chore: bump version to v6.1.0rc1

.github/pull_request_template.md

@@ -18,6 +18,5 @@
 
 - [ ] _The PR has a short but descriptive title, suitable for a changelog_
 - [ ] _Tests added / updated (if applicable)_
-- [ ] _❗Changes to a redux slice have a corresponding migration_
 - [ ] _Documentation added / updated (if applicable)_
 - [ ] _Updated `What's New` copy (if doing a release after this PR)_

.github/workflows/build-container.yml

@@ -45,9 +45,6 @@ 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
@@ -55,11 +52,6 @@ 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/lfs-checks.yml

@@ -1,30 +0,0 @@
-# Checks that large files and LFS-tracked files are properly checked in with pointer format.
-# Uses https://github.com/ppremk/lfs-warning to detect LFS issues.
-
-name: 'lfs checks'
-
-on:
-  push:
-    branches:
-      - 'main'
-  pull_request:
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
-  merge_group:
-  workflow_dispatch:
-
-jobs:
-  lfs-check:
-    runs-on: ubuntu-latest
-    timeout-minutes: 5
-    permissions:
-      # Required to label and comment on the PRs
-      pull-requests: write
-    steps:
-      - name: checkout
-        uses: actions/checkout@v4
-
-      - name: check lfs files
-        uses: ppremk/lfs-warning@v3.3

.github/workflows/typegen-checks.yml

@@ -39,18 +39,6 @@ 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

.gitignore

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

docker/.env.sample

@@ -22,10 +22,6 @@
 ## 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

@@ -43,6 +43,7 @@ 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 \
@@ -73,18 +74,20 @@ 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 \
-    ulimit -n 30000 && \
-    uv sync --extra $GPU_DRIVER --frozen
-
-# Link amdgpu.ids for ROCm builds
-# contributed by https://github.com/Rubonnek
-RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
-    ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids" && groupadd render
+    if [ "$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"
 
+# 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"
+
 RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
 
 COPY docker/docker-entrypoint.sh ./
@@ -102,6 +105,8 @@ COPY invokeai ${INVOKEAI_SRC}/invokeai
 RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
     --mount=type=bind,source=uv.lock,target=uv.lock \
-    ulimit -n 30000 && \
-    uv pip install -e .[$GPU_DRIVER]
+    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 .
 

docker/Dockerfile-rocm-full

@@ -1,136 +0,0 @@
-# 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

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

docker/docker-entrypoint.sh

@@ -21,17 +21,6 @@ _=$(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 "$service_name"
-  docker compose --profile "$profile" logs -f
+  docker compose 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 database.
+Returns True if a model with the given key exists in the databsae.
 
 #### 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 overridden by providing a dictionary in the
+The probed values can be overriden 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 initialization routines when
+The `start` method is called by the API intialization 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/Software engineer; your friendly neighbourhood cluster-autoscaler
+- @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
 - @sunija - Standalone version
 - @brandon (Brandon Rising) - Platform, Infrastructure, Backend Systems
 - @ryanjdick (Ryan Dick) - Machine Learning & Training

docs/contributing/frontend/workflows.md

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

docs/installation/manual.md

@@ -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 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)
+7. Determine the `PyPI` index URL to use for installation, if any. This is necessary to get the right version of torch installed.
 
     === "Invoke v5.12 and later"
 
-        - 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.**
+        - If you are on Windows or Linux with an Nvidia GPU, use `https://download.pytorch.org/whl/cu128`.
+        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
+        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.2.4`.
+        - **In all other cases, do not use an index.**
 
     === "Invoke v5.10.0 to v5.11.0"
 
-        - If you are on Windows or Linux with an Nvidia GPU, use `--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`.
+        - 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`.
         - **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 `--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`.
+        - 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`.
         - **In all other cases, do not use an index.**
 
     === "Invoke v4"
 
-        - 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`.
+        - 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`.
         - **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 torch backend in the previous step, you'll need to set the backend like this:
+    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:
 
     ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --torch-backend=<VERSION> --force-reinstall
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
     ```
 
 9. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:

docs/installation/quick_start.md

@@ -33,45 +33,30 @@ Hardware requirements vary significantly depending on model and image output siz
 
 More detail on system requirements can be found [here](./requirements.md).
 
-## Step 2: Download and Set Up the Launcher
+## Step 2: Download
 
-The Launcher manages your Invoke install. Follow these instructions to download and set up the Launcher.
+Download the most recent launcher for your operating system:
 
-!!! info "Instructions for each OS"
+- [Download for Windows](https://download.invoke.ai/Invoke%20Community%20Edition.exe)
+- [Download for macOS](https://download.invoke.ai/Invoke%20Community%20Edition.dmg)
+- [Download for Linux](https://download.invoke.ai/Invoke%20Community%20Edition.AppImage)
 
-    === "Windows"
+## Step 3: Install or Update
 
-        - [Download for Windows](https://github.com/invoke-ai/launcher/releases/latest/download/Invoke.Community.Edition.Setup.latest.exe)
-        - Run the `EXE` to install the Launcher and start it.
-        - A desktop shortcut will be created; use this to run the Launcher in the future.
-        - You can delete the `EXE` file you downloaded.
-
-    === "macOS"
-
-        - [Download for macOS](https://github.com/invoke-ai/launcher/releases/latest/download/Invoke.Community.Edition-latest-arm64.dmg)
-        - Open the `DMG` and drag the app into `Applications`.
-        - Run the Launcher using its entry in `Applications`.
-        - You can delete the `DMG` file you downloaded.
-
-    === "Linux"
-
-        - [Download for Linux](https://github.com/invoke-ai/launcher/releases/latest/download/Invoke.Community.Edition-latest.AppImage)
-        - You may need to edit the `AppImage` file properties and make it executable.
-        - Optionally move the file to a location that does not require admin privileges and add a desktop shortcut for it.
-        - Run the Launcher by double-clicking the `AppImage` or the shortcut you made.
-
-## Step 3: Install Invoke
-
-Run the Launcher you just set up if you haven't already. Click **Install** and follow the instructions to install (or update) Invoke.
+Run the launcher you just downloaded, click **Install** and follow the instructions to get set up.
 
 If you have an existing Invoke installation, you can select it and let the launcher manage the install. You'll be able to update or launch the installation.
 
-!!! tip "Updating"
+!!! warning "Problem running the launcher on macOS"
 
-    The Launcher will check for updates for itself _and_ Invoke.
+    macOS may not allow you to run the launcher. We are working to resolve this by signing the launcher executable. Until that is done, you can manually flag the launcher as safe:
 
-    - When the Launcher detects an update is available for itself, you'll get a small popup window. Click through this and the Launcher will update itself.
-    - When the Launcher detects an update for Invoke, you'll see a small green alert in the Launcher. Click that and follow the instructions to update Invoke.
+    - Open the **Invoke Community Edition.dmg** file.
+    - Drag the launcher to **Applications**.
+    - Open a terminal.
+    - Run `xattr -d 'com.apple.quarantine' /Applications/Invoke\ Community\ Edition.app`.
+
+    You should now be able to run the launcher.
 
 ## Step 4: Launch
 

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).
 
-### Create Latent Noise
+### 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

@@ -10,7 +10,6 @@ from invokeai.app.services.board_images.board_images_default import BoardImagesS
 from invokeai.app.services.board_records.board_records_sqlite import SqliteBoardRecordStorage
 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
@@ -152,7 +151,6 @@ 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,
@@ -183,7 +181,6 @@ class ApiDependencies:
             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_videos.py

@@ -1,39 +0,0 @@
-from fastapi import Body, HTTPException
-from fastapi.routing import APIRouter
-
-from invokeai.app.services.videos_common import AddVideosToBoardResult, RemoveVideosFromBoardResult
-
-board_videos_router = APIRouter(prefix="/v1/board_videos", tags=["boards"])
-
-
-@board_videos_router.post(
-    "/batch",
-    operation_id="add_videos_to_board",
-    responses={
-        201: {"description": "Videos were added to board successfully"},
-    },
-    status_code=201,
-    response_model=AddVideosToBoardResult,
-)
-async def add_videos_to_board(
-    board_id: str = Body(description="The id of the board to add to"),
-    video_ids: list[str] = Body(description="The ids of the videos to add", embed=True),
-) -> AddVideosToBoardResult:
-    """Adds a list of videos to a board"""
-    raise HTTPException(status_code=501, detail="Not implemented")
-
-
-@board_videos_router.post(
-    "/batch/delete",
-    operation_id="remove_videos_from_board",
-    responses={
-        201: {"description": "Videos were removed from board successfully"},
-    },
-    status_code=201,
-    response_model=RemoveVideosFromBoardResult,
-)
-async def remove_videos_from_board(
-    video_ids: list[str] = Body(description="The ids of the videos to remove", embed=True),
-) -> RemoveVideosFromBoardResult:
-    """Removes a list of videos from their board, if they had one"""
-    raise HTTPException(status_code=501, detail="Not implemented")

invokeai/app/api/routers/client_state.py

@@ -1,58 +0,0 @@
-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/session_queue.py

@@ -7,6 +7,7 @@ from pydantic import BaseModel, Field
 from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.services.session_processor.session_processor_common import SessionProcessorStatus
 from invokeai.app.services.session_queue.session_queue_common import (
+    QUEUE_ITEM_STATUS,
     Batch,
     BatchStatus,
     CancelAllExceptCurrentResult,
@@ -17,7 +18,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     DeleteByDestinationResult,
     EnqueueBatchResult,
     FieldIdentifier,
-    ItemIdsResult,
     PruneResult,
     RetryItemsResult,
     SessionQueueCountsByDestination,
@@ -25,7 +25,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueItemNotFoundError,
     SessionQueueStatus,
 )
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.shared.pagination import CursorPaginatedResults
 
 session_queue_router = APIRouter(prefix="/v1/queue", tags=["queue"])
 
@@ -68,6 +68,36 @@ async def enqueue_batch(
         raise HTTPException(status_code=500, detail=f"Unexpected error while enqueuing batch: {e}")
 
 
+@session_queue_router.get(
+    "/{queue_id}/list",
+    operation_id="list_queue_items",
+    responses={
+        200: {"model": CursorPaginatedResults[SessionQueueItem]},
+    },
+)
+async def list_queue_items(
+    queue_id: str = Path(description="The queue id to perform this operation on"),
+    limit: int = Query(default=50, description="The number of items to fetch"),
+    status: Optional[QUEUE_ITEM_STATUS] = Query(default=None, description="The status of items to fetch"),
+    cursor: Optional[int] = Query(default=None, description="The pagination cursor"),
+    priority: int = Query(default=0, description="The pagination cursor priority"),
+    destination: Optional[str] = Query(default=None, description="The destination of queue items to fetch"),
+) -> CursorPaginatedResults[SessionQueueItem]:
+    """Gets cursor-paginated queue items"""
+
+    try:
+        return ApiDependencies.invoker.services.session_queue.list_queue_items(
+            queue_id=queue_id,
+            limit=limit,
+            status=status,
+            cursor=cursor,
+            priority=priority,
+            destination=destination,
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Unexpected error while listing all items: {e}")
+
+
 @session_queue_router.get(
     "/{queue_id}/list_all",
     operation_id="list_all_queue_items",
@@ -89,56 +119,6 @@ async def list_all_queue_items(
         raise HTTPException(status_code=500, detail=f"Unexpected error while listing all queue items: {e}")
 
 
-@session_queue_router.get(
-    "/{queue_id}/item_ids",
-    operation_id="get_queue_item_ids",
-    responses={
-        200: {"model": ItemIdsResult},
-    },
-)
-async def get_queue_item_ids(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    order_dir: SQLiteDirection = Query(default=SQLiteDirection.Descending, description="The order of sort"),
-) -> ItemIdsResult:
-    """Gets all queue item ids that match the given parameters"""
-    try:
-        return ApiDependencies.invoker.services.session_queue.get_queue_item_ids(queue_id=queue_id, order_dir=order_dir)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Unexpected error while listing all queue item ids: {e}")
-
-
-@session_queue_router.post(
-    "/{queue_id}/items_by_ids",
-    operation_id="get_queue_items_by_item_ids",
-    responses={200: {"model": list[SessionQueueItem]}},
-)
-async def get_queue_items_by_item_ids(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    item_ids: list[int] = Body(
-        embed=True, description="Object containing list of queue item ids to fetch queue items for"
-    ),
-) -> list[SessionQueueItem]:
-    """Gets queue items for the specified queue item ids. Maintains order of item ids."""
-    try:
-        session_queue_service = ApiDependencies.invoker.services.session_queue
-
-        # Fetch queue items preserving the order of requested item ids
-        queue_items: list[SessionQueueItem] = []
-        for item_id in item_ids:
-            try:
-                queue_item = session_queue_service.get_queue_item(item_id=item_id)
-                if queue_item.queue_id != queue_id:  # Auth protection for items from other queues
-                    continue
-                queue_items.append(queue_item)
-            except Exception:
-                # Skip missing queue items - they may have been deleted between item id fetch and queue item fetch
-                continue
-
-        return queue_items
-    except Exception:
-        raise HTTPException(status_code=500, detail="Failed to get queue items")
-
-
 @session_queue_router.put(
     "/{queue_id}/processor/resume",
     operation_id="resume",
@@ -374,10 +354,7 @@ async def get_queue_item(
 ) -> SessionQueueItem:
     """Gets a queue item"""
     try:
-        queue_item = ApiDependencies.invoker.services.session_queue.get_queue_item(item_id=item_id)
-        if queue_item.queue_id != queue_id:
-            raise HTTPException(status_code=404, detail=f"Queue item with id {item_id} not found in queue {queue_id}")
-        return queue_item
+        return ApiDependencies.invoker.services.session_queue.get_queue_item(item_id)
     except SessionQueueItemNotFoundError:
         raise HTTPException(status_code=404, detail=f"Queue item with id {item_id} not found in queue {queue_id}")
     except Exception as e:

invokeai/app/api/routers/videos.py

@@ -1,119 +0,0 @@
-from typing import Optional
-
-from fastapi import Body, HTTPException, Path, Query
-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.videos_common import (
-    DeleteVideosResult,
-    StarredVideosResult,
-    UnstarredVideosResult,
-    VideoDTO,
-    VideoIdsResult,
-    VideoRecordChanges,
-)
-
-videos_router = APIRouter(prefix="/v1/videos", tags=["videos"])
-
-
-@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 HTTPException(status_code=501, detail="Not implemented")
-
-
-@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 HTTPException(status_code=501, detail="Not implemented")
-
-
-@videos_router.post("/delete", operation_id="delete_videos_from_list", response_model=DeleteVideosResult)
-async def delete_videos_from_list(
-    video_ids: list[str] = Body(description="The list of ids of videos to delete", embed=True),
-) -> DeleteVideosResult:
-    raise HTTPException(status_code=501, detail="Not implemented")
-
-
-@videos_router.post("/star", operation_id="star_videos_in_list", response_model=StarredVideosResult)
-async def star_videos_in_list(
-    video_ids: list[str] = Body(description="The list of ids of videos to star", embed=True),
-) -> StarredVideosResult:
-    raise HTTPException(status_code=501, detail="Not implemented")
-
-
-@videos_router.post("/unstar", operation_id="unstar_videos_in_list", response_model=UnstarredVideosResult)
-async def unstar_videos_in_list(
-    video_ids: list[str] = Body(description="The list of ids of videos to unstar", embed=True),
-) -> UnstarredVideosResult:
-    raise HTTPException(status_code=501, detail="Not implemented")
-
-
-@videos_router.delete("/uncategorized", operation_id="delete_uncategorized_videos", response_model=DeleteVideosResult)
-async def delete_uncategorized_videos() -> DeleteVideosResult:
-    """Deletes all videos that are uncategorized"""
-
-    raise HTTPException(status_code=501, detail="Not implemented")
-
-
-@videos_router.get("/", operation_id="list_video_dtos", response_model=OffsetPaginatedResults[VideoDTO])
-async def list_video_dtos(
-    is_intermediate: Optional[bool] = Query(default=None, description="Whether to list intermediate videos."),
-    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]:
-    """Lists video DTOs"""
-
-    raise HTTPException(status_code=501, detail="Not implemented")
-
-
-@videos_router.get("/ids", operation_id="get_video_ids")
-async def get_video_ids(
-    is_intermediate: Optional[bool] = Query(default=None, description="Whether to list intermediate videos."),
-    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"),
-) -> VideoIdsResult:
-    """Gets ordered list of video ids with metadata for optimistic updates"""
-
-    raise HTTPException(status_code=501, detail="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 HTTPException(status_code=501, detail="Not implemented")

invokeai/app/api_app.py

@@ -18,9 +18,7 @@ from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.app.api.routers import (
     app_info,
     board_images,
-    board_videos,
     boards,
-    client_state,
     download_queue,
     images,
     model_manager,
@@ -28,7 +26,6 @@ from invokeai.app.api.routers import (
     session_queue,
     style_presets,
     utilities,
-    videos,
     workflows,
 )
 from invokeai.app.api.sockets import SocketIO
@@ -127,16 +124,13 @@ 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(boards.boards_router, prefix="/api")
 app.include_router(board_images.board_images_router, prefix="/api")
-app.include_router(board_videos.board_videos_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)
 
@@ -161,12 +155,6 @@ 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

@@ -17,7 +17,6 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_cogview4
 
 # TODO(ryand): This is effectively a copy of SD3ImageToLatentsInvocation and a subset of ImageToLatentsInvocation. We
 # should refactor to avoid this duplication.
@@ -39,11 +38,7 @@ class CogView4ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     @staticmethod
     def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
-        assert isinstance(vae_info.model, AutoencoderKL)
-        estimated_working_memory = estimate_vae_working_memory_cogview4(
-            operation="encode", image_tensor=image_tensor, vae=vae_info.model
-        )
-        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+        with vae_info as vae:
             assert isinstance(vae, AutoencoderKL)
 
             vae.disable_tiling()
@@ -67,8 +62,6 @@ 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)
-        assert isinstance(vae_info.model, AutoencoderKL)
-
         latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")

invokeai/app/invocations/cogview4_latents_to_image.py

@@ -6,6 +6,7 @@ from einops import rearrange
 from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -19,7 +20,6 @@ from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
 from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_cogview4
 
 # TODO(ryand): This is effectively a copy of SD3LatentsToImageInvocation and a subset of LatentsToImageInvocation. We
 # should refactor to avoid this duplication.
@@ -39,15 +39,22 @@ class CogView4LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     latents: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection)
     vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection)
 
+    def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoencoderKL) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
+        out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
+        element_size = next(vae.parameters()).element_size()
+        scaling_constant = 2200  # Determined experimentally.
+        working_memory = out_h * out_w * element_size * scaling_constant
+        return int(working_memory)
+
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL))
-        estimated_working_memory = estimate_vae_working_memory_cogview4(
-            operation="decode", image_tensor=latents, vae=vae_info.model
-        )
+        estimated_working_memory = self._estimate_working_memory(latents, vae_info.model)
         with (
             SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes),
             vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae),

invokeai/app/invocations/fields.py

@@ -1,12 +1,11 @@
 from enum import Enum
 from typing import Any, Callable, Optional, Tuple
 
-from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter
+from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, model_validator
 from pydantic.fields import _Unset
 from pydantic_core import PydanticUndefined
 
 from invokeai.app.util.metaenum import MetaEnum
-from invokeai.backend.image_util.segment_anything.shared import BoundingBox
 from invokeai.backend.util.logging import InvokeAILogger
 
 logger = InvokeAILogger.get_logger()
@@ -65,16 +64,12 @@ class UIType(str, Enum, metaclass=MetaEnum):
     Imagen3Model = "Imagen3ModelField"
     Imagen4Model = "Imagen4ModelField"
     ChatGPT4oModel = "ChatGPT4oModelField"
-    Gemini2_5Model = "Gemini2_5ModelField"
     FluxKontextModel = "FluxKontextModelField"
-    Veo3Model = "Veo3ModelField"
-    RunwayModel = "RunwayModelField"
     # endregion
 
     # region Misc Field Types
     Scheduler = "SchedulerField"
     Any = "AnyField"
-    Video = "VideoField"
     # endregion
 
     # region Internal Field Types
@@ -229,12 +224,6 @@ class ImageField(BaseModel):
     image_name: str = Field(description="The name of the image")
 
 
-class VideoField(BaseModel):
-    """A video primitive field"""
-
-    video_id: str = Field(description="The id of the video")
-
-
 class BoardField(BaseModel):
     """A board primitive field"""
 
@@ -332,9 +321,14 @@ class ConditioningField(BaseModel):
     )
 
 
-class BoundingBoxField(BoundingBox):
+class BoundingBoxField(BaseModel):
     """A bounding box primitive value."""
 
+    x_min: int = Field(ge=0, description="The minimum x-coordinate of the bounding box (inclusive).")
+    x_max: int = Field(ge=0, description="The maximum x-coordinate of the bounding box (exclusive).")
+    y_min: int = Field(ge=0, description="The minimum y-coordinate of the bounding box (inclusive).")
+    y_max: int = Field(ge=0, description="The maximum y-coordinate of the bounding box (exclusive).")
+
     score: Optional[float] = Field(
         default=None,
         ge=0.0,
@@ -343,6 +337,21 @@ class BoundingBoxField(BoundingBox):
         "when the bounding box was produced by a detector and has an associated confidence score.",
     )
 
+    @model_validator(mode="after")
+    def check_coords(self):
+        if self.x_min > self.x_max:
+            raise ValueError(f"x_min ({self.x_min}) is greater than x_max ({self.x_max}).")
+        if self.y_min > self.y_max:
+            raise ValueError(f"y_min ({self.y_min}) is greater than y_max ({self.y_max}).")
+        return self
+
+    def tuple(self) -> Tuple[int, int, int, int]:
+        """
+        Returns the bounding box as a tuple suitable for use with PIL's `Image.crop()` method.
+        This method returns a tuple of the form (left, upper, right, lower) == (x_min, y_min, x_max, y_max).
+        """
+        return (self.x_min, self.y_min, self.x_max, self.y_max)
+
 
 class MetadataField(RootModel[dict[str, Any]]):
     """

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="4.1.0",
+    version="4.0.0",
 )
 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: FluxKontextConditioningField | list[FluxKontextConditioningField] | None = InputField(
+    kontext_conditioning: Optional[FluxKontextConditioningField] = InputField(
         default=None,
         description="FLUX Kontext conditioning (reference image).",
         input=Input.Connection,
@@ -328,21 +328,6 @@ 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).
@@ -400,6 +385,19 @@ 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_decode.py

@@ -3,6 +3,7 @@ from einops import rearrange
 from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -17,7 +18,6 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_flux
 
 
 @invocation(
@@ -39,11 +39,17 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
     )
 
+    def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoEncoder) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
+        out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
+        element_size = next(vae.parameters()).element_size()
+        scaling_constant = 2200  # Determined experimentally.
+        working_memory = out_h * out_w * element_size * scaling_constant
+        return int(working_memory)
+
     def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
-        assert isinstance(vae_info.model, AutoEncoder)
-        estimated_working_memory = estimate_vae_working_memory_flux(
-            operation="decode", image_tensor=latents, vae=vae_info.model
-        )
+        estimated_working_memory = self._estimate_working_memory(latents, vae_info.model)
         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

invokeai/app/invocations/flux_vae_encode.py

@@ -15,7 +15,6 @@ from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.model_manager import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_flux
 
 
 @invocation(
@@ -42,12 +41,8 @@ class FluxVaeEncodeInvocation(BaseInvocation):
         # 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.
-        assert isinstance(vae_info.model, AutoEncoder)
-        estimated_working_memory = estimate_vae_working_memory_flux(
-            operation="encode", image_tensor=image_tensor, vae=vae_info.model
-        )
         generator = torch.Generator(device=TorchDevice.choose_torch_device()).manual_seed(0)
-        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+        with vae_info 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)

invokeai/app/invocations/image.py

@@ -1347,96 +1347,3 @@ 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

@@ -27,7 +27,6 @@ from invokeai.backend.model_manager import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
 from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_sd15_sdxl
 
 
 @invocation(
@@ -53,24 +52,11 @@ 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)
 
-    @classmethod
+    @staticmethod
     def vae_encode(
-        cls,
-        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
     ) -> torch.Tensor:
-        assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
-        estimated_working_memory = estimate_vae_working_memory_sd15_sdxl(
-            operation="encode",
-            image_tensor=image_tensor,
-            vae=vae_info.model,
-            tile_size=tile_size if tiled else None,
-            fp32=upcast,
-        )
-        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+        with vae_info as vae:
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             orig_dtype = vae.dtype
             if upcast:
@@ -127,7 +113,6 @@ 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:
@@ -135,11 +120,7 @@ class ImageToLatentsInvocation(BaseInvocation):
 
         context.util.signal_progress("Running VAE encoder")
         latents = self.vae_encode(
-            vae_info=vae_info,
-            upcast=self.fp32,
-            tiled=self.tiled or context.config.get().force_tiled_decode,
-            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
         )
 
         latents = latents.to("cpu")

invokeai/app/invocations/latents_to_image.py

@@ -27,7 +27,6 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
 from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
 from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_sd15_sdxl
 
 
 @invocation(
@@ -54,6 +53,39 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     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, latents: torch.Tensor, use_tiling: bool, vae: AutoencoderKL | AutoencoderTiny
+    ) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        # It was found experimentally that the peak working memory scales linearly with the number of pixels and the
+        # element size (precision). This estimate is accurate for both SD1 and SDXL.
+        element_size = 4 if self.fp32 else 2
+        scaling_constant = 2200  # Determined experimentally.
+
+        if use_tiling:
+            tile_size = self.tile_size
+            if tile_size == 0:
+                tile_size = vae.tile_sample_min_size
+                assert isinstance(tile_size, int)
+            out_h = tile_size
+            out_w = tile_size
+            working_memory = out_h * out_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:
+            out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
+            out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
+            working_memory = out_h * out_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)
+
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
@@ -62,13 +94,8 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
-        estimated_working_memory = estimate_vae_working_memory_sd15_sdxl(
-            operation="decode",
-            image_tensor=latents,
-            vae=vae_info.model,
-            tile_size=self.tile_size if use_tiling else None,
-            fp32=self.fp32,
-        )
+
+        estimated_working_memory = self._estimate_working_memory(latents, use_tiling, vae_info.model)
         with (
             SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes),
             vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae),

invokeai/app/invocations/primitives.py

@@ -27,7 +27,6 @@ from invokeai.app.invocations.fields import (
     SD3ConditioningField,
     TensorField,
     UIComponent,
-    VideoField,
 )
 from invokeai.app.services.images.images_common import ImageDTO
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -288,30 +287,6 @@ class ImageCollectionInvocation(BaseInvocation):
         return ImageCollectionOutput(collection=self.collection)
 
 
-# endregion
-
-# region Video
-
-
-@invocation_output("video_output")
-class VideoOutput(BaseInvocationOutput):
-    """Base class for nodes that output a video"""
-
-    video: VideoField = OutputField(description="The output video")
-    width: int = OutputField(description="The width of the video in pixels")
-    height: int = OutputField(description="The height of the video in pixels")
-    duration_seconds: float = OutputField(description="The duration of the video in seconds")
-
-    @classmethod
-    def build(cls, video_id: str, width: int, height: int, duration_seconds: float) -> "VideoOutput":
-        return cls(
-            video=VideoField(video_id=video_id),
-            width=width,
-            height=height,
-            duration_seconds=duration_seconds,
-        )
-
-
 # endregion
 
