chore: bump version to v5.4.1

.dockerignore

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

.git-blame-ignore-revs

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

.gitattributes

@@ -2,5 +2,4 @@
 # Only affects text files and ignores other file types.
 # For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
 * text=auto
-docker/** text eol=lf
-tests/test_model_probe/stripped_models/** filter=lfs diff=lfs merge=lfs -text
+docker/** text eol=lf

.github/CODEOWNERS

@@ -1,12 +1,12 @@
 # continuous integration
-/.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr @jazzhaiku
+/.github/workflows/  @lstein @blessedcoolant @hipsterusername @ebr
 
 # documentation
 /docs/ @lstein @blessedcoolant @hipsterusername @Millu
 /mkdocs.yml @lstein  @blessedcoolant @hipsterusername @Millu
 
 # nodes
-/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising @hipsterusername @jazzhaiku
+/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising @hipsterusername
 
 # installation and configuration
 /pyproject.toml  @lstein @blessedcoolant @hipsterusername
@@ -22,7 +22,7 @@
 /invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp @hipsterusername
 
 # generation, model management, postprocessing
-/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick @hipsterusername @jazzhaiku
+/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising @ryanjdick @hipsterusername
 
 # front ends
 /invokeai/frontend/CLI @lstein @hipsterusername

.github/workflows/build-container.yml

@@ -76,6 +76,9 @@ jobs:
             latest=${{ matrix.gpu-driver == 'cuda' && github.ref == 'refs/heads/main' }}
             suffix=-${{ matrix.gpu-driver }},onlatest=false
 
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
         with:
@@ -97,12 +100,10 @@ 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: ${{ steps.meta.outputs.labels }}
-          # cache-from: |
-          #   type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
-          #   type=gha,scope=main-${{ matrix.gpu-driver }}
-          # cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
+          cache-from: |
+            type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
+            type=gha,scope=main-${{ matrix.gpu-driver }}
+          cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}

.github/workflows/frontend-checks.yml

@@ -44,12 +44,7 @@ jobs:
       - name: check for changed frontend files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
+        uses: tj-actions/changed-files@v42
         with:
           files_yaml: |
             frontend:

.github/workflows/frontend-tests.yml

@@ -44,12 +44,7 @@ jobs:
       - name: check for changed frontend files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
+        uses: tj-actions/changed-files@v42
         with:
           files_yaml: |
             frontend:

.github/workflows/python-checks.yml

@@ -34,9 +34,6 @@ 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:
@@ -46,12 +43,7 @@ jobs:
       - name: check for changed python files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
+        uses: tj-actions/changed-files@v42
         with:
           files_yaml: |
             python:
@@ -60,19 +52,25 @@ jobs:
               - '!invokeai/frontend/web/**'
               - 'tests/**'
 
-      - name: setup uv
+      - name: setup python
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        uses: astral-sh/setup-uv@v5
+        uses: actions/setup-python@v5
         with:
-          version: '0.6.10'
-          enable-cache: true
+          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==0.6.0
+        shell: bash
 
       - name: ruff check
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: uv tool run ruff@0.11.2 check --output-format=github .
+        run: ruff check --output-format=github .
         shell: bash
 
       - name: ruff format
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}
-        run: uv tool run ruff@0.11.2 format --check .
+        run: ruff format --check .
         shell: bash

.github/workflows/python-tests.yml

@@ -39,15 +39,24 @@ 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-24.04
+            os: ubuntu-22.04
             extra-index-url: 'https://download.pytorch.org/whl/cpu'
             github-env: $GITHUB_ENV
           - platform: macos-default
@@ -61,22 +70,14 @@ 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
-        uses: nschloe/action-cached-lfs-checkout@f46300cd8952454b9f0a21a3d133d4bd5684cfc2
+        uses: actions/checkout@v4
 
       - name: check for changed python files
         if: ${{ inputs.always_run != true }}
         id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
+        uses: tj-actions/changed-files@v42
         with:
           files_yaml: |
             python:
@@ -85,25 +86,20 @@ 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:
-          UV_INDEX: ${{ matrix.extra-index-url }}
-        run: uv pip install --editable ".[test]"
+          PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
+        run: >
+          pip3 install --editable=".[test]"
 
       - name: run pytest
         if: ${{ steps.changed-files.outputs.python_any_changed == 'true' || inputs.always_run == true }}

