wip

Previously it logged the whole config and flooded the terminal output.

.dockerignore

@@ -1,11 +1,9 @@
 *
 !invokeai
 !pyproject.toml
-!uv.lock
 !docker/docker-entrypoint.sh
 !LICENSE
 
-**/dist
 **/node_modules
 **/__pycache__
-**/*.egg-info
+**/*.egg-info

.github/CODEOWNERS

@@ -2,11 +2,11 @@
 /.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr @jazzhaiku
 
 # documentation
-/docs/ @lstein @blessedcoolant @hipsterusername @psychedelicious
-/mkdocs.yml @lstein @blessedcoolant @hipsterusername @psychedelicious
+/docs/ @lstein @blessedcoolant @hipsterusername @Millu
+/mkdocs.yml @lstein  @blessedcoolant @hipsterusername @Millu
 
 # nodes
-/invokeai/app/ @blessedcoolant @psychedelicious @brandonrising @hipsterusername @jazzhaiku
+/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising @hipsterusername @jazzhaiku
 
 # installation and configuration
 /pyproject.toml  @lstein @blessedcoolant @hipsterusername
@@ -22,7 +22,7 @@
 /invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
 
 # generation, model management, postprocessing
-/invokeai/backend  @lstein @blessedcoolant @brandonrising @hipsterusername @jazzhaiku
+/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick @hipsterusername @jazzhaiku
 
 # front ends
 /invokeai/frontend/CLI @lstein @hipsterusername

.github/workflows/build-container.yml

@@ -97,8 +97,6 @@ jobs:
           context: .
           file: docker/Dockerfile
           platforms: ${{ env.PLATFORMS }}
-          build-args: |
-            GPU_DRIVER=${{ matrix.gpu-driver }}
           push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' || github.event.inputs.push-to-registry }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}

.github/workflows/build-installer.yml

@@ -1,6 +1,6 @@
-# Builds and uploads python build artifacts.
+# Builds and uploads the installer and python build artifacts.
 
-name: build wheel
+name: build installer
 
 on:
   workflow_dispatch:
@@ -17,7 +17,7 @@ jobs:
       - name: setup python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.12'
+          python-version: '3.10'
           cache: pip
           cache-dependency-path: pyproject.toml
 
@@ -27,12 +27,19 @@ jobs:
       - name: setup frontend
         uses: ./.github/actions/install-frontend-deps
 
-      - name: build wheel
-        id: build_wheel
-        run: ./scripts/build_wheel.sh
+      - name: create installer
+        id: create_installer
+        run: ./create_installer.sh
+        working-directory: installer
 
       - name: upload python distribution artifact
         uses: actions/upload-artifact@v4
         with:
           name: dist
-          path: ${{ steps.build_wheel.outputs.DIST_PATH }}
+          path: ${{ steps.create_installer.outputs.DIST_PATH }}
+
+      - name: upload installer artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: installer
+          path: ${{ steps.create_installer.outputs.INSTALLER_PATH }}

.github/workflows/release.yml

@@ -49,7 +49,7 @@ jobs:
       always_run: true
 
   build:
-    uses: ./.github/workflows/build-wheel.yml
+    uses: ./.github/workflows/build-installer.yml
 
   publish-testpypi:
     runs-on: ubuntu-latest

.nvmrc

@@ -1 +1 @@
-v22.14.0
+v22.12.0

Makefile

@@ -16,7 +16,7 @@ help:
 	@echo "frontend-build           Build the frontend in order to run on localhost:9090"
 	@echo "frontend-dev             Run the frontend in developer mode on localhost:5173"
 	@echo "frontend-typegen         Generate types for the frontend from the OpenAPI schema"
-	@echo "wheel            				Build the wheel for the current version"
+	@echo "installer-zip            Build the installer .zip file for the current version"
 	@echo "tag-release              Tag the GitHub repository with the current version (use at release time only!)"
 	@echo "openapi                  Generate the OpenAPI schema for the app, outputting to stdout"
 	@echo "docs                     Serve the mkdocs site with live reload"
@@ -64,13 +64,13 @@ frontend-dev:
 frontend-typegen:
 	cd invokeai/frontend/web && python ../../../scripts/generate_openapi_schema.py | pnpm typegen
 
-# Tag the release
-wheel:
-	cd scripts && ./build_wheel.sh
+# Installer zip file
+installer-zip:
+	cd installer && ./create_installer.sh
 
 # Tag the release
 tag-release:
-	cd scripts && ./tag_release.sh
+	cd installer && ./tag_release.sh
 
 # Generate the OpenAPI Schema for the app
 openapi:

docker/Dockerfile

@@ -1,6 +1,77 @@
 # syntax=docker/dockerfile:1.4
 
-#### Web UI ------------------------------------
+## Builder stage
+
+FROM library/ubuntu:24.04 AS builder
+
+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,sharing=locked \
+    --mount=type=cache,target=/var/lib/apt,sharing=locked \
+    apt update && apt-get install -y \
+        build-essential \
+        git
+
+# Install `uv` for package management
+COPY --from=ghcr.io/astral-sh/uv:0.6.0 /uv /uvx /bin/
+
+ENV VIRTUAL_ENV=/opt/venv
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+ENV INVOKEAI_SRC=/opt/invokeai
+ENV PYTHON_VERSION=3.11
+ENV UV_PYTHON=3.11
+ENV UV_COMPILE_BYTECODE=1
+ENV UV_LINK_MODE=copy
+ENV UV_PROJECT_ENVIRONMENT="$VIRTUAL_ENV"
+ENV UV_INDEX="https://download.pytorch.org/whl/cu124"
+
+ARG GPU_DRIVER=cuda
+# unused but available
+ARG BUILDPLATFORM
+
+# Switch to the `ubuntu` user to work around dependency issues with uv-installed python
+RUN mkdir -p ${VIRTUAL_ENV} && \
+    mkdir -p ${INVOKEAI_SRC} && \
+    chmod -R a+w /opt && \
+    mkdir ~ubuntu/.cache && chown ubuntu: ~ubuntu/.cache
+USER ubuntu
+
+# Install python
+RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
+  uv python install ${PYTHON_VERSION}
+
+WORKDIR ${INVOKEAI_SRC}
+
+# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
+# bind-mount instead of copy to defer adding sources to the image until next layer.
+#
+# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
+# x86_64/CUDA is the default
+RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
+    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
+    --mount=type=bind,source=invokeai/version,target=invokeai/version \
+    if [ "$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/rocm6.1"; \
+    fi && \
+    uv sync --no-install-project
+
+# Now that the bulk of the dependencies have been installed, copy in the project files that change more frequently.
+COPY invokeai invokeai
+COPY pyproject.toml .
+
+RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
+    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
+    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/rocm6.1"; \
+    fi && \
+    uv sync
+
+
+#### Build the Web UI ------------------------------------
 
 FROM docker.io/node:22-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
@@ -14,100 +85,69 @@ RUN --mount=type=cache,target=/pnpm/store \
     pnpm install --frozen-lockfile
 RUN npx vite build
 
-## Backend ---------------------------------------
+#### Runtime stage ---------------------------------------
 
-FROM library/ubuntu:24.04
+FROM library/ubuntu:24.04 AS runtime
 
 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
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
 
-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 \
-    UV_INDEX="https://download.pytorch.org/whl/cu124" \
-    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}
+RUN apt update && apt install -y --no-install-recommends \
+        git \
+        curl \
+        vim \
+        tmux \
+        ncdu \
+        iotop \
+        bzip2 \
+        gosu \
+        magic-wormhole \
+        libglib2.0-0 \
+        libgl1 \
+        libglx-mesa0 \
+        build-essential \
+        libopencv-dev \
+        libstdc++-10-dev &&\
+    apt-get clean && apt-get autoclean
 
-ARG GPU_DRIVER=cuda
+ENV INVOKEAI_SRC=/opt/invokeai
+ENV VIRTUAL_ENV=/opt/venv
+ENV UV_PROJECT_ENVIRONMENT="$VIRTUAL_ENV"
+ENV PYTHON_VERSION=3.11
+ENV INVOKEAI_ROOT=/invokeai
+ENV INVOKEAI_HOST=0.0.0.0
+ENV INVOKEAI_PORT=9090
+ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
+ENV CONTAINER_UID=${CONTAINER_UID:-1000}
+ENV CONTAINER_GID=${CONTAINER_GID:-1000}
 
 # Install `uv` for package management
-COPY --from=ghcr.io/astral-sh/uv:0.6.9 /uv /uvx /bin/
+# and install python for the ubuntu user (expected to exist on ubuntu >=24.x)
+# this is too tiny to optimize with multi-stage builds, but maybe we'll come back to it
+COPY --from=ghcr.io/astral-sh/uv:0.6.0 /uv /uvx /bin/
+USER ubuntu
+RUN uv python install ${PYTHON_VERSION}
+USER root
 
-# 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 ${INVOKEAI_SRC}
-
-# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
-# bind-mount instead of copy to defer adding sources to the image until next layer.
-#
-# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
-# x86_64/CUDA is the default
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=uv.lock,target=uv.lock \
-    # this is just to get the package manager to recognize that the project exists, without making changes to the docker layer
-    --mount=type=bind,source=invokeai/version,target=invokeai/version \
-    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then UV_INDEX="https://download.pytorch.org/whl/cpu"; \
-    elif [ "$GPU_DRIVER" = "rocm" ]; then UV_INDEX="https://download.pytorch.org/whl/rocm6.2"; \
-    fi && \
-    uv sync --frozen
-
-# build patchmatch
-RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
-RUN python -c "from patchmatch import patch_match"
+# --link requires buldkit w/ dockerfile syntax 1.4
+COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
+COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
 
 # 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"
+
+WORKDIR ${INVOKEAI_SRC}
+
+# build patchmatch
+RUN cd /usr/lib/$(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 ${INVOKEAI_SRC}/invokeai
-
-# this should not increase image size because we've already installed dependencies
-# in a previous layer
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=uv.lock,target=uv.lock \
-    if [ "$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 .
-

docs/RELEASE.md

@@ -60,11 +60,16 @@ Next, these jobs run and must pass. They are the same jobs that are run for ever
 - **`frontend-checks`**: runs `prettier` (format), `eslint` (lint), `dpdm` (circular refs), `tsc` (static type check) and `knip` (unused imports)
 - **`typegen-checks`**: ensures the frontend and backend types are synced
 
-#### `build-wheel` Job
+#### `build-installer` Job
 
-This sets up both python and frontend dependencies and builds the python package. Internally, this runs `./scripts/build_wheel.sh` and uploads `dist.zip`, which contains the wheel and unarchived build.
+This sets up both python and frontend dependencies and builds the python package. Internally, this runs `installer/create_installer.sh` and uploads two artifacts:
 
-You don't need to download or test these artifacts.
+- **`dist`**: the python distribution, to be published on PyPI
+- **`InvokeAI-installer-${VERSION}.zip`**: the legacy install scripts
+
+You don't need to download either of these files.
+
+> The legacy install scripts are no longer used, but we haven't updated the workflow to skip building them.
 
 #### Sanity Check & Smoke Test
 
@@ -74,7 +79,7 @@ It's possible to test the python package before it gets published to PyPI. We've
 
 But, if you want to be extra-super careful, here's how to test it:
 
-- Download the `dist.zip` build artifact from the `build-wheel` job
+- Download the `dist.zip` build artifact from the `build-installer` job
 - Unzip it and find the wheel file
 - Create a fresh Invoke install by following the [manual install guide](https://invoke-ai.github.io/InvokeAI/installation/manual/) - but instead of installing from PyPI, install from the wheel
 - Test the app

docs/contributing/dev-environment.md

@@ -41,7 +41,7 @@ If you just want to use Invoke, you should use the [launcher][launcher link].
      With the modifications made, the install command should look something like this:
 
       ```sh
-      uv pip install -e ".[dev,test,docs,xformers]" --python 3.12 --python-preference only-managed --index=https://download.pytorch.org/whl/cu124 --reinstall
+      uv pip install -e ".[dev,test,docs,xformers]" --python 3.11 --python-preference only-managed --index=https://download.pytorch.org/whl/cu124 --reinstall
       ```
 
 6. At this point, you should have Invoke installed, a venv set up and activated, and the server running. But you will see a warning in the terminal that no UI was found. If you go to the URL for the server, you won't get a UI.

docs/installation/legacy_scripts.md

@@ -0,0 +1,121 @@
+# Legacy Scripts
+
+!!! warning "Legacy Scripts"
+
+    We recommend using the Invoke Launcher to install and update Invoke. It's a desktop application for Windows, macOS and Linux. It takes care of a lot of nitty gritty details for you.
+
+    Follow the [quick start guide](./quick_start.md) to get started.
+
+!!! tip "Use the installer to update"
+
+    Using the installer for updates will not erase any of your data (images, models, boards, etc). It only updates the core libraries used to run Invoke.
+
+    Simply use the same path you installed to originally to update your existing installation.
+
+Both release and pre-release versions can be installed using the installer. It also supports install through a wheel if needed.
+
+Be sure to review the [installation requirements] and ensure your system has everything it needs to install Invoke.
+
+## Getting the Latest Installer
+
+Download the `InvokeAI-installer-vX.Y.Z.zip` file from the [latest release] page. It is at the bottom of the page, under **Assets**.
+
+After unzipping the installer, you should have a `InvokeAI-Installer` folder with some files inside, including `install.bat` and `install.sh`.
+
+## Running the Installer
+
+!!! tip
+
+    Windows users should first double-click the `WinLongPathsEnabled.reg` file to prevent a failed installation due to long file paths.
+
+Double-click the install script:
+
+=== "Windows"
+
+    ```sh
+    install.bat
+    ```
+
+=== "Linux/macOS"
+
+    ```sh
+    install.sh
+    ```
+
+!!! info "Running the Installer from the commandline"
+
+    You can also run the install script from cmd/powershell (Windows) or terminal (Linux/macOS).
+
+!!! warning "Untrusted Publisher (Windows)"
+
+    You may get a popup saying the file comes from an `Untrusted Publisher`. Click `More Info` and `Run Anyway` to get past this.
+
+The installation process is simple, with a few prompts:
+
+- Select the version to install. Unless you have a specific reason to install a specific version, select the default (the latest version).
+- Select location for the install. Be sure you have enough space in this folder for the base application, as described in the [installation requirements].
+- Select a GPU device.
+
+!!! info "Slow Installation"
+
+    The installer needs to download several GB of data and install it all. It may appear to get stuck at 99.9% when installing `pytorch` or during a step labeled "Installing collected packages".
+
+    If it is stuck for over 10 minutes, something has probably gone wrong and you should close the window and restart.
+
+## Running the Application
+
+Find the install location you selected earlier. Double-click the launcher script to run the app:
+
+=== "Windows"
+
+    ```sh
+    invoke.bat
+    ```
+
+=== "Linux/macOS"
+
+    ```sh
+    invoke.sh
+    ```
+
+Choose the first option to run the UI. After a series of startup messages, you'll see something like this:
+
+```sh
+Uvicorn running on http://127.0.0.1:9090 (Press CTRL+C to quit)
+```
+
+Copy the URL into your browser and you should see the UI.
+
+## Improved Outpainting with PatchMatch
+
+PatchMatch is an extra add-on that can improve outpainting. Windows users are in luck - it works out of the box.
+
+On macOS and Linux, a few extra steps are needed to set it up. See the [PatchMatch installation guide](./patchmatch.md).
+
+## First-time Setup
+
+You will need to [install some models] before you can generate.
+
+Check the [configuration docs] for details on configuring the application.
+
+## Updating
+
+Updating is exactly the same as installing - download the latest installer, choose the latest version, enter your existing installation path, and the app will update. None of your data (images, models, boards, etc) will be erased.
+
+!!! info "Dependency Resolution Issues"
+
+    We've found that pip's dependency resolution can cause issues when upgrading packages. One very common problem was pip "downgrading" torch from CUDA to CPU, but things broke in other novel ways.
+
+    The installer doesn't have this kind of problem, so we use it for updating as well.
+
+## Installation Issues
+
+If you have installation issues, please review the [FAQ]. You can also [create an issue] or ask for help on [discord].
+
+[installation requirements]: ./requirements.md
+[FAQ]: ../faq.md
+[install some models]: ./models.md
+[configuration docs]: ../configuration.md
+[latest release]: https://github.com/invoke-ai/InvokeAI/releases/latest
+[create an issue]: https://github.com/invoke-ai/InvokeAI/issues
+[discord]: https://discord.gg/ZmtBAhwWhy

docs/installation/manual.md

@@ -43,10 +43,10 @@ The following commands vary depending on the version of Invoke being installed a
 3. Create a virtual environment in that directory:
 
     ```sh
-    uv venv --relocatable --prompt invoke --python 3.12 --python-preference only-managed .venv
+    uv venv --relocatable --prompt invoke --python 3.11 --python-preference only-managed .venv
     ```
 
-    This command creates a portable virtual environment at `.venv` complete with a portable python 3.12. It doesn't matter if your system has no python installed, or has a different version - `uv` will handle everything.
+    This command creates a portable virtual environment at `.venv` complete with a portable python 3.11. It doesn't matter if your system has no python installed, or has a different version - `uv` will handle everything.
 
 4. Activate the virtual environment:
 
@@ -64,7 +64,7 @@ The following commands vary depending on the version of Invoke being installed a
 
 5. Choose a version to install. Review the [GitHub releases page](https://github.com/invoke-ai/InvokeAI/releases).
 
-6. Determine the package specifier to use when installing. This is a performance optimization.
+6. Determine the package package specifier to use when installing. This is a performance optimization.
 
     - 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`.
@@ -88,13 +88,13 @@ The following commands vary depending on the version of Invoke being installed a
 8. Install the `invokeai` package. Substitute the package specifier and version.
 
     ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --force-reinstall
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.11 --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:
 
     ```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 3.11 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
     ```
 
 9. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:

docs/installation/quick_start.md

@@ -49,9 +49,9 @@ If you have an existing Invoke installation, you can select it and let the launc
 
 !!! warning "Problem running the launcher on macOS"
 
-    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:
+    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 either use the [legacy scripts](./legacy_scripts.md) to install, or manually flag the launcher as safe:
 
-    - Open the **Invoke Community Edition.dmg** file.
+    - Open the **Invoke-Installer-mac-arm64.dmg** file.
     - Drag the launcher to **Applications**.
     - Open a terminal.
     - Run `xattr -d 'com.apple.quarantine' /Applications/Invoke\ Community\ Edition.app`.
@@ -117,6 +117,7 @@ If you still have problems, ask for help on the Invoke [discord](https://discord
 
 - You can install the Invoke application as a python package. See our [manual install](./manual.md) docs.
 - You can run Invoke with docker. See our [docker install](./docker.md) docs.
+- You can still use our legacy scripts to install and run Invoke. See the [legacy scripts](./legacy_scripts.md) docs.
 
 ## Need Help?
 

docs/installation/requirements.md

@@ -41,7 +41,7 @@ The requirements below are rough guidelines for best performance. GPUs with less
 
     You don't need to do this if you are installing with the [Invoke Launcher](./quick_start.md).
 
-Invoke requires python 3.10 through 3.12. If you don't already have one of these versions installed, we suggest installing 3.12, as it will be supported for longer.
+Invoke requires python 3.10 or 3.11. If you don't already have one of these versions installed, we suggest installing 3.11, as it will be supported for longer.
 
 Check that your system has an up-to-date Python installed by running `python3 --version` in the terminal (Linux, macOS) or cmd/powershell (Windows).
 
@@ -49,19 +49,19 @@ Check that your system has an up-to-date Python installed by running `python3 --
 
     === "Windows"
 
-        - Install python with [an official installer].
+        - Install python 3.11 with [an official installer].
         - The installer includes an option to add python to your PATH. Be sure to enable this. If you missed it, re-run the installer, choose to modify an existing installation, and tick that checkbox.
         - You may need to install [Microsoft Visual C++ Redistributable].
 
     === "macOS"
 
-        - Install python with [an official installer].
+        - Install python 3.11 with [an official installer].
         - If model installs fail with a certificate error, you may need to run this command (changing the python version to match what you have installed): `/Applications/Python\ 3.10/Install\ Certificates.command`
         - If you haven't already, you will need to install the XCode CLI Tools by running `xcode-select --install` in a terminal.
 
     === "Linux"
 
-        - Installing python varies depending on your system. We recommend [using `uv` to manage your python installation](https://docs.astral.sh/uv/concepts/python-versions/#installing-a-python-version).
+        - Installing python varies depending on your system. On Ubuntu, you can use the [deadsnakes PPA](https://launchpad.net/~deadsnakes/+archive/ubuntu/ppa).
         - You'll need to install `libglib2.0-0` and `libgl1-mesa-glx` for OpenCV to work. For example, on a Debian system: `sudo apt update && sudo apt install -y libglib2.0-0 libgl1-mesa-glx`
 
 ## Drivers

installer/create_installer.sh

@@ -32,6 +32,12 @@ if [[ ! -z ${CI} ]]; then
     echo
     echo -e "${BCYAN}CI environment detected${RESET}"
     echo
+else
+    echo
+    echo -e "${BYELLOW}This script must be run from the installer directory!${RESET}"
+    echo "The current working directory is $(pwd)"
+    read -p "If that looks right, press any key to proceed, or CTRL-C to exit..."
+    echo
 fi
 
 echo -e "${BGREEN}HEAD${RESET}:"
@@ -71,8 +77,42 @@ fi
 
 rm -rf ../build
 
-python3 -m build --outdir ../dist/ ../.
+python3 -m build --outdir dist/ ../.
 
+# ----------------------
+
+echo
+echo "Building installer zip files for InvokeAI ${VERSION}..."
+echo
+
+# get rid of any old ones
+rm -f *.zip
+rm -rf InvokeAI-Installer
+
+# copy content
+mkdir InvokeAI-Installer
+for f in templates *.txt *.reg; do
+    cp -r ${f} InvokeAI-Installer/
+done
+mkdir InvokeAI-Installer/lib
+cp lib/*.py InvokeAI-Installer/lib
+
+# Install scripts
+# Mac/Linux
+cp install.sh.in InvokeAI-Installer/install.sh
+chmod a+x InvokeAI-Installer/install.sh
+
+# Windows
+cp install.bat.in InvokeAI-Installer/install.bat
+cp WinLongPathsEnabled.reg InvokeAI-Installer/
+
+FILENAME=InvokeAI-installer-$VERSION.zip
+
+# Zip everything up
+zip -r ${FILENAME} InvokeAI-Installer
+
+echo
+echo -e "${BGREEN}Built installer: ./${FILENAME}${RESET}"
 echo -e "${BGREEN}Built PyPi distribution: ./dist${RESET}"
 
 # clean up, but only if we are not in a github action
@@ -85,7 +125,9 @@ fi
 if [[ ! -z ${CI} ]]; then
     echo
     echo "Setting GitHub action outputs..."
-    echo "DIST_PATH=./dist/" >>$GITHUB_OUTPUT
+    echo "INSTALLER_FILENAME=${FILENAME}" >>$GITHUB_OUTPUT
+    echo "INSTALLER_PATH=installer/${FILENAME}" >>$GITHUB_OUTPUT
+    echo "DIST_PATH=installer/dist/" >>$GITHUB_OUTPUT
 fi
 
 exit 0

installer/install.bat.in

@@ -0,0 +1,128 @@
+@echo off
+setlocal EnableExtensions EnableDelayedExpansion
+
+@rem This script requires the user to install Python 3.10 or higher. All other
+@rem requirements are downloaded as needed.
+
+@rem change to the script's directory
+PUSHD "%~dp0"
+
+set "no_cache_dir=--no-cache-dir"
+if "%1" == "use-cache" (
+    set "no_cache_dir="
+)
+
+@rem Config
+@rem The version in the next line is replaced by an up to date release number
+@rem when create_installer.sh is run. Change the release number there.
+set INSTRUCTIONS=https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
+set TROUBLESHOOTING=https://invoke-ai.github.io/InvokeAI/help/FAQ/
+set PYTHON_URL=https://www.python.org/downloads/windows/
+set MINIMUM_PYTHON_VERSION=3.10.0
+set PYTHON_URL=https://www.python.org/downloads/release/python-3109/
+
+set err_msg=An error has occurred and the script could not continue.
+
+@rem --------------------------- Intro -------------------------------
+echo This script will install InvokeAI and its dependencies.
+echo.
+echo BEFORE YOU START PLEASE MAKE SURE TO DO THE FOLLOWING
+echo 1. Install python 3.10 or 3.11. Python version 3.9 is no longer supported.
+echo 2. Double-click on the file WinLongPathsEnabled.reg in order to
+echo    enable long path support on your system.
+echo 3. Install the Visual C++ core libraries.
+echo    Please download and install the libraries from:
+echo    https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170
+echo.
+echo See %INSTRUCTIONS% for more details.
+echo.
+echo FOR THE BEST USER EXPERIENCE WE SUGGEST MAXIMIZING THIS WINDOW NOW.
+pause
+
+@rem ---------------------------- check Python version ---------------
+echo ***** Checking and Updating Python *****
+
+call python --version >.tmp1 2>.tmp2
+if %errorlevel% == 1 (
+   set err_msg=Please install Python 3.10-11. See %INSTRUCTIONS% for details.
+   goto err_exit
+)
+
+for /f "tokens=2" %%i in (.tmp1) do set python_version=%%i
+if "%python_version%" == "" (
+   set err_msg=No python was detected on your system. Please install Python version %MINIMUM_PYTHON_VERSION% or higher. We recommend Python 3.10.12 from %PYTHON_URL%
+   goto err_exit
+)
+
+call :compareVersions %MINIMUM_PYTHON_VERSION% %python_version%
+if %errorlevel% == 1 (
+   set err_msg=Your version of Python is too low. You need at least %MINIMUM_PYTHON_VERSION% but you have %python_version%. We recommend Python 3.10.12 from %PYTHON_URL%
+   goto err_exit
+)
+
+@rem Cleanup
+del /q .tmp1 .tmp2
+
+@rem -------------- Install and Configure ---------------
+
+call python .\lib\main.py
+pause
+exit /b
+
+@rem ------------------------ Subroutines ---------------
+@rem routine to do comparison of semantic version numbers
+@rem found at https://stackoverflow.com/questions/15807762/compare-version-numbers-in-batch-file
+:compareVersions
+::
+:: Compares two version numbers and returns the result in the ERRORLEVEL
+::
+:: Returns 1 if version1 > version2
+::         0 if version1 = version2
+::        -1 if version1 < version2
+::
+:: The nodes must be delimited by . or , or -
+::
+:: Nodes are normally strictly numeric, without a 0 prefix. A letter suffix
+:: is treated as a separate node
+::
+setlocal enableDelayedExpansion
+set "v1=%~1"
+set "v2=%~2"
+call :divideLetters v1
+call :divideLetters v2
+:loop
+call :parseNode "%v1%" n1 v1
+call :parseNode "%v2%" n2 v2
+if %n1% gtr %n2% exit /b 1
+if %n1% lss %n2% exit /b -1
+if not defined v1 if not defined v2 exit /b 0
+if not defined v1 exit /b -1
+if not defined v2 exit /b 1
+goto :loop
+
+
+:parseNode  version  nodeVar  remainderVar
+for /f "tokens=1* delims=.,-" %%A in ("%~1") do (
+  set "%~2=%%A"
+  set "%~3=%%B"
+)
+exit /b
+
+
+:divideLetters  versionVar
+for %%C in (a b c d e f g h i j k l m n o p q r s t u v w x y z) do set "%~1=!%~1:%%C=.%%C!"
+exit /b
+
+:err_exit
+echo %err_msg%
+echo The installer will exit now.
+pause
+exit /b
+
+pause
+
+:Trim
+SetLocal EnableDelayedExpansion
+set Params=%*
+for /f "tokens=1*" %%a in ("!Params!") do EndLocal & set %1=%%b
+exit /b