 # region DenoiseMask

invokeai/app/invocations/sd3_image_to_latents.py

@@ -17,7 +17,6 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_sd3
 
 
 @invocation(
@@ -35,11 +34,7 @@ class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     @staticmethod
     def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
-        assert isinstance(vae_info.model, AutoencoderKL)
-        estimated_working_memory = estimate_vae_working_memory_sd3(
-            operation="encode", image_tensor=image_tensor, vae=vae_info.model
-        )
-        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+        with vae_info as vae:
             assert isinstance(vae, AutoencoderKL)
 
             vae.disable_tiling()
@@ -63,8 +58,6 @@ 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)
-        assert isinstance(vae_info.model, AutoencoderKL)
-
         latents = self.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
         latents = latents.to("cpu")

invokeai/app/invocations/sd3_latents_to_image.py

@@ -6,6 +6,7 @@ from einops import rearrange
 from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -19,7 +20,6 @@ from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.stable_diffusion.extensions.seamless import SeamlessExt
 from invokeai.backend.util.devices import TorchDevice
-from invokeai.backend.util.vae_working_memory import estimate_vae_working_memory_sd3
 
 
 @invocation(
@@ -41,15 +41,22 @@ class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
     )
 
+    def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoencoderKL) -> int:
+        """Estimate the working memory required by the invocation in bytes."""
+        out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
+        out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
+        element_size = next(vae.parameters()).element_size()
+        scaling_constant = 2200  # Determined experimentally.
+        working_memory = out_h * out_w * element_size * scaling_constant
+        return int(working_memory)
+
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL))
-        estimated_working_memory = estimate_vae_working_memory_sd3(
-            operation="decode", image_tensor=latents, vae=vae_info.model
-        )
+        estimated_working_memory = self._estimate_working_memory(latents, vae_info.model)
         with (
             SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes),
             vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae),

invokeai/app/invocations/segment_anything.py

@@ -1,75 +1,72 @@
-from itertools import zip_longest
+from enum import Enum
 from pathlib import Path
 from typing import Literal
 
 import numpy as np
 import torch
 from PIL import Image
-from pydantic import BaseModel, Field, model_validator
+from pydantic import BaseModel, Field
+from transformers import AutoProcessor
 from transformers.models.sam import SamModel
 from transformers.models.sam.processing_sam import SamProcessor
-from transformers.models.sam2 import Sam2Model
-from transformers.models.sam2.processing_sam2 import Sam2Processor
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import BoundingBoxField, ImageField, InputField, TensorField
 from invokeai.app.invocations.primitives import MaskOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.image_util.segment_anything.mask_refinement import mask_to_polygon, polygon_to_mask
-from invokeai.backend.image_util.segment_anything.segment_anything_2_pipeline import SegmentAnything2Pipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
-from invokeai.backend.image_util.segment_anything.shared import SAMInput, SAMPoint
 
-SegmentAnythingModelKey = Literal[
-    "segment-anything-base",
-    "segment-anything-large",
-    "segment-anything-huge",
-    "segment-anything-2-tiny",
-    "segment-anything-2-small",
-    "segment-anything-2-base",
-    "segment-anything-2-large",
-]
+SegmentAnythingModelKey = Literal["segment-anything-base", "segment-anything-large", "segment-anything-huge"]
 SEGMENT_ANYTHING_MODEL_IDS: dict[SegmentAnythingModelKey, str] = {
     "segment-anything-base": "facebook/sam-vit-base",
     "segment-anything-large": "facebook/sam-vit-large",
     "segment-anything-huge": "facebook/sam-vit-huge",
-    "segment-anything-2-tiny": "facebook/sam2.1-hiera-tiny",
-    "segment-anything-2-small": "facebook/sam2.1-hiera-small",
-    "segment-anything-2-base": "facebook/sam2.1-hiera-base-plus",
-    "segment-anything-2-large": "facebook/sam2.1-hiera-large",
 }
 
 
-class SAMPointsField(BaseModel):
-    points: list[SAMPoint] = Field(..., description="The points of the object", min_length=1)
+class SAMPointLabel(Enum):
+    negative = -1
+    neutral = 0
+    positive = 1
 
-    def to_list(self) -> list[list[float]]:
+
+class SAMPoint(BaseModel):
+    x: int = Field(..., description="The x-coordinate of the point")
+    y: int = Field(..., description="The y-coordinate of the point")
+    label: SAMPointLabel = Field(..., description="The label of the point")
+
+
+class SAMPointsField(BaseModel):
+    points: list[SAMPoint] = Field(..., description="The points of the object")
+
+    def to_list(self) -> list[list[int]]:
         return [[point.x, point.y, point.label.value] for point in self.points]
 
 
 @invocation(
     "segment_anything",
     title="Segment Anything",
-    tags=["prompt", "segmentation", "sam", "sam2"],
+    tags=["prompt", "segmentation"],
     category="segmentation",
-    version="1.3.0",
+    version="1.2.0",
 )
 class SegmentAnythingInvocation(BaseInvocation):
-    """Runs a Segment Anything Model (SAM or SAM2)."""
+    """Runs a Segment Anything Model."""
 
     # Reference:
     # - https://arxiv.org/pdf/2304.02643
     # - https://huggingface.co/docs/transformers/v4.43.3/en/model_doc/grounding-dino#grounded-sam
     # - https://github.com/NielsRogge/Transformers-Tutorials/blob/a39f33ac1557b02ebfb191ea7753e332b5ca933f/Grounding%20DINO/GroundingDINO_with_Segment_Anything.ipynb
 
-    model: SegmentAnythingModelKey = InputField(description="The Segment Anything model to use (SAM or SAM2).")
+    model: SegmentAnythingModelKey = InputField(description="The Segment Anything model to use.")
     image: ImageField = InputField(description="The image to segment.")
     bounding_boxes: list[BoundingBoxField] | None = InputField(
-        default=None, description="The bounding boxes to prompt the model with."
+        default=None, description="The bounding boxes to prompt the SAM model with."
     )
     point_lists: list[SAMPointsField] | None = InputField(
         default=None,
-        description="The list of point lists to prompt the model with. Each list of points represents a single object.",
+        description="The list of point lists to prompt the SAM model with. Each list of points represents a single object.",
     )
     apply_polygon_refinement: bool = InputField(
         description="Whether to apply polygon refinement to the masks. This will smooth the edges of the masks slightly and ensure that each mask consists of a single closed polygon (before merging).",
@@ -80,18 +77,14 @@ class SegmentAnythingInvocation(BaseInvocation):
         default="all",
     )
 
-    @model_validator(mode="after")
-    def validate_points_and_boxes_len(self):
-        if self.point_lists is not None and self.bounding_boxes is not None:
-            if len(self.point_lists) != len(self.bounding_boxes):
-                raise ValueError("If both point_lists and bounding_boxes are provided, they must have the same length.")
-        return self
-
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> MaskOutput:
         # The models expect a 3-channel RGB image.
         image_pil = context.images.get_pil(self.image.image_name, mode="RGB")
 
+        if self.point_lists is not None and self.bounding_boxes is not None:
+            raise ValueError("Only one of point_lists or bounding_box can be provided.")
+
         if (not self.bounding_boxes or len(self.bounding_boxes) == 0) and (
             not self.point_lists or len(self.point_lists) == 0
         ):
@@ -118,38 +111,26 @@ class SegmentAnythingInvocation(BaseInvocation):
             # model, and figure out how to make it work in the pipeline.
             # torch_dtype=TorchDevice.choose_torch_dtype(),
         )
-        sam_processor = SamProcessor.from_pretrained(model_path, local_files_only=True)
+
+        sam_processor = AutoProcessor.from_pretrained(model_path, local_files_only=True)
+        assert isinstance(sam_processor, SamProcessor)
         return SegmentAnythingPipeline(sam_model=sam_model, sam_processor=sam_processor)
 
-    @staticmethod
-    def _load_sam_2_model(model_path: Path):
-        sam2_model = Sam2Model.from_pretrained(model_path, local_files_only=True)
-        sam2_processor = Sam2Processor.from_pretrained(model_path, local_files_only=True)
-        return SegmentAnything2Pipeline(sam2_model=sam2_model, sam2_processor=sam2_processor)
-
     def _segment(self, context: InvocationContext, image: Image.Image) -> list[torch.Tensor]:
-        """Use Segment Anything (SAM or SAM2) to generate masks given an image + a set of bounding boxes."""
+        """Use Segment Anything (SAM) to generate masks given an image + a set of bounding boxes."""
+        # Convert the bounding boxes to the SAM input format.
+        sam_bounding_boxes = (
+            [[bb.x_min, bb.y_min, bb.x_max, bb.y_max] for bb in self.bounding_boxes] if self.bounding_boxes else None
+        )
+        sam_points = [p.to_list() for p in self.point_lists] if self.point_lists else None
 
-        source = SEGMENT_ANYTHING_MODEL_IDS[self.model]
-        inputs: list[SAMInput] = []
-        for bbox_field, point_field in zip_longest(self.bounding_boxes or [], self.point_lists or [], fillvalue=None):
-            inputs.append(
-                SAMInput(
-                    bounding_box=bbox_field,
-                    points=point_field.points if point_field else None,
-                )
-            )
-
-        if "sam2" in source:
-            loader = SegmentAnythingInvocation._load_sam_2_model
-            with context.models.load_remote_model(source=source, loader=loader) as pipeline:
-                assert isinstance(pipeline, SegmentAnything2Pipeline)
-                masks = pipeline.segment(image=image, inputs=inputs)
-        else:
-            loader = SegmentAnythingInvocation._load_sam_model
-            with context.models.load_remote_model(source=source, loader=loader) as pipeline:
-                assert isinstance(pipeline, SegmentAnythingPipeline)
-                masks = pipeline.segment(image=image, inputs=inputs)
+        with (
+            context.models.load_remote_model(
+                source=SEGMENT_ANYTHING_MODEL_IDS[self.model], loader=SegmentAnythingInvocation._load_sam_model
+            ) as sam_pipeline,
+        ):
+            assert isinstance(sam_pipeline, SegmentAnythingPipeline)
+            masks = sam_pipeline.segment(image=image, bounding_boxes=sam_bounding_boxes, point_lists=sam_points)
 
         masks = self._process_masks(masks)
         if self.apply_polygon_refinement:

invokeai/app/services/board_image_records/board_image_records_base.py

@@ -49,11 +49,3 @@ class BoardImageRecordStorageBase(ABC):
     ) -> int:
         """Gets the number of images for a board."""
         pass
-
-    @abstractmethod
-    def get_asset_count_for_board(
-        self,
-        board_id: str,
-    ) -> int:
-        """Gets the number of assets for a board."""
-        pass

invokeai/app/services/board_image_records/board_image_records_sqlite.py

@@ -3,8 +3,6 @@ from typing import Optional, cast
 
 from invokeai.app.services.board_image_records.board_image_records_base import BoardImageRecordStorageBase
 from invokeai.app.services.image_records.image_records_common import (
-    ASSETS_CATEGORIES,
-    IMAGE_CATEGORIES,
     ImageCategory,
     ImageRecord,
     deserialize_image_record,
@@ -153,38 +151,15 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
 
     def get_image_count_for_board(self, board_id: str) -> int:
         with self._db.transaction() as cursor:
-            # Convert the enum values to unique list of strings
-            category_strings = [c.value for c in set(IMAGE_CATEGORIES)]
-            # Create the correct length of placeholders
-            placeholders = ",".join("?" * len(category_strings))
             cursor.execute(
-                f"""--sql
+                """--sql
                     SELECT COUNT(*)
                     FROM board_images
                     INNER JOIN images ON board_images.image_name = images.image_name
-                    WHERE images.is_intermediate = FALSE AND images.image_category IN ( {placeholders} )
+                    WHERE images.is_intermediate = FALSE
                     AND board_images.board_id = ?;
                     """,
-                (*category_strings, board_id),
-            )
-            count = cast(int, cursor.fetchone()[0])
-        return count
-
-    def get_asset_count_for_board(self, board_id: str) -> int:
-        with self._db.transaction() as cursor:
-            # Convert the enum values to unique list of strings
-            category_strings = [c.value for c in set(ASSETS_CATEGORIES)]
-            # Create the correct length of placeholders
-            placeholders = ",".join("?" * len(category_strings))
-            cursor.execute(
-                f"""--sql
-                    SELECT COUNT(*)
-                    FROM board_images
-                    INNER JOIN images ON board_images.image_name = images.image_name
-                    WHERE images.is_intermediate = FALSE AND images.image_category IN ( {placeholders} )
-                    AND board_images.board_id = ?;
-                    """,
-                (*category_strings, board_id),
+                (board_id,),
             )
             count = cast(int, cursor.fetchone()[0])
         return count

invokeai/app/services/boards/boards_common.py

@@ -12,20 +12,12 @@ 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."""
-    asset_count: int = Field(description="The number of assets in the board.")
-    """The number of assets 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, asset_count: int, video_count: int
-) -> BoardDTO:
+def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_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,
-        asset_count=asset_count,
-        video_count=video_count,
     )

invokeai/app/services/boards/boards_default.py

@@ -17,7 +17,7 @@ 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, 0, 0)
+        return board_record_to_dto(board_record, None, 0)
 
     def get_dto(self, board_id: str) -> BoardDTO:
         board_record = self.__invoker.services.board_records.get(board_id)
@@ -27,9 +27,7 @@ class BoardService(BoardServiceABC):
         else:
             cover_image_name = None
         image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
-        asset_count = self.__invoker.services.board_image_records.get_asset_count_for_board(board_id)
-        video_count = 0  # noop for OSS
-        return board_record_to_dto(board_record, cover_image_name, image_count, asset_count, video_count)
+        return board_record_to_dto(board_record, cover_image_name, image_count)
 
     def update(
         self,
@@ -44,9 +42,7 @@ class BoardService(BoardServiceABC):
             cover_image_name = None
 
         image_count = self.__invoker.services.board_image_records.get_image_count_for_board(board_id)
-        asset_count = self.__invoker.services.board_image_records.get_asset_count_for_board(board_id)
-        video_count = 0  # noop for OSS
-        return board_record_to_dto(board_record, cover_image_name, image_count, asset_count, video_count)
+        return board_record_to_dto(board_record, cover_image_name, image_count)
 
     def delete(self, board_id: str) -> None:
         self.__invoker.services.board_records.delete(board_id)
@@ -71,9 +67,7 @@ class BoardService(BoardServiceABC):
                 cover_image_name = None
 
             image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
-            asset_count = self.__invoker.services.board_image_records.get_asset_count_for_board(r.board_id)
-            video_count = 0  # noop for OSS
-            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count, asset_count, video_count))
+            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
 
         return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
 
@@ -90,8 +84,6 @@ class BoardService(BoardServiceABC):
                 cover_image_name = None
 
             image_count = self.__invoker.services.board_image_records.get_image_count_for_board(r.board_id)
-            asset_count = self.__invoker.services.board_image_records.get_asset_count_for_board(r.board_id)
-            video_count = 0  # noop for OSS
-            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count, asset_count, video_count))
+            board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
 
         return board_dtos

invokeai/app/services/bulk_download/bulk_download_default.py

@@ -150,15 +150,4 @@ class BulkDownloadService(BulkDownloadBase):
     def _is_valid_path(self, path: Union[str, Path]) -> bool:
         """Validates the path given for a bulk download."""
         path = path if isinstance(path, Path) else Path(path)
-
-        # Resolve the path to handle any path traversal attempts (e.g., ../)
-        resolved_path = path.resolve()
-
-        # The path may not traverse out of the bulk downloads folder or its subfolders
-        does_not_traverse = resolved_path.parent == self._bulk_downloads_folder.resolve()
-
-        # The path must exist and be a .zip file
-        does_exist = resolved_path.exists()
-        is_zip_file = resolved_path.suffix == ".zip"
-
-        return does_exist and is_zip_file and does_not_traverse
+        return path.exists()

invokeai/app/services/client_state_persistence/client_state_persistence_base.py

@@ -1,42 +0,0 @@
-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

@@ -1,65 +0,0 @@
-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

@@ -107,7 +107,6 @@ 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)
@@ -197,7 +196,6 @@ 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/events/events_common.py

@@ -234,8 +234,8 @@ class QueueItemStatusChangedEvent(QueueItemEventBase):
     error_type: Optional[str] = Field(default=None, description="The error type, if any")
     error_message: Optional[str] = Field(default=None, description="The error message, if any")
     error_traceback: Optional[str] = Field(default=None, description="The error traceback, if any")
-    created_at: str = Field(description="The timestamp when the queue item was created")
-    updated_at: str = Field(description="The timestamp when the queue item was last updated")
+    created_at: Optional[str] = Field(default=None, description="The timestamp when the queue item was created")
+    updated_at: Optional[str] = Field(default=None, description="The timestamp when the queue item was last updated")
     started_at: Optional[str] = Field(default=None, description="The timestamp when the queue item was started")
     completed_at: Optional[str] = Field(default=None, description="The timestamp when the queue item was completed")
     batch_status: BatchStatus = Field(description="The status of the batch")
@@ -258,8 +258,8 @@ class QueueItemStatusChangedEvent(QueueItemEventBase):
             error_type=queue_item.error_type,
             error_message=queue_item.error_message,
             error_traceback=queue_item.error_traceback,
-            created_at=str(queue_item.created_at),
-            updated_at=str(queue_item.updated_at),
+            created_at=str(queue_item.created_at) if queue_item.created_at else None,
+            updated_at=str(queue_item.updated_at) if queue_item.updated_at else None,
             started_at=str(queue_item.started_at) if queue_item.started_at else None,
             completed_at=str(queue_item.completed_at) if queue_item.completed_at else None,
             batch_status=batch_status,

invokeai/app/services/image_records/image_records_common.py

@@ -58,15 +58,6 @@ class ImageCategory(str, Enum, metaclass=MetaEnum):
     """OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes."""
 
 
-IMAGE_CATEGORIES: list[ImageCategory] = [ImageCategory.GENERAL]
-ASSETS_CATEGORIES: list[ImageCategory] = [
-    ImageCategory.CONTROL,
-    ImageCategory.MASK,
-    ImageCategory.USER,
-    ImageCategory.OTHER,
-]
-
-
 class InvalidImageCategoryException(ValueError):
     """Raised when a provided value is not a valid ImageCategory.
 

invokeai/app/services/invocation_services.py

@@ -17,7 +17,6 @@ if TYPE_CHECKING:
     from invokeai.app.services.board_records.board_records_base import BoardRecordStorageBase
     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
@@ -74,7 +73,6 @@ class InvocationServices:
         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
@@ -104,4 +102,3 @@ class InvocationServices:
         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 move, rmtree
+from shutil import copyfile, copytree, move, rmtree
 from tempfile import mkdtemp
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union
 
@@ -51,7 +51,6 @@ from invokeai.backend.model_manager.metadata import (
 from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMetadata
 from invokeai.backend.model_manager.search import ModelSearch
 from invokeai.backend.model_manager.taxonomy import ModelRepoVariant, ModelSourceType
-from invokeai.backend.model_manager.util.lora_metadata_extractor import apply_lora_metadata
 from invokeai.backend.util import InvokeAILogger
 from invokeai.backend.util.catch_sigint import catch_sigint
 from invokeai.backend.util.devices import TorchDevice
@@ -186,15 +185,13 @@ class ModelInstallService(ModelInstallServiceBase):
         info: AnyModelConfig = self._probe(Path(model_path), config)  # type: ignore
 
         if preferred_name := config.name:
-            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}"
+            preferred_name = Path(preferred_name).with_suffix(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._move_model(model_path, dest_path)
+            new_path = self._copy_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()}"
@@ -619,17 +616,30 @@ 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):
@@ -657,10 +667,6 @@ class ModelInstallService(ModelInstallServiceBase):
 
         info = info or self._probe(model_path, config)
 
-        # Apply LoRA metadata if applicable
-        model_images_path = self.app_config.models_path / "model_images"
-        apply_lora_metadata(info, model_path.resolve(), model_images_path)
-
         model_path = model_path.resolve()
 
         # Models in the Invoke-managed models dir should use relative paths.

invokeai/app/services/model_load/model_load_default.py

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

invokeai/app/services/model_records/model_records_base.py

@@ -15,7 +15,6 @@ from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     ControlAdapterDefaultSettings,
-    LoraModelDefaultSettings,
     MainModelDefaultSettings,
 )
 from invokeai.backend.model_manager.taxonomy import (
@@ -84,8 +83,8 @@ class ModelRecordChanges(BaseModelExcludeNull):
     file_size: Optional[int] = Field(description="Size of model file", default=None)
     format: Optional[str] = Field(description="format of model file", default=None)
     trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
-    default_settings: Optional[MainModelDefaultSettings | LoraModelDefaultSettings | ControlAdapterDefaultSettings] = (
-        Field(description="Default settings for this model", default=None)
+    default_settings: Optional[MainModelDefaultSettings | ControlAdapterDefaultSettings] = Field(
+        description="Default settings for this model", default=None
     )
 
     # Checkpoint-specific changes

invokeai/app/services/session_queue/session_queue_base.py

@@ -2,6 +2,7 @@ from abc import ABC, abstractmethod
 from typing import Any, Coroutine, Optional
 
 from invokeai.app.services.session_queue.session_queue_common import (
+    QUEUE_ITEM_STATUS,
     Batch,
     BatchStatus,
     CancelAllExceptCurrentResult,
@@ -14,7 +15,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     EnqueueBatchResult,
     IsEmptyResult,
     IsFullResult,
-    ItemIdsResult,
     PruneResult,
     RetryItemsResult,
     SessionQueueCountsByDestination,
@@ -22,7 +22,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueStatus,
 )
 from invokeai.app.services.shared.graph import GraphExecutionState
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.shared.pagination import CursorPaginatedResults
 
 
 class SessionQueueBase(ABC):
@@ -135,6 +135,19 @@ class SessionQueueBase(ABC):
         """Deletes all queue items except in-progress items"""
         pass
 
+    @abstractmethod
+    def list_queue_items(
+        self,
+        queue_id: str,
+        limit: int,
+        priority: int,
+        cursor: Optional[int] = None,
+        status: Optional[QUEUE_ITEM_STATUS] = None,
+        destination: Optional[str] = None,
+    ) -> CursorPaginatedResults[SessionQueueItem]:
+        """Gets a page of session queue items"""
+        pass
+
     @abstractmethod
     def list_all_queue_items(
         self,
@@ -144,18 +157,9 @@ class SessionQueueBase(ABC):
         """Gets all queue items that match the given parameters"""
         pass
 
-    @abstractmethod
-    def get_queue_item_ids(
-        self,
-        queue_id: str,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-    ) -> ItemIdsResult:
-        """Gets all queue item ids that match the given parameters"""
-        pass
-
     @abstractmethod
     def get_queue_item(self, item_id: int) -> SessionQueueItem:
-        """Gets a session queue item by ID for a given queue"""
+        """Gets a session queue item by ID"""
         pass
 
     @abstractmethod

invokeai/app/services/session_queue/session_queue_common.py

@@ -176,14 +176,6 @@ DEFAULT_QUEUE_ID = "default"
 
 QUEUE_ITEM_STATUS = Literal["pending", "in_progress", "completed", "failed", "canceled"]
 
-
-class ItemIdsResult(BaseModel):
-    """Response containing ordered item ids with metadata for optimistic updates."""
-
-    item_ids: list[int] = Field(description="Ordered list of item ids")
-    total_count: int = Field(description="Total number of queue items matching the query")
-
-
 NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue])
 
 

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -22,7 +22,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     EnqueueBatchResult,
     IsEmptyResult,
     IsFullResult,
-    ItemIdsResult,
     PruneResult,
     RetryItemsResult,
     SessionQueueCountsByDestination,
@@ -34,7 +33,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     prepare_values_to_insert,
 )
 from invokeai.app.services.shared.graph import GraphExecutionState
-from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
+from invokeai.app.services.shared.pagination import CursorPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 
 
@@ -588,6 +587,59 @@ class SqliteSessionQueue(SessionQueueBase):
             )
         return self.get_queue_item(item_id)
 
+    def list_queue_items(
+        self,
+        queue_id: str,
+        limit: int,
+        priority: int,
+        cursor: Optional[int] = None,
+        status: Optional[QUEUE_ITEM_STATUS] = None,
+        destination: Optional[str] = None,
+    ) -> CursorPaginatedResults[SessionQueueItem]:
+        with self._db.transaction() as cursor_:
+            item_id = cursor
+            query = """--sql
+                SELECT *
+                FROM session_queue
+                WHERE queue_id = ?
+            """
+            params: list[Union[str, int]] = [queue_id]
+
+            if status is not None:
+                query += """--sql
+                    AND status = ?
+                    """
+                params.append(status)
+
+            if destination is not None:
+                query += """---sql
+                    AND destination = ?
+                """
+                params.append(destination)
+
+            if item_id is not None:
+                query += """--sql
+                    AND (priority < ?) OR (priority = ? AND item_id > ?)
+                    """
+                params.extend([priority, priority, item_id])
+
+            query += """--sql
+                ORDER BY
+                    priority DESC,
+                    item_id ASC
+                LIMIT ?
+                """
+            params.append(limit + 1)
+            cursor_.execute(query, params)
+            results = cast(list[sqlite3.Row], cursor_.fetchall())
+        items = [SessionQueueItem.queue_item_from_dict(dict(result)) for result in results]
+        has_more = False
+        if len(items) > limit:
+            # remove the extra item
+            items.pop()
+            has_more = True
+        return CursorPaginatedResults(items=items, limit=limit, has_more=has_more)
+
     def list_all_queue_items(
         self,
         queue_id: str,
@@ -619,26 +671,6 @@ class SqliteSessionQueue(SessionQueueBase):
         items = [SessionQueueItem.queue_item_from_dict(dict(result)) for result in results]
         return items
 
-    def get_queue_item_ids(
-        self,
-        queue_id: str,
-        order_dir: SQLiteDirection = SQLiteDirection.Descending,
-    ) -> ItemIdsResult:
-        with self._db.transaction() as cursor_:
-            query = f"""--sql
-                SELECT item_id
-                FROM session_queue
-                WHERE queue_id = ?
-                ORDER BY created_at {order_dir.value}
-                """
-            query_params = [queue_id]
-
-            cursor_.execute(query, query_params)
-            result = cast(list[sqlite3.Row], cursor_.fetchall())
-        item_ids = [row[0] for row in result]
-
-        return ItemIdsResult(item_ids=item_ids, total_count=len(item_ids))
-
     def get_queue_status(self, queue_id: str) -> SessionQueueStatus:
         with self._db.transaction() as cursor:
             cursor.execute(

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

@@ -23,7 +23,6 @@ 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
 
 
@@ -64,7 +63,6 @@ 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

@@ -1,40 +0,0 @@
-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/videos_common.py

