chore: bump version to v6.5.0rc1

Currently translated at 98.6% (2050 of 2079 strings)

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

.github/workflows/build-container.yml

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

.github/workflows/lfs-checks.yml

@@ -0,0 +1,30 @@
+# 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

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

docker/.env.sample

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

docker/Dockerfile

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

docker/Dockerfile-rocm-full

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

docker/docker-compose.yml

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

docker/docker-entrypoint.sh

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

docker/run.sh

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

docs/contributing/MODEL_MANAGER.md

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

docs/contributing/contributors.md

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

docs/installation/manual.md

@@ -69,34 +69,34 @@ The following commands vary depending on the version of Invoke being installed a
     - If you have an Nvidia 20xx series GPU or older, use `invokeai[xformers]`.
     - If you have an Nvidia 30xx series GPU or newer, or do not have an Nvidia GPU, use `invokeai`.
 
-7. Determine the `PyPI` index URL to use for installation, if any. This is necessary to get the right version of torch installed.
+7. Determine the torch backend to use for installation, if any. This is necessary to get the right version of torch installed. This is acheived by using [UV's built in torch support.](https://docs.astral.sh/uv/guides/integration/pytorch/#automatic-backend-selection)
 
     === "Invoke v5.12 and later"
 
-        - If you are on Windows or Linux with an Nvidia GPU, use `https://download.pytorch.org/whl/cu128`.
-        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
-        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.2.4`.
-        - **In all other cases, do not use an index.**
+        - If you are on Windows or Linux with an Nvidia GPU, use `--torch-backend=cu128`.
+        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
+        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm6.3`.
+        - **In all other cases, do not use a torch backend.**
 
     === "Invoke v5.10.0 to v5.11.0"
 
-        - If you are on Windows or Linux with an Nvidia GPU, use `https://download.pytorch.org/whl/cu126`.
-        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
-        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.2.4`.
+        - If you are on Windows or Linux with an Nvidia GPU, use `--torch-backend=cu126`.
+        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
+        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm6.2.4`.
         - **In all other cases, do not use an index.**
 
     === "Invoke v5.0.0 to v5.9.1"
 
-        - If you are on Windows with an Nvidia GPU, use `https://download.pytorch.org/whl/cu124`.
-        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
-        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.1`.
+        - If you are on Windows with an Nvidia GPU, use `--torch-backend=cu124`.
+        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
+        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm6.1`.
         - **In all other cases, do not use an index.**
 
     === "Invoke v4"
 
-        - If you are on Windows with an Nvidia GPU, use `https://download.pytorch.org/whl/cu124`.
-        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
-        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm5.2`.
+        - If you are on Windows with an Nvidia GPU, use `--torch-backend=cu124`.
+        - If you are on Linux with no GPU, use `--torch-backend=cpu`.
+        - If you are on Linux with an AMD GPU, use `--torch-backend=rocm5.2`.
         - **In all other cases, do not use an index.**
 
 8. Install the `invokeai` package. Substitute the package specifier and version.
@@ -105,10 +105,10 @@ The following commands vary depending on the version of Invoke being installed a
     uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --force-reinstall
     ```
 
-    If you determined you needed to use a `PyPI` index URL in the previous step, you'll need to add `--index=<INDEX_URL>` like this:
+    If you determined you needed to use a torch backend in the previous step, you'll need to set the backend like this:
 
     ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --torch-backend=<VERSION> --force-reinstall
     ```
 
 9. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:

docs/installation/quick_start.md

@@ -33,30 +33,45 @@ 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
+## Step 2: Download and Set Up the Launcher
 
-Download the most recent launcher for your operating system:
+The Launcher manages your Invoke install. Follow these instructions to download and set up the Launcher.
 
-- [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)
+!!! info "Instructions for each OS"
 
-## Step 3: Install or Update
+    === "Windows"
 
-Run the launcher you just downloaded, click **Install** and follow the instructions to get set up.
+        - [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.
 
 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.
 
-!!! warning "Problem running the launcher on macOS"
+!!! tip "Updating"
 
-    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:
+    The Launcher will check for updates for itself _and_ 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.
+    - 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.
 
 ## 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).
 
-### Noise
+### Create Latent Noise
 
 An initial noise tensor is necessary for the latent diffusion process. As a result, the Denoising node requires a noise node input.  
 

invokeai/app/api/dependencies.py

@@ -10,6 +10,7 @@ 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
@@ -151,6 +152,7 @@ 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,
@@ -181,6 +183,7 @@ 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/client_state.py

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

invokeai/app/api_app.py

@@ -19,6 +19,7 @@ from invokeai.app.api.routers import (
     app_info,
     board_images,
     boards,
+    client_state,
     download_queue,
     images,
     model_manager,
@@ -131,6 +132,7 @@ app.include_router(app_info.app_router, prefix="/api")
 app.include_router(session_queue.session_queue_router, prefix="/api")
 app.include_router(workflows.workflows_router, prefix="/api")
 app.include_router(style_presets.style_presets_router, prefix="/api")
+app.include_router(client_state.client_state_router, prefix="/api")
 
 app.openapi = get_openapi_func(app)
 
@@ -155,6 +157,12 @@ def overridden_redoc() -> HTMLResponse:
 
 web_root_path = Path(list(web_dir.__path__)[0])
 
+if app_config.unsafe_disable_picklescan:
+    logger.warning(
+        "The unsafe_disable_picklescan option is enabled. This disables malware scanning while installing and"
+        "loading models, which may allow malicious code to be executed. Use at your own risk."
+    )
+
 try:
     app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
 except RuntimeError:

invokeai/app/invocations/cogview4_image_to_latents.py

@@ -17,6 +17,7 @@ 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.
@@ -38,7 +39,11 @@ class CogView4ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     @staticmethod
     def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
-        with vae_info as vae:
+        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):
             assert isinstance(vae, AutoencoderKL)
 
             vae.disable_tiling()
@@ -62,6 +67,8 @@ 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

@@ -6,7 +6,6 @@ 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,
@@ -20,6 +19,7 @@ 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.
@@ -39,22 +39,15 @@ 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 = self._estimate_working_memory(latents, vae_info.model)
+        estimated_working_memory = estimate_vae_working_memory_cogview4(
+            operation="decode", image_tensor=latents, vae=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/flux_denoise.py

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

invokeai/app/invocations/flux_vae_decode.py

@@ -3,7 +3,6 @@ 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,
@@ -18,6 +17,7 @@ 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(
@@ -39,17 +39,11 @@ 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:
-        estimated_working_memory = self._estimate_working_memory(latents, vae_info.model)
+        assert isinstance(vae_info.model, AutoEncoder)
+        estimated_working_memory = estimate_vae_working_memory_flux(
+            operation="decode", image_tensor=latents, vae=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

@@ -15,6 +15,7 @@ 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(
@@ -41,8 +42,12 @@ 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 as vae:
+        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
             assert isinstance(vae, AutoEncoder)
             vae_dtype = next(iter(vae.parameters())).dtype
             image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)

invokeai/app/invocations/image.py

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

invokeai/app/invocations/image_to_latents.py

@@ -27,6 +27,7 @@ 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(
@@ -52,11 +53,24 @@ 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)
 
-    @staticmethod
+    @classmethod
     def vae_encode(
-        vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor, tile_size: int = 0
+        cls,
+        vae_info: LoadedModel,
+        upcast: bool,
+        tiled: bool,
+        image_tensor: torch.Tensor,
+        tile_size: int = 0,
     ) -> torch.Tensor:
-        with vae_info as vae:
+        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):
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
             orig_dtype = vae.dtype
             if upcast:
@@ -113,6 +127,7 @@ 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:
@@ -120,7 +135,11 @@ class ImageToLatentsInvocation(BaseInvocation):
 
         context.util.signal_progress("Running VAE encoder")
         latents = self.vae_encode(
-            vae_info=vae_info, upcast=self.fp32, tiled=self.tiled, image_tensor=image_tensor, tile_size=self.tile_size
+            vae_info=vae_info,
+            upcast=self.fp32,
+            tiled=self.tiled or context.config.get().force_tiled_decode,
+            image_tensor=image_tensor,
+            tile_size=self.tile_size,
         )
 
         latents = latents.to("cpu")

invokeai/app/invocations/latents_to_image.py

@@ -27,6 +27,7 @@ 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(
@@ -53,39 +54,6 @@ 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)
@@ -94,8 +62,13 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
-
-        estimated_working_memory = self._estimate_working_memory(latents, use_tiling, vae_info.model)
+        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,
+        )
         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/sd3_image_to_latents.py

@@ -17,6 +17,7 @@ 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(
@@ -34,7 +35,11 @@ class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     @staticmethod
     def vae_encode(vae_info: LoadedModel, image_tensor: torch.Tensor) -> torch.Tensor:
-        with vae_info as vae:
+        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):
             assert isinstance(vae, AutoencoderKL)
 
             vae.disable_tiling()
@@ -58,6 +63,8 @@ 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

@@ -6,7 +6,6 @@ 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,
@@ -20,6 +19,7 @@ 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(
@@ -41,22 +41,15 @@ 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 = self._estimate_working_memory(latents, vae_info.model)
+        estimated_working_memory = estimate_vae_working_memory_sd3(
+            operation="decode", image_tensor=latents, vae=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/services/client_state_persistence/client_state_persistence_base.py

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

invokeai/app/services/client_state_persistence/client_state_persistence_sqlite.py

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

invokeai/app/services/config/config_default.py

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

invokeai/app/services/invocation_services.py

@@ -17,6 +17,7 @@ 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
@@ -73,6 +74,7 @@ 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
@@ -102,3 +104,4 @@ 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 copyfile, copytree, move, rmtree
+from shutil import move, rmtree
 from tempfile import mkdtemp
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union
 
@@ -186,13 +186,15 @@ class ModelInstallService(ModelInstallServiceBase):
         info: AnyModelConfig = self._probe(Path(model_path), config)  # type: ignore
 
         if preferred_name := config.name:
-            preferred_name = Path(preferred_name).with_suffix(model_path.suffix)
+            if Path(model_path).is_file():
+                # Careful! Don't use pathlib.Path(...).with_suffix - it can will strip everything after the first dot.
+                preferred_name = f"{preferred_name}{model_path.suffix}"
 
         dest_path = (
             self.app_config.models_path / info.base.value / info.type.value / (preferred_name or model_path.name)
         )
         try:
-            new_path = self._copy_model(model_path, dest_path)
+            new_path = self._move_model(model_path, dest_path)
         except FileExistsError as excp:
             raise DuplicateModelException(
                 f"A model named {model_path.name} is already installed at {dest_path.as_posix()}"
@@ -617,30 +619,17 @@ class ModelInstallService(ModelInstallServiceBase):
         self.record_store.update_model(key, ModelRecordChanges(path=model.path))
         return model
 
-    def _copy_model(self, old_path: Path, new_path: Path) -> Path:
-        if old_path == new_path:
-            return old_path
-        new_path.parent.mkdir(parents=True, exist_ok=True)
-        if old_path.is_dir():
-            copytree(old_path, new_path)
-        else:
-            copyfile(old_path, new_path)
-        return new_path
-
     def _move_model(self, old_path: Path, new_path: Path) -> Path:
         if old_path == new_path:
             return old_path
 
+        if new_path.exists():
+            raise FileExistsError(f"Cannot move {old_path} to {new_path}: destination already exists")
+
         new_path.parent.mkdir(parents=True, exist_ok=True)
 
-        # if path already exists then we jigger the name to make it unique
-        counter: int = 1
-        while new_path.exists():
-            path = new_path.with_stem(new_path.stem + f"_{counter:02d}")
-            if not path.exists():
-                new_path = path
-            counter += 1
         move(old_path, new_path)
+
         return new_path
 
     def _probe(self, model_path: Path, config: Optional[ModelRecordChanges] = None):

invokeai/app/services/model_load/model_load_default.py

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

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

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

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

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

invokeai/backend/flux/denoise.py

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

invokeai/backend/flux/extensions/kontext_extension.py

@@ -1,15 +1,14 @@
-import einops
-import numpy as np
 import torch
+import torch.nn.functional as F
+import torchvision.transforms as T
 from einops import repeat
-from PIL import Image
 
 from invokeai.app.invocations.fields import FluxKontextConditioningField
-from invokeai.app.invocations.flux_vae_encode import FluxVaeEncodeInvocation
 from invokeai.app.invocations.model import VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.flux.sampling_utils import pack
-from invokeai.backend.flux.util import PREFERED_KONTEXT_RESOLUTIONS
+from invokeai.backend.util.devices import TorchDevice
 
 
 def generate_img_ids_with_offset(
@@ -19,8 +18,10 @@ def generate_img_ids_with_offset(
     device: torch.device,
     dtype: torch.dtype,
     idx_offset: int = 0,
+    h_offset: int = 0,
+    w_offset: int = 0,
 ) -> torch.Tensor:
-    """Generate tensor of image position ids with an optional offset.
+    """Generate tensor of image position ids with optional index and spatial offsets.
 
     Args:
         latent_height (int): Height of image in latent space (after packing, this becomes h//2).
@@ -28,7 +29,9 @@ def generate_img_ids_with_offset(
         batch_size (int): Number of images in the batch.
         device (torch.device): Device to create tensors on.
         dtype (torch.dtype): Data type for the tensors.
-        idx_offset (int): Offset to add to the first dimension of the image ids.
+        idx_offset (int): Offset to add to the first dimension of the image ids (default: 0).
+        h_offset (int): Spatial offset for height/y-coordinates in latent space (default: 0).
+        w_offset (int): Spatial offset for width/x-coordinates in latent space (default: 0).
 
