếp Vừa Layers Vào Canvas",
-                "desc": "Căn chỉnh để góc nhìn vừa vặn với tất cả layer."
+                "desc": "Căn chỉnh để góc nhìn vừa vặn với tất cả layer nhìn thấy dược."
             },
             "setZoomTo800Percent": {
                 "desc": "Phóng to canvas lên 800%.",
@@ -473,6 +474,24 @@
             "toggleNonRasterLayers": {
                 "title": "Bật/Tắt Layer Không Thuộc Dạng Raster",
                 "desc": "Hiện hoặc ẩn tất cả layer không thuộc dạng raster (Layer Điều Khiển Được, Lớp Phủ Inpaint, Chỉ Dẫn Khu Vực)."
+            },
+            "invertMask": {
+                "title": "Đảo Ngược Lớp Phủ",
+                "desc": "Đảo ngược lớp phủ inpaint được chọn, tạo một lớp phủ mới với độ trong suốt đối nghịch."
+            },
+            "fitBboxToMasks": {
+                "title": "Xếp Vừa Hộp Giới Hạn Vào Lớp Phủ",
+                "desc": "Tự động điểu chỉnh hộp giới hạn tạo sinh vừa vặn vào lớp phủ inpaint nhìn thấy được"
+            },
+            "applySegmentAnything": {
+                "title": "Áp Dụng Segment Anything",
+                "desc": "Áp dụng lớp phủ Segment Anything hiện tại.",
+                "key": "enter"
+            },
+            "cancelSegmentAnything": {
+                "title": "Huỷ Segment Anything",
+                "desc": "Huỷ hoạt động Segment Anything hiện tại.",
+                "key": "esc"
             }
         },
         "workflows": {
@@ -602,6 +621,10 @@
             "clearSelection": {
                 "desc": "Xoá phần lựa chọn hiện tại nếu có.",
                 "title": "Xoá Phần Lựa Chọn"
+            },
+            "starImage": {
+                "title": "Dấu/Huỷ Sao Hình Ảnh",
+                "desc": "Đánh dấu sao hoặc huỷ đánh dấu sao ảnh được chọn."
             }
         },
         "app": {
@@ -661,6 +684,11 @@
             "selectModelsTab": {
                 "desc": "Chọn tab Model (Mô Hình).",
                 "title": "Chọn Tab Model"
+            },
+            "selectGenerateTab": {
+                "title": "Chọn Tab Tạo Sinh",
+                "desc": "Chọn tab Tạo Sinh.",
+                "key": "1"
             }
         },
         "searchHotkeys": "Tìm Phím tắt",
@@ -1090,7 +1118,23 @@
         "unknownField_withName": "Vùng Dữ Liệu Không Rõ \"{{name}}\"",
         "unexpectedField_withName": "Sai Vùng Dữ Liệu \"{{name}}\"",
         "unknownFieldEditWorkflowToFix_withName": "Workflow chứa vùng dữ liệu không rõ \"{{name}}\".\nHãy biên tập workflow để sửa lỗi.",
-        "missingField_withName": "Thiếu Vùng Dữ Liệu \"{{name}}\""
+        "missingField_withName": "Thiếu Vùng Dữ Liệu \"{{name}}\"",
+        "layout": {
+            "autoLayout": "Bố Cục Tự Động",
+            "layeringStrategy": "Chiến Lược Phân Layer",
+            "networkSimplex": "Network Simplex",
+            "longestPath": "Đường Đi Dài Nhất",
+            "nodeSpacing": "Khoảng Cách Node",
+            "layerSpacing": "Khoảng Cách Layer",
+            "layoutDirection": "Hướng Bố Cục",
