build: downgrade python to 3.11 in pins

The launcher will query this file to get the pins needed for installation

.dockerignore

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

.git-blame-ignore-revs

@@ -1,2 +1,5 @@
 b3dccfaeb636599c02effc377cdd8a87d658256c
 218b6d0546b990fc449c876fb99f44b50c4daa35
+182580ff6970caed400be178c5b888514b75d7f2
+8e9d5c1187b0d36da80571ce4c8ba9b3a37b6c46
+99aac5870e1092b182e6c5f21abcaab6936a4ad1

.github/workflows/build-container.yml

@@ -97,6 +97,8 @@ 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

@@ -17,7 +17,7 @@ jobs:
       - name: setup python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.10'
+          python-version: '3.12'
           cache: pip
           cache-dependency-path: pyproject.toml
 

.github/workflows/python-checks.yml

@@ -34,6 +34,9 @@ on:
 
 jobs:
   python-checks:
+    env:
+      # uv requires a venv by default - but for this, we can simply use the system python
+      UV_SYSTEM_PYTHON: 1
     runs-on: ubuntu-latest
     timeout-minutes: 5 # expected run time: <1 min
     steps:
@@ -57,25 +60,19 @@ jobs:
               - '!invokeai/frontend/web/**'
               - 'tests/**'
 
-      - name: setup python
+      - name: setup uv
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        uses: actions/setup-python@v5
+        uses: astral-sh/setup-uv@v5
         with:
-          python-version: '3.10'
-          cache: pip
-          cache-dependency-path: pyproject.toml
-
-      - name: install ruff
-        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: pip install ruff==0.9.9
-        shell: bash
+          version: '0.6.10'
+          enable-cache: true
 
       - name: ruff check
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: ruff check --output-format=github .
+        run: uv tool run ruff@0.11.2 check --output-format=github .
         shell: bash
 
       - name: ruff format
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: ruff format --check .
+        run: uv tool run ruff@0.11.2 format --check .
         shell: bash

.github/workflows/python-tests.yml

@@ -39,24 +39,15 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - '3.10'
           - '3.11'
+          - '3.12'
         platform:
-          - linux-cuda-11_7
-          - linux-rocm-5_2
           - linux-cpu
           - macos-default
           - windows-cpu
         include:
-          - platform: linux-cuda-11_7
-            os: ubuntu-22.04
-            github-env: $GITHUB_ENV
-          - platform: linux-rocm-5_2
-            os: ubuntu-22.04
-            extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
-            github-env: $GITHUB_ENV
           - platform: linux-cpu
-            os: ubuntu-22.04
+            os: ubuntu-24.04
             extra-index-url: 'https://download.pytorch.org/whl/cpu'
             github-env: $GITHUB_ENV
           - platform: macos-default
@@ -70,6 +61,8 @@ jobs:
     timeout-minutes: 15 # expected run time: 2-6 min, depending on platform
     env:
       PIP_USE_PEP517: '1'
+      UV_SYSTEM_PYTHON: 1
+
     steps:
       - name: checkout
         # https://github.com/nschloe/action-cached-lfs-checkout
@@ -92,20 +85,25 @@ jobs:
               - '!invokeai/frontend/web/**'
               - 'tests/**'
 
+      - name: setup uv
+        if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
+        uses: astral-sh/setup-uv@v5
+        with:
+          version: '0.6.10'
+          enable-cache: true
+          python-version: ${{ matrix.python-version }}
+
       - name: setup python
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          cache: pip
-          cache-dependency-path: pyproject.toml
 
       - name: install dependencies
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
         env:
-          PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
-        run: >
-          pip3 install --editable=".[test]"
+          UV_INDEX: ${{ matrix.extra-index-url }}
+        run: uv pip install --editable ".[test]"
 
       - name: run pytest
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}

.github/workflows/typegen-checks.yml

@@ -54,17 +54,25 @@ jobs:
               - 'pyproject.toml'
               - 'invokeai/**'
 
+      - name: setup uv
+        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
+        uses: astral-sh/setup-uv@v5
+        with:
+          version: '0.6.10'
+          enable-cache: true
+          python-version: '3.11'
+
       - name: setup python
         if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
         uses: actions/setup-python@v5
         with:
-          python-version: '3.10'
-          cache: pip
-          cache-dependency-path: pyproject.toml
+          python-version: '3.11'
 
-      - name: install python dependencies
+      - name: install dependencies
         if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        run: pip3 install --use-pep517 --editable="."
+        env:
+          UV_INDEX: ${{ matrix.extra-index-url }}
+        run: uv pip install --editable .
 
       - name: install frontend dependencies
         if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
@@ -77,7 +85,7 @@ jobs:
 
       - name: generate schema
         if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        run: make frontend-typegen
+        run: cd invokeai/frontend/web && uv run ../../../scripts/generate_openapi_schema.py | pnpm typegen
         shell: bash
 
       - name: compare files

.nvmrc

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

docker/Dockerfile

@@ -1,77 +1,6 @@
 # syntax=docker/dockerfile:1.4
 
-## 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 ------------------------------------
+#### Web UI ------------------------------------
 
 FROM docker.io/node:22-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
@@ -85,69 +14,89 @@ RUN --mount=type=cache,target=/pnpm/store \
     pnpm install --frozen-lockfile
 RUN npx vite build
 
-#### Runtime stage ---------------------------------------
+## Backend ---------------------------------------
 
-FROM library/ubuntu:24.04 AS runtime
+FROM library/ubuntu:24.04
 
 ARG DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV PYTHONDONTWRITEBYTECODE=1
+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
 
-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
+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}
 
-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}
+ARG GPU_DRIVER=cuda
 
 # Install `uv` for package management
-# 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
+COPY --from=ghcr.io/astral-sh/uv:0.6.9 /uv /uvx /bin/
 
-# --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"
+# 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 amdgpu.ids for ROCm builds
+# contributed by https://github.com/Rubonnek
+RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
+    ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
+
 RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
 
 COPY docker/docker-entrypoint.sh ./
 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

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 3.11 --python-preference only-managed --index=https://download.pytorch.org/whl/cu124 --reinstall
+      uv pip install -e ".[dev,test,docs,xformers]" --python 3.12 --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/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 3.11 --python-preference only-managed .venv
+    uv venv --relocatable --prompt invoke --python 3.12 --python-preference only-managed .venv
     ```
 
-    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.
+    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.
 
 4. Activate the virtual environment:
 
@@ -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 3.11 --python-preference only-managed --force-reinstall
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --force-reinstall
     ```
 
     If you determined you needed to use a `PyPI` index URL in the previous step, you'll need to add `--index=<INDEX_URL>` like this:
 
     ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.11 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
     ```
 
 9. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:

docs/installation/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 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.
+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.
 
 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 3.11 with [an official installer].
+        - Install python 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 3.11 with [an official installer].
+        - Install python 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. On Ubuntu, you can use the [deadsnakes PPA](https://launchpad.net/~deadsnakes/+archive/ubuntu/ppa).
+        - 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).
         - 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

invokeai/app/api/dependencies.py

@@ -37,7 +37,13 @@ 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 ConditioningFieldData
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
+    BasicConditioningInfo,
+    ConditioningFieldData,
+    FLUXConditioningInfo,
+    SD3ConditioningInfo,
+    SDXLConditioningInfo,
+)
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -101,10 +107,25 @@ class ApiDependencies:
         images = ImageService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         tensors = ObjectSerializerForwardCache(
-            ObjectSerializerDisk[torch.Tensor](output_folder / "tensors", ephemeral=True)
+            ObjectSerializerDisk[torch.Tensor](
+                output_folder / "tensors",
+                safe_globals=[torch.Tensor],
+                ephemeral=True,
+            ),
+            max_cache_size=0,
         )
         conditioning = ObjectSerializerForwardCache(
-            ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
+            ObjectSerializerDisk[ConditioningFieldData](
+                output_folder / "conditioning",
+                safe_globals=[
+                    ConditioningFieldData,
+                    BasicConditioningInfo,
+                    SDXLConditioningInfo,
+                    FLUXConditioningInfo,
+                    SD3ConditioningInfo,
+                ],
+                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/app_info.py

@@ -12,6 +12,7 @@ from pydantic import BaseModel, Field
 
 from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.invocations.upscale import ESRGAN_MODELS
+from invokeai.app.services.config.config_default import InvokeAIAppConfig, get_config
 from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
 from invokeai.backend.image_util.infill_methods.patchmatch import PatchMatch
 from invokeai.backend.util.logging import logging
@@ -99,7 +100,7 @@ async def get_app_deps() -> AppDependencyVersions:
 
 
 @app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
-async def get_config() -> AppConfig:
+async def get_config_() -> AppConfig:
     infill_methods = ["lama", "tile", "cv2", "color"]  # TODO: add mosaic back
     if PatchMatch.patchmatch_available():
         infill_methods.append("patchmatch")
@@ -121,6 +122,21 @@ async def get_config() -> AppConfig:
     )
 
 
+class InvokeAIAppConfigWithSetFields(BaseModel):
+    """InvokeAI App Config with model fields set"""
+
+    set_fields: set[str] = Field(description="The set fields")
+    config: InvokeAIAppConfig = Field(description="The InvokeAI App Config")
+
+
+@app_router.get(
+    "/runtime_config", operation_id="get_runtime_config", status_code=200, response_model=InvokeAIAppConfigWithSetFields
+)
+async def get_runtime_config() -> InvokeAIAppConfigWithSetFields:
+    config = get_config()
+    return InvokeAIAppConfigWithSetFields(set_fields=config.model_fields_set, config=config)
+
+
 @app_router.get(
     "/logging",
     operation_id="get_log_level",

invokeai/app/api/routers/images.py

@@ -96,6 +96,22 @@ async def upload_image(
         raise HTTPException(status_code=500, detail="Failed to create image")
 
 
+class ImageUploadEntry(BaseModel):
+    image_dto: ImageDTO = Body(description="The image DTO")
+    presigned_url: str = Body(description="The URL to get the presigned URL for the image upload")
+
+
+@images_router.post("/", operation_id="create_image_upload_entry")
+async def create_image_upload_entry(
+    width: int = Body(description="The width of the image"),
+    height: int = Body(description="The height of the image"),
+    board_id: Optional[str] = Body(default=None, description="The board to add this image to, if any"),
+) -> ImageUploadEntry:
+    """Uploads an image from a URL, not implemented"""
+
+    raise HTTPException(status_code=501, detail="Not implemented")
+
+
 @images_router.delete("/i/{image_name}", operation_id="delete_image")
 async def delete_image(
     image_name: str = Path(description="The name of the image to delete"),

invokeai/app/api/routers/model_manager.py

@@ -28,12 +28,10 @@ from invokeai.app.services.model_records import (
     UnknownModelException,
 )
 from invokeai.app.util.suppress_output import SuppressOutput
+from invokeai.backend.model_manager import BaseModelType, ModelFormat, ModelType
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
     MainCheckpointConfig,
-    ModelFormat,
-    ModelType,
 )
 from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch

invokeai/app/invocations/baseinvocation.py

@@ -8,6 +8,7 @@ import sys
 import warnings
 from abc import ABC, abstractmethod
 from enum import Enum
+from functools import lru_cache
 from inspect import signature
 from typing import (
     TYPE_CHECKING,
@@ -27,7 +28,6 @@ import semver
 from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, create_model
 from pydantic.fields import FieldInfo
 from pydantic_core import PydanticUndefined
-from typing_extensions import TypeAliasType
 
 from invokeai.app.invocations.fields import (
     FieldKind,
@@ -100,37 +100,6 @@ class BaseInvocationOutput(BaseModel):
     All invocation outputs must use the `@invocation_output` decorator to provide their unique type.
     """
 