.github/workflows/typegen-checks.yml

@@ -1,98 +0,0 @@
-# Runs typegen schema quality checks.
-# Frontend types should match the server.
-#
-# Checks for changes to files before running the checks.
-# If always_run is true, always runs the checks.
-
-name: 'typegen checks'
-
-on:
-  push:
-    branches:
-      - 'main'
-  pull_request:
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
-  merge_group:
-  workflow_dispatch:
-    inputs:
-      always_run:
-        description: 'Always run the checks'
-        required: true
-        type: boolean
-        default: true
-  workflow_call:
-    inputs:
-      always_run:
-        description: 'Always run the checks'
-        required: true
-        type: boolean
-        default: true
-
-jobs:
-  typegen-checks:
-    runs-on: ubuntu-22.04
-    timeout-minutes: 15 # expected run time: <5 min
-    steps:
-      - name: checkout
-        uses: actions/checkout@v4
-
-      - name: check for changed files
-        if: ${{ inputs.always_run != true }}
-        id: changed-files
-        # Pinned to the _hash_ for v45.0.9 to prevent supply-chain attacks.
-        # See:
-        # - CVE-2025-30066
-        # - https://www.stepsecurity.io/blog/harden-runner-detection-tj-actions-changed-files-action-is-compromised
-        # - https://github.com/tj-actions/changed-files/issues/2463
-        uses: tj-actions/changed-files@a284dc1814e3fd07f2e34267fc8f81227ed29fb8
-        with:
-          files_yaml: |
-            src:
-              - '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: '3.11'
-
-      - name: install dependencies
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        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 }}
-        uses: ./.github/actions/install-frontend-deps
-
-      - name: copy schema
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        run: cp invokeai/frontend/web/src/services/api/schema.ts invokeai/frontend/web/src/services/api/schema_orig.ts
-        shell: bash
-
-      - name: generate schema
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        run: cd invokeai/frontend/web && uv run ../../../scripts/generate_openapi_schema.py | pnpm typegen
-        shell: bash
-
-      - name: compare files
-        if: ${{ steps.changed-files.outputs.src_any_changed == 'true' || inputs.always_run == true }}
-        run: |
-          if ! diff invokeai/frontend/web/src/services/api/schema.ts invokeai/frontend/web/src/services/api/schema_orig.ts; then
-            echo "Files are different!";
-            exit 1;
-          fi
-        shell: bash

.nvmrc

@@ -1 +0,0 @@
-v22.14.0

README.md

@@ -30,12 +30,51 @@ Invoke is available in two editions:
 |----------------------------------------------------------------------------------------------------------------------------|
 |  [Installation and Updates][installation docs] - [Documentation and Tutorials][docs home] - [Bug Reports][github issues] - [Contributing][contributing docs]  | 
 
-# Installation
+</div>
 
-To get started with Invoke, [Download the Installer](https://www.invoke.com/downloads).
+## Quick Start
 
-For detailed step by step instructions, or for instructions on manual/docker installations, visit our documentation on [Installation and Updates][installation docs]
+1. Download and unzip the installer from the bottom of the [latest release][latest release link].
+2. Run the installer script.
 
+   - **Windows**: Double-click on the `install.bat` script.
+   - **macOS**: Open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press enter.
+   - **Linux**: Run `install.sh`.
+
+3. When prompted, enter a location for the install and select your GPU type.
+4. Once the install finishes, find the directory you selected during install. The default location is `C:\Users\Username\invokeai` for Windows or `~/invokeai` for Linux/macOS.
+5. Run the launcher script (`invoke.bat` for Windows, `invoke.sh` for macOS and Linux) the same way you ran the installer script in step 2.
+6. Select option 1 to start the application. Once it starts up, open your browser and go to <http://localhost:9090>.
+7. Open the model manager tab to install a starter model and then you'll be ready to generate.
+
+More detail, including hardware requirements and manual install instructions, are available in the [installation documentation][installation docs].
+
+## Docker Container
+
+We publish official container images in Github Container Registry: https://github.com/invoke-ai/InvokeAI/pkgs/container/invokeai. Both CUDA and ROCm images are available. Check the above link for relevant tags.
+
+> [!IMPORTANT]
+> Ensure that Docker is set up to use the GPU. Refer to [NVIDIA][nvidia docker docs] or [AMD][amd docker docs] documentation.
+
+### Generate!
+
+Run the container, modifying the command as necessary:
+
+```bash
+docker run --runtime=nvidia --gpus=all --publish 9090:9090 ghcr.io/invoke-ai/invokeai
+```
+
+Then open `http://localhost:9090` and install some models using the Model Manager tab to begin generating.
+
+For ROCm, add `--device /dev/kfd --device /dev/dri` to the `docker run` command.
+
+### Persist your data
+
+You will likely want to persist your workspace outside of the container. Use the `--volume /home/myuser/invokeai:/invokeai` flag to mount some local directory (using its **absolute** path) to the `/invokeai` path inside the container. Your generated images and models will reside there. You can use this directory with other InvokeAI installations, or switch between runtime directories as needed.
+
+### DIY
+
+Build your own image and customize the environment to match your needs using our `docker-compose` stack. See [README.md](./docker/README.md) in the [docker](./docker) directory.
 