+            "layoutDirectionRight": "Phải",
+            "layoutDirectionDown": "Xuống",
+            "alignment": "Căn Chỉnh Node",
+            "alignmentUL": "Trên Cùng Bên Trái",
+            "alignmentDL": "Dưới Cùng Bên Trái",
+            "alignmentUR": "Trên Cùng Bên Phải",
+            "alignmentDR": "Dưới Cùng Bên Phải"
+        }
     },
     "popovers": {
         "paramCFGRescaleMultiplier": {
@@ -1597,7 +1641,7 @@
             "modelIncompatibleScaledBboxHeight": "Chiều dài hộp giới hạn theo tỉ lệ là {{height}} nhưng {{model}} yêu cầu bội số của {{multiple}}",
             "modelIncompatibleScaledBboxWidth": "Chiều rộng hộp giới hạn theo tỉ lệ là {{width}} nhưng {{model}} yêu cầu bội số của {{multiple}}",
             "modelDisabledForTrial": "Tạo sinh với {{modelName}} là không thể với tài khoản trial. Vào phần thiết lập tài khoản để nâng cấp.",
-            "fluxKontextMultipleReferenceImages": "Chỉ có thể dùng 1 Ảnh Mẫu cùng lúc với Flux Kontext",
+            "fluxKontextMultipleReferenceImages": "Chỉ có thể dùng 1 Ảnh Mẫu cùng lúc với LUX Kontext thông qua BFL API",
             "promptExpansionPending": "Trong quá trình mở rộng lệnh",
             "promptExpansionResultPending": "Hãy chấp thuận hoặc huỷ bỏ kết quả mở rộng lệnh của bạn"
         },
@@ -2192,7 +2236,11 @@
             "off": "Tắt",
             "switchOnStart": "Khi Bắt Đầu",
             "switchOnFinish": "Khi Kết Thúc"
-        }
+        },
+        "fitBboxToMasks": "Xếp Vừa Hộp Giới Hạn Vào Lớp Phủ",
+        "invertMask": "Đảo Ngược Lớp Phủ",
+        "maxRefImages": "Ảnh Mẫu Tối Đa",
+        "useAsReferenceImage": "Dùng Làm Ảnh Mẫu"
     },
     "stylePresets": {
         "negativePrompt": "Lệnh Tiêu Cực",
@@ -2354,20 +2402,28 @@
         "noValidLayerAdapters": "Không có Layer Adaper Phù Hợp",
         "promptGenerationStarted": "Trình tạo sinh lệnh khởi động",
         "uploadAndPromptGenerationFailed": "Thất bại khi tải lên ảnh để tạo sinh lệnh",
-        "promptExpansionFailed": "Có vấn đề xảy ra. Hãy thử mở rộng lệnh lại."
+        "promptExpansionFailed": "Có vấn đề xảy ra. Hãy thử mở rộng lệnh lại.",
+        "maskInverted": "Đã Đảo Ngược Lớp Phủ",
+        "maskInvertFailed": "Thất Bại Khi Đảo Ngược Lớp Phủ",
+        "noVisibleMasks": "Không Có Lớp Phủ Đang Hiển Thị",
+        "noVisibleMasksDesc": "Tạo hoặc bật ít nhất một lớp phủ inpaint để đảo ngược",
+        "noInpaintMaskSelected": "Không Có Lớp Phủ Inpant Được Chọn",
+        "noInpaintMaskSelectedDesc": "Chọn một lớp phủ inpaint để đảo ngược",
+        "invalidBbox": "Hộp Giới Hạn Không Hợp Lệ",
+        "invalidBboxDesc": "Hợp giới hạn có kích thước không hợp lệ"
     },
     "ui": {
         "tabs": {
             "gallery": "Thư Viện Ảnh",
             "models": "Models",
-            "generation": "Generation (Máy Tạo Sinh)",
             "upscaling": "Upscale (Nâng Cấp Chất Lượng Hình Ảnh)",
             "canvas": "Canvas (Vùng Ảnh)",
             "upscalingTab": "$t(common.tab) $t(ui.tabs.upscaling)",