@@ -1,179 +0,0 @@
-import datetime
-from typing import Optional, Union
-
-from pydantic import BaseModel, Field, StrictBool, StrictStr
-
-from invokeai.app.util.misc import get_iso_timestamp
-from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
-
-VIDEO_DTO_COLS = ", ".join(
-    [
-        "videos." + c
-        for c in [
-            "video_id",
-            "width",
-            "height",
-            "session_id",
-            "node_id",
-            "is_intermediate",
-            "created_at",
-            "updated_at",
-            "deleted_at",
-            "starred",
-        ]
-    ]
-)
-
-
-class VideoRecord(BaseModelExcludeNull):
-    """Deserialized video record without metadata."""
-
-    video_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. This may be different from the width in metadata."""
-    height: int = Field(description="The height of the video in px.")
-    """The actual height of the video in px. This may be different from the height in metadata."""
-    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."""
-    deleted_at: Optional[Union[datetime.datetime, str]] = Field(
-        default=None, description="The deleted timestamp of the video."
-    )
-    """The deleted timestamp of the video."""
-    is_intermediate: bool = Field(description="Whether this is an intermediate video.")
-    """Whether this is an intermediate video."""
-    session_id: Optional[str] = Field(
-        default=None,
-        description="The session ID that generated this video, if it is a generated video.",
-    )
-    """The session ID that generated this video, if it is a generated video."""
-    node_id: Optional[str] = Field(
-        default=None,
-        description="The node ID that generated this video, if it is a generated video.",
-    )
-    """The node ID that generated this video, if it is a generated video."""
-    starred: bool = Field(description="Whether this video is starred.")
-    """Whether this video is starred."""
-
-
-class VideoRecordChanges(BaseModelExcludeNull):
-    """A set of changes to apply to a video record.
-
-    Only limited changes are valid:
-      - `session_id`: change the session associated with a video
-      - `is_intermediate`: change the video's `is_intermediate` flag
-      - `starred`: change whether the video is starred
-    """
-
-    session_id: Optional[StrictStr] = Field(
-        default=None,
-        description="The video's new session ID.",
-    )
-    """The video's new session ID."""
-    is_intermediate: Optional[StrictBool] = Field(default=None, description="The video's new `is_intermediate` flag.")
-    """The video's new `is_intermediate` flag."""
-    starred: Optional[StrictBool] = Field(default=None, description="The video's new `starred` state")
-    """The video's new `starred` state."""
-
-
-def deserialize_video_record(video_dict: dict) -> VideoRecord:
-    """Deserializes a video record."""
-
-    # Retrieve all the values, setting "reasonable" defaults if they are not present.
-    video_id = video_dict.get("video_id", "unknown")
-    width = video_dict.get("width", 0)
-    height = video_dict.get("height", 0)
-    session_id = video_dict.get("session_id", None)
-    node_id = video_dict.get("node_id", None)
-    created_at = video_dict.get("created_at", get_iso_timestamp())
-    updated_at = video_dict.get("updated_at", get_iso_timestamp())
-    deleted_at = video_dict.get("deleted_at", get_iso_timestamp())
-    is_intermediate = video_dict.get("is_intermediate", False)
-    starred = video_dict.get("starred", False)
-
-    return VideoRecord(
-        video_id=video_id,
-        width=width,
-        height=height,
-        session_id=session_id,
-        node_id=node_id,
-        created_at=created_at,
-        updated_at=updated_at,
-        deleted_at=deleted_at,
-        is_intermediate=is_intermediate,
-        starred=starred,
-    )
-
-
-class VideoCollectionCounts(BaseModel):
-    starred_count: int = Field(description="The number of starred videos in the collection.")
-    unstarred_count: int = Field(description="The number of unstarred videos in the collection.")
-
-
-class VideoIdsResult(BaseModel):
-    """Response containing ordered video ids with metadata for optimistic updates."""
-
-    video_ids: list[str] = Field(description="Ordered list of video ids")
-    starred_count: int = Field(description="Number of starred videos (when starred_first=True)")
-    total_count: int = Field(description="Total number of videos matching the query")
-
-
-class VideoUrlsDTO(BaseModelExcludeNull):
-    """The URLs for an image and its thumbnail."""
-
-    video_id: str = Field(description="The unique id of the video.")
-    """The unique id of the video."""
-    video_url: str = Field(description="The URL of the video.")
-    """The URL of the video."""
-    thumbnail_url: str = Field(description="The URL of the video's thumbnail.")
-    """The URL of the video's thumbnail."""
-
-
-class VideoDTO(VideoRecord, VideoUrlsDTO):
-    """Deserialized video record, enriched for the frontend."""
-
-    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."""
-
-
-def video_record_to_dto(
-    video_record: VideoRecord,
-    video_url: str,
-    thumbnail_url: str,
-    board_id: Optional[str],
-) -> VideoDTO:
-    """Converts a video record to a video DTO."""
-    return VideoDTO(
-        **video_record.model_dump(),
-        video_url=video_url,
-        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 DeleteVideosResult(ResultWithAffectedBoards):
-    deleted_videos: list[str] = Field(description="The ids of the videos that were deleted")
-
-
-class StarredVideosResult(ResultWithAffectedBoards):
-    starred_videos: list[str] = Field(description="The ids of the videos that were starred")
-
-
-class UnstarredVideosResult(ResultWithAffectedBoards):
-    unstarred_videos: list[str] = Field(description="The ids of the videos that were unstarred")
-
-
-class AddVideosToBoardResult(ResultWithAffectedBoards):
-    added_videos: list[str] = Field(description="The video ids that were added to the board")
-
-
-class RemoveVideosFromBoardResult(ResultWithAffectedBoards):
-    removed_videos: list[str] = Field(description="The video ids that were removed from their board")

invokeai/backend/flux/denoise.py

@@ -112,7 +112,7 @@ def denoise(
         )
 
         # Slice prediction to only include the main image tokens
-        if img_cond_seq is not None:
+        if img_input_ids is not None:
             pred = pred[:, :original_seq_len]
 
         step_cfg_scale = cfg_scale[step_index]
@@ -125,26 +125,9 @@ 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=neg_img_input,
-                img_ids=neg_img_input_ids,
+                img=img,
+                img_ids=img_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,
@@ -157,10 +140,6 @@ 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

@@ -1,14 +1,15 @@
+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.util.devices import TorchDevice
+from invokeai.backend.flux.util import PREFERED_KONTEXT_RESOLUTIONS
 
 
 def generate_img_ids_with_offset(
@@ -18,10 +19,8 @@ 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 optional index and spatial offsets.
+    """Generate tensor of image position ids with an optional offset.
 
     Args:
         latent_height (int): Height of image in latent space (after packing, this becomes h//2).
@@ -29,9 +28,7 @@ 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 (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).
+        idx_offset (int): Offset to add to the first dimension of the image ids.
 
     Returns:
         torch.Tensor: Image position ids with shape [batch_size, (latent_height//2 * latent_width//2), 3].
@@ -45,10 +42,6 @@ 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)
@@ -56,13 +49,13 @@ def generate_img_ids_with_offset(
     # Set the batch offset for all positions
     img_ids[..., 0] = idx_offset
 
-    # Create y-coordinate indices (vertical positions) with spatial offset
-    y_indices = torch.arange(packed_height, device=device, dtype=dtype) + packed_h_offset
+    # Create y-coordinate indices (vertical positions)
+    y_indices = torch.arange(packed_height, device=device, dtype=dtype)
     # Broadcast y_indices to match the spatial dimensions [packed_height, 1]
     img_ids[..., 1] = y_indices[:, None]
 
-    # Create x-coordinate indices (horizontal positions) with spatial offset
-    x_indices = torch.arange(packed_width, device=device, dtype=dtype) + packed_w_offset
+    # Create x-coordinate indices (horizontal positions)
+    x_indices = torch.arange(packed_width, device=device, dtype=dtype)
     # Broadcast x_indices to match the spatial dimensions [1, packed_width]
     img_ids[..., 2] = x_indices[None, :]
 
@@ -80,14 +73,14 @@ class KontextExtension:
 
     def __init__(
         self,
-        kontext_conditioning: list[FluxKontextConditioningField],
+        kontext_conditioning: FluxKontextConditioningField,
         context: InvocationContext,
         vae_field: VAEField,
         device: torch.device,
         dtype: torch.dtype,
     ):
         """
-        Initializes the KontextExtension, pre-processing the reference images
+        Initializes the KontextExtension, pre-processing the reference image
         into latents and positional IDs.
         """
         self._context = context
@@ -100,116 +93,54 @@ class KontextExtension:
         self.kontext_latents, self.kontext_ids = self._prepare_kontext()
 
     def _prepare_kontext(self) -> tuple[torch.Tensor, torch.Tensor]:
-        """Encodes the reference images and prepares their concatenated latents and IDs with spatial tiling."""
-        all_latents = []
-        all_ids = []
+        """Encodes the reference image and prepares its latents and IDs."""
+        image = self._context.images.get_pil(self.kontext_conditioning.image.image_name)
 
-        # 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
+        # Calculate aspect ratio of input image
+        width, height = image.size
+        aspect_ratio = width / height
 
+        # Find the closest preferred resolution by aspect ratio
+        _, target_width, target_height = min(
+            ((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS), key=lambda x: x[0]
+        )
+
+        # Apply BFL's scaling formula
+        # This ensures compatibility with the model's training
+        scaled_width = 2 * int(target_width / 16)
+        scaled_height = 2 * int(target_height / 16)
+
+        # Resize to the exact resolution used during training
+        image = image.convert("RGB")
+        final_width = 8 * scaled_width
+        final_height = 8 * scaled_height
+        image = image.resize((final_width, final_height), Image.Resampling.LANCZOS)
+
+        # Convert to tensor with same normalization as BFL
+        image_np = np.array(image)
+        image_tensor = torch.from_numpy(image_np).float() / 127.5 - 1.0
+        image_tensor = einops.rearrange(image_tensor, "h w c -> 1 c h w")
+        image_tensor = image_tensor.to(self._device)
+
+        # Continue with VAE encoding
         vae_info = self._context.models.load(self._vae_field.vae)
+        kontext_latents_unpacked = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
-        for idx, kontext_field in enumerate(self.kontext_conditioning):
-            image = self._context.images.get_pil(kontext_field.image.image_name)
+        # Extract tensor dimensions
+        batch_size, _, latent_height, latent_width = kontext_latents_unpacked.shape
 
-            # Convert to RGB
-            image = image.convert("RGB")
+        # 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 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
+        return kontext_latents_packed, kontext_ids
 
     def ensure_batch_size(self, target_batch_size: int) -> None:
         """Ensures the kontext latents and IDs match the target batch size by repeating if necessary."""

invokeai/backend/image_util/imwatermark/vendor.py

@@ -1,304 +0,0 @@
-# This file is vendored from https://github.com/ShieldMnt/invisible-watermark
-#
-# `invisible-watermark` is MIT licensed as of August 23, 2025, when the code was copied into this repo.
-#
-# Why we vendored it in:
-# `invisible-watermark` has a dependency on `opencv-python`, which conflicts with Invoke's dependency on
-# `opencv-contrib-python`. It's easier to copy the code over than complicate the installation process by
-# requiring an extra post-install step of removing `opencv-python` and installing `opencv-contrib-python`.
-
-import struct
-import uuid
-import base64
-import cv2
-import numpy as np
-import pywt
-
-
-class WatermarkEncoder(object):
-    def __init__(self, content=b""):
-        seq = np.array([n for n in content], dtype=np.uint8)
-        self._watermarks = list(np.unpackbits(seq))
-        self._wmLen = len(self._watermarks)
-        self._wmType = "bytes"
-
-    def set_by_ipv4(self, addr):
-        bits = []
-        ips = addr.split(".")
-        for ip in ips:
-            bits += list(np.unpackbits(np.array([ip % 255], dtype=np.uint8)))
-        self._watermarks = bits
-        self._wmLen = len(self._watermarks)
-        self._wmType = "ipv4"
-        assert self._wmLen == 32
-
-    def set_by_uuid(self, uid):
-        u = uuid.UUID(uid)
-        self._wmType = "uuid"
-        seq = np.array([n for n in u.bytes], dtype=np.uint8)
-        self._watermarks = list(np.unpackbits(seq))
-        self._wmLen = len(self._watermarks)
-
-    def set_by_bytes(self, content):
-        self._wmType = "bytes"
-        seq = np.array([n for n in content], dtype=np.uint8)
-        self._watermarks = list(np.unpackbits(seq))
-        self._wmLen = len(self._watermarks)
-
-    def set_by_b16(self, b16):
-        content = base64.b16decode(b16)
-        self.set_by_bytes(content)
-        self._wmType = "b16"
-
-    def set_by_bits(self, bits=[]):
-        self._watermarks = [int(bit) % 2 for bit in bits]
-        self._wmLen = len(self._watermarks)
-        self._wmType = "bits"
-
-    def set_watermark(self, wmType="bytes", content=""):
-        if wmType == "ipv4":
-            self.set_by_ipv4(content)
-        elif wmType == "uuid":
-            self.set_by_uuid(content)
-        elif wmType == "bits":
-            self.set_by_bits(content)
-        elif wmType == "bytes":
-            self.set_by_bytes(content)
-        elif wmType == "b16":
-            self.set_by_b16(content)
-        else:
-            raise NameError("%s is not supported" % wmType)
-
-    def get_length(self):
-        return self._wmLen
-
-    # @classmethod
-    # def loadModel(cls):
-    #     RivaWatermark.loadModel()
-
-    def encode(self, cv2Image, method="dwtDct", **configs):
-        (r, c, channels) = cv2Image.shape
-        if r * c < 256 * 256:
-            raise RuntimeError("image too small, should be larger than 256x256")
-
-        if method == "dwtDct":
-            embed = EmbedMaxDct(self._watermarks, wmLen=self._wmLen, **configs)
-            return embed.encode(cv2Image)
-        # elif method == 'dwtDctSvd':
-        #     embed = EmbedDwtDctSvd(self._watermarks, wmLen=self._wmLen, **configs)
-        #     return embed.encode(cv2Image)
-        # elif method == 'rivaGan':
-        #     embed = RivaWatermark(self._watermarks, self._wmLen)
-        #     return embed.encode(cv2Image)
-        else:
-            raise NameError("%s is not supported" % method)
-
-
-class WatermarkDecoder(object):
-    def __init__(self, wm_type="bytes", length=0):
-        self._wmType = wm_type
-        if wm_type == "ipv4":
-            self._wmLen = 32
-        elif wm_type == "uuid":
-            self._wmLen = 128
-        elif wm_type == "bytes":
-            self._wmLen = length
-        elif wm_type == "bits":
-            self._wmLen = length
-        elif wm_type == "b16":
-            self._wmLen = length
-        else:
-            raise NameError("%s is unsupported" % wm_type)
-
-    def reconstruct_ipv4(self, bits):
-        ips = [str(ip) for ip in list(np.packbits(bits))]
-        return ".".join(ips)
-
-    def reconstruct_uuid(self, bits):
-        nums = np.packbits(bits)
-        bstr = b""
-        for i in range(16):
-            bstr += struct.pack(">B", nums[i])
-
-        return str(uuid.UUID(bytes=bstr))
-
-    def reconstruct_bits(self, bits):
-        # return ''.join([str(b) for b in bits])
-        return bits
-
-    def reconstruct_b16(self, bits):
-        bstr = self.reconstruct_bytes(bits)
-        return base64.b16encode(bstr)
-
-    def reconstruct_bytes(self, bits):
-        nums = np.packbits(bits)
-        bstr = b""
-        for i in range(self._wmLen // 8):
-            bstr += struct.pack(">B", nums[i])
-        return bstr
-
-    def reconstruct(self, bits):
-        if len(bits) != self._wmLen:
-            raise RuntimeError("bits are not matched with watermark length")
-
-        if self._wmType == "ipv4":
-            return self.reconstruct_ipv4(bits)
-        elif self._wmType == "uuid":
-            return self.reconstruct_uuid(bits)
-        elif self._wmType == "bits":
-            return self.reconstruct_bits(bits)
-        elif self._wmType == "b16":
-            return self.reconstruct_b16(bits)
-        else:
-            return self.reconstruct_bytes(bits)
-
-    def decode(self, cv2Image, method="dwtDct", **configs):
-        (r, c, channels) = cv2Image.shape
-        if r * c < 256 * 256:
-            raise RuntimeError("image too small, should be larger than 256x256")
-
-        bits = []
-        if method == "dwtDct":
-            embed = EmbedMaxDct(watermarks=[], wmLen=self._wmLen, **configs)
-            bits = embed.decode(cv2Image)
-        # elif method == 'dwtDctSvd':
-        #     embed = EmbedDwtDctSvd(watermarks=[], wmLen=self._wmLen, **configs)
-        #     bits = embed.decode(cv2Image)
-        # elif method == 'rivaGan':
-        #     embed = RivaWatermark(watermarks=[], wmLen=self._wmLen, **configs)
-        #     bits = embed.decode(cv2Image)
-        else:
-            raise NameError("%s is not supported" % method)
-        return self.reconstruct(bits)
-
-    # @classmethod
-    # def loadModel(cls):
-    #     RivaWatermark.loadModel()
-
-
-class EmbedMaxDct(object):
-    def __init__(self, watermarks=[], wmLen=8, scales=[0, 36, 36], block=4):
-        self._watermarks = watermarks
-        self._wmLen = wmLen
-        self._scales = scales
-        self._block = block
-
-    def encode(self, bgr):
-        (row, col, channels) = bgr.shape
-
-        yuv = cv2.cvtColor(bgr, cv2.COLOR_BGR2YUV)
-
-        for channel in range(2):
-            if self._scales[channel] <= 0:
-                continue
-
-            ca1, (h1, v1, d1) = pywt.dwt2(yuv[: row // 4 * 4, : col // 4 * 4, channel], "haar")
-            self.encode_frame(ca1, self._scales[channel])
-
-            yuv[: row // 4 * 4, : col // 4 * 4, channel] = pywt.idwt2((ca1, (v1, h1, d1)), "haar")
-
-        bgr_encoded = cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR)
-        return bgr_encoded
-
-    def decode(self, bgr):
-        (row, col, channels) = bgr.shape
-
-        yuv = cv2.cvtColor(bgr, cv2.COLOR_BGR2YUV)
-
-        scores = [[] for i in range(self._wmLen)]
-        for channel in range(2):
-            if self._scales[channel] <= 0:
-                continue
-
-            ca1, (h1, v1, d1) = pywt.dwt2(yuv[: row // 4 * 4, : col // 4 * 4, channel], "haar")
-
-            scores = self.decode_frame(ca1, self._scales[channel], scores)
-
-        avgScores = list(map(lambda l: np.array(l).mean(), scores))
-
-        bits = np.array(avgScores) * 255 > 127
-        return bits
-
-    def decode_frame(self, frame, scale, scores):
-        (row, col) = frame.shape
-        num = 0
-
-        for i in range(row // self._block):
-            for j in range(col // self._block):
-                block = frame[
-                    i * self._block : i * self._block + self._block, j * self._block : j * self._block + self._block
-                ]
-
-                score = self.infer_dct_matrix(block, scale)
-                # score = self.infer_dct_svd(block, scale)
-                wmBit = num % self._wmLen
-                scores[wmBit].append(score)
-                num = num + 1
-
-        return scores
-
-    def diffuse_dct_svd(self, block, wmBit, scale):
-        u, s, v = np.linalg.svd(cv2.dct(block))
-
-        s[0] = (s[0] // scale + 0.25 + 0.5 * wmBit) * scale
-        return cv2.idct(np.dot(u, np.dot(np.diag(s), v)))
-
-    def infer_dct_svd(self, block, scale):
-        u, s, v = np.linalg.svd(cv2.dct(block))
-
-        score = 0
-        score = int((s[0] % scale) > scale * 0.5)
-        return score
-        if score >= 0.5:
-            return 1.0
-        else:
-            return 0.0
-
-    def diffuse_dct_matrix(self, block, wmBit, scale):
-        pos = np.argmax(abs(block.flatten()[1:])) + 1
-        i, j = pos // self._block, pos % self._block
-        val = block[i][j]
-        if val >= 0.0:
-            block[i][j] = (val // scale + 0.25 + 0.5 * wmBit) * scale
-        else:
-            val = abs(val)
-            block[i][j] = -1.0 * (val // scale + 0.25 + 0.5 * wmBit) * scale
-        return block
-
-    def infer_dct_matrix(self, block, scale):
-        pos = np.argmax(abs(block.flatten()[1:])) + 1
-        i, j = pos // self._block, pos % self._block
-
-        val = block[i][j]
-        if val < 0:
-            val = abs(val)
-
-        if (val % scale) > 0.5 * scale:
-            return 1
-        else:
-            return 0
-
-    def encode_frame(self, frame, scale):
-        """
-        frame is a matrix (M, N)
-
-        we get K (watermark bits size) blocks (self._block x self._block)
-
-        For i-th block, we encode watermark[i] bit into it
-        """
-        (row, col) = frame.shape
-        num = 0
-        for i in range(row // self._block):
-            for j in range(col // self._block):
-                block = frame[
-                    i * self._block : i * self._block + self._block, j * self._block : j * self._block + self._block
-                ]
-                wmBit = self._watermarks[(num % self._wmLen)]
-
-                diffusedBlock = self.diffuse_dct_matrix(block, wmBit, scale)
-                # diffusedBlock = self.diffuse_dct_svd(block, wmBit, scale)
-                frame[
-                    i * self._block : i * self._block + self._block, j * self._block : j * self._block + self._block
-                ] = diffusedBlock
-
-                num = num + 1

invokeai/backend/image_util/invisible_watermark.py

@@ -6,10 +6,13 @@ configuration variable, that allows the watermarking to be supressed.
 
 import cv2
 import numpy as np
+from imwatermark import WatermarkEncoder
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.image_util.imwatermark.vendor import WatermarkEncoder
+from invokeai.app.services.config.config_default import get_config
+
+config = get_config()
 
 
 class InvisibleWatermark:

invokeai/backend/image_util/segment_anything/segment_anything_2_pipeline.py

@@ -1,109 +0,0 @@
-from typing import Optional
-
-import torch
-from PIL import Image
-
-# Import SAM2 components - these should be available in transformers 4.56.0+
-from transformers.models.sam2 import Sam2Model
-from transformers.models.sam2.processing_sam2 import Sam2Processor
-
-from invokeai.backend.image_util.segment_anything.shared import SAMInput
-from invokeai.backend.raw_model import RawModel
-
-
-class SegmentAnything2Pipeline(RawModel):
-    """A wrapper class for the transformers SAM2 model and processor that makes it compatible with the model manager."""
-
-    def __init__(self, sam2_model: Sam2Model, sam2_processor: Sam2Processor):
-        """Initialize the SAM2 pipeline.
-
-        Args:
-            sam2_model: The SAM2 model
-            sam2_processor: The SAM2 processor (can be Sam2Processor or Sam2VideoProcessor)
-        """
-        self._sam2_model = sam2_model
-        self._sam2_processor = sam2_processor
-
-    def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None):
-        # HACK: The SAM2 pipeline may not work on MPS devices. We only allow it to be moved to CPU or CUDA.
-        if device is not None and device.type not in {"cpu", "cuda"}:
-            device = None
-        self._sam2_model.to(device=device, dtype=dtype)
-
-    def calc_size(self) -> int:
-        # HACK: Fix the circular import issue.
-        from invokeai.backend.model_manager.load.model_util import calc_module_size
-
-        return calc_module_size(self._sam2_model)
-
-    def segment(
-        self,
-        image: Image.Image,
-        inputs: list[SAMInput],
-    ) -> torch.Tensor:
-        """Segment the image using the provided inputs.
-
-        Args:
-            image: The image to segment.
-            inputs: A list of SAMInput objects containing bounding boxes and/or point lists.
-
-        Returns:
-            torch.Tensor: The segmentation masks. dtype: torch.bool. shape: [num_masks, channels, height, width].
-        """
-
-        input_boxes: list[list[float]] = []
-        input_points: list[list[list[float]]] = []
-        input_labels: list[list[int]] = []
-
-        for i in inputs:
-            box: list[float] | None = None
-            points: list[list[float]] | None = None
-            labels: list[int] | None = None
-
-            if i.bounding_box is not None:
-                box: list[float] | None = [
-                    i.bounding_box.x_min,
-                    i.bounding_box.y_min,
-                    i.bounding_box.x_max,
-                    i.bounding_box.y_max,
-                ]
-
-            if i.points is not None:
-                points = []
-                labels = []
-                for point in i.points:
-                    points.append([point.x, point.y])
-                    labels.append(point.label.value)
-
-            if box is not None:
-                input_boxes.append(box)
-            if points is not None:
-                input_points.append(points)
-            if labels is not None:
-                input_labels.append(labels)
-
-        batched_input_boxes = [input_boxes] if input_boxes else None
-        batched_input_points = [input_points] if input_points else None
-        batched_input_labels = [input_labels] if input_labels else None
-
-        processed_inputs = self._sam2_processor(
-            images=image,
-            input_boxes=batched_input_boxes,
-            input_points=batched_input_points,
-            input_labels=batched_input_labels,
-            return_tensors="pt",
-        ).to(self._sam2_model.device)
-
-        # Generate masks using the SAM2 model
-        outputs = self._sam2_model(**processed_inputs)
-
-        # Post-process the masks to get the final segmentation
-        masks = self._sam2_processor.post_process_masks(
-            masks=outputs.pred_masks,
-            original_sizes=processed_inputs.original_sizes,
-            reshaped_input_sizes=processed_inputs.reshaped_input_sizes,
-        )
-
-        # There should be only one batch.
-        assert len(masks) == 1
-        return masks[0]

invokeai/backend/image_util/segment_anything/segment_anything_pipeline.py

@@ -1,13 +1,20 @@
-from typing import Optional
+from typing import Optional, TypeAlias
 
 import torch
 from PIL import Image
 from transformers.models.sam import SamModel
 from transformers.models.sam.processing_sam import SamProcessor
 
-from invokeai.backend.image_util.segment_anything.shared import SAMInput
 from invokeai.backend.raw_model import RawModel
 
+# Type aliases for the inputs to the SAM model.
+ListOfBoundingBoxes: TypeAlias = list[list[int]]
+"""A list of bounding boxes. Each bounding box is in the format [xmin, ymin, xmax, ymax]."""
+ListOfPoints: TypeAlias = list[list[int]]
+"""A list of points. Each point is in the format [x, y]."""
+ListOfPointLabels: TypeAlias = list[int]
+"""A list of SAM point labels. Each label is an integer where -1 is background, 0 is neutral, and 1 is foreground."""
+
 
 class SegmentAnythingPipeline(RawModel):
     """A wrapper class for the transformers SAM model and processor that makes it compatible with the model manager."""
@@ -31,65 +38,55 @@ class SegmentAnythingPipeline(RawModel):
     def segment(
         self,
         image: Image.Image,
-        inputs: list[SAMInput],
+        bounding_boxes: list[list[int]] | None = None,
+        point_lists: list[list[list[int]]] | None = None,
     ) -> torch.Tensor:
-        """Segment the image using the provided inputs.
+        """Run the SAM model.
+
+        Either bounding_boxes or point_lists must be provided. If both are provided, bounding_boxes will be used and
+        point_lists will be ignored.
 
         Args:
-            image: The image to segment.
-            inputs: A list of SAMInput objects containing bounding boxes and/or point lists.
+            image (Image.Image): The image to segment.
+            bounding_boxes (list[list[int]]): The bounding box prompts. Each bounding box is in the format
+                [xmin, ymin, xmax, ymax].
+            point_lists (list[list[list[int]]]): The points prompts. Each point is in the format [x, y, label].
+                `label` is an integer where -1 is background, 0 is neutral, and 1 is foreground.
 
         Returns:
             torch.Tensor: The segmentation masks. dtype: torch.bool. shape: [num_masks, channels, height, width].
         """
 
-        input_boxes: list[list[float]] = []
-        input_points: list[list[list[float]]] = []
-        input_labels: list[list[int]] = []
+        # Prep the inputs:
+        # - Create a list of bounding boxes or points and labels.
+        # - Add a batch dimension of 1 to the inputs.
+        if bounding_boxes:
+            input_boxes: list[ListOfBoundingBoxes] | None = [bounding_boxes]
+            input_points: list[ListOfPoints] | None = None
+            input_labels: list[ListOfPointLabels] | None = None
+        elif point_lists:
+            input_boxes: list[ListOfBoundingBoxes] | None = None
+            input_points: list[ListOfPoints] | None = []
+            input_labels: list[ListOfPointLabels] | None = []
+            for point_list in point_lists:
+                input_points.append([[p[0], p[1]] for p in point_list])
+                input_labels.append([p[2] for p in point_list])
 
-        for i in inputs:
-            box: list[float] | None = None
-            points: list[list[float]] | None = None
-            labels: list[int] | None = None
+        else:
+            raise ValueError("Either bounding_boxes or points and labels must be provided.")
 