 ## Troubleshooting, FAQ and Support
 

SECURITY.md

@@ -1,14 +0,0 @@
-# Security Policy
-
-## Supported Versions
-
-Only the latest version of Invoke will receive security updates. 
-We do not currently maintain multiple versions of the application with updates.
-
-## Reporting a Vulnerability
-
-To report a vulnerability, contact the Invoke team directly at security@invoke.ai
-
-At this time, we do not maintain a formal bug bounty program. 
-
-You can also share identified security issues with our team on huntr.com

docker/Dockerfile

@@ -1,8 +1,58 @@
 # syntax=docker/dockerfile:1.4
 
-#### Web UI ------------------------------------
+## Builder stage
 
-FROM docker.io/node:22-slim AS web-builder
+FROM library/ubuntu:23.04 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 \
+        git \
+        python3-venv \
+        python3-pip \
+        build-essential
+
+ENV INVOKEAI_SRC=/opt/invokeai
+ENV VIRTUAL_ENV=/opt/venv/invokeai
+
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+ARG GPU_DRIVER=cuda
+ARG TARGETPLATFORM="linux/amd64"
+# unused but available
+ARG BUILDPLATFORM
+
+WORKDIR ${INVOKEAI_SRC}
+
+COPY invokeai ./invokeai
+COPY pyproject.toml ./
+
+# Editable mode helps use the same image for development:
+# the local working copy can be bind-mounted into the image
+# at path defined by ${INVOKEAI_SRC}
+# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
+# x86_64/CUDA is default
+RUN --mount=type=cache,target=/root/.cache/pip \
+    python3 -m venv ${VIRTUAL_ENV} &&\
+    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
+    elif [ "$GPU_DRIVER" = "rocm" ]; then \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm6.1"; \
+    else \
+        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu124"; \
+    fi &&\
+
+    # xformers + triton fails to install on arm64
+    if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
+        pip install $extra_index_url_arg -e ".[xformers]"; \
+    else \
+        pip install $extra_index_url_arg -e "."; \
+    fi
+
+# #### Build the Web UI ------------------------------------
+
+FROM node:20-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
 ENV PATH="$PNPM_HOME:$PATH"
 RUN corepack use pnpm@8.x
@@ -14,89 +64,61 @@ RUN --mount=type=cache,target=/pnpm/store \
     pnpm install --frozen-lockfile
 RUN npx vite build
 
-## Backend ---------------------------------------
+#### Runtime stage ---------------------------------------
 
-FROM library/ubuntu:24.04
+FROM library/ubuntu:23.04 AS runtime
 
 ARG DEBIAN_FRONTEND=noninteractive
-RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
-RUN --mount=type=cache,target=/var/cache/apt \
-    --mount=type=cache,target=/var/lib/apt \
-    apt update && apt install -y --no-install-recommends \
-    ca-certificates \
-    git \
-    gosu \
-    libglib2.0-0 \
-    libgl1 \
-    libglx-mesa0 \
-    build-essential \
-    libopencv-dev \
-    libstdc++-10-dev
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
 
-ENV \
-    PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1 \
-    VIRTUAL_ENV=/opt/venv \
-    INVOKEAI_SRC=/opt/invokeai \
-    PYTHON_VERSION=3.12 \
-    UV_PYTHON=3.12 \
-    UV_COMPILE_BYTECODE=1 \
-    UV_MANAGED_PYTHON=1 \
-    UV_LINK_MODE=copy \
-    UV_PROJECT_ENVIRONMENT=/opt/venv \
-    UV_INDEX="https://download.pytorch.org/whl/cu124" \
-    INVOKEAI_ROOT=/invokeai \
-    INVOKEAI_HOST=0.0.0.0 \
-    INVOKEAI_PORT=9090 \
-    PATH="/opt/venv/bin:$PATH" \
-    CONTAINER_UID=${CONTAINER_UID:-1000} \
-    CONTAINER_GID=${CONTAINER_GID:-1000}
+RUN apt update && apt install -y --no-install-recommends \
+        git \
+        curl \
+        vim \
+        tmux \
+        ncdu \
+        iotop \
+        bzip2 \
+        gosu \
+        magic-wormhole \
+        libglib2.0-0 \
+        libgl1-mesa-glx \
+        python3-venv \
+        python3-pip \
+        build-essential \
+        libopencv-dev \
+        libstdc++-10-dev &&\
+    apt-get clean && apt-get autoclean
 