installer/install.sh.in

@@ -0,0 +1,40 @@
+#!/bin/bash
+
+# make sure we are not already in a venv
+# (don't need to check status)
+deactivate >/dev/null 2>&1
+scriptdir=$(dirname "$0")
+cd $scriptdir
+
+function version { echo "$@" | awk -F. '{ printf("%d%03d%03d%03d\n", $1,$2,$3,$4); }'; }
+
+MINIMUM_PYTHON_VERSION=3.10.0
+MAXIMUM_PYTHON_VERSION=3.11.100
+PYTHON=""
+for candidate in python3.11 python3.10 python3 python ; do
+    if ppath=`which $candidate 2>/dev/null`; then
+        # when using `pyenv`, the executable for an inactive Python version will exist but will not be operational
+        # we check that this found executable can actually run
+        if [ $($candidate --version &>/dev/null; echo ${PIPESTATUS}) -gt 0 ]; then continue; fi
+
+        python_version=$($ppath -V | awk '{ print $2 }')
+        if [ $(version $python_version) -ge $(version "$MINIMUM_PYTHON_VERSION") ]; then
+            if [ $(version $python_version) -le $(version "$MAXIMUM_PYTHON_VERSION") ]; then
+                PYTHON=$ppath
+                break
+            fi
+        fi
+    fi
+done
+
+if [ -z "$PYTHON" ]; then
+    echo "A suitable Python interpreter could not be found"
+    echo "Please install Python $MINIMUM_PYTHON_VERSION or higher (maximum $MAXIMUM_PYTHON_VERSION) before running this script. See instructions at $INSTRUCTIONS for help."
+    read -p "Press any key to exit"
+    exit -1
+fi
+
+echo "For the best user experience we suggest enlarging or maximizing this window now."
+
+exec $PYTHON ./lib/main.py ${@}
+read -p "Press any key to exit"

installer/lib/installer.py

@@ -0,0 +1,438 @@
+# Copyright (c) 2023 Eugene Brodsky (https://github.com/ebr)
+"""
+InvokeAI installer script
+"""
+
+import locale
+import os
+import platform
+import re
+import shutil
+import subprocess
+import sys
+import venv
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from typing import Optional, Tuple
+
+SUPPORTED_PYTHON = ">=3.10.0,<=3.11.100"
+INSTALLER_REQS = ["rich", "semver", "requests", "plumbum", "prompt-toolkit"]
+BOOTSTRAP_VENV_PREFIX = "invokeai-installer-tmp"
+DOCS_URL = "https://invoke-ai.github.io/InvokeAI/"
+DISCORD_URL = "https://discord.gg/ZmtBAhwWhy"
+
+OS = platform.uname().system
+ARCH = platform.uname().machine
+VERSION = "latest"
+
+
+def get_version_from_wheel_filename(wheel_filename: str) -> str:
+    match = re.search(r"-(\d+\.\d+\.\d+)", wheel_filename)
+    if match:
+        version = match.group(1)
+        return version
+    else:
+        raise ValueError(f"Could not extract version from wheel filename: {wheel_filename}")
+
+
+class Installer:
+    """
+    Deploys an InvokeAI installation into a given path
+    """
+
+    reqs: list[str] = INSTALLER_REQS
+
+    def __init__(self) -> None:
+        if os.getenv("VIRTUAL_ENV") is not None:
+            print("A virtual environment is already activated. Please 'deactivate' before installation.")
+            sys.exit(-1)
+        self.bootstrap()
+        self.available_releases = get_github_releases()
+
+    def mktemp_venv(self) -> TemporaryDirectory[str]:
+        """
+        Creates a temporary virtual environment for the installer itself
+
+        :return: path to the created virtual environment directory
+        :rtype: TemporaryDirectory
+        """
+
+        # Cleaning up temporary directories on Windows results in a race condition
+        # and a stack trace.
+        # `ignore_cleanup_errors` was only added in Python 3.10
+        if OS == "Windows" and int(platform.python_version_tuple()[1]) >= 10:
+            venv_dir = TemporaryDirectory(prefix=BOOTSTRAP_VENV_PREFIX, ignore_cleanup_errors=True)
+        else:
+            venv_dir = TemporaryDirectory(prefix=BOOTSTRAP_VENV_PREFIX)
+
+        venv.create(venv_dir.name, with_pip=True)
+        self.venv_dir = venv_dir
+        set_sys_path(Path(venv_dir.name))
+
+        return venv_dir
+
+    def bootstrap(self, verbose: bool = False) -> TemporaryDirectory[str] | None:
+        """
+        Bootstrap the installer venv with packages required at install time
+        """
+
+        print("Initializing the installer. This may take a minute - please wait...")
+
+        venv_dir = self.mktemp_venv()
+        pip = get_pip_from_venv(Path(venv_dir.name))
+
+        cmd = [pip, "install", "--require-virtualenv", "--use-pep517"]
+        cmd.extend(self.reqs)
+
+        try:
+            # upgrade pip to the latest version to avoid a confusing message
+            res = upgrade_pip(Path(venv_dir.name))
+            if verbose:
+                print(res)
+
+            # run the install prerequisites installation
+            res = subprocess.check_output(cmd).decode()
+
+            if verbose:
+                print(res)
+
+            return venv_dir
+        except subprocess.CalledProcessError as e:
+            print(e)
+
+    def app_venv(self, venv_parent: Path) -> Path:
+        """
+        Create a virtualenv for the InvokeAI installation
+        """
+
+        venv_dir = venv_parent / ".venv"
+
+        # Prefer to copy python executables
+        # so that updates to system python don't break InvokeAI
+        try:
+            venv.create(venv_dir, with_pip=True)
+        # If installing over an existing environment previously created with symlinks,
+        # the executables will fail to copy. Keep symlinks in that case
+        except shutil.SameFileError:
+            venv.create(venv_dir, with_pip=True, symlinks=True)
+
+        return venv_dir
+
+    def install(
+        self,
+        root: str = "~/invokeai",
+        yes_to_all: bool = False,
+        find_links: Optional[str] = None,
+        wheel: Optional[Path] = None,
+    ) -> None:
+        """Install the InvokeAI application into the given runtime path
+
+        Args:
+            root: Destination path for the installation
+            yes_to_all: Accept defaults to all questions
+            find_links: A local directory to search for requirement wheels before going to remote indexes
+            wheel: A wheel file to install
+        """
+
+        import messages
+
+        if wheel:
+            messages.installing_from_wheel(wheel.name)
+            version = get_version_from_wheel_filename(wheel.name)
+        else:
+            messages.welcome(self.available_releases)
+            version = messages.choose_version(self.available_releases)
+
+        auto_dest = Path(os.environ.get("INVOKEAI_ROOT", root)).expanduser().resolve()
+        destination = auto_dest if yes_to_all else messages.dest_path(root)
+        if destination is None:
+            print("Could not find or create the destination directory. Installation cancelled.")
+            sys.exit(0)
+
+        # create the venv for the app
+        self.venv = self.app_venv(venv_parent=destination)
+
+        self.instance = InvokeAiInstance(runtime=destination, venv=self.venv, version=version)
+
+        # install dependencies and the InvokeAI application
+        (extra_index_url, optional_modules) = get_torch_source() if not yes_to_all else (None, None)
+        self.instance.install(extra_index_url, optional_modules, find_links, wheel)
+
+        # install the launch/update scripts into the runtime directory
+        self.instance.install_user_scripts()
+
+        message = f"""
+*** Installation Successful ***
+
+To start the application, run:
+    {destination}/invoke.{"bat" if sys.platform == "win32" else "sh"}
+
+For more information, troubleshooting and support, visit our docs at:
+    {DOCS_URL}
+
+Join the community on Discord:
+    {DISCORD_URL}
+"""
+        print(message)
+
+
+class InvokeAiInstance:
+    """
+    Manages an installed instance of InvokeAI, comprising a virtual environment and a runtime directory.
+    The virtual environment *may* reside within the runtime directory.
+    A single runtime directory *may* be shared by multiple virtual environments, though this isn't currently tested or supported.
+    """
+
+    def __init__(self, runtime: Path, venv: Path, version: str = "stable") -> None:
+        self.runtime = runtime
+        self.venv = venv
+        self.pip = get_pip_from_venv(venv)
+        self.version = version
+
+        set_sys_path(venv)
+        os.environ["INVOKEAI_ROOT"] = str(self.runtime.expanduser().resolve())
+        os.environ["VIRTUAL_ENV"] = str(self.venv.expanduser().resolve())
+        upgrade_pip(venv)
+
+    def get(self) -> tuple[Path, Path]:
+        """
+        Get the location of the virtualenv directory for this installation
+
+        :return: Paths of the runtime and the venv directory
+        :rtype: tuple[Path, Path]
+        """
+
+        return (self.runtime, self.venv)
+
+    def install(
+        self,
+        extra_index_url: Optional[str] = None,
+        optional_modules: Optional[str] = None,
+        find_links: Optional[str] = None,
+        wheel: Optional[Path] = None,
+    ):
+        """Install the package from PyPi or a wheel, if provided.
+
+        Args:
+            extra_index_url: the "--extra-index-url ..." line for pip to look in extra indexes.
+            optional_modules: optional modules to install using "[module1,module2]" format.
+            find_links: path to a directory containing wheels to be searched prior to going to the internet
+            wheel: a wheel file to install
+        """
+
+        import messages
+
+        # not currently used, but may be useful for "install most recent version" option
+        if self.version == "prerelease":
+            version = None
+            pre_flag = "--pre"
+        elif self.version == "stable":
+            version = None
+            pre_flag = None
+        else:
+            version = self.version
+            pre_flag = None
+
+        src = "invokeai"
+        if optional_modules:
+            src += optional_modules
+        if version:
+            src += f"=={version}"
+
+        messages.simple_banner("Installing the InvokeAI Application :art:")
+
+        from plumbum import FG, ProcessExecutionError, local
+
+        pip = local[self.pip]
+
+        # Uninstall xformers if it is present; the correct version of it will be reinstalled if needed
+        _ = pip["uninstall", "-yqq", "xformers"] & FG
+
+        pipeline = pip[
+            "install",
+            "--require-virtualenv",
+            "--force-reinstall",
+            "--use-pep517",
+            str(src) if not wheel else str(wheel),
+            "--find-links" if find_links is not None else None,
+            find_links,
+            "--extra-index-url" if extra_index_url is not None else None,
+            extra_index_url,
+            pre_flag if not wheel else None,  # Ignore the flag if we are installing a wheel
+        ]
+
+        try:
+            _ = pipeline & FG
+        except ProcessExecutionError as e:
+            print(f"Error: {e}")
+            print(
+                "Could not install InvokeAI. Please try downloading the latest version of the installer and install again."
+            )
+            sys.exit(1)
+
+    def install_user_scripts(self):
+        """
+        Copy the launch and update scripts to the runtime dir
+        """
+
+        ext = "bat" if OS == "Windows" else "sh"
+
+        scripts = ["invoke"]
+
+        for script in scripts:
+            src = Path(__file__).parent / ".." / "templates" / f"{script}.{ext}.in"
+            dest = self.runtime / f"{script}.{ext}"
+            shutil.copy(src, dest)
+            os.chmod(dest, 0o0755)
+
+
+### Utility functions ###
+
+
+def get_pip_from_venv(venv_path: Path) -> str:
+    """
+    Given a path to a virtual environment, get the absolute path to the `pip` executable
+    in a cross-platform fashion. Does not validate that the pip executable
+    actually exists in the virtualenv.
+
+    :param venv_path: Path to the virtual environment
+    :type venv_path: Path
+    :return: Absolute path to the pip executable
+    :rtype: str
+    """
+
+    pip = "Scripts\\pip.exe" if OS == "Windows" else "bin/pip"
+    return str(venv_path.expanduser().resolve() / pip)
+
+
+def upgrade_pip(venv_path: Path) -> str | None:
+    """
+    Upgrade the pip executable in the given virtual environment
+    """
+
+    python = "Scripts\\python.exe" if OS == "Windows" else "bin/python"
+    python = str(venv_path.expanduser().resolve() / python)
+
+    try:
+        result = subprocess.check_output([python, "-m", "pip", "install", "--upgrade", "pip"]).decode(
+            encoding=locale.getpreferredencoding()
+        )
+    except subprocess.CalledProcessError as e:
+        print(e)
+        result = None
+
+    return result
+
+
+def set_sys_path(venv_path: Path) -> None:
+    """
+    Given a path to a virtual environment, set the sys.path, in a cross-platform fashion,
+    such that packages from the given venv may be imported in the current process.
+    Ensure that the packages from system environment are not visible (emulate
+    the virtual env 'activate' script) - this doesn't work on Windows yet.
+
+    :param venv_path: Path to the virtual environment
+    :type venv_path: Path
+    """
+
+    # filter out any paths in sys.path that may be system- or user-wide
+    # but leave the temporary bootstrap virtualenv as it contains packages we
+    # temporarily need at install time
+    sys.path = list(filter(lambda p: not p.endswith("-packages") or p.find(BOOTSTRAP_VENV_PREFIX) != -1, sys.path))
+
+    # determine site-packages/lib directory location for the venv
+    lib = "Lib" if OS == "Windows" else f"lib/python{sys.version_info.major}.{sys.version_info.minor}"
+
+    # add the site-packages location to the venv
+    sys.path.append(str(Path(venv_path, lib, "site-packages").expanduser().resolve()))
+
+
+def get_github_releases() -> tuple[list[str], list[str]] | None:
+    """
+    Query Github for published (pre-)release versions.
+    Return a tuple where the first element is a list of stable releases and the second element is a list of pre-releases.
+    Return None if the query fails for any reason.
+    """
+
+    import requests
+
+    ## get latest releases using github api
+    url = "https://api.github.com/repos/invoke-ai/InvokeAI/releases"
+    releases: list[str] = []
+    pre_releases: list[str] = []
+    try:
+        res = requests.get(url)
+        res.raise_for_status()
+        tag_info = res.json()
+        for tag in tag_info:
+            if not tag["prerelease"]:
+                releases.append(tag["tag_name"].lstrip("v"))
+            else:
+                pre_releases.append(tag["tag_name"].lstrip("v"))
+    except requests.HTTPError as e:
+        print(f"Error: {e}")
+        print("Could not fetch version information from GitHub. Please check your network connection and try again.")
+        return
+    except Exception as e:
+        print(f"Error: {e}")
+        print("An unexpected error occurred while trying to fetch version information from GitHub. Please try again.")
+        return
+
+    releases.sort(reverse=True)
+    pre_releases.sort(reverse=True)
+
+    return releases, pre_releases
+
+
+def get_torch_source() -> Tuple[str | None, str | None]:
+    """
+    Determine the extra index URL for pip to use for torch installation.
+    This depends on the OS and the graphics accelerator in use.
+    This is only applicable to Windows and Linux, since PyTorch does not
+    offer accelerated builds for macOS.
+
+    Prefer CUDA-enabled wheels if the user wasn't sure of their GPU, as it will fallback to CPU if possible.
+
+    A NoneType return means just go to PyPi.
+
+    :return: tuple consisting of (extra index url or None, optional modules to load or None)
+    :rtype: list
+    """
+
+    from messages import GpuType, select_gpu
+
+    # device can be one of: "cuda", "rocm", "cpu", "cuda_and_dml, autodetect"
+    device = select_gpu()
+
+    # The correct extra index URLs for torch are inconsistent, see https://pytorch.org/get-started/locally/#start-locally
+
+    url = None
+    optional_modules: str | None = None
+    if OS == "Linux":
+        if device == GpuType.ROCM:
+            url = "https://download.pytorch.org/whl/rocm6.1"
+        elif device == GpuType.CPU:
+            url = "https://download.pytorch.org/whl/cpu"
+        elif device == GpuType.CUDA:
+            url = "https://download.pytorch.org/whl/cu124"
+            optional_modules = "[onnx-cuda]"
+        elif device == GpuType.CUDA_WITH_XFORMERS:
+            url = "https://download.pytorch.org/whl/cu124"
+            optional_modules = "[xformers,onnx-cuda]"
+    elif OS == "Windows":
+        if device == GpuType.CUDA:
+            url = "https://download.pytorch.org/whl/cu124"
+            optional_modules = "[onnx-cuda]"
+        elif device == GpuType.CUDA_WITH_XFORMERS:
+            url = "https://download.pytorch.org/whl/cu124"
+            optional_modules = "[xformers,onnx-cuda]"
+        elif device.value == "cpu":
+            # CPU  uses the default PyPi index, no optional modules
+            pass
+    elif OS == "Darwin":
+        # macOS uses the default PyPi index, no optional modules
+        pass
+
+    # Fall back to defaults
+
+    return (url, optional_modules)

installer/lib/main.py

@@ -0,0 +1,57 @@
+"""
+InvokeAI Installer
+"""
+
+import argparse
+import os
+from pathlib import Path
+
+from installer import Installer
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "-r",
+        "--root",
+        dest="root",
+        type=str,
+        help="Destination path for installation",
+        default=os.environ.get("INVOKEAI_ROOT") or "~/invokeai",
+    )
+    parser.add_argument(
+        "-y",
+        "--yes",
+        "--yes-to-all",
+        dest="yes_to_all",
+        action="store_true",
+        help="Assume default answers to all questions",
+        default=False,
+    )
+
+    parser.add_argument(
+        "--find-links",
+        dest="find_links",
+        help="Specifies a directory of local wheel files to be searched prior to searching the online repositories.",
+        type=Path,
+        default=None,
+    )
+
+    parser.add_argument(
+        "--wheel",
+        dest="wheel",
+        help="Specifies a wheel for the InvokeAI package. Used for troubleshooting or testing prereleases.",
+        type=Path,
+        default=None,
+    )
+
+    args = parser.parse_args()
+
+    inst = Installer()
+
+    try:
+        inst.install(**args.__dict__)
+    except KeyboardInterrupt:
+        print("\n")
+        print("Ctrl-C pressed. Aborting.")
+        print("Come back soon!")

installer/lib/messages.py

@@ -0,0 +1,342 @@
+# Copyright (c) 2023 Eugene Brodsky (https://github.com/ebr)
+"""
+Installer user interaction
+"""
+
+import os
+import platform
+from enum import Enum
+from pathlib import Path
+from typing import Optional
+
+from prompt_toolkit import prompt
+from prompt_toolkit.completion import FuzzyWordCompleter, PathCompleter
+from prompt_toolkit.validation import Validator
+from rich import box, print
+from rich.console import Console, Group, group
+from rich.panel import Panel
+from rich.prompt import Confirm
+from rich.style import Style
+from rich.syntax import Syntax
+from rich.text import Text
+
+OS = platform.uname().system
+ARCH = platform.uname().machine
+
+if OS == "Windows":
+    # Windows terminals look better without a background colour
+    console = Console(style=Style(color="grey74"))
+else:
+    console = Console(style=Style(color="grey74", bgcolor="grey19"))
+
+
+def welcome(available_releases: tuple[list[str], list[str]] | None = None) -> None:
+    @group()
+    def text():
+        if (platform_specific := _platform_specific_help()) is not None:
+            yield platform_specific
+            yield ""
+        yield Text.from_markup(
+            "Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.",
+            justify="center",
+        )
+        if available_releases is not None:
+            latest_stable = available_releases[0][0]
+            last_pre = available_releases[1][0]
+            yield ""
+            yield Text.from_markup(
+                f"[red3]🠶[/] Latest stable release (recommended): [b bright_white]{latest_stable}", justify="center"
+            )
+            yield Text.from_markup(
+                f"[red3]🠶[/] Last published pre-release version: [b bright_white]{last_pre}", justify="center"
+            )
+
+    console.rule()
+    print(
+        Panel(
+            title="[bold wheat1]Welcome to the InvokeAI Installer",
+            renderable=text(),
+            box=box.DOUBLE,
+            expand=True,
+            padding=(1, 2),
+            style=Style(bgcolor="grey23", color="orange1"),
+            subtitle=f"[bold grey39]{OS}-{ARCH}",
+        )
+    )
+    console.line()
+
+
+def installing_from_wheel(wheel_filename: str) -> None:
+    """Display a message about installing from a wheel"""
+
+    @group()
+    def text():
+        yield Text.from_markup(f"You are installing from a wheel file: [bold]{wheel_filename}\n")
+        yield Text.from_markup(
+            "[bold orange3]If you are not sure why you are doing this, you should cancel and install InvokeAI normally."
+        )
+
+    console.print(
+        Panel(
+            title="Installing from Wheel",
+            renderable=text(),
+            box=box.DOUBLE,
+            expand=True,
+            padding=(1, 2),
+        )
+    )
+
+    should_proceed = Confirm.ask("Do you want to proceed?")
+
+    if not should_proceed:
+        console.print("Installation cancelled.")
+        exit()
+
+
+def choose_version(available_releases: tuple[list[str], list[str]] | None = None) -> str:
+    """
+    Prompt the user to choose an Invoke version to install
+    """
+
+    # short circuit if we couldn't get a version list
+    # still try to install the latest stable version
+    if available_releases is None:
+        return "stable"
+
+    console.print(":grey_question: [orange3]Please choose an Invoke version to install.")
+
+    choices = available_releases[0] + available_releases[1]
+
+    response = prompt(
+        message=f"   <Enter> to install the recommended release ({choices[0]}). <Tab> or type to pick a version: ",
+        complete_while_typing=True,
+        completer=FuzzyWordCompleter(choices),
+    )
+    console.print(f"   Version {choices[0] if response == '' else response} will be installed.")
+
+    console.line()
+
+    return "stable" if response == "" else response
+
+
+def confirm_install(dest: Path) -> bool:
+    if dest.exists():
+        print(f":stop_sign: Directory {dest} already exists!")
+        print("   Is this location correct?")
+        default = False
+    else:
+        print(f":file_folder: InvokeAI will be installed in {dest}")
+        default = True
+
+    dest_confirmed = Confirm.ask("   Please confirm:", default=default)
+
+    console.line()
+
+    return dest_confirmed
+
+
+def dest_path(dest: Optional[str | Path] = None) -> Path | None:
+    """
+    Prompt the user for the destination path and create the path
+
+    :param dest: a filesystem path, defaults to None
+    :type dest: str, optional
+    :return: absolute path to the created installation directory
+    :rtype: Path
+    """
+
+    if dest is not None:
+        dest = Path(dest).expanduser().resolve()
+    else:
+        dest = Path.cwd().expanduser().resolve()
+    prev_dest = init_path = dest
+    dest_confirmed = False
+
+    while not dest_confirmed:
+        browse_start = (dest or Path.cwd()).expanduser().resolve()
+
+        path_completer = PathCompleter(
+            only_directories=True,
+            expanduser=True,
+            get_paths=lambda: [str(browse_start)],  # noqa: B023
+            # get_paths=lambda: [".."].extend(list(browse_start.iterdir()))
+        )
+
+        console.line()
+
+        console.print(f":grey_question: [orange3]Please select the install destination:[/] \\[{browse_start}]: ")
+        selected = prompt(
+            ">>> ",
+            complete_in_thread=True,
+            completer=path_completer,
+            default=str(browse_start) + os.sep,
+            vi_mode=True,
+            complete_while_typing=True,
+            # Test that this is not needed on Windows
+            # complete_style=CompleteStyle.READLINE_LIKE,
+        )
+        prev_dest = dest
+        dest = Path(selected)
+
+        console.line()
+
+        dest_confirmed = confirm_install(dest.expanduser().resolve())
+
+        if not dest_confirmed:
+            dest = prev_dest
+
+    dest = dest.expanduser().resolve()
+
+    try:
+        dest.mkdir(exist_ok=True, parents=True)
+        return dest
+    except PermissionError:
+        console.print(
+            f"Failed to create directory {dest} due to insufficient permissions",
+            style=Style(color="red"),
+            highlight=True,
+        )
+    except OSError:
+        console.print_exception()
+
+    if Confirm.ask("Would you like to try again?"):
+        dest_path(init_path)
+    else:
+        console.rule("Goodbye!")
+
+
+class GpuType(Enum):
+    CUDA_WITH_XFORMERS = "xformers"
+    CUDA = "cuda"
+    ROCM = "rocm"
+    CPU = "cpu"
+
+
+def select_gpu() -> GpuType:
+    """
+    Prompt the user to select the GPU driver
+    """
+
+    if ARCH == "arm64" and OS != "Darwin":
+        print(f"Only CPU acceleration is available on {ARCH} architecture. Proceeding with that.")
+        return GpuType.CPU
+
+    nvidia = (
+        "an [gold1 b]NVIDIA[/] RTX 3060 or newer GPU using CUDA",
+        GpuType.CUDA,
+    )
+    vintage_nvidia = (
+        "an [gold1 b]NVIDIA[/] RTX 20xx or older GPU using CUDA+xFormers",
+        GpuType.CUDA_WITH_XFORMERS,
+    )
+    amd = (
+        "an [gold1 b]AMD[/] GPU using ROCm",
+        GpuType.ROCM,
+    )
+    cpu = (
+        "Do not install any GPU support, use CPU for generation (slow)",
+        GpuType.CPU,
+    )
+
+    options = []
+    if OS == "Windows":
+        options = [nvidia, vintage_nvidia, cpu]
+    if OS == "Linux":
+        options = [nvidia, vintage_nvidia, amd, cpu]
+    elif OS == "Darwin":
+        options = [cpu]
+
+    if len(options) == 1:
+        return options[0][1]
+
+    options = {str(i): opt for i, opt in enumerate(options, 1)}
+
+    console.rule(":space_invader: GPU (Graphics Card) selection :space_invader:")
+    console.print(
+        Panel(
+            Group(
+                "\n".join(
+                    [
+                        f"Detected the [gold1]{OS}-{ARCH}[/] platform",
+                        "",
+                        "See [deep_sky_blue1]https://invoke-ai.github.io/InvokeAI/installation/requirements/[/] to ensure your system meets the minimum requirements.",
+                        "",
+                        "[red3]🠶[/] [b]Your GPU drivers must be correctly installed before using InvokeAI![/] [red3]🠴[/]",
+                    ]
+                ),
+                "",
+                "Please select the type of GPU installed in your computer.",
+                Panel(
+                    "\n".join([f"[dark_goldenrod b i]{i}[/] [dark_red]🢒[/]{opt[0]}" for (i, opt) in options.items()]),
+                    box=box.MINIMAL,
+                ),
+            ),
+            box=box.MINIMAL,
+            padding=(1, 1),
+        )
+    )
+    choice = prompt(
+        "Please make your selection: ",
+        validator=Validator.from_callable(
+            lambda n: n in options.keys(), error_message="Please select one the above options"
+        ),
+    )
+
+    return options[choice][1]
+
+
+def simple_banner(message: str) -> None:
+    """
+    A simple banner with a message, defined here for styling consistency
+
+    :param message: The message to display
+    :type message: str
+    """
+
+    console.rule(message)
+
+
+# TODO this does not yet work correctly
+def windows_long_paths_registry() -> None:
+    """
+    Display a message about applying the Windows long paths registry fix
+    """
+
+    with open(str(Path(__file__).parent / "WinLongPathsEnabled.reg"), "r", encoding="utf-16le") as code:
+        syntax = Syntax(code.read(), line_numbers=True, lexer="regedit")
+
+    console.print(
+        Panel(
+            Group(
+                "\n".join(
+                    [
+                        "We will now apply a registry fix to enable long paths on Windows. InvokeAI needs this to function correctly. We are asking your permission to modify the Windows Registry on your behalf.",
+                        "",
+                        "This is the change that will be applied:",
+                        str(syntax),
+                    ]
+                )
+            ),
+            title="Windows Long Paths registry fix",
+            box=box.HORIZONTALS,
+            padding=(1, 1),
+        )
+    )
+
+
+def _platform_specific_help() -> Text | None:
+    if OS == "Darwin":
+        text = Text.from_markup(
+            """[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/]."""
+        )
+    elif OS == "Windows":
+        text = Text.from_markup(
+            """[b wheat1]Windows Users![/]\n\nBefore you start, please do the following:
+  1. Double-click on the file [b wheat1]WinLongPathsEnabled.reg[/] in order to
+     enable long path support on your system.
+  2. Make sure you have the [b wheat1]Visual C++ core libraries[/] installed. If not, install from
+     [deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]"""
+        )
+    else:
+        return
+    return text