             "modelsTab": "$t(common.tab) $t(ui.tabs.models)",
             "queue": "Queue (Hàng Đợi)",
             "workflows": "Workflow (Luồng Làm Việc)",
-            "workflowsTab": "$t(common.tab) $t(ui.tabs.workflows)"
+            "workflowsTab": "$t(common.tab) $t(ui.tabs.workflows)",
+            "generate": "Tạo Sinh"
         },
         "launchpad": {
             "workflowsTitle": "Đi sâu hơn với Workflow.",
@@ -2415,8 +2471,43 @@
                     "promptAdvice": "Khi upscale, dùng lệnh để mô tả phương thức và phong cách. Tránh mô tả các chi tiết cụ thể trong ảnh.",
                     "styleAdvice": "Upscale thích hợp nhất cho phong cách chung của ảnh."
                 },
-                "scale": "Kích Thước"
+                "scale": "Kích Thước",
+                "creativityAndStructure": {
+                    "title": "Độ Sáng Tạo & Cấu Trúc Mặc Định",
+                    "conservative": "Bảo toàn",
+                    "balanced": "Cân bằng",
+                    "creative": "Sáng tạo",
+                    "artistic": "Thẩm mỹ"
+                }
+            },
+            "createNewWorkflowFromScratch": "Tạo workflow mới từ đầu",
+            "browseAndLoadWorkflows": "Duyệt và tải workflow có sẵn",
+            "addStyleRef": {
+                "title": "Thêm Phong Cách Mẫu",
+                "description": "Thêm ảnh để chuyển đổi diện mạo của nó."
+            },
+            "editImage": {
+                "title": "Biên Tập Ảnh",
+                "description": "Thêm ảnh để chỉnh sửa."
+            },
+            "generateFromText": {
+                "title": "Tạo Sinh Từ Chữ",
+                "description": "Nhập lệnh vào và Kích Hoạt."
+            },
+            "useALayoutImage": {
+                "title": "Dùng Bố Cục Ảnh",
+                "description": "Thêm ảnh để điều khiển bố cục."
+            },
+            "generate": {
+                "canvasCalloutTitle": "Đang tìm cách để điều khiển, chỉnh sửa, và làm lại ảnh?",
+                "canvasCalloutLink": "Vào Canvas cho nhiều tính năng hơn."
             }
+        },
+        "panels": {
+            "launchpad": "Launchpad",
+            "workflowEditor": "Trình Biên Tập Workflow",
+            "imageViewer": "Trình Xem Ảnh",
+            "canvas": "Canvas"
         }
     },
     "workflows": {
@@ -2588,9 +2679,8 @@
         "watchRecentReleaseVideos": "Xem Video Phát Hành Mới Nhất",
         "watchUiUpdatesOverview": "Xem Tổng Quan Về Những Cập Nhật Cho Giao Diện Người Dùng",
         "items": [
-            "Tạo sinh ảnh nhanh hơn với Launchpad và thẻ Tạo Sinh đã cơ bản hoá.",
-            "Biên tập với lệnh bằng Flux Kontext Dev.",
-            "Xuất ra file PSD, ẩn số lượng lớn lớp phủ, sắp xếp model & ảnh — tất cả cho một giao diện đã thiết kế lại để chuyên điều khiển."
+            "Trạng thái Studio được lưu vào server, giúp bạn tiếp tục công việc ở mọi thiết bị.",
+            "Hỗ trợ nhiều ảnh mẫu cho FLUX KONTEXT (chỉ cho model trên máy)."
         ]
     },
     "upsell": {