-            if i.bounding_box is not None:
-                box: list[float] | None = [
-                    i.bounding_box.x_min,
-                    i.bounding_box.y_min,
-                    i.bounding_box.x_max,
-                    i.bounding_box.y_max,
-                ]
-
-            if i.points is not None:
-                points = []
-                labels = []
-                for point in i.points:
-                    points.append([point.x, point.y])
-                    labels.append(point.label.value)
-
-            if box is not None:
-                input_boxes.append(box)
-            if points is not None:
-                input_points.append(points)
-            if labels is not None:
-                input_labels.append(labels)
-
-        batched_input_boxes = [input_boxes] if input_boxes else None
-        batched_input_points = input_points if input_points else None
-        batched_input_labels = input_labels if input_labels else None
-
-        processed_inputs = self._sam_processor(
+        inputs = self._sam_processor(
             images=image,
-            input_boxes=batched_input_boxes,
-            input_points=batched_input_points,
-            input_labels=batched_input_labels,
+            input_boxes=input_boxes,
+            input_points=input_points,
+            input_labels=input_labels,
             return_tensors="pt",
         ).to(self._sam_model.device)
-        outputs = self._sam_model(**processed_inputs)
+        outputs = self._sam_model(**inputs)
         masks = self._sam_processor.post_process_masks(
             masks=outputs.pred_masks,
-            original_sizes=processed_inputs.original_sizes,
-            reshaped_input_sizes=processed_inputs.reshaped_input_sizes,
+            original_sizes=inputs.original_sizes,
+            reshaped_input_sizes=inputs.reshaped_input_sizes,
         )
 
         # There should be only one batch.

invokeai/backend/image_util/segment_anything/shared.py

@@ -1,49 +0,0 @@
-from enum import Enum
-
-from pydantic import BaseModel, model_validator
-from pydantic.fields import Field
-
-
-class BoundingBox(BaseModel):
-    x_min: int = Field(..., description="The minimum x-coordinate of the bounding box (inclusive).")
-    x_max: int = Field(..., description="The maximum x-coordinate of the bounding box (exclusive).")
-    y_min: int = Field(..., description="The minimum y-coordinate of the bounding box (inclusive).")
-    y_max: int = Field(..., description="The maximum y-coordinate of the bounding box (exclusive).")
-
-    @model_validator(mode="after")
-    def check_coords(self):
-        if self.x_min > self.x_max:
-            raise ValueError(f"x_min ({self.x_min}) is greater than x_max ({self.x_max}).")
-        if self.y_min > self.y_max:
-            raise ValueError(f"y_min ({self.y_min}) is greater than y_max ({self.y_max}).")
-        return self
-
-    def tuple(self) -> tuple[int, int, int, int]:
-        """
-        Returns the bounding box as a tuple suitable for use with PIL's `Image.crop()` method.
-        This method returns a tuple of the form (left, upper, right, lower) == (x_min, y_min, x_max, y_max).
-        """
-        return (self.x_min, self.y_min, self.x_max, self.y_max)
-
-
-class SAMPointLabel(Enum):
-    negative = -1
-    neutral = 0
-    positive = 1
-
-
-class SAMPoint(BaseModel):
-    x: int = Field(..., description="The x-coordinate of the point")
-    y: int = Field(..., description="The y-coordinate of the point")
-    label: SAMPointLabel = Field(..., description="The label of the point")
-
-
-class SAMInput(BaseModel):
-    bounding_box: BoundingBox | None = Field(None, description="The bounding box to use for segmentation")
-    points: list[SAMPoint] | None = Field(None, description="The points to use for segmentation")
-
-    @model_validator(mode="after")
-    def check_input(self):
-        if not self.bounding_box and not self.points:
-            raise ValueError("Either bounding_box or points must be provided")
-        return self

invokeai/backend/model_manager/config.py

@@ -90,11 +90,6 @@ class MainModelDefaultSettings(BaseModel):
     model_config = ConfigDict(extra="forbid")
 
 
-class LoraModelDefaultSettings(BaseModel):
-    weight: float | None = Field(default=None, ge=-1, le=2, description="Default weight for this model")
-    model_config = ConfigDict(extra="forbid")
-
-
 class ControlAdapterDefaultSettings(BaseModel):
     # This could be narrowed to controlnet processor nodes, but they change. Leaving this a string is safer.
     preprocessor: str | None
@@ -292,9 +287,6 @@ class LoRAConfigBase(ABC, BaseModel):
 
     type: Literal[ModelType.LoRA] = ModelType.LoRA
     trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
-    default_settings: Optional[LoraModelDefaultSettings] = Field(
-        description="Default settings for this model", default=None
-    )
 
     @classmethod
     def flux_lora_format(cls, mod: ModelOnDisk):
@@ -500,15 +492,6 @@ class MainConfigBase(ABC, BaseModel):
     variant: AnyVariant = ModelVariantType.Normal
 
 
-class VideoConfigBase(ABC, BaseModel):
-    type: Literal[ModelType.Video] = ModelType.Video
-    trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
-    default_settings: Optional[MainModelDefaultSettings] = Field(
-        description="Default settings for this model", default=None
-    )
-    variant: AnyVariant = ModelVariantType.Normal
-
-
 class MainCheckpointConfig(CheckpointConfigBase, MainConfigBase, LegacyProbeMixin, ModelConfigBase):
     """Model config for main checkpoint models."""
 
@@ -666,21 +649,6 @@ class ApiModelConfig(MainConfigBase, ModelConfigBase):
         raise NotImplementedError("API models are not parsed from disk.")
 
 
-class VideoApiModelConfig(VideoConfigBase, ModelConfigBase):
-    """Model config for API-based video models."""
-
-    format: Literal[ModelFormat.Api] = ModelFormat.Api
-
-    @classmethod
-    def matches(cls, mod: ModelOnDisk) -> bool:
-        # API models are not stored on disk, so we can't match them.
-        return False
-
-    @classmethod
-    def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
-        raise NotImplementedError("API models are not parsed from disk.")
-
-
 def get_model_discriminator_value(v: Any) -> str:
     """
     Computes the discriminator value for a model config.
@@ -750,13 +718,12 @@ AnyModelConfig = Annotated[
         Annotated[FluxReduxConfig, FluxReduxConfig.get_tag()],
         Annotated[LlavaOnevisionConfig, LlavaOnevisionConfig.get_tag()],
         Annotated[ApiModelConfig, ApiModelConfig.get_tag()],
-        Annotated[VideoApiModelConfig, VideoApiModelConfig.get_tag()],
     ],
     Discriminator(get_model_discriminator_value),
 ]
 
 AnyModelConfigValidator = TypeAdapter(AnyModelConfig)
-AnyDefaultSettings: TypeAlias = Union[MainModelDefaultSettings, LoraModelDefaultSettings, ControlAdapterDefaultSettings]
+AnyDefaultSettings: TypeAlias = Union[MainModelDefaultSettings, ControlAdapterDefaultSettings]
 
 
 class ModelConfigFactory:

invokeai/backend/model_manager/legacy_probe.py

@@ -9,7 +9,6 @@ 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,
@@ -23,7 +22,6 @@ from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     ControlAdapterDefaultSettings,
     InvalidModelConfigException,
-    LoraModelDefaultSettings,
     MainModelDefaultSettings,
     ModelConfigFactory,
     SubmodelDefinition,
@@ -218,8 +216,6 @@ class ModelProbe(object):
         if not fields["default_settings"]:
             if fields["type"] in {ModelType.ControlNet, ModelType.T2IAdapter, ModelType.ControlLoRa}:
                 fields["default_settings"] = get_default_settings_control_adapters(fields["name"])
-            if fields["type"] in {ModelType.LoRA}:
-                fields["default_settings"] = get_default_settings_lora()
             elif fields["type"] is ModelType.Main:
                 fields["default_settings"] = get_default_settings_main(fields["base"])
 
@@ -497,21 +493,9 @@ class ModelProbe(object):
         # scan model
         scan_result = pscan.scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
-            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.")
+            raise Exception(f"The model {model_name} 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 {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.")
+            raise Exception(f"Error scanning model {model_name} for malware. Aborting import.")
 
 
 # Probing utilities
@@ -546,10 +530,6 @@ def get_default_settings_control_adapters(model_name: str) -> Optional[ControlAd
     return None
 
 
-def get_default_settings_lora() -> LoraModelDefaultSettings:
-    return LoraModelDefaultSettings()
-
-
 def get_default_settings_main(model_base: BaseModelType) -> Optional[MainModelDefaultSettings]:
     if model_base is BaseModelType.StableDiffusion1 or model_base is BaseModelType.StableDiffusion2:
         return MainModelDefaultSettings(width=512, height=512)

invokeai/backend/model_manager/model_on_disk.py

@@ -6,17 +6,13 @@ 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."""
@@ -83,24 +79,8 @@ class ModelOnDisk:
         with SilenceWarnings():
             if path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin")):
                 scan_result = scan_file_path(path)
-                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.")
+                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.")
                 checkpoint = torch.load(path, map_location="cpu")
                 assert isinstance(checkpoint, dict)
             elif path.suffix.endswith(".gguf"):

invokeai/backend/model_manager/starter_models.py

@@ -149,29 +149,13 @@ 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,
@@ -596,14 +580,13 @@ t2i_sketch_sdxl = StarterModel(
 )
 # endregion
 # region SpandrelImageToImage
-animesharp_v4_rcan = StarterModel(
-    name="2x-AnimeSharpV4_RCAN",
+realesrgan_anime = StarterModel(
+    name="RealESRGAN_x4plus_anime_6B",
     base=BaseModelType.Any,
-    source="https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV4/2x-AnimeSharpV4_RCAN.safetensors",
-    description="A 2x upscaling model (optimized for anime images).",
+    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).",
     type=ModelType.SpandrelImageToImage,
 )
-
 realesrgan_x4 = StarterModel(
     name="RealESRGAN_x4plus",
     base=BaseModelType.Any,
@@ -749,7 +732,7 @@ STARTER_MODELS: list[StarterModel] = [
     t2i_lineart_sdxl,
     t2i_sketch_sdxl,
     realesrgan_x4,
-    animesharp_v4_rcan,
+    realesrgan_anime,
     realesrgan_x2,
     swinir,
     t5_base_encoder,
@@ -760,8 +743,6 @@ STARTER_MODELS: list[StarterModel] = [
     llava_onevision,
     flux_fill,
     cogview4,
-    flux_krea,
-    flux_krea_quantized,
 ]
 
 sd1_bundle: list[StarterModel] = [
@@ -813,7 +794,6 @@ flux_bundle: list[StarterModel] = [
     flux_redux,
     flux_fill,
     flux_kontext_quantized,
-    flux_krea_quantized,
 ]
 
 STARTER_BUNDLES: dict[str, StarterModelBundle] = {

invokeai/backend/model_manager/taxonomy.py

@@ -28,11 +28,8 @@ class BaseModelType(str, Enum):
     CogView4 = "cogview4"
     Imagen3 = "imagen3"
     Imagen4 = "imagen4"
-    Gemini2_5 = "gemini-2.5"
     ChatGPT4o = "chatgpt-4o"
     FluxKontext = "flux-kontext"
-    Veo3 = "veo3"
-    Runway = "runway"
 
 
 class ModelType(str, Enum):
@@ -54,7 +51,6 @@ class ModelType(str, Enum):
     SigLIP = "siglip"
     FluxRedux = "flux_redux"
     LlavaOnevision = "llava_onevision"
-    Video = "video"
 
 
 class SubModelType(str, Enum):

invokeai/backend/model_manager/util/lora_metadata_extractor.py

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

invokeai/backend/model_manager/util/model_util.py

@@ -8,12 +8,8 @@ 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]:
@@ -63,21 +59,9 @@ 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:
-                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.")
+                raise Exception(f"The model at {path} 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} for malware, but picklescan is disabled. "
-                        "Proceeding with caution."
-                    )
-                else:
-                    raise RuntimeError(f"Error scanning the model at {path} for malware. Aborting import.")
+                raise Exception(f"Error scanning model at {path} for malware. Aborting import.")
 
         checkpoint = torch.load(path, map_location=torch.device("meta"))
     return checkpoint

invokeai/backend/patches/lora_conversions/flux_diffusers_lora_conversion_utils.py

@@ -18,25 +18,16 @@ def is_state_dict_likely_in_flux_diffusers_format(state_dict: Dict[str, torch.Te
     # First, check that all keys end in "lora_A.weight" or "lora_B.weight" (i.e. are in PEFT format).
     all_keys_in_peft_format = all(k.endswith(("lora_A.weight", "lora_B.weight")) for k in state_dict.keys())
 
-    # Check if keys use transformer prefix
-    transformer_prefix_keys = [
+    # Next, check that this is likely a FLUX model by spot-checking a few keys.
+    expected_keys = [
         "transformer.single_transformer_blocks.0.attn.to_q.lora_A.weight",
         "transformer.single_transformer_blocks.0.attn.to_q.lora_B.weight",
         "transformer.transformer_blocks.0.attn.add_q_proj.lora_A.weight",
         "transformer.transformer_blocks.0.attn.add_q_proj.lora_B.weight",
     ]
-    transformer_keys_present = all(k in state_dict for k in transformer_prefix_keys)
+    all_expected_keys_present = all(k in state_dict for k in expected_keys)
 
-    # Check if keys use base_model.model prefix
-    base_model_prefix_keys = [
-        "base_model.model.single_transformer_blocks.0.attn.to_q.lora_A.weight",
-        "base_model.model.single_transformer_blocks.0.attn.to_q.lora_B.weight",
-        "base_model.model.transformer_blocks.0.attn.add_q_proj.lora_A.weight",
-        "base_model.model.transformer_blocks.0.attn.add_q_proj.lora_B.weight",
-    ]
-    base_model_keys_present = all(k in state_dict for k in base_model_prefix_keys)
-
-    return all_keys_in_peft_format and (transformer_keys_present or base_model_keys_present)
+    return all_keys_in_peft_format and all_expected_keys_present
 
 
 def lora_model_from_flux_diffusers_state_dict(
@@ -58,16 +49,8 @@ def lora_layers_from_flux_diffusers_grouped_state_dict(
     https://github.com/huggingface/diffusers/blob/55ac421f7bb12fd00ccbef727be4dc2f3f920abb/scripts/convert_flux_to_diffusers.py
     """
 
-    # Determine which prefix is used and remove it from all keys.
-    # Check if any key starts with "base_model.model." prefix
-    has_base_model_prefix = any(k.startswith("base_model.model.") for k in grouped_state_dict.keys())
-
-    if has_base_model_prefix:
-        # Remove the "base_model.model." prefix from all keys.
-        grouped_state_dict = {k.replace("base_model.model.", ""): v for k, v in grouped_state_dict.items()}
-    else:
-        # Remove the "transformer." prefix from all keys.
-        grouped_state_dict = {k.replace("transformer.", ""): v for k, v in grouped_state_dict.items()}
+    # Remove the "transformer." prefix from all keys.
+    grouped_state_dict = {k.replace("transformer.", ""): v for k, v in grouped_state_dict.items()}
 
     # Constants for FLUX.1
     num_double_layers = 19

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

invokeai/backend/quantization/scripts/load_flux_model_bnb_nf4.py

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

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("Initialize T5 on meta device"):
+    with log_time("Intialize T5 on meta device"):
         model_config = AutoConfig.from_pretrained(model_path)
         with accelerate.init_empty_weights():
             model = AutoModelForTextEncoding.from_config(model_config)

invokeai/backend/util/hotfixes.py

@@ -5,11 +5,7 @@ import torch
 from diffusers.configuration_utils import ConfigMixin, register_to_config
 from diffusers.loaders.single_file_model import FromOriginalModelMixin
 from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor
-from diffusers.models.controlnets.controlnet import (
-    ControlNetConditioningEmbedding,
-    ControlNetOutput,
-    zero_module,
-)
+from diffusers.models.controlnet import ControlNetConditioningEmbedding, ControlNetOutput, zero_module
 from diffusers.models.embeddings import (
     TextImageProjection,
     TextImageTimeEmbedding,
@@ -779,15 +775,7 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
 
 
 diffusers.ControlNetModel = ControlNetModel
-# Patch both the new and legacy module paths for compatibility
-try:
-    diffusers.models.controlnets.controlnet.ControlNetModel = ControlNetModel
-except Exception:
-    # Fallback for environments still exposing the legacy path
-    try:
-        diffusers.models.controlnet.ControlNetModel = ControlNetModel
-    except Exception:
-        pass
+diffusers.models.controlnet.ControlNetModel = ControlNetModel
 
 
 # patch LoRACompatibleConv to use original Conv2D forward function

invokeai/backend/util/vae_working_memory.py

@@ -1,117 +0,0 @@
-from typing import Literal
-
-import torch
-from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
-from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
-
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
-from invokeai.backend.flux.modules.autoencoder import AutoEncoder
-
-
-def estimate_vae_working_memory_sd15_sdxl(
-    operation: Literal["encode", "decode"],
-    image_tensor: torch.Tensor,
-    vae: AutoencoderKL | AutoencoderTiny,
-    tile_size: int | None,
-    fp32: bool,
-) -> int:
-    """Estimate the working memory required to encode or decode the given tensor."""
-    # It was found experimentally that the peak working memory scales linearly with the number of pixels and the
-    # element size (precision). This estimate is accurate for both SD1 and SDXL.
-    element_size = 4 if fp32 else 2
-
-    # This constant is determined experimentally and takes into consideration both allocated and reserved memory. See #8414
-    # Encoding uses ~45% the working memory as decoding.
-    scaling_constant = 2200 if operation == "decode" else 1100
-
-    latent_scale_factor_for_operation = LATENT_SCALE_FACTOR if operation == "decode" else 1
-
-    if tile_size is not None:
-        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 = latent_scale_factor_for_operation * image_tensor.shape[-2]
-        w = latent_scale_factor_for_operation * image_tensor.shape[-1]
-        working_memory = h * w * element_size * scaling_constant
-
-    if fp32:
-        # If we are running in FP32, then we should account for the likely increase in model size (~250MB).
-        working_memory += 250 * 2**20
-
-    print(f"estimate_vae_working_memory_sd15_sdxl: {int(working_memory)}")
-
-    return int(working_memory)
-
-
-def estimate_vae_working_memory_cogview4(
-    operation: Literal["encode", "decode"], image_tensor: torch.Tensor, vae: AutoencoderKL
-) -> int:
-    """Estimate the working memory required by the invocation in bytes."""
-    latent_scale_factor_for_operation = LATENT_SCALE_FACTOR if operation == "decode" else 1
-
-    h = latent_scale_factor_for_operation * image_tensor.shape[-2]
-    w = latent_scale_factor_for_operation * image_tensor.shape[-1]
-    element_size = next(vae.parameters()).element_size()
-
-    # This constant is determined experimentally and takes into consideration both allocated and reserved memory. See #8414
-    # Encoding uses ~45% the working memory as decoding.
-    scaling_constant = 2200 if operation == "decode" else 1100
-    working_memory = h * w * element_size * scaling_constant
-
-    print(f"estimate_vae_working_memory_cogview4: {int(working_memory)}")
-
-    return int(working_memory)
-
-
-def estimate_vae_working_memory_flux(
-    operation: Literal["encode", "decode"], image_tensor: torch.Tensor, vae: AutoEncoder
-) -> int:
-    """Estimate the working memory required by the invocation in bytes."""
-
-    latent_scale_factor_for_operation = LATENT_SCALE_FACTOR if operation == "decode" else 1
-
-    out_h = latent_scale_factor_for_operation * image_tensor.shape[-2]
-    out_w = latent_scale_factor_for_operation * image_tensor.shape[-1]
-    element_size = next(vae.parameters()).element_size()
-
-    # This constant is determined experimentally and takes into consideration both allocated and reserved memory. See #8414
-    # Encoding uses ~45% the working memory as decoding.
-    scaling_constant = 2200 if operation == "decode" else 1100
-
-    working_memory = out_h * out_w * element_size * scaling_constant
-
-    print(f"estimate_vae_working_memory_flux: {int(working_memory)}")
-
-    return int(working_memory)
-
-
-def estimate_vae_working_memory_sd3(
-    operation: Literal["encode", "decode"], 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
-
-    latent_scale_factor_for_operation = LATENT_SCALE_FACTOR if operation == "decode" else 1
-
-    h = latent_scale_factor_for_operation * image_tensor.shape[-2]
-    w = latent_scale_factor_for_operation * image_tensor.shape[-1]
-    element_size = next(vae.parameters()).element_size()
-
-    # This constant is determined experimentally and takes into consideration both allocated and reserved memory. See #8414
-    # Encoding uses ~45% the working memory as decoding.
-    scaling_constant = 2200 if operation == "decode" else 1100
-
-    working_memory = h * w * element_size * scaling_constant
-
-    print(f"estimate_vae_working_memory_sd3: {int(working_memory)}")
-
-    return int(working_memory)

invokeai/frontend/web/.eslintignore

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

invokeai/frontend/web/.eslintrc.js

@@ -0,0 +1,88 @@
+module.exports = {
+  extends: ['@invoke-ai/eslint-config-react'],
+  plugins: ['path', 'i18next'],
+  rules: {
+    // TODO(psyche): Enable this rule. Requires no default exports in components - many changes.
+    'react-refresh/only-export-components': 'off',
+    // TODO(psyche): Enable this rule. Requires a lot of eslint-disable-next-line comments.
+    '@typescript-eslint/consistent-type-assertions': 'off',
+    // https://github.com/qdanik/eslint-plugin-path
+    'path/no-relative-imports': ['error', { maxDepth: 0 }],
+    // https://github.com/edvardchen/eslint-plugin-i18next/blob/HEAD/docs/rules/no-literal-string.md
+    // TODO: ENABLE THIS RULE BEFORE v6.0.0
+    // 'i18next/no-literal-string': 'error',
+    // https://eslint.org/docs/latest/rules/no-console
+    'no-console': 'warn',
+    // https://eslint.org/docs/latest/rules/no-promise-executor-return
+    'no-promise-executor-return': 'error',
+    // https://eslint.org/docs/latest/rules/require-await
+    'require-await': 'error',
+    // Restrict setActiveTab calls to only use-navigation-api.tsx
+    'no-restricted-syntax': [
+      'error',
+      {
+        selector: 'CallExpression[callee.name="setActiveTab"]',
+        message:
+          'setActiveTab() can only be called from use-navigation-api.tsx. Use navigationApi.switchToTab() instead.',
+      },
+    ],
+    // TODO: ENABLE THIS RULE BEFORE v6.0.0
+    'react/display-name': 'off',
+    'no-restricted-properties': [
+      'error',
+      {
+        object: 'crypto',
+        property: 'randomUUID',
+        message: 'Use of crypto.randomUUID is not allowed as it is not available in all browsers.',
+      },
+      {
+        object: 'navigator',
+        property: 'clipboard',
+        message:
+          'The Clipboard API is not available by default in Firefox. Use the `useClipboard` hook instead, which wraps clipboard access to prevent errors.',
+      },
+    ],
+    'no-restricted-imports': [
+      'error',
+      {
+        paths: [
+          {
+            name: 'lodash-es',
+            importNames: ['isEqual'],
+            message: 'Please use objectEquals from @observ33r/object-equals instead.',
+          },
+          {
+            name: 'lodash-es',
+            message: 'Please use es-toolkit instead.',
+          },
+          {
+            name: 'es-toolkit',
+            importNames: ['isEqual'],
+            message: 'Please use objectEquals from @observ33r/object-equals instead.',
+          },
+        ],
+      },
+    ],
+  },
+  overrides: [
+    /**
+     * Allow setActiveTab calls only in use-navigation-api.tsx
+     */
+    {
+      files: ['**/use-navigation-api.tsx'],
+      rules: {
+        'no-restricted-syntax': 'off',
+      },
+    },
+    /**
+     * Overrides for stories
+     */
+    {
+      files: ['*.stories.tsx'],
+      rules: {
+        // We may not have i18n available in stories.
+        'i18next/no-literal-string': 'off',
+      },
+    },
+  ],
+};

invokeai/frontend/web/.gitignore

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

invokeai/frontend/web/.prettierignore

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

invokeai/frontend/web/.prettierrc.js

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

invokeai/frontend/web/.prettierrc.json

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

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

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

invokeai/frontend/web/.storybook/main.ts

@@ -2,13 +2,19 @@ import type { StorybookConfig } from '@storybook/react-vite';
 
 const config: StorybookConfig = {
   stories: ['../src/**/*.mdx', '../src/**/*.stories.@(js|jsx|mjs|ts|tsx)'],
-  addons: ['@storybook/addon-links', '@storybook/addon-docs'],
-
+  addons: [
+    '@storybook/addon-links',
+    '@storybook/addon-essentials',
+    '@storybook/addon-interactions',
+    '@storybook/addon-storysource',
+  ],
   framework: {
     name: '@storybook/react-vite',
     options: {},
   },
-
+  docs: {
+    autodocs: 'tag',
+  },
   core: {
     disableTelemetry: true,
   },

invokeai/frontend/web/.storybook/manager.ts

@@ -1,5 +1,5 @@
-import { addons } from 'storybook/manager-api';
-import { themes } from 'storybook/theming';
+import { addons } from '@storybook/manager-api';
+import { themes } from '@storybook/theming';
 
 addons.setConfig({
   theme: themes.dark,

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

@@ -1,18 +1,17 @@
-import type { Preview } from '@storybook/react-vite';
-import { themes } from 'storybook/theming';
-import { $store } from 'app/store/nanostores/store';
+import { Preview } from '@storybook/react';
+import { themes } from '@storybook/theming';
 import i18n from 'i18next';
 import { initReactI18next } from 'react-i18next';
 import { Provider } from 'react-redux';
-
+import ThemeLocaleProvider from '../src/app/components/ThemeLocaleProvider';
+import { $baseUrl } from '../src/app/store/nanostores/baseUrl';
+import { createStore } from '../src/app/store/store';
 // TODO: Disabled for IDE performance issues with our translation JSON
 // eslint-disable-next-line @typescript-eslint/ban-ts-comment
 // @ts-ignore
 import translationEN from '../public/locales/en.json';
-import ThemeLocaleProvider from '../src/app/components/ThemeLocaleProvider';
-import { $baseUrl } from '../src/app/store/nanostores/baseUrl';
-import { createStore } from '../src/app/store/store';
 import { ReduxInit } from './ReduxInit';
+import { $store } from 'app/store/nanostores/store';
 
 i18n.use(initReactI18next).init({
   lng: 'en',
@@ -26,7 +25,7 @@ i18n.use(initReactI18next).init({
   returnNull: false,
 });
 
-const store = createStore();
+const store = createStore(undefined, false);
 $store.set(store);
 $baseUrl.set('http://localhost:9090');
 
@@ -47,7 +46,6 @@ const preview: Preview = {
   parameters: {
     docs: {
       theme: themes.dark,
-      codePanel: true,
     },
   },
 };

invokeai/frontend/web/CLAUDE.md

@@ -1,39 +0,0 @@
-# Bash commands
-
-All commands should be run from `<REPO_ROOT>/invokeai/frontend/web/`.
-
-- `pnpm lint:prettier`: check formatting
-- `pnpm lint:eslint`: check for linting issues
-- `pnpm lint:knip`: check for unused dependencies
-- `pnpm lint:dpdm`: check for dependency cycles
-- `pnpm lint:tsc`: check for TypeScript issues
-- `pnpm lint`: run all checks
-- `pnpm fix`: automatically fix issues where possible
-- `pnpm test:no-watch`: run the test suite
-
-# Writing Tests
-
-This repo uses `vitest` for unit tests.
-
-Tests should be colocated with the code they test, and should use the `.test.ts` suffix.
-
-Tests do not need to be written for code that is trivial or has no logic (e.g. simple type definitions, re-exports, etc.). We currently do not do UI tests.
-
-# Agents
-
-- Use @agent-javascript-pro and @agent-typescript-pro for JavaScript and TypeScript code generation and assistance.
-- Use @frontend-developer for general frontend development tasks.
-
-## Workflow
-
-Split up tasks into smaller subtasks and handle them one at a time using an agent. Ensure each subtask is completed before moving on to the next.
-
-Each agent should maintain a work log in a markdown file.
-
-When an agent completes a task, it should:
-
-1. Summarize the changes made.
-2. List any files that were added, modified, or deleted.
-3. Commit the changes with a descriptive commit message.
-
-DO NOT PUSH ANY CHANGES TO THE REMOTE REPOSITORY.