installer/readme.txt

@@ -0,0 +1,52 @@
+InvokeAI
+
+Project homepage: https://github.com/invoke-ai/InvokeAI
+
+Preparations:
+
+  You will need to install Python 3.10 or higher for this installer
+  to work. Instructions are given here:
+  https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/
+
+  Before you start the installer, please open up your system's command
+  line window (Terminal or Command) and type the commands:
+
+     python --version
+
+  If all is well, it will print "Python 3.X.X", where the version number
+  is at least 3.10.*, and not higher than 3.11.*.
+
+  If this works, check the version of the Python package manager, pip:
+
+     pip --version
+
+  You should get a message that indicates that the pip package
+  installer was derived from Python 3.10 or 3.11. For example:
+  "pip 22.0.1 from /usr/bin/pip (python 3.10)"
+
+Long Paths on Windows:
+
+  If you are on Windows, you will need to enable Windows Long Paths to
+  run InvokeAI successfully. If you're not sure what this is, you
+  almost certainly need to do this.
+
+  Simply double-click the "WinLongPathsEnabled.reg" file located in
+  this directory, and approve the Windows warnings. Note that you will
+  need to have admin privileges in order to do this.
+
+Launching the installer:
+
+  Windows: double-click the 'install.bat' file (while keeping it inside
+  the InvokeAI-Installer folder).
+
+  Linux and Mac: Please open the terminal application and run
+  './install.sh' (while keeping it inside the InvokeAI-Installer
+  folder).
+
+The installer will create a directory of your choice and install the
+InvokeAI application within it. This directory contains everything you need to run
+invokeai. Once InvokeAI is up and running, you may delete the
+InvokeAI-Installer folder at your convenience.
+
+For more information, please see
+https://invoke-ai.github.io/InvokeAI/installation/INSTALL_AUTOMATED/

installer/templates/invoke.bat.in

@@ -0,0 +1,54 @@
+@echo off
+
+PUSHD "%~dp0"
+setlocal
+
+call .venv\Scripts\activate.bat
+set INVOKEAI_ROOT=.
+
+:start
+echo Desired action:
+echo 1. Generate images with the browser-based interface
+echo 2. Open the developer console
+echo 3. Command-line help
+echo Q - Quit
+echo.
+echo To update, download and run the installer from https://github.com/invoke-ai/InvokeAI/releases/latest
+echo.
+set /P choice="Please enter 1-4, Q: [1] "
+if not defined choice set choice=1
+IF /I "%choice%" == "1" (
+    echo Starting the InvokeAI browser-based UI..
+    python .venv\Scripts\invokeai-web.exe %*
+) ELSE IF /I "%choice%" == "2" (
+    echo Developer Console
+    echo Python command is:
+    where python
+    echo Python version is:
+    python --version
+    echo *************************
+    echo You are now in the system shell, with the local InvokeAI Python virtual environment activated,
+    echo so that you can troubleshoot this InvokeAI installation as necessary.
+    echo *************************
+    echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
+    call cmd /k
+) ELSE IF /I "%choice%" == "3" (
+    echo Displaying command line help...
+    python .venv\Scripts\invokeai-web.exe --help %*
+    pause
+    exit /b
+) ELSE IF /I "%choice%" == "q" (
+    echo Goodbye!
+    goto ending
+) ELSE (
+    echo Invalid selection
+    pause
+    exit /b
+)
+goto start
+
+endlocal
+pause
+
+:ending
+exit /b

installer/templates/invoke.sh.in

@@ -0,0 +1,87 @@
+#!/bin/bash
+
+# MIT License
+
+# Coauthored by Lincoln Stein, Eugene Brodsky and Joshua Kimsey
+# Copyright 2023, The InvokeAI Development Team
+
+####
+# This launch script assumes that:
+# 1. it is located in the runtime directory,
+# 2. the .venv is also located in the runtime directory and is named exactly that
+#
+# If both of the above are not true, this script will likely not work as intended.
+# Activate the virtual environment and run `invoke.py` directly.
+####
+
+set -eu
+
+# Ensure we're in the correct folder in case user's CWD is somewhere else
+scriptdir=$(dirname $(readlink -f "$0"))
+cd "$scriptdir"
+
+. .venv/bin/activate
+
+export INVOKEAI_ROOT="$scriptdir"
+
+# Stash the CLI args - when we prompt for user input, `$@` is overwritten
+PARAMS=$@
+
+# This setting allows torch to fall back to CPU for operations that are not supported by MPS on macOS.
+if [ "$(uname -s)" == "Darwin" ]; then
+    export PYTORCH_ENABLE_MPS_FALLBACK=1
+fi
+
+# Primary function for the case statement to determine user input
+do_choice() {
+    case $1 in
+    1)
+        clear
+        printf "Generate images with a browser-based interface\n"
+        invokeai-web $PARAMS
+        ;;
+    2)
+        clear
+        printf "Open the developer console\n"
+        file_name=$(basename "${BASH_SOURCE[0]}")
+        bash --init-file "$file_name"
+        ;;
+    3)
+        clear
+        printf "Command-line help\n"
+        invokeai-web --help
+        ;;
+    *)
+        clear
+        printf "Exiting...\n"
+        exit
+        ;;
+    esac
+    clear
+}
+
+# Command-line interface for launching Invoke functions
+do_line_input() {
+    clear
+    printf "What would you like to do?\n"
+    printf "1: Generate images using the browser-based interface\n"
+    printf "2: Open the developer console\n"
+    printf "3: Command-line help\n"
+    printf "Q: Quit\n\n"
+    printf "To update, download and run the installer from https://github.com/invoke-ai/InvokeAI/releases/latest\n\n"
+    read -p "Please enter 1-4, Q: [1] " yn
+    choice=${yn:='1'}
+    do_choice $choice
+    clear
+}
+
+# Main IF statement for launching Invoke, and for checking if the user is in the developer console
+if [ "$0" != "bash" ]; then
+    while true; do
+        do_line_input
+    done
+else # in developer console
+    python --version
+    printf "Press ^D to exit\n"
+    export PS1="(InvokeAI) \u@\h \w> "
+fi

invokeai/app/api/dependencies.py

@@ -37,13 +37,7 @@ from invokeai.app.services.style_preset_records.style_preset_records_sqlite impo
 from invokeai.app.services.urls.urls_default import LocalUrlService
 from invokeai.app.services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
 from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_disk import WorkflowThumbnailFileStorageDisk
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    BasicConditioningInfo,
-    ConditioningFieldData,
-    FLUXConditioningInfo,
-    SD3ConditioningInfo,
-    SDXLConditioningInfo,
-)
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -107,25 +101,10 @@ class ApiDependencies:
         images = ImageService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         tensors = ObjectSerializerForwardCache(
-            ObjectSerializerDisk[torch.Tensor](
-                output_folder / "tensors",
-                safe_globals=[torch.Tensor],
-                ephemeral=True,
-            ),
-            max_cache_size=0,
+            ObjectSerializerDisk[torch.Tensor](output_folder / "tensors", ephemeral=True)
         )
         conditioning = ObjectSerializerForwardCache(
-            ObjectSerializerDisk[ConditioningFieldData](
-                output_folder / "conditioning",
-                safe_globals=[
-                    ConditioningFieldData,
-                    BasicConditioningInfo,
-                    SDXLConditioningInfo,
-                    FLUXConditioningInfo,
-                    SD3ConditioningInfo,
-                ],
-                ephemeral=True,
-            ),
+            ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
         )
         download_queue_service = DownloadQueueService(app_config=configuration, event_bus=events)
         model_images_service = ModelImageFileStorageDisk(model_images_folder / "model_images")

invokeai/app/api/routers/session_queue.py

@@ -1,10 +1,13 @@
-from typing import Optional
+import json
+from typing import Any, Optional
 
 from fastapi import Body, Path, Query
 from fastapi.routing import APIRouter
 from pydantic import BaseModel, Field
 
 from invokeai.app.api.dependencies import ApiDependencies
+from invokeai.app.invocations.fields import BoardField
+from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.services.session_processor.session_processor_common import SessionProcessorStatus
 from invokeai.app.services.session_queue.session_queue_common import (
     QUEUE_ITEM_STATUS,
@@ -23,6 +26,7 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueItemDTO,
     SessionQueueStatus,
 )
+from invokeai.app.services.shared.compose_pydantic_model import compose_model_from_fields
 from invokeai.app.services.shared.pagination import CursorPaginatedResults
 
 session_queue_router = APIRouter(prefix="/v1/queue", tags=["queue"])
@@ -35,10 +39,15 @@ class SessionQueueAndProcessorStatus(BaseModel):
     processor: SessionProcessorStatus
 
 
-class ValidationRunData(BaseModel):
-    workflow_id: str = Field(description="The id of the workflow being published.")
-    input_fields: list[FieldIdentifier] = Body(description="The input fields for the published workflow")
-    output_fields: list[FieldIdentifier] = Body(description="The output fields for the published workflow")
+class SimpleModelIdentifer(BaseModel):
+    id: str = Field(description="The model id")
+
+
+model_field_overrides = {ModelIdentifierField: (SimpleModelIdentifer, Field(description="The model identifier"))}
+
+
+def model_field_filter(field_type: type[Any]) -> bool:
+    return field_type not in {BoardField, Optional[BoardField]}
 
 
 @session_queue_router.post(
@@ -52,13 +61,52 @@ async def enqueue_batch(
     queue_id: str = Path(description="The queue id to perform this operation on"),
     batch: Batch = Body(description="Batch to process"),
     prepend: bool = Body(default=False, description="Whether or not to prepend this batch in the queue"),
-    validation_run_data: Optional[ValidationRunData] = Body(
-        default=None,
-        description="The validation run data to use for this batch. This is only used if this is a validation run.",
+    is_api_validation_run: bool = Body(
+        default=False,
+        description="Whether or not this is a validation run.",
+    ),
+    api_input_fields: Optional[list[FieldIdentifier]] = Body(
+        default=None, description="The fields that were used as input to the API"
+    ),
+    api_output_fields: Optional[list[FieldIdentifier]] = Body(
+        default=None, description="The fields that were used as output from the API"
     ),
 ) -> EnqueueBatchResult:
     """Processes a batch and enqueues the output graphs for execution."""
 
+    if is_api_validation_run:
+        session_count = batch.get_session_count()
+        assert session_count == 1, "API validation run only supports single session batches"
+
+        if api_input_fields:
+            composed_model = compose_model_from_fields(
+                g=batch.graph,
+                field_identifiers=api_input_fields,
+                composed_model_class_name="APIInputModel",
+                model_field_overrides=model_field_overrides,
+                model_field_filter=model_field_filter,
+            )
+            json_schema = composed_model.model_json_schema(mode="validation")
+            print("API Input Model")
+            print(json.dumps(json_schema))
+
+        if api_output_fields:
+            composed_model = compose_model_from_fields(
+                g=batch.graph,
+                field_identifiers=api_output_fields,
+                composed_model_class_name="APIOutputModel",
+            )
+            json_schema = composed_model.model_json_schema(mode="validation")
+            print("API Output Model")
+            print(json.dumps(json_schema))
+
+        print("graph")
+        print(batch.graph.model_dump_json())
+
+        if batch.workflow is not None:
+            print("workflow")
+            print(batch.workflow.model_dump_json())
+
     return await ApiDependencies.invoker.services.session_queue.enqueue_batch(
         queue_id=queue_id, batch=batch, prepend=prepend
     )

invokeai/app/api/routers/workflows.py

@@ -1,4 +1,5 @@
 import io
+import random
 import traceback
 from typing import Optional
 
@@ -24,6 +25,37 @@ from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_common import
 IMAGE_MAX_AGE = 31536000
 workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
 
+ids = {
+    "6614752a-0420-4d81-98fc-e110069d4f38": random.choice([True, False]),
+    "default_5e8b008d-c697-45d0-8883-085a954c6ace": random.choice([True, False]),
+    "4b2b297a-0d47-4f43-8113-ebbf3f403089": random.choice([True, False]),
+    "d0ce602a-049e-4368-97ae-977b49eed042": random.choice([True, False]),
+    "f170a187-fd74-40b8-ba9c-00de173ea4b9": random.choice([True, False]),
+    "default_f96e794f-eb3e-4d01-a960-9b4e43402bcf": random.choice([True, False]),
+    "default_cbf0e034-7b54-4b2c-b670-3b1e2e4b4a88": random.choice([True, False]),
+    "default_dec5a2e9-f59c-40d9-8869-a056751d79b8": random.choice([True, False]),
+    "default_dbe46d95-22aa-43fb-9c16-94400d0ce2fd": random.choice([True, False]),
+    "default_d7a1c60f-ca2f-4f90-9e33-75a826ca6d8f": random.choice([True, False]),
+    "default_e71d153c-2089-43c7-bd2c-f61f37d4c1c1": random.choice([True, False]),
+    "default_7dde3e36-d78f-4152-9eea-00ef9c8124ed": random.choice([True, False]),
+    "default_444fe292-896b-44fd-bfc6-c0b5d220fffc": random.choice([True, False]),
+    "default_2d05e719-a6b9-4e64-9310-b875d3b2f9d2": random.choice([True, False]),
+    "acae7e87-070b-4999-9074-c5b593c86618": random.choice([True, False]),
+    "3008fc77-1521-49c7-ba95-94c5a4508d1d": random.choice([True, False]),
+    "default_686bb1d0-d086-4c70-9fa3-2f600b922023": random.choice([True, False]),
+    "36905c46-e768-4dc3-8ecd-e55fe69bf03c": random.choice([True, False]),
+    "7c3e4951-183b-40ef-a890-28eef4d50097": random.choice([True, False]),
+    "7a053b2f-64e4-4152-80e9-296006e77131": random.choice([True, False]),
+    "27d4f1be-4156-46e9-8d22-d0508cd72d4f": random.choice([True, False]),
+    "e881dc06-70d2-438f-b007-6f3e0c3c0e78": random.choice([True, False]),
+    "265d2244-a1d7-495c-a2eb-88217f5eae37": random.choice([True, False]),
+    "caebcbc7-2bf0-41c4-b553-106b585fddda": random.choice([True, False]),
+    "a7998705-474e-417d-bd37-a2a9480beedf": random.choice([True, False]),
+    "554d94b5-94b3-4d8e-8aed-51ebfc9deea5": random.choice([True, False]),
+    "e6898540-c1bc-408b-b944-c1e242cddbcd": random.choice([True, False]),
+    "363b0960-ab2c-4902-8df3-f592d6194bb3": random.choice([True, False]),
+}
+
 
 @workflows_router.get(
     "/i/{workflow_id}",
@@ -39,6 +71,8 @@ async def get_workflow(
     try:
         thumbnail_url = ApiDependencies.invoker.services.workflow_thumbnails.get_url(workflow_id)
         workflow = ApiDependencies.invoker.services.workflow_records.get(workflow_id)
+        workflow.is_published = ids.get(workflow_id, False)
+        workflow.workflow.is_published = ids.get(workflow_id, False)
         return WorkflowRecordWithThumbnailDTO(thumbnail_url=thumbnail_url, **workflow.model_dump())
     except WorkflowNotFoundError:
         raise HTTPException(status_code=404, detail="Workflow not found")
@@ -110,7 +144,7 @@ async def list_workflows(
 ) -> PaginatedResults[WorkflowRecordListItemWithThumbnailDTO]:
     """Gets a page of workflows"""
     workflows_with_thumbnails: list[WorkflowRecordListItemWithThumbnailDTO] = []
-    workflows = ApiDependencies.invoker.services.workflow_records.get_many(
+    workflow_record_list_items = ApiDependencies.invoker.services.workflow_records.get_many(
         order_by=order_by,
         direction=direction,
         page=page,
@@ -121,19 +155,21 @@ async def list_workflows(
         has_been_opened=has_been_opened,
         is_published=is_published,
     )
-    for workflow in workflows.items:
+    for item in workflow_record_list_items.items:
+        data = item.model_dump()
+        data["is_published"] = ids.get(item.workflow_id, False)
         workflows_with_thumbnails.append(
             WorkflowRecordListItemWithThumbnailDTO(
-                thumbnail_url=ApiDependencies.invoker.services.workflow_thumbnails.get_url(workflow.workflow_id),
-                **workflow.model_dump(),
+                thumbnail_url=ApiDependencies.invoker.services.workflow_thumbnails.get_url(item.workflow_id),
+                **data,
             )
         )
     return PaginatedResults[WorkflowRecordListItemWithThumbnailDTO](
         items=workflows_with_thumbnails,
-        total=workflows.total,
-        page=workflows.page,
-        pages=workflows.pages,
-        per_page=workflows.per_page,
+        total=workflow_record_list_items.total,
+        page=workflow_record_list_items.page,
+        pages=workflow_record_list_items.pages,
+        per_page=workflow_record_list_items.per_page,
     )
 
 

invokeai/app/invocations/controlnet.py

@@ -1,128 +0,0 @@
-# Invocations for ControlNet image preprocessors
-# initial implementation by Gregg Helt, 2023
-from typing import List, Union
-
-from pydantic import BaseModel, Field, field_validator, model_validator
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    ImageField,
-    InputField,
-    OutputField,
-    UIType,
-)
-from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, heuristic_resize
-from invokeai.backend.image_util.util import np_to_pil, pil_to_np
-
-
-class ControlField(BaseModel):
-    image: ImageField = Field(description="The control image")
-    control_model: ModelIdentifierField = Field(description="The ControlNet model to use")
-    control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
-    begin_step_percent: float = Field(
-        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
-    )
-    end_step_percent: float = Field(
-        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
-    )
-    control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
-    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
-
-    @field_validator("control_weight")
-    @classmethod
-    def validate_control_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self):
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
-
-@invocation_output("control_output")
-class ControlOutput(BaseInvocationOutput):
-    """node output for ControlNet info"""
-
-    # Outputs
-    control: ControlField = OutputField(description=FieldDescriptions.control)
-
-
-@invocation("controlnet", title="ControlNet - SD1.5, SDXL", tags=["controlnet"], category="controlnet", version="1.1.3")
-class ControlNetInvocation(BaseInvocation):
-    """Collects ControlNet info to pass to other nodes"""
-
-    image: ImageField = InputField(description="The control image")
-    control_model: ModelIdentifierField = InputField(
-        description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
-    )
-    control_weight: Union[float, List[float]] = InputField(
-        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
-    )
-    begin_step_percent: float = InputField(
-        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
-    )
-    end_step_percent: float = InputField(
-        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
-    )
-    control_mode: CONTROLNET_MODE_VALUES = InputField(default="balanced", description="The control mode used")
-    resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
-
-    @field_validator("control_weight")
-    @classmethod
-    def validate_control_weight(cls, v):
-        validate_weights(v)
-        return v
-
-    @model_validator(mode="after")
-    def validate_begin_end_step_percent(self) -> "ControlNetInvocation":
-        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
-        return self
-
-    def invoke(self, context: InvocationContext) -> ControlOutput:
-        return ControlOutput(
-            control=ControlField(
-                image=self.image,
-                control_model=self.control_model,
-                control_weight=self.control_weight,
-                begin_step_percent=self.begin_step_percent,
-                end_step_percent=self.end_step_percent,
-                control_mode=self.control_mode,
-                resize_mode=self.resize_mode,
-            ),
-        )
-
-
-@invocation(
-    "heuristic_resize",
-    title="Heuristic Resize",
-    tags=["image, controlnet"],
-    category="image",
-    version="1.0.1",
-    classification=Classification.Prototype,
-)
-class HeuristicResizeInvocation(BaseInvocation):
-    """Resize an image using a heuristic method. Preserves edge maps."""
-
-    image: ImageField = InputField(description="The image to resize")
-    width: int = InputField(default=512, ge=1, description="The width to resize to (px)")
-    height: int = InputField(default=512, ge=1, description="The height to resize to (px)")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name, "RGB")
-        np_img = pil_to_np(image)
-        np_resized = heuristic_resize(np_img, (self.width, self.height))
-        resized = np_to_pil(np_resized)
-        image_dto = context.images.save(image=resized)
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/controlnet_image_processors.py