invokeai/frontend/web/public/locales/zh-CN.json

@@ -1772,7 +1772,6 @@
     },
     "ui": {
         "tabs": {
-            "generation": "生成",
             "queue": "队列",
             "canvas": "画布",
             "upscaling": "放大中",

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

@@ -3,9 +3,9 @@ import { useStore } from '@nanostores/react';
 import { GlobalHookIsolator } from 'app/components/GlobalHookIsolator';
 import { GlobalModalIsolator } from 'app/components/GlobalModalIsolator';
 import { $didStudioInit, type StudioInitAction } from 'app/hooks/useStudioInitAction';
+import { clearStorage } from 'app/store/enhancers/reduxRemember/driver';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import Loading from 'common/components/Loading/Loading';
-import { useClearStorage } from 'common/hooks/useClearStorage';
 import { AppContent } from 'features/ui/components/AppContent';
 import { memo, useCallback } from 'react';
 import { ErrorBoundary } from 'react-error-boundary';
@@ -21,13 +21,12 @@ interface Props {
 
 const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
   const didStudioInit = useStore($didStudioInit);
-  const clearStorage = useClearStorage();
 
   const handleReset = useCallback(() => {
     clearStorage();
     location.reload();
     return false;
-  }, [clearStorage]);
+  }, []);
 
   return (
     <ThemeLocaleProvider>

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

@@ -5,6 +5,7 @@ import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
 import { $didStudioInit } from 'app/hooks/useStudioInitAction';
 import type { LoggingOverrides } from 'app/logging/logger';
 import { $loggingOverrides, configureLogging } from 'app/logging/logger';
+import { addStorageListeners } from 'app/store/enhancers/reduxRemember/driver';
 import { $accountSettingsLink } from 'app/store/nanostores/accountSettingsLink';
 import { $authToken } from 'app/store/nanostores/authToken';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
@@ -35,7 +36,7 @@ import {
 import type { WorkflowCategory } from 'features/nodes/types/workflow';
 import type { ToastConfig } from 'features/toast/toast';
 import type { PropsWithChildren, ReactNode } from 'react';
-import React, { lazy, memo, useEffect, useLayoutEffect, useMemo } from 'react';
+import React, { lazy, memo, useEffect, useLayoutEffect, useState } from 'react';
 import { Provider } from 'react-redux';
 import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
 import { $socketOptions } from 'services/events/stores';
@@ -70,6 +71,7 @@ interface Props extends PropsWithChildren {
    * If provided, overrides in-app navigation to the model manager
    */
   onClickGoToModelManager?: () => void;
+  storagePersistDebounce?: number;
 }
 
 const InvokeAIUI = ({
@@ -96,7 +98,11 @@ const InvokeAIUI = ({
   loggingOverrides,
   onClickGoToModelManager,
   whatsNew,
+  storagePersistDebounce = 2000,
 }: Props) => {
+  const [store, setStore] = useState<ReturnType<typeof createStore> | undefined>(undefined);
+  const [didRehydrate, setDidRehydrate] = useState(false);
+
   useLayoutEffect(() => {
     /*
      * We need to configure logging before anything else happens - useLayoutEffect ensures we set this at the first
@@ -308,22 +314,30 @@ const InvokeAIUI = ({
     };
   }, [isDebugging]);
 
-  const store = useMemo(() => {
-    return createStore(projectId);
-  }, [projectId]);
-
   useEffect(() => {
+    const onRehydrated = () => {
+      setDidRehydrate(true);
+    };
+    const store = createStore({ persist: true, persistDebounce: storagePersistDebounce, onRehydrated });
+    setStore(store);
     $store.set(store);
     if (import.meta.env.MODE === 'development') {
       window.$store = $store;
     }
+    const removeStorageListeners = addStorageListeners();
     return () => {
+      removeStorageListeners();
+      setStore(undefined);
       $store.set(undefined);
       if (import.meta.env.MODE === 'development') {
         window.$store = undefined;
       }
     };
-  }, [store]);
+  }, [storagePersistDebounce]);
+
+  if (!store || !didRehydrate) {
+    return <Loading />;
+  }
 
   return (
     <React.StrictMode>

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

@@ -93,5 +93,7 @@ export const configureLogging = (
     localStorage.setItem('ROARR_FILTER', filter);
   }
 
-  ROARR.write = createLogWriter();
+  const styleOutput = localStorage.getItem('ROARR_STYLE_OUTPUT') === 'false' ? false : true;
+
+  ROARR.write = createLogWriter({ styleOutput });
 };

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

@@ -1,3 +1,2 @@
-export const STORAGE_PREFIX = '@@invokeai-';
 export const EMPTY_ARRAY = [];
 export const EMPTY_OBJECT = {};

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/driver.ts

@@ -1,40 +1,209 @@
+import { logger } from 'app/logging/logger';
 import { StorageError } from 'app/store/enhancers/reduxRemember/errors';
+import { $authToken } from 'app/store/nanostores/authToken';
 import { $projectId } from 'app/store/nanostores/projectId';
+import { $queueId } from 'app/store/nanostores/queueId';
 import type { UseStore } from 'idb-keyval';
-import { clear, createStore as createIDBKeyValStore, get, set } from 'idb-keyval';
-import { atom } from 'nanostores';
+import { createStore as idbCreateStore, del as idbDel, get as idbGet } from 'idb-keyval';
 import type { Driver } from 'redux-remember';
+import { serializeError } from 'serialize-error';
+import { buildV1Url, getBaseUrl } from 'services/api';
+import type { JsonObject } from 'type-fest';
 