     Returns:
         torch.Tensor: Image position ids with shape [batch_size, (latent_height//2 * latent_width//2), 3].
@@ -42,6 +45,10 @@ def generate_img_ids_with_offset(
     packed_height = latent_height // 2
     packed_width = latent_width // 2
 
+    # Convert spatial offsets from latent space to packed space
+    packed_h_offset = h_offset // 2
+    packed_w_offset = w_offset // 2
+
     # Create base tensor for position IDs with shape [packed_height, packed_width, 3]
     # The 3 channels represent: [batch_offset, y_position, x_position]
     img_ids = torch.zeros(packed_height, packed_width, 3, device=device, dtype=dtype)
@@ -49,13 +56,13 @@ def generate_img_ids_with_offset(
     # Set the batch offset for all positions
     img_ids[..., 0] = idx_offset
 
-    # Create y-coordinate indices (vertical positions)
-    y_indices = torch.arange(packed_height, device=device, dtype=dtype)
+    # Create y-coordinate indices (vertical positions) with spatial offset
+    y_indices = torch.arange(packed_height, device=device, dtype=dtype) + packed_h_offset
     # Broadcast y_indices to match the spatial dimensions [packed_height, 1]
     img_ids[..., 1] = y_indices[:, None]
 
-    # Create x-coordinate indices (horizontal positions)
-    x_indices = torch.arange(packed_width, device=device, dtype=dtype)
+    # Create x-coordinate indices (horizontal positions) with spatial offset
+    x_indices = torch.arange(packed_width, device=device, dtype=dtype) + packed_w_offset
     # Broadcast x_indices to match the spatial dimensions [1, packed_width]
     img_ids[..., 2] = x_indices[None, :]
 
@@ -73,14 +80,14 @@ class KontextExtension:
 
     def __init__(
         self,
-        kontext_conditioning: FluxKontextConditioningField,
+        kontext_conditioning: list[FluxKontextConditioningField],
         context: InvocationContext,
         vae_field: VAEField,
         device: torch.device,
         dtype: torch.dtype,
     ):
         """
-        Initializes the KontextExtension, pre-processing the reference image
+        Initializes the KontextExtension, pre-processing the reference images
         into latents and positional IDs.
         """
         self._context = context
@@ -93,54 +100,116 @@ class KontextExtension:
         self.kontext_latents, self.kontext_ids = self._prepare_kontext()
 
     def _prepare_kontext(self) -> tuple[torch.Tensor, torch.Tensor]:
-        """Encodes the reference image and prepares its latents and IDs."""
-        image = self._context.images.get_pil(self.kontext_conditioning.image.image_name)
+        """Encodes the reference images and prepares their concatenated latents and IDs with spatial tiling."""
+        all_latents = []
+        all_ids = []
 
-        # Calculate aspect ratio of input image
-        width, height = image.size
-        aspect_ratio = width / height
+        # Track cumulative dimensions for spatial tiling
+        # These track the running extent of the virtual canvas in latent space
+        canvas_h = 0  # Running canvas height
+        canvas_w = 0  # Running canvas width
 
-        # Find the closest preferred resolution by aspect ratio
-        _, target_width, target_height = min(
-            ((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS), key=lambda x: x[0]
-        )
-
-        # Apply BFL's scaling formula
-        # This ensures compatibility with the model's training
-        scaled_width = 2 * int(target_width / 16)
-        scaled_height = 2 * int(target_height / 16)
-
-        # Resize to the exact resolution used during training
-        image = image.convert("RGB")
-        final_width = 8 * scaled_width
-        final_height = 8 * scaled_height
-        image = image.resize((final_width, final_height), Image.Resampling.LANCZOS)
-
-        # Convert to tensor with same normalization as BFL
-        image_np = np.array(image)
-        image_tensor = torch.from_numpy(image_np).float() / 127.5 - 1.0
-        image_tensor = einops.rearrange(image_tensor, "h w c -> 1 c h w")
-        image_tensor = image_tensor.to(self._device)
-
-        # Continue with VAE encoding
         vae_info = self._context.models.load(self._vae_field.vae)
-        kontext_latents_unpacked = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=image_tensor)
 
-        # Extract tensor dimensions
-        batch_size, _, latent_height, latent_width = kontext_latents_unpacked.shape
+        for idx, kontext_field in enumerate(self.kontext_conditioning):
+            image = self._context.images.get_pil(kontext_field.image.image_name)
 
-        # Pack the latents and generate IDs
-        kontext_latents_packed = pack(kontext_latents_unpacked).to(self._device, self._dtype)
-        kontext_ids = generate_img_ids_with_offset(
-            latent_height=latent_height,
-            latent_width=latent_width,
-            batch_size=batch_size,
-            device=self._device,
-            dtype=self._dtype,
-            idx_offset=1,
-        )
+            # Convert to RGB
+            image = image.convert("RGB")
 
-        return kontext_latents_packed, kontext_ids
+            # Convert to tensor using torchvision transforms for consistency
+            transformation = T.Compose(
+                [
+                    T.ToTensor(),  # Converts PIL image to tensor and scales to [0, 1]
+                ]
+            )
+            image_tensor = transformation(image)
+            # Convert from [0, 1] to [-1, 1] range expected by VAE
+            image_tensor = image_tensor * 2.0 - 1.0
+            image_tensor = image_tensor.unsqueeze(0)  # Add batch dimension
+            image_tensor = image_tensor.to(self._device)
+
+            # Continue with VAE encoding
+            # Don't sample from the distribution for reference images - use the mean (matching ComfyUI)
+            # Estimate working memory for encode operation (50% of decode memory requirements)
+            img_h = image_tensor.shape[-2]
+            img_w = image_tensor.shape[-1]
+            element_size = next(vae_info.model.parameters()).element_size()
+            scaling_constant = 1100  # 50% of decode scaling constant (2200)
+            estimated_working_memory = int(img_h * img_w * element_size * scaling_constant)
+
+            with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+                assert isinstance(vae, AutoEncoder)
+                vae_dtype = next(iter(vae.parameters())).dtype
+                image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)
+                # Use sample=False to get the distribution mean without noise
+                kontext_latents_unpacked = vae.encode(image_tensor, sample=False)
+                TorchDevice.empty_cache()
+
+            # Extract tensor dimensions
+            batch_size, _, latent_height, latent_width = kontext_latents_unpacked.shape
+
+            # Pad latents to be compatible with patch_size=2
+            # This ensures dimensions are even for the pack() function
+            pad_h = (2 - latent_height % 2) % 2
+            pad_w = (2 - latent_width % 2) % 2
+            if pad_h > 0 or pad_w > 0:
+                kontext_latents_unpacked = F.pad(kontext_latents_unpacked, (0, pad_w, 0, pad_h), mode="circular")
+                # Update dimensions after padding
+                _, _, latent_height, latent_width = kontext_latents_unpacked.shape
+
+            # Pack the latents
+            kontext_latents_packed = pack(kontext_latents_unpacked).to(self._device, self._dtype)
+
+            # Determine spatial offsets for this reference image
+            h_offset = 0
+            w_offset = 0
+
+            if idx > 0:  # First image starts at (0, 0)
+                # Calculate potential canvas dimensions for each tiling option
+                # Option 1: Tile vertically (below existing content)
+                potential_h_vertical = canvas_h + latent_height
+
+                # Option 2: Tile horizontally (to the right of existing content)
+                potential_w_horizontal = canvas_w + latent_width
+
+                # Choose arrangement that minimizes the maximum dimension
+                # This keeps the canvas closer to square, optimizing attention computation
+                if potential_h_vertical > potential_w_horizontal:
+                    # Tile horizontally (to the right of existing images)
+                    w_offset = canvas_w
+                    canvas_w = canvas_w + latent_width
+                    canvas_h = max(canvas_h, latent_height)
+                else:
+                    # Tile vertically (below existing images)
+                    h_offset = canvas_h
+                    canvas_h = canvas_h + latent_height
+                    canvas_w = max(canvas_w, latent_width)
+            else:
+                # First image - just set canvas dimensions
+                canvas_h = latent_height
+                canvas_w = latent_width
+
+            # Generate IDs with both index offset and spatial offsets
+            kontext_ids = generate_img_ids_with_offset(
+                latent_height=latent_height,
+                latent_width=latent_width,
+                batch_size=batch_size,
+                device=self._device,
+                dtype=self._dtype,
+                idx_offset=1,  # All reference images use index=1 (matching ComfyUI implementation)
+                h_offset=h_offset,
+                w_offset=w_offset,
+            )
+
+            all_latents.append(kontext_latents_packed)
+            all_ids.append(kontext_ids)
+
+        # Concatenate all latents and IDs along the sequence dimension
+        concatenated_latents = torch.cat(all_latents, dim=1)  # Concatenate along sequence dimension
+        concatenated_ids = torch.cat(all_ids, dim=1)  # Concatenate along sequence dimension
+
+        return concatenated_latents, concatenated_ids
 
     def ensure_batch_size(self, target_batch_size: int) -> None:
         """Ensures the kontext latents and IDs match the target batch size by repeating if necessary."""

invokeai/backend/image_util/imwatermark/vendor.py

@@ -0,0 +1,304 @@
+# 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

@@ -6,13 +6,10 @@ 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.app.services.config.config_default import get_config
-
-config = get_config()
+from invokeai.backend.image_util.imwatermark.vendor import WatermarkEncoder
 
 
 class InvisibleWatermark:

invokeai/backend/model_manager/legacy_probe.py

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

invokeai/backend/model_manager/model_on_disk.py

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

invokeai/backend/model_manager/starter_models.py

@@ -149,13 +149,29 @@ flux_kontext = StarterModel(
     dependencies=[t5_base_encoder, flux_vae, clip_l_encoder],
 )
 flux_kontext_quantized = StarterModel(
-    name="FLUX.1 Kontext dev (Quantized)",
+    name="FLUX.1 Kontext dev (quantized)",
     base=BaseModelType.Flux,
     source="https://huggingface.co/unsloth/FLUX.1-Kontext-dev-GGUF/resolve/main/flux1-kontext-dev-Q4_K_M.gguf",
     description="FLUX.1 Kontext dev quantized (q4_k_m). Total size with dependencies: ~14GB",
     type=ModelType.Main,
     dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
 )
+flux_krea = StarterModel(
+    name="FLUX.1 Krea dev",
+    base=BaseModelType.Flux,
+    source="https://huggingface.co/InvokeAI/FLUX.1-Krea-dev/resolve/main/flux1-krea-dev.safetensors",
+    description="FLUX.1 Krea dev. Total size with dependencies: ~33GB",
+    type=ModelType.Main,
+    dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
+)
+flux_krea_quantized = StarterModel(
+    name="FLUX.1 Krea dev (quantized)",
+    base=BaseModelType.Flux,
+    source="https://huggingface.co/InvokeAI/FLUX.1-Krea-dev-GGUF/resolve/main/flux1-krea-dev-Q4_K_M.gguf",
+    description="FLUX.1 Krea dev quantized (q4_k_m). Total size with dependencies: ~14GB",
+    type=ModelType.Main,
+    dependencies=[t5_8b_quantized_encoder, flux_vae, clip_l_encoder],
+)
 sd35_medium = StarterModel(
     name="SD3.5 Medium",
     base=BaseModelType.StableDiffusion3,
@@ -580,13 +596,14 @@ t2i_sketch_sdxl = StarterModel(
 )
 # endregion
 # region SpandrelImageToImage
-realesrgan_anime = StarterModel(
-    name="RealESRGAN_x4plus_anime_6B",
+animesharp_v4_rcan = StarterModel(
+    name="2x-AnimeSharpV4_RCAN",
     base=BaseModelType.Any,
-    source="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
-    description="A Real-ESRGAN 4x upscaling model (optimized for anime images).",
+    source="https://github.com/Kim2091/Kim2091-Models/releases/download/2x-AnimeSharpV4/2x-AnimeSharpV4_RCAN.safetensors",
+    description="A 2x upscaling model (optimized for anime images).",
     type=ModelType.SpandrelImageToImage,
 )
+
 realesrgan_x4 = StarterModel(
     name="RealESRGAN_x4plus",
     base=BaseModelType.Any,
@@ -732,7 +749,7 @@ STARTER_MODELS: list[StarterModel] = [
     t2i_lineart_sdxl,
     t2i_sketch_sdxl,
     realesrgan_x4,
-    realesrgan_anime,
+    animesharp_v4_rcan,
     realesrgan_x2,
     swinir,
     t5_base_encoder,
@@ -743,6 +760,8 @@ STARTER_MODELS: list[StarterModel] = [
     llava_onevision,
     flux_fill,
     cogview4,
+    flux_krea,
+    flux_krea_quantized,
 ]
 
 sd1_bundle: list[StarterModel] = [
@@ -794,6 +813,7 @@ flux_bundle: list[StarterModel] = [
     flux_redux,
     flux_fill,
     flux_kontext_quantized,
+    flux_krea_quantized,
 ]
 
 STARTER_BUNDLES: dict[str, StarterModelBundle] = {

invokeai/backend/model_manager/util/model_util.py

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

invokeai/backend/patches/lora_conversions/flux_diffusers_lora_conversion_utils.py

@@ -18,16 +18,25 @@ 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())
 
-    # Next, check that this is likely a FLUX model by spot-checking a few keys.
-    expected_keys = [
+    # Check if keys use transformer prefix
+    transformer_prefix_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",
     ]
-    all_expected_keys_present = all(k in state_dict for k in expected_keys)
+    transformer_keys_present = all(k in state_dict for k in transformer_prefix_keys)
 
-    return all_keys_in_peft_format and all_expected_keys_present
+    # 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)
 
 
 def lora_model_from_flux_diffusers_state_dict(
@@ -49,8 +58,16 @@ def lora_layers_from_flux_diffusers_grouped_state_dict(
     https://github.com/huggingface/diffusers/blob/55ac421f7bb12fd00ccbef727be4dc2f3f920abb/scripts/convert_flux_to_diffusers.py
     """
 
-    # Remove the "transformer." prefix from all keys.
-    grouped_state_dict = {k.replace("transformer.", ""): v for k, v in grouped_state_dict.items()}
+    # 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()}
 
     # 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("Intialize FLUX transformer on meta device"):
+    with log_time("Initialize FLUX transformer on meta device"):
         # TODO(ryand): Determine if this is a schnell model or a dev model and load the appropriate config.
         p = params["flux-schnell"]
 

invokeai/backend/quantization/scripts/load_flux_model_bnb_nf4.py

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

invokeai/backend/quantization/scripts/quantize_t5_xxl_bnb_llm_int8.py

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

invokeai/backend/util/vae_working_memory.py

@@ -0,0 +1,117 @@
+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/.gitignore