@@ -0,0 +1,716 @@
+# Invocations for ControlNet image preprocessors
+# initial implementation by Gregg Helt, 2023
+# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
+from builtins import bool, float
+from pathlib import Path
+from typing import Dict, List, Literal, Union
+
+import cv2
+import numpy as np
+from controlnet_aux import (
+    ContentShuffleDetector,
+    LeresDetector,
+    MediapipeFaceDetector,
+    MidasDetector,
+    MLSDdetector,
+    NormalBaeDetector,
+    PidiNetDetector,
+    SamDetector,
+    ZoeDetector,
+)
+from controlnet_aux.util import HWC3, ade_palette
+from PIL import Image
+from pydantic import BaseModel, Field, field_validator, model_validator
+from transformers import pipeline
+from transformers.pipelines import DepthEstimationPipeline
+
+from invokeai.app.invocations.baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Classification,
+    invocation,
+    invocation_output,
+)
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    ImageField,
+    InputField,
+    OutputField,
+    UIType,
+    WithBoard,
+    WithMetadata,
+)
+from invokeai.app.invocations.model import ModelIdentifierField
+from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES, heuristic_resize
+from invokeai.backend.image_util.canny import get_canny_edges
+from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
+from invokeai.backend.image_util.dw_openpose import DWPOSE_MODELS, DWOpenposeDetector
+from invokeai.backend.image_util.hed import HEDProcessor
+from invokeai.backend.image_util.lineart import LineartProcessor
+from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor
+from invokeai.backend.image_util.util import np_to_pil, pil_to_np
+
+
+class ControlField(BaseModel):
+    image: ImageField = Field(description="The control image")
+    control_model: ModelIdentifierField = Field(description="The ControlNet model to use")
+    control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
+    begin_step_percent: float = Field(
+        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+    )
+    end_step_percent: float = Field(
+        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
+    )
+    control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
+    resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
+
+    @field_validator("control_weight")
+    @classmethod
+    def validate_control_weight(cls, v):
+        validate_weights(v)
+        return v
+
+    @model_validator(mode="after")
+    def validate_begin_end_step_percent(self):
+        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
+        return self
+
+
+@invocation_output("control_output")
+class ControlOutput(BaseInvocationOutput):
+    """node output for ControlNet info"""
+
+    # Outputs
+    control: ControlField = OutputField(description=FieldDescriptions.control)
+
+
+@invocation("controlnet", title="ControlNet - SD1.5, SDXL", tags=["controlnet"], category="controlnet", version="1.1.3")
+class ControlNetInvocation(BaseInvocation):
+    """Collects ControlNet info to pass to other nodes"""
+
+    image: ImageField = InputField(description="The control image")
+    control_model: ModelIdentifierField = InputField(
+        description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
+    )
+    control_weight: Union[float, List[float]] = InputField(
+        default=1.0, ge=-1, le=2, description="The weight given to the ControlNet"
+    )
+    begin_step_percent: float = InputField(
+        default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
+    )
+    end_step_percent: float = InputField(
+        default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
+    )
+    control_mode: CONTROLNET_MODE_VALUES = InputField(default="balanced", description="The control mode used")
+    resize_mode: CONTROLNET_RESIZE_VALUES = InputField(default="just_resize", description="The resize mode used")
+
+    @field_validator("control_weight")
+    @classmethod
+    def validate_control_weight(cls, v):
+        validate_weights(v)
+        return v
+
+    @model_validator(mode="after")
+    def validate_begin_end_step_percent(self) -> "ControlNetInvocation":
+        validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
+        return self
+
+    def invoke(self, context: InvocationContext) -> ControlOutput:
+        return ControlOutput(
+            control=ControlField(
+                image=self.image,
+                control_model=self.control_model,
+                control_weight=self.control_weight,
+                begin_step_percent=self.begin_step_percent,
+                end_step_percent=self.end_step_percent,
+                control_mode=self.control_mode,
+                resize_mode=self.resize_mode,
+            ),
+        )
+
+
+# This invocation exists for other invocations to subclass it - do not register with @invocation!
+class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
+    """Base class for invocations that preprocess images for ControlNet"""
+
+    image: ImageField = InputField(description="The image to process")
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        # superclass just passes through image without processing
+        return image
+
+    def load_image(self, context: InvocationContext) -> Image.Image:
+        # allows override for any special formatting specific to the preprocessor
+        return context.images.get_pil(self.image.image_name, "RGB")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        self._context = context
+        raw_image = self.load_image(context)
+        # image type should be PIL.PngImagePlugin.PngImageFile ?
+        processed_image = self.run_processor(raw_image)
+
+        # currently can't see processed image in node UI without a showImage node,
+        #    so for now setting image_type to RESULT instead of INTERMEDIATE so will get saved in gallery
+        image_dto = context.images.save(image=processed_image)
+
+        """Builds an ImageOutput and its ImageField"""
+        processed_image_field = ImageField(image_name=image_dto.image_name)
+        return ImageOutput(
+            image=processed_image_field,
+            # width=processed_image.width,
+            width=image_dto.width,
+            # height=processed_image.height,
+            height=image_dto.height,
+            # mode=processed_image.mode,
+        )
+
+
+@invocation(
+    "canny_image_processor",
+    title="Canny Processor",
+    tags=["controlnet", "canny"],
+    category="controlnet",
+    version="1.3.3",
+    classification=Classification.Deprecated,
+)
+class CannyImageProcessorInvocation(ImageProcessorInvocation):
+    """Canny edge detection for ControlNet"""
+
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    low_threshold: int = InputField(
+        default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)"
+    )
+    high_threshold: int = InputField(
+        default=200, ge=0, le=255, description="The high threshold of the Canny pixel gradient (0-255)"
+    )
+
+    def load_image(self, context: InvocationContext) -> Image.Image:
+        # Keep alpha channel for Canny processing to detect edges of transparent areas
+        return context.images.get_pil(self.image.image_name, "RGBA")
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        processed_image = get_canny_edges(
+            image,
+            self.low_threshold,
+            self.high_threshold,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+        )
+        return processed_image
+
+
+@invocation(
+    "hed_image_processor",
+    title="HED (softedge) Processor",
+    tags=["controlnet", "hed", "softedge"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class HedImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies HED edge detection to image"""
+
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    # safe not supported in controlnet_aux v0.0.3
+    # safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
+    scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        hed_processor = HEDProcessor()
+        processed_image = hed_processor.run(
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+            # safe not supported in controlnet_aux v0.0.3
+            # safe=self.safe,
+            scribble=self.scribble,
+        )
+        return processed_image
+
+
+@invocation(
+    "lineart_image_processor",
+    title="Lineart Processor",
+    tags=["controlnet", "lineart"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class LineartImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies line art processing to image"""
+
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    coarse: bool = InputField(default=False, description="Whether to use coarse mode")
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        lineart_processor = LineartProcessor()
+        processed_image = lineart_processor.run(
+            image, detect_resolution=self.detect_resolution, image_resolution=self.image_resolution, coarse=self.coarse
+        )
+        return processed_image
+
+
+@invocation(
+    "lineart_anime_image_processor",
+    title="Lineart Anime Processor",
+    tags=["controlnet", "lineart", "anime"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies line art anime processing to image"""
+
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        processor = LineartAnimeProcessor()
+        processed_image = processor.run(
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+        )
+        return processed_image
+
+
+@invocation(
+    "midas_depth_image_processor",
+    title="Midas Depth Processor",
+    tags=["controlnet", "midas"],
+    category="controlnet",
+    version="1.2.4",
+    classification=Classification.Deprecated,
+)
+class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies Midas depth processing to image"""
+
+    a_mult: float = InputField(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
+    bg_th: float = InputField(default=0.1, ge=0, description="Midas parameter `bg_th`")
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    # depth_and_normal not supported in controlnet_aux v0.0.3
+    # depth_and_normal: bool = InputField(default=False, description="whether to use depth and normal mode")
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        # TODO: replace from_pretrained() calls with context.models.download_and_cache() (or similar)
+        midas_processor = MidasDetector.from_pretrained("lllyasviel/Annotators")
+        processed_image = midas_processor(
+            image,
+            a=np.pi * self.a_mult,
+            bg_th=self.bg_th,
+            image_resolution=self.image_resolution,
+            detect_resolution=self.detect_resolution,
+            # dept_and_normal not supported in controlnet_aux v0.0.3
+            # depth_and_normal=self.depth_and_normal,
+        )
+        return processed_image
+
+
+@invocation(
+    "normalbae_image_processor",
+    title="Normal BAE Processor",
+    tags=["controlnet"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies NormalBae processing to image"""
+
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
+        processed_image = normalbae_processor(
+            image, detect_resolution=self.detect_resolution, image_resolution=self.image_resolution
+        )
+        return processed_image
+
+
+@invocation(
+    "mlsd_image_processor",
+    title="MLSD Processor",
+    tags=["controlnet", "mlsd"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class MlsdImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies MLSD processing to image"""
+
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    thr_v: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_v`")
+    thr_d: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_d`")
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        mlsd_processor = MLSDdetector.from_pretrained("lllyasviel/Annotators")
+        processed_image = mlsd_processor(
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+            thr_v=self.thr_v,
+            thr_d=self.thr_d,
+        )
+        return processed_image
+
+
+@invocation(
+    "pidi_image_processor",
+    title="PIDI Processor",
+    tags=["controlnet", "pidi"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class PidiImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies PIDI processing to image"""
+
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
+    scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        pidi_processor = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
+        processed_image = pidi_processor(
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+            safe=self.safe,
+            scribble=self.scribble,
+        )
+        return processed_image
+
+
+@invocation(
+    "content_shuffle_image_processor",
+    title="Content Shuffle Processor",
+    tags=["controlnet", "contentshuffle"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies content shuffle processing to image"""
+
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+    h: int = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
+    w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
+    f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        content_shuffle_processor = ContentShuffleDetector()
+        processed_image = content_shuffle_processor(
+            image,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+            h=self.h,
+            w=self.w,
+            f=self.f,
+        )
+        return processed_image
+
+
+# should work with controlnet_aux >= 0.0.4 and timm <= 0.6.13
+@invocation(
+    "zoe_depth_image_processor",
+    title="Zoe (Depth) Processor",
+    tags=["controlnet", "zoe", "depth"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies Zoe depth processing to image"""
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        zoe_depth_processor = ZoeDetector.from_pretrained("lllyasviel/Annotators")
+        processed_image = zoe_depth_processor(image)
+        return processed_image
+
+
+@invocation(
+    "mediapipe_face_processor",
+    title="Mediapipe Face Processor",
+    tags=["controlnet", "mediapipe", "face"],
+    category="controlnet",
+    version="1.2.4",
+    classification=Classification.Deprecated,
+)
+class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
+    """Applies mediapipe face processing to image"""
+
+    max_faces: int = InputField(default=1, ge=1, description="Maximum number of faces to detect")
+    min_confidence: float = InputField(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        mediapipe_face_processor = MediapipeFaceDetector()
+        processed_image = mediapipe_face_processor(
+            image,
+            max_faces=self.max_faces,
+            min_confidence=self.min_confidence,
+            image_resolution=self.image_resolution,
+            detect_resolution=self.detect_resolution,
+        )
+        return processed_image
+
+
+@invocation(
+    "leres_image_processor",
+    title="Leres (Depth) Processor",
+    tags=["controlnet", "leres", "depth"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class LeresImageProcessorInvocation(ImageProcessorInvocation):
+    """Applies leres processing to image"""
+
+    thr_a: float = InputField(default=0, description="Leres parameter `thr_a`")
+    thr_b: float = InputField(default=0, description="Leres parameter `thr_b`")
+    boost: bool = InputField(default=False, description="Whether to use boost mode")
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
+        processed_image = leres_processor(
+            image,
+            thr_a=self.thr_a,
+            thr_b=self.thr_b,
+            boost=self.boost,
+            detect_resolution=self.detect_resolution,
+            image_resolution=self.image_resolution,
+        )
+        return processed_image
+
+
+@invocation(
+    "tile_image_processor",
+    title="Tile Resample Processor",
+    tags=["controlnet", "tile"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class TileResamplerProcessorInvocation(ImageProcessorInvocation):
+    """Tile resampler processor"""
+
+    # res: int = InputField(default=512, ge=0, le=1024, description="The pixel resolution for each tile")
+    down_sampling_rate: float = InputField(default=1.0, ge=1.0, le=8.0, description="Down sampling rate")
+
+    # tile_resample copied from sd-webui-controlnet/scripts/processor.py
+    def tile_resample(
+        self,
+        np_img: np.ndarray,
+        res=512,  # never used?
+        down_sampling_rate=1.0,
+    ):
+        np_img = HWC3(np_img)
+        if down_sampling_rate < 1.1:
+            return np_img
+        H, W, C = np_img.shape
+        H = int(float(H) / float(down_sampling_rate))
+        W = int(float(W) / float(down_sampling_rate))
+        np_img = cv2.resize(np_img, (W, H), interpolation=cv2.INTER_AREA)
+        return np_img
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        np_img = np.array(image, dtype=np.uint8)
+        processed_np_image = self.tile_resample(
+            np_img,
+            # res=self.tile_size,
+            down_sampling_rate=self.down_sampling_rate,
+        )
+        processed_image = Image.fromarray(processed_np_image)
+        return processed_image
+
+
+@invocation(
+    "segment_anything_processor",
+    title="Segment Anything Processor",
+    tags=["controlnet", "segmentanything"],
+    category="controlnet",
+    version="1.2.4",
+    classification=Classification.Deprecated,
+)
+class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
+    """Applies segment anything processing to image"""
+
+    detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        # segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
+        segment_anything_processor = SamDetectorReproducibleColors.from_pretrained(
+            "ybelkada/segment-anything", subfolder="checkpoints"
+        )
+        np_img = np.array(image, dtype=np.uint8)
+        processed_image = segment_anything_processor(
+            np_img, image_resolution=self.image_resolution, detect_resolution=self.detect_resolution
+        )
+        return processed_image
+
+
+class SamDetectorReproducibleColors(SamDetector):
+    # overriding SamDetector.show_anns() method to use reproducible colors for segmentation image
+    #     base class show_anns() method randomizes colors,
+    #     which seems to also lead to non-reproducible image generation
+    # so using ADE20k color palette instead
+    def show_anns(self, anns: List[Dict]):
+        if len(anns) == 0:
+            return
+        sorted_anns = sorted(anns, key=(lambda x: x["area"]), reverse=True)
+        h, w = anns[0]["segmentation"].shape
+        final_img = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
+        palette = ade_palette()
+        for i, ann in enumerate(sorted_anns):
+            m = ann["segmentation"]
+            img = np.empty((m.shape[0], m.shape[1], 3), dtype=np.uint8)
+            # doing modulo just in case number of annotated regions exceeds number of colors in palette
+            ann_color = palette[i % len(palette)]
+            img[:, :] = ann_color
+            final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
+        return np.array(final_img, dtype=np.uint8)
+
+
+@invocation(
+    "color_map_image_processor",
+    title="Color Map Processor",
+    tags=["controlnet"],
+    category="controlnet",
+    version="1.2.3",
+    classification=Classification.Deprecated,
+)
+class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
+    """Generates a color map from the provided image"""
+
+    color_map_tile_size: int = InputField(default=64, ge=1, description=FieldDescriptions.tile_size)
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        np_image = np.array(image, dtype=np.uint8)
+        height, width = np_image.shape[:2]
+
+        width_tile_size = min(self.color_map_tile_size, width)
+        height_tile_size = min(self.color_map_tile_size, height)
+
+        color_map = cv2.resize(
+            np_image,
+            (width // width_tile_size, height // height_tile_size),
+            interpolation=cv2.INTER_CUBIC,
+        )
+        color_map = cv2.resize(color_map, (width, height), interpolation=cv2.INTER_NEAREST)
+        color_map = Image.fromarray(color_map)
+        return color_map
+
+
+DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small", "small_v2"]
+# DepthAnything V2 Small model is licensed under Apache 2.0 but not the base and large models.
+DEPTH_ANYTHING_MODELS = {
+    "large": "LiheYoung/depth-anything-large-hf",
+    "base": "LiheYoung/depth-anything-base-hf",
+    "small": "LiheYoung/depth-anything-small-hf",
+    "small_v2": "depth-anything/Depth-Anything-V2-Small-hf",
+}
+
+
+@invocation(
+    "depth_anything_image_processor",
+    title="Depth Anything Processor",
+    tags=["controlnet", "depth", "depth anything"],
+    category="controlnet",
+    version="1.1.3",
+    classification=Classification.Deprecated,
+)
+class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
+    """Generates a depth map based on the Depth Anything algorithm"""
+
+    model_size: DEPTH_ANYTHING_MODEL_SIZES = InputField(
+        default="small_v2", description="The size of the depth model to use"
+    )
+    resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        def load_depth_anything(model_path: Path):
+            depth_anything_pipeline = pipeline(model=str(model_path), task="depth-estimation", local_files_only=True)
+            assert isinstance(depth_anything_pipeline, DepthEstimationPipeline)
+            return DepthAnythingPipeline(depth_anything_pipeline)
+
+        with self._context.models.load_remote_model(
+            source=DEPTH_ANYTHING_MODELS[self.model_size], loader=load_depth_anything
+        ) as depth_anything_detector:
+            assert isinstance(depth_anything_detector, DepthAnythingPipeline)
+            depth_map = depth_anything_detector.generate_depth(image)
+
+            # Resizing to user target specified size
+            new_height = int(image.size[1] * (self.resolution / image.size[0]))
+            depth_map = depth_map.resize((self.resolution, new_height))
+
+            return depth_map
+
+
+@invocation(
+    "dw_openpose_image_processor",
+    title="DW Openpose Image Processor",
+    tags=["controlnet", "dwpose", "openpose"],
+    category="controlnet",
+    version="1.1.1",
+    classification=Classification.Deprecated,
+)
+class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
+    """Generates an openpose pose from an image using DWPose"""
+
+    draw_body: bool = InputField(default=True)
+    draw_face: bool = InputField(default=False)
+    draw_hands: bool = InputField(default=False)
+    image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
+
+    def run_processor(self, image: Image.Image) -> Image.Image:
+        onnx_det = self._context.models.download_and_cache_model(DWPOSE_MODELS["yolox_l.onnx"])
+        onnx_pose = self._context.models.download_and_cache_model(DWPOSE_MODELS["dw-ll_ucoco_384.onnx"])
+
+        dw_openpose = DWOpenposeDetector(onnx_det=onnx_det, onnx_pose=onnx_pose)
+        processed_image = dw_openpose(
+            image,
+            draw_face=self.draw_face,
+            draw_hands=self.draw_hands,
+            draw_body=self.draw_body,
+            resolution=self.image_resolution,
+        )
+        return processed_image
+
+
+@invocation(
+    "heuristic_resize",
+    title="Heuristic Resize",
+    tags=["image, controlnet"],
+    category="image",
+    version="1.0.1",
+    classification=Classification.Prototype,
+)
+class HeuristicResizeInvocation(BaseInvocation):
+    """Resize an image using a heuristic method. Preserves edge maps."""
+
+    image: ImageField = InputField(description="The image to resize")
+    width: int = InputField(default=512, ge=1, description="The width to resize to (px)")
+    height: int = InputField(default=512, ge=1, description="The height to resize to (px)")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.images.get_pil(self.image.image_name, "RGB")
+        np_img = pil_to_np(image)
+        np_resized = heuristic_resize(np_img, (self.width, self.height))
+        resized = np_to_pil(np_resized)
+        image_dto = context.images.save(image=resized)
+        return ImageOutput.build(image_dto)

invokeai/app/invocations/denoise_latents.py

@@ -22,7 +22,7 @@ from transformers import CLIPVisionModelWithProjection
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
-from invokeai.app.invocations.controlnet import ControlField
+from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.fields import (
     ConditioningField,
     DenoiseMaskField,

invokeai/app/invocations/dw_openpose.py

@@ -4,7 +4,7 @@ from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import ImageField, InputField, WithBoard, WithMetadata
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
+from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector2
 
 
 @invocation(
@@ -25,20 +25,20 @@ class DWOpenposeDetectionInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         image = context.images.get_pil(self.image.image_name, "RGB")
 
-        onnx_det_path = context.models.download_and_cache_model(DWOpenposeDetector.get_model_url_det())
-        onnx_pose_path = context.models.download_and_cache_model(DWOpenposeDetector.get_model_url_pose())
+        onnx_det_path = context.models.download_and_cache_model(DWOpenposeDetector2.get_model_url_det())
+        onnx_pose_path = context.models.download_and_cache_model(DWOpenposeDetector2.get_model_url_pose())
 
         loaded_session_det = context.models.load_local_model(
-            onnx_det_path, DWOpenposeDetector.create_onnx_inference_session
+            onnx_det_path, DWOpenposeDetector2.create_onnx_inference_session
         )
         loaded_session_pose = context.models.load_local_model(
-            onnx_pose_path, DWOpenposeDetector.create_onnx_inference_session
+            onnx_pose_path, DWOpenposeDetector2.create_onnx_inference_session
         )
 
         with loaded_session_det as session_det, loaded_session_pose as session_pose:
             assert isinstance(session_det, ort.InferenceSession)
             assert isinstance(session_pose, ort.InferenceSession)
-            detector = DWOpenposeDetector(session_det=session_det, session_pose=session_pose)
+            detector = DWOpenposeDetector2(session_det=session_det, session_pose=session_pose)
             detected_image = detector.run(
                 image,
                 draw_face=self.draw_face,

invokeai/app/invocations/flux_redux.py

@@ -1,5 +1,4 @@
-import math
-from typing import Literal, Optional
+from typing import Optional
 
 import torch
 from PIL import Image
@@ -40,15 +39,12 @@ class FluxReduxOutput(BaseInvocationOutput):
     )
 
 
-DOWNSAMPLING_FUNCTIONS = Literal["nearest", "bilinear", "bicubic", "area", "nearest-exact"]
-
-
 @invocation(
     "flux_redux",
     title="FLUX Redux",
     tags=["ip_adapter", "control"],
     category="ip_adapter",
-    version="2.1.0",
+    version="2.0.0",
     classification=Classification.Beta,
 )
 class FluxReduxInvocation(BaseInvocation):
@@ -65,53 +61,18 @@ class FluxReduxInvocation(BaseInvocation):
         title="FLUX Redux Model",
         ui_type=UIType.FluxReduxModel,
     )
-    downsampling_factor: int = InputField(
-        ge=1,
-        le=9,
-        default=1,
-        description="Redux Downsampling Factor (1-9)",
-    )
-    downsampling_function: DOWNSAMPLING_FUNCTIONS = InputField(
-        default="area",
-        description="Redux Downsampling Function",
-    )
-    weight: float = InputField(
-        ge=0,
-        le=1,
-        default=1.0,
-        description="Redux weight (0.0-1.0)",
-    )
 
     def invoke(self, context: InvocationContext) -> FluxReduxOutput:
         image = context.images.get_pil(self.image.image_name, "RGB")
 
         encoded_x = self._siglip_encode(context, image)
         redux_conditioning = self._flux_redux_encode(context, encoded_x)
-        if self.downsampling_factor > 1 or self.weight != 1.0:
-            redux_conditioning = self._downsample_weight(context, redux_conditioning)
 
         tensor_name = context.tensors.save(redux_conditioning)
         return FluxReduxOutput(
             redux_cond=FluxReduxConditioningField(conditioning=TensorField(tensor_name=tensor_name), mask=self.mask)
         )
 
-    @torch.no_grad()
-    def _downsample_weight(self, context: InvocationContext, redux_conditioning: torch.Tensor) -> torch.Tensor:
-        # Downsampling derived from https://github.com/kaibioinfo/ComfyUI_AdvancedRefluxControl
-        (b, t, h) = redux_conditioning.shape
-        m = int(math.sqrt(t))
-        if self.downsampling_factor > 1:
-            redux_conditioning = redux_conditioning.view(b, m, m, h)
-            redux_conditioning = torch.nn.functional.interpolate(
-                redux_conditioning.transpose(1, -1),
-                size=(m // self.downsampling_factor, m // self.downsampling_factor),
-                mode=self.downsampling_function,
-            )
-            redux_conditioning = redux_conditioning.transpose(1, -1).reshape(b, -1, h)
-        if self.weight != 1.0:
-            redux_conditioning = redux_conditioning * self.weight * self.weight
-        return redux_conditioning
-
     @torch.no_grad()
     def _siglip_encode(self, context: InvocationContext, image: Image.Image) -> torch.Tensor:
         siglip_model_config = self._get_siglip_model(context)

invokeai/app/invocations/metadata_linked.py

@@ -14,7 +14,7 @@ from invokeai.app.invocations.baseinvocation import (
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.controlnet import ControlField, ControlNetInvocation
+from invokeai.app.invocations.controlnet_image_processors import ControlField, ControlNetInvocation
 from invokeai.app.invocations.denoise_latents import DenoiseLatentsInvocation
 from invokeai.app.invocations.fields import (
     FieldDescriptions,

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -9,7 +9,7 @@ from pydantic import field_validator
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
-from invokeai.app.invocations.controlnet import ControlField
+from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.denoise_latents import DenoiseLatentsInvocation, get_scheduler
 from invokeai.app.invocations.fields import (
     ConditioningField,

invokeai/app/run_app.py

@@ -31,12 +31,6 @@ def run_app() -> None:
     if app_config.pytorch_cuda_alloc_conf:
         configure_torch_cuda_allocator(app_config.pytorch_cuda_alloc_conf, logger)
 
-    # This import must happen after configure_torch_cuda_allocator() is called, because the module imports torch.
-    from invokeai.backend.util.devices import TorchDevice
-
-    torch_device_name = TorchDevice.get_torch_device_name()
-    logger.info(f"Using torch device: {torch_device_name}")
-
     # Import from startup_utils here to avoid importing torch before configure_torch_cuda_allocator() is called.
     from invokeai.app.util.startup_utils import (
         apply_monkeypatches,

invokeai/app/services/object_serializer/object_serializer_disk.py

@@ -21,16 +21,10 @@ class ObjectSerializerDisk(ObjectSerializerBase[T]):
     """Disk-backed storage for arbitrary python objects. Serialization is handled by `torch.save` and `torch.load`.
 
     :param output_dir: The folder where the serialized objects will be stored
-    :param safe_globals: A list of types to be added to the safe globals for torch serialization
     :param ephemeral: If True, objects will be stored in a temporary directory inside the given output_dir and cleaned up on exit
     """
 
-    def __init__(
-        self,
-        output_dir: Path,
-        safe_globals: list[type],
-        ephemeral: bool = False,
-    ) -> None:
+    def __init__(self, output_dir: Path, ephemeral: bool = False):
         super().__init__()
         self._ephemeral = ephemeral
         self._base_output_dir = output_dir
@@ -48,8 +42,6 @@ class ObjectSerializerDisk(ObjectSerializerBase[T]):
         self._output_dir = Path(self._tempdir.name) if self._tempdir else self._base_output_dir
         self.__obj_class_name: Optional[str] = None
 
-        torch.serialization.add_safe_globals(safe_globals) if safe_globals else None
-
     def load(self, name: str) -> T:
         file_path = self._get_path(name)
         try:

invokeai/app/services/session_queue/session_queue_base.py

@@ -33,7 +33,12 @@ class SessionQueueBase(ABC):
         pass
 
     @abstractmethod
-    def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> Coroutine[Any, Any, EnqueueBatchResult]:
+    def enqueue_batch(
+        self,
+        queue_id: str,
+        batch: Batch,
+        prepend: bool,
+    ) -> Coroutine[Any, Any, EnqueueBatchResult]:
         """Enqueues all permutations of a batch for execution."""
         pass
 

invokeai/app/services/session_queue/session_queue_common.py

@@ -157,6 +157,28 @@ class Batch(BaseModel):
         v.validate_self()
         return v
 
+    def get_session_count(self) -> int:
+        """
+        Calculates the number of sessions that would be created by the batch, without incurring the overhead of actually
+        creating them, as is done in `create_session_nfv_tuples()`.
+
+        The count is used to communicate to the user how many sessions were _requested_ to be created, as opposed to how
+        many were _actually_ created (which may be less due to the maximum number of sessions).
+
+        If the session count has already been calculated, return the cached value.
+        """
+        if not self.data:
+            return self.runs
+        data = []
+        for batch_datum_list in self.data:
+            to_zip = []
+            for batch_datum in batch_datum_list:
+                batch_data_items = range(len(batch_datum.items))
+                to_zip.append(batch_data_items)
+            data.append(list(zip(*to_zip, strict=True)))
+        data_product = list(product(*data))
+        return len(data_product) * self.runs
+
     model_config = ConfigDict(
         json_schema_extra={
             "required": [
@@ -247,10 +269,6 @@ class SessionQueueItemWithoutGraph(BaseModel):
         default=False,
         description="Whether this queue item is an API validation run.",
     )
-    published_workflow_id: Optional[str] = Field(
-        default=None,
-        description="The ID of the published workflow associated with this queue item",
-    )
     api_input_fields: Optional[list[FieldIdentifier]] = Field(
         default=None, description="The fields that were used as input to the API"
     )
@@ -556,28 +574,6 @@ def create_session_nfv_tuples(batch: Batch, maximum: int) -> Generator[tuple[str
             count += 1
 
 
-def calc_session_count(batch: Batch) -> int:
-    """
-    Calculates the number of sessions that would be created by the batch, without incurring the overhead of actually
-    creating them, as is done in `create_session_nfv_tuples()`.
-
-    The count is used to communicate to the user how many sessions were _requested_ to be created, as opposed to how
-    many were _actually_ created (which may be less due to the maximum number of sessions).
-    """
-    # TODO: Should this be a class method on Batch?
-    if not batch.data:
-        return batch.runs
-    data = []
-    for batch_datum_list in batch.data:
-        to_zip = []
-        for batch_datum in batch_datum_list:
-            batch_data_items = range(len(batch_datum.items))
-            to_zip.append(batch_data_items)
-        data.append(list(zip(*to_zip, strict=True)))
-    data_product = list(product(*data))
-    return len(data_product) * batch.runs
-
-
 ValueToInsertTuple: TypeAlias = tuple[
     str,  # queue_id
     str,  # session (as stringified JSON)

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -28,7 +28,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueItemNotFoundError,
     SessionQueueStatus,
     ValueToInsertTuple,
-    calc_session_count,
     prepare_values_to_insert,
 )
 from invokeai.app.services.shared.graph import GraphExecutionState
@@ -118,7 +117,8 @@ class SqliteSessionQueue(SessionQueueBase):
             if prepend:
                 priority = self._get_highest_priority(queue_id) + 1
 