invokeai/frontend/web/eslint.config.mjs

@@ -1,246 +0,0 @@
-import js from '@eslint/js';
-import typescriptEslint from '@typescript-eslint/eslint-plugin';
-import typescriptParser from '@typescript-eslint/parser';
-import pluginI18Next from 'eslint-plugin-i18next';
-import pluginImport from 'eslint-plugin-import';
-import pluginPath from 'eslint-plugin-path';
-import pluginReact from 'eslint-plugin-react';
-import pluginReactHooks from 'eslint-plugin-react-hooks';
-import pluginReactRefresh from 'eslint-plugin-react-refresh';
-import pluginSimpleImportSort from 'eslint-plugin-simple-import-sort';
-import pluginStorybook from 'eslint-plugin-storybook';
-import pluginUnusedImports from 'eslint-plugin-unused-imports';
-import globals from 'globals';
-
-export default [
-  js.configs.recommended,
-
-  {
-    languageOptions: {
-      parser: typescriptParser,
-      parserOptions: {
-        ecmaFeatures: {
-          jsx: true,
-        },
-      },
-      globals: {
-        ...globals.browser,
-        ...globals.node,
-        GlobalCompositeOperation: 'readonly',
-        RequestInit: 'readonly',
-      },
-    },
-
-    files: ['**/*.ts', '**/*.tsx', '**/*.js', '**/*.jsx'],
-
-    plugins: {
-      react: pluginReact,
-      '@typescript-eslint': typescriptEslint,
-      'react-hooks': pluginReactHooks,
-      import: pluginImport,
-      'unused-imports': pluginUnusedImports,
-      'simple-import-sort': pluginSimpleImportSort,
-      'react-refresh': pluginReactRefresh.configs.vite,
-      path: pluginPath,
-      i18next: pluginI18Next,
-      storybook: pluginStorybook,
-    },
-
-    rules: {
-      ...typescriptEslint.configs.recommended.rules,
-      ...pluginReact.configs.recommended.rules,
-      ...pluginReact.configs['jsx-runtime'].rules,
-      ...pluginReactHooks.configs.recommended.rules,
-      ...pluginStorybook.configs.recommended.rules,
-
-      'react/jsx-no-bind': [
-        'error',
-        {
-          allowBind: true,
-        },
-      ],
-
-      'react/jsx-curly-brace-presence': [
-        'error',
-        {
-          props: 'never',
-          children: 'never',
-        },
-      ],
-
-      'react-hooks/exhaustive-deps': 'error',
-
-      curly: 'error',
-      'no-var': 'error',
-      'brace-style': 'error',
-      'prefer-template': 'error',
-      radix: 'error',
-      'space-before-blocks': 'error',
-      eqeqeq: 'error',
-      'one-var': ['error', 'never'],
-      'no-eval': 'error',
-      'no-extend-native': 'error',
-      'no-implied-eval': 'error',
-      'no-label-var': 'error',
-      'no-return-assign': 'error',
-      'no-sequences': 'error',
-      'no-template-curly-in-string': 'error',
-      'no-throw-literal': 'error',
-      'no-unmodified-loop-condition': 'error',
-      'import/no-duplicates': 'error',
-      'import/prefer-default-export': 'off',
-      'unused-imports/no-unused-imports': 'error',
-
-      'unused-imports/no-unused-vars': [
-        'error',
-        {
-          vars: 'all',
-          varsIgnorePattern: '^_',
-          args: 'after-used',
-          argsIgnorePattern: '^_',
-        },
-      ],
-
-      'simple-import-sort/imports': 'error',
-      'simple-import-sort/exports': 'error',
-      '@typescript-eslint/no-unused-vars': 'off',
-
-      '@typescript-eslint/ban-ts-comment': [
-        'error',
-        {
-          'ts-expect-error': 'allow-with-description',
-          'ts-ignore': true,
-          'ts-nocheck': true,
-          'ts-check': false,
-          minimumDescriptionLength: 10,
-        },
-      ],
-
-      '@typescript-eslint/no-empty-interface': [
-        'error',
-        {
-          allowSingleExtends: true,
-        },
-      ],
-
-      '@typescript-eslint/consistent-type-imports': [
-        'error',
-        {
-          prefer: 'type-imports',
-          fixStyle: 'separate-type-imports',
-          disallowTypeAnnotations: true,
-        },
-      ],
-
-      '@typescript-eslint/no-import-type-side-effects': 'error',
-
-      '@typescript-eslint/consistent-type-assertions': [
-        'error',
-        {
-          assertionStyle: 'as',
-        },
-      ],
-
-      'path/no-relative-imports': [
-        'error',
-        {
-          maxDepth: 0,
-        },
-      ],
-
-      'no-console': 'warn',
-      'no-promise-executor-return': 'error',
-      'require-await': 'error',
-
-      'no-restricted-syntax': [
-        'error',
-        {
-          selector: 'CallExpression[callee.name="setActiveTab"]',
-          message:
-            'setActiveTab() can only be called from use-navigation-api.tsx. Use navigationApi.switchToTab() instead.',
-        },
-      ],
-
-      'no-restricted-properties': [
-        'error',
-        {
-          object: 'crypto',
-          property: 'randomUUID',
-          message: 'Use of crypto.randomUUID is not allowed as it is not available in all browsers.',
-        },
-        {
-          object: 'navigator',
-          property: 'clipboard',
-          message:
-            'The Clipboard API is not available by default in Firefox. Use the `useClipboard` hook instead, which wraps clipboard access to prevent errors.',
-        },
-      ],
-
-      // Typescript handles this for us: https://eslint.org/docs/latest/rules/no-redeclare#handled_by_typescript
-      'no-redeclare': 'off',
-
-      'no-restricted-imports': [
-        'error',
-        {
-          paths: [
-            {
-              name: 'lodash-es',
-              importNames: ['isEqual'],
-              message: 'Please use objectEquals from @observ33r/object-equals instead.',
-            },
-            {
-              name: 'lodash-es',
-              message: 'Please use es-toolkit instead.',
-            },
-            {
-              name: 'es-toolkit',
-              importNames: ['isEqual'],
-              message: 'Please use objectEquals from @observ33r/object-equals instead.',
-            },
-            {
-              name: 'zod/v3',
-              message: 'Import from zod instead.',
-            },
-          ],
-        },
-      ],
-    },
-
-    settings: {
-      react: {
-        version: 'detect',
-      },
-    },
-  },
-
-  {
-    files: ['**/use-navigation-api.tsx'],
-    rules: {
-      'no-restricted-syntax': 'off',
-    },
-  },
-
-  {
-    files: ['**/*.stories.tsx'],
-    rules: {
-      'i18next/no-literal-string': 'off',
-    },
-  },
-
-  {
-    ignores: [
-      '**/dist/',
-      '**/static/',
-      '**/.husky/',
-      '**/node_modules/',
-      '**/patches/',
-      '**/stats.html',
-      '**/index.html',
-      '**/.yarn/',
-      '**/*.scss',
-      'src/services/api/schema.ts',
-      '.prettierrc.js',
-      '.storybook',
-    ],
-  },
-];

invokeai/frontend/web/knip.ts

@@ -17,7 +17,6 @@ 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

@@ -45,31 +45,30 @@
     "@dagrejs/dagre": "^1.1.5",
     "@dagrejs/graphlib": "^2.2.4",
     "@fontsource-variable/inter": "^5.2.6",
-    "@invoke-ai/ui-library": "^0.0.47",
+    "@invoke-ai/ui-library": "^0.0.46",
     "@nanostores/react": "^1.0.0",
-    "@observ33r/object-equals": "^1.1.5",
+    "@observ33r/object-equals": "^1.1.4",
     "@reduxjs/toolkit": "2.8.2",
     "@roarr/browser-log-writer": "^1.3.0",
-    "@xyflow/react": "^12.8.2",
-    "ag-psd": "^28.2.2",
+    "@xyflow/react": "^12.7.1",
+    "ag-psd": "^28.2.1",
     "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.2",
     "cmdk": "^1.1.1",
     "compare-versions": "^6.1.1",
-    "dockview": "^4.7.1",
-    "es-toolkit": "^1.39.7",
+    "dockview": "^4.4.0",
+    "es-toolkit": "^1.39.5",
     "filesize": "^10.1.6",
     "fracturedjsonjs": "^4.1.0",
     "framer-motion": "^11.10.0",
-    "i18next": "^25.3.2",
+    "i18next": "^25.2.1",
     "i18next-http-backend": "^3.0.2",
     "idb-keyval": "6.2.1",
     "jsondiffpatch": "^0.7.3",
-    "konva": "^9.3.22",
+    "konva": "^9.3.20",
     "linkify-react": "^4.3.1",
     "linkifyjs": "^4.3.1",
     "lru-cache": "^11.1.0",
-    "media-chrome": "^4.13.0",
     "mtwist": "^1.0.2",
     "nanoid": "^5.1.5",
     "nanostores": "^1.0.1",
@@ -84,11 +83,10 @@
     "react-dom": "^18.3.1",
     "react-dropzone": "^14.3.8",
     "react-error-boundary": "^5.0.0",
-    "react-hook-form": "^7.60.0",
+    "react-hook-form": "^7.58.1",
     "react-hotkeys-hook": "4.5.0",
     "react-i18next": "^15.5.3",
     "react-icons": "^5.5.0",
-    "react-player": "^3.3.1",
     "react-redux": "9.2.0",
     "react-resizable-panels": "^3.0.3",
     "react-textarea-autosize": "^8.5.9",
@@ -105,7 +103,7 @@
     "use-debounce": "^10.0.5",
     "use-device-pixel-ratio": "^1.1.2",
     "uuid": "^11.1.0",
-    "zod": "^4.0.10",
+    "zod": "^3.25.67",
     "zod-validation-error": "^3.5.2"
   },
   "peerDependencies": {
@@ -113,44 +111,39 @@
     "react-dom": "^18.2.0"
   },
   "devDependencies": {
-    "@eslint/js": "^9.31.0",
-    "@storybook/addon-docs": "^9.0.17",
-    "@storybook/addon-links": "^9.0.17",
-    "@storybook/react-vite": "^9.0.17",
+    "@invoke-ai/eslint-config-react": "^0.0.14",
+    "@invoke-ai/prettier-config-react": "^0.0.7",
+    "@storybook/addon-essentials": "^8.6.12",
+    "@storybook/addon-interactions": "^8.6.12",
+    "@storybook/addon-links": "^8.6.12",
+    "@storybook/addon-storysource": "^8.6.12",
+    "@storybook/manager-api": "^8.6.12",
+    "@storybook/react": "^8.6.12",
+    "@storybook/react-vite": "^8.6.12",
+    "@storybook/theming": "^8.6.12",
     "@types/node": "^22.15.1",
     "@types/react": "^18.3.11",
     "@types/react-dom": "^18.3.0",
     "@types/uuid": "^10.0.0",
-    "@typescript-eslint/eslint-plugin": "^8.37.0",
-    "@typescript-eslint/parser": "^8.37.0",
     "@vitejs/plugin-react-swc": "^3.9.0",
     "@vitest/coverage-v8": "^3.1.2",
     "@vitest/ui": "^3.1.2",
     "concurrently": "^9.1.2",
     "csstype": "^3.1.3",
     "dpdm": "^3.14.0",
-    "eslint": "^9.31.0",
-    "eslint-plugin-i18next": "^6.1.2",
-    "eslint-plugin-import": "^2.29.1",
-    "eslint-plugin-path": "^2.0.3",
-    "eslint-plugin-react": "^7.33.2",
-    "eslint-plugin-react-hooks": "^5.2.0",
-    "eslint-plugin-react-refresh": "^0.4.5",
-    "eslint-plugin-simple-import-sort": "^12.0.0",
-    "eslint-plugin-storybook": "^9.0.17",
-    "eslint-plugin-unused-imports": "^4.1.4",
-    "globals": "^16.3.0",
+    "eslint": "^8.57.1",
+    "eslint-plugin-i18next": "^6.1.1",
+    "eslint-plugin-path": "^1.3.0",
     "knip": "^5.61.3",
-    "magic-string": "^0.30.17",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^7.6.1",
     "prettier": "^3.5.3",
-    "rollup-plugin-visualizer": "^6.0.3",
-    "storybook": "^9.0.17",
+    "rollup-plugin-visualizer": "^5.14.0",
+    "storybook": "^8.6.12",
     "tsafe": "^1.8.5",
     "type-fest": "^4.40.0",
     "typescript": "^5.8.3",
-    "vite": "^7.0.5",
+    "vite": "^7.0.2",
     "vite-plugin-css-injected-by-js": "^3.5.2",
     "vite-plugin-dts": "^4.5.3",
     "vite-plugin-eslint": "^1.8.1",

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

@@ -14,7 +14,8 @@
     "gallery": {
         "galleryImageSize": "حجم الصورة",
         "gallerySettings": "إعدادات المعرض",
-        "autoSwitchNewImages": "التبديل التلقائي إلى الصور الجديدة"
+        "autoSwitchNewImages": "التبديل التلقائي إلى الصور الجديدة",
+        "noImagesInGallery": "لا توجد صور في المعرض"
     },
     "modelManager": {
         "modelManager": "مدير النموذج",
@@ -61,10 +62,12 @@
         "infillMethod": "طريقة التعبئة",
         "tileSize": "حجم البلاطة",
         "copyImage": "نسخ الصورة",
+        "downloadImage": "تحميل الصورة",
         "usePrompt": "استخدم المحث",
         "useSeed": "استخدام البذور",
         "useAll": "استخدام الكل",
-        "info": "معلومات"
+        "info": "معلومات",
+        "showOptionsPanel": "إظهار لوحة الخيارات"
     },
     "settings": {
         "models": "موديلات",