-// Create a custom idb-keyval store (just needed to customize the name)
-const $idbKeyValStore = atom<UseStore>(createIDBKeyValStore('invoke', 'invoke-store'));
+const log = logger('system');
 
-export const clearIdbKeyValStore = () => {
-  clear($idbKeyValStore.get());
+const getUrl = (endpoint: 'get_by_key' | 'set_by_key' | 'delete', key?: string) => {
+  const baseUrl = getBaseUrl();
+  const query: Record<string, string> = {};
+  if (key) {
+    query['key'] = key;
+  }
+
+  const path = buildV1Url(`client_state/${$queueId.get()}/${endpoint}`, query);
+  const url = `${baseUrl}/${path}`;
+  return url;
 };
 
-// Create redux-remember driver, wrapping idb-keyval
-export const idbKeyValDriver: Driver = {
-  getItem: (key) => {
-    try {
-      return get(key, $idbKeyValStore.get());
-    } catch (originalError) {
-      throw new StorageError({
-        key,
-        projectId: $projectId.get(),
-        originalError,
-      });
-    }
-  },
-  setItem: (key, value) => {
-    try {
-      return set(key, value, $idbKeyValStore.get());
-    } catch (originalError) {
-      throw new StorageError({
-        key,
-        value,
-        projectId: $projectId.get(),
-        originalError,
-      });
-    }
-  },
+const getHeaders = () => {
+  const headers = new Headers();
+  const authToken = $authToken.get();
+  const projectId = $projectId.get();
+  if (authToken) {
+    headers.set('Authorization', `Bearer ${authToken}`);
+  }
+  if (projectId) {
+    headers.set('project-id', projectId);
+  }
+  return headers;
+};
+
+// Persistence happens per slice. To track when persistence is in progress, maintain a ref count, incrementing
+// it when a slice is being persisted and decrementing it when the persistence is done.
+let persistRefCount = 0;
+
+// Keep track of the last persisted state for each key to avoid unnecessary network requests.
+//
+// `redux-remember` persists individual slices of state, so we can implicity denylist a slice by not giving it a
+// persist config.
+//
+// However, we may need to avoid persisting individual _fields_ of a slice. `redux-remember` does not provide a
+// way to do this directly.
+//
+// To accomplish this, we add a layer of logic on top of the `redux-remember`. In the state serializer function
+// provided to `redux-remember`, we can omit certain fields from the state that we do not want to persist. See
+// the implementation in `store.ts` for this logic.
+//
+// This logic is unknown to `redux-remember`. When an omitted field changes, it will still attempt to persist the
+// whole slice, even if the final, _serialized_ slice value is unchanged.
+//
+// To avoid unnecessary network requests, we keep track of the last persisted state for each key in this map.
+// If the value to be persisted is the same as the last persisted value, we will skip the network request.
+const lastPersistedState = new Map<string, string | undefined>();
+
+// As of v6.3.0, we use server-backed storage for client state. This replaces the previous IndexedDB-based storage,
+// which was implemented using `idb-keyval`.
+//
+// To facilitate a smooth transition, we implement a migration strategy that attempts to retrieve values from IndexedDB
+// and persist them to the new server-backed storage. This is done on a best-effort basis.
+
+// These constants were used in the previous IndexedDB-based storage implementation.
+const IDB_DB_NAME = 'invoke';
+const IDB_STORE_NAME = 'invoke-store';
+const IDB_STORAGE_PREFIX = '@@invokeai-';
+
+// Lazy store creation
+let _idbKeyValStore: UseStore | null = null;
+const getIdbKeyValStore = () => {
+  if (_idbKeyValStore === null) {
+    _idbKeyValStore = idbCreateStore(IDB_DB_NAME, IDB_STORE_NAME);
+  }
+  return _idbKeyValStore;
+};
+
+const getIdbKey = (key: string) => {
+  return `${IDB_STORAGE_PREFIX}${key}`;
+};
+
+const getItem = async (key: string) => {
+  try {
+    const url = getUrl('get_by_key', key);
+    const headers = getHeaders();
+    const res = await fetch(url, { method: 'GET', headers });
+    if (!res.ok) {
+      throw new Error(`Response status: ${res.status}`);
+    }
+    const value = await res.json();
+