-    _output_classes: ClassVar[set[BaseInvocationOutput]] = set()
-    _typeadapter: ClassVar[Optional[TypeAdapter[Any]]] = None
-    _typeadapter_needs_update: ClassVar[bool] = False
-
-    @classmethod
-    def register_output(cls, output: BaseInvocationOutput) -> None:
-        """Registers an invocation output."""
-        cls._output_classes.add(output)
-        cls._typeadapter_needs_update = True
-
-    @classmethod
-    def get_outputs(cls) -> Iterable[BaseInvocationOutput]:
-        """Gets all invocation outputs."""
-        return cls._output_classes
-
-    @classmethod
-    def get_typeadapter(cls) -> TypeAdapter[Any]:
-        """Gets a pydantc TypeAdapter for the union of all invocation output types."""
-        if not cls._typeadapter or cls._typeadapter_needs_update:
-            AnyInvocationOutput = TypeAliasType(
-                "AnyInvocationOutput", Annotated[Union[tuple(cls._output_classes)], Field(discriminator="type")]
-            )
-            cls._typeadapter = TypeAdapter(AnyInvocationOutput)
-            cls._typeadapter_needs_update = False
-        return cls._typeadapter
-
-    @classmethod
-    def get_output_types(cls) -> Iterable[str]:
-        """Gets all invocation output types."""
-        return (i.get_type() for i in BaseInvocationOutput.get_outputs())
-
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseInvocationOutput]) -> None:
         """Adds various UI-facing attributes to the invocation output's OpenAPI schema."""
@@ -173,76 +142,16 @@ class BaseInvocation(ABC, BaseModel):
     All invocations must use the `@invocation` decorator to provide their unique type.
     """
 
-    _invocation_classes: ClassVar[set[BaseInvocation]] = set()
-    _typeadapter: ClassVar[Optional[TypeAdapter[Any]]] = None
-    _typeadapter_needs_update: ClassVar[bool] = False
-
     @classmethod
     def get_type(cls) -> str:
         """Gets the invocation's type, as provided by the `@invocation` decorator."""
         return cls.model_fields["type"].default
 
-    @classmethod
-    def register_invocation(cls, invocation: BaseInvocation) -> None:
-        """Registers an invocation."""
-        cls._invocation_classes.add(invocation)
-        cls._typeadapter_needs_update = True
-
-    @classmethod
-    def get_typeadapter(cls) -> TypeAdapter[Any]:
-        """Gets a pydantc TypeAdapter for the union of all invocation types."""
-        if not cls._typeadapter or cls._typeadapter_needs_update:
-            AnyInvocation = TypeAliasType(
-                "AnyInvocation", Annotated[Union[tuple(cls.get_invocations())], Field(discriminator="type")]
-            )
-            cls._typeadapter = TypeAdapter(AnyInvocation)
-            cls._typeadapter_needs_update = False
-        return cls._typeadapter
-
-    @classmethod
-    def invalidate_typeadapter(cls) -> None:
-        """Invalidates the typeadapter, forcing it to be rebuilt on next access. If the invocation allowlist or
-        denylist is changed, this should be called to ensure the typeadapter is updated and validation respects
-        the updated allowlist and denylist."""
-        cls._typeadapter_needs_update = True
-
-    @classmethod
-    def get_invocations(cls) -> Iterable[BaseInvocation]:
-        """Gets all invocations, respecting the allowlist and denylist."""
-        app_config = get_config()
-        allowed_invocations: set[BaseInvocation] = set()
-        for sc in cls._invocation_classes:
-            invocation_type = sc.get_type()
-            is_in_allowlist = (
-                invocation_type in app_config.allow_nodes if isinstance(app_config.allow_nodes, list) else True
-            )
-            is_in_denylist = (
-                invocation_type in app_config.deny_nodes if isinstance(app_config.deny_nodes, list) else False
-            )
-            if is_in_allowlist and not is_in_denylist:
-                allowed_invocations.add(sc)
-        return allowed_invocations
-
-    @classmethod
-    def get_invocations_map(cls) -> dict[str, BaseInvocation]:
-        """Gets a map of all invocation types to their invocation classes."""
-        return {i.get_type(): i for i in BaseInvocation.get_invocations()}
-
-    @classmethod
-    def get_invocation_types(cls) -> Iterable[str]:
-        """Gets all invocation types."""
-        return (i.get_type() for i in BaseInvocation.get_invocations())
-
     @classmethod
     def get_output_annotation(cls) -> BaseInvocationOutput:
         """Gets the invocation's output annotation (i.e. the return annotation of its `invoke()` method)."""
         return signature(cls.invoke).return_annotation
 
-    @classmethod
-    def get_invocation_for_type(cls, invocation_type: str) -> BaseInvocation | None:
-        """Gets the invocation class for a given invocation type."""
-        return cls.get_invocations_map().get(invocation_type)
-
     @staticmethod
     def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseInvocation]) -> None:
         """Adds various UI-facing attributes to the invocation's OpenAPI schema."""
@@ -340,6 +249,105 @@ class BaseInvocation(ABC, BaseModel):
 TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
 
 
+class InvocationRegistry:
+    _invocation_classes: ClassVar[set[type[BaseInvocation]]] = set()
+    _output_classes: ClassVar[set[type[BaseInvocationOutput]]] = set()
+
+    @classmethod
+    def register_invocation(cls, invocation: type[BaseInvocation]) -> None:
+        """Registers an invocation."""
+        cls._invocation_classes.add(invocation)
+        cls.invalidate_invocation_typeadapter()
+
+    @classmethod
+    @lru_cache(maxsize=1)
+    def get_invocation_typeadapter(cls) -> TypeAdapter[Any]:
+        """Gets a pydantic TypeAdapter for the union of all invocation types.
+
+        This is used to parse serialized invocations into the correct invocation class.
+
+        This method is cached to avoid rebuilding the TypeAdapter on every access. If the invocation allowlist or
+        denylist is changed, the cache should be cleared to ensure the TypeAdapter is updated and validation respects
+        the updated allowlist and denylist.
+
+        @see https://docs.pydantic.dev/latest/concepts/type_adapter/
+        """
+        return TypeAdapter(Annotated[Union[tuple(cls.get_invocation_classes())], Field(discriminator="type")])
+
+    @classmethod
+    def invalidate_invocation_typeadapter(cls) -> None:
+        """Invalidates the cached invocation type adapter."""
+        cls.get_invocation_typeadapter.cache_clear()
+
+    @classmethod
+    def get_invocation_classes(cls) -> Iterable[type[BaseInvocation]]:
+        """Gets all invocations, respecting the allowlist and denylist."""
+        app_config = get_config()
+        allowed_invocations: set[type[BaseInvocation]] = set()
+        for sc in cls._invocation_classes:
+            invocation_type = sc.get_type()
+            is_in_allowlist = (
+                invocation_type in app_config.allow_nodes if isinstance(app_config.allow_nodes, list) else True
+            )
+            is_in_denylist = (
+                invocation_type in app_config.deny_nodes if isinstance(app_config.deny_nodes, list) else False
+            )
+            if is_in_allowlist and not is_in_denylist:
+                allowed_invocations.add(sc)
+        return allowed_invocations
+
+    @classmethod
+    def get_invocations_map(cls) -> dict[str, type[BaseInvocation]]:
+        """Gets a map of all invocation types to their invocation classes."""
+        return {i.get_type(): i for i in cls.get_invocation_classes()}
+
+    @classmethod
+    def get_invocation_types(cls) -> Iterable[str]:
+        """Gets all invocation types."""
+        return (i.get_type() for i in cls.get_invocation_classes())
+
+    @classmethod
+    def get_invocation_for_type(cls, invocation_type: str) -> type[BaseInvocation] | None:
+        """Gets the invocation class for a given invocation type."""
+        return cls.get_invocations_map().get(invocation_type)
+
+    @classmethod
+    def register_output(cls, output: "type[TBaseInvocationOutput]") -> None:
+        """Registers an invocation output."""
+        cls._output_classes.add(output)
+        cls.invalidate_output_typeadapter()
+
+    @classmethod
+    def get_output_classes(cls) -> Iterable[type[BaseInvocationOutput]]:
+        """Gets all invocation outputs."""
+        return cls._output_classes
+
+    @classmethod
+    @lru_cache(maxsize=1)
+    def get_output_typeadapter(cls) -> TypeAdapter[Any]:
+        """Gets a pydantic TypeAdapter for the union of all invocation output types.
+
+        This is used to parse serialized invocation outputs into the correct invocation output class.
+
+        This method is cached to avoid rebuilding the TypeAdapter on every access. If the invocation allowlist or
+        denylist is changed, the cache should be cleared to ensure the TypeAdapter is updated and validation respects
+        the updated allowlist and denylist.
+
+        @see https://docs.pydantic.dev/latest/concepts/type_adapter/
+        """
+        return TypeAdapter(Annotated[Union[tuple(cls._output_classes)], Field(discriminator="type")])
+
+    @classmethod
+    def invalidate_output_typeadapter(cls) -> None:
+        """Invalidates the cached invocation output type adapter."""
+        cls.get_output_typeadapter.cache_clear()
+
+    @classmethod
+    def get_output_types(cls) -> Iterable[str]:
+        """Gets all invocation output types."""
+        return (i.get_type() for i in cls.get_output_classes())
+
+
 RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
     "id",
     "is_intermediate",
@@ -453,8 +461,8 @@ def invocation(
         node_pack = cls.__module__.split(".")[0]
 
         # Handle the case where an existing node is being clobbered by the one we are registering
-        if invocation_type in BaseInvocation.get_invocation_types():
-            clobbered_invocation = BaseInvocation.get_invocation_for_type(invocation_type)
+        if invocation_type in InvocationRegistry.get_invocation_types():
+            clobbered_invocation = InvocationRegistry.get_invocation_for_type(invocation_type)
             # This should always be true - we just checked if the invocation type was in the set
             assert clobbered_invocation is not None
 
@@ -539,8 +547,7 @@ def invocation(
         )
         cls.__doc__ = docstring
 
-        # TODO: how to type this correctly? it's typed as ModelMetaclass, a private class in pydantic
-        BaseInvocation.register_invocation(cls)  # type: ignore
+        InvocationRegistry.register_invocation(cls)
 
         return cls
 
@@ -565,7 +572,7 @@ def invocation_output(
         if re.compile(r"^\S+$").match(output_type) is None:
             raise ValueError(f'"output_type" must consist of non-whitespace characters, got "{output_type}"')
 
-        if output_type in BaseInvocationOutput.get_output_types():
+        if output_type in InvocationRegistry.get_output_types():
             raise ValueError(f'Invocation type "{output_type}" already exists')
 
         validate_fields(cls.model_fields, output_type)
@@ -586,7 +593,7 @@ def invocation_output(
         )
         cls.__doc__ = docstring
 
-        BaseInvocationOutput.register_output(cls)  # type: ignore # TODO: how to type this correctly?
+        InvocationRegistry.register_output(cls)
 
         return cls
 

invokeai/app/invocations/create_gradient_mask.py

@@ -19,7 +19,8 @@ from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
 from invokeai.app.invocations.model import UNetField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager import LoadedModel
-from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
+from invokeai.backend.model_manager.config import MainConfigBase
+from invokeai.backend.model_manager.taxonomy import ModelVariantType
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 
 

invokeai/app/invocations/denoise_latents.py

@@ -39,8 +39,8 @@ from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.model_manager import BaseModelType, ModelVariantType
 from invokeai.backend.model_manager.config import AnyModelConfig
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelVariantType
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw

invokeai/app/invocations/flux_control_lora_loader.py

@@ -1,7 +1,6 @@
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -25,7 +24,6 @@ class FluxControlLoRALoaderOutput(BaseInvocationOutput):
     tags=["lora", "model", "flux"],
     category="model",
     version="1.1.1",
-    classification=Classification.Prototype,
 )
 class FluxControlLoRALoaderInvocation(BaseInvocation):
     """LoRA model and Image to use with FLUX transformer generation."""

invokeai/app/invocations/flux_controlnet.py

@@ -3,7 +3,6 @@ from pydantic import BaseModel, Field, field_validator, model_validator
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -52,7 +51,6 @@ class FluxControlNetOutput(BaseInvocationOutput):
     tags=["controlnet", "flux"],
     category="controlnet",
     version="1.0.0",
-    classification=Classification.Prototype,
 )
 class FluxControlNetInvocation(BaseInvocation):
     """Collect FLUX ControlNet info to pass to other nodes."""

invokeai/app/invocations/flux_denoise.py

@@ -10,7 +10,7 @@ from PIL import Image
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
     FieldDescriptions,
@@ -49,7 +49,7 @@ from invokeai.backend.flux.sampling_utils import (
     unpack,
 )
 from invokeai.backend.flux.text_conditioning import FluxReduxConditioning, FluxTextConditioning
-from invokeai.backend.model_manager.config import ModelFormat, ModelVariantType
+from invokeai.backend.model_manager.taxonomy import ModelFormat, ModelVariantType
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
@@ -64,7 +64,6 @@ from invokeai.backend.util.devices import TorchDevice
     tags=["image", "flux"],
     category="image",
     version="3.3.0",
-    classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Run denoising process with a FLUX transformer model."""