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

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

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

invokeai/frontend/web/eslint.config.mjs

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

invokeai/frontend/web/knip.ts

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

invokeai/frontend/web/package.json

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

invokeai/frontend/web/pnpm-lock.yaml

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

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

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

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

@@ -38,6 +38,7 @@
         "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:",
@@ -114,6 +115,9 @@
         "t2iAdapter": "T2I Adapter",
         "positivePrompt": "Positive Prompt",
         "negativePrompt": "Negative Prompt",
+        "removeNegativePrompt": "Remove Negative Prompt",
+        "addNegativePrompt": "Add Negative Prompt",
+        "selectYourModel": "Select Your Model",
         "discordLabel": "Discord",
         "dontAskMeAgain": "Don't ask me again",
         "dontShowMeThese": "Don't show me these",
@@ -610,10 +614,18 @@
                 "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.",
@@ -763,6 +775,7 @@
         "allPrompts": "All Prompts",
         "cfgScale": "CFG scale",
         "cfgRescaleMultiplier": "$t(parameters.cfgRescaleMultiplier)",
+        "clipSkip": "$t(parameters.clipSkip)",
         "createdBy": "Created By",
         "generationMode": "Generation Mode",
         "guidance": "Guidance",
@@ -865,6 +878,9 @@
         "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",
@@ -1235,7 +1251,7 @@
             "modelIncompatibleScaledBboxWidth": "Scaled bbox width is {{width}} but {{model}} requires multiple of {{multiple}}",
             "modelIncompatibleScaledBboxHeight": "Scaled bbox height is {{height}} but {{model}} requires multiple of {{multiple}}",
             "fluxModelMultipleControlLoRAs": "Can only use 1 Control LoRA at a time",
-            "fluxKontextMultipleReferenceImages": "Can only use 1 Reference Image at a time with Flux Kontext",
+            "fluxKontextMultipleReferenceImages": "Can only use 1 Reference Image at a time with FLUX Kontext via BFL API",
             "canvasIsFiltering": "Canvas is busy (filtering)",
             "canvasIsTransforming": "Canvas is busy (transforming)",
             "canvasIsRasterizing": "Canvas is busy (rasterizing)",
@@ -1283,6 +1299,7 @@
         "remixImage": "Remix Image",
         "usePrompt": "Use Prompt",
         "useSeed": "Use Seed",
+        "useClipSkip": "Use CLIP Skip",
         "width": "Width",
         "gaussianBlur": "Gaussian Blur",
         "boxBlur": "Box Blur",
@@ -1933,8 +1950,11 @@
             "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",
@@ -2066,6 +2086,8 @@
         "asControlLayer": "As $t(controlLayers.controlLayer)",
         "asControlLayerResize": "As $t(controlLayers.controlLayer) (Resize)",
         "referenceImage": "Reference Image",
+        "maxRefImages": "Max Ref Images",
+        "useAsReferenceImage": "Use as Reference Image",
         "regionalReferenceImage": "Regional Reference Image",
         "globalReferenceImage": "Global Reference Image",
         "sendingToCanvas": "Staging Generations on Canvas",
@@ -2174,7 +2196,8 @@
             "rgReferenceImagesNotSupported": "regional Reference Images not supported for selected base model",
             "rgAutoNegativeNotSupported": "Auto-Negative not supported for selected base model",
             "rgNoRegion": "no region drawn",
-            "fluxFillIncompatibleWithControlLoRA": "Control LoRA is not compatible with FLUX Fill"
+            "fluxFillIncompatibleWithControlLoRA": "Control LoRA is not compatible with FLUX Fill",
+            "bboxHidden": "Bounding box is hidden (shift+o to toggle)"
         },
         "errors": {
             "unableToFindImage": "Unable to find image",
@@ -2533,7 +2556,7 @@
     },
     "ui": {
         "tabs": {
-            "generation": "Generation",
+            "generate": "Generate",
             "canvas": "Canvas",
             "workflows": "Workflows",
             "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
@@ -2544,6 +2567,12 @@
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
             "gallery": "Gallery"
         },
+        "panels": {
+            "launchpad": "Launchpad",
+            "workflowEditor": "Workflow Editor",
+            "imageViewer": "Image Viewer",
+            "canvas": "Canvas"
+        },
         "launchpad": {
             "workflowsTitle": "Go deep with Workflows.",
             "upscalingTitle": "Upscale and add detail.",
@@ -2551,6 +2580,28 @@
             "generateTitle": "Generate images from text prompts.",
             "modelGuideText": "Want to learn what prompts work best for each model?",
             "modelGuideLink": "Check out our Model Guide.",
+            "createNewWorkflowFromScratch": "Create a new Workflow from scratch",
+            "browseAndLoadWorkflows": "Browse and load existing workflows",
+            "addStyleRef": {
+                "title": "Add a Style Reference",
+                "description": "Add an image to transfer its look."
+            },
+            "editImage": {
+                "title": "Edit Image",
+                "description": "Add an image to refine."
+            },
+            "generateFromText": {
+                "title": "Generate from Text",
+                "description": "Enter a prompt and Invoke."
+            },
+            "useALayoutImage": {
+                "title": "Use a Layout Image",
+                "description": "Add an image to control composition."
+            },
+            "generate": {
+                "canvasCalloutTitle": "Looking to get more control, edit, and iterate on your images?",
+                "canvasCalloutLink": "Navigate to Canvas for more capabilities."
+            },
             "workflows": {
                 "description": "Workflows are reusable templates that automate image generation tasks, allowing you to quickly perform complex operations and get consistent results.",
                 "learnMoreLink": "Learn more about creating workflows",
@@ -2587,6 +2638,13 @@
                 "upscaleModel": "Upscale Model",
                 "model": "Model",
                 "scale": "Scale",
+                "creativityAndStructure": {
+                    "title": "Creativity & Structure Defaults",
+                    "conservative": "Conservative",
+                    "balanced": "Balanced",
+                    "creative": "Creative",
+                    "artistic": "Artistic"
+                },
                 "helpText": {
                     "promptAdvice": "When upscaling, use a prompt that describes the medium and style. Avoid describing specific content details in the image.",
                     "styleAdvice": "Upscaling works best with the general style of your image."
@@ -2631,10 +2689,8 @@
     "whatsNew": {
         "whatsNewInInvoke": "What's New in Invoke",
         "items": [
-            "New setting to send all Canvas generations directly to the Gallery.",
-            "New Invert Mask (Shift+V) and Fit BBox to Mask (Shift+B) capabilities.",
-            "Expanded support for Model Thumbnails and configurations.",
-            "Various other quality of life updates and fixes"
+            "Misc QoL: Toggle Bbox visibility, highlight nodes with errors, prevent adding node fields to Builder form multiple times, CLIP Skip metadata recallable",
+            "Reduced VRAM usage for multiple Kontext Ref images and VAE encoding"
         ],
         "readReleaseNotes": "Read Release Notes",
         "watchRecentReleaseVideos": "Watch Recent Release Videos",

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

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

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

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

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

@@ -128,7 +128,10 @@
         "search": "Cerca",
         "clear": "Cancella",
         "compactView": "Vista compatta",
-        "fullView": "Vista completa"
+        "fullView": "Vista completa",
+        "removeNegativePrompt": "Rimuovi prompt negativo",
+        "addNegativePrompt": "Aggiungi prompt negativo",
+        "selectYourModel": "Seleziona il modello"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -254,8 +257,8 @@
                 "desc": "Attiva/disattiva il pannello destro."
             },
             "resetPanelLayout": {
-                "title": "Ripristina il layout del pannello",
-                "desc": "Ripristina le dimensioni e il layout predefiniti dei pannelli sinistro e destro."
+                "title": "Ripristina lo schema del pannello",
+                "desc": "Ripristina le dimensioni e lo schema predefiniti dei pannelli sinistro e destro."
             },
             "togglePanels": {
                 "title": "Attiva/disattiva i pannelli",
@@ -410,6 +413,10 @@
             "cancelSegmentAnything": {
                 "title": "Annulla Segment Anything",
                 "desc": "Annulla l'operazione Segment Anything corrente."
+            },
+            "fitBboxToLayers": {
+                "title": "Adatta il riquadro di delimitazione ai livelli",
+                "desc": "Regola automaticamente il riquadro di delimitazione della generazione per adattarlo ai livelli visibili"
             }
         },
         "workflows": {
@@ -539,6 +546,10 @@
             "galleryNavUpAlt": {
                 "desc": "Uguale a Naviga verso l'alto, ma seleziona l'immagine da confrontare, aprendo la modalità di confronto se non è già aperta.",
                 "title": "Naviga verso l'alto (Confronta immagine)"
+            },
+            "starImage": {
+                "desc": "Aggiungi/Rimuovi contrassegno all'immagine selezionata.",
+                "title": "Aggiungi / Rimuovi contrassegno immagine"
             }
         }
     },
@@ -707,7 +718,10 @@
             "bundleDescription": "Ogni pacchetto include modelli essenziali per ogni famiglia di modelli e modelli base selezionati per iniziare.",
             "browseAll": "Oppure scopri tutti i modelli disponibili:"
         },
-        "launchpadTab": "Rampa di lancio"
+        "launchpadTab": "Rampa di lancio",
+        "installBundle": "Installa pacchetto",
+        "installBundleMsg1": "Vuoi davvero installare il pacchetto {{bundleName}}?",
+        "installBundleMsg2": "Questo pacchetto installerà i seguenti {{count}} modelli:"
     },
     "parameters": {
         "images": "Immagini",
@@ -794,7 +808,7 @@
             "modelIncompatibleScaledBboxWidth": "La larghezza scalata del riquadro è {{width}} ma {{model}} richiede multipli di {{multiple}}",
             "modelIncompatibleScaledBboxHeight": "L'altezza scalata del riquadro è {{height}} ma {{model}} richiede multipli di {{multiple}}",
             "modelDisabledForTrial": "La generazione con {{modelName}} non è disponibile per gli account di prova. Accedi alle impostazioni del tuo account per effettuare l'upgrade.",
-            "fluxKontextMultipleReferenceImages": "È possibile utilizzare solo 1 immagine di riferimento alla volta con Flux Kontext",
+            "fluxKontextMultipleReferenceImages": "È possibile utilizzare solo 1 immagine di riferimento alla volta con FLUX Kontext tramite BFL API",
             "promptExpansionResultPending": "Accetta o ignora il risultato dell'espansione del prompt",
             "promptExpansionPending": "Espansione del prompt in corso"
         },
@@ -824,7 +838,8 @@
         "coherenceMinDenoise": "Min rid. rumore",
         "recallMetadata": "Richiama i metadati",
         "disabledNoRasterContent": "Disabilitato (nessun contenuto Raster)",
-        "modelDisabledForTrial": "La generazione con {{modelName}} non è disponibile per gli account di prova. Visita le <LinkComponent>impostazioni account</LinkComponent> per effettuare l'upgrade."
+        "modelDisabledForTrial": "La generazione con {{modelName}} non è disponibile per gli account di prova. Visita le <LinkComponent>impostazioni account</LinkComponent> per effettuare l'upgrade.",
+        "useClipSkip": "Usa CLIP Skip"
     },
     "settings": {
         "models": "Modelli",
@@ -1162,7 +1177,19 @@
         "unexpectedField_withName": "Campo \"{{name}}\" inaspettato",
         "missingSourceOrTargetHandle": "Identificatore del nodo sorgente o di destinazione mancante",
         "layout": {
-            "alignmentDR": "In basso a destra"
+            "alignmentDR": "In basso a destra",
+            "autoLayout": "Schema automatico",
+            "nodeSpacing": "Spaziatura nodi",
+            "layerSpacing": "Spaziatura livelli",
+            "layeringStrategy": "Strategia livelli",
+            "longestPath": "Percorso più lungo",
+            "layoutDirection": "Direzione schema",
+            "layoutDirectionRight": "A destra",
+            "layoutDirectionDown": "In basso",
+            "alignment": "Allineamento nodi",
+            "alignmentUL": "In alto a sinistra",
+            "alignmentDL": "In basso a sinistra",
+            "alignmentUR": "In alto a destra"
         }
     },
     "boards": {
@@ -1211,7 +1238,8 @@
         "updateBoardError": "Errore durante l'aggiornamento della bacheca",
         "uncategorizedImages": "Immagini non categorizzate",
         "deleteAllUncategorizedImages": "Elimina tutte le immagini non categorizzate",
-        "deletedImagesCannotBeRestored": "Le immagini eliminate non possono essere ripristinate."
+        "deletedImagesCannotBeRestored": "Le immagini eliminate non possono essere ripristinate.",
+        "locateInGalery": "Trova nella Galleria"
     },
     "queue": {
         "queueFront": "Aggiungi all'inizio della coda",
@@ -1240,7 +1268,7 @@
         "batchQueuedDesc_other": "Aggiunte {{count}} sessioni a {{direction}} della coda",
         "graphQueued": "Grafico in coda",
         "batch": "Lotto",
-        "clearQueueAlertDialog": "Lo svuotamento della coda annulla immediatamente tutti gli elementi in elaborazione e cancella completamente la coda. I filtri in sospeso verranno annullati.",
+        "clearQueueAlertDialog": "La cancellazione della coda annulla immediatamente tutti gli elementi in elaborazione e cancella completamente la coda. I filtri in sospeso verranno annullati e l'area di lavoro della Tela verrà reimpostata.",
         "pending": "In attesa",
         "completedIn": "Completato in",
         "resumeFailed": "Problema nel riavvio dell'elaborazione",
@@ -1296,7 +1324,8 @@
         "retrySucceeded": "Elemento rieseguito",
         "retryItem": "Riesegui elemento",
         "retryFailed": "Problema riesecuzione elemento",
-        "credits": "Crediti"
+        "credits": "Crediti",
+        "cancelAllExceptCurrent": "Annulla tutto tranne quello corrente"
     },
     "models": {
         "noMatchingModels": "Nessun modello corrispondente",
@@ -1711,7 +1740,7 @@
         "structure": {
             "heading": "Struttura",
             "paragraphs": [
-                "La struttura determina quanto l'immagine finale rispecchierà il layout dell'originale. Una struttura bassa permette cambiamenti significativi, mentre una struttura alta conserva la composizione e il layout originali."
+                "La struttura determina quanto l'immagine finale rispecchierà lo schema dell'originale. Un valore struttura basso permette cambiamenti significativi, mentre un valore struttura alto conserva la composizione e lo schema originali."
             ]
         },
         "fluxDevLicense": {
@@ -1877,7 +1906,7 @@
         "opened": "Aperto",
         "convertGraph": "Converti grafico",
         "loadWorkflow": "$t(common.load) Flusso di lavoro",
-        "autoLayout": "Disposizione automatica",
+        "autoLayout": "Schema automatico",
         "loadFromGraph": "Carica il flusso di lavoro dal grafico",
         "userWorkflows": "Flussi di lavoro utente",
         "projectWorkflows": "Flussi di lavoro del progetto",
@@ -1957,7 +1986,9 @@
             "publishInProgress": "Pubblicazione in corso",
             "selectingOutputNode": "Selezione del nodo di uscita",
             "selectingOutputNodeDesc": "Fare clic su un nodo per selezionarlo come nodo di uscita del flusso di lavoro.",
-            "errorWorkflowHasUnpublishableNodes": "Il flusso di lavoro ha nodi di estrazione lotto, generatore o metadati"
+            "errorWorkflowHasUnpublishableNodes": "Il flusso di lavoro ha nodi di estrazione lotto, generatore o metadati",
+            "showShuffle": "Mostra Mescola",
+            "shuffle": "Mescola"
         },
         "loadMore": "Carica altro",
         "searchPlaceholder": "Cerca per nome, descrizione o etichetta",
@@ -2438,7 +2469,8 @@
             "ipAdapterIncompatibleBaseModel": "modello base dell'immagine di riferimento incompatibile",
             "ipAdapterNoImageSelected": "nessuna immagine di riferimento selezionata",
             "rgAutoNegativeNotSupported": "Auto-Negativo non supportato per il modello base selezionato",
-            "fluxFillIncompatibleWithControlLoRA": "Il controllo LoRA non è compatibile con FLUX Fill"
+            "fluxFillIncompatibleWithControlLoRA": "Il controllo LoRA non è compatibile con FLUX Fill",
+            "bboxHidden": "Il riquadro di delimitazione è nascosto (Shift+o per attivarlo)"
         },
         "pasteTo": "Incolla su",
         "pasteToBboxDesc": "Nuovo livello (nel riquadro di delimitazione)",
@@ -2478,11 +2510,12 @@
             "off": "Spento"
         },
         "invertMask": "Inverti maschera",
-        "fitBboxToMasks": "Adatta il riquadro di delimitazione alle maschere"
+        "fitBboxToMasks": "Adatta il riquadro di delimitazione alle maschere",
+        "maxRefImages": "Max Immagini di rif.to",
+        "useAsReferenceImage": "Usa come immagine di riferimento"
     },
     "ui": {
         "tabs": {
-            "generation": "Generazione",
             "canvas": "Tela",
             "workflows": "Flussi di lavoro",
             "workflowsTab": "$t(ui.tabs.workflows) $t(common.tab)",
@@ -2491,7 +2524,8 @@
             "queue": "Coda",
             "upscaling": "Amplia",
             "upscalingTab": "$t(ui.tabs.upscaling) $t(common.tab)",
-            "gallery": "Galleria"
+            "gallery": "Galleria",
+            "generate": "Genera"
         },
         "launchpad": {
             "workflowsTitle": "Approfondisci i flussi di lavoro.",
@@ -2539,8 +2573,43 @@
                 "helpText": {
                     "promptAdvice": "Durante l'ampliamento, utilizza un prompt che descriva il mezzo e lo stile. Evita di descrivere dettagli specifici del contenuto dell'immagine.",
                     "styleAdvice": "L'ampliamento funziona meglio con lo stile generale dell'immagine."
+                },
+                "creativityAndStructure": {
+                    "title": "Creatività e struttura predefinite",
+                    "conservative": "Conservativo",
+                    "balanced": "Bilanciato",
+                    "creative": "Creativo",
+                    "artistic": "Artistico"
                 }
+            },
+            "createNewWorkflowFromScratch": "Crea un nuovo flusso di lavoro da zero",
+            "browseAndLoadWorkflows": "Sfoglia e carica i flussi di lavoro esistenti",
+            "addStyleRef": {
+                "title": "Aggiungi un riferimento di stile",
+                "description": "Aggiungi un'immagine per trasferirne l'aspetto."
+            },
+            "editImage": {
+                "title": "Modifica immagine",
+                "description": "Aggiungi un'immagine da perfezionare."
+            },
+            "generateFromText": {
+                "title": "Genera da testo",
+                "description": "Inserisci un prompt e genera."
+            },
+            "useALayoutImage": {
+                "description": "Aggiungi un'immagine per controllare la composizione.",
+                "title": "Usa una immagine guida"
+            },
+            "generate": {
+                "canvasCalloutTitle": "Vuoi avere più controllo, modificare e affinare le tue immagini?",
+                "canvasCalloutLink": "Per ulteriori funzionalità, vai su Tela."
             }
+        },
+        "panels": {
+            "launchpad": "Rampa di lancio",
+            "workflowEditor": "Editor del flusso di lavoro",
+            "imageViewer": "Visualizzatore immagini",
+            "canvas": "Tela"
         }
     },
     "upscaling": {
@@ -2631,9 +2700,8 @@
         "watchRecentReleaseVideos": "Guarda i video su questa versione",
         "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
         "items": [
-            "Genera immagini più velocemente con le nuove Rampe di lancio e una scheda Genera semplificata.",
-            "Modifica con prompt utilizzando Flux Kontext Dev.",
-            "Esporta in PSD, nascondi sovrapposizioni in blocco, organizza modelli e immagini: il tutto in un'interfaccia riprogettata e pensata per il controllo."
+            "Vari QoL: attiva/disattiva la visibilità del Riquadro di delimitazione, evidenzia i nodi con errori, evita di aggiungere più volte i campi dei nodi al modulo Generatore, i metadati CLIP Skip ora richiamabili",
+            "Utilizzo ridotto di VRAM per immagini di riferimento Kontext multiple e codifica VAE"
         ]
     },
     "system": {