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

@@ -24,6 +24,7 @@
         "ipAdapter": "IP Adapter",
         "auto": "Auto",
         "controlNet": "ControlNet",
+        "imageFailedToLoad": "Kann Bild nicht laden",
         "modelManager": "Model Manager",
         "learnMore": "Mehr erfahren",
         "loading": "Lade",
@@ -51,6 +52,7 @@
         "somethingWentWrong": "Etwas ist schief gelaufen",
         "copyError": "$t(gallery.copy) Fehler",
         "input": "Eingabe",
+        "notInstalled": "Nicht $t(common.installed)",
         "alpha": "Alpha",
         "red": "Rot",
         "green": "Grün",
@@ -60,8 +62,11 @@
         "direction": "Richtung",
         "save": "Speichern",
         "created": "Erstellt",
+        "prevPage": "Vorherige Seite",
+        "nextPage": "Nächste Seite",
         "unknownError": "Unbekannter Fehler",
         "aboutDesc": "Verwenden Sie Invoke für die Arbeit? Siehe hier:",
+        "localSystem": "Lokales System",
         "orderBy": "Ordnen nach",
         "saveAs": "Speichern als",
         "updated": "Aktualisiert",
@@ -72,6 +77,7 @@
         "selected": "Ausgewählt",
         "beta": "Beta",
         "editor": "Editor",
+        "goTo": "Gehe zu",
         "positivePrompt": "Positiv-Prompt",
         "negativePrompt": "Negativ-Prompt",
         "tab": "Tabulator",
@@ -100,6 +106,7 @@
         "values": "Werte",
         "min": "Min",
         "max": "Max",
+        "resetToDefaults": "Auf Standard zurücksetzen",
         "seed": "Seed",
         "row": "Reihe",
         "column": "Spalte",
@@ -128,12 +135,14 @@
         "galleryImageSize": "Bildgröße",
         "gallerySettings": "Galerie-Einstellungen",
         "autoSwitchNewImages": "Auto-Wechsel zu neuen Bildern",
+        "noImagesInGallery": "Keine Bilder in der Galerie",
         "loading": "Lade",
         "deleteImage_one": "Lösche Bild",
         "deleteImage_other": "Lösche {{count}} Bilder",
         "copy": "Kopieren",
         "download": "Runterladen",
         "featuresWillReset": "Wenn Sie dieses Bild löschen, werden diese Funktionen sofort zurückgesetzt.",
+        "unableToLoad": "Galerie kann nicht geladen werden",
         "downloadSelection": "Auswahl herunterladen",
         "currentlyInUse": "Dieses Bild wird derzeit in den folgenden Funktionen verwendet:",
         "deleteImagePermanent": "Gelöschte Bilder können nicht wiederhergestellt werden.",
@@ -173,12 +182,16 @@
         "gallery": "Galerie",
         "sortDirection": "Sortierreihenfolge",
         "sideBySide": "Nebeneinander",
+        "openViewer": "Viewer öffnen",
         "viewerImage": "Viewer-Bild",
         "exitCompare": "Vergleichen beenden",
+        "closeViewer": "Viewer schließen",
+        "selectAnImageToCompare": "Wählen Sie ein Bild zum Vergleichen",
         "stretchToFit": "Strecken bis es passt",
         "displayBoardSearch": "Board durchsuchen",
         "displaySearch": "Bild suchen",
         "go": "Los",
+        "jump": "Springen",
         "assetsTab": "Dateien, die Sie zur Verwendung in Ihren Projekten hochgeladen haben.",
         "imagesTab": "Bilder, die Sie in Invoke erstellt und gespeichert haben.",
         "boardsSettings": "Ordnereinstellungen",
@@ -197,6 +210,10 @@
                 "title": "Bbox Werkzeug",
                 "desc": "Bbox Werkzeug auswählen."
             },
+            "setFillToWhite": {
+                "title": "Farbe auf Weiß einstellen",
+                "desc": "Setzt die aktuelle Werkzeugfarbe auf weiß."
+            },
             "title": "Leinwand",
             "selectBrushTool": {
                 "title": "Pinselwerkzeug",
@@ -561,6 +578,7 @@
         "urlOrLocalPath": "URL oder lokaler Pfad",
         "install": "Installieren",
         "textualInversions": "Textuelle Inversionen",
+        "ipAdapters": "IP-Adapter",
         "modelImageUpdated": "Modellbild aktualisiert",
         "path": "Pfad",
         "pathToConfig": "Pfad zur Konfiguration",
@@ -583,6 +601,7 @@
         "repoVariant": "Repo Variante",
         "learnMoreAboutSupportedModels": "Erfahren Sie mehr über die Modelle, die wir unterstützen",
         "clipEmbed": "CLIP einbetten",
+        "starterModelsInModelManager": "Modelle für Ihren Start finden Sie im Modell-Manager",
         "noModelsInstalledDesc1": "Installiere Modelle mit dem",
         "modelImageUpdateFailed": "Modellbild-Update fehlgeschlagen",
         "prune": "Bereinigen",
@@ -642,9 +661,11 @@
         "scaledHeight": "Skaliert H",
         "infillMethod": "Infill-Methode",
         "tileSize": "Kachelgröße",
+        "downloadImage": "Bild herunterladen",
         "usePrompt": "Prompt verwenden",
         "useSeed": "Seed verwenden",
         "useAll": "Alle verwenden",
+        "showOptionsPanel": "Optionsleiste zeigen",
         "copyImage": "Bild kopieren",
         "denoisingStrength": "Stärke der Entrauschung",
         "symmetry": "Symmetrie",
@@ -660,6 +681,10 @@
         "remixImage": "Remix des Bilds erstellen",
         "imageActions": "Weitere Bildaktionen",
         "invoke": {
+            "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), Skalierte Bbox-Breite ist {{width}}",
+            "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), Skalierte Bbox-Höhe ist {{height}}",
+            "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), Bbox-Breite ist {{width}}",
+            "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), Bbox-Höhe ist {{height}}",
             "noNodesInGraph": "Keine Knoten im Graphen",
             "canvasIsTransforming": "Leinwand ist beschäftigt (wird transformiert)",
             "canvasIsRasterizing": "Leinwand ist beschäftigt (wird gerastert)",
@@ -686,8 +711,7 @@
         "gaussianBlur": "Gaußsche Unschärfe",
         "sendToUpscale": "An Hochskalieren senden",
         "useCpuNoise": "CPU-Rauschen verwenden",
-        "sendToCanvas": "An Leinwand senden",
-        "disabledNoRasterContent": "Deaktiviert (kein Rasterinhalt)"
+        "sendToCanvas": "An Leinwand senden"
     },
     "settings": {
         "displayInProgress": "Zwischenbilder anzeigen",
@@ -725,6 +749,7 @@
         "parametersNotSet": "Parameter nicht zurückgerufen",
         "addedToBoard": "Dem Board hinzugefügt",
         "loadedWithWarnings": "Workflow mit Warnungen geladen",
+        "imageSaved": "Bild gespeichert",
         "linkCopied": "Link kopiert",
         "problemCopyingLayer": "Ebene kann nicht kopiert werden",
         "problemSavingLayer": "Ebene kann nicht gespeichert werden",
@@ -735,6 +760,8 @@
         "prunedQueue": "Warteschlange bereinigt",
         "modelAddedSimple": "Modell zur Warteschlange hinzugefügt",
         "parametersSet": "Parameter zurückgerufen",
+        "imageNotLoadedDesc": "Bild konnte nicht gefunden werden",
+        "setControlImage": "Als Kontrollbild festlegen",
         "sentToUpscale": "An Vergrößerung gesendet",
         "parameterNotSetDescWithMessage": "{{parameter}} kann nicht zurückgerufen werden: {{message}}",
         "unableToLoadImageMetadata": "Bildmetadaten können nicht geladen werden",
@@ -747,6 +774,7 @@
         "parameterSet": "Parameter zurückgerufen",
         "importFailed": "Import fehlgeschlagen",
         "importSuccessful": "Import erfolgreich",
+        "setNodeField": "Als Knotenfeld festlegen",
         "somethingWentWrong": "Etwas ist schief gelaufen",
         "workflowLoaded": "Arbeitsablauf geladen",
         "workflowDeleted": "Arbeitsablauf gelöscht",
@@ -754,13 +782,14 @@
         "layerCopiedToClipboard": "Ebene in die Zwischenablage kopiert",
         "sentToCanvas": "An Leinwand gesendet",
         "problemDeletingWorkflow": "Problem beim Löschen des Arbeitsablaufs",
+        "uploadFailedInvalidUploadDesc_withCount_one": "Darf maximal 1 PNG-, JPEG- oder WEBP-Bild sein.",
+        "uploadFailedInvalidUploadDesc_withCount_other": "Dürfen maximal {{count}} PNG-, JPEG- oder WEBP-Bild sein.",
         "problemRetrievingWorkflow": "Problem beim Abrufen des Arbeitsablaufs",
         "uploadFailedInvalidUploadDesc": "Müssen PNG-, JPEG- oder WEBP-Bilder sein.",
         "pasteSuccess": "Eingefügt in {{destination}}",
         "pasteFailed": "Einfügen fehlgeschlagen",
         "unableToCopy": "Kopieren nicht möglich",
-        "unableToCopyDesc_theseSteps": "diese Schritte",
-        "noVisibleRasterLayers": "Keine sichtbaren Rasterebenen"
+        "unableToCopyDesc_theseSteps": "diese Schritte"
     },
     "accessibility": {
         "uploadImage": "Bild hochladen",
@@ -812,13 +841,13 @@
         "archiveBoard": "Ordner archivieren",
         "archived": "Archiviert",
         "noBoards": "Kein {{boardType}} Ordner",
+        "hideBoards": "Ordner verstecken",
+        "viewBoards": "Ordner ansehen",
         "deletedPrivateBoardsCannotbeRestored": "Gelöschte Boards können nicht wiederhergestellt werden. Wenn Sie „Nur Board löschen“ wählen, werden die Bilder in einen privaten, nicht kategorisierten Status für den Ersteller des Bildes versetzt.",
         "assetsWithCount_one": "{{count}} in der Sammlung",
         "assetsWithCount_other": "{{count}} in der Sammlung",
         "deletedBoardsCannotbeRestored": "Gelöschte Ordner können nicht wiederhergestellt werden. Die Auswahl von \"Nur Ordner löschen\" verschiebt Bilder in einen unkategorisierten Zustand.",
-        "updateBoardError": "Fehler beim Aktualisieren des Ordners",
-        "uncategorizedImages": "Nicht kategorisierte Bilder",
-        "deleteAllUncategorizedImages": "Alle nicht kategorisierten Bilder löschen"
+        "updateBoardError": "Fehler beim Aktualisieren des Ordners"
     },
     "queue": {
         "status": "Status",
@@ -873,6 +902,7 @@
         "batchQueuedDesc_other": "{{count}} Einträge an {{direction}} der Wartschlange hinzugefügt",
         "openQueue": "Warteschlange öffnen",
         "batchFailedToQueue": "Fehler beim Einreihen in die Stapelverarbeitung",
+        "batchFieldValues": "Stapelverarbeitungswerte",
         "batchQueued": "Stapelverarbeitung eingereiht",
         "graphQueued": "Graph eingereiht",
         "graphFailedToQueue": "Fehler beim Einreihen des Graphen",
@@ -919,6 +949,8 @@
         "allPrompts": "Alle Prompts",
         "imageDimensions": "Bilder Auslösungen",
         "parameterSet": "Parameter {{parameter}} setzen",
+        "recallParameter": "{{label}} Abrufen",
+        "parsingFailed": "Parsing Fehlgeschlagen",
         "canvasV2Metadata": "Leinwand",
         "guidance": "Führung",
         "seamlessXAxis": "Nahtlose X Achse",
@@ -1162,9 +1194,6 @@
                 "Die Kantengröße des Kohärenzdurchlaufs."
             ],
             "heading": "Kantengröße"
-        },
-        "rasterLayer": {
-            "heading": "Rasterebene"
         }
     },
     "invocationCache": {
@@ -1201,7 +1230,9 @@
         "collectionFieldType": "{{name}} (Sammlung)",
         "connectionWouldCreateCycle": "Verbindung würde einen Kreislauf/cycle schaffen",
         "inputMayOnlyHaveOneConnection": "Eingang darf nur eine Verbindung haben",
+        "hideLegendNodes": "Feldtyp-Legende ausblenden",
         "integer": "Ganze Zahl",
+        "addLinearView": "Zur linearen Ansicht hinzufügen",
         "currentImageDescription": "Zeigt das aktuelle Bild im Node-Editor an",
         "ipAdapter": "IP-Adapter",
         "hideMinimapnodes": "Miniatur-Kartenansicht ausblenden",
@@ -1210,6 +1241,7 @@
         "reloadNodeTemplates": "Knoten-Vorlagen neu laden",
         "newWorkflow": "Neuer Arbeitsablauf / Workflow",
         "newWorkflowDesc": "Einen neuen Arbeitsablauf erstellen?",
+        "noFieldsLinearview": "Keine Felder zur linearen Ansicht hinzugefügt",
         "clearWorkflow": "Workflow löschen",
         "clearWorkflowDesc": "Diesen Arbeitsablauf löschen und neu starten?",
         "noConnectionInProgress": "Es besteht keine Verbindung",
@@ -1217,6 +1249,7 @@
         "nodeVersion": "Knoten Version",
         "node": "Knoten",
         "nodeSearch": "Knoten suchen",
+        "removeLinearView": "Entfernen aus Linear View",
         "nodeOutputs": "Knoten-Ausgänge",
         "nodeTemplate": "Knoten-Vorlage",
         "nodeType": "Knotentyp",
@@ -1227,6 +1260,7 @@
         "clearWorkflowDesc2": "Ihr aktueller Arbeitsablauf hat ungespeicherte Änderungen.",
         "scheduler": "Planer",
         "showMinimapnodes": "MiniMap anzeigen",
+        "showLegendNodes": "Feldtyp-Legende anzeigen",
         "executionStateCompleted": "Erledigt",
         "downloadWorkflow": "Workflow JSON herunterladen",
         "executionStateInProgress": "In Bearbeitung",
@@ -1236,6 +1270,7 @@
         "fieldTypesMustMatch": "Feldtypen müssen übereinstimmen",
         "fitViewportNodes": "An Ansichtsgröße anpassen",
         "loadingNodes": "Lade Nodes...",
+        "mismatchedVersion": "Ungültiger Knoten: Knoten {{node}} vom Typ {{type}} hat keine passende Version (Update versuchen?)",
         "fullyContainNodesHelp": "Nodes müssen vollständig innerhalb der Auswahlbox sein, um ausgewählt werden zu können",
         "noWorkflow": "Kein Workflow",
         "executionStateError": "Fehler",
@@ -1247,7 +1282,9 @@
         "sourceNodeDoesNotExist": "Ungültiger Rand: Quell- / Ausgabe-Knoten {{node}} existiert nicht",
         "updateAllNodes": "Update Knoten",
         "allNodesUpdated": "Alle Knoten aktualisiert",
+        "unknownTemplate": "Unbekannte Vorlage",
         "updateApp": "Update App",
+        "unknownInput": "Unbekannte Eingabe: {{name}}",
         "unknownNodeType": "Unbekannter Knotentyp",
         "float": "Kommazahlen",
         "enum": "Aufzählung",
@@ -1263,6 +1300,7 @@
         "workflowAuthor": "Autor",
         "graph": "Graph",
         "workflowDescription": "Kurze Beschreibung",
+        "versionUnknown": " Version unbekannt",
         "workflow": "Arbeitsablauf",
         "noGraph": "Kein Graph",
         "version": "Version",
@@ -1280,6 +1318,7 @@
         "unknownErrorValidatingWorkflow": "Unbekannter Fehler beim Validieren des Arbeitsablaufes",
         "inputFieldTypeParseError": "Typ des Eingabefelds {{node}}.{{field}} kann nicht analysiert werden ({{message}})",
         "workflowSettings": "Arbeitsablauf Editor Einstellungen",
+        "unableToLoadWorkflow": "Arbeitsablauf kann nicht geladen werden",
         "viewMode": "In linearen Ansicht verwenden",
         "unableToValidateWorkflow": "Arbeitsablauf kann nicht validiert werden",
         "outputFieldTypeParseError": "Typ des Ausgabefelds {{node}}.{{field}} kann nicht analysiert werden ({{message}})",
@@ -1295,6 +1334,7 @@
         "arithmeticSequence": "Arithmetische Folge",
         "noBatchGroup": "keine Gruppe",
         "generatorNoValues": "leer",
+        "generatorLoading": "wird geladen",
         "generatorLoadFromFile": "Aus Datei laden",
         "showEdgeLabels": "Kantenbeschriftungen anzeigen",
         "downloadWorkflowError": "Fehler beim Herunterladen des Arbeitsablaufs",
@@ -1302,11 +1342,14 @@
         "description": "Beschreibung",
         "loadWorkflowDesc": "Arbeitsablauf laden?",
         "loadWorkflowDesc2": "Ihr aktueller Arbeitsablauf enthält nicht gespeicherte Änderungen.",
+        "loadingTemplates": "Lade {{name}}",
         "missingSourceOrTargetHandle": "Fehlender Quell- oder Zielgriff",
         "missingSourceOrTargetNode": "Fehlender Quell- oder Zielknoten",
         "showEdgeLabelsHelp": "Beschriftungen an Kanten anzeigen, um die verknüpften Knoten zu kennzeichnen"
     },
     "hrf": {
+        "enableHrf": "Korrektur für hohe Auflösungen",
+        "upscaleMethod": "Vergrößerungsmethode",
         "metadata": {
             "strength": "Auflösungs-Fix Stärke",
             "enabled": "Auflösungs-Fix aktiviert",
@@ -1317,9 +1360,11 @@
     "models": {
         "noMatchingModels": "Keine passenden Modelle",
         "loading": "lade",
+        "noMatchingLoRAs": "Keine passenden LoRAs",
         "noModelsAvailable": "Keine Modelle verfügbar",
         "selectModel": "Wählen ein Modell aus",
         "noRefinerModelsInstalled": "Keine SDXL Refiner-Modelle installiert",
+        "noLoRAsInstalled": "Keine LoRAs installiert",
         "addLora": "LoRA hinzufügen",
         "defaultVAE": "Standard VAE",
         "lora": "LoRA",
@@ -1349,23 +1394,31 @@
         "workflows": "Arbeitsabläufe",
         "workflowName": "Arbeitsablauf-Name",
         "saveWorkflowAs": "Arbeitsablauf speichern als",
+        "searchWorkflows": "Suche Arbeitsabläufe",
         "newWorkflowCreated": "Neuer Arbeitsablauf erstellt",
         "problemSavingWorkflow": "Problem beim Speichern des Arbeitsablaufs",
+        "problemLoading": "Problem beim Laden von Arbeitsabläufen",
         "downloadWorkflow": "Speichern als",
         "savingWorkflow": "Speichere Arbeitsablauf...",
         "saveWorkflow": "Arbeitsablauf speichern",
         "noWorkflows": "Keine Arbeitsabläufe",
         "workflowLibrary": "Bibliothek",
         "unnamedWorkflow": "Unbenannter Arbeitsablauf",
+        "noDescription": "Keine Beschreibung",
+        "clearWorkflowSearchFilter": "Suchfilter zurücksetzen",
         "workflowEditorMenu": "Arbeitsablauf-Editor Menü",
         "deleteWorkflow": "Arbeitsablauf löschen",
         "workflowSaved": "Arbeitsablauf gespeichert",
         "uploadWorkflow": "Aus Datei laden",
+        "openWorkflow": "Arbeitsablauf öffnen",
         "saveWorkflowToProject": "Arbeitsablauf in Projekt speichern",
         "workflowCleared": "Arbeitsablauf gelöscht",
         "loading": "Lade Arbeitsabläufe",
         "name": "Name",
         "ascending": "Aufsteigend",
+        "defaultWorkflows": "Standard Arbeitsabläufe",
+        "userWorkflows": "Benutzer Arbeitsabläufe",
+        "projectWorkflows": "Projekt Arbeitsabläufe",
         "opened": "Geöffnet",
         "loadWorkflow": "Arbeitsablauf $t(common.load)",
         "updated": "Aktualisiert",
@@ -1378,11 +1431,10 @@
         "autoLayout": "Auto Layout",
         "copyShareLink": "Teilen-Link kopieren",
         "download": "Herunterladen",
-        "convertGraph": "Graph konvertieren",
-        "yourWorkflows": "Ihre Arbeitsabläufe",
-        "recentlyOpened": "Kürzlich geöffnet"
+        "convertGraph": "Graph konvertieren"
     },
     "sdxl": {
+        "concatPromptStyle": "Verknüpfen von Prompt & Stil",
         "scheduler": "Planer",
         "steps": "Schritte"
     },
@@ -1392,17 +1444,12 @@
     "prompt": {
         "noMatchingTriggers": "Keine passenden Trigger",
         "addPromptTrigger": "Prompt-Trigger hinzufügen",
-        "compatibleEmbeddings": "Kompatible Einbettungen",
-        "replace": "Ersetzen",
-        "discard": "Verwerfen",
-        "generateFromImage": "Prompt aus Bild generieren",
-        "expandCurrentPrompt": "Aktuelle Prompt erweitern",
-        "uploadImageForPromptGeneration": "Bild zur Prompt-Generierung hochladen",
-        "expandingPrompt": "Prompt wird erweitert..."
+        "compatibleEmbeddings": "Kompatible Einbettungen"
     },
     "ui": {
         "tabs": {
             "queue": "Warteschlange",
+            "generation": "Erzeugung",
             "gallery": "Galerie",
             "models": "Modelle",
             "upscaling": "Hochskalierung",
@@ -1526,26 +1573,30 @@
             "newGlobalReferenceImage": "Neues globales Referenzbild",
             "newRegionalReferenceImage": "Neues regionales Referenzbild",
             "newControlLayer": "Neue Kontroll-Ebene",
-            "newRasterLayer": "Neue Rasterebene"
+            "newRasterLayer": "Neue Raster-Ebene"
         },
         "rectangle": "Rechteck",
         "saveCanvasToGallery": "Leinwand in Galerie speichern",
-        "newRasterLayerError": "Problem beim Erstellen einer Rasterebene",
+        "newRasterLayerError": "Problem beim Erstellen einer Raster-Ebene",
         "saveLayerToAssets": "Ebene in Galerie speichern",
         "deleteReferenceImage": "Referenzbild löschen",
         "referenceImage": "Referenzbild",
         "opacity": "Opazität",
         "removeBookmark": "Lesezeichen entfernen",
-        "rasterLayer": "Rasterebene",
+        "rasterLayer": "Raster-Ebene",
+        "rasterLayers_withCount_visible": "Raster-Ebenen ({{count}})",
+        "controlLayers_withCount_visible": "Kontroll-Ebenen ({{count}})",
         "deleteSelected": "Ausgewählte löschen",
         "newRegionalReferenceImageError": "Problem beim Erstellen eines regionalen Referenzbilds",
         "newControlLayerOk": "Kontroll-Ebene erstellt",
         "newControlLayerError": "Problem beim Erstellen einer Kontroll-Ebene",
-        "newRasterLayerOk": "Rasterebene erstellt",
+        "newRasterLayerOk": "Raster-Layer erstellt",
         "moveToFront": "Nach vorne bringen",
         "copyToClipboard": "In die Zwischenablage kopieren",
+        "controlLayers_withCount_hidden": "Kontroll-Ebenen ({{count}} ausgeblendet)",
         "clearCaches": "Cache leeren",
         "controlLayer": "Kontroll-Ebene",
+        "rasterLayers_withCount_hidden": "Raster-Ebenen ({{count}} ausgeblendet)",
         "transparency": "Transparenz",
         "canvas": "Leinwand",
         "global": "Global",
@@ -1568,7 +1619,9 @@
         "weight": "Gewichtung",
         "addReferenceImage": "$t(controlLayers.referenceImage) hinzufügen",
         "addInpaintMask": "$t(controlLayers.inpaintMask) hinzufügen",
+        "addGlobalReferenceImage": "$t(controlLayers.globalReferenceImage) hinzufügen",
         "regionalGuidance": "Regionale Führung",
+        "globalReferenceImages_withCount_visible": "Globale Referenzbilder ({{count}})",
         "addPositivePrompt": "$t(controlLayers.prompt) hinzufügen",
         "locked": "Gesperrt",
         "showHUD": "HUD anzeigen",
@@ -1576,12 +1629,16 @@
         "addRasterLayer": "$t(controlLayers.rasterLayer) hinzufügen",
         "addRegionalGuidance": "$t(controlLayers.regionalGuidance) hinzufügen",
         "addControlLayer": "$t(controlLayers.controlLayer) hinzufügen",
+        "newCanvasSession": "Neue Leinwand-Sitzung",
         "replaceLayer": "Ebene ersetzen",
+        "newGallerySession": "Neue Galerie-Sitzung",
         "unlocked": "Entsperrt",
         "showProgressOnCanvas": "Fortschritt auf Leinwand anzeigen",
         "controlMode": {
             "balanced": "Ausgewogen"
         },
+        "globalReferenceImages_withCount_hidden": "Globale Referenzbilder ({{count}} ausgeblendet)",
+        "sendToGallery": "An Galerie senden",
         "stagingArea": {
             "accept": "Annehmen",
             "next": "Nächste",
@@ -1589,6 +1646,8 @@
             "discard": "Verwerfen",
             "previous": "Vorherige"
         },
+        "regionalGuidance_withCount_visible": "Regionale Führung ({{count}})",
+        "regionalGuidance_withCount_hidden": "Regionale Führung ({{count}} ausgeblendet)",
         "settings": {
             "snapToGrid": {
                 "on": "Ein",
@@ -1598,6 +1657,8 @@
         },
         "layer_one": "Ebene",
         "layer_other": "Ebenen",
+        "layer_withCount_one": "Ebene ({{count}})",
+        "layer_withCount_other": "Ebenen ({{count}})",
         "fill": {
             "fillStyle": "Füllstil",
             "diagonal": "Diagonal",
@@ -1621,14 +1682,7 @@
             "filterType": "Filtertyp",
             "filter": "Filter"
         },
-        "bookmark": "Lesezeichen für Schnell-Umschalten",
-        "asRasterLayer": "Als $t(controlLayers.rasterLayer)",
-        "asRasterLayerResize": "Als $t(controlLayers.rasterLayer) (Größe anpassen)",
-        "rasterLayer_withCount_one": "$t(controlLayers.rasterLayer)",
-        "rasterLayer_withCount_other": "Rasterebenen",
-        "newRasterLayer": "Neue $t(controlLayers.rasterLayer)",
-        "showNonRasterLayers": "Nicht-Rasterebenen anzeigen (Umschalt+H)",
-        "hideNonRasterLayers": "Nicht-Rasterebenen ausblenden (Umschalt+H)"
+        "bookmark": "Lesezeichen für Schnell-Umschalten"
     },
     "upsell": {
         "shareAccess": "Zugang teilen",

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

@@ -38,13 +38,10 @@
         "deletedImagesCannotBeRestored": "Deleted images cannot be restored.",
         "hideBoards": "Hide Boards",
         "loading": "Loading...",
-        "locateInGalery": "Locate in Gallery",
         "menuItemAutoAdd": "Auto-add to this Board",
         "move": "Move",
         "movingImagesToBoard_one": "Moving {{count}} image to board:",
         "movingImagesToBoard_other": "Moving {{count}} images to board:",
-        "movingVideosToBoard_one": "Moving {{count}} video to board:",
-        "movingVideosToBoard_other": "Moving {{count}} videos to board:",
         "myBoard": "My Board",
         "noBoards": "No {{boardType}} Boards",
         "noMatching": "No matching Boards",
@@ -61,8 +58,6 @@
         "imagesWithCount_other": "{{count}} images",
         "assetsWithCount_one": "{{count}} asset",
         "assetsWithCount_other": "{{count}} assets",
-        "videosWithCount_one": "{{count}} video",
-        "videosWithCount_other": "{{count}} videos",
         "updateBoardError": "Error updating board"
     },
     "accordions": {
@@ -104,7 +99,6 @@
         "copy": "Copy",
         "copyError": "$t(gallery.copy) Error",
         "clipboard": "Clipboard",
-        "crop": "Crop",
         "on": "On",
         "off": "Off",
         "or": "or",
@@ -120,9 +114,6 @@
         "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",
@@ -243,10 +234,7 @@
         "resultSubtitle": "Choose how to handle the expanded prompt:",
         "replace": "Replace",
         "insert": "Insert",
-        "discard": "Discard",
-        "noPromptHistory": "No prompt history recorded.",
-        "noMatchingPrompts": "No matching prompts in history.",
-        "toSwitchBetweenPrompts": "to switch between prompts."
+        "discard": "Discard"
     },
     "queue": {
         "queue": "Queue",
@@ -265,7 +253,6 @@
         "cancel": "Cancel",
         "cancelAllExceptCurrentQueueItemAlertDialog": "Canceling all queue items except the current one will stop pending items but allow the in-progress one to finish.",
         "cancelAllExceptCurrentQueueItemAlertDialog2": "Are you sure you want to cancel all pending queue items?",
-        "cancelAllExceptCurrent": "Cancel All Except Current",
         "cancelAllExceptCurrentTooltip": "Cancel All Except Current Item",
         "cancelTooltip": "Cancel Current Item",
         "cancelSucceeded": "Item Canceled",
@@ -286,7 +273,7 @@
         "retryItem": "Retry Item",
         "cancelBatchSucceeded": "Batch Canceled",
         "cancelBatchFailed": "Problem Canceling Batch",
-        "clearQueueAlertDialog": "Clearing the queue immediately cancels any processing items and clears the queue entirely. Pending filters will be canceled and the Canvas Staging Area will be reset.",
+        "clearQueueAlertDialog": "Clearing the queue immediately cancels any processing items and clears the queue entirely. Pending filters will be canceled.",
         "clearQueueAlertDialog2": "Are you sure you want to clear the queue?",
         "current": "Current",
         "next": "Next",
@@ -302,7 +289,7 @@
         "completedIn": "Completed in",
         "batch": "Batch",
         "origin": "Origin",
-        "destination": "Dest",
+        "destination": "Destination",
         "upscaling": "Upscaling",
         "canvas": "Canvas",
         "generation": "Generation",
@@ -328,13 +315,7 @@
         "iterations_other": "Iterations",
         "generations_one": "Generation",
         "generations_other": "Generations",
-        "batchSize": "Batch Size",
-        "createdAt": "Created At",
-        "completedAt": "Completed At",
-        "sortColumn": "Sort Column",
-        "sortBy": "Sort by {{column}}",
-        "sortOrderAscending": "Ascending",
-        "sortOrderDescending": "Descending"
+        "batchSize": "Batch Size"
     },
     "invocationCache": {
         "invocationCache": "Invocation Cache",
@@ -375,9 +356,6 @@
         "deleteImage_one": "Delete Image",
         "deleteImage_other": "Delete {{count}} Images",
         "deleteImagePermanent": "Deleted images cannot be restored.",
-        "deleteVideo_one": "Delete Video",
-        "deleteVideo_other": "Delete {{count}} Videos",
-        "deleteVideoPermanent": "Deleted videos cannot be restored.",
         "displayBoardSearch": "Board Search",
         "displaySearch": "Image Search",
         "download": "Download",
@@ -397,10 +375,9 @@
         "sortDirection": "Sort Direction",
         "showStarredImagesFirst": "Show Starred Images First",
         "noImageSelected": "No Image Selected",
-        "noVideoSelected": "No Video Selected",
         "noImagesInGallery": "No Images to Display",
-        "starImage": "Star",
-        "unstarImage": "Unstar",
+        "starImage": "Star Image",
+        "unstarImage": "Unstar Image",
         "unableToLoad": "Unable to load Gallery",
         "deleteSelection": "Delete Selection",
         "downloadSelection": "Download Selection",
@@ -429,9 +406,7 @@
         "openViewer": "Open Viewer",
         "closeViewer": "Close Viewer",
         "move": "Move",
-        "useForPromptGeneration": "Use for Prompt Generation",
-        "videos": "Videos",
-        "videosTab": "Videos you've created and saved within Invoke."
+        "useForPromptGeneration": "Use for Prompt Generation"
     },
     "hotkeys": {
         "hotkeys": "Hotkeys",
@@ -476,22 +451,10 @@
                 "title": "Select the Queue Tab",
                 "desc": "Selects the Queue tab."
             },
-            "selectVideoTab": {
-                "title": "Select the Video Tab",
-                "desc": "Selects the Video tab."
-            },
             "focusPrompt": {
                 "title": "Focus Prompt",
                 "desc": "Move cursor focus to the positive prompt."
             },
-            "promptHistoryPrev": {
-                "title": "Previous Prompt in History",
-                "desc": "When the prompt is focused, move to the previous (older) prompt in your history."
-            },
-            "promptHistoryNext": {
-                "title": "Next Prompt in History",
-                "desc": "When the prompt is focused, move to the next (newer) prompt in your history."
-            },
             "toggleLeftPanel": {
                 "title": "Toggle Left Panel",
                 "desc": "Show or hide the left panel."
@@ -507,16 +470,8 @@
             "togglePanels": {
                 "title": "Toggle Panels",
                 "desc": "Show or hide both left and right panels at once."
-            },
-            "selectGenerateTab": {
-                "title": "Select the Generate Tab",
-                "desc": "Selects the Generate tab.",
-                "key": "1"
             }
         },
-        "video": {
-            "title": "Video"
-        },
         "canvas": {
             "title": "Canvas",
             "selectBrushTool": {
@@ -607,13 +562,9 @@
                 "title": "Prev Layer",
                 "desc": "Select the previous layer in the list."
             },
-            "setFillColorsToDefault": {
-                "title": "Set Colors to Default",
-                "desc": "Set the current tool colors to default."
-            },
-            "toggleFillColor": {
-                "title": "Toggle Fill Color",
-                "desc": "Toggle the current tool fill color."
+            "setFillToWhite": {
+                "title": "Set Color to White",
+                "desc": "Set the current tool color to white."
             },
             "filterSelected": {
                 "title": "Filter",
@@ -623,10 +574,6 @@
                 "title": "Transform",
                 "desc": "Transform the selected layer."
             },
-            "invertMask": {
-                "title": "Invert Mask",
-                "desc": "Invert the selected inpaint mask, creating a new mask with opposite transparency."
-            },
             "applyFilter": {
                 "title": "Apply Filter",
                 "desc": "Apply the pending filter to the selected layer."
@@ -652,28 +599,6 @@
             "toggleNonRasterLayers": {
                 "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"
-            },
-            "toggleBbox": {
-                "title": "Toggle Bbox Visibility",
-                "desc": "Hide or show the generation bounding box"
-            },
-            "applySegmentAnything": {
-                "title": "Apply Segment Anything",
-                "desc": "Apply the current Segment Anything mask.",
-                "key": "enter"
-            },
-            "cancelSegmentAnything": {
-                "title": "Cancel Segment Anything",
-                "desc": "Cancel the current Segment Anything operation.",
-                "key": "esc"
             }
         },
         "workflows": {
@@ -803,33 +728,23 @@
             "deleteSelection": {
                 "title": "Delete",
                 "desc": "Delete all selected images. By default, you will be prompted to confirm deletion. If the images are currently in use in the app, you will be warned."
-            },
-            "starImage": {
-                "title": "Star/Unstar Image",
-                "desc": "Star or unstar the selected image."
             }
         }
     },
-    "lora": {
-        "weight": "Weight"
-    },
     "metadata": {
         "allPrompts": "All Prompts",
         "cfgScale": "CFG scale",
         "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
-        "clipSkip": "$t(parameters.clipSkip)",
         "createdBy": "Created By",
         "generationMode": "Generation Mode",
         "guidance": "Guidance",
         "height": "Height",
         "imageDetails": "Image Details",
-        "videoDetails": "Video Details",
         "imageDimensions": "Image Dimensions",
         "metadata": "Metadata",
         "model": "Model",
         "negativePrompt": "Negative Prompt",
         "noImageDetails": "No image details found",
-        "noVideoDetails": "No video details found",
         "noMetaData": "No metadata found",
         "noRecallParameters": "No parameters to recall found",
         "parameterSet": "Parameter {{parameter}} set",
@@ -847,11 +762,7 @@
         "vae": "VAE",
         "width": "Width",
         "workflow": "Workflow",
-        "canvasV2Metadata": "Canvas Layers",
-        "videoModel": "Model",
-        "videoDuration": "Duration",
-        "videoAspectRatio": "Aspect Ratio",
-        "videoResolution": "Resolution"
+        "canvasV2Metadata": "Canvas Layers"
     },
     "modelManager": {
         "active": "active",
@@ -926,9 +837,6 @@
         "install": "Install",
         "installAll": "Install All",
         "installRepo": "Install Repo",
-        "installBundle": "Install Bundle",
-        "installBundleMsg1": "Are you sure you want to install the {{bundleName}} bundle?",
-        "installBundleMsg2": "This bundle will install the following {{count}} models:",
         "ipAdapters": "IP Adapters",
         "learnMoreAboutSupportedModels": "Learn more about the models we support",
         "load": "Load",
@@ -1217,27 +1125,10 @@
         "addItem": "Add Item",
         "generateValues": "Generate Values",
         "floatRangeGenerator": "Float Range Generator",
-        "integerRangeGenerator": "Integer Range Generator",
-        "layout": {
-            "autoLayout": "Auto Layout",
-            "layeringStrategy": "Layering Strategy",
-            "networkSimplex": "Network Simplex",
-            "longestPath": "Longest Path",
-            "nodeSpacing": "Node Spacing",
-            "layerSpacing": "Layer Spacing",
-            "layoutDirection": "Layout Direction",
-            "layoutDirectionRight": "Right",
-            "layoutDirectionDown": "Down",
-            "alignment": "Node Alignment",
-            "alignmentUL": "Top Left",
-            "alignmentDL": "Bottom Left",
-            "alignmentUR": "Top Right",
-            "alignmentDR": "Bottom Right"
-        }
+        "integerRangeGenerator": "Integer Range Generator"
     },
     "parameters": {
         "aspect": "Aspect",
-        "duration": "Duration",
         "lockAspectRatio": "Lock Aspect Ratio",
         "swapDimensions": "Swap Dimensions",
         "setToOptimalSize": "Optimize size for model",
@@ -1262,15 +1153,9 @@
         "height": "Height",
         "imageFit": "Fit Initial Image To Output Size",
         "images": "Images",
-        "images_withCount_one": "Image",
-        "images_withCount_other": "Images",
-        "videos_withCount_one": "Video",
-        "videos_withCount_other": "Videos",
         "infillMethod": "Infill Method",
         "infillColorValue": "Fill Color",
         "info": "Info",
-        "startingFrameImage": "Start Frame",
-        "startingFrameImageAspectRatioWarning": "Image aspect ratio does not match the video aspect ratio ({{videoAspectRatio}}). This could lead to unexpected cropping during video generation.",
         "invoke": {
             "addingImagesTo": "Adding images to",
             "modelDisabledForTrial": "Generating with {{modelName}} is not available on trial accounts. Visit your account settings to upgrade.",
@@ -1294,7 +1179,6 @@
             "batchNodeCollectionSizeMismatchNoGroupId": "Batch group collection size mismatch",
             "batchNodeCollectionSizeMismatch": "Collection size mismatch on Batch {{batchGroupId}}",
             "noModelSelected": "No model selected",
-            "noStartingFrameImage": "No starting frame image",
             "noT5EncoderModelSelected": "No T5 Encoder model selected for FLUX generation",
             "noFLUXVAEModelSelected": "No VAE model selected for FLUX generation",
             "noCLIPEmbedModelSelected": "No CLIP Embed model selected for FLUX generation",
@@ -1307,7 +1191,7 @@
             "modelIncompatibleScaledBboxWidth": "Scaled bbox width is {{width}} but {{model}} requires multiple of {{multiple}}",
             "modelIncompatibleScaledBboxHeight": "Scaled bbox height is {{height}} but {{model}} requires multiple of {{multiple}}",
             "fluxModelMultipleControlLoRAs": "Can only use 1 Control LoRA at a time",
-            "incompatibleLoRAs": "Incompatible LoRA(s) added",
+            "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)",
@@ -1317,8 +1201,7 @@
             "noNodesInGraph": "No nodes in graph",
             "systemDisconnected": "System disconnected",
             "promptExpansionPending": "Prompt expansion in progress",
-            "promptExpansionResultPending": "Please accept or discard your prompt expansion result",
-            "videoIsDisabled": "Video generation is not enabled for {{accountType}} accounts."
+            "promptExpansionResultPending": "Please accept or discard your prompt expansion result"
         },
         "maskBlur": "Mask Blur",
         "negativePromptPlaceholder": "Negative Prompt",
@@ -1336,11 +1219,9 @@
         "seamlessXAxis": "Seamless X Axis",
         "seamlessYAxis": "Seamless Y Axis",
         "seed": "Seed",
-        "videoActions": "Video Actions",
         "imageActions": "Image Actions",
         "sendToCanvas": "Send To Canvas",
         "sendToUpscale": "Send To Upscale",
-        "sendToVideo": "Send To Video",
         "showOptionsPanel": "Show Side Panel (O or T)",
         "shuffle": "Shuffle Seed",
         "steps": "Steps",
@@ -1352,19 +1233,16 @@
         "postProcessing": "Post-Processing (Shift + U)",
         "processImage": "Process Image",
         "upscaling": "Upscaling",
-        "video": "Video",
         "useAll": "Use All",
         "useSize": "Use Size",
         "useCpuNoise": "Use CPU Noise",
         "remixImage": "Remix Image",
         "usePrompt": "Use Prompt",
         "useSeed": "Use Seed",
-        "useClipSkip": "Use CLIP Skip",
         "width": "Width",
         "gaussianBlur": "Gaussian Blur",
         "boxBlur": "Box Blur",
         "staged": "Staged",
-        "resolution": "Resolution",
         "modelDisabledForTrial": "Generating with {{modelName}} is not available on trial accounts. Visit your <LinkComponent>account settings</LinkComponent> to upgrade."
     },
     "dynamicPrompts": {
@@ -1442,8 +1320,8 @@
         "addedToBoard": "Added to board {{name}}'s assets",
         "addedToUncategorized": "Added to board $t(boards.uncategorized)'s assets",
         "baseModelChanged": "Base Model Changed",
-        "baseModelChangedCleared_one": "Updated, cleared or disabled {{count}} incompatible submodel",
-        "baseModelChangedCleared_other": "Updated, cleared or disabled {{count}} incompatible submodels",
+        "baseModelChangedCleared_one": "Cleared or disabled {{count}} incompatible submodel",
+        "baseModelChangedCleared_other": "Cleared or disabled {{count}} incompatible submodels",
         "canceled": "Processing Canceled",
         "connected": "Connected to Server",
         "imageCopied": "Image Copied",
@@ -1529,15 +1407,7 @@
         "sentToUpscale": "Sent to Upscale",
         "promptGenerationStarted": "Prompt generation started",
         "uploadAndPromptGenerationFailed": "Failed to upload image and generate prompt",
-        "promptExpansionFailed": "We ran into an issue. Please try prompt expansion again.",
-        "maskInverted": "Mask Inverted",
-        "maskInvertFailed": "Failed to Invert Mask",
-        "noVisibleMasks": "No Visible Masks",
-        "noVisibleMasksDesc": "Create or enable at least one inpaint mask to invert",
-        "noInpaintMaskSelected": "No Inpaint Mask Selected",
-        "noInpaintMaskSelectedDesc": "Select an inpaint mask to invert",
-        "invalidBbox": "Invalid Bounding Box",
-        "invalidBboxDesc": "The bounding box has no valid dimensions"
+        "promptExpansionFailed": "We ran into an issue. Please try prompt expansion again."
     },
     "popovers": {
         "clipSkip": {
@@ -2011,11 +1881,8 @@
             "zoomToNode": "Zoom to Node",
             "nodeFieldTooltip": "To add a node field, click the small plus sign button on the field in the Workflow Editor, or drag the field by its name into the form.",
             "addToForm": "Add to Form",
-            "removeFromForm": "Remove from Form",
             "label": "Label",
             "showDescription": "Show Description",
-            "showShuffle": "Show Shuffle",
-            "shuffle": "Shuffle",
             "component": "Component",
             "numberInput": "Number Input",
             "singleLine": "Single Line",
@@ -2073,7 +1940,6 @@
         "canvas": "Canvas",
         "bookmark": "Bookmark for Quick Switch",
         "fitBboxToLayers": "Fit Bbox To Layers",
-        "fitBboxToMasks": "Fit Bbox To Masks",
         "removeBookmark": "Remove Bookmark",
         "saveCanvasToGallery": "Save Canvas to Gallery",
         "saveBboxToGallery": "Save Bbox to Gallery",
@@ -2096,24 +1962,6 @@
         "pullBboxIntoLayerError": "Problem Pulling BBox Into Layer",
         "pullBboxIntoReferenceImageOk": "Bbox Pulled Into ReferenceImage",
         "pullBboxIntoReferenceImageError": "Problem Pulling BBox Into ReferenceImage",
-        "addAdjustments": "Add Adjustments",
-        "removeAdjustments": "Remove Adjustments",
-        "adjustments": {
-            "simple": "Simple",
-            "curves": "Curves",
-            "heading": "Adjustments",
-            "expand": "Expand adjustments",
-            "collapse": "Collapse adjustments",
-            "brightness": "Brightness",
-            "contrast": "Contrast",
-            "saturation": "Saturation",
-            "temperature": "Temperature",
-            "tint": "Tint",
-            "sharpness": "Sharpness",
-            "finish": "Finish",
-            "reset": "Reset",
-            "master": "Master"
-        },
         "regionIsEmpty": "Selected region is empty",
         "mergeVisible": "Merge Visible",
         "mergeDown": "Merge Down",
@@ -2156,7 +2004,6 @@
         "rasterLayer": "Raster Layer",
         "controlLayer": "Control Layer",
         "inpaintMask": "Inpaint Mask",
-        "invertMask": "Invert Mask",
         "regionalGuidance": "Regional Guidance",
         "referenceImageRegional": "Reference Image (Regional)",
         "referenceImageGlobal": "Reference Image (Global)",
@@ -2165,8 +2012,6 @@
         "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",
@@ -2275,8 +2120,7 @@
             "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",
-            "bboxHidden": "Bounding box is hidden (shift+o to toggle)"
+            "fluxFillIncompatibleWithControlLoRA": "Control LoRA is not compatible with FLUX Fill"
         },
         "errors": {
             "unableToFindImage": "Unable to find image",
@@ -2312,8 +2156,6 @@
         },
         "fill": {
             "fillColor": "Fill Color",
-            "bgFillColor": "Background Color",
-            "fgFillColor": "Foreground Color",
             "fillStyle": "Fill Style",
             "solid": "Solid",
             "grid": "Grid",
@@ -2485,21 +2327,12 @@
             "saveAs": "Save As",
             "cancel": "Cancel",
             "process": "Process",
-            "desc": "Select a single target object. After selection is complete, click <Bold>Apply</Bold> to discard everything outside the selected area, or save the selection as a new layer.",
-            "visualModeDesc": "Visual mode uses box and point inputs to select an object.",
-            "visualMode1": "Click and drag to draw a box around the object you want to select. You may get better results by drawing the box a bit larger or smaller than the object.",
-            "visualMode2": "Click to add a green <Bold>include</Bold> point, or shift-click to add a red <Bold>exclude</Bold> point to tell the model what to include or exclude.",
-            "visualMode3": "Points can be used to refine a box selection or used independently.",
-            "promptModeDesc": "Prompt mode uses text input to select an object.",
-            "promptMode1": "Type a brief description of the object you want to select.",
-            "promptMode2": "Use simple language, avoiding complex descriptions or multiple objects.",
+            "help1": "Select a single target object. Add <Bold>Include</Bold> and <Bold>Exclude</Bold> points to indicate which parts of the layer are part of the target object.",
+            "help2": "Start with one <Bold>Include</Bold> point within the target object. Add more points to refine the selection. Fewer points typically produce better results.",
+            "help3": "Invert the selection to select everything except the target object.",
             "clickToAdd": "Click on the layer to add a point",
             "dragToMove": "Drag a point to move it",
-            "clickToRemove": "Click on a point to remove it",
-            "model": "Model",
-            "segmentAnything1": "Segment Anything 1",
-            "segmentAnything2": "Segment Anything 2",
-            "prompt": "Selection Prompt"
+            "clickToRemove": "Click on a point to remove it"
         },
         "settings": {
             "snapToGrid": {
@@ -2646,7 +2479,7 @@
     },
     "ui": {
         "tabs": {
-            "generate": "Generate",
+            "generation": "Generation",
             "canvas": "Canvas",
             "workflows": "Workflows",
             "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
@@ -2655,54 +2488,15 @@
             "queue": "Queue",
             "upscaling": "Upscaling",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
-            "video": "Video",
             "gallery": "Gallery"
         },
-        "panels": {
-            "launchpad": "Launchpad",
-            "workflowEditor": "Workflow Editor",
-            "imageViewer": "Viewer",
-            "canvas": "Canvas",
-            "video": "Video"
-        },
         "launchpad": {
             "workflowsTitle": "Go deep with Workflows.",
             "upscalingTitle": "Upscale and add detail.",
             "canvasTitle": "Edit and refine on Canvas.",
             "generateTitle": "Generate images from text prompts.",
-            "videoTitle": "Generate videos from text prompts.",
-            "video": {
-                "startingFrameCalloutTitle": "Add a Starting Frame",
-                "startingFrameCalloutDesc": "Add an image to control the first frame of your video."
-            },
-            "addStartingFrame": {
-                "title": "Add a Starting Frame",
-                "description": "Add an image to control the first frame of your video."
-            },
             "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",
@@ -2739,13 +2533,6 @@
                 "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."
@@ -2753,10 +2540,6 @@
             }
         }
     },
-    "video": {
-        "noVideoSelected": "No video selected",
-        "selectFromGallery": "Select a video from the gallery to play"
-    },
     "system": {
         "enableLogging": "Enable Logging",
         "logLevel": {
@@ -2794,9 +2577,9 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "Select Object v2: Improved object selection with point and box inputs or text prompts.",
-            "Raster Layer Adjustments: Easily adjust layer brightness, contrast, saturation, curves and more.",
-            "Prompt History: Review and quickly recall your last 100 prompts."
+            "Generate images faster with new Launchpads and a simplified Generate tab.",
+            "Edit with prompts using Flux Kontext Dev.",
+            "Export to PSD, bulk-hide overlays, organize models & images — all in a reimagined interface built for control."
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",