invokeai/app/invocations/flux_fill.py

@@ -31,7 +31,7 @@ class FluxFillOutput(BaseInvocationOutput):
     tags=["inpaint"],
     category="inpaint",
     version="1.0.0",
-    classification=Classification.Prototype,
+    classification=Classification.Beta,
 )
 class FluxFillInvocation(BaseInvocation):
     """Prepare the FLUX Fill conditioning data."""

invokeai/app/invocations/flux_ip_adapter.py

@@ -4,7 +4,7 @@ from typing import List, Literal, Union
 from pydantic import field_validator, model_validator
 from typing_extensions import Self
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import InputField, UIType
 from invokeai.app.invocations.ip_adapter import (
     CLIP_VISION_MODEL_MAP,
@@ -28,7 +28,6 @@ from invokeai.backend.model_manager.config import (
     tags=["ip_adapter", "control"],
     category="ip_adapter",
     version="1.0.0",
-    classification=Classification.Prototype,
 )
 class FluxIPAdapterInvocation(BaseInvocation):
     """Collects FLUX IP-Adapter info to pass to other nodes."""

invokeai/app/invocations/flux_lora_loader.py

@@ -3,14 +3,13 @@ from typing import Optional
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 from invokeai.app.invocations.model import CLIPField, LoRAField, ModelIdentifierField, T5EncoderField, TransformerField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType
 
 
 @invocation_output("flux_lora_loader_output")
@@ -32,7 +31,6 @@ class FluxLoRALoaderOutput(BaseInvocationOutput):
     tags=["lora", "model", "flux"],
     category="model",
     version="1.2.1",
-    classification=Classification.Prototype,
 )
 class FluxLoRALoaderInvocation(BaseInvocation):
     """Apply a LoRA model to a FLUX transformer and/or text encoder."""
@@ -111,7 +109,6 @@ class FluxLoRALoaderInvocation(BaseInvocation):
     tags=["lora", "model", "flux"],
     category="model",
     version="1.3.1",
-    classification=Classification.Prototype,
 )
 class FLUXLoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to a FLUX transformer."""

invokeai/app/invocations/flux_model_loader.py

@@ -3,7 +3,6 @@ from typing import Literal
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -17,8 +16,8 @@ from invokeai.app.util.t5_model_identifier import (
 from invokeai.backend.flux.util import max_seq_lengths
 from invokeai.backend.model_manager.config import (
     CheckpointConfigBase,
-    SubModelType,
 )
+from invokeai.backend.model_manager.taxonomy import SubModelType
 
 
 @invocation_output("flux_model_loader_output")
@@ -41,7 +40,6 @@ class FluxModelLoaderOutput(BaseInvocationOutput):
     tags=["model", "flux"],
     category="model",
     version="1.0.6",
-    classification=Classification.Prototype,
 )
 class FluxModelLoaderInvocation(BaseInvocation):
     """Loads a flux base model, outputting its submodels."""

invokeai/app/invocations/flux_redux.py

@@ -23,7 +23,8 @@ from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.redux.flux_redux_model import FluxReduxModel
-from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType
+from invokeai.backend.model_manager import BaseModelType, ModelType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.starter_models import siglip
 from invokeai.backend.sig_lip.sig_lip_pipeline import SigLipPipeline
 from invokeai.backend.util.devices import TorchDevice
@@ -44,7 +45,7 @@ class FluxReduxOutput(BaseInvocationOutput):
     tags=["ip_adapter", "control"],
     category="ip_adapter",
     version="2.0.0",
-    classification=Classification.Prototype,
+    classification=Classification.Beta,
 )
 class FluxReduxInvocation(BaseInvocation):
     """Runs a FLUX Redux model to generate a conditioning tensor."""

invokeai/app/invocations/flux_text_encoder.py

@@ -4,7 +4,7 @@ from typing import Iterator, Literal, Optional, Tuple
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer, T5TokenizerFast
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     FluxConditioningField,
@@ -17,7 +17,7 @@ from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import FluxConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.modules.conditioner import HFEncoder
-from invokeai.backend.model_manager.config import ModelFormat
+from invokeai.backend.model_manager import ModelFormat
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX, FLUX_LORA_T5_PREFIX
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
@@ -30,7 +30,6 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import Condit
     tags=["prompt", "conditioning", "flux"],
     category="conditioning",
     version="1.1.2",
-    classification=Classification.Prototype,
 )
 class FluxTextEncoderInvocation(BaseInvocation):
     """Encodes and preps a prompt for a flux image."""

invokeai/app/invocations/ideal_size.py

@@ -6,7 +6,7 @@ from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField
 from invokeai.app.invocations.model import UNetField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType
 
 
 @invocation_output("ideal_size_output")

invokeai/app/invocations/image.py

@@ -355,7 +355,6 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
     tags=["image", "unsharp_mask"],
     category="image",
     version="1.2.2",
-    classification=Classification.Beta,
 )
 class UnsharpMaskInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Applies an unsharp mask filter to an image"""
@@ -1090,12 +1089,13 @@ class CanvasV2MaskAndCropInvocation(BaseInvocation, WithMetadata, WithBoard):
 
 
 @invocation(
-    "expand_mask_with_fade", title="Expand Mask with Fade", tags=["image", "mask"], category="image", version="1.0.0"
+    "expand_mask_with_fade", title="Expand Mask with Fade", tags=["image", "mask"], category="image", version="1.0.1"
 )
 class ExpandMaskWithFadeInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Expands a mask with a fade effect. The mask uses black to indicate areas to keep from the generated image and white for areas to discard.
     The mask is thresholded to create a binary mask, and then a distance transform is applied to create a fade effect.
     The fade size is specified in pixels, and the mask is expanded by that amount. The result is a mask with a smooth transition from black to white.
+    If the fade size is 0, the mask is returned as-is.
     """
 
     mask: ImageField = InputField(description="The mask to expand")
@@ -1105,6 +1105,11 @@ class ExpandMaskWithFadeInvocation(BaseInvocation, WithMetadata, WithBoard):
     def invoke(self, context: InvocationContext) -> ImageOutput:
         pil_mask = context.images.get_pil(self.mask.image_name, mode="L")
 
+        if self.fade_size_px == 0:
+            # If the fade size is 0, just return the mask as-is.
+            image_dto = context.images.save(image=pil_mask, image_category=ImageCategory.MASK)
+            return ImageOutput.build(image_dto)
+
         np_mask = numpy.array(pil_mask)
 
         # Threshold the mask to create a binary mask - 0 for black, 255 for white
@@ -1142,8 +1147,21 @@ class ExpandMaskWithFadeInvocation(BaseInvocation, WithMetadata, WithBoard):
         coeffs = numpy.polyfit(x_control, y_control, 3)
         poly = numpy.poly1d(coeffs)
 
-        # Evaluate and clip the smooth mapping
-        feather = numpy.clip(poly(d_norm), 0, 1)
+        # Evaluate the polynomial
+        feather = poly(d_norm)
+
+        # The polynomial fit isn't perfect. Points beyond the fade distance are likely to be slightly less than 1.0,
+        # even though the control points indicate that they should be exactly 1.0. This is due to the nature of the
+        # polynomial fit, which is a best approximation of the control points but not an exact match.
+
+        # When this occurs, the area outside the mask and fade-out will not be 100% transparent. For example, it may
+        # have an alpha value of 1 instead of 0. So we must force pixels at or beyond the fade distance to exactly 1.0.
+
+        # Force pixels at or beyond the fade distance to exactly 1.0
+        feather = numpy.where(d_norm >= 1.0, 1.0, feather)
+
+        # Clip any other values to ensure they're in the valid range [0,1]
+        feather = numpy.clip(feather, 0, 1)
 
         # Build final image.
         np_result = numpy.where(black_mask == 1, 0, (feather * 255).astype(numpy.uint8))
@@ -1265,7 +1283,6 @@ class ImageNoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     category="image",
     version="1.0.0",
     tags=["image", "crop"],
-    classification=Classification.Beta,
 )
 class CropImageToBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Crop an image to the given bounding box. If the bounding box is omitted, the image is cropped to the non-transparent pixels."""
@@ -1292,7 +1309,6 @@ class CropImageToBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoard):
     category="image",
     version="1.0.0",
     tags=["image", "crop"],
-    classification=Classification.Beta,
 )
 class PasteImageIntoBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Paste the source image into the target image at the given bounding box.

invokeai/app/invocations/ip_adapter.py

@@ -13,10 +13,8 @@ from invokeai.app.services.model_records.model_records_base import ModelRecordCh
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
     IPAdapterCheckpointConfig,
     IPAdapterInvokeAIConfig,
-    ModelType,
 )
 from invokeai.backend.model_manager.starter_models import (
     StarterModel,
@@ -24,6 +22,7 @@ from invokeai.backend.model_manager.starter_models import (
     ip_adapter_sd_image_encoder,
     ip_adapter_sdxl_image_encoder,
 )
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType
 
 
 class IPAdapterField(BaseModel):

invokeai/app/invocations/llava_onevision_vllm.py

@@ -4,7 +4,7 @@ import torch
 from PIL.Image import Image
 from pydantic import field_validator
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, UIComponent, UIType
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.invocations.primitives import StringOutput
@@ -13,7 +13,14 @@ from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
 from invokeai.backend.util.devices import TorchDevice
 
 
-@invocation("llava_onevision_vllm", title="LLaVA OneVision VLLM", tags=["vllm"], category="vllm", version="1.0.0")
+@invocation(
+    "llava_onevision_vllm",
+    title="LLaVA OneVision VLLM",
+    tags=["vllm"],
+    category="vllm",
+    version="1.0.0",
+    classification=Classification.Beta,
+)
 class LlavaOnevisionVllmInvocation(BaseInvocation):
     """Run a LLaVA OneVision VLLM model."""
 

invokeai/app/invocations/mask.py

@@ -4,7 +4,6 @@ from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
-    Classification,
     InvocationContext,
     invocation,
 )
@@ -58,7 +57,6 @@ class RectangleMaskInvocation(BaseInvocation, WithMetadata):
     tags=["conditioning"],
     category="conditioning",
     version="1.0.0",
-    classification=Classification.Beta,
 )
 class AlphaMaskToTensorInvocation(BaseInvocation):
     """Convert a mask image to a tensor. Opaque regions are 1 and transparent regions are 0."""
@@ -87,7 +85,6 @@ class AlphaMaskToTensorInvocation(BaseInvocation):
     tags=["conditioning"],
     category="conditioning",
     version="1.1.0",
-    classification=Classification.Beta,
 )
 class InvertTensorMaskInvocation(BaseInvocation):
     """Inverts a tensor mask."""
@@ -234,7 +231,6 @@ WHITE = ColorField(r=255, g=255, b=255, a=255)
     tags=["mask"],
     category="mask",
     version="1.0.0",
-    classification=Classification.Beta,
 )
 class GetMaskBoundingBoxInvocation(BaseInvocation):
     """Gets the bounding box of the given mask image."""

invokeai/app/invocations/metadata_linked.py

@@ -43,7 +43,7 @@ from invokeai.app.invocations.primitives import BooleanOutput, FloatOutput, Inte
 from invokeai.app.invocations.scheduler import SchedulerOutput
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField, T2IAdapterInvocation
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import ModelType, SubModelType
+from invokeai.backend.model_manager.taxonomy import ModelType, SubModelType
 from invokeai.backend.stable_diffusion.schedulers.schedulers import SCHEDULER_NAME_VALUES
 from invokeai.version import __version__
 

invokeai/app/invocations/model.py

@@ -6,7 +6,6 @@ from pydantic import BaseModel, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -15,10 +14,8 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.shared.models import FreeUConfig
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
-    ModelType,
-    SubModelType,
 )
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType, SubModelType
 
 
 class ModelIdentifierField(BaseModel):
@@ -126,7 +123,6 @@ class ModelIdentifierOutput(BaseInvocationOutput):
     tags=["model"],
     category="model",
     version="1.0.1",
-    classification=Classification.Prototype,
 )
 class ModelIdentifierInvocation(BaseInvocation):
     """Selects any model, outputting it its identifier. Be careful with this one! The identifier will be accepted as

invokeai/app/invocations/sd3_denoise.py

@@ -6,7 +6,7 @@ from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
 from torchvision.transforms.functional import resize as tv_resize
 from tqdm import tqdm
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
@@ -23,7 +23,7 @@ from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.invocations.sd3_text_encoder import SD3_T5_MAX_SEQ_LEN
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.sampling_utils import clip_timestep_schedule_fractional
-from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.model_manager import BaseModelType
 from invokeai.backend.sd3.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import SD3ConditioningInfo
@@ -36,7 +36,6 @@ from invokeai.backend.util.devices import TorchDevice
     tags=["image", "sd3"],
     category="image",
     version="1.1.1",
-    classification=Classification.Prototype,
 )
 class SD3DenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Run denoising process with a SD3 model."""