-ARG GPU_DRIVER=cuda
 
-# Install `uv` for package management
-COPY --from=ghcr.io/astral-sh/uv:0.6.9 /uv /uvx /bin/
+ENV INVOKEAI_SRC=/opt/invokeai
+ENV VIRTUAL_ENV=/opt/venv/invokeai
+ENV INVOKEAI_ROOT=/invokeai
+ENV INVOKEAI_HOST=0.0.0.0
+ENV INVOKEAI_PORT=9090
+ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
+ENV CONTAINER_UID=${CONTAINER_UID:-1000}
+ENV CONTAINER_GID=${CONTAINER_GID:-1000}
 
-# Install python & allow non-root user to use it by traversing the /root dir without read permissions
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv python install ${PYTHON_VERSION} && \
-    # chmod --recursive a+rX /root/.local/share/uv/python
-    chmod 711 /root
-
-WORKDIR ${INVOKEAI_SRC}
-
-# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
-# bind-mount instead of copy to defer adding sources to the image until next layer.
-#
-# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
-# x86_64/CUDA is the default
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=uv.lock,target=uv.lock \
-    # this is just to get the package manager to recognize that the project exists, without making changes to the docker layer
-    --mount=type=bind,source=invokeai/version,target=invokeai/version \
-    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then UV_INDEX="https://download.pytorch.org/whl/cpu"; \
-    elif [ "$GPU_DRIVER" = "rocm" ]; then UV_INDEX="https://download.pytorch.org/whl/rocm6.2"; \
-    fi && \
-    uv sync --frozen
-
-# build patchmatch
-RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
-RUN python -c "from patchmatch import patch_match"
+# --link requires buldkit w/ dockerfile syntax 1.4
+COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
+COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
 
 # Link amdgpu.ids for ROCm builds
 # contributed by https://github.com/Rubonnek
 RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
-    ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
+  ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
+
+WORKDIR ${INVOKEAI_SRC}
+
+# build patchmatch
+RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
+RUN python3 -c "from patchmatch import patch_match"
 
 RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
 
 COPY docker/docker-entrypoint.sh ./
 ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
 CMD ["invokeai-web"]
-
-# --link requires buldkit w/ dockerfile syntax 1.4, does not work with podman
-COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
-
-# add sources last to minimize image changes on code changes
-COPY invokeai ${INVOKEAI_SRC}/invokeai

docker/docker-entrypoint.sh

@@ -16,9 +16,6 @@ set -e -o pipefail
 
 USER_ID=${CONTAINER_UID:-1000}
 USER=ubuntu
-# if the user does not exist, create it. It is expected to be present on ubuntu >=24.x
-_=$(id ${USER} 2>&1) || useradd -u ${USER_ID} ${USER}
-# ensure the UID is correct
 usermod -u ${USER_ID} ${USER} 1>/dev/null
 
 ### Set the $PUBLIC_KEY env var to enable SSH access.
@@ -39,8 +36,6 @@ fi
 mkdir -p "${INVOKEAI_ROOT}"
 chown --recursive ${USER} "${INVOKEAI_ROOT}" || true
 cd "${INVOKEAI_ROOT}"
-export HF_HOME=${HF_HOME:-$INVOKEAI_ROOT/.cache/huggingface}
-export MPLCONFIGDIR=${MPLCONFIGDIR:-$INVOKEAI_ROOT/.matplotlib}
 
 # Run the CMD as the Container User (not root).
 exec gosu ${USER} "$@"

docs/RELEASE.md

@@ -1,50 +1,41 @@
 # Release Process
 
-The Invoke application is published as a python package on [PyPI]. This includes both a source distribution and built distribution (a wheel).
+The app is published in twice, in different build formats.
 
-Most users install it with the [Launcher](https://github.com/invoke-ai/launcher/), others with `pip`.
-
-The launcher uses GitHub as the source of truth for available releases.
-
-## Broad Strokes
-
-- Merge all changes and bump the version in the codebase.
-- Tag the release commit.
-- Wait for the release workflow to complete.
-- Approve the PyPI publish jobs.
-- Write GH release notes.
+- A [PyPI] distribution. This includes both a source distribution and built distribution (a wheel). Users install with `pip install invokeai`. The updater uses this build.
+- An installer on the [InvokeAI Releases Page]. This is a zip file with install scripts and a wheel. This is only used for new installs.
 