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

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

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

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

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

@@ -252,7 +252,10 @@
         "clear": "Dọn Dẹp",
         "compactView": "Chế Độ Xem Gọn",
         "fullView": "Chế Độ Xem Đầy Đủ",
-        "options_withCount_other": "{{count}} thiết lập"
+        "options_withCount_other": "{{count}} thiết lập",
+        "removeNegativePrompt": "Xóa Lệnh Tiêu Cực",
+        "addNegativePrompt": "Thêm Lệnh Tiêu Cực",
+        "selectYourModel": "Chọn Model"
     },
     "prompt": {
         "addPromptTrigger": "Thêm Trigger Cho Lệnh",
@@ -299,7 +302,7 @@
         "pruneTooltip": "Cắt bớt {{item_count}} mục đã hoàn tất",
         "pruneSucceeded": "Đã cắt bớt {{item_count}} mục đã hoàn tất khỏi hàng",
         "clearTooltip": "Huỷ Và Dọn Dẹp Tất Cả Mục",
-        "clearQueueAlertDialog": "Dọn dẹp hàng đợi sẽ ngay lập tức huỷ tất cả mục đang xử lý và làm sạch hàng hoàn toàn. Bộ lọc đang chờ xử lý sẽ bị huỷ bỏ.",
+        "clearQueueAlertDialog": "Dọn dẹp hàng đợi sẽ ngay lập tức huỷ tất cả mục đang xử lý và làm sạch hàng hoàn toàn. Bộ lọc đang chờ xử lý sẽ bị huỷ bỏ và Vùng Dựng Canva sẽ được khởi động lại.",
         "session": "Phiên",
         "item": "Mục",
         "resumeFailed": "Có Vấn Đề Khi Tiếp Tục Bộ Xử Lý",
@@ -343,13 +346,14 @@
         "retrySucceeded": "Mục Đã Thử Lại",
         "retryFailed": "Có Vấn Đề Khi Thử Lại Mục",
         "retryItem": "Thử Lại Mục",
-        "credits": "Nguồn"
+        "credits": "Nguồn",
+        "cancelAllExceptCurrent": "Huỷ Bỏ Tất Cả Ngoại Trừ Mục Hiện Tại"
     },
     "hotkeys": {
         "canvas": {
             "fitLayersToCanvas": {
                 "title": "Xếp Vừa Layers Vào Canvas",
-                "desc": "Căn chỉnh để góc nhìn vừa vặn với tất cả layer."
+                "desc": "Căn chỉnh để góc nhìn vừa vặn với tất cả layer nhìn thấy dược."
             },
             "setZoomTo800Percent": {
                 "desc": "Phóng to canvas lên 800%.",
@@ -473,6 +477,28 @@
             "toggleNonRasterLayers": {
                 "title": "Bật/Tắt Layer Không Thuộc Dạng Raster",
                 "desc": "Hiện hoặc ẩn tất cả layer không thuộc dạng raster (Layer Điều Khiển Được, Lớp Phủ Inpaint, Chỉ Dẫn Khu Vực)."
+            },
+            "invertMask": {
+                "title": "Đảo Ngược Lớp Phủ",
+                "desc": "Đảo ngược lớp phủ inpaint được chọn, tạo một lớp phủ mới với độ trong suốt đối nghịch."
+            },
+            "fitBboxToMasks": {
+                "title": "Xếp Vừa Hộp Giới Hạn Vào Lớp Phủ",
+                "desc": "Tự động điểu chỉnh hộp giới hạn tạo sinh vừa vặn vào lớp phủ inpaint nhìn thấy được"
+            },
+            "applySegmentAnything": {
+                "title": "Áp Dụng Segment Anything",
+                "desc": "Áp dụng lớp phủ Segment Anything hiện tại.",
+                "key": "enter"
+            },
+            "cancelSegmentAnything": {
+                "title": "Huỷ Segment Anything",
+                "desc": "Huỷ hoạt động Segment Anything hiện tại.",
+                "key": "esc"
+            },
+            "fitBboxToLayers": {
+                "title": "Xếp Vừa Hộp Giới Hạn Vào Layer",
+                "desc": "Tự động điểu chỉnh hộp giới hạn tạo sinh vừa vặn vào layer nhìn thấy được"
             }
         },
         "workflows": {
@@ -602,6 +628,10 @@
             "clearSelection": {
                 "desc": "Xoá phần lựa chọn hiện tại nếu có.",
                 "title": "Xoá Phần Lựa Chọn"
+            },
+            "starImage": {
+                "title": "Dấu/Huỷ Sao Hình Ảnh",
+                "desc": "Đánh dấu sao hoặc huỷ đánh dấu sao ảnh được chọn."
             }
         },
         "app": {
@@ -661,6 +691,11 @@
             "selectModelsTab": {
                 "desc": "Chọn tab Model (Mô Hình).",
                 "title": "Chọn Tab Model"
+            },
+            "selectGenerateTab": {
+                "title": "Chọn Tab Tạo Sinh",
+                "desc": "Chọn tab Tạo Sinh.",
+                "key": "1"
             }
         },
         "searchHotkeys": "Tìm Phím tắt",
@@ -870,7 +905,8 @@
         "recallParameters": "Gợi Nhớ Tham Số",
         "scheduler": "Scheduler",
         "noMetaData": "Không tìm thấy metadata",
-        "imageDimensions": "Kích Thước Ảnh"
+        "imageDimensions": "Kích Thước Ảnh",
+        "clipSkip": "$t(parameters.clipSkip)"
     },
     "accordions": {
         "generation": {
@@ -1090,7 +1126,23 @@
         "unknownField_withName": "Vùng Dữ Liệu Không Rõ \"{{name}}\"",
         "unexpectedField_withName": "Sai Vùng Dữ Liệu \"{{name}}\"",
         "unknownFieldEditWorkflowToFix_withName": "Workflow chứa vùng dữ liệu không rõ \"{{name}}\".\nHãy biên tập workflow để sửa lỗi.",
-        "missingField_withName": "Thiếu Vùng Dữ Liệu \"{{name}}\""
+        "missingField_withName": "Thiếu Vùng Dữ Liệu \"{{name}}\"",
+        "layout": {
+            "autoLayout": "Bố Cục Tự Động",
+            "layeringStrategy": "Chiến Lược Phân Layer",
+            "networkSimplex": "Network Simplex",
+            "longestPath": "Đường Đi Dài Nhất",
+            "nodeSpacing": "Khoảng Cách Node",
+            "layerSpacing": "Khoảng Cách Layer",
+            "layoutDirection": "Hướng Bố Cục",
+            "layoutDirectionRight": "Phải",
+            "layoutDirectionDown": "Xuống",
+            "alignment": "Căn Chỉnh Node",
+            "alignmentUL": "Trên Cùng Bên Trái",
+            "alignmentDL": "Dưới Cùng Bên Trái",
+            "alignmentUR": "Trên Cùng Bên Phải",
+            "alignmentDR": "Dưới Cùng Bên Phải"
+        }
     },
     "popovers": {
         "paramCFGRescaleMultiplier": {
@@ -1597,7 +1649,7 @@
             "modelIncompatibleScaledBboxHeight": "Chiều dài hộp giới hạn theo tỉ lệ là {{height}} nhưng {{model}} yêu cầu bội số của {{multiple}}",
             "modelIncompatibleScaledBboxWidth": "Chiều rộng hộp giới hạn theo tỉ lệ là {{width}} nhưng {{model}} yêu cầu bội số của {{multiple}}",
             "modelDisabledForTrial": "Tạo sinh với {{modelName}} là không thể với tài khoản trial. Vào phần thiết lập tài khoản để nâng cấp.",
-            "fluxKontextMultipleReferenceImages": "Chỉ có thể dùng 1 Ảnh Mẫu cùng lúc với Flux Kontext",
+            "fluxKontextMultipleReferenceImages": "Chỉ có thể dùng 1 Ảnh Mẫu cùng lúc với LUX Kontext thông qua BFL API",
             "promptExpansionPending": "Trong quá trình mở rộng lệnh",
             "promptExpansionResultPending": "Hãy chấp thuận hoặc huỷ bỏ kết quả mở rộng lệnh của bạn"
         },
@@ -1663,7 +1715,8 @@
         "upscaling": "Upscale",
         "tileSize": "Kích Thước Khối",
         "disabledNoRasterContent": "Đã Tắt (Không Có Nội Dung Dạng Raster)",
-        "modelDisabledForTrial": "Tạo sinh với {{modelName}} là không thể với tài khoản trial. Vào phần <LinkComponent>thiết lập tài khoản</LinkComponent> để nâng cấp."
+        "modelDisabledForTrial": "Tạo sinh với {{modelName}} là không thể với tài khoản trial. Vào phần <LinkComponent>thiết lập tài khoản</LinkComponent> để nâng cấp.",
+        "useClipSkip": "Dùng CLIP Skip"
     },
     "dynamicPrompts": {
         "seedBehaviour": {
@@ -2154,7 +2207,8 @@
             "rgReferenceImagesNotSupported": "Ảnh Mẫu Khu Vực không được hỗ trợ cho model cơ sở được chọn",
             "rgAutoNegativeNotSupported": "Tự Động Đảo Chiều không được hỗ trợ cho model cơ sở được chọn",
             "rgNoRegion": "không có khu vực được vẽ",
-            "fluxFillIncompatibleWithControlLoRA": "LoRA Điều Khiển Được không tương tích với FLUX Fill"
+            "fluxFillIncompatibleWithControlLoRA": "LoRA Điều Khiển Được không tương tích với FLUX Fill",
+            "bboxHidden": "Hộp giới hạn đang ẩn (shift+o để bật/tắt)"
         },
         "pasteTo": "Dán Vào",
         "pasteToAssets": "Tài Nguyên",
@@ -2192,7 +2246,11 @@
             "off": "Tắt",
             "switchOnStart": "Khi Bắt Đầu",
             "switchOnFinish": "Khi Kết Thúc"
-        }
+        },
+        "fitBboxToMasks": "Xếp Vừa Hộp Giới Hạn Vào Lớp Phủ",
+        "invertMask": "Đảo Ngược Lớp Phủ",
+        "maxRefImages": "Ảnh Mẫu Tối Đa",
+        "useAsReferenceImage": "Dùng Làm Ảnh Mẫu"
     },
     "stylePresets": {
         "negativePrompt": "Lệnh Tiêu Cực",
@@ -2354,20 +2412,28 @@
         "noValidLayerAdapters": "Không có Layer Adaper Phù Hợp",
         "promptGenerationStarted": "Trình tạo sinh lệnh khởi động",
         "uploadAndPromptGenerationFailed": "Thất bại khi tải lên ảnh để tạo sinh lệnh",
-        "promptExpansionFailed": "Có vấn đề xảy ra. Hãy thử mở rộng lệnh lại."
+        "promptExpansionFailed": "Có vấn đề xảy ra. Hãy thử mở rộng lệnh lại.",
+        "maskInverted": "Đã Đảo Ngược Lớp Phủ",
+        "maskInvertFailed": "Thất Bại Khi Đảo Ngược Lớp Phủ",
+        "noVisibleMasks": "Không Có Lớp Phủ Đang Hiển Thị",
+        "noVisibleMasksDesc": "Tạo hoặc bật ít nhất một lớp phủ inpaint để đảo ngược",
+        "noInpaintMaskSelected": "Không Có Lớp Phủ Inpant Được Chọn",
+        "noInpaintMaskSelectedDesc": "Chọn một lớp phủ inpaint để đảo ngược",
+        "invalidBbox": "Hộp Giới Hạn Không Hợp Lệ",
+        "invalidBboxDesc": "Hợp giới hạn có kích thước không hợp lệ"
     },
     "ui": {
         "tabs": {
             "gallery": "Thư Viện Ảnh",
             "models": "Models",
-            "generation": "Generation (Máy Tạo Sinh)",
             "upscaling": "Upscale (Nâng Cấp Chất Lượng Hình Ảnh)",
             "canvas": "Canvas (Vùng Ảnh)",
             "upscalingTab": "$t(common.tab) $t(ui.tabs.upscaling)",
             "modelsTab": "$t(common.tab) $t(ui.tabs.models)",
             "queue": "Queue (Hàng Đợi)",
             "workflows": "Workflow (Luồng Làm Việc)",
-            "workflowsTab": "$t(common.tab) $t(ui.tabs.workflows)"
+            "workflowsTab": "$t(common.tab) $t(ui.tabs.workflows)",
+            "generate": "Tạo Sinh"
         },
         "launchpad": {
             "workflowsTitle": "Đi sâu hơn với Workflow.",
@@ -2415,8 +2481,43 @@
                     "promptAdvice": "Khi upscale, dùng lệnh để mô tả phương thức và phong cách. Tránh mô tả các chi tiết cụ thể trong ảnh.",
                     "styleAdvice": "Upscale thích hợp nhất cho phong cách chung của ảnh."
                 },
-                "scale": "Kích Thước"
+                "scale": "Kích Thước",
+                "creativityAndStructure": {
+                    "title": "Độ Sáng Tạo & Cấu Trúc Mặc Định",
+                    "conservative": "Bảo toàn",
+                    "balanced": "Cân bằng",
+                    "creative": "Sáng tạo",
+                    "artistic": "Thẩm mỹ"
+                }
+            },
+            "createNewWorkflowFromScratch": "Tạo workflow mới từ đầu",
+            "browseAndLoadWorkflows": "Duyệt và tải workflow có sẵn",
+            "addStyleRef": {
+                "title": "Thêm Phong Cách Mẫu",
+                "description": "Thêm ảnh để chuyển đổi diện mạo của nó."
+            },
+            "editImage": {
+                "title": "Biên Tập Ảnh",
+                "description": "Thêm ảnh để chỉnh sửa."
+            },
+            "generateFromText": {
+                "title": "Tạo Sinh Từ Chữ",
+                "description": "Nhập lệnh vào và Kích Hoạt."
+            },
+            "useALayoutImage": {
+                "title": "Dùng Bố Cục Ảnh",
+                "description": "Thêm ảnh để điều khiển bố cục."
+            },
+            "generate": {
+                "canvasCalloutTitle": "Đang tìm cách để điều khiển, chỉnh sửa, và làm lại ảnh?",
+                "canvasCalloutLink": "Vào Canvas cho nhiều tính năng hơn."
             }
+        },
+        "panels": {
+            "launchpad": "Launchpad",
+            "workflowEditor": "Trình Biên Tập Workflow",
+            "imageViewer": "Trình Xem Ảnh",
+            "canvas": "Canvas"
         }
     },
     "workflows": {
@@ -2588,9 +2689,8 @@
         "watchRecentReleaseVideos": "Xem Video Phát Hành Mới Nhất",
         "watchUiUpdatesOverview": "Xem Tổng Quan Về Những Cập Nhật Cho Giao Diện Người Dùng",
         "items": [
-            "Tạo sinh ảnh nhanh hơn với Launchpad và thẻ Tạo Sinh đã cơ bản hoá.",
-            "Biên tập với lệnh bằng Flux Kontext Dev.",
-            "Xuất ra file PSD, ẩn số lượng lớn lớp phủ, sắp xếp model & ảnh — tất cả cho một giao diện đã thiết kế lại để chuyên điều khiển."
+            "Trạng thái Studio được lưu vào server, giúp bạn tiếp tục công việc ở mọi thiết bị.",
+            "Hỗ trợ nhiều ảnh mẫu cho FLUX KONTEXT (chỉ cho model trên máy)."
         ]
     },
     "upsell": {