invokeai/app/invocations/sd3_image_to_latents.py

@@ -2,7 +2,7 @@ import einops
 import torch
 from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -25,7 +25,6 @@ from invokeai.backend.util.devices import TorchDevice
     tags=["image", "latents", "vae", "i2l", "sd3"],
     category="image",
     version="1.0.1",
-    classification=Classification.Prototype,
 )
 class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates latents from an image."""

invokeai/app/invocations/sd3_model_loader.py

@@ -3,7 +3,6 @@ from typing import Optional
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -14,7 +13,7 @@ from invokeai.app.util.t5_model_identifier import (
     preprocess_t5_encoder_model_identifier,
     preprocess_t5_tokenizer_model_identifier,
 )
-from invokeai.backend.model_manager.config import SubModelType
+from invokeai.backend.model_manager.taxonomy import SubModelType
 
 
 @invocation_output("sd3_model_loader_output")
@@ -34,7 +33,6 @@ class Sd3ModelLoaderOutput(BaseInvocationOutput):
     tags=["model", "sd3"],
     category="model",
     version="1.0.1",
-    classification=Classification.Prototype,
 )
 class Sd3ModelLoaderInvocation(BaseInvocation):
     """Loads a SD3 base model, outputting its submodels."""

invokeai/app/invocations/sd3_text_encoder.py

@@ -11,12 +11,12 @@ from transformers import (
     T5TokenizerFast,
 )
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
 from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import SD3ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import ModelFormat
+from invokeai.backend.model_manager.taxonomy import ModelFormat
 from invokeai.backend.patches.layer_patcher import LayerPatcher
 from invokeai.backend.patches.lora_conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
@@ -33,7 +33,6 @@ SD3_T5_MAX_SEQ_LEN = 256
     tags=["prompt", "conditioning", "sd3"],
     category="conditioning",
     version="1.0.1",
-    classification=Classification.Prototype,
 )
 class Sd3TextEncoderInvocation(BaseInvocation):
     """Encodes and preps a prompt for a SD3 image."""

invokeai/app/invocations/sdxl.py

@@ -2,7 +2,7 @@ from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocati
 from invokeai.app.invocations.fields import FieldDescriptions, InputField, OutputField, UIType
 from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, UNetField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager import SubModelType
+from invokeai.backend.model_manager.taxonomy import SubModelType
 
 
 @invocation_output("sdxl_model_loader_output")

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

@@ -7,7 +7,7 @@ from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 from diffusers.schedulers.scheduling_utils import SchedulerMixin
 from pydantic import field_validator
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.controlnet_image_processors import ControlField
 from invokeai.app.invocations.denoise_latents import DenoiseLatentsInvocation, get_scheduler
@@ -56,7 +56,6 @@ def crop_controlnet_data(control_data: ControlNetData, latent_region: TBLR) -> C
     title="Tiled Multi-Diffusion Denoise - SD1.5, SDXL",
     tags=["upscale", "denoise"],
     category="latents",
-    classification=Classification.Beta,
     version="1.0.1",
 )
 class TiledMultiDiffusionDenoiseLatents(BaseInvocation):

invokeai/app/invocations/tiles.py

@@ -7,7 +7,6 @@ from pydantic import BaseModel
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
-    Classification,
     invocation,
     invocation_output,
 )
@@ -40,7 +39,6 @@ class CalculateImageTilesOutput(BaseInvocationOutput):
     tags=["tiles"],
     category="tiles",
     version="1.0.1",
-    classification=Classification.Beta,
 )
 class CalculateImageTilesInvocation(BaseInvocation):
     """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
@@ -74,7 +72,6 @@ class CalculateImageTilesInvocation(BaseInvocation):
     tags=["tiles"],
     category="tiles",
     version="1.1.1",
-    classification=Classification.Beta,
 )
 class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
     """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
@@ -117,7 +114,6 @@ class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
     tags=["tiles"],
     category="tiles",
     version="1.0.1",
-    classification=Classification.Beta,
 )
 class CalculateImageTilesMinimumOverlapInvocation(BaseInvocation):
     """Calculate the coordinates and overlaps of tiles that cover a target image shape."""
@@ -168,7 +164,6 @@ class TileToPropertiesOutput(BaseInvocationOutput):
     tags=["tiles"],
     category="tiles",
     version="1.0.1",
-    classification=Classification.Beta,
 )
 class TileToPropertiesInvocation(BaseInvocation):
     """Split a Tile into its individual properties."""
@@ -201,7 +196,6 @@ class PairTileImageOutput(BaseInvocationOutput):
     tags=["tiles"],
     category="tiles",
     version="1.0.1",
-    classification=Classification.Beta,
 )
 class PairTileImageInvocation(BaseInvocation):
     """Pair an image with its tile properties."""
@@ -230,7 +224,6 @@ BLEND_MODES = Literal["Linear", "Seam"]
     tags=["tiles"],
     category="tiles",
     version="1.1.1",
-    classification=Classification.Beta,
 )
 class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Merge multiple tile images into a single image."""

invokeai/app/run_app.py

@@ -41,16 +41,15 @@ def run_app() -> None:
     )
 
     # Find an open port, and modify the config accordingly.
-    orig_config_port = app_config.port
-    app_config.port = find_open_port(app_config.port)
-    if orig_config_port != app_config.port:
+    first_open_port = find_open_port(app_config.port)
+    if app_config.port != first_open_port:
+        orig_config_port = app_config.port
+        app_config.port = first_open_port
         logger.warning(f"Port {orig_config_port} is already in use. Using port {app_config.port}.")
 
     # Miscellaneous startup tasks.
     apply_monkeypatches()
     register_mime_types()
-    if app_config.dev_reload:
-        enable_dev_reload()
     check_cudnn(logger)
 
     # Initialize the app and event loop.
@@ -61,6 +60,11 @@ def run_app() -> None:
     # core nodes have been imported so that we can catch when a custom node clobbers a core node.
     load_custom_nodes(custom_nodes_path=app_config.custom_nodes_path, logger=logger)
 
+    if app_config.dev_reload:
+        # load_custom_nodes seems to bypass jurrigged's import sniffer, so be sure to call it *after* they're already
+        # imported.
+        enable_dev_reload(custom_nodes_path=app_config.custom_nodes_path)
+
     # Start the server.
     config = uvicorn.Config(
         app=app,

invokeai/app/services/events/events_base.py

@@ -44,7 +44,8 @@ if TYPE_CHECKING:
         SessionQueueItem,
         SessionQueueStatus,
     )
-    from invokeai.backend.model_manager.config import AnyModelConfig, SubModelType
+    from invokeai.backend.model_manager import SubModelType
+    from invokeai.backend.model_manager.config import AnyModelConfig
 
 
 class EventServiceBase:

invokeai/app/services/events/events_common.py

@@ -16,7 +16,8 @@ from invokeai.app.services.session_queue.session_queue_common import (
 )
 from invokeai.app.services.shared.graph import AnyInvocation, AnyInvocationOutput
 from invokeai.app.util.misc import get_timestamp
-from invokeai.backend.model_manager.config import AnyModelConfig, SubModelType
+from invokeai.backend.model_manager import SubModelType
+from invokeai.backend.model_manager.config import AnyModelConfig
 
 if TYPE_CHECKING:
     from invokeai.app.services.download.download_base import DownloadJob

invokeai/app/services/model_install/model_install_common.py

@@ -10,9 +10,9 @@ from typing_extensions import Annotated
 
 from invokeai.app.services.download import DownloadJob, MultiFileDownloadJob
 from invokeai.app.services.model_records import ModelRecordChanges
-from invokeai.backend.model_manager import AnyModelConfig, ModelRepoVariant
-from invokeai.backend.model_manager.config import ModelSourceType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant, ModelSourceType
 
 
 class InstallStatus(str, Enum):

invokeai/app/services/model_install/model_install_default.py

@@ -39,8 +39,6 @@ from invokeai.backend.model_manager.config import (
     CheckpointConfigBase,
     InvalidModelConfigException,
     ModelConfigBase,
-    ModelRepoVariant,
-    ModelSourceType,
 )
 from invokeai.backend.model_manager.legacy_probe import ModelProbe
 from invokeai.backend.model_manager.metadata import (
@@ -52,6 +50,7 @@ from invokeai.backend.model_manager.metadata import (
 )
 from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMetadata
 from invokeai.backend.model_manager.search import ModelSearch
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant, ModelSourceType
 from invokeai.backend.util import InvokeAILogger
 from invokeai.backend.util.catch_sigint import catch_sigint
 from invokeai.backend.util.devices import TorchDevice

invokeai/app/services/model_load/model_load_base.py

@@ -5,9 +5,10 @@ from abc import ABC, abstractmethod
 from pathlib import Path
 from typing import Callable, Optional
 
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.load import LoadedModel, LoadedModelWithoutConfig
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.taxonomy import AnyModel, SubModelType
 
 
 class ModelLoadServiceBase(ABC):

invokeai/app/services/model_load/model_load_default.py

@@ -11,7 +11,7 @@ from torch import load as torch_load
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_load.model_load_base import ModelLoadServiceBase
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.load import (
     LoadedModel,
     LoadedModelWithoutConfig,
@@ -20,6 +20,7 @@ from invokeai.backend.model_manager.load import (
 )
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import AnyModel, SubModelType
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 

invokeai/app/services/model_manager/__init__.py

@@ -1,16 +1,12 @@
 """Initialization file for model manager service."""
 
 from invokeai.app.services.model_manager.model_manager_default import ModelManagerService, ModelManagerServiceBase
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelType, SubModelType
+from invokeai.backend.model_manager import AnyModelConfig
 from invokeai.backend.model_manager.load import LoadedModel
 
 __all__ = [
     "ModelManagerServiceBase",
     "ModelManagerService",
-    "AnyModel",
     "AnyModelConfig",
-    "BaseModelType",
-    "ModelType",
-    "SubModelType",
     "LoadedModel",
 ]

invokeai/app/services/model_records/model_records_base.py

@@ -14,10 +14,12 @@ from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
-    ClipVariantType,
     ControlAdapterDefaultSettings,
     MainModelDefaultSettings,
+)
+from invokeai.backend.model_manager.taxonomy import (
+    BaseModelType,
+    ClipVariantType,
     ModelFormat,
     ModelSourceType,
     ModelType,

invokeai/app/services/model_records/model_records_sql.py

@@ -60,11 +60,9 @@ from invokeai.app.services.shared.pagination import PaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
     ModelConfigFactory,
-    ModelFormat,
-    ModelType,
 )
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType
 
 
 class ModelRecordServiceSQL(ModelRecordServiceBase):

invokeai/app/services/object_serializer/object_serializer_disk.py

@@ -21,10 +21,16 @@ 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, ephemeral: bool = False):
+    def __init__(
+        self,
+        output_dir: Path,
+        safe_globals: list[type],
+        ephemeral: bool = False,
+    ) -> None:
         super().__init__()
         self._ephemeral = ephemeral
         self._base_output_dir = output_dir
@@ -42,6 +48,8 @@ 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/shared/graph.py

@@ -21,6 +21,7 @@ from invokeai.app.invocations import *  # noqa: F401 F403
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    InvocationRegistry,
     invocation,
     invocation_output,
 )
@@ -283,7 +284,7 @@ class AnyInvocation(BaseInvocation):
     @classmethod
     def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
         def validate_invocation(v: Any) -> "AnyInvocation":
-            return BaseInvocation.get_typeadapter().validate_python(v)
+            return InvocationRegistry.get_invocation_typeadapter().validate_python(v)
 
         return core_schema.no_info_plain_validator_function(validate_invocation)
 