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

@@ -47,8 +47,11 @@
         "editor": "Editor",
         "orderBy": "Ordenar por",
         "file": "Archivo",
+        "goTo": "Ir a",
+        "imageFailedToLoad": "No se puede cargar la imagen",
         "saveAs": "Guardar Como",
         "somethingWentWrong": "Algo salió mal",
+        "nextPage": "Página Siguiente",
         "selected": "Seleccionado",
         "tab": "Tabulador",
         "positivePrompt": "Prompt Positivo",
@@ -58,6 +61,7 @@
         "unknown": "Desconocido",
         "input": "Entrada",
         "template": "Plantilla",
+        "prevPage": "Página Anterior",
         "red": "Rojo",
         "alpha": "Transparencia",
         "outputs": "Resultados",
@@ -90,6 +94,8 @@
         "edit": "Editar",
         "safetensors": "Safetensors",
         "toResolve": "Para resolver",
+        "localSystem": "Sistema local",
+        "notInstalled": "No $t(common.installed)",
         "outpaint": "outpaint",
         "simple": "Sencillo",
         "close": "Cerrar"
@@ -98,6 +104,7 @@
         "galleryImageSize": "Tamaño de la imagen",
         "gallerySettings": "Ajustes de la galería",
         "autoSwitchNewImages": "Auto seleccionar Imágenes nuevas",
+        "noImagesInGallery": "No hay imágenes para mostrar",
         "deleteImage_one": "Eliminar Imagen",
         "deleteImage_many": "Eliminar {{count}} Imágenes",
         "deleteImage_other": "Eliminar {{count}} Imágenes",
@@ -111,7 +118,9 @@
         "selectForCompare": "Seleccionar para comparar",
         "alwaysShowImageSizeBadge": "Mostrar siempre las dimensiones de la imagen",
         "currentlyInUse": "Esta imagen se utiliza actualmente con las siguientes funciones:",
+        "unableToLoad": "No se puede cargar la galería",
         "selectAllOnPage": "Seleccionar todo en la página",
+        "selectAnImageToCompare": "Seleccione una imagen para comparar",
         "bulkDownloadFailed": "Error en la descarga",
         "compareHelp2": "Presione <Kbd> M </Kbd> para recorrer los modos de comparación.",
         "move": "Mover",
@@ -136,6 +145,7 @@
         "exitBoardSearch": "Finalizar búsqueda",
         "exitSearch": "Salir de la búsqueda de imágenes",
         "featuresWillReset": "Si elimina esta imagen, dichas funciones se restablecerán inmediatamente.",
+        "jump": "Omitir",
         "loading": "Cargando",
         "newestFirst": "La más nueva primero",
         "unstarImage": "Dejar de ser favorita",
@@ -153,7 +163,9 @@
         "boardsSettings": "Ajustes de los tableros",
         "imagesSettings": "Configuración de imágenes de la galería",
         "compareHelp3": "Presione <Kbd> C </Kbd> para intercambiar las imágenes comparadas.",
-        "showArchivedBoards": "Mostrar paneles archivados"
+        "showArchivedBoards": "Mostrar paneles archivados",
+        "closeViewer": "Cerrar visor",
+        "openViewer": "Abrir visor"
     },
     "modelManager": {
         "modelManager": "Gestor de Modelos",
@@ -227,10 +239,12 @@
         "scaledHeight": "Alto escalado",
         "infillMethod": "Método de relleno",
         "tileSize": "Tamaño del mosaico",
+        "downloadImage": "Descargar imagen",
         "usePrompt": "Usar Entrada",
         "useSeed": "Usar Semilla",
         "useAll": "Usar Todo",
         "info": "Información",
+        "showOptionsPanel": "Mostrar panel lateral (O o T)",
         "symmetry": "Simetría",
         "copyImage": "Copiar la imagen",
         "general": "General",
@@ -309,6 +323,8 @@
         "hideMinimapnodes": "Ocultar el minimapa",
         "fitViewportNodes": "Ajustar la vista",
         "zoomOutNodes": "Alejar",
+        "hideLegendNodes": "Ocultar la leyenda del tipo de campo",
+        "showLegendNodes": "Mostrar la leyenda del tipo de campo",
         "showMinimapnodes": "Mostrar el minimapa",
         "reloadNodeTemplates": "Recargar las plantillas de nodos",
         "loadWorkflow": "Cargar el flujo de trabajo",
@@ -345,6 +361,7 @@
         "assetsWithCount_one": "{{count}} activo",
         "assetsWithCount_many": "{{count}} activos",
         "assetsWithCount_other": "{{count}} activos",
+        "hideBoards": "Ocultar paneles",
         "addPrivateBoard": "Agregar un panel privado",
         "addSharedBoard": "Añadir panel compartido",
         "boards": "Paneles",
@@ -355,6 +372,7 @@
         "noBoards": "No hay paneles {{boardType}}",
         "shared": "Paneles compartidos",
         "deletedPrivateBoardsCannotbeRestored": "Los paneles eliminados no se pueden restaurar. Al elegir \"Eliminar solo el panel\", las imágenes se colocan en un estado privado y sin categoría para el creador de la imagen.",
+        "viewBoards": "Ver paneles",
         "private": "Paneles privados",
         "updateBoardError": "No se pudo actualizar el panel"
     },
@@ -381,6 +399,7 @@
     "ui": {
         "tabs": {
             "canvas": "Lienzo",
+            "generation": "Generación",
             "queue": "Cola",
             "workflows": "Flujos de trabajo",
             "models": "Modelos",
@@ -443,6 +462,7 @@
         "other": "Otro",
         "queueFront": "Añadir al principio de la cola",
         "gallery": "Galería",
+        "batchFieldValues": "Valores de procesamiento por lotes",
         "session": "Sesión",
         "notReady": "La cola aún no está lista",
         "graphQueued": "Gráfico en cola",
@@ -475,11 +495,15 @@
         "layer_one": "Capa",
         "layer_many": "Capas",
         "layer_other": "Capas",
+        "layer_withCount_one": "({{count}}) capa",
+        "layer_withCount_many": "({{count}}) capas",
+        "layer_withCount_other": "({{count}}) capas",
         "copyToClipboard": "Copiar al portapapeles"
     },
     "whatsNew": {
         "readReleaseNotes": "Leer las notas de la versión",
         "watchRecentReleaseVideos": "Ver videos de versiones recientes",
+        "watchUiUpdatesOverview": "Descripción general de las actualizaciones de la interfaz de usuario de Watch",
         "whatsNewInInvoke": "Novedades en Invoke",
         "items": [
             "<StrongComponent>SD 3.5</StrongComponent>: compatibilidad con SD 3.5 Medium y Large."
@@ -504,11 +528,13 @@
     },
     "hrf": {
         "hrf": "Solución de alta resolución",
+        "enableHrf": "Activar corrección de alta resolución",
         "metadata": {
             "enabled": "Corrección de alta resolución activada",
             "strength": "Forzar la corrección de alta resolución",
             "method": "Método de corrección de alta resolución"
-        }
+        },
+        "upscaleMethod": "Método de expansión"
     },
     "prompt": {
         "addPromptTrigger": "Añadir activador de los avisos",
@@ -566,6 +592,10 @@
                 "title": "Ajustar capas al lienzo",
                 "desc": "Escala y posiciona la vista para que se ajuste a todas las capas visibles."
             },
+            "setFillToWhite": {
+                "title": "Establecer color en blanco",
+                "desc": "Establece el color actual de la herramienta en blanco."
+            },
             "resetSelected": {
                 "title": "Restablecer capa",
                 "desc": "Restablecer la capa seleccionada. Solo se aplica a Máscara de retoque y Guía regional."
@@ -839,8 +869,10 @@
         "seed": "Semilla",
         "strength": "Forzar imagen a imagen",
         "recallParameters": "Parámetros de recuperación",
+        "recallParameter": "Recuperar {{label}}",
         "steps": "Pasos",
-        "noRecallParameters": "Sin parámetros para recuperar"
+        "noRecallParameters": "Sin parámetros para recuperar",
+        "parsingFailed": "Error al analizar"
     },
     "system": {
         "logLevel": {

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

@@ -28,6 +28,7 @@
     "gallery": {
         "galleryImageSize": "Kuvan koko",
         "gallerySettings": "Gallerian asetukset",
-        "autoSwitchNewImages": "Vaihda uusiin kuviin automaattisesti"
+        "autoSwitchNewImages": "Vaihda uusiin kuviin automaattisesti",
+        "noImagesInGallery": "Ei kuvia galleriassa"
     }
 }