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

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

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

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

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

@@ -5,6 +5,7 @@ import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
 import { $didStudioInit } from 'app/hooks/useStudioInitAction';
 import type { LoggingOverrides } from 'app/logging/logger';
 import { $loggingOverrides, configureLogging } from 'app/logging/logger';
+import { addStorageListeners } from 'app/store/enhancers/reduxRemember/driver';
 import { $accountSettingsLink } from 'app/store/nanostores/accountSettingsLink';
 import { $authToken } from 'app/store/nanostores/authToken';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
@@ -35,7 +36,7 @@ import {
 import type { WorkflowCategory } from 'features/nodes/types/workflow';
 import type { ToastConfig } from 'features/toast/toast';
 import type { PropsWithChildren, ReactNode } from 'react';
-import React, { lazy, memo, useEffect, useLayoutEffect, useMemo } from 'react';
+import React, { lazy, memo, useEffect, useLayoutEffect, useState } from 'react';
 import { Provider } from 'react-redux';
 import { addMiddleware, resetMiddlewares } from 'redux-dynamic-middlewares';
 import { $socketOptions } from 'services/events/stores';
@@ -70,6 +71,7 @@ interface Props extends PropsWithChildren {
    * If provided, overrides in-app navigation to the model manager
    */
   onClickGoToModelManager?: () => void;
+  storagePersistDebounce?: number;
 }
 
 const InvokeAIUI = ({
@@ -96,7 +98,11 @@ const InvokeAIUI = ({
   loggingOverrides,
   onClickGoToModelManager,
   whatsNew,
+  storagePersistDebounce = 300,
 }: Props) => {
+  const [store, setStore] = useState<ReturnType<typeof createStore> | undefined>(undefined);
+  const [didRehydrate, setDidRehydrate] = useState(false);
+
   useLayoutEffect(() => {
     /*
      * We need to configure logging before anything else happens - useLayoutEffect ensures we set this at the first
@@ -308,22 +314,30 @@ const InvokeAIUI = ({
     };
   }, [isDebugging]);
 
-  const store = useMemo(() => {
-    return createStore(projectId);
-  }, [projectId]);
-
   useEffect(() => {
+    const onRehydrated = () => {
+      setDidRehydrate(true);
+    };
+    const store = createStore({ persist: true, persistDebounce: storagePersistDebounce, onRehydrated });
+    setStore(store);
     $store.set(store);
     if (import.meta.env.MODE === 'development') {
       window.$store = $store;
     }
+    const removeStorageListeners = addStorageListeners();
     return () => {
+      removeStorageListeners();
+      setStore(undefined);
       $store.set(undefined);
       if (import.meta.env.MODE === 'development') {
         window.$store = undefined;
       }
     };
-  }, [store]);
+  }, [storagePersistDebounce]);
+
+  if (!store || !didRehydrate) {
+    return <Loading />;
+  }
 
   return (
     <React.StrictMode>

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1,4 +1,4 @@
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { selectRefImagesSlice } from 'features/controlLayers/store/refImagesSlice';
 import { selectCanvasSlice } from 'features/controlLayers/store/selectors';
 import { getImageUsage } from 'features/deleteImageModal/store/state';

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

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

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

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

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

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

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

@@ -1,5 +1,5 @@
 import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { imagesApi } from 'services/api/endpoints/images';
 
 const log = logger('gallery');

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

@@ -1,5 +1,5 @@
 import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { imagesApi } from 'services/api/endpoints/images';
 
 const log = logger('gallery');

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

@@ -1,7 +1,6 @@
 import { isAnyOf } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { RootState } from 'app/store/store';
+import type { AppStartListening, RootState } from 'app/store/store';
 import { omit } from 'es-toolkit/compat';
 import { imageUploadedClientSide } from 'features/gallery/store/actions';
 import { selectListBoardsQueryArgs } from 'features/gallery/store/gallerySelectors';

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

@@ -1,5 +1,5 @@
 import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { bboxSyncedToOptimalDimension, rgRefImageModelChanged } from 'features/controlLayers/store/canvasSlice';
 import { buildSelectIsStaging, selectCanvasSessionId } from 'features/controlLayers/store/canvasStagingAreaSlice';
 import { loraDeleted } from 'features/controlLayers/store/lorasSlice';

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

@@ -1,6 +1,5 @@
 import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
-import type { AppDispatch, RootState } from 'app/store/store';
+import type { AppDispatch, AppStartListening, RootState } from 'app/store/store';
 import { controlLayerModelChanged, rgRefImageModelChanged } from 'features/controlLayers/store/canvasSlice';
 import { loraDeleted } from 'features/controlLayers/store/lorasSlice';
 import {

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

@@ -1,4 +1,4 @@
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import type { AppStartListening } from 'app/store/store';
 import { isNil } from 'es-toolkit';
 import { bboxHeightChanged, bboxWidthChanged } from 'features/controlLayers/store/canvasSlice';
 import { buildSelectIsStaging, selectCanvasSessionId } from 'features/controlLayers/store/canvasStagingAreaSlice';

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

@@ -1,8 +1,8 @@
 import { objectEquals } from '@observ33r/object-equals';
 import { createAction } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
-import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
 import { $baseUrl } from 'app/store/nanostores/baseUrl';
+import type { AppStartListening } from 'app/store/store';
 import { atom } from 'nanostores';
 import { api } from 'services/api';
 import { modelsApi } from 'services/api/endpoints/models';

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

@@ -1,159 +1,165 @@
-import type { ThunkDispatch, UnknownAction } from '@reduxjs/toolkit';
-import { autoBatchEnhancer, combineReducers, configureStore } from '@reduxjs/toolkit';
+import type { ThunkDispatch, TypedStartListening, UnknownAction } from '@reduxjs/toolkit';
+import { addListener, combineReducers, configureStore, createAction, createListenerMiddleware } from '@reduxjs/toolkit';
 import { logger } from 'app/logging/logger';
-import { idbKeyValDriver } from 'app/store/enhancers/reduxRemember/driver';
 import { errorHandler } from 'app/store/enhancers/reduxRemember/errors';
+import { addAdHocPostProcessingRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/addAdHocPostProcessingRequestedListener';
+import { addAnyEnqueuedListener } from 'app/store/middleware/listenerMiddleware/listeners/anyEnqueued';
+import { addAppConfigReceivedListener } from 'app/store/middleware/listenerMiddleware/listeners/appConfigReceived';
+import { addAppStartedListener } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
+import { addBatchEnqueuedListener } from 'app/store/middleware/listenerMiddleware/listeners/batchEnqueued';
+import { addDeleteBoardAndImagesFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/boardAndImagesDeleted';
+import { addBoardIdSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/boardIdSelected';
+import { addBulkDownloadListeners } from 'app/store/middleware/listenerMiddleware/listeners/bulkDownload';
+import { addGetOpenAPISchemaListener } from 'app/store/middleware/listenerMiddleware/listeners/getOpenAPISchema';
+import { addImageAddedToBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageAddedToBoard';
+import { addImageRemovedFromBoardFulfilledListener } from 'app/store/middleware/listenerMiddleware/listeners/imageRemovedFromBoard';
+import { addModelSelectedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelSelected';
+import { addModelsLoadedListener } from 'app/store/middleware/listenerMiddleware/listeners/modelsLoaded';
+import { addSetDefaultSettingsListener } from 'app/store/middleware/listenerMiddleware/listeners/setDefaultSettings';
+import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketConnected';
 import { deepClone } from 'common/util/deepClone';
 import { keys, mergeWith, omit, pick } from 'es-toolkit/compat';
-import { changeBoardModalSlice } from 'features/changeBoardModal/store/slice';
-import { canvasSettingsPersistConfig, canvasSettingsSlice } from 'features/controlLayers/store/canvasSettingsSlice';
-import { canvasPersistConfig, canvasSlice, canvasUndoableConfig } from 'features/controlLayers/store/canvasSlice';
-import {
-  canvasSessionSlice,
-  canvasStagingAreaPersistConfig,
-} from 'features/controlLayers/store/canvasStagingAreaSlice';
-import { lorasPersistConfig, lorasSlice } from 'features/controlLayers/store/lorasSlice';
-import { paramsPersistConfig, paramsSlice } from 'features/controlLayers/store/paramsSlice';
-import { refImagesPersistConfig, refImagesSlice } from 'features/controlLayers/store/refImagesSlice';
-import { dynamicPromptsPersistConfig, dynamicPromptsSlice } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
-import { galleryPersistConfig, gallerySlice } from 'features/gallery/store/gallerySlice';
-import { modelManagerV2PersistConfig, modelManagerV2Slice } from 'features/modelManagerV2/store/modelManagerV2Slice';
-import { nodesPersistConfig, nodesSlice, nodesUndoableConfig } from 'features/nodes/store/nodesSlice';
-import { workflowLibraryPersistConfig, workflowLibrarySlice } from 'features/nodes/store/workflowLibrarySlice';
-import { workflowSettingsPersistConfig, workflowSettingsSlice } from 'features/nodes/store/workflowSettingsSlice';
-import { upscalePersistConfig, upscaleSlice } from 'features/parameters/store/upscaleSlice';
-import { queueSlice } from 'features/queue/store/queueSlice';
-import { stylePresetPersistConfig, stylePresetSlice } from 'features/stylePresets/store/stylePresetSlice';
-import { configSlice } from 'features/system/store/configSlice';
-import { systemPersistConfig, systemSlice } from 'features/system/store/systemSlice';
-import { uiPersistConfig, uiSlice } from 'features/ui/store/uiSlice';
+import { changeBoardModalSliceConfig } from 'features/changeBoardModal/store/slice';
+import { canvasSettingsSliceConfig } from 'features/controlLayers/store/canvasSettingsSlice';
+import { canvasSliceConfig } from 'features/controlLayers/store/canvasSlice';
+import { canvasSessionSliceConfig } from 'features/controlLayers/store/canvasStagingAreaSlice';
+import { lorasSliceConfig } from 'features/controlLayers/store/lorasSlice';
+import { paramsSliceConfig } from 'features/controlLayers/store/paramsSlice';
+import { refImagesSliceConfig } from 'features/controlLayers/store/refImagesSlice';
+import { dynamicPromptsSliceConfig } from 'features/dynamicPrompts/store/dynamicPromptsSlice';
+import { gallerySliceConfig } from 'features/gallery/store/gallerySlice';
+import { modelManagerSliceConfig } from 'features/modelManagerV2/store/modelManagerV2Slice';
+import { nodesSliceConfig } from 'features/nodes/store/nodesSlice';
+import { workflowLibrarySliceConfig } from 'features/nodes/store/workflowLibrarySlice';
+import { workflowSettingsSliceConfig } from 'features/nodes/store/workflowSettingsSlice';
+import { upscaleSliceConfig } from 'features/parameters/store/upscaleSlice';
+import { queueSliceConfig } from 'features/queue/store/queueSlice';
+import { stylePresetSliceConfig } from 'features/stylePresets/store/stylePresetSlice';
+import { configSliceConfig } from 'features/system/store/configSlice';
+import { systemSliceConfig } from 'features/system/store/systemSlice';
+import { uiSliceConfig } from 'features/ui/store/uiSlice';
 import { diff } from 'jsondiffpatch';
 import dynamicMiddlewares from 'redux-dynamic-middlewares';
 import type { SerializeFunction, UnserializeFunction } from 'redux-remember';
-import { rememberEnhancer, rememberReducer } from 'redux-remember';
+import { REMEMBER_REHYDRATED, rememberEnhancer, rememberReducer } from 'redux-remember';
 import undoable, { newHistory } from 'redux-undo';
 import { serializeError } from 'serialize-error';
 import { api } from 'services/api';
 import { authToastMiddleware } from 'services/api/authToastMiddleware';
 import type { JsonObject } from 'type-fest';
 