@@ -294,7 +295,7 @@ class AnyInvocation(BaseInvocation):
         # Nodes are too powerful, we have to make our own OpenAPI schema manually
         # No but really, because the schema is dynamic depending on loaded nodes, we need to generate it manually
         oneOf: list[dict[str, str]] = []
-        names = [i.__name__ for i in BaseInvocation.get_invocations()]
+        names = [i.__name__ for i in InvocationRegistry.get_invocation_classes()]
         for name in sorted(names):
             oneOf.append({"$ref": f"#/components/schemas/{name}"})
         return {"oneOf": oneOf}
@@ -304,7 +305,7 @@ class AnyInvocationOutput(BaseInvocationOutput):
     @classmethod
     def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler):
         def validate_invocation_output(v: Any) -> "AnyInvocationOutput":
-            return BaseInvocationOutput.get_typeadapter().validate_python(v)
+            return InvocationRegistry.get_output_typeadapter().validate_python(v)
 
         return core_schema.no_info_plain_validator_function(validate_invocation_output)
 
@@ -316,7 +317,7 @@ class AnyInvocationOutput(BaseInvocationOutput):
         # No but really, because the schema is dynamic depending on loaded nodes, we need to generate it manually
 
         oneOf: list[dict[str, str]] = []
-        names = [i.__name__ for i in BaseInvocationOutput.get_outputs()]
+        names = [i.__name__ for i in InvocationRegistry.get_output_classes()]
         for name in sorted(names):
             oneOf.append({"$ref": f"#/components/schemas/{name}"})
         return {"oneOf": oneOf}

invokeai/app/services/shared/invocation_context.py

@@ -20,14 +20,10 @@ from invokeai.app.services.session_processor.session_processor_common import Pro
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 from invokeai.app.util.step_callback import flux_step_callback, stable_diffusion_step_callback
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.load_base import LoadedModel, LoadedModelWithoutConfig
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
 

invokeai/app/util/custom_openapi.py

@@ -4,7 +4,10 @@ from fastapi import FastAPI
 from fastapi.openapi.utils import get_openapi
 from pydantic.json_schema import models_json_schema
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, UIConfigBase
+from invokeai.app.invocations.baseinvocation import (
+    InvocationRegistry,
+    UIConfigBase,
+)
 from invokeai.app.invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.services.events.events_common import EventBase
@@ -56,14 +59,14 @@ def get_openapi_func(
         invocation_output_map_required: list[str] = []
 
         # We need to manually add all outputs to the schema - pydantic doesn't add them because they aren't used directly.
-        for output in BaseInvocationOutput.get_outputs():
+        for output in InvocationRegistry.get_output_classes():
             json_schema = output.model_json_schema(mode="serialization", ref_template="#/components/schemas/{model}")
             move_defs_to_top_level(openapi_schema, json_schema)
             openapi_schema["components"]["schemas"][output.__name__] = json_schema
 
         # Technically, invocations are added to the schema by pydantic, but we still need to manually set their output
         # property, so we'll just do it all manually.
-        for invocation in BaseInvocation.get_invocations():
+        for invocation in InvocationRegistry.get_invocation_classes():
             json_schema = invocation.model_json_schema(
                 mode="serialization", ref_template="#/components/schemas/{model}"
             )

invokeai/app/util/startup_utils.py

@@ -1,6 +1,7 @@
 import logging
 import mimetypes
 import socket
+from pathlib import Path
 
 import torch
 
@@ -33,7 +34,16 @@ def check_cudnn(logger: logging.Logger) -> None:
             )
 
 
-def enable_dev_reload() -> None:
+def invokeai_source_dir() -> Path:
+    # `invokeai.__file__` doesn't always work for editable installs
+    this_module_path = Path(__file__).resolve()
+    # https://youtrack.jetbrains.com/issue/PY-38382/Unresolved-reference-spec-but-this-is-standard-builtin
+    # noinspection PyUnresolvedReferences
+    depth = len(__spec__.parent.split("."))
+    return this_module_path.parents[depth - 1]
+
+
+def enable_dev_reload(custom_nodes_path=None) -> None:
     """Enable hot reloading on python file changes during development."""
     from invokeai.backend.util.logging import InvokeAILogger
 
@@ -44,7 +54,10 @@ def enable_dev_reload() -> None:
             'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.'
         ) from e
     else:
-        jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
+        paths = [str(invokeai_source_dir() / "*.py")]
+        if custom_nodes_path:
+            paths.append(str(custom_nodes_path / "*.py"))
+        jurigged.watch(pattern=paths, logger=InvokeAILogger.get_logger(name="jurigged").info)
 
 
 def apply_monkeypatches() -> None:

invokeai/app/util/step_callback.py

@@ -5,7 +5,7 @@ import torch
 from PIL import Image
 
 from invokeai.app.services.session_processor.session_processor_common import CanceledException
-from invokeai.backend.model_manager.config import BaseModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 
 # fast latents preview matrix for sdxl

invokeai/app/util/t5_model_identifier.py

@@ -1,5 +1,5 @@
 from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.backend.model_manager.config import BaseModelType, SubModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType, SubModelType
 
 
 def preprocess_t5_encoder_model_identifier(model_identifier: ModelIdentifierField) -> ModelIdentifierField:

invokeai/app/util/ti_utils.py

@@ -4,7 +4,7 @@ from typing import List, Tuple
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.model_records import UnknownModelException
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import BaseModelType, ModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType
 from invokeai.backend.textual_inversion import TextualInversionModelRaw
 
 

invokeai/backend/flux/flux_state_dict_utils.py

@@ -0,0 +1,23 @@
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from invokeai.backend.model_manager.legacy_probe import CkptType
+
+
+def get_flux_in_channels_from_state_dict(state_dict: "CkptType") -> int | None:
+    """Gets the in channels from the state dict."""
+
+    # "Standard" FLUX models use "img_in.weight", but some community fine tunes use
+    # "model.diffusion_model.img_in.weight". Known models that use the latter key:
+    # - https://civitai.com/models/885098?modelVersionId=990775
+    # - https://civitai.com/models/1018060?modelVersionId=1596255
+    # - https://civitai.com/models/978314/ultrareal-fine-tune?modelVersionId=1413133
+
+    keys = {"img_in.weight", "model.diffusion_model.img_in.weight"}
+
+    for key in keys:
+        val = state_dict.get(key)
+        if val is not None:
+            return val.shape[1]
+
+    return None

invokeai/backend/image_util/infill_methods/lama.py

@@ -6,8 +6,8 @@ import torch
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-from invokeai.backend.model_manager.config import AnyModel
 from invokeai.backend.model_manager.load.model_cache.utils import get_effective_device
+from invokeai.backend.model_manager.taxonomy import AnyModel
 
 
 def norm_img(np_img):

invokeai/backend/image_util/normal_bae/nets/submodules/efficientnet_repo/geffnet/conv2d_layers.py

@@ -16,7 +16,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from .config import *
+from .config import is_exportable, is_scriptable
 
 
 # From PyTorch internals

invokeai/backend/image_util/normal_bae/nets/submodules/efficientnet_repo/geffnet/efficientnet_builder.py

@@ -5,8 +5,8 @@ Copyright 2020 Ross Wightman
 import re
 from copy import deepcopy
 
-from .conv2d_layers import *
-from geffnet.activations import *
+from .conv2d_layers import CondConv2d, get_condconv_initializer, math, partial, select_conv2d
+from geffnet.activations import F, get_act_layer, nn, sigmoid, torch
 
 __all__ = ['get_bn_args_tf', 'resolve_bn_args', 'resolve_se_args', 'resolve_act_layer', 'make_divisible',
            'round_channels', 'drop_connect', 'SqueezeExcite', 'ConvBnAct', 'DepthwiseSeparableConv',

invokeai/backend/image_util/normal_bae/nets/submodules/efficientnet_repo/geffnet/gen_efficientnet.py

@@ -32,7 +32,9 @@ import torch.nn.functional as F
 from .config import layer_config_kwargs, is_scriptable
 from .conv2d_layers import select_conv2d
 from .helpers import load_pretrained
-from .efficientnet_builder import *
+from .efficientnet_builder import (BN_EPS_TF_DEFAULT, EfficientNetBuilder, decode_arch_def,
+                                   initialize_weight_default, initialize_weight_goog,
+                                   resolve_act_layer, resolve_bn_args, round_channels)
 
 __all__ = ['GenEfficientNet', 'mnasnet_050', 'mnasnet_075', 'mnasnet_100', 'mnasnet_b1', 'mnasnet_140',
            'semnasnet_050', 'semnasnet_075', 'semnasnet_100', 'mnasnet_a1', 'semnasnet_140', 'mnasnet_small',

invokeai/backend/image_util/normal_bae/nets/submodules/efficientnet_repo/geffnet/mobilenetv3.py

@@ -13,7 +13,9 @@ from .activations import get_act_fn, get_act_layer, HardSwish
 from .config import layer_config_kwargs
 from .conv2d_layers import select_conv2d
 from .helpers import load_pretrained
-from .efficientnet_builder import *
+from .efficientnet_builder import (BN_EPS_TF_DEFAULT, EfficientNetBuilder, decode_arch_def,
+                                   initialize_weight_default, initialize_weight_goog,
+                                   resolve_act_layer, resolve_bn_args, round_channels)
 
 __all__ = ['mobilenetv3_rw', 'mobilenetv3_large_075', 'mobilenetv3_large_100', 'mobilenetv3_large_minimal_100',
            'mobilenetv3_small_075', 'mobilenetv3_small_100', 'mobilenetv3_small_minimal_100',

invokeai/backend/image_util/realesrgan/realesrgan.py

@@ -10,7 +10,7 @@ from cv2.typing import MatLike
 from tqdm import tqdm
 
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
-from invokeai.backend.model_manager.config import AnyModel
+from invokeai.backend.model_manager.taxonomy import AnyModel
 from invokeai.backend.util.devices import TorchDevice
 
 """

invokeai/backend/llava_onevision_model.py

@@ -47,3 +47,10 @@ class LlavaOnevisionModel(RawModel):
 
     def to(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None) -> None:
         self._vllm_model.to(device=device, dtype=dtype)
+
+    def calc_size(self) -> int:
+        """Get size of the model in memory in bytes."""
+        # HACK(ryand): Fix this issue with circular imports.
+        from invokeai.backend.model_manager.load.model_util import calc_module_size
+
+        return calc_module_size(self._vllm_model)

invokeai/backend/model_manager/__init__.py

@@ -1,37 +1,45 @@
 """Re-export frequently-used symbols from the Model Manager backend."""
 
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
     InvalidModelConfigException,
     ModelConfigBase,
     ModelConfigFactory,
+)
+from invokeai.backend.model_manager.legacy_probe import ModelProbe
+from invokeai.backend.model_manager.load import LoadedModel
+from invokeai.backend.model_manager.search import ModelSearch
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    AnyVariant,
+    BaseModelType,
+    ClipVariantType,
     ModelFormat,
     ModelRepoVariant,
+    ModelSourceType,
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
     SubModelType,
 )
-from invokeai.backend.model_manager.legacy_probe import ModelProbe
-from invokeai.backend.model_manager.load import LoadedModel
-from invokeai.backend.model_manager.search import ModelSearch
 
 __all__ = [
-    "AnyModel",
     "AnyModelConfig",
-    "BaseModelType",
-    "ModelRepoVariant",
     "InvalidModelConfigException",
     "LoadedModel",
     "ModelConfigFactory",
-    "ModelFormat",
     "ModelProbe",
     "ModelSearch",
+    "ModelConfigBase",
+    "AnyModel",
+    "AnyVariant",
+    "BaseModelType",
+    "ClipVariantType",
+    "ModelFormat",
+    "ModelRepoVariant",
+    "ModelSourceType",
     "ModelType",
     "ModelVariantType",
     "SchedulerPredictionType",
     "SubModelType",
-    "ModelConfigBase",
 ]

invokeai/backend/model_manager/config.py

@@ -30,153 +30,41 @@ from inspect import isabstract
 from pathlib import Path
 from typing import ClassVar, Literal, Optional, TypeAlias, Union
 
-import diffusers
-import onnxruntime as ort
-import safetensors.torch
-import torch
-from diffusers.models.modeling_utils import ModelMixin
-from picklescan.scanner import scan_file_path
 from pydantic import BaseModel, ConfigDict, Discriminator, Field, Tag, TypeAdapter
 from typing_extensions import Annotated, Any, Dict
 
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.model_hash.hash_validator import validate_hash
-from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
-from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
-from invokeai.backend.raw_model import RawModel
+from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS
+from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
+from invokeai.backend.model_manager.taxonomy import (
+    AnyVariant,
+    BaseModelType,
+    ClipVariantType,
+    FluxLoRAFormat,
+    ModelFormat,
+    ModelRepoVariant,
+    ModelSourceType,
+    ModelType,
+    ModelVariantType,
+    SchedulerPredictionType,
+    SubModelType,
+)
+from invokeai.backend.model_manager.util.model_util import lora_token_vector_length
 from invokeai.backend.stable_diffusion.schedulers.schedulers import SCHEDULER_NAME_VALUES
-from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 logger = logging.getLogger(__name__)
 
-# ModelMixin is the base class for all diffusers and transformers models
-# RawModel is the InvokeAI wrapper class for ip_adapters, loras, textual_inversion and onnx runtime
-AnyModel = Union[
-    ModelMixin, RawModel, torch.nn.Module, Dict[str, torch.Tensor], diffusers.DiffusionPipeline, ort.InferenceSession
-]
-
 
 class InvalidModelConfigException(Exception):
     """Exception for when config parser doesn't recognize this combination of model type and format."""
 