+    // Best-effort migration from IndexedDB to the new storage system
+    log.trace({ key, value }, 'Server-backed storage value retrieved');
+
+    if (!value) {
+      const idbKey = getIdbKey(key);
+      try {
+        // It's a bit tricky to query IndexedDB directly to check if value exists, so we use `idb-keyval` to do it.
+        // Thing is, `idb-keyval` requires you to create a store to query it. End result - we are creating a store
+        // even if we don't use it for anything besides checking if the key is present.
+        const idbKeyValStore = getIdbKeyValStore();
+        const idbValue = await idbGet(idbKey, idbKeyValStore);
+        if (idbValue) {
+          log.debug(
+            { key, idbKey, idbValue },
+            'No value in server-backed storage, but found value in IndexedDB - attempting migration'
+          );
+          await idbDel(idbKey, idbKeyValStore);
+          await setItem(key, idbValue);
+          log.debug({ key, idbKey, idbValue }, 'Migration successful');
+          return idbValue;
+        }
+      } catch (error) {
+        // Just log if IndexedDB retrieval fails - this is a best-effort migration.
+        log.debug(
+          { key, idbKey, error: serializeError(error) } as JsonObject,
+          'Error checking for or migrating from IndexedDB'
+        );
+      }
+    }
+
+    lastPersistedState.set(key, value);
+    log.trace({ key, last: lastPersistedState.get(key), next: value }, `Getting state for ${key}`);
+    return value;
+  } catch (originalError) {
+    throw new StorageError({
+      key,
+      projectId: $projectId.get(),
+      originalError,
+    });
+  }
+};
+
+const setItem = async (key: string, value: string) => {
+  try {
+    persistRefCount++;
+    if (lastPersistedState.get(key) === value) {
+      log.trace(
+        { key, last: lastPersistedState.get(key), next: value },
+        `Skipping persist for ${key} as value is unchanged`
+      );
+      return value;
+    }
+    log.trace({ key, last: lastPersistedState.get(key), next: value }, `Persisting state for ${key}`);
+    const url = getUrl('set_by_key', key);
+    const headers = getHeaders();
+    const res = await fetch(url, { method: 'POST', headers, body: value });
+    if (!res.ok) {
+      throw new Error(`Response status: ${res.status}`);
+    }
+    const resultValue = await res.json();
+    lastPersistedState.set(key, resultValue);
+    return resultValue;
+  } catch (originalError) {
+    throw new StorageError({
+      key,
+      value,
+      projectId: $projectId.get(),
+      originalError,
+    });
+  } finally {
+    persistRefCount--;
+    if (persistRefCount < 0) {
+      log.trace('Persist ref count is negative, resetting to 0');
+      persistRefCount = 0;
+    }
+  }
+};
+
+export const reduxRememberDriver: Driver = { getItem, setItem };
+
+export const clearStorage = async () => {
+  try {
+    persistRefCount++;
+    const url = getUrl('delete');
+    const headers = getHeaders();
+    const res = await fetch(url, { method: 'POST', headers });
+    if (!res.ok) {
+      throw new Error(`Response status: ${res.status}`);
+    }
+  } catch {
+    log.error('Failed to reset client state');
+  } finally {
+    persistRefCount--;
+    lastPersistedState.clear();
+    if (persistRefCount < 0) {
+      log.trace('Persist ref count is negative, resetting to 0');
+      persistRefCount = 0;
+    }
+  }
+};
+
+export const addStorageListeners = () => {
+  const onBeforeUnload = (e: BeforeUnloadEvent) => {
+    if (persistRefCount > 0) {
+      e.preventDefault();
+    }
+  };
+  window.addEventListener('beforeunload', onBeforeUnload);
+
+  return () => {
+    window.removeEventListener('beforeunload', onBeforeUnload);
+  };
 };

invokeai/frontend/web/src/app/store/enhancers/reduxRemember/errors.ts

@@ -33,8 +33,9 @@ export class StorageError extends Error {
   }
 }
 
+const log = logger('system');
+
 export const errorHandler = (err: PersistError | RehydrateError) => {
-  const log = logger('system');
   if (err instanceof PersistError) {
     log.error({ error: serializeError(err) }, 'Problem persisting state');
   } else if (err instanceof RehydrateError) {