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

@@ -27,15 +27,21 @@
         "error": "Erreur",
         "installed": "Installé",
         "format": "format",
+        "goTo": "Aller à",
         "input": "Entrée",
         "linear": "Linéaire",
+        "localSystem": "Système local",
         "learnMore": "En savoir plus",
         "modelManager": "Gestionnaire de modèle",
+        "notInstalled": "Non $t(common.installed)",
         "openInNewTab": "Ouvrir dans un nouvel onglet",
         "somethingWentWrong": "Une erreur s'est produite",
         "created": "Créé",
         "tab": "Onglet",
         "folder": "Dossier",
+        "imageFailedToLoad": "Impossible de charger l'Image",
+        "prevPage": "Page précédente",
+        "nextPage": "Page suivante",
         "selected": "Sélectionné",
         "save": "Enregistrer",
         "updated": "Mis à jour",
@@ -105,6 +111,7 @@
         "min": "Min",
         "max": "Max",
         "values": "Valeurs",
+        "resetToDefaults": "Réinitialiser par défaut",
         "seed": "Graine",
         "combinatorial": "Combinatoire"
     },
@@ -112,9 +119,11 @@
         "galleryImageSize": "Taille de l'image",
         "gallerySettings": "Paramètres de la galerie",
         "autoSwitchNewImages": "Basculer automatiquement vers de nouvelles images",
+        "noImagesInGallery": "Aucune image à afficher",
         "bulkDownloadRequestedDesc": "Votre demande de téléchargement est en cours de traitement. Cela peut prendre quelques instants.",
         "deleteSelection": "Supprimer la sélection",
         "selectAllOnPage": "Séléctionner toute la page",
+        "unableToLoad": "Impossible de charger la Galerie",
         "featuresWillReset": "Si vous supprimez cette image, ces fonctionnalités vont être réinitialisés.",
         "loading": "Chargement",
         "sortDirection": "Direction de tri",
@@ -140,6 +149,7 @@
         "openInViewer": "Ouvrir dans le Visualiseur",
         "showArchivedBoards": "Montrer les Planches archivées",
         "selectForCompare": "Séléctionner pour comparaison",
+        "selectAnImageToCompare": "Séléctionner une Image à comparer",
         "exitCompare": "Sortir de la comparaison",
         "compareHelp2": "Appuyez sur <Kbd>M</Kbd> pour faire défiler les modes de comparaison.",
         "swapImages": "Échanger les Images",
@@ -147,7 +157,10 @@
         "compareHelp1": "Maintenir <Kbd>Alt</Kbd> lors du clic d'une image dans la galerie ou en utilisant les flèches du clavier pour changer l'Image à comparer.",
         "compareHelp3": "Appuyer sur <Kbd>C</Kbd> pour échanger les images à comparer.",
         "image": "image",
+        "openViewer": "Ouvrir le Visualisateur",
+        "closeViewer": "Fermer le Visualisateur",
         "currentlyInUse": "Cette image est actuellement utilisée dans ces fonctionalités :",
+        "jump": "Sauter",
         "starImage": "Marquer l'Image",
         "download": "Téléchargement",
         "deleteImage_one": "Supprimer l'Image",
@@ -234,6 +247,7 @@
         "metadata": "Métadonnées",
         "scanFolder": "Scanner le dossier",
         "inplaceInstallDesc": "Installez les modèles sans copier les fichiers. Lors de l'utilisation du modèle, il sera chargé depuis cet emplacement. Si cette option est désactivée, le(s) fichier(s) du modèle seront copiés dans le répertoire des modèles géré par Invoke lors de l'installation.",
+        "ipAdapters": "Adaptateurs IP",
         "installQueue": "File d'attente d'installation",
         "modelImageDeleteFailed": "Échec de la suppression de l'image du modèle",
         "modelName": "Nom du modèle",
@@ -274,6 +288,7 @@
         "scanFolderHelper": "Le dossier sera analysé de manière récursive à la recherche de modèles. Cela peut prendre quelques instants pour des dossiers très volumineux.",
         "clipEmbed": "Intégration CLIP",
         "spandrelImageToImage": "Image vers Image (Spandrel)",
+        "starterModelsInModelManager": "Les modèles de démarrage peuvent être trouvés dans le gestionnaire de modèles",
         "t5Encoder": "Encodeur T5",
         "learnMoreAboutSupportedModels": "En savoir plus sur les modèles que nous prenons en charge",
         "includesNModels": "Contient {{n}} modèles et leurs dépendances",
@@ -331,10 +346,12 @@
         "infillMethod": "Méthode de Remplissage",
         "tileSize": "Taille des Tuiles",
         "copyImage": "Copier Image",
+        "downloadImage": "Télécharger Image",
         "usePrompt": "Utiliser la suggestion",
         "useSeed": "Utiliser la graine",
         "useAll": "Tout utiliser",
         "info": "Info",
+        "showOptionsPanel": "Afficher le panneau latéral (O ou T)",
         "invoke": {
             "noPrompts": "Aucun prompts généré",
             "missingInputForField": "entrée manquante",
@@ -345,16 +362,21 @@
             "noModelSelected": "Aucun modèle sélectionné",
             "noNodesInGraph": "Aucun nœud dans le graphique",
             "systemDisconnected": "Système déconnecté",
+            "fluxModelIncompatibleBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), la hauteur de la bounding box est {{height}}",
+            "fluxModelIncompatibleScaledBboxHeight": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), la hauteur de la bounding box est {{height}}",
             "noFLUXVAEModelSelected": "Aucun modèle VAE sélectionné pour la génération FLUX",
             "canvasIsTransforming": "La Toile est occupée (en transformation)",
             "canvasIsRasterizing": "La Toile est occupée (en rastérisation)",
             "noCLIPEmbedModelSelected": "Aucun modèle CLIP Embed sélectionné pour la génération FLUX",
             "canvasIsFiltering": "La Toile est occupée (en filtration)",
+            "fluxModelIncompatibleBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), la largeur de la bounding box est {{width}}",
             "noT5EncoderModelSelected": "Aucun modèle T5 Encoder sélectionné pour la génération FLUX",
+            "fluxModelIncompatibleScaledBboxWidth": "$t(parameters.invoke.fluxRequiresDimensionsToBeMultipleOf16), la largeur de la bounding box mise à l'échelle est {{width}}",
             "canvasIsCompositing": "La Toile est occupée (en composition)",
             "collectionTooFewItems": "trop peu d'éléments, minimum {{minItems}}",
             "collectionTooManyItems": "trop d'éléments, maximum {{maxItems}}",
             "canvasIsSelectingObject": "La toile est occupée (sélection d'objet)",
+            "emptyBatches": "lots vides",
             "batchNodeNotConnected": "Noeud de lots non connecté : {{label}}",
             "fluxModelMultipleControlLoRAs": "Vous ne pouvez utiliser qu'un seul Control LoRA à la fois",
             "collectionNumberLTMin": "{{value}} < {{minimum}} (incl. min)",
@@ -446,7 +468,9 @@
         "informationalPopoversDisabled": "Pop-ups d'information désactivés",
         "informationalPopoversDisabledDesc": "Les pop-ups d'information ont été désactivés. Activez-les dans les paramètres.",
         "confirmOnNewSession": "Confirmer lors d'une nouvelle session",
+        "modelDescriptionsDisabledDesc": "Les descriptions des modèles dans les menus déroulants ont été désactivées. Activez-les dans les paramètres.",
         "enableModelDescriptions": "Activer les descriptions de modèle dans les menus déroulants",
+        "modelDescriptionsDisabled": "Descriptions de modèle dans les menus déroulants désactivés",
         "showDetailedInvocationProgress": "Afficher les détails de progression"
     },
     "toast": {
@@ -462,14 +486,17 @@
         "addedToBoard": "Ajouté aux ressources de la planche {{name}}",
         "workflowLoaded": "Workflow chargé",
         "connected": "Connecté au serveur",
+        "setNodeField": "Définir comme champ de nœud",
         "imageUploadFailed": "Échec de l'importation de l'image",
         "loadedWithWarnings": "Workflow chargé avec des avertissements",
         "imageUploaded": "Image importée",
         "modelAddedSimple": "Modèle ajouté à la file d'attente",
+        "setControlImage": "Définir comme image de contrôle",
         "workflowDeleted": "Workflow supprimé",
         "baseModelChangedCleared_one": "Effacé ou désactivé {{count}} sous-modèle incompatible",
         "baseModelChangedCleared_many": "Effacé ou désactivé {{count}} sous-modèles incompatibles",
         "baseModelChangedCleared_other": "Effacé ou désactivé {{count}} sous-modèles incompatibles",
+        "invalidUpload": "Importation invalide",
         "problemDownloadingImage": "Impossible de télécharger l'image",
         "problemRetrievingWorkflow": "Problème de récupération du Workflow",
         "problemDeletingWorkflow": "Problème de suppression du Workflow",
@@ -483,10 +510,12 @@
         "errorCopied": "Erreur copiée",
         "parametersSet": "Paramètres rappelés",
         "somethingWentWrong": "Quelque chose a échoué",
+        "imageSaved": "Image enregistrée",
         "unableToLoadStylePreset": "Impossible de charger le préréglage de style",
         "stylePresetLoaded": "Préréglage de style chargé",
         "parameterNotSetDescWithMessage": "Impossible de rappeler {{parameter}} : {{message}}",
         "importFailed": "Importation échouée",
+        "imageSavingFailed": "Échec de l'enregistrement de l'image",
         "importSuccessful": "Importation réussie",
         "outOfMemoryError": "Erreur de mémoire insuffisante",
         "sessionRef": "Session : {{sessionId}}",
@@ -494,11 +523,16 @@
         "parameterSetDesc": "Rappelé {{parameter}}",
         "parameterNotSetDesc": "Impossible de rappeler {{parameter}}",
         "layerCopiedToClipboard": "Calque copié dans le presse-papiers",
+        "layerSavedToAssets": "Calque enregistré dans les ressources",
         "problemCopyingLayer": "Impossible de copier la couche",
         "baseModelChanged": "Modèle de base changé",
         "problemSavingLayer": "Impossible d'enregistrer la couche",
+        "imageNotLoadedDesc": "Image introuvable",
         "linkCopied": "Lien copié",
         "imagesWillBeAddedTo": "Les images Importées seront ajoutées au ressources de la Planche {{boardName}}.",
+        "uploadFailedInvalidUploadDesc_withCount_one": "Doit être au maximum une image PNG ou JPEG.",
+        "uploadFailedInvalidUploadDesc_withCount_many": "Doit être au maximum {{count}} images PNG ou JPEG.",
+        "uploadFailedInvalidUploadDesc_withCount_other": "Doit être au maximum {{count}} images PNG ou JPEG.",
         "addedToUncategorized": "Ajouté aux ressources de la planche $t(boards.uncategorized)",
         "pasteSuccess": "Collé à {{destination}}",
         "pasteFailed": "Échec du collage",
@@ -546,6 +580,8 @@
         "movingImagesToBoard_one": "Déplacer {{count}} image à cette planche :",
         "movingImagesToBoard_many": "Déplacer {{count}} images à cette planche :",
         "movingImagesToBoard_other": "Déplacer {{count}} image à cette planche :",
+        "viewBoards": "Voir les Planches",
+        "hideBoards": "Cacher les Planches",
         "noBoards": "Pas de Planches {{boardType}}",
         "shared": "Planches Partagées",
         "searchBoard": "Chercher les Planches...",
@@ -645,6 +681,7 @@
         "batchQueued": "Lot ajouté à la file d'attente",
         "gallery": "Galerie",
         "notReady": "Impossible d'ajouter à la file d'attente",
+        "batchFieldValues": "Valeurs Champ Lot",
         "front": "début",
         "graphQueued": "Graph ajouté à la file d'attente",
         "other": "Autre",
@@ -675,11 +712,13 @@
         "compatibleEmbeddings": "Embeddings Compatibles"
     },
     "hrf": {
+        "upscaleMethod": "Méthode d'Agrandissement",
         "metadata": {
             "enabled": "Correction Haute Résolution Activée",
             "strength": "Force de la Correction Haute Résolution",
             "method": "Méthode de la Correction Haute Résolution"
         },
+        "enableHrf": "Activer la Correction Haute Résolution",
         "hrf": "Correction Haute Résolution"
     },
     "invocationCache": {
@@ -862,6 +901,10 @@
                 "desc": "Définit le zoom de la toile à 400 %.",
                 "title": "Zoomer à 400 %"
             },
+            "setFillToWhite": {
+                "title": "Définir la couleur sur blanc",
+                "desc": "Définir la couleur de l'outil actuel sur blanc."
+            },
             "transformSelected": {
                 "title": "Transformer",
                 "desc": "Transforme la couche sélectionnée."
@@ -1447,7 +1490,8 @@
         "showDynamicPrompts": "Afficher les Prompts dynamiques",
         "dynamicPrompts": "Prompts Dynamiques",
         "promptsPreview": "Prévisualisation des Prompts",
-        "loading": "Génération des Pompts Dynamiques..."
+        "loading": "Génération des Pompts Dynamiques...",
+        "promptsToGenerate": "Prompts à générer"
     },
     "metadata": {
         "positivePrompt": "Prompt Positif",
@@ -1475,12 +1519,18 @@
         "recallParameters": "Rappeler les paramètres",
         "imageDimensions": "Dimensions de l'image",
         "parameterSet": "Paramètre {{parameter}} défini",
+        "parsingFailed": "L'analyse a échoué",
+        "recallParameter": "Rappeler {{label}}",
         "canvasV2Metadata": "Toile",
         "guidance": "Guide",
         "seamlessXAxis": "Axe X sans bords",
         "seamlessYAxis": "Axe Y sans bords"
     },
     "sdxl": {
+        "freePromptStyle": "Écriture de Prompt manuelle",
+        "concatPromptStyle": "Lier Prompt & Style",
+        "negStylePrompt": "Style Prompt Négatif",
+        "posStylePrompt": "Style Prompt Positif",
         "refinerStart": "Démarrer le Refiner",
         "denoisingStrength": "Force de débruitage",
         "steps": "Étapes",
@@ -1497,6 +1547,8 @@
     "nodes": {
         "showMinimapnodes": "Afficher la MiniCarte",
         "fitViewportNodes": "Ajuster la Vue",
+        "hideLegendNodes": "Masquer la légende du type de champ",
+        "showLegendNodes": "Afficher la légende du type de champ",
         "hideMinimapnodes": "Masquer MiniCarte",
         "zoomOutNodes": "Dézoomer",
         "zoomInNodes": "Zoomer",
@@ -1520,7 +1572,9 @@
         "colorCodeEdges": "Code de couleur des connexions",
         "colorCodeEdgesHelp": "Code couleur des connexions en fonction de leurs champs connectés",
         "currentImage": "Image actuelle",
+        "noFieldsLinearview": "Aucun champ ajouté à la vue linéaire",
         "float": "Flottant",
+        "mismatchedVersion": "Nœud invalide : le nœud {{node}} de type {{type}} a une version incompatible (essayez de mettre à jour ?)",
         "missingTemplate": "Nœud invalide : le nœud {{node}} de type {{type}} modèle manquant (non installé ?)",
         "noWorkflow": "Pas de Workflow",
         "validateConnectionsHelp": "Prévenir la création de connexions invalides et l'invocation de graphes invalides",
@@ -1531,10 +1585,12 @@
         "scheduler": "Planificateur",
         "notes": "Notes",
         "notesDescription": "Ajouter des notes sur votre workflow",
+        "unableToLoadWorkflow": "Impossible de charger le Workflow",
         "addNode": "Ajouter un nœud",
         "problemSettingTitle": "Problème lors de définition du Titre",
         "connectionWouldCreateCycle": "La connexion créerait un cycle",
         "currentImageDescription": "Affiche l'image actuelle dans l'éditeur de nœuds",
+        "versionUnknown": " Version inconnue",
         "cannotConnectInputToInput": "Impossible de connecter l'entrée à l'entrée",
         "addNodeToolTip": "Ajouter un nœud (Shift+A, Espace)",
         "fullyContainNodesHelp": "Les nœuds doivent être entièrement à l'intérieur de la zone de sélection pour être sélectionnés",
@@ -1550,6 +1606,7 @@
         "nodeSearch": "Rechercher des nœuds",
         "collection": "Collection",
         "noOutputRecorded": "Aucun résultat enregistré",
+        "removeLinearView": "Retirer de la vue linéaire",
         "snapToGrid": "Aligner sur la grille",
         "workflow": "Workflow",
         "updateApp": "Mettre à jour l'application",
@@ -1558,6 +1615,7 @@
         "noConnectionInProgress": "Aucune connexion en cours",
         "nodeType": "Type de nœud",
         "workflowContact": "Contact",
+        "unknownTemplate": "Modèle inconnu",
         "unknownNode": "Nœud inconnu",
         "workflowVersion": "Version",
         "string": "Chaîne de caractères",
@@ -1571,6 +1629,7 @@
         "cannotDuplicateConnection": "Impossible de créer des connexions en double",
         "resetToDefaultValue": "Réinitialiser à la valeur par défaut",
         "unknownNodeType": "Type de nœud inconnu",
+        "unknownInput": "Entrée inconnue : {{name}}",
         "prototypeDesc": "Cette invocation est un prototype. Elle peut subir des modifications majeures lors des mises à jour de l'application et peut être supprimée à tout moment.",
         "nodePack": "Paquet de nœuds",
         "sourceNodeDoesNotExist": "Connexion invalide : le nœud source/de sortie {{node}} n'existe pas",
@@ -1585,6 +1644,7 @@
         "clearWorkflow": "Effacer le Workflow",
         "clearWorkflowDesc": "Effacer ce workflow et en commencer un nouveau ?",
         "unsupportedArrayItemType": "type d'élément de tableau non pris en charge \"{{type}}\"",
+        "addLinearView": "Ajouter à la vue linéaire",
         "collectionOrScalarFieldType": "{{name}} (Unique ou Collection)",
         "unableToExtractEnumOptions": "impossible d'extraire les options d'énumération",
         "unsupportedAnyOfLength": "trop de membres dans l'union ({{count}})",
@@ -1592,6 +1652,7 @@
         "viewMode": "Utiliser en vue linéaire",
         "collectionFieldType": "{{name}} (Collection)",
         "newWorkflow": "Nouveau Workflow",
+        "reorderLinearView": "Réorganiser la vue linéaire",
         "outputFieldTypeParseError": "Impossible d'analyser le type du champ de sortie {{node}}.{{field}} ({{message}})",
         "unsupportedMismatchedUnion": "type CollectionOrScalar non concordant avec les types de base {{firstType}} et {{secondType}}",
         "unableToParseFieldType": "impossible d'analyser le type de champ",
@@ -1625,9 +1686,13 @@
         "arithmeticSequence": "Séquence Arithmétique",
         "uniformRandomDistribution": "Distribution Aléatoire Uniforme",
         "noBatchGroup": "aucun groupe",
+        "generatorLoading": "chargement",
         "generatorLoadFromFile": "Charger depuis un Fichier",
         "dynamicPromptsRandom": "Prompts Dynamiques (Aléatoire)",
+        "integerRangeGenerator": "Générateur d'interval d'entiers",
+        "generateValues": "Générer Valeurs",
         "linearDistribution": "Distribution Linéaire",
+        "floatRangeGenerator": "Générateur d'interval de nombres décimaux",
         "generatorNRandomValues_one": "{{count}} valeur aléatoire",
         "generatorNRandomValues_many": "{{count}} valeurs aléatoires",
         "generatorNRandomValues_other": "{{count}} valeurs aléatoires",
@@ -1647,6 +1712,7 @@
         "generatorImagesCategory": "Catégorie",
         "generatorImagesFromBoard": "Images de la Planche",
         "missingSourceOrTargetHandle": "Manque de gestionnaire source ou cible",
+        "loadingTemplates": "Chargement de {{name}}",
         "loadWorkflowDesc2": "Votre workflow actuel contient des modifications non enregistrées.",
         "generatorImages_one": "{{count}} image",
         "generatorImages_many": "{{count}} images",
@@ -1657,8 +1723,10 @@
         "noModelsAvailable": "Aucun modèle disponible",
         "loading": "chargement",
         "selectModel": "Sélectionner un modèle",
+        "noMatchingLoRAs": "Aucun LoRA correspondant",
         "lora": "LoRA",
         "noRefinerModelsInstalled": "Aucun modèle SDXL Refiner installé",
+        "noLoRAsInstalled": "Aucun LoRA installé",
         "addLora": "Ajouter LoRA",
         "defaultVAE": "VAE par défaut",
         "concepts": "Concepts"
@@ -1666,8 +1734,11 @@
     "workflows": {
         "workflowLibrary": "Bibliothèque",
         "loading": "Chargement des Workflows",
+        "searchWorkflows": "Chercher des Workflows",
         "workflowCleared": "Workflow effacé",
+        "noDescription": "Aucune description",
         "deleteWorkflow": "Supprimer le Workflow",
+        "openWorkflow": "Ouvrir le Workflow",
         "uploadWorkflow": "Charger à partir d'un fichier",
         "workflowName": "Nom du Workflow",
         "unnamedWorkflow": "Workflow sans nom",
@@ -1680,6 +1751,8 @@
         "problemSavingWorkflow": "Problème de sauvegarde du Workflow",
         "workflowEditorMenu": "Menu de l'Éditeur de Workflow",
         "newWorkflowCreated": "Nouveau Workflow créé",
+        "clearWorkflowSearchFilter": "Réinitialiser le filtre de recherche de Workflow",
+        "problemLoading": "Problème de chargement des Workflows",
         "workflowSaved": "Workflow enregistré",
         "noWorkflows": "Pas de Workflows",
         "ascending": "Ascendant",
@@ -1692,6 +1765,9 @@
         "opened": "Ouvert",
         "name": "Nom",
         "autoLayout": "Mise en page automatique",
+        "defaultWorkflows": "Workflows par défaut",
+        "userWorkflows": "Workflows de l'utilisateur",
+        "projectWorkflows": "Workflows du projet",
         "copyShareLink": "Copier le lien de partage",
         "chooseWorkflowFromLibrary": "Choisir le Workflow dans la Bibliothèque",
         "edit": "Modifer",
@@ -1708,6 +1784,7 @@
             "multiLine": "Multi Ligne",
             "headingPlaceholder": "En-tête vide",
             "emptyRootPlaceholderEditMode": "Faites glisser un élément de formulaire ou un champ de nœud ici pour commencer.",
+            "emptyRootPlaceholderViewMode": "Cliquez sur Modifier pour commencer à créer un formulaire pour ce workflow.",
             "containerPlaceholder": "Conteneur Vide",
             "row": "Ligne",
             "column": "Colonne",
@@ -1721,8 +1798,10 @@
             "builder": "Constructeur de Formulaire",
             "resetAllNodeFields": "Réinitialiser tous les champs de nœud",
             "deleteAllElements": "Supprimer tous les éléments de formulaire",
+            "workflowBuilderAlphaWarning": "Le constructeur de workflow est actuellement en version alpha. Il peut y avoir des changements majeurs avant la version stable.",
             "showDescription": "Afficher la description"
-        }
+        },
+        "openLibrary": "Ouvrir la Bibliothèque"
     },
     "whatsNew": {
         "whatsNewInInvoke": "Quoi de neuf dans Invoke",
@@ -1731,7 +1810,8 @@
             "<StrongComponent>FLUX Guidage Régional (bêta)</StrongComponent> : Notre version bêta de FLUX Guidage Régional est en ligne pour le contrôle des prompt régionaux.",
             "Autres améliorations : mise en file d'attente par lots plus rapide, meilleur redimensionnement, sélecteur de couleurs amélioré et nœuds de métadonnées."
         ],
-        "readReleaseNotes": "Notes de version"
+        "readReleaseNotes": "Notes de version",
+        "watchUiUpdatesOverview": "Aperçu des mises à jour de l'interface utilisateur"
     },
     "ui": {
         "tabs": {
@@ -1740,6 +1820,7 @@
             "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",
@@ -1748,6 +1829,7 @@
     },
     "controlLayers": {
         "newLayerFromImage": "Nouvelle couche à partir de l'image",
+        "sendToGalleryDesc": "Appuyer sur Invoker génère et enregistre une image unique dans votre galerie.",
         "sendToCanvas": "Envoyer vers la Toile",
         "globalReferenceImage": "Image de référence globale",
         "newCanvasFromImage": "Nouvelle Toile à partir de l'image",
@@ -1903,6 +1985,7 @@
         },
         "bookmark": "Marque-page pour Changement Rapide",
         "saveLayerToAssets": "Enregistrer la couche dans les ressources",
+        "stagingOnCanvas": "Mise en attente des images sur",
         "enableTransparencyEffect": "Activer l'effet de transparence",
         "hidingType": "Masquer {{type}}",
         "settings": {
@@ -1933,6 +2016,11 @@
         "disableAutoNegative": "Désactiver l'Auto Négatif",
         "addNegativePrompt": "Ajouter $t(controlLayers.negativePrompt)",
         "addRegionalGuidance": "Ajouter $t(controlLayers.regionalGuidance)",
+        "controlLayers_withCount_hidden": "Control Layers ({{count}} cachées)",
+        "rasterLayers_withCount_hidden": "Couche de Rastérisation ({{count}} cachées)",
+        "regionalGuidance_withCount_hidden": "Guidage Régional ({{count}} caché)",
+        "rasterLayers_withCount_visible": "Couche de Rastérisation ({{count}})",
+        "inpaintMasks_withCount_visible": "Masques de remplissage ({{count}})",
         "layer_one": "Couche",
         "layer_many": "Couches",
         "layer_other": "Couches",
@@ -1982,6 +2070,8 @@
             "next": "Suivant",
             "saveToGallery": "Enregistrer dans la galerie"
         },
+        "viewProgressOnCanvas": "Voir les progrès et les sorties de la scène sur la <Btn>Toile</Btn>.",
+        "sendToCanvasDesc": "Appuyer sur Invoker met en attente votre travail en cours sur la toile.",
         "mergeVisibleError": "Erreur lors de la fusion des calques visibles",
         "mergeVisibleOk": "Couches visibles fusionnées",
         "clearHistory": "Effacer l'historique",
@@ -1990,6 +2080,8 @@
         "duplicate": "Dupliquer",
         "enableAutoNegative": "Activer l'Auto Négatif",
         "showHUD": "Afficher HUD",
+        "sendToGallery": "Envoyer à la galerie",
+        "sendingToGallery": "Envoi des générations à la galerie",
         "disableTransparencyEffect": "Désactiver l'effet de transparence",
         "HUD": {
             "entityStatus": {
@@ -2006,11 +2098,16 @@
         "opacity": "Opacité",
         "savedToGalleryError": "Erreur lors de l'enregistrement dans la galerie",
         "addInpaintMask": "Ajouter $t(controlLayers.inpaintMask)",
+        "newCanvasSessionDesc": "Cela effacera la toile et tous les paramètres, sauf votre sélection de modèle. Les générations seront mises en attente sur la toile.",
         "canvas": "Toile",
         "savedToGalleryOk": "Enregistré dans la galerie",
         "addPositivePrompt": "Ajouter $t(controlLayers.prompt)",
         "showProgressOnCanvas": "Afficher la progression sur la Toile",
+        "newGallerySession": "Nouvelle session de galerie",
+        "newCanvasSession": "Nouvelle session de toile",
         "showingType": "Afficher {{type}}",
+        "viewProgressInViewer": "Voir les progrès et les résultats dans le <Btn>Visionneur d'images</Btn>.",
+        "deletePrompt": "Supprimer le prompt",
         "addControlLayer": "Ajouter $t(controlLayers.controlLayer)",
         "global": "Global",
         "newGlobalReferenceImageOk": "Image de référence globale créée",
@@ -2024,6 +2121,16 @@
         "newRasterLayerError": "Problème de création de couche de rastérisation",
         "negativePrompt": "Prompt négatif",
         "weight": "Poids",
+        "globalReferenceImages_withCount_hidden": "Images de référence globales ({{count}} cachées)",
+        "inpaintMasks_withCount_hidden": "Masques de remplissage ({{count}} cachés)",
+        "regionalGuidance_withCount_visible": "Guidage Régional ({{count}})",
+        "globalReferenceImage_withCount_one": "$t(controlLayers.globalReferenceImage)",
+        "globalReferenceImage_withCount_many": "Images de référence globales",
+        "globalReferenceImage_withCount_other": "Images de référence globales",
+        "layer_withCount_one": "Couche {{count}}",
+        "layer_withCount_many": "Couches {{count}}",
+        "layer_withCount_other": "Couches {{count}}",
+        "globalReferenceImages_withCount_visible": "Images de référence globales ({{count}})",
         "controlMode": {
             "controlMode": "Mode de contrôle",
             "balanced": "Équilibré",
@@ -2047,14 +2154,18 @@
         },
         "fitBboxToLayers": "Ajuster la bounding box aux calques",
         "regionIsEmpty": "La zone sélectionnée est vide",
+        "controlLayers_withCount_visible": "Couches de contrôle ({{count}})",
         "cropLayerToBbox": "Rogner la couche selon la bounding box",
+        "sendingToCanvas": "Mise en attente des Générations sur la Toile",
         "copyToClipboard": "Copier dans le presse-papiers",
         "regionalGuidance_withCount_one": "$t(controlLayers.regionalGuidance)",
         "regionalGuidance_withCount_many": "Guidage Régional",
         "regionalGuidance_withCount_other": "Guidage Régional",
+        "newGallerySessionDesc": "Cela effacera la toile et tous les paramètres, sauf votre sélection de modèle. Les générations seront envoyées à la galerie.",
         "inpaintMask_withCount_one": "$t(controlLayers.inpaintMask)",
         "inpaintMask_withCount_many": "Remplir les masques",
         "inpaintMask_withCount_other": "Remplir les masques",
+        "newImg2ImgCanvasFromImage": "Nouvelle Img2Img à partir de l'image",
         "bboxOverlay": "Afficher la superposition des Bounding Box",
         "moveToFront": "Déplacer vers le permier plan",
         "moveToBack": "Déplacer vers l'arrière plan",
@@ -2069,6 +2180,7 @@
         "inpaintMask": "Masque de remplissage",
         "deleteReferenceImage": "Supprimer l'image de référence",
         "addReferenceImage": "Ajouter $t(controlLayers.referenceImage)",
+        "addGlobalReferenceImage": "Ajouter $t(controlLayers.globalReferenceImage)",
         "removeBookmark": "Supprimer le marque-page",
         "regionalGuidance": "Guide régional",
         "regionalReferenceImage": "Image de référence régionale",
@@ -2097,12 +2209,16 @@
             "pointType": "Type de point",
             "exclude": "Exclure",
             "process": "Traiter",
-            "reset": "Réinitialiser"
+            "reset": "Réinitialiser",
+            "help1": "Sélectionnez un seul objet cible. Ajoutez des points <Bold>Inclure</Bold> et <Bold>Exclure</Bold> pour indiquer quelles parties de la couche font partie de l'objet cible.",
+            "help2": "Commencez par un point <Bold>Inclure</Bold> au sein de l'objet cible. Ajoutez d'autres points pour affiner la sélection. Moins de points produisent généralement de meilleurs résultats.",
+            "help3": "Inversez la sélection pour sélectionner tout sauf l'objet cible."
         },
         "convertRegionalGuidanceTo": "Convertir $t(controlLayers.regionalGuidance) vers",
         "copyRasterLayerTo": "Copier $t(controlLayers.rasterLayer) vers",
         "newControlLayer": "Nouveau $t(controlLayers.controlLayer)",
         "newRegionalGuidance": "Nouveau $t(controlLayers.regionalGuidance)",
+        "replaceCurrent": "Remplacer Actuel",
         "convertControlLayerTo": "Convertir $t(controlLayers.controlLayer) vers",
         "convertInpaintMaskTo": "Convertir $t(controlLayers.inpaintMask) vers",
         "copyControlLayerTo": "Copier $t(controlLayers.controlLayer) vers",
@@ -2148,7 +2264,9 @@
         "pasteToBboxDesc": "Nouvelle couche (dans Bbox)",
         "pasteToCanvasDesc": "Nouvelle couche (dans la Toile)",
         "useImage": "Utiliser l'image",
-        "referenceImageEmptyState": "<UploadButton>Séléctionner une image</UploadButton> ou faites glisser une image depuis la <GalleryButton>galerie</GalleryButton> sur cette couche pour commencer."
+        "pastedTo": "Collé à {{destination}}",
+        "referenceImageEmptyState": "<UploadButton>Séléctionner une image</UploadButton> ou faites glisser une image depuis la <GalleryButton>galerie</GalleryButton> sur cette couche pour commencer.",
+        "referenceImageGlobal": "Image de référence (Globale)"
     },
     "upscaling": {
         "exceedsMaxSizeDetails": "La limite maximale d'agrandissement est de {{maxUpscaleDimension}}x{{maxUpscaleDimension}} pixels. Veuillez essayer une image plus petite ou réduire votre sélection d'échelle.",
@@ -2257,8 +2375,65 @@
     },
     "supportVideos": {
         "watch": "Regarder",
+        "videos": {
+            "upscaling": {
+                "description": "Comment améliorer la résolution des images avec les outils d'Invoke pour les agrandir.",
+                "title": "Upscaling"
+            },
+            "howDoIGenerateAndSaveToTheGallery": {
+                "description": "Étapes pour générer et enregistrer des images dans la galerie.",
+                "title": "Comment générer et enregistrer dans la galerie ?"
+            },
+            "usingControlLayersAndReferenceGuides": {
+                "title": "Utilisation des couche de contrôle et des guides de référence",
+                "description": "Apprenez à guider la création de vos images avec des couche de contrôle et des images de référence."
+            },
+            "exploringAIModelsAndConceptAdapters": {
+                "description": "Plongez dans les modèles d'IA et découvrez comment utiliser les adaptateurs de concepts pour un contrôle créatif.",
+                "title": "Exploration des modèles d'IA et des adaptateurs de concepts"
+            },
+            "howDoIUseControlNetsAndControlLayers": {
+                "title": "Comment utiliser les réseaux de contrôle et les couches de contrôle ?",
+                "description": "Apprenez à appliquer des couches de contrôle et des ControlNets à vos images."
+            },
+            "creatingAndComposingOnInvokesControlCanvas": {
+                "description": "Apprenez à composer des images en utilisant le canvas de contrôle d'Invoke.",
+                "title": "Créer et composer sur le canvas de contrôle d'Invoke"
+            },
+            "howDoIEditOnTheCanvas": {
+                "title": "Comment puis-je modifier sur la toile ?",
+                "description": "Guide pour éditer des images directement sur la toile."
+            },
+            "howDoIDoImageToImageTransformation": {
+                "title": "Comment effectuer une transformation d'image à image ?",
+                "description": "Tutoriel sur la réalisation de transformations d'image à image dans Invoke."
+            },
+            "howDoIUseGlobalIPAdaptersAndReferenceImages": {
+                "title": "Comment utiliser les IP Adapters globaux et les images de référence ?",
+                "description": "Introduction à l'ajout d'images de référence et IP Adapters globaux."
+            },
+            "howDoIUseInpaintMasks": {
+                "title": "Comment utiliser les masques d'inpainting ?",
+                "description": "Comment appliquer des masques de retourche pour la correction et la variation d'image."
+            },
+            "creatingYourFirstImage": {
+                "title": "Créer votre première image",
+                "description": "Introduction à la création d'une image à partir de zéro en utilisant les outils d'Invoke."
+            },
+            "understandingImageToImageAndDenoising": {
+                "title": "Comprendre l'Image-à-Image et le Débruitage",
+                "description": "Aperçu des transformations d'image à image et du débruitage dans Invoke."
+            },
+            "howDoIOutpaint": {
+                "title": "Comment effectuer un outpainting ?",
+                "description": "Guide pour l'extension au-delà des bordures de l'image originale."
+            }
+        },
         "gettingStarted": "Commencer",
-        "supportVideos": "Vidéos d'assistance"
+        "studioSessionsDesc1": "Consultez le <StudioSessionsPlaylistLink /> pour des approfondissements sur Invoke.",
+        "studioSessionsDesc2": "Rejoignez notre <DiscordLink /> pour participer aux sessions en direct et poser vos questions. Les sessions sont ajoutée dans la playlist la semaine suivante.",
+        "supportVideos": "Vidéos d'assistance",
+        "controlCanvas": "Contrôler la toile"
     },
     "modelCache": {
         "clear": "Effacer le cache du modèle",

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

@@ -50,7 +50,8 @@
     "gallery": {
         "galleryImageSize": "גודל תמונה",
         "gallerySettings": "הגדרות גלריה",
-        "autoSwitchNewImages": "החלף אוטומטית לתמונות חדשות"
+        "autoSwitchNewImages": "החלף אוטומטית לתמונות חדשות",
+        "noImagesInGallery": "אין תמונות בגלריה"
     },
     "parameters": {
         "images": "תמונות",
@@ -69,10 +70,12 @@
         "tileSize": "גודל אריח",
         "symmetry": "סימטריה",
         "copyImage": "העתקת תמונה",
+        "downloadImage": "הורדת תמונה",
         "usePrompt": "שימוש בבקשה",
         "useSeed": "שימוש בזרע",
         "useAll": "שימוש בהכל",
         "info": "פרטים",
+        "showOptionsPanel": "הצג חלונית אפשרויות",
         "shuffle": "ערבוב",
         "noiseThreshold": "סף רעש",
         "perlinNoise": "רעש פרלין",