-import { STORAGE_PREFIX } from './constants';
+import { reduxRememberDriver } from './enhancers/reduxRemember/driver';
 import { actionSanitizer } from './middleware/devtools/actionSanitizer';
 import { actionsDenylist } from './middleware/devtools/actionsDenylist';
 import { stateSanitizer } from './middleware/devtools/stateSanitizer';
-import { listenerMiddleware } from './middleware/listenerMiddleware';
+import { addArchivedOrDeletedBoardListener } from './middleware/listenerMiddleware/listeners/addArchivedOrDeletedBoardListener';
+import { addImageUploadedFulfilledListener } from './middleware/listenerMiddleware/listeners/imageUploaded';
+
+export const listenerMiddleware = createListenerMiddleware();
 
 const log = logger('system');
 
-const allReducers = {
-  [api.reducerPath]: api.reducer,
-  [gallerySlice.name]: gallerySlice.reducer,
-  [nodesSlice.name]: undoable(nodesSlice.reducer, nodesUndoableConfig),
-  [systemSlice.name]: systemSlice.reducer,
-  [configSlice.name]: configSlice.reducer,
-  [uiSlice.name]: uiSlice.reducer,
-  [dynamicPromptsSlice.name]: dynamicPromptsSlice.reducer,
-  [changeBoardModalSlice.name]: changeBoardModalSlice.reducer,
-  [modelManagerV2Slice.name]: modelManagerV2Slice.reducer,
-  [queueSlice.name]: queueSlice.reducer,
-  [canvasSlice.name]: undoable(canvasSlice.reducer, canvasUndoableConfig),
-  [workflowSettingsSlice.name]: workflowSettingsSlice.reducer,
-  [upscaleSlice.name]: upscaleSlice.reducer,
-  [stylePresetSlice.name]: stylePresetSlice.reducer,
-  [paramsSlice.name]: paramsSlice.reducer,
-  [canvasSettingsSlice.name]: canvasSettingsSlice.reducer,
-  [canvasSessionSlice.name]: canvasSessionSlice.reducer,
-  [lorasSlice.name]: lorasSlice.reducer,
-  [workflowLibrarySlice.name]: workflowLibrarySlice.reducer,
-  [refImagesSlice.name]: refImagesSlice.reducer,
+// When adding a slice, add the config to the SLICE_CONFIGS object below, then add the reducer to ALL_REDUCERS.
+const SLICE_CONFIGS = {
+  [canvasSessionSliceConfig.slice.reducerPath]: canvasSessionSliceConfig,
+  [canvasSettingsSliceConfig.slice.reducerPath]: canvasSettingsSliceConfig,
+  [canvasSliceConfig.slice.reducerPath]: canvasSliceConfig,
+  [changeBoardModalSliceConfig.slice.reducerPath]: changeBoardModalSliceConfig,
+  [configSliceConfig.slice.reducerPath]: configSliceConfig,
+  [dynamicPromptsSliceConfig.slice.reducerPath]: dynamicPromptsSliceConfig,
+  [gallerySliceConfig.slice.reducerPath]: gallerySliceConfig,
+  [lorasSliceConfig.slice.reducerPath]: lorasSliceConfig,
+  [modelManagerSliceConfig.slice.reducerPath]: modelManagerSliceConfig,
+  [nodesSliceConfig.slice.reducerPath]: nodesSliceConfig,
+  [paramsSliceConfig.slice.reducerPath]: paramsSliceConfig,
+  [queueSliceConfig.slice.reducerPath]: queueSliceConfig,
+  [refImagesSliceConfig.slice.reducerPath]: refImagesSliceConfig,
+  [stylePresetSliceConfig.slice.reducerPath]: stylePresetSliceConfig,
+  [systemSliceConfig.slice.reducerPath]: systemSliceConfig,
+  [uiSliceConfig.slice.reducerPath]: uiSliceConfig,
+  [upscaleSliceConfig.slice.reducerPath]: upscaleSliceConfig,
+  [workflowLibrarySliceConfig.slice.reducerPath]: workflowLibrarySliceConfig,
+  [workflowSettingsSliceConfig.slice.reducerPath]: workflowSettingsSliceConfig,
 };
 
-const rootReducer = combineReducers(allReducers);
+// TS makes it really hard to dynamically create this object :/ so it's just hardcoded here.
+// Remember to wrap undoable reducers in `undoable()`!
+const ALL_REDUCERS = {
+  [api.reducerPath]: api.reducer,
+  [canvasSessionSliceConfig.slice.reducerPath]: canvasSessionSliceConfig.slice.reducer,
+  [canvasSettingsSliceConfig.slice.reducerPath]: canvasSettingsSliceConfig.slice.reducer,
+  // Undoable!
+  [canvasSliceConfig.slice.reducerPath]: undoable(
+    canvasSliceConfig.slice.reducer,
+    canvasSliceConfig.undoableConfig?.reduxUndoOptions
+  ),
+  [changeBoardModalSliceConfig.slice.reducerPath]: changeBoardModalSliceConfig.slice.reducer,
+  [configSliceConfig.slice.reducerPath]: configSliceConfig.slice.reducer,
+  [dynamicPromptsSliceConfig.slice.reducerPath]: dynamicPromptsSliceConfig.slice.reducer,
+  [gallerySliceConfig.slice.reducerPath]: gallerySliceConfig.slice.reducer,
+  [lorasSliceConfig.slice.reducerPath]: lorasSliceConfig.slice.reducer,
+  [modelManagerSliceConfig.slice.reducerPath]: modelManagerSliceConfig.slice.reducer,
+  // Undoable!
+  [nodesSliceConfig.slice.reducerPath]: undoable(
+    nodesSliceConfig.slice.reducer,
+    nodesSliceConfig.undoableConfig?.reduxUndoOptions
+  ),
+  [paramsSliceConfig.slice.reducerPath]: paramsSliceConfig.slice.reducer,
+  [queueSliceConfig.slice.reducerPath]: queueSliceConfig.slice.reducer,
+  [refImagesSliceConfig.slice.reducerPath]: refImagesSliceConfig.slice.reducer,
+  [stylePresetSliceConfig.slice.reducerPath]: stylePresetSliceConfig.slice.reducer,
+  [systemSliceConfig.slice.reducerPath]: systemSliceConfig.slice.reducer,
+  [uiSliceConfig.slice.reducerPath]: uiSliceConfig.slice.reducer,
+  [upscaleSliceConfig.slice.reducerPath]: upscaleSliceConfig.slice.reducer,
+  [workflowLibrarySliceConfig.slice.reducerPath]: workflowLibrarySliceConfig.slice.reducer,
+  [workflowSettingsSliceConfig.slice.reducerPath]: workflowSettingsSliceConfig.slice.reducer,
+};
+
+const rootReducer = combineReducers(ALL_REDUCERS);
 
 const rememberedRootReducer = rememberReducer(rootReducer);
 
-/* eslint-disable-next-line @typescript-eslint/no-explicit-any */
-export type PersistConfig<T = any> = {
-  /**
-   * The name of the slice.
-   */
-  name: keyof typeof allReducers;
-  /**
-   * The initial state of the slice.
-   */
-  initialState: T;
-  /**
-   * Migrate the state to the current version during rehydration.
-   * @param state The rehydrated state.
-   * @returns A correctly-shaped state.
-   */
-  migrate: (state: unknown) => T;
-  /**
-   * Keys to omit from the persisted state.
-   */
-  persistDenylist: (keyof T)[];
-};
-
-const persistConfigs: { [key in keyof typeof allReducers]?: PersistConfig } = {
-  [galleryPersistConfig.name]: galleryPersistConfig,
-  [nodesPersistConfig.name]: nodesPersistConfig,
-  [systemPersistConfig.name]: systemPersistConfig,
-  [uiPersistConfig.name]: uiPersistConfig,
-  [dynamicPromptsPersistConfig.name]: dynamicPromptsPersistConfig,
-  [modelManagerV2PersistConfig.name]: modelManagerV2PersistConfig,
-  [canvasPersistConfig.name]: canvasPersistConfig,
-  [workflowSettingsPersistConfig.name]: workflowSettingsPersistConfig,
-  [upscalePersistConfig.name]: upscalePersistConfig,
-  [stylePresetPersistConfig.name]: stylePresetPersistConfig,
-  [paramsPersistConfig.name]: paramsPersistConfig,
-  [canvasSettingsPersistConfig.name]: canvasSettingsPersistConfig,
-  [canvasStagingAreaPersistConfig.name]: canvasStagingAreaPersistConfig,
-  [lorasPersistConfig.name]: lorasPersistConfig,
-  [workflowLibraryPersistConfig.name]: workflowLibraryPersistConfig,
-  [refImagesSlice.name]: refImagesPersistConfig,
-};
-
 const unserialize: UnserializeFunction = (data, key) => {
-  const persistConfig = persistConfigs[key as keyof typeof persistConfigs];
-  if (!persistConfig) {
+  const sliceConfig = SLICE_CONFIGS[key as keyof typeof SLICE_CONFIGS];
+  if (!sliceConfig?.persistConfig) {
     throw new Error(`No persist config for slice "${key}"`);
   }
+  const { getInitialState, persistConfig, undoableConfig } = sliceConfig;
   let state;
   try {
-    const { initialState, migrate } = persistConfig;
+    const initialState = getInitialState();
     const parsed = JSON.parse(data);
 
     // strip out old keys
     const stripped = pick(deepClone(parsed), keys(initialState));
-    // run (additive) migrations
-    const migrated = migrate(stripped);
     /*
      * Merge in initial state as default values, covering any missing keys. You might be tempted to use _.defaultsDeep,
      * but that merges arrays by index and partial objects by key. Using an identity function as the customizer results
      * in behaviour like defaultsDeep, but doesn't overwrite any values that are not undefined in the migrated state.
      */
-    const transformed = mergeWith(migrated, initialState, (objVal) => objVal);
+    const unPersistDenylisted = mergeWith(stripped, initialState, (objVal) => objVal);
+    // run (additive) migrations
+    const migrated = persistConfig.migrate(unPersistDenylisted);
 
     log.debug(
       {
-        persistedData: parsed,
-        rehydratedData: transformed,
-        diff: diff(parsed, transformed) as JsonObject, // this is always serializable
+        persistedData: parsed as JsonObject,
+        rehydratedData: migrated as JsonObject,
+        diff: diff(data, migrated) as JsonObject,
       },
       `Rehydrated slice "${key}"`
     );
-    state = transformed;
+    state = migrated;
   } catch (err) {
     log.warn(
       { error: serializeError(err as Error) },
       `Error rehydrating slice "${key}", falling back to default initial state`
     );
-    state = persistConfig.initialState;
+    state = getInitialState();
   }
 
-  // If the slice is undoable, we need to wrap it in a new history - only nodes and canvas are undoable at the moment.
-  // TODO(psyche): make this automatic & remove the hard-coding for specific slices.
-  if (key === nodesSlice.name || key === canvasSlice.name) {
+  // Undoable slices must be wrapped in a history!
+  if (undoableConfig) {
     return newHistory([], state, []);
   } else {
     return state;
@@ -161,43 +167,53 @@ const unserialize: UnserializeFunction = (data, key) => {
 };
 
 const serialize: SerializeFunction = (data, key) => {
-  const persistConfig = persistConfigs[key as keyof typeof persistConfigs];
-  if (!persistConfig) {
+  const sliceConfig = SLICE_CONFIGS[key as keyof typeof SLICE_CONFIGS];
+  if (!sliceConfig?.persistConfig) {
     throw new Error(`No persist config for slice "${key}"`);
   }
-  // Heuristic to determine if the slice is undoable - could just hardcode it in the persistConfig
-  const isUndoable = 'present' in data && 'past' in data && 'future' in data && '_latestUnfiltered' in data;
-  const result = omit(isUndoable ? data.present : data, persistConfig.persistDenylist);
+
+  const result = omit(
+    sliceConfig.undoableConfig ? data.present : data,
+    sliceConfig.persistConfig.persistDenylist ?? []
+  );
+
   return JSON.stringify(result);
 };
 
-export const createStore = (uniqueStoreKey?: string, persist = true) =>
-  configureStore({
+const PERSISTED_KEYS = Object.values(SLICE_CONFIGS)
+  .filter((sliceConfig) => !!sliceConfig.persistConfig)
+  .map((sliceConfig) => sliceConfig.slice.reducerPath);
+
+export const createStore = (options?: { persist?: boolean; persistDebounce?: number; onRehydrated?: () => void }) => {
+  const store = configureStore({
     reducer: rememberedRootReducer,
     middleware: (getDefaultMiddleware) =>
       getDefaultMiddleware({
+        // serializableCheck: false,
+        // immutableCheck: false,
         serializableCheck: import.meta.env.MODE === 'development',
         immutableCheck: import.meta.env.MODE === 'development',
       })
         .concat(api.middleware)
         .concat(dynamicMiddlewares)
         .concat(authToastMiddleware)
-        // .concat(getDebugLoggerMiddleware())
+        // .concat(getDebugLoggerMiddleware({ withDiff: true, withNextState: true }))
         .prepend(listenerMiddleware.middleware),
     enhancers: (getDefaultEnhancers) => {
-      const _enhancers = getDefaultEnhancers().concat(autoBatchEnhancer());
-      if (persist) {
-        _enhancers.push(
-          rememberEnhancer(idbKeyValDriver, keys(persistConfigs), {
-            persistDebounce: 300,
+      const enhancers = getDefaultEnhancers();
+      if (options?.persist) {
+        return enhancers.prepend(
+          rememberEnhancer(reduxRememberDriver, PERSISTED_KEYS, {
+            persistDebounce: options?.persistDebounce ?? 2000,
             serialize,
             unserialize,
-            prefix: uniqueStoreKey ? `${STORAGE_PREFIX}${uniqueStoreKey}-` : STORAGE_PREFIX,
+            prefix: '',
             errorHandler,
           })
         );
+      } else {
+        return enhancers;
       }
-      return _enhancers;
     },
     devTools: {
       actionSanitizer,
@@ -212,9 +228,62 @@ export const createStore = (uniqueStoreKey?: string, persist = true) =>
     },
   });
 