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

@@ -1,73 +0,0 @@
-import type { TypedStartListening } from '@reduxjs/toolkit';
-import { addListener, createListenerMiddleware } from '@reduxjs/toolkit';
-import { addAdHocPostProcessingRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/addAdHocPostProcessingRequestedListener';
-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';
-import { addBatchEnqueuedListener } from 'app/store/middleware/listenerMiddleware/listeners/batchEnqueued';
-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 { addGetOpenAPISchemaListener } from 'app/store/middleware/listenerMiddleware/listeners/getOpenAPISchema';
-import { addImageAddedToBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageAddedToBoard';
-import { addImageRemovedFromBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageRemovedFromBoard';
-import { 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';
-import { addSetDefaultSettingsListener } from 'app/store/middleware/listenerMiddleware/listeners/setDefaultSettings';
-import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketConnected';
-import type { AppDispatch, RootState } from 'app/store/store';
-
-import { addArchivedOrDeletedBoardListener } from './listeners/addArchivedOrDeletedBoardListener';
-
-export const listenerMiddleware = createListenerMiddleware();
-
-export type AppStartListening = TypedStartListening<RootState, AppDispatch>;
-
-const startAppListening = listenerMiddleware.startListening as AppStartListening;
-
-export const addAppListener = addListener.withTypes<RootState, AppDispatch>();
-
-/**
- * The RTK listener middleware is a lightweight alternative sagas/observables.
- *
- * Most side effect logic should live in a listener.
- */
-
-// Image uploaded
-addImageUploadedFulfilledListener(startAppListening);
-
-// Image deleted
-addDeleteBoardAndImagesFulfilledListener(startAppListening);
-
-// User Invoked
-addAnyEnqueuedListener(startAppListening);
-addBatchEnqueuedListener(startAppListening);
-
-// Socket.IO
-addSocketConnectedEventListener(startAppListening);
-
-// Gallery bulk download
-addBulkDownloadListeners(startAppListening);
-
-// Boards
-addImageAddedToBoardFulfilledListener(startAppListening);
-addImageRemovedFromBoardFulfilledListener(startAppListening);
-addBoardIdSelectedListener(startAppListening);
-addArchivedOrDeletedBoardListener(startAppListening);
-
-// Node schemas
-addGetOpenAPISchemaListener(startAppListening);
-
-// Models
-addModelSelectedListener(startAppListening);
-
-// app startup
-addAppStartedListener(startAppListening);
-addModelsLoadedListener(startAppListening);
-addAppConfigReceivedListener(startAppListening);
-
-// Ad-hoc upscale workflwo
-addAdHocPostProcessingRequestedListener(startAppListening);
-
-addSetDefaultSettingsListener(startAppListening);

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

@@ -1,6 +1,6 @@
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { buildAdHocPostProcessingGraph } from 'features/nodes/util/graph/buildAdHocPostProcessingGraph';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';

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

@@ -1,5 +1,5 @@
 import { isAnyOf } from '@reduxjs/toolkit';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';
 import {
   autoAddBoardIdChanged,

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

@@ -1,4 +1,4 @@
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { queueApi, selectQueueStatus } from 'services/api/endpoints/queue';
 
 export const addAnyEnqueuedListener = (startAppListening: AppStartListening) => {

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

@@ -1,4 +1,4 @@
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { setInfillMethod } from 'features/controlLayers/store/paramsSlice';
 import { shouldUseNSFWCheckerChanged, shouldUseWatermarkerChanged } from 'features/system/store/systemSlice';
 import { appInfoApi } from 'services/api/endpoints/appInfo';