-
-class BaseModelType(str, Enum):
-    """Base model type."""
-
-    Any = "any"
-    StableDiffusion1 = "sd-1"
-    StableDiffusion2 = "sd-2"
-    StableDiffusion3 = "sd-3"
-    StableDiffusionXL = "sdxl"
-    StableDiffusionXLRefiner = "sdxl-refiner"
-    Flux = "flux"
-    # Kandinsky2_1 = "kandinsky-2.1"
-
-
-class ModelType(str, Enum):
-    """Model type."""
-
-    ONNX = "onnx"
-    Main = "main"
-    VAE = "vae"
-    LoRA = "lora"
-    ControlLoRa = "control_lora"
-    ControlNet = "controlnet"  # used by model_probe
-    TextualInversion = "embedding"
-    IPAdapter = "ip_adapter"
-    CLIPVision = "clip_vision"
-    CLIPEmbed = "clip_embed"
-    T2IAdapter = "t2i_adapter"
-    T5Encoder = "t5_encoder"
-    SpandrelImageToImage = "spandrel_image_to_image"
-    SigLIP = "siglip"
-    FluxRedux = "flux_redux"
-    LlavaOnevision = "llava_onevision"
-
-
-class SubModelType(str, Enum):
-    """Submodel type."""
-
-    UNet = "unet"
-    Transformer = "transformer"
-    TextEncoder = "text_encoder"
-    TextEncoder2 = "text_encoder_2"
-    TextEncoder3 = "text_encoder_3"
-    Tokenizer = "tokenizer"
-    Tokenizer2 = "tokenizer_2"
-    Tokenizer3 = "tokenizer_3"
-    VAE = "vae"
-    VAEDecoder = "vae_decoder"
-    VAEEncoder = "vae_encoder"
-    Scheduler = "scheduler"
-    SafetyChecker = "safety_checker"
-
-
-class ClipVariantType(str, Enum):
-    """Variant type."""
-
-    L = "large"
-    G = "gigantic"
-
-
-class ModelVariantType(str, Enum):
-    """Variant type."""
-
-    Normal = "normal"
-    Inpaint = "inpaint"
-    Depth = "depth"
-
-
-class ModelFormat(str, Enum):
-    """Storage format of model."""
-
-    Diffusers = "diffusers"
-    Checkpoint = "checkpoint"
-    LyCORIS = "lycoris"
-    ONNX = "onnx"
-    Olive = "olive"
-    EmbeddingFile = "embedding_file"
-    EmbeddingFolder = "embedding_folder"
-    InvokeAI = "invokeai"
-    T5Encoder = "t5_encoder"
-    BnbQuantizedLlmInt8b = "bnb_quantized_int8b"
-    BnbQuantizednf4b = "bnb_quantized_nf4b"
-    GGUFQuantized = "gguf_quantized"
-
-
-class SchedulerPredictionType(str, Enum):
-    """Scheduler prediction type."""
-
-    Epsilon = "epsilon"
-    VPrediction = "v_prediction"
-    Sample = "sample"
-
-
-class ModelRepoVariant(str, Enum):
-    """Various hugging face variants on the diffusers format."""
-
-    Default = ""  # model files without "fp16" or other qualifier
-    FP16 = "fp16"
-    FP32 = "fp32"
-    ONNX = "onnx"
-    OpenVINO = "openvino"
-    Flax = "flax"
-
-
-class ModelSourceType(str, Enum):
-    """Model source type."""
-
-    Path = "path"
-    Url = "url"
-    HFRepoID = "hf_repo_id"
+    pass
 
 
 DEFAULTS_PRECISION = Literal["fp16", "fp32"]
 
 
-AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]
-
-
 class SubmodelDefinition(BaseModel):
     path_or_prefix: str
     model_type: ModelType
@@ -207,68 +95,6 @@ class ControlAdapterDefaultSettings(BaseModel):
     model_config = ConfigDict(extra="forbid")
 
 
-class ModelOnDisk:
-    """A utility class representing a model stored on disk."""
-
-    def __init__(self, path: Path, hash_algo: HASHING_ALGORITHMS = "blake3_single"):
-        self.path = path
-        self.format_type = ModelFormat.Diffusers if path.is_dir() else ModelFormat.Checkpoint
-        if self.path.suffix in {".safetensors", ".bin", ".pt", ".ckpt"}:
-            self.name = path.stem
-        else:
-            self.name = path.name
-        self.hash_algo = hash_algo
-
-    def hash(self):
-        return ModelHash(algorithm=self.hash_algo).hash(self.path)
-
-    def size(self):
-        if self.format_type == ModelFormat.Checkpoint:
-            return self.path.stat().st_size
-        return sum(file.stat().st_size for file in self.path.rglob("*"))
-
-    def component_paths(self):
-        if self.format_type == ModelFormat.Checkpoint:
-            return {self.path}
-        extensions = {".safetensors", ".pt", ".pth", ".ckpt", ".bin", ".gguf"}
-        return {f for f in self.path.rglob("*") if f.suffix in extensions}
-
-    def repo_variant(self):
-        if self.format_type == ModelFormat.Checkpoint:
-            return None
-
-        weight_files = list(self.path.glob("**/*.safetensors"))
-        weight_files.extend(list(self.path.glob("**/*.bin")))
-        for x in weight_files:
-            if ".fp16" in x.suffixes:
-                return ModelRepoVariant.FP16
-            if "openvino_model" in x.name:
-                return ModelRepoVariant.OpenVINO
-            if "flax_model" in x.name:
-                return ModelRepoVariant.Flax
-            if x.suffix == ".onnx":
-                return ModelRepoVariant.ONNX
-        return ModelRepoVariant.Default
-
-    @staticmethod
-    def load_state_dict(path: Path):
-        with SilenceWarnings():
-            if path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin")):
-                scan_result = scan_file_path(path)
-                if scan_result.infected_files != 0 or scan_result.scan_err:
-                    raise RuntimeError(f"The model {path.stem} is potentially infected by malware. Aborting import.")
-                checkpoint = torch.load(path, map_location="cpu")
-            elif path.suffix.endswith(".gguf"):
-                checkpoint = gguf_sd_loader(path, compute_dtype=torch.float32)
-            elif path.suffix.endswith(".safetensors"):
-                checkpoint = safetensors.torch.load_file(path)
-            else:
-                raise ValueError(f"Unrecognized model extension: {path.suffix}")
-
-        state_dict = checkpoint.get("state_dict", checkpoint)
-        return state_dict
-
-
 class MatchSpeed(int, Enum):
     """Represents the estimated runtime speed of a config's 'matches' method."""
 
@@ -343,16 +169,18 @@ class ModelConfigBase(ABC, BaseModel):
         Created to deprecate ModelProbe.probe
         """
         candidates = ModelConfigBase._USING_CLASSIFY_API
-        sorted_by_match_speed = sorted(candidates, key=lambda cls: cls._MATCH_SPEED)
+        sorted_by_match_speed = sorted(candidates, key=lambda cls: (cls._MATCH_SPEED, cls.__name__))
         mod = ModelOnDisk(model_path, hash_algo)
 
         for config_cls in sorted_by_match_speed:
             try:
-                return config_cls.from_model_on_disk(mod, **overrides)
-            except InvalidModelConfigException:
-                logger.debug(f"ModelConfig '{config_cls.__name__}' failed to parse '{mod.path}', trying next config")
+                if not config_cls.matches(mod):
+                    continue
             except Exception as e:
-                logger.error(f"Unexpected exception while parsing '{config_cls.__name__}': {e}, trying next config")
+                logger.warning(f"Unexpected exception while matching {mod.name} to '{config_cls.__name__}': {e}")
+                continue
+            else:
+                return config_cls.from_model_on_disk(mod, **overrides)
 
         raise InvalidModelConfigException("No valid config found")
 
@@ -392,12 +220,12 @@ class ModelConfigBase(ABC, BaseModel):
         if "source_type" in overrides:
             overrides["source_type"] = ModelSourceType(overrides["source_type"])
 
+        if "variant" in overrides:
+            overrides["variant"] = ModelVariantType(overrides["variant"])
+
     @classmethod
     def from_model_on_disk(cls, mod: ModelOnDisk, **overrides):
         """Creates an instance of this config or raises InvalidModelConfigException."""
-        if not cls.matches(mod):
-            raise InvalidModelConfigException(f"Path {mod.path} does not match {cls.__name__} format")
-
         fields = cls.parse(mod)
         cls.cast_overrides(overrides)
         fields.update(overrides)
@@ -454,6 +282,38 @@ class LoRAConfigBase(ABC, BaseModel):
     type: Literal[ModelType.LoRA] = ModelType.LoRA
     trigger_phrases: Optional[set[str]] = Field(description="Set of trigger phrases for this model", default=None)
 
+    @classmethod
+    def flux_lora_format(cls, mod: ModelOnDisk):
+        key = "FLUX_LORA_FORMAT"
+        if key in mod.cache:
+            return mod.cache[key]
+
+        from invokeai.backend.patches.lora_conversions.formats import flux_format_from_state_dict
+
+        sd = mod.load_state_dict(mod.path)
+        value = flux_format_from_state_dict(sd)
+        mod.cache[key] = value
+        return value
+
+    @classmethod
+    def base_model(cls, mod: ModelOnDisk) -> BaseModelType:
+        if cls.flux_lora_format(mod):
+            return BaseModelType.Flux
+
+        state_dict = mod.load_state_dict()
+        # If we've gotten here, we assume that the model is a Stable Diffusion model
+        token_vector_length = lora_token_vector_length(state_dict)
+        if token_vector_length == 768:
+            return BaseModelType.StableDiffusion1
+        elif token_vector_length == 1024:
+            return BaseModelType.StableDiffusion2
+        elif token_vector_length == 1280:
+            return BaseModelType.StableDiffusionXL  # recognizes format at https://civitai.com/models/224641
+        elif token_vector_length == 2048:
+            return BaseModelType.StableDiffusionXL
+        else:
+            raise InvalidModelConfigException("Unknown LoRA type")
+
 
 class T5EncoderConfigBase(ABC, BaseModel):
     """Base class for diffusers-style models."""
@@ -469,11 +329,40 @@ class T5EncoderBnbQuantizedLlmInt8bConfig(T5EncoderConfigBase, LegacyProbeMixin,
     format: Literal[ModelFormat.BnbQuantizedLlmInt8b] = ModelFormat.BnbQuantizedLlmInt8b
 
 
-class LoRALyCORISConfig(LoRAConfigBase, LegacyProbeMixin, ModelConfigBase):
+class LoRALyCORISConfig(LoRAConfigBase, ModelConfigBase):
     """Model config for LoRA/Lycoris models."""
 