+  // Once-off listener to support waiting for rehydration before rendering the app
+  startAppListening({
+    actionCreator: createAction(REMEMBER_REHYDRATED),
+    effect: (action, { unsubscribe }) => {
+      unsubscribe();
+      options?.onRehydrated?.();
+    },
+  });
+
+  return store;
+};
+
 export type AppStore = ReturnType<typeof createStore>;
 export type RootState = ReturnType<AppStore['getState']>;
-// eslint-disable-next-line @typescript-eslint/no-explicit-any
+/* eslint-disable-next-line @typescript-eslint/no-explicit-any */
 export type AppThunkDispatch = ThunkDispatch<RootState, any, UnknownAction>;
 export type AppDispatch = ReturnType<typeof createStore>['dispatch'];
 export type AppGetState = ReturnType<typeof createStore>['getState'];
+export type AppStartListening = TypedStartListening<RootState, AppDispatch>;
+
+export const addAppListener = addListener.withTypes<RootState, AppDispatch>();
+
+const startAppListening = listenerMiddleware.startListening as AppStartListening;
+addImageUploadedFulfilledListener(startAppListening);
+
+// Image deleted
+addDeleteBoardAndImagesFulfilledListener(startAppListening);
+
+// User Invoked
+addAnyEnqueuedListener(startAppListening);
+addBatchEnqueuedListener(startAppListening);
+
+// Socket.IO
+addSocketConnectedEventListener(startAppListening);
+
+// Gallery bulk download
+addBulkDownloadListeners(startAppListening);
+
+// Boards
+addImageAddedToBoardFulfilledListener(startAppListening);
+addImageRemovedFromBoardFulfilledListener(startAppListening);
+addBoardIdSelectedListener(startAppListening);
+addArchivedOrDeletedBoardListener(startAppListening);
+
+// Node schemas
+addGetOpenAPISchemaListener(startAppListening);
+
+// Models
+addModelSelectedListener(startAppListening);
+
+// app startup
+addAppStartedListener(startAppListening);
+addModelsLoadedListener(startAppListening);
+addAppConfigReceivedListener(startAppListening);
+
+// Ad-hoc upscale workflwo
+addAdHocPostProcessingRequestedListener(startAppListening);
+
+addSetDefaultSettingsListener(startAppListening);

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

@@ -0,0 +1,46 @@
+import type { Slice } from '@reduxjs/toolkit';
+import type { UndoableOptions } from 'redux-undo';
+import type { ZodType } from 'zod';
+
+type StateFromSlice<T extends Slice> = T extends Slice<infer U> ? U : never;
+
+export type SliceConfig<T extends Slice> = {
+  /**
+   * The redux slice (return of createSlice).
+   */
+  slice: T;
+  /**
+   * The zod schema for the slice.
+   */
+  schema: ZodType<StateFromSlice<T>>;
+  /**
+   * A function that returns the initial state of the slice.
+   */
+  getInitialState: () => StateFromSlice<T>;
+  /**
+   * The optional persist configuration for this slice. If omitted, the slice will not be persisted.
+   */
+  persistConfig?: {
+    /**
+     * Migrate the state to the current version during rehydration. This method should throw an error if the migration
+     * fails.
+     *
+     * @param state The rehydrated state.
+     * @returns A correctly-shaped state.
+     */
+    migrate: (state: unknown) => StateFromSlice<T>;
+    /**
+     * Keys to omit from the persisted state.
+     */
+    persistDenylist?: (keyof StateFromSlice<T>)[];
+  };
+  /**
+   * The optional undoable configuration for this slice. If omitted, the slice will not be undoable.
+   */
+  undoableConfig?: {
+    /**
+     * The options to be passed into redux-undo.
+     */
+    reduxUndoOptions: UndoableOptions<StateFromSlice<T>>;
+  };
+};

invokeai/frontend/web/src/app/types/invokeai.ts

@@ -1,130 +1,300 @@
-import type { FilterType } from 'features/controlLayers/store/filters';
-import type { ParameterPrecision, ParameterScheduler } from 'features/parameters/types/parameterSchemas';
-import type { TabName } from 'features/ui/store/uiTypes';
+import { zFilterType } from 'features/controlLayers/store/filters';
+import { zParameterPrecision, zParameterScheduler } from 'features/parameters/types/parameterSchemas';
+import { zTabName } from 'features/ui/store/uiTypes';
 import type { PartialDeep } from 'type-fest';
+import z from 'zod';
 
-/**
- * A disable-able application feature
- */
-export type AppFeature =
-  | 'faceRestore'
-  | 'upscaling'
-  | 'lightbox'
-  | 'modelManager'
-  | 'githubLink'
-  | 'discordLink'
-  | 'bugLink'
-  | 'aboutModal'
-  | 'localization'
-  | 'consoleLogging'
-  | 'dynamicPrompting'
-  | 'batches'
-  | 'syncModels'
-  | 'multiselect'
-  | 'pauseQueue'
-  | 'resumeQueue'
-  | 'invocationCache'
-  | 'modelCache'
-  | 'bulkDownload'
-  | 'starterModels'
-  | 'hfToken'
-  | 'retryQueueItem'
-  | 'cancelAndClearAll'
-  | 'chatGPT4oHigh'
-  | 'modelRelationships';
-/**
- * A disable-able Stable Diffusion feature
- */
-export type SDFeature =
-  | 'controlNet'
-  | 'noise'
-  | 'perlinNoise'
-  | 'noiseThreshold'
-  | 'variation'
-  | 'symmetry'
-  | 'seamless'
-  | 'hires'
-  | 'lora'
-  | 'embedding'
-  | 'vae'
-  | 'hrf';
+const zAppFeature = z.enum([
+  'faceRestore',
+  'upscaling',
+  'lightbox',
+  'modelManager',
+  'githubLink',
+  'discordLink',
+  'bugLink',
+  'aboutModal',
+  'localization',
+  'consoleLogging',
+  'dynamicPrompting',
+  'batches',
+  'syncModels',
+  'multiselect',
+  'pauseQueue',
+  'resumeQueue',
+  'invocationCache',
+  'modelCache',
+  'bulkDownload',
+  'starterModels',
+  'hfToken',
+  'retryQueueItem',
+  'cancelAndClearAll',
+  'chatGPT4oHigh',
+  'modelRelationships',
+]);
+export type AppFeature = z.infer<typeof zAppFeature>;
 
-export type NumericalParameterConfig = {
-  initial: number;
-  sliderMin: number;
-  sliderMax: number;
-  numberInputMin: number;
-  numberInputMax: number;
-  fineStep: number;
-  coarseStep: number;
-};
+const zSDFeature = z.enum([
+  'controlNet',
+  'noise',
+  'perlinNoise',
+  'noiseThreshold',
+  'variation',
+  'symmetry',
+  'seamless',
+  'hires',
+  'lora',
+  'embedding',
+  'vae',
+  'hrf',
+]);
+export type SDFeature = z.infer<typeof zSDFeature>;
+
+const zNumericalParameterConfig = z.object({
+  initial: z.number().default(512),
+  sliderMin: z.number().default(64),
+  sliderMax: z.number().default(1536),
+  numberInputMin: z.number().default(64),
+  numberInputMax: z.number().default(4096),
+  fineStep: z.number().default(8),
+  coarseStep: z.number().default(64),
+});
+export type NumericalParameterConfig = z.infer<typeof zNumericalParameterConfig>;
 
 /**
  * Configuration options for the InvokeAI UI.
  * Distinct from system settings which may be changed inside the app.
  */
-export type AppConfig = {
+export const zAppConfig = z.object({
   /**
    * Whether or not we should update image urls when image loading errors
    */
-  shouldUpdateImagesOnConnect: boolean;
-  shouldFetchMetadataFromApi: boolean;
+  shouldUpdateImagesOnConnect: z.boolean(),
+  shouldFetchMetadataFromApi: z.boolean(),
   /**
    * Sets a size limit for outputs on the upscaling tab. This is a maximum dimension, so the actual max number of pixels
    * will be the square of this value.
    */
-  maxUpscaleDimension?: number;
-  allowPrivateBoards: boolean;
-  allowPrivateStylePresets: boolean;
-  allowClientSideUpload: boolean;
-  allowPublishWorkflows: boolean;
-  allowPromptExpansion: boolean;
-  disabledTabs: TabName[];
-  disabledFeatures: AppFeature[];
-  disabledSDFeatures: SDFeature[];
-  nodesAllowlist: string[] | undefined;
-  nodesDenylist: string[] | undefined;
-  metadataFetchDebounce?: number;
-  workflowFetchDebounce?: number;
-  isLocal?: boolean;
-  shouldShowCredits: boolean;
-  sd: {
-    defaultModel?: string;
-    disabledControlNetModels: string[];
-    disabledControlNetProcessors: FilterType[];
+  maxUpscaleDimension: z.number().optional(),
+  allowPrivateBoards: z.boolean(),
+  allowPrivateStylePresets: z.boolean(),
+  allowClientSideUpload: z.boolean(),
+  allowPublishWorkflows: z.boolean(),
+  allowPromptExpansion: z.boolean(),
+  disabledTabs: z.array(zTabName),
+  disabledFeatures: z.array(zAppFeature),
+  disabledSDFeatures: z.array(zSDFeature),
+  nodesAllowlist: z.array(z.string()).optional(),
+  nodesDenylist: z.array(z.string()).optional(),
+  metadataFetchDebounce: z.number().int().optional(),
+  workflowFetchDebounce: z.number().int().optional(),
+  isLocal: z.boolean().optional(),
+  shouldShowCredits: z.boolean().optional(),
+  sd: z.object({
+    defaultModel: z.string().optional(),
+    disabledControlNetModels: z.array(z.string()),
+    disabledControlNetProcessors: z.array(zFilterType),
     // Core parameters
-    iterations: NumericalParameterConfig;
-    width: NumericalParameterConfig; // initial value comes from model
-    height: NumericalParameterConfig; // initial value comes from model
-    steps: NumericalParameterConfig;
-    guidance: NumericalParameterConfig;
-    cfgRescaleMultiplier: NumericalParameterConfig;
-    img2imgStrength: NumericalParameterConfig;
-    scheduler?: ParameterScheduler;
-    vaePrecision?: ParameterPrecision;
+    iterations: zNumericalParameterConfig,
+    width: zNumericalParameterConfig,
+    height: zNumericalParameterConfig,
+    steps: zNumericalParameterConfig,
+    guidance: zNumericalParameterConfig,
+    cfgRescaleMultiplier: zNumericalParameterConfig,
+    img2imgStrength: zNumericalParameterConfig,
+    scheduler: zParameterScheduler.optional(),
+    vaePrecision: zParameterPrecision.optional(),
     // Canvas
-    boundingBoxHeight: NumericalParameterConfig; // initial value comes from model
-    boundingBoxWidth: NumericalParameterConfig; // initial value comes from model
-    scaledBoundingBoxHeight: NumericalParameterConfig; // initial value comes from model
-    scaledBoundingBoxWidth: NumericalParameterConfig; // initial value comes from model
-    canvasCoherenceStrength: NumericalParameterConfig;
-    canvasCoherenceEdgeSize: NumericalParameterConfig;
-    infillTileSize: NumericalParameterConfig;
-    infillPatchmatchDownscaleSize: NumericalParameterConfig;
+    boundingBoxHeight: zNumericalParameterConfig,
+    boundingBoxWidth: zNumericalParameterConfig,
+    scaledBoundingBoxHeight: zNumericalParameterConfig,
+    scaledBoundingBoxWidth: zNumericalParameterConfig,
+    canvasCoherenceStrength: zNumericalParameterConfig,
+    canvasCoherenceEdgeSize: zNumericalParameterConfig,
+    infillTileSize: zNumericalParameterConfig,
+    infillPatchmatchDownscaleSize: zNumericalParameterConfig,
     // Misc advanced
-    clipSkip: NumericalParameterConfig; // slider and input max are ignored for this, because the values depend on the model
-    maskBlur: NumericalParameterConfig;
-    hrfStrength: NumericalParameterConfig;
-    dynamicPrompts: {
-      maxPrompts: NumericalParameterConfig;
-    };
-    ca: {
-      weight: NumericalParameterConfig;
-    };
-  };
-  flux: {
-    guidance: NumericalParameterConfig;
-  };
-};
+    clipSkip: zNumericalParameterConfig, // slider and input max are ignored for this, because the values depend on the model
+    maskBlur: zNumericalParameterConfig,
+    hrfStrength: zNumericalParameterConfig,
+    dynamicPrompts: z.object({
+      maxPrompts: zNumericalParameterConfig,
+    }),
+    ca: z.object({
+      weight: zNumericalParameterConfig,
+    }),
+  }),
+  flux: z.object({
+    guidance: zNumericalParameterConfig,
+  }),
+});
 
+export type AppConfig = z.infer<typeof zAppConfig>;
 export type PartialAppConfig = PartialDeep<AppConfig>;
+
+export const getDefaultAppConfig = (): AppConfig => ({
+  isLocal: true,
+  shouldUpdateImagesOnConnect: false,
+  shouldFetchMetadataFromApi: false,
+  allowPrivateBoards: false,
+  allowPrivateStylePresets: false,
+  allowClientSideUpload: false,
+  allowPublishWorkflows: false,
+  allowPromptExpansion: false,
+  shouldShowCredits: false,
+  disabledTabs: [],
+  disabledFeatures: ['lightbox', 'faceRestore', 'batches'] satisfies AppFeature[],
+  disabledSDFeatures: ['variation', 'symmetry', 'hires', 'perlinNoise', 'noiseThreshold'] satisfies SDFeature[],
+  sd: {
+    disabledControlNetModels: [],
+    disabledControlNetProcessors: [],
+    iterations: {
+      initial: 1,
+      sliderMin: 1,
+      sliderMax: 1000,
+      numberInputMin: 1,
+      numberInputMax: 10000,
+      fineStep: 1,
+      coarseStep: 1,
+    },
+    width: zNumericalParameterConfig.parse({}), // initial value comes from model
+    height: zNumericalParameterConfig.parse({}), // initial value comes from model
+    boundingBoxWidth: zNumericalParameterConfig.parse({}), // initial value comes from model
+    boundingBoxHeight: zNumericalParameterConfig.parse({}), // initial value comes from model
+    scaledBoundingBoxWidth: zNumericalParameterConfig.parse({}), // initial value comes from model
+    scaledBoundingBoxHeight: zNumericalParameterConfig.parse({}), // initial value comes from model
+    scheduler: 'dpmpp_3m_k' as const,
+    vaePrecision: 'fp32' as const,
+    steps: {
+      initial: 30,
+      sliderMin: 1,
+      sliderMax: 100,
+      numberInputMin: 1,
+      numberInputMax: 500,
+      fineStep: 1,
+      coarseStep: 1,
+    },
+    guidance: {
+      initial: 7,
+      sliderMin: 1,
+      sliderMax: 20,
+      numberInputMin: 1,
+      numberInputMax: 200,
+      fineStep: 0.1,
+      coarseStep: 0.5,
+    },
+    img2imgStrength: {
+      initial: 0.7,
+      sliderMin: 0,
+      sliderMax: 1,
+      numberInputMin: 0,
+      numberInputMax: 1,
+      fineStep: 0.01,
+      coarseStep: 0.05,
+    },
+    canvasCoherenceStrength: {
+      initial: 0.3,
+      sliderMin: 0,
+      sliderMax: 1,
+      numberInputMin: 0,
+      numberInputMax: 1,
+      fineStep: 0.01,
+      coarseStep: 0.05,
+    },
+    hrfStrength: {
+      initial: 0.45,
+      sliderMin: 0,
+      sliderMax: 1,
+      numberInputMin: 0,
+      numberInputMax: 1,
+      fineStep: 0.01,
+      coarseStep: 0.05,
+    },
+    canvasCoherenceEdgeSize: {
+      initial: 16,
+      sliderMin: 0,
+      sliderMax: 128,
+      numberInputMin: 0,
+      numberInputMax: 1024,
+      fineStep: 8,
+      coarseStep: 16,
+    },
+    cfgRescaleMultiplier: {
+      initial: 0,
+      sliderMin: 0,
+      sliderMax: 0.99,
+      numberInputMin: 0,
+      numberInputMax: 0.99,
+      fineStep: 0.05,
+      coarseStep: 0.1,
+    },
+    clipSkip: {
+      initial: 0,
+      sliderMin: 0,
+      sliderMax: 12, // determined by model selection, unused in practice
+      numberInputMin: 0,
+      numberInputMax: 12, // determined by model selection, unused in practice
+      fineStep: 1,
+      coarseStep: 1,
+    },
+    infillPatchmatchDownscaleSize: {
+      initial: 1,
+      sliderMin: 1,
+      sliderMax: 10,
+      numberInputMin: 1,
+      numberInputMax: 10,
+      fineStep: 1,
+      coarseStep: 1,
+    },
+    infillTileSize: {
+      initial: 32,
+      sliderMin: 16,
+      sliderMax: 64,
+      numberInputMin: 16,
+      numberInputMax: 256,
+      fineStep: 1,
+      coarseStep: 1,
+    },
+    maskBlur: {
+      initial: 16,
+      sliderMin: 0,
+      sliderMax: 128,
+      numberInputMin: 0,
+      numberInputMax: 512,
+      fineStep: 1,
+      coarseStep: 1,
+    },
+    ca: {
+      weight: {
+        initial: 1,
+        sliderMin: 0,
+        sliderMax: 2,
+        numberInputMin: -1,
+        numberInputMax: 2,
+        fineStep: 0.01,
+        coarseStep: 0.05,
+      },
+    },
+    dynamicPrompts: {
+      maxPrompts: {
+        initial: 100,
+        sliderMin: 1,
+        sliderMax: 1000,
+        numberInputMin: 1,
+        numberInputMax: 10000,
+        fineStep: 1,
+        coarseStep: 10,
+      },
+    },
+  },
+  flux: {
+    guidance: {
+      initial: 4,
+      sliderMin: 2,
+      sliderMax: 6,
+      numberInputMin: 1,
+      numberInputMax: 20,
+      fineStep: 0.1,
+      coarseStep: 0.5,
+    },
+  },
+});