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

@@ -1,5 +1,5 @@
 import { createAction } from '@reduxjs/toolkit';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { selectLastSelectedImage } from 'features/gallery/store/gallerySelectors';
 import { imageSelected } from 'features/gallery/store/gallerySlice';
 import { imagesApi } from 'services/api/endpoints/images';

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

@@ -1,5 +1,5 @@
 import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { truncate } from 'es-toolkit/compat';
 import { zPydanticValidationError } from 'features/system/store/zodSchemas';
 import { toast } from 'features/toast/toast';

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

@@ -1,4 +1,4 @@
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { selectRefImagesSlice } from 'features/controlLayers/store/refImagesSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import { getImageUsage } from 'features/deleteImageModal/store/state';
@@ -11,7 +11,7 @@ export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppS
   startAppListening({
     matcher: imagesApi.endpoints.deleteBoardAndImages.matchFulfilled,
     effect: (action, { dispatch, getState }) => {
-      const { deleted_images } = action.payload;
+      const { deleted_resources } = action.payload;
 
       // Remove all deleted images from the UI
 
@@ -23,8 +23,8 @@ export const addDeleteBoardAndImagesFulfilledListener = (startAppListening: AppS
       const upscale = selectUpscaleSlice(state);
       const refImages = selectRefImagesSlice(state);
 
-      deleted_images.forEach((image_name) => {
-        const imageUsage = getImageUsage(nodes, canvas, upscale, refImages, image_name);
+      deleted_resources.forEach((resource_id) => {
+        const imageUsage = getImageUsage(nodes, canvas, upscale, refImages, resource_id);
 
         if (imageUsage.isNodesImage && !wasNodeEditorReset) {
           dispatch(nodeEditorReset());

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

@@ -1,5 +1,5 @@
 import { isAnyOf } from '@reduxjs/toolkit';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { selectGetImageNamesQueryArgs, selectSelectedBoardId } from 'features/gallery/store/gallerySelectors';
 import { boardIdSelected, galleryViewChanged, imageSelected } from 'features/gallery/store/gallerySlice';
 import { imagesApi } from 'services/api/endpoints/images';

invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/bulkDownload.tsx

@@ -1,5 +1,5 @@
 import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { toast } from 'features/toast/toast';
 import { t } from 'i18next';
 import { imagesApi } from 'services/api/endpoints/images';

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

@@ -1,5 +1,5 @@
 import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { parseify } from 'common/util/serialize';
 import { size } from 'es-toolkit/compat';
 import { $templates } from 'features/nodes/store/nodesSlice';

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

@@ -1,23 +0,0 @@
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { imagesApi } from 'services/api/endpoints/images';
-
-const log = logger('gallery');
-
-export const addImageAddedToBoardFulfilledListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    matcher: imagesApi.endpoints.addImageToBoard.matchFulfilled,
-    effect: (action) => {
-      const { board_id, image_name } = action.meta.arg.originalArgs;
-      log.debug({ board_id, image_name }, 'Image added to board');
-    },
-  });
-
-  startAppListening({
-    matcher: imagesApi.endpoints.addImageToBoard.matchRejected,
-    effect: (action) => {
-      const { board_id, image_name } = action.meta.arg.originalArgs;
-      log.debug({ board_id, image_name }, 'Problem adding image to board');
-    },
-  });
-};

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

@@ -1,23 +0,0 @@
-import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import { imagesApi } from 'services/api/endpoints/images';
-
-const log = logger('gallery');
-
-export const addImageRemovedFromBoardFulfilledListener = (startAppListening: AppStartListening) => {
-  startAppListening({
-    matcher: imagesApi.endpoints.removeImageFromBoard.matchFulfilled,
-    effect: (action) => {
-      const imageDTO = action.meta.arg.originalArgs;
-      log.debug({ imageDTO }, 'Image removed from board');
-    },
-  });
-
-  startAppListening({
-    matcher: imagesApi.endpoints.removeImageFromBoard.matchRejected,
-    effect: (action) => {
-      const imageDTO = action.meta.arg.originalArgs;
-      log.debug({ imageDTO }, 'Problem removing image from board');
-    },
-  });
-};