 ## General Prep
 
-Make a developer call-out for PRs to merge. Merge and test things out. Bump the version by editing `invokeai/version/invokeai_version.py`.
+Make a developer call-out for PRs to merge. Merge and test things out.
+
+While the release workflow does not include end-to-end tests, it does pause before publishing so you can download and test the final build.
 
 ## Release Workflow
 
 The `release.yml` workflow runs a number of jobs to handle code checks, tests, build and publish on PyPI.
 
-It is triggered on **tag push**, when the tag matches `v*`.
+It is triggered on **tag push**, when the tag matches `v*`. It doesn't matter if you've prepped a release branch like `release/v3.5.0` or are releasing from `main` - it works the same.
+
+> Because commits are reference-counted, it is safe to create a release branch, tag it, let the workflow run, then delete the branch. So long as the tag exists, that commit will exist.
 
 ### Triggering the Workflow
 
-Ensure all commits that should be in the release are merged, and you have pulled them locally.
+Run `make tag-release` to tag the current commit and kick off the workflow.
 
-Double-check that you have checked out the commit that will represent the release (typically the latest commit on `main`).
-
-Run `make tag-release` to tag the current commit and kick off the workflow. You will be prompted to provide a message - use the version specifier.
-
-If this version's tag already exists for some reason (maybe you had to make a last minute change), the script will overwrite it.
-
-> In case you cannot use the Make target, the release may also be dispatched [manually] via GH.
+The release may also be dispatched [manually].
 
 ### Workflow Jobs and Process
 
-The workflow consists of a number of concurrently-run checks and tests, then two final publish jobs.
+The workflow consists of a number of concurrently-run jobs, and two final publish jobs.
 
 The publish jobs require manual approval and are only run if the other jobs succeed.
 
 #### `check-version` Job
 
-This job ensures that the `invokeai` python package version specifier matches the tag for the release. The version specifier is pulled from the `__version__` variable in `invokeai/version/invokeai_version.py`.
+This job checks that the git ref matches the app version. It matches the ref against the `__version__` variable in `invokeai/version/invokeai_version.py`.
+
+When the workflow is triggered by tag push, the ref is the tag. If the workflow is run manually, the ref is the target selected from the **Use workflow from** dropdown.
 
 This job uses [samuelcolvin/check-python-version].
 
@@ -52,52 +43,62 @@ This job uses [samuelcolvin/check-python-version].
 
 #### Check and Test Jobs
 
-Next, these jobs run and must pass. They are the same jobs that are run for every PR.
-
 - **`python-tests`**: runs `pytest` on matrix of platforms
 - **`python-checks`**: runs `ruff` (format and lint)
 - **`frontend-tests`**: runs `vitest`
 - **`frontend-checks`**: runs `prettier` (format), `eslint` (lint), `dpdm` (circular refs), `tsc` (static type check) and `knip` (unused imports)
-- **`typegen-checks`**: ensures the frontend and backend types are synced
+
+> **TODO** We should add `mypy` or `pyright` to the **`check-python`** job.
+
+> **TODO** We should add an end-to-end test job that generates an image.
 
 #### `build-installer` Job
 
 This sets up both python and frontend dependencies and builds the python package. Internally, this runs `installer/create_installer.sh` and uploads two artifacts:
 
 - **`dist`**: the python distribution, to be published on PyPI
-- **`InvokeAI-installer-${VERSION}.zip`**: the legacy install scripts
-
-You don't need to download either of these files.
-
-> The legacy install scripts are no longer used, but we haven't updated the workflow to skip building them.
+- **`InvokeAI-installer-${VERSION}.zip`**: the installer to be included in the GitHub release
 
 #### Sanity Check & Smoke Test
 
-At this point, the release workflow pauses as the remaining publish jobs require approval.
+At this point, the release workflow pauses as the remaining publish jobs require approval. Time to test the installer.
 