     format: Literal[ModelFormat.LyCORIS] = ModelFormat.LyCORIS
 
+    @classmethod
+    def matches(cls, mod: ModelOnDisk) -> bool:
+        if mod.path.is_dir():
+            return False
+
+        # Avoid false positive match against ControlLoRA and Diffusers
+        if cls.flux_lora_format(mod) in [FluxLoRAFormat.Control, FluxLoRAFormat.Diffusers]:
+            return False
+
+        state_dict = mod.load_state_dict()
+        for key in state_dict.keys():
+            if type(key) is int:
+                continue
+
+            if key.startswith(("lora_te_", "lora_unet_", "lora_te1_", "lora_te2_", "lora_transformer_")):
+                return True
+            # "lora_A.weight" and "lora_B.weight" are associated with models in PEFT format. We don't support all PEFT
+            # LoRA models, but as of the time of writing, we support Diffusers FLUX PEFT LoRA models.
+            if key.endswith(("to_k_lora.up.weight", "to_q_lora.down.weight", "lora_A.weight", "lora_B.weight")):
+                return True
+
+        return False
+
+    @classmethod
+    def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
+        return {
+            "base": cls.base_model(mod),
+        }
+
 
 class ControlAdapterConfigBase(ABC, BaseModel):
     default_settings: Optional[ControlAdapterDefaultSettings] = Field(
@@ -497,11 +386,26 @@ class ControlLoRADiffusersConfig(ControlAdapterConfigBase, LegacyProbeMixin, Mod
     format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
 
 
-class LoRADiffusersConfig(LoRAConfigBase, LegacyProbeMixin, ModelConfigBase):
+class LoRADiffusersConfig(LoRAConfigBase, ModelConfigBase):
     """Model config for LoRA/Diffusers models."""
 
     format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
 
+    @classmethod
+    def matches(cls, mod: ModelOnDisk) -> bool:
+        if mod.path.is_file():
+            return cls.flux_lora_format(mod) == FluxLoRAFormat.Diffusers
+
+        suffixes = ["bin", "safetensors"]
+        weight_files = [mod.path / f"pytorch_lora_weights.{sfx}" for sfx in suffixes]
+        return any(wf.exists() for wf in weight_files)
+
+    @classmethod
+    def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
+        return {
+            "base": cls.base_model(mod),
+        }
+
 
 class VAECheckpointConfig(CheckpointConfigBase, LegacyProbeMixin, ModelConfigBase):
     """Model config for standalone VAE models."""
@@ -673,7 +577,7 @@ class LlavaOnevisionConfig(DiffusersConfigBase, ModelConfigBase):
 
     @classmethod
     def matches(cls, mod: ModelOnDisk) -> bool:
-        if mod.format_type == ModelFormat.Checkpoint:
+        if mod.path.is_file():
             return False
 
         config_path = mod.path / "config.json"

invokeai/backend/model_manager/legacy_probe.py

@@ -14,27 +14,30 @@ from invokeai.backend.flux.controlnet.state_dict_utils import (
     is_state_dict_instantx_controlnet,
     is_state_dict_xlabs_controlnet,
 )
+from invokeai.backend.flux.flux_state_dict_utils import get_flux_in_channels_from_state_dict
 from invokeai.backend.flux.ip_adapter.state_dict_utils import is_state_dict_xlabs_ip_adapter
 from invokeai.backend.flux.redux.flux_redux_state_dict_utils import is_state_dict_likely_flux_redux
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    AnyVariant,
-    BaseModelType,
     ControlAdapterDefaultSettings,
     InvalidModelConfigException,
     MainModelDefaultSettings,
     ModelConfigFactory,
+    SubmodelDefinition,
+)
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import ConfigLoader
+from invokeai.backend.model_manager.taxonomy import (
+    AnyVariant,
+    BaseModelType,
     ModelFormat,
     ModelRepoVariant,
     ModelSourceType,
     ModelType,
     ModelVariantType,
     SchedulerPredictionType,
-    SubmodelDefinition,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import ConfigLoader
 from invokeai.backend.model_manager.util.model_util import (
     get_clip_variant_type,
     lora_token_vector_length,
@@ -562,7 +565,14 @@ class CheckpointProbeBase(ProbeBase):
         state_dict = self.checkpoint.get("state_dict") or self.checkpoint
 
         if base_type == BaseModelType.Flux:
-            in_channels = state_dict["img_in.weight"].shape[1]
+            in_channels = get_flux_in_channels_from_state_dict(state_dict)
+
+            if in_channels is None:
+                # If we cannot find the in_channels, we assume that this is a normal variant. Log a warning.
+                logger.warning(
+                    f"{self.model_path} does not have img_in.weight or model.diffusion_model.img_in.weight key. Assuming normal variant."
+                )
+                return ModelVariantType.Normal
 
             # FLUX Model variant types are distinguished by input channels:
             # - Unquantized Dev and Schnell have in_channels=64

invokeai/backend/model_manager/load/load_base.py

@@ -13,12 +13,11 @@ import torch
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.taxonomy import AnyModel, SubModelType
 
 
 class LoadedModelWithoutConfig:

invokeai/backend/model_manager/load/load_default.py

@@ -6,18 +6,16 @@ from pathlib import Path
 from typing import Optional
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_manager import (
-    AnyModel,
-    AnyModelConfig,
-    InvalidModelConfigException,
-    SubModelType,
-)
-from invokeai.backend.model_manager.config import DiffusersConfigBase
+from invokeai.backend.model_manager.config import AnyModelConfig, DiffusersConfigBase, InvalidModelConfigException
 from invokeai.backend.model_manager.load.load_base import LoadedModel, ModelLoaderBase
 from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
 from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache, get_model_cache_key
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_fs
 from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    SubModelType,
+)
 from invokeai.backend.util.devices import TorchDevice
 
 

invokeai/backend/model_manager/load/model_cache/model_cache.py

@@ -9,7 +9,6 @@ from typing import Any, Callable, Dict, List, Optional
 import psutil
 import torch
 
-from invokeai.backend.model_manager import AnyModel, SubModelType
 from invokeai.backend.model_manager.load.memory_snapshot import MemorySnapshot
 from invokeai.backend.model_manager.load.model_cache.cache_record import CacheRecord
 from invokeai.backend.model_manager.load.model_cache.cache_stats import CacheStats
@@ -23,6 +22,7 @@ from invokeai.backend.model_manager.load.model_cache.torch_module_autocast.torch
     apply_custom_layers_to_model,
 )
 from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
+from invokeai.backend.model_manager.taxonomy import AnyModel, SubModelType
 from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.backend.util.prefix_logger_adapter import PrefixedLoggerAdapter

invokeai/backend/model_manager/load/model_loader_registry.py

@@ -20,13 +20,10 @@ from typing import Callable, Dict, Optional, Tuple, Type, TypeVar
 
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
-    BaseModelType,
     ModelConfigBase,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load import ModelLoaderBase
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType, SubModelType
 
 
 class ModelLoaderRegistryBase(ABC):

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

@@ -4,16 +4,12 @@ from typing import Optional
 from transformers import CLIPVisionModelWithProjection
 
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
     DiffusersConfigBase,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)

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

@@ -5,19 +5,19 @@ from typing import Optional
 
 from diffusers import ControlNetModel
 
-from invokeai.backend.model_manager import (
-    AnyModel,
-    AnyModelConfig,
-)
 from invokeai.backend.model_manager.config import (
-    BaseModelType,
+    AnyModelConfig,
     ControlNetCheckpointConfig,
+)
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    BaseModelType,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 
 
 @ModelLoaderRegistry.register(

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

@@ -27,15 +27,8 @@ from invokeai.backend.flux.model import Flux
 from invokeai.backend.flux.modules.autoencoder import AutoEncoder
 from invokeai.backend.flux.redux.flux_redux_model import FluxReduxModel
 from invokeai.backend.flux.util import ae_params, params
-from invokeai.backend.model_manager import (
-    AnyModel,
-    AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
-)
 from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
     CheckpointConfigBase,
     CLIPEmbedDiffusersConfig,
     ControlNetCheckpointConfig,
@@ -51,6 +44,13 @@ from invokeai.backend.model_manager.config import (
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+)
 from invokeai.backend.model_manager.util.model_util import (
     convert_bundle_to_flux_transformer_checkpoint,
 )

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

@@ -8,18 +8,16 @@ from typing import Any, Optional
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.models.modeling_utils import ModelMixin
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import AnyModelConfig, DiffusersConfigBase, InvalidModelConfigException
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import (
     AnyModel,
-    AnyModelConfig,
     BaseModelType,
-    InvalidModelConfigException,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.config import DiffusersConfigBase
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T2IAdapter, format=ModelFormat.Diffusers)

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

@@ -7,8 +7,9 @@ from typing import Optional
 import torch
 
 from invokeai.backend.ip_adapter.ip_adapter import build_ip_adapter
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
+from invokeai.backend.model_manager.config import AnyModelConfig
 from invokeai.backend.model_manager.load import ModelLoader, ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.raw_model import RawModel
 
 

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

@@ -3,15 +3,11 @@ from typing import Optional
 
 from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LlavaOnevision, format=ModelFormat.Diffusers)

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

@@ -9,17 +9,17 @@ import torch
 from safetensors.torch import load_file
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import AnyModelConfig
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import (
     AnyModel,
-    AnyModelConfig,
     BaseModelType,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_cache.model_cache import ModelCache
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import (
     is_state_dict_likely_flux_control,
     lora_model_from_flux_control_state_dict,

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

@@ -5,16 +5,16 @@
 from pathlib import Path
 from typing import Optional
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import AnyModelConfig
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import (
     AnyModel,
-    AnyModelConfig,
     BaseModelType,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.ONNX, format=ModelFormat.ONNX)

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

@@ -2,15 +2,11 @@ from pathlib import Path
 from typing import Optional
 
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.sig_lip.sig_lip_pipeline import SigLipPipeline
 
 

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

@@ -4,15 +4,11 @@ from typing import Optional
 import torch
 
 from invokeai.backend.model_manager.config import (
-    AnyModel,
     AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    SubModelType,
 )
 from invokeai.backend.model_manager.load.load_default import ModelLoader
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 
 

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

@@ -11,16 +11,8 @@ from diffusers import (
     StableDiffusionXLPipeline,
 )
 
-from invokeai.backend.model_manager import (
-    AnyModel,
-    AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-    ModelVariantType,
-    SubModelType,
-)
 from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
     CheckpointConfigBase,
     DiffusersConfigBase,
     MainCheckpointConfig,
@@ -28,6 +20,14 @@ from invokeai.backend.model_manager.config import (
 from invokeai.backend.model_manager.load.model_cache.model_cache import get_model_cache_key
 from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    ModelVariantType,
+    SubModelType,
+)
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 VARIANT_TO_IN_CHANNEL_MAP = {

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

@@ -4,16 +4,16 @@
 from pathlib import Path
 from typing import Optional
 
-from invokeai.backend.model_manager import (
+from invokeai.backend.model_manager.config import AnyModelConfig
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import (
     AnyModel,
-    AnyModelConfig,
     BaseModelType,
     ModelFormat,
     ModelType,
     SubModelType,
 )
-from invokeai.backend.model_manager.load.load_default import ModelLoader
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
 from invokeai.backend.textual_inversion import TextualInversionModelRaw
 
 

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

@@ -5,15 +5,16 @@ from typing import Optional
 
 from diffusers import AutoencoderKL
 
-from invokeai.backend.model_manager import (
-    AnyModelConfig,
+from invokeai.backend.model_manager.config import AnyModelConfig, VAECheckpointConfig
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
     BaseModelType,
     ModelFormat,
     ModelType,
+    SubModelType,
 )
-from invokeai.backend.model_manager.config import AnyModel, SubModelType, VAECheckpointConfig
-from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
-from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
 
 
 @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.VAE, format=ModelFormat.Diffusers)

invokeai/backend/model_manager/load/model_util.py

@@ -15,7 +15,8 @@ from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import D
 from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
 from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
-from invokeai.backend.model_manager.config import AnyModel
+from invokeai.backend.llava_onevision_model import LlavaOnevisionModel
+from invokeai.backend.model_manager.taxonomy import AnyModel
 from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
 from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.sig_lip.sig_lip_pipeline import SigLipPipeline
@@ -50,6 +51,7 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
             SegmentAnythingPipeline,
             DepthAnythingPipeline,
             SigLipPipeline,
+            LlavaOnevisionModel,
         ),
     ):
         return model.calc_size()