-            requested_count = calc_session_count(batch)
+            requested_count = batch.get_session_count()
+
             values_to_insert = prepare_values_to_insert(
                 queue_id=queue_id,
                 batch=batch,

invokeai/app/services/shared/compose_pydantic_model.py

@@ -0,0 +1,204 @@
+from copy import deepcopy
+from typing import Any, Callable, TypeAlias, get_args
+
+from pydantic import BaseModel, ConfigDict, create_model
+from pydantic.fields import FieldInfo
+
+from invokeai.app.services.session_queue.session_queue_common import FieldIdentifier
+from invokeai.app.services.shared.graph import Graph
+
+DictOfFieldsMetadata: TypeAlias = dict[str, tuple[type[Any], FieldInfo]]
+
+
+class ComposedFieldMetadata(BaseModel):
+    node_id: str
+    field_name: str
+    field_type_class_name: str
+
+
+def dedupe_field_name(field_metadata: DictOfFieldsMetadata, field_name: str) -> str:
+    """Given a field name, return a name that is not already in the field metadata.
+    If the field name is not in the field metadata, return the field name.
+    If the field name is in the field metadata, generate a new name by appending an underscore and integer to the field name, starting with 2.
+    """
+
+    if field_name not in field_metadata:
+        return field_name
+
+    i = 2
+    while True:
+        new_field_name = f"{field_name}_{i}"
+        if new_field_name not in field_metadata:
+            return new_field_name
+        i += 1
+
+
+def compose_model_from_fields(
+    g: Graph,
+    field_identifiers: list[FieldIdentifier],
+    composed_model_class_name: str = "ComposedModel",
+    model_field_overrides: dict[type[Any], tuple[type[Any], FieldInfo]] | None = None,
+    model_field_filter: Callable[[type[Any]], bool] | None = None,
+) -> type[BaseModel]:
+    """Given a graph and a list of field identifiers, create a new pydantic model composed of the fields of the nodes in the graph.
+
+    The resultant model can be used to validate a JSON payload that contains the fields of the nodes in the graph, or generate an
+    OpenAPI schema for the model.
+
+    Args:
+        g: The graph containing the nodes whose fields will be composed into the new model.
+        field_identifiers: A list of FieldIdentifier instances, each representing a field on a node in the graph.
+        model_name: The name of the composed model.
+        kind: The kind of model to create. Must be "input" or "output". Defaults to "input".
+        model_field_overrides: A dictionary mapping type annotations to tuples of (new_type_annotation, new_field_info).
+            This can be used to override the type annotation and field info of a field in the composed model. For example,
+            if `ModelIdentifierField` should be replaced by a string, the dictionary would look like this:
+            ```python
+            {ModelIdentifierField: (str, Field(description="The model id."))}
+            ```
+        model_field_filter: A function that takes a type annotation and returns True if the field should be included in the composed model.
+            If None, all fields will be included. For example, to omit `BoardField` fields, the filter would look like this:
+            ```python
+            def model_field_filter(field_type: type[Any]) -> bool:
+                return field_type not in {BoardField}
+            ```
+            Optional fields - or any other complex field types like unions - must be explicitly included in the filter. For example,
+            to omit `BoardField` _and_ `Optional[BoardField]`:
+            ```python
+            def model_field_filter(field_type: type[Any]) -> bool:
+                return field_type not in {BoardField, Optional[BoardField]}
+            ```
+            Note that the filter is applied to the type annotation of the field, not the field itself.
+
+    Example usage:
+    ```python
+    # Create some nodes.
+    add_node = AddInvocation()
+    sub_node = SubtractInvocation()
+    color_node = ColorInvocation()
+
+    # Create a graph with the nodes.
+    g = Graph(
+        nodes={
+            add_node.id: add_node,
+            sub_node.id: sub_node,
+            color_node.id: color_node,
+        }
+    )
+
+    # Select the fields to compose.
+    fields_to_compose = [
+        FieldIdentifier(node_id=add_node.id, field_name="a"),
+        FieldIdentifier(node_id=sub_node.id, field_name="a"),  # this will be deduped to "a_2"
+        FieldIdentifier(node_id=add_node.id, field_name="b"),
+        FieldIdentifier(node_id=color_node.id, field_name="color"),
+    ]
+
+    # Compose the model from the fields.
+    composed_model = compose_model_from_fields(g, fields_to_compose, model_name="ComposedModel")
+
+    # Generate the OpenAPI schema for the model.
+    json_schema = composed_model.model_json_schema(mode="validation")
+    ```
+    """
+
+    # Temp storage for the composed fields. Pydantic needs a type annotation and instance of FieldInfo to create a model.
+    field_metadata: DictOfFieldsMetadata = {}
+    model_field_overrides = model_field_overrides or {}
+
+    # The list of required fields. This is used to ensure the composed model's fields retain their required state.
+    required: list[str] = []
+
+    for field_identifier in field_identifiers:
+        node_id = field_identifier.node_id
+        field_name = field_identifier.field_name
+
+        # Pull the node instance from the graph so we can introspect it.
+        node_instance = g.nodes[node_id]
+
+        if field_identifier.kind == "input":
+            # Get the class of the node. This will be a BaseInvocation subclass, e.g. AddInvocation, DenoiseLatentsInvocation, etc.
+            pydantic_model = type(node_instance)
+        else:
+            # Otherwise the the type of the node's output class. This will be a BaseInvocationOutput subclass, e.g. IntegerOutput, ImageOutput, etc.
+            pydantic_model = type(node_instance).get_output_annotation()
+
+        # Get the FieldInfo instance for the field. For example:
+        # a: int = Field(..., description="The first number to add.")
+        #          ^^^^^ The return value of this Field call is the FieldInfo instance (Field is a function).
+        og_field_info = pydantic_model.model_fields[field_name]
+
+        # Get the type annotation of the field. For example:
+        # a: int = Field(..., description="The first number to add.")
+        #    ^^^ this is the type annotation
+        og_field_type = og_field_info.annotation
+
+        # Apparently pydantic allows fields without type annotations. We don't support that.
+        assert og_field_type is not None, (
+            f"{field_identifier.kind.capitalize()} field {field_name} on node {node_id} has no type annotation."
+        )
+
+        # Now that we have the type annotation, we can apply the filter to see if we should include the field in the composed model.
+        if model_field_filter and not model_field_filter(og_field_type):
+            continue
+
+        # Ok, we want this type of field. Retrieve any overrides for the field type. This is a dictionary mapping
+        # type annotations to tuples of (override_type_annotation, override_field_info).
+        (override_field_type, override_field_info) = model_field_overrides.get(og_field_type, (None, None))
+
+        # The override tuple's first element is the new type annotation, if it exists.
+        composed_field_type = override_field_type if override_field_type is not None else og_field_type
+
+        # Create a deep copy of the FieldInfo instance (or override it if it exists) so we can modify it without
+        # affecting the original. This is important because we are going to modify the FieldInfo instance and
+        # don't want to affect the original model's schema.
+        composed_field_info = deepcopy(override_field_info if override_field_info is not None else og_field_info)
+
+        json_schema_extra = og_field_info.json_schema_extra if isinstance(og_field_info.json_schema_extra, dict) else {}
+
+        # The field's original required state is stored in the json_schema_extra dict. For more information about why,
+        # see the definition of `InputField` in invokeai/app/invocations/fields.py.
+        #
+        # Add the field to the required list if it is required, which we will use when creating the composed model.
+        if json_schema_extra.get("orig_required", False):
+            required.append(field_name)
+
+        # Invocation fields have some extra metadata, used by the UI to render the field in the frontend. This data is
+        # included in the OpenAPI schema for each field. For example, we add a "ui_order" field, which the UI uses to
+        # sort fields when rendering them.
+        #
+        # The composed model's OpenAPI schema should not have this information. It should only have a standard OpenAPI
+        # schema for the field. We need to strip out the UI-specific metadata from the FieldInfo instance before adding
+        # it to the composed model.
+        #
+        # We will replace this metadata with some custom metadata:
+        # - node_id: The id of the node that this field belongs to.
+        # - field_name: The name of the field on the node.
+        # - original_data_type: The original data type of the field.
+
+        field_type_class = get_args(og_field_type)[0] if hasattr(og_field_type, "__args__") else og_field_type
+        field_type_class_name = field_type_class.__name__
+
+        composed_field_metadata = ComposedFieldMetadata(
+            node_id=node_id,
+            field_name=field_name,
+            field_type_class_name=field_type_class_name,
+        )
+
+        composed_field_info.json_schema_extra = {
+            "composed_field_extra": composed_field_metadata.model_dump(),
+        }
+
+        # Override the name, title and description if overrides are provided. Dedupe the field name if necessary.
+        final_field_name = dedupe_field_name(field_metadata, field_name)
+
+        # Store the field metadata.
+        field_metadata.update({final_field_name: (composed_field_type, composed_field_info)})
+
+    # Splat in the composed fields to create the new model. There are type errors here because create_model's kwargs are not typed,
+    # and for some reason pydantic's ConfigDict doesn't like lists in `json_schema_extra`. Anyways, the inputs here are correct.
+    return create_model(
+        composed_model_class_name,
+        **field_metadata,
+        __config__=ConfigDict(json_schema_extra={"required": required}),
+    )

invokeai/app/util/startup_utils.py

@@ -65,6 +65,9 @@ def apply_monkeypatches() -> None:
 
     import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
 
+    if torch.backends.mps.is_available():
+        import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+
 
 def register_mime_types() -> None:
     """Register additional mime types for windows."""

invokeai/backend/image_util/dw_openpose/__init__.py

@@ -5,14 +5,62 @@ import huggingface_hub
 import numpy as np
 import onnxruntime as ort
 import torch
+from controlnet_aux.util import resize_image
 from PIL import Image
 
 from invokeai.backend.image_util.dw_openpose.onnxdet import inference_detector
 from invokeai.backend.image_util.dw_openpose.onnxpose import inference_pose
 from invokeai.backend.image_util.dw_openpose.utils import NDArrayInt, draw_bodypose, draw_facepose, draw_handpose
+from invokeai.backend.image_util.dw_openpose.wholebody import Wholebody
 from invokeai.backend.image_util.util import np_to_pil
 from invokeai.backend.util.devices import TorchDevice
 
+DWPOSE_MODELS = {
+    "yolox_l.onnx": "https://huggingface.co/yzd-v/DWPose/resolve/main/yolox_l.onnx?download=true",
+    "dw-ll_ucoco_384.onnx": "https://huggingface.co/yzd-v/DWPose/resolve/main/dw-ll_ucoco_384.onnx?download=true",
+}
+
+
+def draw_pose(
+    pose: Dict[str, NDArrayInt | Dict[str, NDArrayInt]],
+    H: int,
+    W: int,
+    draw_face: bool = True,
+    draw_body: bool = True,
+    draw_hands: bool = True,
+    resolution: int = 512,
+) -> Image.Image:
+    bodies = pose["bodies"]
+    faces = pose["faces"]
+    hands = pose["hands"]
+
+    assert isinstance(bodies, dict)
+    candidate = bodies["candidate"]
+
+    assert isinstance(bodies, dict)
+    subset = bodies["subset"]
+
+    canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
+
+    if draw_body:
+        canvas = draw_bodypose(canvas, candidate, subset)
+
+    if draw_hands:
+        assert isinstance(hands, np.ndarray)
+        canvas = draw_handpose(canvas, hands)
+
+    if draw_face:
+        assert isinstance(hands, np.ndarray)
+        canvas = draw_facepose(canvas, faces)  # type: ignore
+
+    dwpose_image: Image.Image = resize_image(
+        canvas,
+        resolution,
+    )
+    dwpose_image = Image.fromarray(dwpose_image)
+
+    return dwpose_image
+
 
 class DWOpenposeDetector:
     """
@@ -20,6 +68,62 @@ class DWOpenposeDetector:
     Credits: https://github.com/IDEA-Research/DWPose
     """
 
+    def __init__(self, onnx_det: Path, onnx_pose: Path) -> None:
+        self.pose_estimation = Wholebody(onnx_det=onnx_det, onnx_pose=onnx_pose)
+
+    def __call__(
+        self,
+        image: Image.Image,
+        draw_face: bool = False,
+        draw_body: bool = True,
+        draw_hands: bool = False,
+        resolution: int = 512,
+    ) -> Image.Image:
+        np_image = np.array(image)
+        H, W, C = np_image.shape
+
+        with torch.no_grad():
+            candidate, subset = self.pose_estimation(np_image)
+            nums, keys, locs = candidate.shape
+            candidate[..., 0] /= float(W)
+            candidate[..., 1] /= float(H)
+            body = candidate[:, :18].copy()
+            body = body.reshape(nums * 18, locs)
+            score = subset[:, :18]
+            for i in range(len(score)):
+                for j in range(len(score[i])):
+                    if score[i][j] > 0.3:
+                        score[i][j] = int(18 * i + j)
+                    else:
+                        score[i][j] = -1
+
+            un_visible = subset < 0.3
+            candidate[un_visible] = -1
+
+            # foot = candidate[:, 18:24]
+
+            faces = candidate[:, 24:92]
+
+            hands = candidate[:, 92:113]
+            hands = np.vstack([hands, candidate[:, 113:]])
+
+            bodies = {"candidate": body, "subset": score}
+            pose = {"bodies": bodies, "hands": hands, "faces": faces}
+
+            return draw_pose(
+                pose, H, W, draw_face=draw_face, draw_hands=draw_hands, draw_body=draw_body, resolution=resolution
+            )
+
+
+class DWOpenposeDetector2:
+    """
+    Code from the original implementation of the DW Openpose Detector.
+    Credits: https://github.com/IDEA-Research/DWPose
+
+    This implementation is similar to DWOpenposeDetector, with some alterations to allow the onnx models to be loaded
+    and managed by the model manager.
+    """
+
     hf_repo_id = "yzd-v/DWPose"
     hf_filename_onnx_det = "yolox_l.onnx"
     hf_filename_onnx_pose = "dw-ll_ucoco_384.onnx"
@@ -109,7 +213,7 @@ class DWOpenposeDetector:
             bodies = {"candidate": body, "subset": score}
             pose = {"bodies": bodies, "hands": hands, "faces": faces}
 
-            return DWOpenposeDetector.draw_pose(
+            return DWOpenposeDetector2.draw_pose(
                 pose, H, W, draw_face=draw_face, draw_hands=draw_hands, draw_body=draw_body
             )
 

invokeai/backend/image_util/dw_openpose/utils.py

@@ -3,6 +3,7 @@
 import math
 
 import cv2
+import matplotlib
 import numpy as np
 import numpy.typing as npt
 
@@ -126,13 +127,11 @@ def draw_handpose(canvas: NDArrayInt, all_hand_peaks: NDArrayInt) -> NDArrayInt:
             x2 = int(x2 * W)
             y2 = int(y2 * H)
             if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
-                hsv_color = np.array([[[ie / float(len(edges)) * 180, 255, 255]]], dtype=np.uint8)
-                rgb_color = cv2.cvtColor(hsv_color, cv2.COLOR_HSV2RGB)[0, 0]
                 cv2.line(
                     canvas,
                     (x1, y1),
                     (x2, y2),
-                    rgb_color.tolist(),
+                    matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255,
                     thickness=2,
                 )
 

invokeai/backend/image_util/dw_openpose/wholebody.py

@@ -0,0 +1,44 @@
+# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
+# Modified pathing to suit Invoke
+
+
+from pathlib import Path
+
+import numpy as np
+import onnxruntime as ort
+
+from invokeai.app.services.config.config_default import get_config
+from invokeai.backend.image_util.dw_openpose.onnxdet import inference_detector
+from invokeai.backend.image_util.dw_openpose.onnxpose import inference_pose
+from invokeai.backend.util.devices import TorchDevice
+
+config = get_config()
+
+
+class Wholebody:
+    def __init__(self, onnx_det: Path, onnx_pose: Path):
+        device = TorchDevice.choose_torch_device()
+
+        providers = ["CUDAExecutionProvider"] if device.type == "cuda" else ["CPUExecutionProvider"]
+
+        self.session_det = ort.InferenceSession(path_or_bytes=onnx_det, providers=providers)
+        self.session_pose = ort.InferenceSession(path_or_bytes=onnx_pose, providers=providers)
+
+    def __call__(self, oriImg):
+        det_result = inference_detector(self.session_det, oriImg)
+        keypoints, scores = inference_pose(self.session_pose, det_result, oriImg)
+
+        keypoints_info = np.concatenate((keypoints, scores[..., None]), axis=-1)
+        # compute neck joint
+        neck = np.mean(keypoints_info[:, [5, 6]], axis=1)
+        # neck score when visualizing pred
+        neck[:, 2:4] = np.logical_and(keypoints_info[:, 5, 2:4] > 0.3, keypoints_info[:, 6, 2:4] > 0.3).astype(int)
+        new_keypoints_info = np.insert(keypoints_info, 17, neck, axis=1)
+        mmpose_idx = [17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3]
+        openpose_idx = [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17]
+        new_keypoints_info[:, openpose_idx] = new_keypoints_info[:, mmpose_idx]
+        keypoints_info = new_keypoints_info
+
+        keypoints, scores = keypoints_info[..., :2], keypoints_info[..., 2]
+
+        return keypoints, scores

invokeai/backend/model_manager/load/model_util.py

@@ -6,7 +6,6 @@ import logging
 from pathlib import Path
 from typing import Optional
 
-import onnxruntime as ort
 import torch
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
@@ -56,16 +55,6 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
         ),
     ):
         return model.calc_size()
-    elif isinstance(model, ort.InferenceSession):
-        if model._model_bytes is not None:
-            # If the model is already loaded, return the size of the model bytes
-            return len(model._model_bytes)
-        elif model._model_path is not None:
-            # If the model is not loaded, return the size of the model path
-            return calc_model_size_by_fs(Path(model._model_path))
-        else:
-            # If neither is available, return 0
-            return 0
     elif isinstance(
         model,
         (

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -69,9 +69,6 @@ class SD3ConditioningInfo:
 
 @dataclass
 class ConditioningFieldData:
-    # If you change this class, adding more types, you _must_ update the instantiation of ObjectSerializerDisk in
-    # invokeai/app/api/dependencies.py, adding the types to the list of safe globals. If you do not, torch will be
-    # unable to deserialize the object and will raise an error.
     conditionings: (
         List[BasicConditioningInfo]
         | List[SDXLConditioningInfo]

invokeai/backend/util/mps_fixes.py

@@ -0,0 +1,245 @@
+import math
+
+import diffusers
+import torch
+
+if torch.backends.mps.is_available():
+    torch.empty = torch.zeros
+
+
+_torch_layer_norm = torch.nn.functional.layer_norm
+
+
+def new_layer_norm(input, normalized_shape, weight=None, bias=None, eps=1e-05):
+    if input.device.type == "mps" and input.dtype == torch.float16:
+        input = input.float()
+        if weight is not None:
+            weight = weight.float()
+        if bias is not None:
+            bias = bias.float()
+        return _torch_layer_norm(input, normalized_shape, weight, bias, eps).half()
+    else:
+        return _torch_layer_norm(input, normalized_shape, weight, bias, eps)
+
+
+torch.nn.functional.layer_norm = new_layer_norm
+
+
+_torch_tensor_permute = torch.Tensor.permute
+
+
+def new_torch_tensor_permute(input, *dims):
+    result = _torch_tensor_permute(input, *dims)
+    if input.device == "mps" and input.dtype == torch.float16:
+        result = result.contiguous()
+    return result
+
+
+torch.Tensor.permute = new_torch_tensor_permute
+
+
+_torch_lerp = torch.lerp
+
+
+def new_torch_lerp(input, end, weight, *, out=None):
+    if input.device.type == "mps" and input.dtype == torch.float16:
+        input = input.float()
+        end = end.float()
+        if isinstance(weight, torch.Tensor):
+            weight = weight.float()
+        if out is not None:
+            out_fp32 = torch.zeros_like(out, dtype=torch.float32)
+        else:
+            out_fp32 = None
+        result = _torch_lerp(input, end, weight, out=out_fp32)
+        if out is not None:
+            out.copy_(out_fp32.half())
+            del out_fp32
+        return result.half()
+
+    else:
+        return _torch_lerp(input, end, weight, out=out)
+
+
+torch.lerp = new_torch_lerp
+
+
+_torch_interpolate = torch.nn.functional.interpolate
+
+
+def new_torch_interpolate(
+    input,
+    size=None,
+    scale_factor=None,
+    mode="nearest",
+    align_corners=None,
+    recompute_scale_factor=None,
+    antialias=False,
+):
+    if input.device.type == "mps" and input.dtype == torch.float16:
+        return _torch_interpolate(
+            input.float(), size, scale_factor, mode, align_corners, recompute_scale_factor, antialias
+        ).half()
+    else:
+        return _torch_interpolate(input, size, scale_factor, mode, align_corners, recompute_scale_factor, antialias)
+
+
+torch.nn.functional.interpolate = new_torch_interpolate
+
+# TODO: refactor it
+_SlicedAttnProcessor = diffusers.models.attention_processor.SlicedAttnProcessor
+
+
+class ChunkedSlicedAttnProcessor:
+    r"""
+    Processor for implementing sliced attention.
+
+    Args:
+        slice_size (`int`, *optional*):
+            The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
+            `attention_head_dim` must be a multiple of the `slice_size`.
+    """
+
+    def __init__(self, slice_size):
+        assert isinstance(slice_size, int)
+        slice_size = 1  # TODO: maybe implement chunking in batches too when enough memory
+        self.slice_size = slice_size
+        self._sliced_attn_processor = _SlicedAttnProcessor(slice_size)
+
+    def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):
+        if self.slice_size != 1 or attn.upcast_attention:
+            return self._sliced_attn_processor(attn, hidden_states, encoder_hidden_states, attention_mask)
+
+        residual = hidden_states
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+        dim = query.shape[-1]
+        query = attn.head_to_batch_dim(query)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        batch_size_attention, query_tokens, _ = query.shape
+        hidden_states = torch.zeros(
+            (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
+        )
+
+        chunk_tmp_tensor = torch.empty(
+            self.slice_size, query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
+        )
+
+        for i in range(batch_size_attention // self.slice_size):
+            start_idx = i * self.slice_size
+            end_idx = (i + 1) * self.slice_size
+
+            query_slice = query[start_idx:end_idx]
+            key_slice = key[start_idx:end_idx]
+            attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
+
+            self.get_attention_scores_chunked(
+                attn,
+                query_slice,
+                key_slice,
+                attn_mask_slice,
+                hidden_states[start_idx:end_idx],
+                value[start_idx:end_idx],
+                chunk_tmp_tensor,
+            )
+
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+    def get_attention_scores_chunked(self, attn, query, key, attention_mask, hidden_states, value, chunk):
+        # batch size = 1
+        assert query.shape[0] == 1
+        assert key.shape[0] == 1
+        assert value.shape[0] == 1
+        assert hidden_states.shape[0] == 1
+
+        # dtype = query.dtype
+        if attn.upcast_attention:
+            query = query.float()
+            key = key.float()
+
+        # out_item_size = query.dtype.itemsize
+        # if attn.upcast_attention:
+        #    out_item_size = torch.float32.itemsize
+        out_item_size = query.element_size()
+        if attn.upcast_attention:
+            out_item_size = 4
+
+        chunk_size = 2**29
+
+        out_size = query.shape[1] * key.shape[1] * out_item_size
+        chunks_count = min(query.shape[1], math.ceil((out_size - 1) / chunk_size))
+        chunk_step = max(1, int(query.shape[1] / chunks_count))
+
+        key = key.transpose(-1, -2)
+
+        def _get_chunk_view(tensor, start, length):
+            if start + length > tensor.shape[1]:
+                length = tensor.shape[1] - start
+            # print(f"view: [{tensor.shape[0]},{tensor.shape[1]},{tensor.shape[2]}] - start: {start}, length: {length}")
+            return tensor[:, start : start + length]
+
+        for chunk_pos in range(0, query.shape[1], chunk_step):
+            if attention_mask is not None:
+                torch.baddbmm(
+                    _get_chunk_view(attention_mask, chunk_pos, chunk_step),
+                    _get_chunk_view(query, chunk_pos, chunk_step),
+                    key,
+                    beta=1,
+                    alpha=attn.scale,
+                    out=chunk,
+                )
+            else:
+                torch.baddbmm(
+                    torch.zeros((1, 1, 1), device=query.device, dtype=query.dtype),
+                    _get_chunk_view(query, chunk_pos, chunk_step),
+                    key,
+                    beta=0,
+                    alpha=attn.scale,
+                    out=chunk,
+                )
+            chunk = chunk.softmax(dim=-1)
+            torch.bmm(chunk, value, out=_get_chunk_view(hidden_states, chunk_pos, chunk_step))
+
+        # del chunk
+
+
+diffusers.models.attention_processor.SlicedAttnProcessor = ChunkedSlicedAttnProcessor

invokeai/frontend/web/package.json

@@ -62,7 +62,7 @@
     "@nanostores/react": "^0.7.3",
     "@reduxjs/toolkit": "2.6.1",
     "@roarr/browser-log-writer": "^1.3.0",
-    "@xyflow/react": "^12.5.3",
+    "@xyflow/react": "^12.5.1",
     "async-mutex": "^0.5.0",
     "chakra-react-select": "^4.9.2",
     "cmdk": "^1.0.0",
@@ -162,6 +162,5 @@
   },
   "engines": {
     "pnpm": "8"
-  },
-  "packageManager": "pnpm@8.15.9+sha512.499434c9d8fdd1a2794ebf4552b3b25c0a633abcee5bb15e7b5de90f32f47b513aca98cd5cfd001c31f0db454bc3804edccd578501e4ca293a6816166bbd9f81"
+  }
 }

invokeai/frontend/web/pnpm-lock.yaml

@@ -36,8 +36,8 @@ dependencies:
     specifier: ^1.3.0
     version: 1.3.0
   '@xyflow/react':
-    specifier: ^12.5.3
-    version: 12.5.3(@types/react@18.3.11)(react-dom@18.3.1)(react@18.3.1)
+    specifier: ^12.5.1
+    version: 12.5.1(@types/react@18.3.11)(react-dom@18.3.1)(react@18.3.1)
   async-mutex:
     specifier: ^0.5.0
     version: 0.5.0
@@ -3951,8 +3951,8 @@ packages:
     resolution: {integrity: sha512-N8tkAACJx2ww8vFMneJmaAgmjAG1tnVBZJRLRcx061tmsLRZHSEZSLuGWnwPtunsSLvSqXQ2wfp7Mgqg1I+2dQ==}
     dev: false
 
-  /@xyflow/react@12.5.3(@types/react@18.3.11)(react-dom@18.3.1)(react@18.3.1):
-    resolution: {integrity: sha512-saovy/aQRoW8qQoIqMFUtmC3F6oEV7n6+J1pVbhSG45NI/hOFvK0qozsIPKqX5Va6lGQnkl/o53NHLja3NiweQ==}
+  /@xyflow/react@12.5.1(@types/react@18.3.11)(react-dom@18.3.1)(react@18.3.1):
+    resolution: {integrity: sha512-jMKQVqGwCz0x6pUyvxTIuCMbyehfua7CfEEWDj29zQSHigQpCy0/5d8aOmZrqK4cwur/pVHLQomT6Rm10gXfHg==}
     peerDependencies:
       react: '>=17'
       react-dom: '>=17'
@@ -9123,8 +9123,8 @@ packages:
       react: 18.3.1
     dev: false
 
-  /use-sync-external-store@1.5.0(react@18.3.1):
-    resolution: {integrity: sha512-Rb46I4cGGVBmjamjphe8L/UnvJD+uPPtTkNvX5mZgqdbavhI4EbgIWJiIHXJ8bc/i9EQGPRh4DwEURJ552Do0A==}
+  /use-sync-external-store@1.4.0(react@18.3.1):
+    resolution: {integrity: sha512-9WXSPC5fMv61vaupRkCKCxsPxBocVnwakBEkMIHHpkTTg6icbJtg6jzgtLDm4bl3cSHAca52rYWih0k4K3PfHw==}
     peerDependencies:
       react: ^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0
     dependencies:
@@ -9592,5 +9592,5 @@ packages:
     dependencies:
       '@types/react': 18.3.11
       react: 18.3.1
-      use-sync-external-store: 1.5.0(react@18.3.1)
+      use-sync-external-store: 1.4.0(react@18.3.1)
     dev: false

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

@@ -116,10 +116,7 @@
         "combinatorial": "Kombinatorisch",
         "saveChanges": "Änderungen speichern",
         "error_withCount_one": "{{count}} Fehler",
-        "error_withCount_other": "{{count}} Fehler",
-        "value": "Wert",
-        "label": "Label",
-        "systemInformation": "Systeminformationen"
+        "error_withCount_other": "{{count}} Fehler"
     },
     "gallery": {
         "galleryImageSize": "Bildgröße",
@@ -698,10 +695,7 @@
         "guidance": "Führung",
         "coherenceMode": "Modus",
         "recallMetadata": "Metadaten abrufen",
-        "gaussianBlur": "Gaußsche Unschärfe",
-        "sendToUpscale": "An Hochskalieren senden",
-        "useCpuNoise": "CPU-Rauschen verwenden",
-        "sendToCanvas": "An Leinwand senden"
+        "gaussianBlur": "Gaußsche Unschärfe"
     },
     "settings": {
         "displayInProgress": "Zwischenbilder anzeigen",
@@ -1334,8 +1328,7 @@
         "loadWorkflowDesc2": "Ihr aktueller Arbeitsablauf enthält nicht gespeicherte Änderungen.",
         "loadingTemplates": "Lade {{name}}",
         "missingSourceOrTargetHandle": "Fehlender Quell- oder Zielgriff",
-        "missingSourceOrTargetNode": "Fehlender Quell- oder Zielknoten",
-        "showEdgeLabelsHelp": "Beschriftungen an Kanten anzeigen, um die verknüpften Knoten zu kennzeichnen"
+        "missingSourceOrTargetNode": "Fehlender Quell- oder Zielknoten"
     },
     "hrf": {
         "enableHrf": "Korrektur für hohe Auflösungen",