-It's possible to test the python package before it gets published to PyPI. We've never had problems with it, so it's not necessary to do this.
+Because the installer pulls from PyPI, and we haven't published to PyPI yet, you will need to install from the wheel:
 
-But, if you want to be extra-super careful, here's how to test it:
+- Download and unzip `dist.zip` and the installer from the **Summary** tab of the workflow
+- Run the installer script using the `--wheel` CLI arg, pointing at the wheel:
 
-- Download the `dist.zip` build artifact from the `build-installer` job
-- Unzip it and find the wheel file
-- Create a fresh Invoke install by following the [manual install guide](https://invoke-ai.github.io/InvokeAI/installation/manual/) - but instead of installing from PyPI, install from the wheel
-- Test the app
+  ```sh
+  ./install.sh --wheel ../InvokeAI-4.0.0rc6-py3-none-any.whl
+  ```
+
+- Install to a temporary directory so you get the new user experience
+- Download a model and generate
+
+> The same wheel file is bundled in the installer and in the `dist` artifact, which is uploaded to PyPI. You should end up with the exactly the same installation as if the installer got the wheel from PyPI.
 
 ##### Something isn't right
 
-If testing reveals any issues, no worries. Cancel the workflow, which will cancel the pending publish jobs (you didn't approve them prematurely, right?) and start over.
+If testing reveals any issues, no worries. Cancel the workflow, which will cancel the pending publish jobs (you didn't approve them prematurely, right?).
+
+Now you can start from the top:
+
+- Fix the issues and PR the fixes per usual
+- Get the PR approved and merged per usual
+- Switch to `main` and pull in the fixes
+- Run `make tag-release` to move the tag to `HEAD` (which has the fixes) and kick off the release workflow again
+- Re-do the sanity check
 
 #### PyPI Publish Jobs
 
-The publish jobs will not run if any of the previous jobs fail.
+The publish jobs will run if any of the previous jobs fail.
 
 They use [GitHub environments], which are configured as [trusted publishers] on PyPI.
 
-Both jobs require a @hipsterusername or @psychedelicious to approve them from the workflow's **Summary** tab.
+Both jobs require a maintainer to approve them from the workflow's **Summary** tab.
 
 - Click the **Review deployments** button
-- Select the environment (either `testpypi` or `pypi` - typically you select both)
+- Select the environment (either `testpypi` or `pypi`)
 - Click **Approve and deploy**
 
 > **If the version already exists on PyPI, the publish jobs will fail.** PyPI only allows a given version to be published once - you cannot change it. If version published on PyPI has a problem, you'll need to "fail forward" by bumping the app version and publishing a followup release.
@@ -112,33 +113,46 @@ If there are no incidents, contact @hipsterusername or @lstein, who have owner a
 
 Publishes the distribution on the [Test PyPI] index, using the `testpypi` GitHub environment.
 
-This job is not required for the production PyPI publish, but included just in case you want to test the PyPI release for some reason:
+This job is not required for the production PyPI publish, but included just in case you want to test the PyPI release.
 
-- Approve this publish job without approving the prod publish
-- Let it finish
-- Create a fresh Invoke install by following the [manual install guide](https://invoke-ai.github.io/InvokeAI/installation/manual/), making sure to use the Test PyPI index URL: `https://test.pypi.org/simple/`
-- Test the app
+If approved and successful, you could try out the test release like this:
+
+```sh
+# Create a new virtual environment
+python -m venv ~/.test-invokeai-dist --prompt test-invokeai-dist
+# Install the distribution from Test PyPI
+pip install --index-url https://test.pypi.org/simple/ invokeai
+# Run and test the app
+invokeai-web
+# Cleanup
+deactivate
+rm -rf ~/.test-invokeai-dist
+```
 
 #### `publish-pypi` Job
 
 Publishes the distribution on the production PyPI index, using the `pypi` GitHub environment.
 
-It's a good idea to wait to approve and run this job until you have the release notes ready!
+## Publish the GitHub Release with installer
 
-## Prep and publish the GitHub Release
+Once the release is published to PyPI, it's time to publish the GitHub release.
 