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

@@ -17,12 +17,12 @@ from typing import Optional
 from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 
-from invokeai.backend.model_manager import ModelRepoVariant
 from invokeai.backend.model_manager.metadata.metadata_base import (
     AnyModelRepoMetadata,
     AnyModelRepoMetadataValidator,
     BaseMetadata,
 )
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 
 
 class ModelMetadataFetchBase(ABC):

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

@@ -24,7 +24,6 @@ from huggingface_hub.errors import RepositoryNotFoundError, RevisionNotFoundErro
 from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 
-from invokeai.backend.model_manager.config import ModelRepoVariant
 from invokeai.backend.model_manager.metadata.fetch.fetch_base import ModelMetadataFetchBase
 from invokeai.backend.model_manager.metadata.metadata_base import (
     AnyModelRepoMetadata,
@@ -32,6 +31,7 @@ from invokeai.backend.model_manager.metadata.metadata_base import (
     RemoteModelFile,
     UnknownMetadataException,
 )
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 
 HF_MODEL_RE = r"https?://huggingface.co/([\w\-.]+/[\w\-.]+)"
 

invokeai/backend/model_manager/metadata/metadata_base.py

@@ -23,7 +23,7 @@ from pydantic.networks import AnyHttpUrl
 from requests.sessions import Session
 from typing_extensions import Annotated
 
-from invokeai.backend.model_manager import ModelRepoVariant
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 from invokeai.backend.model_manager.util.select_hf_files import filter_files
 
 

invokeai/backend/model_manager/model_on_disk.py

@@ -0,0 +1,96 @@
+from pathlib import Path
+from typing import Any, Optional, TypeAlias
+
+import safetensors.torch
+import torch
+from picklescan.scanner import scan_file_path
+
+from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS, ModelHash
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
+from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
+from invokeai.backend.util.silence_warnings import SilenceWarnings
+
+StateDict: TypeAlias = dict[str | int, Any]  # When are the keys int?
+
+
+class ModelOnDisk:
+    """A utility class representing a model stored on disk."""
+
+    def __init__(self, path: Path, hash_algo: HASHING_ALGORITHMS = "blake3_single"):
+        self.path = path
+        if self.path.suffix in {".safetensors", ".bin", ".pt", ".ckpt"}:
+            self.name = path.stem
+        else:
+            self.name = path.name
+        self.hash_algo = hash_algo
+        # Having a cache helps users of ModelOnDisk (i.e. configs) to save state
+        # This prevents redundant computations during matching and parsing
+        self.cache = {"_CACHED_STATE_DICTS": {}}
+
+    def hash(self) -> str:
+        return ModelHash(algorithm=self.hash_algo).hash(self.path)
+
+    def size(self) -> int:
+        if self.path.is_file():
+            return self.path.stat().st_size
+        return sum(file.stat().st_size for file in self.path.rglob("*"))
+
+    def component_paths(self) -> set[Path]:
+        if self.path.is_file():
+            return {self.path}
+        extensions = {".safetensors", ".pt", ".pth", ".ckpt", ".bin", ".gguf"}
+        return {f for f in self.path.rglob("*") if f.suffix in extensions}
+
+    def repo_variant(self) -> Optional[ModelRepoVariant]:
+        if self.path.is_file():
+            return None
+
+        weight_files = list(self.path.glob("**/*.safetensors"))
+        weight_files.extend(list(self.path.glob("**/*.bin")))
+        for x in weight_files:
+            if ".fp16" in x.suffixes:
+                return ModelRepoVariant.FP16
+            if "openvino_model" in x.name:
+                return ModelRepoVariant.OpenVINO
+            if "flax_model" in x.name:
+                return ModelRepoVariant.Flax
+            if x.suffix == ".onnx":
+                return ModelRepoVariant.ONNX
+        return ModelRepoVariant.Default
+
+    def load_state_dict(self, path: Optional[Path] = None) -> StateDict:
+        sd_cache = self.cache["_CACHED_STATE_DICTS"]
+
+        if path in sd_cache:
+            return sd_cache[path]
+
+        if not path:
+            components = list(self.component_paths())
+            match components:
+                case []:
+                    raise ValueError("No weight files found for this model")
+                case [p]:
+                    path = p
+                case ps if len(ps) >= 2:
+                    raise ValueError(
+                        f"Multiple weight files found for this model: {ps}. "
+                        f"Please specify the intended file using the 'path' argument"
+                    )
+
+        with SilenceWarnings():
+            if path.suffix.endswith((".ckpt", ".pt", ".pth", ".bin")):
+                scan_result = scan_file_path(path)
+                if scan_result.infected_files != 0 or scan_result.scan_err:
+                    raise RuntimeError(f"The model {path.stem} is potentially infected by malware. Aborting import.")
+                checkpoint = torch.load(path, map_location="cpu")
+                assert isinstance(checkpoint, dict)
+            elif path.suffix.endswith(".gguf"):
+                checkpoint = gguf_sd_loader(path, compute_dtype=torch.float32)
+            elif path.suffix.endswith(".safetensors"):
+                checkpoint = safetensors.torch.load_file(path)
+            else:
+                raise ValueError(f"Unrecognized model extension: {path.suffix}")
+
+        state_dict = checkpoint.get("state_dict", checkpoint)
+        sd_cache[path] = state_dict
+        return state_dict

invokeai/backend/model_manager/starter_models.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from pydantic import BaseModel
 
-from invokeai.backend.model_manager.config import BaseModelType, ModelFormat, ModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType
 
 
 class StarterModelWithoutDependencies(BaseModel):

invokeai/backend/model_manager/taxonomy.py

@@ -0,0 +1,138 @@
+from enum import Enum
+from typing import Dict, TypeAlias, Union
+
+import diffusers
+import onnxruntime as ort
+import torch
+from diffusers import ModelMixin
+
+from invokeai.backend.raw_model import RawModel
+
+# ModelMixin is the base class for all diffusers and transformers models
+# RawModel is the InvokeAI wrapper class for ip_adapters, loras, textual_inversion and onnx runtime
+AnyModel = Union[
+    ModelMixin, RawModel, torch.nn.Module, Dict[str, torch.Tensor], diffusers.DiffusionPipeline, ort.InferenceSession
+]
+
+
+class BaseModelType(str, Enum):
+    """Base model type."""
+
+    Any = "any"
+    StableDiffusion1 = "sd-1"
+    StableDiffusion2 = "sd-2"
+    StableDiffusion3 = "sd-3"
+    StableDiffusionXL = "sdxl"
+    StableDiffusionXLRefiner = "sdxl-refiner"
+    Flux = "flux"
+    # Kandinsky2_1 = "kandinsky-2.1"
+
+
+class ModelType(str, Enum):
+    """Model type."""
+
+    ONNX = "onnx"
+    Main = "main"
+    VAE = "vae"
+    LoRA = "lora"
+    ControlLoRa = "control_lora"
+    ControlNet = "controlnet"  # used by model_probe
+    TextualInversion = "embedding"
+    IPAdapter = "ip_adapter"
+    CLIPVision = "clip_vision"
+    CLIPEmbed = "clip_embed"
+    T2IAdapter = "t2i_adapter"
+    T5Encoder = "t5_encoder"
+    SpandrelImageToImage = "spandrel_image_to_image"
+    SigLIP = "siglip"
+    FluxRedux = "flux_redux"
+    LlavaOnevision = "llava_onevision"
+
+
+class SubModelType(str, Enum):
+    """Submodel type."""
+
+    UNet = "unet"
+    Transformer = "transformer"
+    TextEncoder = "text_encoder"
+    TextEncoder2 = "text_encoder_2"
+    TextEncoder3 = "text_encoder_3"
+    Tokenizer = "tokenizer"
+    Tokenizer2 = "tokenizer_2"
+    Tokenizer3 = "tokenizer_3"
+    VAE = "vae"
+    VAEDecoder = "vae_decoder"
+    VAEEncoder = "vae_encoder"
+    Scheduler = "scheduler"
+    SafetyChecker = "safety_checker"
+
+
+class ClipVariantType(str, Enum):
+    """Variant type."""
+
+    L = "large"
+    G = "gigantic"
+
+
+class ModelVariantType(str, Enum):
+    """Variant type."""
+
+    Normal = "normal"
+    Inpaint = "inpaint"
+    Depth = "depth"
+
+
+class ModelFormat(str, Enum):
+    """Storage format of model."""
+
+    Diffusers = "diffusers"
+    Checkpoint = "checkpoint"
+    LyCORIS = "lycoris"
+    ONNX = "onnx"
+    Olive = "olive"
+    EmbeddingFile = "embedding_file"
+    EmbeddingFolder = "embedding_folder"
+    InvokeAI = "invokeai"
+    T5Encoder = "t5_encoder"
+    BnbQuantizedLlmInt8b = "bnb_quantized_int8b"
+    BnbQuantizednf4b = "bnb_quantized_nf4b"
+    GGUFQuantized = "gguf_quantized"
+
+
+class SchedulerPredictionType(str, Enum):
+    """Scheduler prediction type."""
+
+    Epsilon = "epsilon"
+    VPrediction = "v_prediction"
+    Sample = "sample"
+
+
+class ModelRepoVariant(str, Enum):
+    """Various hugging face variants on the diffusers format."""
+
+    Default = ""  # model files without "fp16" or other qualifier
+    FP16 = "fp16"
+    FP32 = "fp32"
+    ONNX = "onnx"
+    OpenVINO = "openvino"
+    Flax = "flax"
+
+
+class ModelSourceType(str, Enum):
+    """Model source type."""
+
+    Path = "path"
+    Url = "url"
+    HFRepoID = "hf_repo_id"
+
+
+class FluxLoRAFormat(str, Enum):
+    """Flux LoRA formats."""
+
+    Diffusers = "flux.diffusers"
+    Kohya = "flux.kohya"
+    OneTrainer = "flux.onetrainer"
+    Control = "flux.control"
+
+
+AnyVariant: TypeAlias = Union[ModelVariantType, ClipVariantType, None]

invokeai/backend/model_manager/util/model_util.py

@@ -8,7 +8,7 @@ import picklescan.scanner as pscan
 import safetensors
 import torch
 
-from invokeai.backend.model_manager.config import ClipVariantType
+from invokeai.backend.model_manager.taxonomy import ClipVariantType
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
 
 

invokeai/backend/model_manager/util/select_hf_files.py

@@ -17,7 +17,7 @@ from dataclasses import dataclass
 from pathlib import Path
 from typing import Dict, List, Optional, Set
 
-from invokeai.backend.model_manager.config import ModelRepoVariant
+from invokeai.backend.model_manager.taxonomy import ModelRepoVariant
 
 
 def filter_files(

invokeai/backend/patches/lora_conversions/formats.py

@@ -0,0 +1,24 @@
+from invokeai.backend.model_manager.taxonomy import FluxLoRAFormat
+from invokeai.backend.patches.lora_conversions.flux_control_lora_utils import is_state_dict_likely_flux_control
+from invokeai.backend.patches.lora_conversions.flux_diffusers_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_diffusers_format,
+)
+from invokeai.backend.patches.lora_conversions.flux_kohya_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_kohya_format,
+)
+from invokeai.backend.patches.lora_conversions.flux_onetrainer_lora_conversion_utils import (
+    is_state_dict_likely_in_flux_onetrainer_format,
+)
+
+
+def flux_format_from_state_dict(state_dict):
+    if is_state_dict_likely_in_flux_kohya_format(state_dict):
+        return FluxLoRAFormat.Kohya
+    elif is_state_dict_likely_in_flux_onetrainer_format(state_dict):
+        return FluxLoRAFormat.OneTrainer
+    elif is_state_dict_likely_in_flux_diffusers_format(state_dict):
+        return FluxLoRAFormat.Diffusers
+    elif is_state_dict_likely_flux_control(state_dict):
+        return FluxLoRAFormat.Control
+    else:
+        return None

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

@@ -69,6 +69,9 @@ 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/stable_diffusion/extensions/t2i_adapter.py

@@ -8,7 +8,7 @@ from diffusers import T2IAdapter
 from PIL.Image import Image
 
 from invokeai.app.util.controlnet_utils import prepare_control_image
-from invokeai.backend.model_manager import BaseModelType
+from invokeai.backend.model_manager.taxonomy import BaseModelType
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningMode
 from invokeai.backend.stable_diffusion.extension_callback_type import ExtensionCallbackType
 from invokeai.backend.stable_diffusion.extensions.base import ExtensionBase, callback