invokeai/frontend/web/src/common/components/SessionMenuItems.tsx

@@ -1,9 +1,9 @@
 import { MenuItem } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { canvasReset } from 'features/controlLayers/store/actions';
-import { inpaintMaskAdded } from 'features/controlLayers/store/canvasSlice';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { allEntitiesDeleted, inpaintMaskAdded } from 'features/controlLayers/store/canvasSlice';
 import { $canvasManager } from 'features/controlLayers/store/ephemeral';
 import { paramsReset } from 'features/controlLayers/store/paramsSlice';
+import { selectActiveTab } from 'features/ui/store/uiSelectors';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiArrowsCounterClockwiseBold } from 'react-icons/pi';
@@ -11,9 +11,10 @@ import { PiArrowsCounterClockwiseBold } from 'react-icons/pi';
 export const SessionMenuItems = memo(() => {
   const { t } = useTranslation();
   const dispatch = useAppDispatch();
+  const tab = useAppSelector(selectActiveTab);
 
   const resetCanvasLayers = useCallback(() => {
-    dispatch(canvasReset());
+    dispatch(allEntitiesDeleted());
     dispatch(inpaintMaskAdded({ isSelected: true, isBookmarked: true }));
     $canvasManager.get()?.stage.fitBboxToStage();
   }, [dispatch]);
@@ -22,12 +23,16 @@ export const SessionMenuItems = memo(() => {
   }, [dispatch]);
   return (
     <>
-      <MenuItem icon={<PiArrowsCounterClockwiseBold />} onClick={resetCanvasLayers}>
-        {t('controlLayers.resetCanvasLayers')}
-      </MenuItem>
-      <MenuItem icon={<PiArrowsCounterClockwiseBold />} onClick={resetGenerationSettings}>
-        {t('controlLayers.resetGenerationSettings')}
-      </MenuItem>
+      {tab === 'canvas' && (
+        <MenuItem icon={<PiArrowsCounterClockwiseBold />} onClick={resetCanvasLayers}>
+          {t('controlLayers.resetCanvasLayers')}
+        </MenuItem>
+      )}
+      {(tab === 'canvas' || tab === 'generate') && (
+        <MenuItem icon={<PiArrowsCounterClockwiseBold />} onClick={resetGenerationSettings}>
+          {t('controlLayers.resetGenerationSettings')}
+        </MenuItem>
+      )}
     </>
   );
 });

invokeai/frontend/web/src/common/hooks/useClearStorage.ts

@@ -1,11 +0,0 @@
-import { clearIdbKeyValStore } from 'app/store/enhancers/reduxRemember/driver';
-import { useCallback } from 'react';
-
-export const useClearStorage = () => {
-  const clearStorage = useCallback(() => {
-    clearIdbKeyValStore();
-    localStorage.clear();
-  }, []);
-
-  return clearStorage;
-};

invokeai/frontend/web/src/common/util/randomFloat.ts

@@ -0,0 +1,5 @@
+const randomFloat = (min: number, max: number): number => {
+  return Math.random() * (max - min + Number.EPSILON) + min;
+};
+
+export default randomFloat;

invokeai/frontend/web/src/features/changeBoardModal/store/initialState.ts

@@ -1,6 +0,0 @@
-import type { ChangeBoardModalState } from './types';
-
-export const initialState: ChangeBoardModalState = {
-  isModalOpen: false,
-  image_names: [],
-};

invokeai/frontend/web/src/features/changeBoardModal/store/slice.ts

@@ -1,12 +1,20 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
 import type { RootState } from 'app/store/store';
+import type { SliceConfig } from 'app/store/types';
+import z from 'zod';
 
-import { initialState } from './initialState';
+const zChangeBoardModalState = z.object({
+  isModalOpen: z.boolean().default(false),
+  image_names: z.array(z.string()).default(() => []),
+});
+type ChangeBoardModalState = z.infer<typeof zChangeBoardModalState>;
 
-export const changeBoardModalSlice = createSlice({
+const getInitialState = (): ChangeBoardModalState => zChangeBoardModalState.parse({});
+
+const slice = createSlice({
   name: 'changeBoardModal',
-  initialState,
+  initialState: getInitialState(),
   reducers: {
     isModalOpenChanged: (state, action: PayloadAction<boolean>) => {
       state.isModalOpen = action.payload;
@@ -21,6 +29,12 @@ export const changeBoardModalSlice = createSlice({
   },
 });
 
-export const { isModalOpenChanged, imagesToChangeSelected, changeBoardReset } = changeBoardModalSlice.actions;
+export const { isModalOpenChanged, imagesToChangeSelected, changeBoardReset } = slice.actions;
 
 export const selectChangeBoardModalSlice = (state: RootState) => state.changeBoardModal;
+
+export const changeBoardModalSliceConfig: SliceConfig<typeof slice> = {
+  slice,
+  schema: zChangeBoardModalState,
+  getInitialState,
+};

invokeai/frontend/web/src/features/changeBoardModal/store/types.ts

@@ -1,4 +0,0 @@
-export type ChangeBoardModalState = {
-  isModalOpen: boolean;
-  image_names: string[];
-};

invokeai/frontend/web/src/features/controlLayers/components/CanvasAlerts/CanvasAlertsBboxVisibility.tsx

@@ -0,0 +1,24 @@
+import { Alert, AlertIcon, AlertTitle } from '@invoke-ai/ui-library';
+import { useStore } from '@nanostores/react';
+import { useCanvasManager } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
+import { memo } from 'react';
+import { useTranslation } from 'react-i18next';
+
+export const CanvasAlertsBboxVisibility = memo(() => {
+  const { t } = useTranslation();
+  const canvasManager = useCanvasManager();
+  const isBboxHidden = useStore(canvasManager.tool.tools.bbox.$isBboxHidden);
+
+  if (!isBboxHidden) {
+    return null;
+  }
+
+  return (
+    <Alert status="warning" borderRadius="base" fontSize="sm" shadow="md" w="fit-content">
+      <AlertIcon />
+      <AlertTitle>{t('controlLayers.warnings.bboxHidden')}</AlertTitle>
+    </Alert>
+  );
+});
+
+CanvasAlertsBboxVisibility.displayName = 'CanvasAlertsBboxVisibility';

invokeai/frontend/web/src/features/controlLayers/components/RefImage/RefImageImage.tsx

@@ -1,15 +1,20 @@
 import { Flex } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
 import { skipToken } from '@reduxjs/toolkit/query';
+import { useAppSelector, useAppStore } from 'app/store/storeHooks';
 import { UploadImageIconButton } from 'common/hooks/useImageUploadButton';
+import { bboxSizeOptimized, bboxSizeRecalled } from 'features/controlLayers/store/canvasSlice';
+import { useCanvasIsStaging } from 'features/controlLayers/store/canvasStagingAreaSlice';
+import { sizeOptimized, sizeRecalled } from 'features/controlLayers/store/paramsSlice';
 import type { ImageWithDims } from 'features/controlLayers/store/types';
 import type { setGlobalReferenceImageDndTarget, setRegionalGuidanceReferenceImageDndTarget } from 'features/dnd/dnd';
 import { DndDropTarget } from 'features/dnd/DndDropTarget';
 import { DndImage } from 'features/dnd/DndImage';
 import { DndImageIcon } from 'features/dnd/DndImageIcon';
+import { selectActiveTab } from 'features/ui/store/uiSelectors';
 import { memo, useCallback, useEffect } from 'react';
 import { useTranslation } from 'react-i18next';
-import { PiArrowCounterClockwiseBold } from 'react-icons/pi';
+import { PiArrowCounterClockwiseBold, PiRulerBold } from 'react-icons/pi';
 import { useGetImageDTOQuery } from 'services/api/endpoints/images';
 import type { ImageDTO } from 'services/api/types';
 import { $isConnected } from 'services/events/stores';
@@ -29,7 +34,10 @@ export const RefImageImage = memo(
     dndTargetData,
   }: Props<T>) => {
     const { t } = useTranslation();
+    const store = useAppStore();
     const isConnected = useStore($isConnected);
+    const tab = useAppSelector(selectActiveTab);
+    const isStaging = useCanvasIsStaging();
     const { currentData: imageDTO, isError } = useGetImageDTOQuery(image?.image_name ?? skipToken);
     const handleResetControlImage = useCallback(() => {
       onChangeImage(null);
@@ -48,6 +56,20 @@ export const RefImageImage = memo(
       [onChangeImage]
     );
 
+    const recallSizeAndOptimize = useCallback(() => {
+      if (!imageDTO || (tab === 'canvas' && isStaging)) {
+        return;
+      }
+      const { width, height } = imageDTO;
+      if (tab === 'canvas') {
+        store.dispatch(bboxSizeRecalled({ width, height }));
+        store.dispatch(bboxSizeOptimized());
+      } else if (tab === 'generate') {
+        store.dispatch(sizeRecalled({ width, height }));
+        store.dispatch(sizeOptimized());
+      }
+    }, [imageDTO, isStaging, store, tab]);
+
     return (
       <Flex position="relative" w="full" h="full" alignItems="center" data-error={!imageDTO && !image?.image_name}>
         {!imageDTO && (
@@ -69,6 +91,14 @@ export const RefImageImage = memo(
                 tooltip={t('common.reset')}
               />
             </Flex>
+            <Flex position="absolute" flexDir="column" bottom={2} insetInlineEnd={2} gap={1}>
+              <DndImageIcon
+                onClick={recallSizeAndOptimize}
+                icon={<PiRulerBold size={16} />}
+                tooltip={t('parameters.useSize')}
+                isDisabled={!imageDTO || (tab === 'canvas' && isStaging)}
+              />
+            </Flex>
           </>
         )}
         <DndDropTarget dndTarget={dndTarget} dndTargetData={dndTargetData} label={t('gallery.drop')} />

invokeai/frontend/web/src/features/controlLayers/components/RefImage/RefImageList.tsx

@@ -63,6 +63,7 @@ RefImageList.displayName = 'RefImageList';
 const dndTargetData = addGlobalReferenceImageDndTarget.getData();
 
 const MaxRefImages = memo(() => {
+  const { t } = useTranslation();
   return (
     <Button
       position="relative"
@@ -75,7 +76,7 @@ const MaxRefImages = memo(() => {
       borderRadius="base"
       isDisabled
     >
-      Max Ref Images
+      {t('controlLayers.maxRefImages')}
     </Button>
   );
 });
@@ -83,6 +84,7 @@ MaxRefImages.displayName = 'MaxRefImages';
 
 const AddRefImageDropTargetAndButton = memo(() => {
   const { dispatch, getState } = useAppStore();
+  const { t } = useTranslation();
   const tab = useAppSelector(selectActiveTab);
 
   const uploadOptions = useMemo(
@@ -114,7 +116,7 @@ const AddRefImageDropTargetAndButton = memo(() => {
         leftIcon={<PiUploadBold />}
         {...uploadApi.getUploadButtonProps()}
       >
-        Reference Image
+        {t('controlLayers.referenceImage')}
         <input {...uploadApi.getUploadInputProps()} />
         <DndDropTarget label="Drop" dndTarget={addGlobalReferenceImageDndTarget} dndTargetData={dndTargetData} />
       </Button>

invokeai/frontend/web/src/features/controlLayers/components/Toolbar/CanvasToolbar.tsx

@@ -15,6 +15,7 @@ import { useCanvasEntityQuickSwitchHotkey } from 'features/controlLayers/hooks/u
 import { useCanvasFilterHotkey } from 'features/controlLayers/hooks/useCanvasFilterHotkey';
 import { useCanvasInvertMaskHotkey } from 'features/controlLayers/hooks/useCanvasInvertMaskHotkey';
 import { useCanvasResetLayerHotkey } from 'features/controlLayers/hooks/useCanvasResetLayerHotkey';
+import { useCanvasToggleBboxHotkey } from 'features/controlLayers/hooks/useCanvasToggleBboxHotkey';
 import { useCanvasToggleNonRasterLayersHotkey } from 'features/controlLayers/hooks/useCanvasToggleNonRasterLayersHotkey';
 import { useCanvasTransformHotkey } from 'features/controlLayers/hooks/useCanvasTransformHotkey';
 import { useCanvasUndoRedoHotkeys } from 'features/controlLayers/hooks/useCanvasUndoRedoHotkeys';
@@ -31,6 +32,7 @@ export const CanvasToolbar = memo(() => {
   useCanvasFilterHotkey();
   useCanvasInvertMaskHotkey();
   useCanvasToggleNonRasterLayersHotkey();
+  useCanvasToggleBboxHotkey();
 
   return (
     <Flex w="full" gap={2} alignItems="center" px={2}>