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

@@ -1306,10 +1306,7 @@
         "unableToCopy": "Unable to Copy",
         "unableToCopyDesc": "Your browser does not support clipboard access. Firefox users may be able to fix this by following ",
         "unableToCopyDesc_theseSteps": "these steps",
-        "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fill is not compatible with Text to Image or Image to Image. Use other FLUX models for these tasks.",
-        "problemUnpublishingWorkflow": "Problem Unpublishing Workflow",
-        "problemUnpublishingWorkflowDescription": "There was a problem unpublishing the workflow. Please try again.",
-        "workflowUnpublished": "Workflow Unpublished"
+        "fluxFillIncompatibleWithT2IAndI2I": "FLUX Fill is not compatible with Text to Image or Image to Image. Use other FLUX models for these tasks."
     },
     "popovers": {
         "clipSkip": {
@@ -1709,7 +1706,6 @@
         "noRecentWorkflows": "No Recent Workflows",
         "private": "Private",
         "shared": "Shared",
-        "published": "Published",
         "browseWorkflows": "Browse Workflows",
         "deselectAll": "Deselect All",
         "recommended": "Recommended For You",
@@ -1789,8 +1785,8 @@
             "minimum": "Minimum",
             "maximum": "Maximum",
             "publish": "Publish",
-            "unpublish": "Unpublish",
             "published": "Published",
+            "unpublish": "Unpublish",
             "workflowLocked": "Workflow Locked",
             "workflowLockedPublished": "Published workflows are locked for editing.\nYou can unpublish the workflow to edit it, or make a copy of it.",
             "workflowLockedDuringPublishing": "Workflow is locked while configuring for publishing.",
@@ -1817,9 +1813,7 @@
             "publishedWorkflowIsLocked": "Published workflow is locked",
             "publishingValidationRun": "Publishing Validation Run",
             "publishingValidationRunInProgress": "Publishing validation run in progress.",
-            "publishedWorkflowsLocked": "Published workflows are locked and cannot be edited or run. Either unpublish the workflow or save a copy to edit or run this workflow.",
-            "selectingOutputNode": "Selecting output node",
-            "selectingOutputNodeDesc": "Click a node to select it as the workflow's output node."
+            "publishedWorkflowsLocked": "Published workflows are locked and cannot be edited or run. Either unpublish the workflow or save a copy to edit or run this workflow."
         }
     },
     "controlLayers": {
@@ -2020,14 +2014,6 @@
             "composition": "Composition Only",
             "compositionDesc": "Replicates layout & structure while ignoring the reference's style."
         },
-        "fluxReduxImageInfluence": {
-            "imageInfluence": "Image Influence",
-            "lowest": "Lowest",
-            "low": "Low",
-            "medium": "Medium",
-            "high": "High",
-            "highest": "Highest"
-        },
         "fill": {
             "fillColor": "Fill Color",
             "fillStyle": "Fill Style",

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

@@ -115,8 +115,7 @@
         "error_withCount_many": "{{count}} errori",
         "error_withCount_other": "{{count}} errori",
         "value": "Valore",
-        "label": "Etichetta",
-        "systemInformation": "Informazioni di sistema"
+        "label": "Etichetta"
     },
     "gallery": {
         "galleryImageSize": "Dimensione dell'immagine",
@@ -716,8 +715,7 @@
             "collectionNumberLTMin": "{{value}} < {{minimum}} (incr min)",
             "collectionNumberGTExclusiveMax": "{{value}} >= {{exclusiveMaximum}} (excl max)",
             "collectionNumberLTExclusiveMin": "{{value}} <= {{exclusiveMinimum}} (excl min)",
-            "collectionEmpty": "raccolta vuota",
-            "batchNodeCollectionSizeMismatchNoGroupId": "Dimensione della raccolta di gruppo nel Lotto non corrisponde"
+            "collectionEmpty": "raccolta vuota"
         },
         "useCpuNoise": "Usa la CPU per generare rumore",
         "iterations": "Iterazioni",
@@ -1790,37 +1788,7 @@
             "maximum": "Massimo",
             "dropdown": "Elenco a discesa",
             "addOption": "Aggiungi opzione",
-            "resetOptions": "Reimposta opzioni",
-            "publish": "Pubblica",
-            "workflowLocked": "Flusso di lavoro bloccato",
-            "workflowLockedDuringPublishing": "Il flusso di lavoro è bloccato durante la configurazione per la pubblicazione.",
-            "selectOutputNode": "Seleziona nodo di uscita",
-            "changeOutputNode": "Cambia nodo di uscita",
-            "publishedWorkflowOutputs": "Uscite",
-            "noPublishableInputs": "Nessun ingresso pubblicabile",
-            "published": "Pubblicato",
-            "cannotPublish": "Impossibile pubblicare il flusso di lavoro",
-            "noOutputNodeSelected": "Nessun nodo di uscita selezionato",
-            "unpublish": "Annulla pubblicazione",
-            "workflowLockedPublished": "I flussi di lavoro pubblicati sono bloccati per la modifica.\nPuoi annullare la pubblicazione del flusso di lavoro per modificarlo o crearne una copia.",
-            "publishedWorkflowInputs": "Ingressi",
-            "unpublishableInputs": "Questi input non pubblicabili verranno omessi",
-            "publishWarnings": "Avvertenze",
-            "errorWorkflowHasUnsavedChanges": "Il flusso di lavoro presenta modifiche non salvate",
-            "errorWorkflowHasBatchOrGeneratorNodes": "Il flusso di lavoro ha nodi lotto e/o generatori",
-            "errorWorkflowHasInvalidGraph": "Grafico del flusso di lavoro non valido (passare il mouse sul pulsante Invoke per i dettagli)",
-            "errorWorkflowHasNoOutputNode": "Nessun nodo di uscita selezionato",
-            "warningWorkflowHasUnpublishableInputFields": "Il flusso di lavoro presenta alcuni ingressi non pubblicabili: questi verranno omessi dal flusso di lavoro pubblicato",
-            "publishFailed": "Pubblicazione non riuscita",
-            "publishFailedDesc": "Si è verificato un problema durante la pubblicazione del flusso di lavoro. Riprova.",
-            "publishSuccess": "Il tuo flusso di lavoro è in fase di pubblicazione",
-            "publishSuccessDesc": "Controlla il <LinkComponent>pannello di controllo del progetto</LinkComponent> per verificarne l'avanzamento.",
-            "publishedWorkflowIsLocked": "Il flusso di lavoro pubblicato è bloccato",
-            "publishingValidationRun": "Esecuzione della convalida della pubblicazione",
-            "publishingValidationRunInProgress": "È in corso la convalida della pubblicazione.",
-            "publishedWorkflowsLocked": "I flussi di lavoro pubblicati sono bloccati e non possono essere modificati o eseguiti. Annulla la pubblicazione del flusso di lavoro o salva una copia per modificare o eseguire questo flusso di lavoro.",
-            "warningWorkflowHasNoPublishableInputFields": "Nessun campo di ingresso pubblicabile selezionato: il flusso di lavoro pubblicato verrà eseguito solo con i valori predefiniti",
-            "publishInProgress": "Pubblicazione in corso"
+            "resetOptions": "Reimposta opzioni"
         },
         "loadMore": "Carica altro",
         "searchPlaceholder": "Cerca per nome, descrizione o etichetta",
@@ -1837,8 +1805,7 @@
         "noRecentWorkflows": "Nessun flusso di lavoro recente",
         "view": "Visualizza",
         "recommended": "Consigliato per te",
-        "emptyStringPlaceholder": "<stringa vuota>",
-        "published": "Pubblicato"
+        "emptyStringPlaceholder": "<stringa vuota>"
     },
     "accordions": {
         "compositing": {
@@ -2398,9 +2365,8 @@
         "watchRecentReleaseVideos": "Guarda i video su questa versione",
         "watchUiUpdatesOverview": "Guarda le novità dell'interfaccia",
         "items": [
-            "Flussi di lavoro: supporto per menu a discesa di stringhe personalizzate nel Generatore di Flussi di lavoro.",
-            "FLUX: supporto per FLUX Fill in Flussi di lavoro e Tela.",
-            "LLaVA OneVision VLLM: supporto beta nei flussi di lavoro."
+            "Flussi di lavoro: nuova e migliorata libreria dei flussi di lavoro.",
+            "FLUX: supporto per FLUX Redux e FLUX Fill in Flussi di lavoro e Tela."
         ]
     },
     "system": {

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

@@ -237,10 +237,7 @@
         "row": "Hàng",
         "board": "Bảng",
         "saveChanges": "Lưu Thay Đổi",
-        "error_withCount_other": "{{count}} lỗi",
-        "value": "Giá Trị",
-        "label": "Nhãn Tên",
-        "systemInformation": "Thông Tin Hệ Thống"
+        "error_withCount_other": "{{count}} lỗi"
     },
     "prompt": {
         "addPromptTrigger": "Thêm Prompt Trigger",
@@ -2229,7 +2226,7 @@
     "workflows": {
         "delete": "Xoá",
         "descending": "Giảm Dần",
-        "created": "Đã Tạo",
+        "created": "Ngày Tạo",
         "edit": "Chỉnh Sửa",
         "download": "Tải Xuống",
         "copyShareLink": "Sao Chép Liên Kết Chia Sẻ",
@@ -2255,7 +2252,7 @@
         "saveWorkflow": "Lưu Workflow",
         "problemSavingWorkflow": "Có Vấn Đề Khi Lưu Workflow",
         "noDescription": "Không có mô tả",
-        "updated": "Đã Cập Nhật",
+        "updated": "Ngày Cập Nhật",
         "uploadWorkflow": "Tải Từ Tệp",
         "autoLayout": "Bố Trí Tự Động",
         "loadWorkflow": "$t(common.load) Workflow",
@@ -2267,7 +2264,7 @@
         "saveWorkflowToProject": "Lưu Workflow Vào Dự Án",
         "workflowName": "Tên Workflow",
         "workflowLibrary": "Thư Viện Workflow",
-        "opened": "Đã Mở",
+        "opened": "Ngày Mở",
         "deleteWorkflow": "Xoá Workflow",
         "workflowEditorMenu": "Menu Biên Tập Workflow",
         "openLibrary": "Mở Thư Viện",
@@ -2303,42 +2300,7 @@
             "minimum": "Tối Thiểu",
             "maximum": "Tối Đa",
             "containerRowLayout": "Hộp Chứa (bố cục hàng)",
-            "containerColumnLayout": "Hộp Chứa (bố cục cột)",
-            "resetOptions": "Tải Lại Lựa Chọn",
-            "addOption": "Thêm Lựa Chọn",
-            "dropdown": "Danh Sách Thả Xuống",
-            "publish": "Đăng Tải",
-            "published": "Đã Đăng",
-            "workflowLocked": "Workflow Bị Khóa",
-            "workflowLockedDuringPublishing": "Workflow bị khóa khi đang điều chỉnh để đăng tải.",
-            "selectOutputNode": "Chọn Node Đầu Ra",
-            "changeOutputNode": "Đổi Node Đầu Ra",
-            "publishedWorkflowOutputs": "Đầu Ra",
-            "unpublishableInputs": "Những đầu vào không đăng tải được sẽ bị bỏ sót",
-            "noPublishableInputs": "Không có đầu vào không đăng tải được",
-            "noOutputNodeSelected": "Không có node đầu ra được chọn",
-            "publishWarnings": "Cảnh Báo",
-            "errorWorkflowHasUnsavedChanges": "Workflow có các thay đổi chưa lưu",
-            "cannotPublish": "Không thể đăng workflow",
-            "publishedWorkflowInputs": "Đầu Vào",
-            "unpublish": "Chưa Đăng",
-            "workflowLockedPublished": "Workflow được đăng tải sẽ bị khóa không thể biên tập.\nBạn có thể ngừng đăng để chỉnh sửa, hoặc tạo một bản sao của nó.",
-            "errorWorkflowHasBatchOrGeneratorNodes": "Workflow có lô node và/hoặc node sản sinh",
-            "errorWorkflowHasInvalidGraph": "Đồ thị workflow không hợp lệ (di chuột đến nút Khởi Động để xem chi tiết)",
-            "errorWorkflowHasNoOutputNode": "Không có node đầu ra được chọn",
-            "warningWorkflowHasUnpublishableInputFields": "Workflow có một số đầu ra không đăng được - chúng sẽ bị bỏ sót khỏi workflow",
-            "publishFailed": "Đăng Tải Thất Bại",
-            "publishFailedDesc": "Có vấn đề khi đăng tải workflow. Xin vui lòng thử lại.",
-            "publishSuccessDesc": "Kiểm tra <LinkComponent>Bảng Dự Án</LinkComponent> để xem tiến độ.",
-            "publishingValidationRun": "Kiểm Tra Tính Hợp Lệ",
-            "publishedWorkflowsLocked": "Workflow đã đăng sẽ bị khóa và không thể biên tập hoặc chạy nữa. Hoặc là ngừng đăng, hoặc là lưu một bản sao của chính nó để biên tập hay chạy workflow này.",
-            "publishInProgress": "Quá trình đăng tải đang diễn ra",
-            "warningWorkflowHasNoPublishableInputFields": "Không có vùng đầu vào đăng tải được được chọn - workflow sẽ chạy với các giá trị mặc định",
-            "publishSuccess": "Workflow của bạn đã được đăng",
-            "publishedWorkflowIsLocked": "Workflow đã đăng đang bị khóa",
-            "publishingValidationRunInProgress": "Quá trình kiểm tra tính hợp lệ đang diễn ra.",
-            "selectingOutputNodeDesc": "Bấm vào node để biến nó thành node đầu ra của workflow.",
-            "selectingOutputNode": "Chọn node đầu ra"
+            "containerColumnLayout": "Hộp Chứa (bố cục cột)"
         },
         "yourWorkflows": "Workflow Của Bạn",
         "browseWorkflows": "Khám Phá Workflow",
@@ -2354,9 +2316,7 @@
         "view": "Xem",
         "deselectAll": "Huỷ Chọn Tất Cả",
         "noRecentWorkflows": "Không Có Workflows Gần Đây",
-        "recommended": "Có Thể Bạn Sẽ Cần",
-        "emptyStringPlaceholder": "<xâu ký tự trống>",
-        "published": "Đã Đăng"
+        "recommended": "Có Thể Bạn Sẽ Cần"
     },
     "upscaling": {
         "missingUpscaleInitialImage": "Thiếu ảnh dùng để upscale",
@@ -2392,9 +2352,8 @@
         "watchRecentReleaseVideos": "Xem Video Phát Hành Mới Nhất",
         "watchUiUpdatesOverview": "Xem Tổng Quan Về Những Cập Nhật Cho Giao Diện Người Dùng",
         "items": [
-            "Workflow: Hỗ trợ xâu ký tự thả xuống tùy chỉnh trong Trình Tạo Vùng Nhập.",
-            "FLUX: Hỗ trợ FLUX Fill trong Workflow và Canvas.",
-            "LLaVA OneVision VLLM: Hỗ trợ phiên bản Beta trong Workflow."
+            "Workflow: Thư Viện Workflow mới và đã được cải tiến.",
+            "FLUX: Hỗ trợ FLUX Redux & FLUX Fill trong Workflow và Canvas."
         ]
     },
     "upsell": {

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

@@ -1,15 +1,54 @@
-import { Box } from '@invoke-ai/ui-library';
+import { Box, useGlobalModifiersInit } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
-import { GlobalHookIsolator } from 'app/components/GlobalHookIsolator';
-import { GlobalModalIsolator } from 'app/components/GlobalModalIsolator';
+import { GlobalImageHotkeys } from 'app/components/GlobalImageHotkeys';
 import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
-import { $didStudioInit } from 'app/hooks/useStudioInitAction';
+import { $didStudioInit, useStudioInitAction } from 'app/hooks/useStudioInitAction';
+import { useSyncQueueStatus } from 'app/hooks/useSyncQueueStatus';
+import { useLogger } from 'app/logging/useLogger';
+import { useSyncLoggingConfig } from 'app/logging/useSyncLoggingConfig';
+import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import type { PartialAppConfig } from 'app/types/invokeai';
 import Loading from 'common/components/Loading/Loading';
+import { useFocusRegionWatcher } from 'common/hooks/focus';
 import { useClearStorage } from 'common/hooks/useClearStorage';
+import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
+import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardModal';
+import { CanvasPasteModal } from 'features/controlLayers/components/CanvasPasteModal';
+import {
+  NewCanvasSessionDialog,
+  NewGallerySessionDialog,
+} from 'features/controlLayers/components/NewSessionConfirmationAlertDialog';
+import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
+import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
+import { FullscreenDropzone } from 'features/dnd/FullscreenDropzone';
+import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';
+import DeleteBoardModal from 'features/gallery/components/Boards/DeleteBoardModal';
+import { ImageContextMenu } from 'features/gallery/components/ImageContextMenu/ImageContextMenu';
+import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
+import { ShareWorkflowModal } from 'features/nodes/components/sidePanel/workflow/WorkflowLibrary/ShareWorkflowModal';
+import { WorkflowLibraryModal } from 'features/nodes/components/sidePanel/workflow/WorkflowLibrary/WorkflowLibraryModal';
+import { CancelAllExceptCurrentQueueItemConfirmationAlertDialog } from 'features/queue/components/CancelAllExceptCurrentQueueItemConfirmationAlertDialog';
+import { ClearQueueConfirmationsAlertDialog } from 'features/queue/components/ClearQueueConfirmationAlertDialog';
+import { useReadinessWatcher } from 'features/queue/store/readiness';
+import { DeleteStylePresetDialog } from 'features/stylePresets/components/DeleteStylePresetDialog';
+import { StylePresetModal } from 'features/stylePresets/components/StylePresetForm/StylePresetModal';
+import RefreshAfterResetModal from 'features/system/components/SettingsModal/RefreshAfterResetModal';
+import { VideosModal } from 'features/system/components/VideosModal/VideosModal';
+import { configChanged } from 'features/system/store/configSlice';
+import { selectLanguage } from 'features/system/store/systemSelectors';
 import { AppContent } from 'features/ui/components/AppContent';
-import { memo, useCallback } from 'react';
+import { DeleteWorkflowDialog } from 'features/workflowLibrary/components/DeleteLibraryWorkflowConfirmationAlertDialog';
+import { LoadWorkflowConfirmationAlertDialog } from 'features/workflowLibrary/components/LoadWorkflowConfirmationAlertDialog';
+import { LoadWorkflowFromGraphModal } from 'features/workflowLibrary/components/LoadWorkflowFromGraphModal/LoadWorkflowFromGraphModal';
+import { NewWorkflowConfirmationAlertDialog } from 'features/workflowLibrary/components/NewWorkflowConfirmationAlertDialog';
+import { SaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog';
+import i18n from 'i18n';
+import { size } from 'lodash-es';
+import { memo, useCallback, useEffect } from 'react';
 import { ErrorBoundary } from 'react-error-boundary';
+import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
+import { useSocketIO } from 'services/events/useSocketIO';
 
 import AppErrorBoundaryFallback from './AppErrorBoundaryFallback';
 const DEFAULT_CONFIG = {};
@@ -35,10 +74,83 @@ const App = ({ config = DEFAULT_CONFIG, studioInitAction }: Props) => {
         <AppContent />
         {!didStudioInit && <Loading />}
       </Box>
-      <GlobalHookIsolator config={config} studioInitAction={studioInitAction} />
-      <GlobalModalIsolator />
+      <HookIsolator config={config} studioInitAction={studioInitAction} />
+      <ModalIsolator />
     </ErrorBoundary>
   );
 };
 
 export default memo(App);
+
+// Running these hooks in a separate component ensures we do not inadvertently rerender the entire app when they change.
+const HookIsolator = memo(
+  ({ config, studioInitAction }: { config: PartialAppConfig; studioInitAction?: StudioInitAction }) => {
+    const language = useAppSelector(selectLanguage);
+    const logger = useLogger('system');
+    const dispatch = useAppDispatch();
+
+    // singleton!
+    useReadinessWatcher();
+    useSocketIO();
+    useGlobalModifiersInit();
+    useGlobalHotkeys();
+    useGetOpenAPISchemaQuery();
+    useSyncLoggingConfig();
+
+    useEffect(() => {
+      i18n.changeLanguage(language);
+    }, [language]);
+
+    useEffect(() => {
+      if (size(config)) {
+        logger.info({ config }, 'Received config');
+        dispatch(configChanged(config));
+      }
+    }, [dispatch, config, logger]);
+
+    useEffect(() => {
+      dispatch(appStarted());
+    }, [dispatch]);
+
+    useStudioInitAction(studioInitAction);
+    useStarterModelsToast();
+    useSyncQueueStatus();
+    useFocusRegionWatcher();
+
+    return null;
+  }
+);
+HookIsolator.displayName = 'HookIsolator';
+
+const ModalIsolator = memo(() => {
+  return (
+    <>
+      <DeleteImageModal />
+      <ChangeBoardModal />
+      <DynamicPromptsModal />
+      <StylePresetModal />
+      <WorkflowLibraryModal />
+      <CancelAllExceptCurrentQueueItemConfirmationAlertDialog />
+      <ClearQueueConfirmationsAlertDialog />
+      <NewWorkflowConfirmationAlertDialog />
+      <LoadWorkflowConfirmationAlertDialog />
+      <DeleteStylePresetDialog />
+      <DeleteWorkflowDialog />
+      <ShareWorkflowModal />
+      <RefreshAfterResetModal />
+      <DeleteBoardModal />
+      <GlobalImageHotkeys />
+      <NewGallerySessionDialog />
+      <NewCanvasSessionDialog />
+      <ImageContextMenu />
+      <FullscreenDropzone />
+      <VideosModal />
+      <SaveWorkflowAsDialog />
+      <CanvasManagerProviderGate>
+        <CanvasPasteModal />
+      </CanvasManagerProviderGate>
+      <LoadWorkflowFromGraphModal />
+    </>
+  );
+});
+ModalIsolator.displayName = 'ModalIsolator';

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

@@ -1,65 +0,0 @@
-import { useGlobalModifiersInit } from '@invoke-ai/ui-library';
-import type { StudioInitAction } from 'app/hooks/useStudioInitAction';
-import { useStudioInitAction } from 'app/hooks/useStudioInitAction';
-import { useSyncQueueStatus } from 'app/hooks/useSyncQueueStatus';
-import { useLogger } from 'app/logging/useLogger';
-import { useSyncLoggingConfig } from 'app/logging/useSyncLoggingConfig';
-import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import type { PartialAppConfig } from 'app/types/invokeai';
-import { useFocusRegionWatcher } from 'common/hooks/focus';
-import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
-import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
-import { useWorkflowBuilderWatcher } from 'features/nodes/components/sidePanel/workflow/IsolatedWorkflowBuilderWatcher';
-import { useReadinessWatcher } from 'features/queue/store/readiness';
-import { configChanged } from 'features/system/store/configSlice';
-import { selectLanguage } from 'features/system/store/systemSelectors';
-import i18n from 'i18n';
-import { size } from 'lodash-es';
-import { memo, useEffect } from 'react';
-import { useGetOpenAPISchemaQuery } from 'services/api/endpoints/appInfo';
-import { useSocketIO } from 'services/events/useSocketIO';
-
-/**
- * GlobalHookIsolator is a logical component that runs global hooks in an isolated component, so that they do not
- * cause needless re-renders of any other components.
- */
-export const GlobalHookIsolator = memo(
-  ({ config, studioInitAction }: { config: PartialAppConfig; studioInitAction?: StudioInitAction }) => {
-    const language = useAppSelector(selectLanguage);
-    const logger = useLogger('system');
-    const dispatch = useAppDispatch();
-
-    // singleton!
-    useReadinessWatcher();
-    useSocketIO();
-    useGlobalModifiersInit();
-    useGlobalHotkeys();
-    useGetOpenAPISchemaQuery();
-    useSyncLoggingConfig();
-
-    useEffect(() => {
-      i18n.changeLanguage(language);
-    }, [language]);
-
-    useEffect(() => {
-      if (size(config)) {
-        logger.info({ config }, 'Received config');
-        dispatch(configChanged(config));
-      }
-    }, [dispatch, config, logger]);
-
-    useEffect(() => {
-      dispatch(appStarted());
-    }, [dispatch]);
-
-    useStudioInitAction(studioInitAction);
-    useStarterModelsToast();
-    useSyncQueueStatus();
-    useFocusRegionWatcher();
-    useWorkflowBuilderWatcher();
-
-    return null;
-  }
-);
-GlobalHookIsolator.displayName = 'GlobalHookIsolator';

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

@@ -1,64 +0,0 @@
-import { GlobalImageHotkeys } from 'app/components/GlobalImageHotkeys';
-import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardModal';
-import { CanvasPasteModal } from 'features/controlLayers/components/CanvasPasteModal';
-import {
-  NewCanvasSessionDialog,
-  NewGallerySessionDialog,
-} from 'features/controlLayers/components/NewSessionConfirmationAlertDialog';
-import { CanvasManagerProviderGate } from 'features/controlLayers/contexts/CanvasManagerProviderGate';
-import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
-import { FullscreenDropzone } from 'features/dnd/FullscreenDropzone';
-import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';
-import DeleteBoardModal from 'features/gallery/components/Boards/DeleteBoardModal';
-import { ImageContextMenu } from 'features/gallery/components/ImageContextMenu/ImageContextMenu';
-import { ShareWorkflowModal } from 'features/nodes/components/sidePanel/workflow/WorkflowLibrary/ShareWorkflowModal';
-import { WorkflowLibraryModal } from 'features/nodes/components/sidePanel/workflow/WorkflowLibrary/WorkflowLibraryModal';
-import { CancelAllExceptCurrentQueueItemConfirmationAlertDialog } from 'features/queue/components/CancelAllExceptCurrentQueueItemConfirmationAlertDialog';
-import { ClearQueueConfirmationsAlertDialog } from 'features/queue/components/ClearQueueConfirmationAlertDialog';
-import { DeleteStylePresetDialog } from 'features/stylePresets/components/DeleteStylePresetDialog';
-import { StylePresetModal } from 'features/stylePresets/components/StylePresetForm/StylePresetModal';
-import RefreshAfterResetModal from 'features/system/components/SettingsModal/RefreshAfterResetModal';
-import { VideosModal } from 'features/system/components/VideosModal/VideosModal';
-import { DeleteWorkflowDialog } from 'features/workflowLibrary/components/DeleteLibraryWorkflowConfirmationAlertDialog';
-import { LoadWorkflowConfirmationAlertDialog } from 'features/workflowLibrary/components/LoadWorkflowConfirmationAlertDialog';
-import { LoadWorkflowFromGraphModal } from 'features/workflowLibrary/components/LoadWorkflowFromGraphModal/LoadWorkflowFromGraphModal';
-import { NewWorkflowConfirmationAlertDialog } from 'features/workflowLibrary/components/NewWorkflowConfirmationAlertDialog';
-import { SaveWorkflowAsDialog } from 'features/workflowLibrary/components/SaveWorkflowAsDialog';
-import { memo } from 'react';
-
-/**
- * GlobalModalIsolator is a logical component that isolates global modal components, so that they do not cause needless
- * re-renders of any other components.
- */
-export const GlobalModalIsolator = memo(() => {
-  return (
-    <>
-      <DeleteImageModal />
-      <ChangeBoardModal />
-      <DynamicPromptsModal />
-      <StylePresetModal />
-      <WorkflowLibraryModal />
-      <CancelAllExceptCurrentQueueItemConfirmationAlertDialog />
-      <ClearQueueConfirmationsAlertDialog />
-      <NewWorkflowConfirmationAlertDialog />
-      <LoadWorkflowConfirmationAlertDialog />
-      <DeleteStylePresetDialog />
-      <DeleteWorkflowDialog />
-      <ShareWorkflowModal />
-      <RefreshAfterResetModal />
-      <DeleteBoardModal />
-      <GlobalImageHotkeys />
-      <NewGallerySessionDialog />
-      <NewCanvasSessionDialog />
-      <ImageContextMenu />
-      <FullscreenDropzone />
-      <VideosModal />
-      <SaveWorkflowAsDialog />
-      <CanvasManagerProviderGate>
-        <CanvasPasteModal />
-      </CanvasManagerProviderGate>
-      <LoadWorkflowFromGraphModal />
-    </>
-  );
-});
-GlobalModalIsolator.displayName = 'GlobalModalIsolator';

invokeai/frontend/web/src/app/store/middleware/devtools/actionSanitizer.ts

@@ -1,7 +1,20 @@
 import type { UnknownAction } from '@reduxjs/toolkit';
+import { isAnyGraphBuilt } from 'features/nodes/store/actions';
 import { appInfoApi } from 'services/api/endpoints/appInfo';
+import type { Graph } from 'services/api/types';
 
 export const actionSanitizer = <A extends UnknownAction>(action: A): A => {
+  if (isAnyGraphBuilt(action)) {
+    if (action.payload.nodes) {
+      const sanitizedNodes: Graph['nodes'] = {};
+
+      return {
+        ...action,
+        payload: { ...action.payload, nodes: sanitizedNodes },
+      };
+    }
+  }
+
   if (appInfoApi.endpoints.getOpenAPISchema.matchFulfilled(action)) {
     return {
       ...action,