 1. [Draft a new release] on GitHub, choosing the tag that triggered the release.
-2. The **Generate release notes** button automatically inserts the changelog and new contributors. Make sure to select the correct tags for this release and the last stable release. GH often selects the wrong tags - do this manually.
-3. Write the release notes, describing important changes. Contributions from community members should be shouted out. Use the GH-generated changelog to see all contributors. If there are Weblate translation updates, open that PR and shout out every person who contributed a translation.
-4. Check **Set as a pre-release** if it's a pre-release.
-5. Approve and wait for the `publish-pypi` job to finish if you haven't already.
-6. Publish the GH release.
-7. Post the release in Discord in the [releases](https://discord.com/channels/1020123559063990373/1149260708098359327) channel with abbreviated notes. For example:
-   > Invoke v5.7.0 (stable): <https://github.com/invoke-ai/InvokeAI/releases/tag/v5.7.0>
-   >
-   > It's a pretty big one - Form Builder, Metadata Nodes (thanks @SkunkWorxDark!), and much more.
-8. Right click the message in releases and copy the link to it. Then, post that link in the [new-release-discussion](https://discord.com/channels/1020123559063990373/1149506274971631688) channel. For example:
-   > Invoke v5.7.0 (stable): <https://discord.com/channels/1020123559063990373/1149260708098359327/1344521744916021248>
+1. Write the release notes, describing important changes. The **Generate release notes** button automatically inserts the changelog and new contributors, and you can copy/paste the intro from previous releases.
+1. Use `scripts/get_external_contributions.py` to get a list of external contributions to shout out in the release notes.
+1. Upload the zip file created in **`build`** job into the Assets section of the release notes.
+1. Check **Set as a pre-release** if it's a pre-release.
+1. Check **Create a discussion for this release**.
+1. Publish the release.
+1. Announce the release in Discord.
+
+> **TODO** Workflows can create a GitHub release from a template and upload release assets. One popular action to handle this is [ncipollo/release-action]. A future enhancement to the release process could set this up.
+
+## Manual Build
+
+The `build installer` workflow can be dispatched manually. This is useful to test the installer for a given branch or tag.
+
+No checks are run, it just builds.
 
 ## Manual Release
 
@@ -146,10 +160,12 @@ The `release` workflow can be dispatched manually. You must dispatch the workflo
 
 This functionality is available as a fallback in case something goes wonky. Typically, releases should be triggered via tag push as described above.
 
+[InvokeAI Releases Page]: https://github.com/invoke-ai/InvokeAI/releases
 [PyPI]: https://pypi.org/
 [Draft a new release]: https://github.com/invoke-ai/InvokeAI/releases/new
 [Test PyPI]: https://test.pypi.org/
 [version specifier]: https://packaging.python.org/en/latest/specifications/version-specifiers/
+[ncipollo/release-action]: https://github.com/ncipollo/release-action
 [GitHub environments]: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment
 [trusted publishers]: https://docs.pypi.org/trusted-publishers/
 [samuelcolvin/check-python-version]: https://github.com/samuelcolvin/check-python-version