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

@@ -25,6 +25,7 @@ import { addModelsLoadedListener } from 'app/store/middleware/listenerMiddleware
 import { addDynamicPromptsListener } from 'app/store/middleware/listenerMiddleware/listeners/promptChanged';
 import { addSetDefaultSettingsListener } from 'app/store/middleware/listenerMiddleware/listeners/setDefaultSettings';
 import { addSocketConnectedEventListener } from 'app/store/middleware/listenerMiddleware/listeners/socketConnected';
+import { addUpdateAllNodesRequestedListener } from 'app/store/middleware/listenerMiddleware/listeners/updateAllNodesRequested';
 import type { AppDispatch, RootState } from 'app/store/store';
 
 import { addArchivedOrDeletedBoardListener } from './listeners/addArchivedOrDeletedBoardListener';
@@ -84,6 +85,9 @@ addArchivedOrDeletedBoardListener(startAppListening);
 // Node schemas
 addGetOpenAPISchemaListener(startAppListening);
 
+// Workflows
+addUpdateAllNodesRequestedListener(startAppListening);
+
 // Models
 addModelSelectedListener(startAppListening);
 

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

@@ -0,0 +1,69 @@
+import { logger } from 'app/logging/logger';
+import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
+import { updateAllNodesRequested } from 'features/nodes/store/actions';
+import { $templates, nodesChanged } from 'features/nodes/store/nodesSlice';
+import { selectNodes } from 'features/nodes/store/selectors';
+import { NodeUpdateError } from 'features/nodes/types/error';
+import { isInvocationNode } from 'features/nodes/types/invocation';
+import { getNeedsUpdate, updateNode } from 'features/nodes/util/node/nodeUpdate';
+import { toast } from 'features/toast/toast';
+import { t } from 'i18next';
+
+const log = logger('workflows');
+
+export const addUpdateAllNodesRequestedListener = (startAppListening: AppStartListening) => {
+  startAppListening({
+    actionCreator: updateAllNodesRequested,
+    effect: (action, { dispatch, getState }) => {
+      const nodes = selectNodes(getState());
+      const templates = $templates.get();
+
+      let unableToUpdateCount = 0;
+
+      nodes.filter(isInvocationNode).forEach((node) => {
+        const template = templates[node.data.type];
+        if (!template) {
+          unableToUpdateCount++;
+          return;
+        }
+        if (!getNeedsUpdate(node.data, template)) {
+          // No need to increment the count here, since we're not actually updating
+          return;
+        }
+        try {
+          const updatedNode = updateNode(node, template);
+          dispatch(
+            nodesChanged([
+              { type: 'remove', id: updatedNode.id },
+              { type: 'add', item: updatedNode },
+            ])
+          );
+        } catch (e) {
+          if (e instanceof NodeUpdateError) {
+            unableToUpdateCount++;
+          }
+        }
+      });
+
+      if (unableToUpdateCount) {
+        log.warn(
+          t('nodes.unableToUpdateNodes', {
+            count: unableToUpdateCount,
+          })
+        );
+        toast({
+          id: 'UNABLE_TO_UPDATE_NODES',
+          title: t('nodes.unableToUpdateNodes', {
+            count: unableToUpdateCount,
+          }),
+        });
+      } else {
+        toast({
+          id: 'ALL_NODES_UPDATED',
+          title: t('nodes.allNodesUpdated'),
+          status: 'success',
+        });
+      }
+    },
+  });
+};

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

@@ -3,6 +3,7 @@ import { autoBatchEnhancer, combineReducers, configureStore } from '@reduxjs/too
 import { logger } from 'app/logging/logger';
 import { idbKeyValDriver } from 'app/store/enhancers/reduxRemember/driver';
 import { errorHandler } from 'app/store/enhancers/reduxRemember/errors';
+import { getDebugLoggerMiddleware } from 'app/store/middleware/debugLoggerMiddleware';
 import { deepClone } from 'common/util/deepClone';
 import { changeBoardModalSlice } from 'features/changeBoardModal/store/slice';
 import { canvasSettingsPersistConfig, canvasSettingsSlice } from 'features/controlLayers/store/canvasSettingsSlice';
@@ -21,6 +22,7 @@ import { modelManagerV2PersistConfig, modelManagerV2Slice } from 'features/model
 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 { workflowPersistConfig, workflowSlice } from 'features/nodes/store/workflowSlice';
 import { upscalePersistConfig, upscaleSlice } from 'features/parameters/store/upscaleSlice';
 import { queueSlice } from 'features/queue/store/queueSlice';
 import { stylePresetPersistConfig, stylePresetSlice } from 'features/stylePresets/store/stylePresetSlice';
@@ -58,6 +60,7 @@ const allReducers = {
   [changeBoardModalSlice.name]: changeBoardModalSlice.reducer,
   [modelManagerV2Slice.name]: modelManagerV2Slice.reducer,
   [queueSlice.name]: queueSlice.reducer,
+  [workflowSlice.name]: workflowSlice.reducer,
   [hrfSlice.name]: hrfSlice.reducer,
   [canvasSlice.name]: undoable(canvasSlice.reducer, canvasUndoableConfig),
   [workflowSettingsSlice.name]: workflowSettingsSlice.reducer,
@@ -100,6 +103,7 @@ const persistConfigs: { [key in keyof typeof allReducers]?: PersistConfig } = {
   [galleryPersistConfig.name]: galleryPersistConfig,
   [nodesPersistConfig.name]: nodesPersistConfig,
   [systemPersistConfig.name]: systemPersistConfig,
+  [workflowPersistConfig.name]: workflowPersistConfig,
   [uiPersistConfig.name]: uiPersistConfig,
   [dynamicPromptsPersistConfig.name]: dynamicPromptsPersistConfig,
   [modelManagerV2PersistConfig.name]: modelManagerV2PersistConfig,
@@ -172,6 +176,7 @@ export const createStore = (uniqueStoreKey?: string, persist = true) =>
         .concat(api.middleware)
         .concat(dynamicMiddlewares)
         .concat(authToastMiddleware)
+        .concat(getDebugLoggerMiddleware())
         .prepend(listenerMiddleware.middleware),
     enhancers: (getDefaultEnhancers) => {
       const _enhancers = getDefaultEnhancers().concat(autoBatchEnhancer());

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

@@ -28,7 +28,8 @@ export type AppFeature =
   | 'starterModels'
   | 'hfToken'
   | 'retryQueueItem'
-  | 'cancelAndClearAll';
+  | 'cancelAndClearAll'
+  | 'deployWorkflow';
 /**
  * A disable-able Stable Diffusion feature
  */
@@ -74,7 +75,6 @@ export type AppConfig = {
   allowPrivateBoards: boolean;
   allowPrivateStylePresets: boolean;
   allowClientSideUpload: boolean;
-  allowPublishWorkflows: boolean;
   disabledTabs: TabName[];
   disabledFeatures: AppFeature[];
   disabledSDFeatures: SDFeature[];

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/FLUXReduxImageInfluence.tsx

@@ -1,60 +0,0 @@
-import type { ComboboxOnChange } from '@invoke-ai/ui-library';
-import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
-import type { FLUXReduxImageInfluence as FLUXReduxImageInfluenceType } from 'features/controlLayers/store/types';
-import { isFLUXReduxImageInfluence } from 'features/controlLayers/store/types';
-import { memo, useCallback, useMemo } from 'react';
-import { useTranslation } from 'react-i18next';
-import { assert } from 'tsafe';
-
-type Props = {
-  imageInfluence: FLUXReduxImageInfluenceType;
-  onChange: (imageInfluence: FLUXReduxImageInfluenceType) => void;
-};
-
-export const FLUXReduxImageInfluence = memo(({ imageInfluence, onChange }: Props) => {
-  const { t } = useTranslation();
-
-  const options = useMemo(
-    () =>
-      [
-        {
-          label: t('controlLayers.fluxReduxImageInfluence.lowest'),
-          value: 'lowest',
-        },
-        {
-          label: t('controlLayers.fluxReduxImageInfluence.low'),
-          value: 'low',
-        },
-        {
-          label: t('controlLayers.fluxReduxImageInfluence.medium'),
-          value: 'medium',
-        },
-        {
-          label: t('controlLayers.fluxReduxImageInfluence.high'),
-          value: 'high',
-        },
-        {
-          label: t('controlLayers.fluxReduxImageInfluence.highest'),
-          value: 'highest',
-        },
-      ] satisfies { label: string; value: FLUXReduxImageInfluenceType }[],
-    [t]
-  );
-  const _onChange = useCallback<ComboboxOnChange>(
-    (v) => {
-      assert(isFLUXReduxImageInfluence(v?.value));
-      onChange(v.value);
-    },
-    [onChange]
-  );
-  const value = useMemo(() => options.find((o) => o.value === imageInfluence), [options, imageInfluence]);
-
-  return (
-    <FormControl>
-      <FormLabel m={0}>{t('controlLayers.fluxReduxImageInfluence.imageInfluence')}</FormLabel>
-      <Combobox value={value} options={options} onChange={_onChange} />
-    </FormControl>
-  );
-});
-
-FLUXReduxImageInfluence.displayName = 'FLUXReduxImageInfluence';

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/IPAdapterImagePreview.tsx

@@ -61,7 +61,7 @@ export const IPAdapterImagePreview = memo(
         )}
         {imageDTO && (
           <>
-            <DndImage imageDTO={imageDTO} borderWidth={1} borderStyle="solid" />
+            <DndImage imageDTO={imageDTO} />
             <Flex position="absolute" flexDir="column" top={2} insetInlineEnd={2} gap={1}>
               <DndImageIcon
                 onClick={handleResetControlImage}

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/IPAdapterMethod.tsx

@@ -50,7 +50,7 @@ export const IPAdapterMethod = memo(({ method, onChange }: Props) => {
   return (
     <FormControl>
       <InformationalPopover feature="ipAdapterMethod">
-        <FormLabel m={0}>{t('controlLayers.ipAdapterMethod.ipAdapterMethod')}</FormLabel>
+        <FormLabel>{t('controlLayers.ipAdapterMethod.ipAdapterMethod')}</FormLabel>
       </InformationalPopover>
       <Combobox value={value} options={options} onChange={_onChange} />
     </FormControl>

invokeai/frontend/web/src/features/controlLayers/components/IPAdapter/IPAdapterSettings.tsx

@@ -5,7 +5,6 @@ import { BeginEndStepPct } from 'features/controlLayers/components/common/BeginE
 import { CanvasEntitySettingsWrapper } from 'features/controlLayers/components/common/CanvasEntitySettingsWrapper';
 import { Weight } from 'features/controlLayers/components/common/Weight';
 import { CLIPVisionModel } from 'features/controlLayers/components/IPAdapter/CLIPVisionModel';
-import { FLUXReduxImageInfluence } from 'features/controlLayers/components/IPAdapter/FLUXReduxImageInfluence';
 import { IPAdapterMethod } from 'features/controlLayers/components/IPAdapter/IPAdapterMethod';
 import { IPAdapterSettingsEmptyState } from 'features/controlLayers/components/IPAdapter/IPAdapterSettingsEmptyState';
 import { useEntityIdentifierContext } from 'features/controlLayers/contexts/EntityIdentifierContext';
@@ -14,7 +13,6 @@ import { useCanvasIsBusy } from 'features/controlLayers/hooks/useCanvasIsBusy';
 import {
   referenceImageIPAdapterBeginEndStepPctChanged,
   referenceImageIPAdapterCLIPVisionModelChanged,
-  referenceImageIPAdapterFLUXReduxImageInfluenceChanged,
   referenceImageIPAdapterImageChanged,
   referenceImageIPAdapterMethodChanged,
   referenceImageIPAdapterModelChanged,
@@ -22,12 +20,7 @@ import {
 } from 'features/controlLayers/store/canvasSlice';
 import { selectIsFLUX } from 'features/controlLayers/store/paramsSlice';
 import { selectCanvasSlice, selectEntity, selectEntityOrThrow } from 'features/controlLayers/store/selectors';
-import type {
-  CanvasEntityIdentifier,
-  CLIPVisionModelV2,
-  FLUXReduxImageInfluence as FLUXReduxImageInfluenceType,
-  IPMethodV2,
-} from 'features/controlLayers/store/types';
+import type { CanvasEntityIdentifier, CLIPVisionModelV2, IPMethodV2 } from 'features/controlLayers/store/types';
 import type { SetGlobalReferenceImageDndTargetData } from 'features/dnd/dnd';
 import { setGlobalReferenceImageDndTarget } from 'features/dnd/dnd';
 import { memo, useCallback, useMemo } from 'react';
@@ -72,13 +65,6 @@ const IPAdapterSettingsContent = memo(() => {
     [dispatch, entityIdentifier]
   );
 
-  const onChangeFLUXReduxImageInfluence = useCallback(
-    (imageInfluence: FLUXReduxImageInfluenceType) => {
-      dispatch(referenceImageIPAdapterFLUXReduxImageInfluenceChanged({ entityIdentifier, imageInfluence }));
-    },
-    [dispatch, entityIdentifier]
-  );
-
   const onChangeModel = useCallback(
     (modelConfig: IPAdapterModelConfig | FLUXReduxModelConfig) => {
       dispatch(referenceImageIPAdapterModelChanged({ entityIdentifier, modelConfig }));
@@ -130,7 +116,7 @@ const IPAdapterSettingsContent = memo(() => {
             icon={<PiBoundingBoxBold />}
           />
         </Flex>
-        <Flex gap={2} w="full">
+        <Flex gap={2} w="full" alignItems="center">
           {ipAdapter.type === 'ip_adapter' && (
             <Flex flexDir="column" gap={2} w="full">
               {!isFLUX && <IPAdapterMethod method={ipAdapter.method} onChange={onChangeIPMethod} />}
@@ -138,14 +124,6 @@ const IPAdapterSettingsContent = memo(() => {
               <BeginEndStepPct beginEndStepPct={ipAdapter.beginEndStepPct} onChange={onChangeBeginEndStepPct} />
             </Flex>
           )}
-          {ipAdapter.type === 'flux_redux' && (
-            <Flex flexDir="column" gap={2} w="full" alignItems="flex-start">
-              <FLUXReduxImageInfluence
-                imageInfluence={ipAdapter.imageInfluence ?? 'lowest'}
-                onChange={onChangeFLUXReduxImageInfluence}
-              />
-            </Flex>
-          )}
           <Flex alignItems="center" justifyContent="center" h={32} w={32} aspectRatio="1/1" flexGrow={1}>
             <IPAdapterImagePreview
               image={ipAdapter.image}

invokeai/frontend/web/src/features/controlLayers/components/RegionalGuidance/RegionalGuidanceIPAdapterSettings.tsx

@@ -4,7 +4,6 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { BeginEndStepPct } from 'features/controlLayers/components/common/BeginEndStepPct';
 import { Weight } from 'features/controlLayers/components/common/Weight';
 import { CLIPVisionModel } from 'features/controlLayers/components/IPAdapter/CLIPVisionModel';
-import { FLUXReduxImageInfluence } from 'features/controlLayers/components/IPAdapter/FLUXReduxImageInfluence';
 import { IPAdapterImagePreview } from 'features/controlLayers/components/IPAdapter/IPAdapterImagePreview';
 import { IPAdapterMethod } from 'features/controlLayers/components/IPAdapter/IPAdapterMethod';
 import { IPAdapterModel } from 'features/controlLayers/components/IPAdapter/IPAdapterModel';
@@ -16,19 +15,13 @@ import {
   rgIPAdapterBeginEndStepPctChanged,
   rgIPAdapterCLIPVisionModelChanged,
   rgIPAdapterDeleted,
-  rgIPAdapterFLUXReduxImageInfluenceChanged,
   rgIPAdapterImageChanged,
   rgIPAdapterMethodChanged,
   rgIPAdapterModelChanged,
   rgIPAdapterWeightChanged,
 } from 'features/controlLayers/store/canvasSlice';
 import { selectCanvasSlice, selectRegionalGuidanceReferenceImage } from 'features/controlLayers/store/selectors';
-import type {
-  CanvasEntityIdentifier,
-  CLIPVisionModelV2,
-  FLUXReduxImageInfluence as FLUXReduxImageInfluenceType,
-  IPMethodV2,
-} from 'features/controlLayers/store/types';
+import type { CanvasEntityIdentifier, CLIPVisionModelV2, IPMethodV2 } from 'features/controlLayers/store/types';
 import type { SetRegionalGuidanceReferenceImageDndTargetData } from 'features/dnd/dnd';
 import { setRegionalGuidanceReferenceImageDndTarget } from 'features/dnd/dnd';
 import { memo, useCallback, useMemo } from 'react';
@@ -80,13 +73,6 @@ const RegionalGuidanceIPAdapterSettingsContent = memo(({ referenceImageId }: Pro
     [dispatch, entityIdentifier, referenceImageId]
   );
 
-  const onChangeFLUXReduxImageInfluence = useCallback(
-    (imageInfluence: FLUXReduxImageInfluenceType) => {
-      dispatch(rgIPAdapterFLUXReduxImageInfluenceChanged({ entityIdentifier, referenceImageId, imageInfluence }));
-    },
-    [dispatch, entityIdentifier, referenceImageId]
-  );
-
   const onChangeModel = useCallback(
     (modelConfig: IPAdapterModelConfig | FLUXReduxModelConfig) => {
       dispatch(rgIPAdapterModelChanged({ entityIdentifier, referenceImageId, modelConfig }));
@@ -165,14 +151,6 @@ const RegionalGuidanceIPAdapterSettingsContent = memo(({ referenceImageId }: Pro
               <BeginEndStepPct beginEndStepPct={ipAdapter.beginEndStepPct} onChange={onChangeBeginEndStepPct} />
             </Flex>
           )}
-          {ipAdapter.type === 'flux_redux' && (
-            <Flex flexDir="column" gap={2} w="full">
-              <FLUXReduxImageInfluence
-                imageInfluence={ipAdapter.imageInfluence ?? 'lowest'}
-                onChange={onChangeFLUXReduxImageInfluence}
-              />
-            </Flex>
-          )}
           <Flex alignItems="center" justifyContent="center" h={32} w={32} aspectRatio="1/1" flexGrow={1}>
             <IPAdapterImagePreview
               image={ipAdapter.image}

invokeai/frontend/web/src/features/controlLayers/konva/CanvasTool/CanvasColorPickerToolModule.ts

@@ -407,7 +407,8 @@ export class CanvasColorPickerToolModule extends CanvasModuleBase {
 
   onStagePointerUp = (_e: KonvaEventObject<PointerEvent>) => {
     const color = this.$colorUnderCursor.get();
-    this.manager.stateApi.setColor({ ...color, a: color.a / 255 });
+    const settings = this.manager.stateApi.getSettings();
+    this.manager.stateApi.setColor({ ...settings.color, ...color });
   };
 
   onStagePointerMove = (_e: KonvaEventObject<PointerEvent>) => {

invokeai/frontend/web/src/features/controlLayers/store/canvasSlice.ts

@@ -21,7 +21,6 @@ import type {
   ControlLoRAConfig,
   EntityMovedByPayload,
   FillStyle,
-  FLUXReduxImageInfluence,
   RegionalGuidanceReferenceImageState,
   RgbColor,
 } from 'features/controlLayers/store/types';
@@ -627,20 +626,6 @@ export const canvasSlice = createSlice({
       }
       entity.ipAdapter.method = method;
     },
-    referenceImageIPAdapterFLUXReduxImageInfluenceChanged: (
-      state,
-      action: PayloadAction<EntityIdentifierPayload<{ imageInfluence: FLUXReduxImageInfluence }, 'reference_image'>>
-    ) => {
-      const { entityIdentifier, imageInfluence } = action.payload;
-      const entity = selectEntity(state, entityIdentifier);
-      if (!entity) {
-        return;
-      }
-      if (entity.ipAdapter.type !== 'flux_redux') {
-        return;
-      }
-      entity.ipAdapter.imageInfluence = imageInfluence;
-    },
     referenceImageIPAdapterModelChanged: (
       state,
       action: PayloadAction<
@@ -941,26 +926,6 @@ export const canvasSlice = createSlice({
 
       referenceImage.ipAdapter.method = method;
     },
-    rgIPAdapterFLUXReduxImageInfluenceChanged: (
-      state,
-      action: PayloadAction<
-        EntityIdentifierPayload<
-          { referenceImageId: string; imageInfluence: FLUXReduxImageInfluence },
-          'regional_guidance'
-        >
-      >
-    ) => {
-      const { entityIdentifier, referenceImageId, imageInfluence } = action.payload;
-      const referenceImage = selectRegionalGuidanceReferenceImage(state, entityIdentifier, referenceImageId);
-      if (!referenceImage) {
-        return;
-      }
-      if (referenceImage.ipAdapter.type !== 'flux_redux') {
-        return;
-      }
-
-      referenceImage.ipAdapter.imageInfluence = imageInfluence;
-    },
     rgIPAdapterModelChanged: (
       state,
       action: PayloadAction<
@@ -1766,7 +1731,6 @@ export const {
   referenceImageIPAdapterCLIPVisionModelChanged,
   referenceImageIPAdapterWeightChanged,
   referenceImageIPAdapterBeginEndStepPctChanged,
-  referenceImageIPAdapterFLUXReduxImageInfluenceChanged,
   // Regions
   rgAdded,
   // rgRecalled,
@@ -1782,7 +1746,6 @@ export const {
   rgIPAdapterMethodChanged,
   rgIPAdapterModelChanged,
   rgIPAdapterCLIPVisionModelChanged,
-  rgIPAdapterFLUXReduxImageInfluenceChanged,
   // Inpaint mask
   inpaintMaskAdded,
   inpaintMaskConvertedToRegionalGuidance,

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

@@ -233,15 +233,10 @@ const zIPAdapterConfig = z.object({
 });
 export type IPAdapterConfig = z.infer<typeof zIPAdapterConfig>;
 
-const zFLUXReduxImageInfluence = z.enum(['lowest', 'low', 'medium', 'high', 'highest']);
-export const isFLUXReduxImageInfluence = (v: unknown): v is FLUXReduxImageInfluence =>
-  zFLUXReduxImageInfluence.safeParse(v).success;
-export type FLUXReduxImageInfluence = z.infer<typeof zFLUXReduxImageInfluence>;
 const zFLUXReduxConfig = z.object({
   type: z.literal('flux_redux'),
   image: zImageWithDims.nullable(),
   model: zServerValidatedModelIdentifierField.nullable(),
-  imageInfluence: zFLUXReduxImageInfluence.default('highest'),
 });
 export type FLUXReduxConfig = z.infer<typeof zFLUXReduxConfig>;
 

invokeai/frontend/web/src/features/controlLayers/store/util.ts

@@ -75,7 +75,6 @@ export const initialFLUXRedux: FLUXReduxConfig = {
   type: 'flux_redux',
   image: null,
   model: null,
-  imageInfluence: 'highest',
 };
 export const initialT2IAdapter: T2IAdapterConfig = {
   type: 't2i_adapter',

invokeai/frontend/web/src/features/gallery/hooks/useGalleryHotkeys.ts

@@ -49,11 +49,7 @@ export const useGalleryHotkeys = () => {
   useRegisteredHotkeys({
     id: 'galleryNavLeft',
     category: 'gallery',
-    callback: (e) => {
-      // Skip the hotkey if the user is focused on a tab element - the arrow keys are used to navigate between tabs.
-      if (e.target instanceof HTMLElement && e.target.getAttribute('role') === 'tab') {
-        return;
-      }
+    callback: () => {
       if (isOnFirstImageOfView && isPrevEnabled && !queryResult.isFetching) {
         goPrev('arrow');
         return;
@@ -75,11 +71,7 @@ export const useGalleryHotkeys = () => {
   useRegisteredHotkeys({
     id: 'galleryNavRight',
     category: 'gallery',
-    callback: (e) => {
-      // Skip the hotkey if the user is focused on a tab element - the arrow keys are used to navigate between tabs.
-      if (e.target instanceof HTMLElement && e.target.getAttribute('role') === 'tab') {
-        return;
-      }
+    callback: () => {
       if (isOnLastImageOfView && isNextEnabled && !queryResult.isFetching) {
         goNext('arrow');
         return;

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/ClearFlowButton.tsx

@@ -1,7 +1,7 @@
 import { ConfirmationAlertDialog, Flex, IconButton, Text, useDisclosure } from '@invoke-ai/ui-library';
-import { useAppDispatch } from 'app/store/storeHooks';
-import { useDoesWorkflowHaveUnsavedChanges } from 'features/nodes/components/sidePanel/workflow/IsolatedWorkflowBuilderWatcher';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import { nodeEditorReset } from 'features/nodes/store/nodesSlice';
+import { selectWorkflowIsTouched } from 'features/nodes/store/workflowSlice';
 import { toast } from 'features/toast/toast';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -11,7 +11,7 @@ const ClearFlowButton = () => {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
   const { isOpen, onOpen, onClose } = useDisclosure();
-  const doesWorkflowHaveUnsavedChanges = useDoesWorkflowHaveUnsavedChanges();
+  const isTouched = useAppSelector(selectWorkflowIsTouched);
 
   const handleNewWorkflow = useCallback(() => {
     dispatch(nodeEditorReset());
@@ -26,12 +26,12 @@ const ClearFlowButton = () => {
   }, [dispatch, onClose, t]);
 
   const onClick = useCallback(() => {
-    if (doesWorkflowHaveUnsavedChanges) {
+    if (!isTouched) {
       handleNewWorkflow();
       return;
     }
     onOpen();
-  }, [doesWorkflowHaveUnsavedChanges, handleNewWorkflow, onOpen]);
+  }, [handleNewWorkflow, isTouched, onOpen]);
 
   return (
     <>

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/SaveWorkflowButton.tsx

@@ -1,5 +1,6 @@
 import { IconButton } from '@invoke-ai/ui-library';
-import { useDoesWorkflowHaveUnsavedChanges } from 'features/nodes/components/sidePanel/workflow/IsolatedWorkflowBuilderWatcher';
+import { useAppSelector } from 'app/store/storeHooks';
+import { selectWorkflowIsTouched } from 'features/nodes/store/workflowSlice';
 import { useSaveOrSaveAsWorkflow } from 'features/workflowLibrary/hooks/useSaveOrSaveAsWorkflow';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
@@ -7,7 +8,7 @@ import { PiFloppyDiskBold } from 'react-icons/pi';
 
 const SaveWorkflowButton = () => {
   const { t } = useTranslation();
-  const doesWorkflowHaveUnsavedChanges = useDoesWorkflowHaveUnsavedChanges();
+  const isTouched = useAppSelector(selectWorkflowIsTouched);
   const saveOrSaveAsWorkflow = useSaveOrSaveAsWorkflow();
 
   return (
@@ -15,7 +16,7 @@ const SaveWorkflowButton = () => {
       tooltip={t('workflows.saveWorkflow')}
       aria-label={t('workflows.saveWorkflow')}
       icon={<PiFloppyDiskBold />}
-      isDisabled={!doesWorkflowHaveUnsavedChanges}
+      isDisabled={!isTouched}
       onClick={saveOrSaveAsWorkflow}
       pointerEvents="auto"
     />

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/TopCenterPanel.tsx

@@ -1,7 +1,7 @@
 import { Flex } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
 import { WorkflowName } from 'features/nodes/components/sidePanel/WorkflowName';
-import { selectWorkflowName } from 'features/nodes/store/selectors';
+import { selectWorkflowName } from 'features/nodes/store/workflowSlice';
 import { memo } from 'react';
 
 export const TopCenterPanel = memo(() => {

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/TopLeftPanel.tsx

@@ -1,23 +1,19 @@
 import { Alert, AlertDescription, AlertIcon, AlertTitle, Box, Flex } from '@invoke-ai/ui-library';
 import { useStore } from '@nanostores/react';
+import { useAppSelector } from 'app/store/storeHooks';
 import AddNodeButton from 'features/nodes/components/flow/panels/TopPanel/AddNodeButton';
 import UpdateNodesButton from 'features/nodes/components/flow/panels/TopPanel/UpdateNodesButton';
-import {
-  $isInPublishFlow,
-  $isSelectingOutputNode,
-  useIsValidationRunInProgress,
-  useIsWorkflowPublished,
-} from 'features/nodes/components/sidePanel/workflow/publish';
+import { $isInPublishFlow, useIsValidationRunInProgress } from 'features/nodes/components/sidePanel/workflow/publish';
 import { useIsWorkflowEditorLocked } from 'features/nodes/hooks/useIsWorkflowEditorLocked';
+import { selectWorkflowIsPublished } from 'features/nodes/store/workflowSlice';
 import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 
 export const TopLeftPanel = memo(() => {
   const isLocked = useIsWorkflowEditorLocked();
   const isInPublishFlow = useStore($isInPublishFlow);
-  const isPublished = useIsWorkflowPublished();
+  const isPublished = useAppSelector(selectWorkflowIsPublished);
   const isValidationRunInProgress = useIsValidationRunInProgress();
-  const isSelectingOutputNode = useStore($isSelectingOutputNode);
 
   const { t } = useTranslation();
   return (
@@ -38,16 +34,11 @@ export const TopLeftPanel = memo(() => {
                 {t('workflows.builder.publishingValidationRunInProgress')}
               </AlertDescription>
             )}
-            {isInPublishFlow && !isValidationRunInProgress && !isSelectingOutputNode && (
+            {isInPublishFlow && !isValidationRunInProgress && (
               <AlertDescription whiteSpace="pre-wrap">
                 {t('workflows.builder.workflowLockedDuringPublishing')}
               </AlertDescription>
             )}
-            {isInPublishFlow && !isValidationRunInProgress && isSelectingOutputNode && (
-              <AlertDescription whiteSpace="pre-wrap">
-                {t('workflows.builder.selectingOutputNodeDesc')}
-              </AlertDescription>
-            )}
             {isPublished && (
               <AlertDescription whiteSpace="pre-wrap">
                 {t('workflows.builder.workflowLockedPublished')}

invokeai/frontend/web/src/features/nodes/components/flow/panels/TopPanel/UpdateNodesButton.tsx

@@ -1,82 +1,18 @@
 import { IconButton } from '@invoke-ai/ui-library';
-import { logger } from 'app/logging/logger';
-import { useAppStore } from 'app/store/storeHooks';
+import { useAppDispatch } from 'app/store/storeHooks';
 import { useGetNodesNeedUpdate } from 'features/nodes/hooks/useGetNodesNeedUpdate';
-import { $templates, nodesChanged } from 'features/nodes/store/nodesSlice';
-import { selectNodes } from 'features/nodes/store/selectors';
-import { NodeUpdateError } from 'features/nodes/types/error';
-import { isInvocationNode } from 'features/nodes/types/invocation';
-import { getNeedsUpdate, updateNode } from 'features/nodes/util/node/nodeUpdate';
-import { toast } from 'features/toast/toast';
+import { updateAllNodesRequested } from 'features/nodes/store/actions';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiWarningBold } from 'react-icons/pi';
 
-const log = logger('workflows');
-
-const useUpdateNodes = () => {
-  const store = useAppStore();
-  const { t } = useTranslation();
-
-  const updateNodes = useCallback(() => {
-    const nodes = selectNodes(store.getState());
-    const templates = $templates.get();
-
-    let unableToUpdateCount = 0;
-
-    nodes.filter(isInvocationNode).forEach((node) => {
-      const template = templates[node.data.type];
-      if (!template) {
-        unableToUpdateCount++;
-        return;
-      }
-      if (!getNeedsUpdate(node.data, template)) {
-        // No need to increment the count here, since we're not actually updating
-        return;
-      }
-      try {
-        const updatedNode = updateNode(node, template);
-        store.dispatch(
-          nodesChanged([
-            { type: 'remove', id: updatedNode.id },
-            { type: 'add', item: updatedNode },
-          ])
-        );
-      } catch (e) {
-        if (e instanceof NodeUpdateError) {
-          unableToUpdateCount++;
-        }
-      }
-    });
-
-    if (unableToUpdateCount) {
-      log.warn(
-        t('nodes.unableToUpdateNodes', {
-          count: unableToUpdateCount,
-        })
-      );
-      toast({
-        id: 'UNABLE_TO_UPDATE_NODES',
-        title: t('nodes.unableToUpdateNodes', {
-          count: unableToUpdateCount,
-        }),
-      });
-    } else {
-      toast({
-        id: 'ALL_NODES_UPDATED',
-        title: t('nodes.allNodesUpdated'),
-        status: 'success',
-      });
-    }
-  }, [store, t]);
-
-  return updateNodes;
-};
-
 const UpdateNodesButton = () => {
+  const dispatch = useAppDispatch();
   const { t } = useTranslation();
   const nodesNeedUpdate = useGetNodesNeedUpdate();
-  const updateNodes = useUpdateNodes();
+  const handleClickUpdateNodes = useCallback(() => {
+    dispatch(updateAllNodesRequested());
+  }, [dispatch]);
 
   if (!nodesNeedUpdate) {
     return null;
@@ -87,7 +23,7 @@ const UpdateNodesButton = () => {
       tooltip={t('nodes.updateAllNodes')}
       aria-label={t('nodes.updateAllNodes')}
       icon={<PiWarningBold />}
-      onClick={updateNodes}
+      onClick={handleClickUpdateNodes}
       pointerEvents="auto"
       colorScheme="warning"
     />

invokeai/frontend/web/src/features/nodes/components/sidePanel/PublishedWorkflowPanelContent.tsx

@@ -1,37 +1,12 @@
 import { Button, Flex, Heading, Text } from '@invoke-ai/ui-library';
-import { useAppSelector } from 'app/store/storeHooks';
-import { selectWorkflowId } from 'features/nodes/store/selectors';
-import { toast } from 'features/toast/toast';
 import { useSaveOrSaveAsWorkflow } from 'features/workflowLibrary/hooks/useSaveOrSaveAsWorkflow';
-import { memo, useCallback } from 'react';
+import { memo } from 'react';
 import { useTranslation } from 'react-i18next';
 import { PiCopyBold, PiLockOpenBold } from 'react-icons/pi';
-import { useUnpublishWorkflowMutation } from 'services/api/endpoints/workflows';
 
 export const PublishedWorkflowPanelContent = memo(() => {
   const { t } = useTranslation();
   const saveAs = useSaveOrSaveAsWorkflow();
-  const [unpublishWorkflow] = useUnpublishWorkflowMutation();
-  const workflowId = useAppSelector(selectWorkflowId);
-
-  const handleUnpublish = useCallback(async () => {
-    if (workflowId) {
-      try {
-        await unpublishWorkflow(workflowId).unwrap();
-        toast({
-          title: t('toast.workflowUnpublished'),
-          status: 'success',
-        });
-      } catch (error) {
-        toast({
-          title: t('toast.problemUnpublishingWorkflow'),
-          description: t('toast.problemUnpublishingWorkflowDescription'),
-          status: 'error',
-        });
-      }
-    }
-  }, [unpublishWorkflow, workflowId, t]);
-
   return (
     <Flex flexDir="column" w="full" h="full" gap={2} alignItems="center">
       <Heading size="md" pt={32}>
@@ -41,7 +16,7 @@ export const PublishedWorkflowPanelContent = memo(() => {
       <Button size="md" onClick={saveAs} variant="ghost" leftIcon={<PiCopyBold />}>
         {t('common.saveAs')}
       </Button>
-      <Button size="md" onClick={handleUnpublish} variant="ghost" leftIcon={<PiLockOpenBold />}>
+      <Button size="md" onClick={undefined} variant="ghost" leftIcon={<PiLockOpenBold />}>
         {t('workflows.builder.unpublish')}
       </Button>
     </Flex>

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowListMenu/ActiveWorkflowDescription.tsx

@@ -1,7 +1,7 @@
 import { Text, Tooltip } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
 import { linkifyOptions, linkifySx } from 'common/components/linkify';
-import { selectWorkflowDescription } from 'features/nodes/store/selectors';
+import { selectWorkflowDescription } from 'features/nodes/store/workflowSlice';
 import Linkify from 'linkify-react';
 import { memo } from 'react';
 

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowListMenu/ActiveWorkflowNameAndActions.tsx

@@ -1,9 +1,8 @@
 import { Flex, Spacer } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
-import { useIsWorkflowPublished } from 'features/nodes/components/sidePanel/workflow/publish';
 import { WorkflowListMenuTrigger } from 'features/nodes/components/sidePanel/WorkflowListMenu/WorkflowListMenuTrigger';
 import { WorkflowViewEditToggleButton } from 'features/nodes/components/sidePanel/WorkflowViewEditToggleButton';
-import { selectWorkflowMode } from 'features/nodes/store/workflowLibrarySlice';
+import { selectWorkflowIsPublished, selectWorkflowMode } from 'features/nodes/store/workflowSlice';
 import { WorkflowLibraryMenu } from 'features/workflowLibrary/components/WorkflowLibraryMenu/WorkflowLibraryMenu';
 import { memo } from 'react';
 
@@ -11,7 +10,7 @@ import SaveWorkflowButton from './SaveWorkflowButton';
 
 export const ActiveWorkflowNameAndActions = memo(() => {
   const mode = useAppSelector(selectWorkflowMode);
-  const isPublished = useIsWorkflowPublished();
+  const isPublished = useAppSelector(selectWorkflowIsPublished);
 
   return (
     <Flex w="full" alignItems="center" gap={1} minW={0}>

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowListMenu/WorkflowListMenuTrigger.tsx

@@ -1,7 +1,7 @@
 import { Button, Text } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
-import { selectWorkflowName } from 'features/nodes/store/selectors';
 import { useWorkflowLibraryModal } from 'features/nodes/store/workflowLibraryModal';
+import { selectWorkflowName } from 'features/nodes/store/workflowSlice';
 import { useTranslation } from 'react-i18next';
 import { PiFolderOpenFill } from 'react-icons/pi';
 

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowName.tsx

@@ -1,8 +1,6 @@
 import { Flex, Icon, Text, Tooltip } from '@invoke-ai/ui-library';
 import { useAppSelector } from 'app/store/storeHooks';
-import { useDoesWorkflowHaveUnsavedChanges } from 'features/nodes/components/sidePanel/workflow/IsolatedWorkflowBuilderWatcher';
-import { selectWorkflowName } from 'features/nodes/store/selectors';
-import { selectWorkflowMode } from 'features/nodes/store/workflowLibrarySlice';
+import { selectWorkflowIsTouched, selectWorkflowMode, selectWorkflowName } from 'features/nodes/store/workflowSlice';
 import { useTranslation } from 'react-i18next';
 import { PiDotOutlineFill } from 'react-icons/pi';
 
@@ -12,7 +10,7 @@ import { WorkflowWarning } from './viewMode/WorkflowWarning';
 export const WorkflowName = () => {
   const { t } = useTranslation();
   const name = useAppSelector(selectWorkflowName);
-  const doesWorkflowHaveUnsavedChanges = useDoesWorkflowHaveUnsavedChanges();
+  const isTouched = useAppSelector(selectWorkflowIsTouched);
   const mode = useAppSelector(selectWorkflowMode);
 
   return (
@@ -29,10 +27,10 @@ export const WorkflowName = () => {
         </Text>
       )}
 
-      {doesWorkflowHaveUnsavedChanges && mode === 'edit' && (
+      {isTouched && mode === 'edit' && (
         <Tooltip label={t('nodes.newWorkflowDesc2')}>
           <Flex>
-            <Icon as={PiDotOutlineFill} boxSize="20px" color="invokeYellow.500" />
+            <Icon as={PiDotOutlineFill} boxSize="20px" sx={{ color: 'invokeYellow.500' }} />
           </Flex>
         </Tooltip>
       )}

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowViewEditToggleButton.tsx

@@ -1,6 +1,6 @@
 import { IconButton } from '@invoke-ai/ui-library';
 import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { selectWorkflowMode, workflowModeChanged } from 'features/nodes/store/workflowLibrarySlice';
+import { selectWorkflowMode, workflowModeChanged } from 'features/nodes/store/workflowSlice';
 import type { MouseEventHandler } from 'react';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';

invokeai/frontend/web/src/features/nodes/components/sidePanel/WorkflowsTabLeftPanel.tsx

@@ -3,18 +3,18 @@ import { useStore } from '@nanostores/react';
 import { useAppSelector } from 'app/store/storeHooks';
 import { EditModeLeftPanelContent } from 'features/nodes/components/sidePanel/EditModeLeftPanelContent';
 import { PublishedWorkflowPanelContent } from 'features/nodes/components/sidePanel/PublishedWorkflowPanelContent';
-import { $isInPublishFlow, useIsWorkflowPublished } from 'features/nodes/components/sidePanel/workflow/publish';
+import { $isInPublishFlow } from 'features/nodes/components/sidePanel/workflow/publish';
 import { PublishWorkflowPanelContent } from 'features/nodes/components/sidePanel/workflow/PublishWorkflowPanelContent';
 import { ActiveWorkflowDescription } from 'features/nodes/components/sidePanel/WorkflowListMenu/ActiveWorkflowDescription';
 import { ActiveWorkflowNameAndActions } from 'features/nodes/components/sidePanel/WorkflowListMenu/ActiveWorkflowNameAndActions';
-import { selectWorkflowMode } from 'features/nodes/store/workflowLibrarySlice';
+import { selectWorkflowIsPublished, selectWorkflowMode } from 'features/nodes/store/workflowSlice';
 import { memo } from 'react';
 
 import { ViewModeLeftPanelContent } from './viewMode/ViewModeLeftPanelContent';
 
 const WorkflowsTabLeftPanel = () => {
   const mode = useAppSelector(selectWorkflowMode);
-  const isPublished = useIsWorkflowPublished();
+  const isPublished = useAppSelector(selectWorkflowIsPublished);
   const isInPublishFlow = useStore($isInPublishFlow);
 
   return (

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/ContainerElement.tsx

@@ -16,9 +16,7 @@ import { FormElementEditModeHeader } from 'features/nodes/components/sidePanel/b
 import { HeadingElement } from 'features/nodes/components/sidePanel/builder/HeadingElement';
 import { NodeFieldElement } from 'features/nodes/components/sidePanel/builder/NodeFieldElement';
 import { TextElement } from 'features/nodes/components/sidePanel/builder/TextElement';
-import { useElement } from 'features/nodes/components/sidePanel/builder/use-element';
-import { selectFormRootElement } from 'features/nodes/store/selectors';
-import { selectWorkflowMode } from 'features/nodes/store/workflowLibrarySlice';
+import { selectFormRootElement, selectWorkflowMode, useElement } from 'features/nodes/store/workflowSlice';
 import type { ContainerElement } from 'features/nodes/types/workflow';
 import {
   CONTAINER_CLASS_NAME,

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/ContainerElementSettings.tsx

@@ -12,7 +12,7 @@ import {
   Portal,
 } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
-import { formElementContainerDataChanged } from 'features/nodes/store/nodesSlice';
+import { formElementContainerDataChanged } from 'features/nodes/store/workflowSlice';
 import type { ContainerElement } from 'features/nodes/types/workflow';
 import { memo, useCallback } from 'react';
 import { useTranslation } from 'react-i18next';

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/DividerElement.tsx

@@ -1,8 +1,7 @@
 import { useAppSelector } from 'app/store/storeHooks';
 import { DividerElementEditMode } from 'features/nodes/components/sidePanel/builder/DividerElementEditMode';
 import { DividerElementViewMode } from 'features/nodes/components/sidePanel/builder/DividerElementViewMode';
-import { useElement } from 'features/nodes/components/sidePanel/builder/use-element';
-import { selectWorkflowMode } from 'features/nodes/store/workflowLibrarySlice';
+import { selectWorkflowMode, useElement } from 'features/nodes/store/workflowSlice';
 import { isDividerElement } from 'features/nodes/types/workflow';
 import { memo } from 'react';
 

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/FormElementEditModeHeader.tsx

@@ -7,7 +7,7 @@ import { useDepthContext } from 'features/nodes/components/sidePanel/builder/con
 import { NodeFieldElementSettings } from 'features/nodes/components/sidePanel/builder/NodeFieldElementSettings';
 import { useMouseOverFormField } from 'features/nodes/hooks/useMouseOverNode';
 import { useZoomToNode } from 'features/nodes/hooks/useZoomToNode';
-import { formElementRemoved } from 'features/nodes/store/nodesSlice';
+import { formElementRemoved } from 'features/nodes/store/workflowSlice';
 import type { FormElement, NodeFieldElement } from 'features/nodes/types/workflow';
 import { isContainerElement, isNodeFieldElement } from 'features/nodes/types/workflow';
 import { camelCase } from 'lodash-es';

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/HeadingElement.tsx

@@ -1,8 +1,7 @@
 import { useAppSelector } from 'app/store/storeHooks';
 import { HeadingElementEditMode } from 'features/nodes/components/sidePanel/builder/HeadingElementEditMode';
 import { HeadingElementViewMode } from 'features/nodes/components/sidePanel/builder/HeadingElementViewMode';
-import { useElement } from 'features/nodes/components/sidePanel/builder/use-element';
-import { selectWorkflowMode } from 'features/nodes/store/workflowLibrarySlice';
+import { selectWorkflowMode, useElement } from 'features/nodes/store/workflowSlice';
 import { isHeadingElement } from 'features/nodes/types/workflow';
 import { memo } from 'react';
 

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/HeadingElementContentEditable.tsx

@@ -2,7 +2,7 @@ import { useAppDispatch } from 'app/store/storeHooks';
 import { useEditable } from 'common/hooks/useEditable';
 import { AutosizeTextarea } from 'features/nodes/components/sidePanel/builder/AutosizeTextarea';
 import { HeadingElementContent } from 'features/nodes/components/sidePanel/builder/HeadingElementContent';
-import { formElementHeadingDataChanged } from 'features/nodes/store/nodesSlice';
+import { formElementHeadingDataChanged } from 'features/nodes/store/workflowSlice';
 import type { HeadingElement } from 'features/nodes/types/workflow';
 import { memo, useCallback, useRef } from 'react';
 import { useTranslation } from 'react-i18next';

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/NodeFieldElement.tsx

@@ -1,8 +1,7 @@
 import { useAppSelector } from 'app/store/storeHooks';
 import { NodeFieldElementEditMode } from 'features/nodes/components/sidePanel/builder/NodeFieldElementEditMode';
 import { NodeFieldElementViewMode } from 'features/nodes/components/sidePanel/builder/NodeFieldElementViewMode';
-import { useElement } from 'features/nodes/components/sidePanel/builder/use-element';
-import { selectWorkflowMode } from 'features/nodes/store/workflowLibrarySlice';
+import { selectWorkflowMode, useElement } from 'features/nodes/store/workflowSlice';
 import { isNodeFieldElement } from 'features/nodes/types/workflow';
 import { memo } from 'react';
 

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/NodeFieldElementFloatSettings.tsx

@@ -3,7 +3,8 @@ import { useAppDispatch } from 'app/store/storeHooks';
 import { roundDownToMultiple, roundUpToMultiple } from 'common/util/roundDownToMultiple';
 import { useFloatField } from 'features/nodes/components/flow/nodes/Invocation/fields/FloatField/useFloatField';
 import { useInputFieldInstance } from 'features/nodes/hooks/useInputFieldInstance';
-import { fieldFloatValueChanged, formElementNodeFieldDataChanged } from 'features/nodes/store/nodesSlice';
+import { fieldFloatValueChanged } from 'features/nodes/store/nodesSlice';
+import { formElementNodeFieldDataChanged } from 'features/nodes/store/workflowSlice';
 import type { FloatFieldInputInstance, FloatFieldInputTemplate } from 'features/nodes/types/field';
 import { type NodeFieldFloatSettings, zNumberComponent } from 'features/nodes/types/workflow';
 import { constrainNumber } from 'features/nodes/util/constrainNumber';

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/NodeFieldElementIntegerSettings.tsx

@@ -3,7 +3,8 @@ import { useAppDispatch } from 'app/store/storeHooks';
 import { roundDownToMultiple, roundUpToMultiple } from 'common/util/roundDownToMultiple';
 import { useIntegerField } from 'features/nodes/components/flow/nodes/Invocation/fields/IntegerField/useIntegerField';
 import { useInputFieldInstance } from 'features/nodes/hooks/useInputFieldInstance';
-import { fieldIntegerValueChanged, formElementNodeFieldDataChanged } from 'features/nodes/store/nodesSlice';
+import { fieldIntegerValueChanged } from 'features/nodes/store/nodesSlice';
+import { formElementNodeFieldDataChanged } from 'features/nodes/store/workflowSlice';
 import type { IntegerFieldInputInstance, IntegerFieldInputTemplate } from 'features/nodes/types/field';
 import type { NodeFieldIntegerSettings } from 'features/nodes/types/workflow';
 import { zNumberComponent } from 'features/nodes/types/workflow';

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/NodeFieldElementSettings.tsx

@@ -16,7 +16,7 @@ import { NodeFieldElementFloatSettings } from 'features/nodes/components/sidePan
 import { NodeFieldElementIntegerSettings } from 'features/nodes/components/sidePanel/builder/NodeFieldElementIntegerSettings';
 import { NodeFieldElementStringSettings } from 'features/nodes/components/sidePanel/builder/NodeFieldElementStringSettings';
 import { useInputFieldTemplateOrThrow } from 'features/nodes/hooks/useInputFieldTemplateOrThrow';
-import { formElementNodeFieldDataChanged } from 'features/nodes/store/nodesSlice';
+import { formElementNodeFieldDataChanged } from 'features/nodes/store/workflowSlice';
 import {
   isFloatFieldInputTemplate,
   isIntegerFieldInputTemplate,

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/NodeFieldElementStringDropdownSettings.tsx

@@ -1,7 +1,7 @@
 import { Button, ButtonGroup, Divider, Flex, Grid, GridItem, IconButton, Input, Text } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
 import { getOverlayScrollbarsParams, overlayScrollbarsStyles } from 'common/components/OverlayScrollbars/constants';
-import { formElementNodeFieldDataChanged } from 'features/nodes/store/nodesSlice';
+import { formElementNodeFieldDataChanged } from 'features/nodes/store/workflowSlice';
 import { NO_DRAG_CLASS, NO_WHEEL_CLASS } from 'features/nodes/types/constants';
 import { getDefaultStringOption, type NodeFieldStringDropdownSettings } from 'features/nodes/types/workflow';
 import { OverlayScrollbarsComponent } from 'overlayscrollbars-react';

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/NodeFieldElementStringSettings.tsx

@@ -1,6 +1,6 @@
 import { FormControl, FormLabel, Select } from '@invoke-ai/ui-library';
 import { useAppDispatch } from 'app/store/storeHooks';
-import { formElementNodeFieldDataChanged } from 'features/nodes/store/nodesSlice';
+import { formElementNodeFieldDataChanged } from 'features/nodes/store/workflowSlice';
 import { getDefaultStringOption, type NodeFieldStringSettings, zStringComponent } from 'features/nodes/types/workflow';
 import { omit } from 'lodash-es';
 import type { ChangeEvent } from 'react';

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/TextElement.tsx

@@ -1,8 +1,7 @@
 import { useAppSelector } from 'app/store/storeHooks';
 import { TextElementEditMode } from 'features/nodes/components/sidePanel/builder/TextElementEditMode';
 import { TextElementViewMode } from 'features/nodes/components/sidePanel/builder/TextElementViewMode';
-import { useElement } from 'features/nodes/components/sidePanel/builder/use-element';
-import { selectWorkflowMode } from 'features/nodes/store/workflowLibrarySlice';
+import { selectWorkflowMode, useElement } from 'features/nodes/store/workflowSlice';
 import { isTextElement } from 'features/nodes/types/workflow';
 import { memo } from 'react';
 

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/TextElementContentEditable.tsx

@@ -2,7 +2,7 @@ import { useAppDispatch } from 'app/store/storeHooks';
 import { useEditable } from 'common/hooks/useEditable';
 import { AutosizeTextarea } from 'features/nodes/components/sidePanel/builder/AutosizeTextarea';
 import { TextElementContent } from 'features/nodes/components/sidePanel/builder/TextElementContent';
-import { formElementTextDataChanged } from 'features/nodes/store/nodesSlice';
+import { formElementTextDataChanged } from 'features/nodes/store/workflowSlice';
 import type { TextElement } from 'features/nodes/types/workflow';
 import { memo, useCallback, useRef } from 'react';
 import { useTranslation } from 'react-i18next';

invokeai/frontend/web/src/features/nodes/components/sidePanel/builder/WorkflowBuilder.tsx

@@ -11,7 +11,7 @@ import { RootContainerElementEditMode } from 'features/nodes/components/sidePane
 import { buildFormElementDndData, useBuilderDndMonitor } from 'features/nodes/components/sidePanel/builder/dnd-hooks';
 import { WorkflowBuilderEditMenu } from 'features/nodes/components/sidePanel/builder/WorkflowBuilderMenu';
 import { $hasTemplates } from 'features/nodes/store/nodesSlice';
-import { selectIsFormEmpty } from 'features/nodes/store/selectors';
+import { selectIsFormEmpty } from 'features/nodes/store/workflowSlice';
 import type { FormElement } from 'features/nodes/types/workflow';
 import { buildContainer, buildDivider, buildHeading, buildText } from 'features/nodes/types/workflow';
 import type { PropsWithChildren, RefObject } from 'react';