docs/configuration.md

@@ -39,7 +39,7 @@ It has two sections - one for internal use and one for user settings:
 
 ```yaml
 # Internal metadata - do not edit:
-schema_version: 4.0.2
+schema_version: 4
 
 # Put user settings here - see https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/:
 host: 0.0.0.0 # serve the app on your local network
@@ -83,10 +83,6 @@ A subset of settings may be specified using CLI args:
 - `--root`: specify the root directory
 - `--config`: override the default `invokeai.yaml` file location
 
-### Low-VRAM Mode
-
-See the [Low-VRAM mode docs][low-vram] for details on enabling this feature.
-
 ### All Settings
 
 Following the table are additional explanations for certain settings.
@@ -118,10 +114,6 @@ remote_api_tokens:
 
 The provided token will be added as a `Bearer` token to the network requests to download the model files. As far as we know, this works for all model marketplaces that require authorization.
 
-!!! tip "HuggingFace Models"
-
-    If you get an error when installing a HF model using a URL instead of repo id, you may need to [set up a HF API token](https://huggingface.co/settings/tokens) and add an entry for it under `remote_api_tokens`. Use `huggingface.co` for `url_regex`.
-
 #### Model Hashing
 
 Models are hashed during installation, providing a stable identifier for models across all platforms. Hashing is a one-time operation.
@@ -189,4 +181,3 @@ The `log_format` option provides several alternative formats:
 
 [basic guide to yaml files]: https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/
 [Model Marketplace API Keys]: #model-marketplace-api-keys
-[low-vram]: ./features/low-vram.md

docs/contributing/ARCHITECTURE.md

@@ -50,7 +50,7 @@ Applications are built on top of the invoke framework. They should construct `in
 
 ### Web UI
 
-The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/invokeai/frontend` and the backend code is found in `/invokeai/app/api_app.py` and `/invokeai/app/api/`. The code is further organized as such:
+The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/frontend` and the backend code is found in `/ldm/invoke/app/api_app.py` and `/ldm/invoke/app/api/`. The code is further organized as such:
 
 | Component | Description |
 | --- | --- |
@@ -62,7 +62,7 @@ The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.t
 
 ### CLI
 
-The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/invokeai/frontend/cli`.
+The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/ldm/invoke/app/cli_app.py`.
 
 ## Invoke
 
@@ -70,7 +70,7 @@ The Invoke framework provides the interface to the underlying AI systems and is
 
 ### Invoker
 
-The invoker (`/invokeai/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
+The invoker (`/ldm/invoke/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
 - **invocation services**, which are used by invocations to interact with core functionality.
 - **invoker services**, which are used by the invoker to manage sessions and manage the invocation queue.
 
@@ -82,12 +82,12 @@ The session graph does not support looping. This is left as an application probl
 
 ### Invocations
 
-Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/invokeai/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
+Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/ldm/invoke/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
 
 ### Services
 
-Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/invokeai/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
+Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/ldm/invoke/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
 
 ## AI Core
 
-The AI Core is represented by the rest of the code base (i.e. the code outside of `/invokeai/app/`).
+The AI Core is represented by the rest of the code base (i.e. the code outside of `/ldm/invoke/app/`).

docs/contributing/INVOCATIONS.md

@@ -287,8 +287,8 @@ new Invocation ready to be used.
 
 Once you've created a Node, the next step is to share it with the community! The
 best way to do this is to submit a Pull Request to add the Node to the
-[Community Nodes](../nodes/communityNodes.md) list. If you're not sure how to do that,
-take a look a at our [contributing nodes overview](../nodes/contributingNodes.md).
+[Community Nodes](nodes/communityNodes) list. If you're not sure how to do that,
+take a look a at our [contributing nodes overview](contributingNodes).
 
 ## Advanced
 

docs/contributing/MODEL_MANAGER.md

@@ -9,20 +9,20 @@ model. These are the:
   configuration information. Among other things, the record service
   tracks the type of the model, its provenance, and where it can be
   found on disk.
-
+  
 * _ModelInstallServiceBase_ A service for installing models to
   disk. It uses `DownloadQueueServiceBase` to download models and
   their metadata, and `ModelRecordServiceBase` to store that
   information. It is also responsible for managing the InvokeAI
   `models` directory and its contents.
-
+  
 * _DownloadQueueServiceBase_
   A multithreaded downloader responsible
   for downloading models from a remote source to disk. The download
   queue has special methods for downloading repo_id folders from
   Hugging Face, as well as discriminating among model versions in
   Civitai, but can be used for arbitrary content.
-
+  
   * _ModelLoadServiceBase_
   Responsible for loading a model from disk
   into RAM and VRAM and getting it ready for inference.
@@ -207,9 +207,9 @@ for use in the InvokeAI web server. Its signature is:
 
 ```
 def open(
-       cls,
-    config: InvokeAIAppConfig,
-    conn: Optional[sqlite3.Connection] = None,
+       cls, 
+    config: InvokeAIAppConfig, 
+    conn: Optional[sqlite3.Connection] = None, 
     lock: Optional[threading.Lock] = None
     ) -> Union[ModelRecordServiceSQL, ModelRecordServiceFile]:
 ```
@@ -363,7 +363,7 @@ functionality:
 
 * Registering a model config record for a model already located on the
   local filesystem, without moving it or changing its path.
-
+  
 * Installing a model alreadiy located on the local filesystem, by
   moving it into the InvokeAI root directory under the
   `models` folder (or wherever config parameter `models_dir`
@@ -371,21 +371,21 @@ functionality:
 
 * Probing of models to determine their type, base type and other key
   information.
-
+  
 * Interface with the InvokeAI event bus to provide status updates on
   the download, installation and registration process.
-
+  
 * Downloading a model from an arbitrary URL and installing it in
   `models_dir`.
 
 * Special handling for HuggingFace repo_ids to recursively download
   the contents of the repository, paying attention to alternative
   variants such as fp16.
-
+  
 * Saving tags and other metadata about the model into the invokeai database
   when fetching from a repo that provides that type of information,
   (currently only HuggingFace).
-
+  
 ### Initializing the installer
 
 A default installer is created at InvokeAI api startup time and stored
@@ -461,7 +461,7 @@ revision.
 `config` is an optional dict of values that will override the
 autoprobed values for model type, base, scheduler prediction type, and
 so forth. See [Model configuration and
-probing](#model-configuration-and-probing) for details.
+probing](#Model-configuration-and-probing) for details.
 
 `access_token` is an optional access token for accessing resources
 that need authentication.
@@ -494,7 +494,7 @@ source8 = URLModelSource(url='https://civitai.com/api/download/models/63006', ac
 
 for source in [source1, source2, source3, source4, source5, source6, source7]:
    install_job = installer.install_model(source)
-
+   
 source2job = installer.wait_for_installs(timeout=120)
 for source in sources:
     job = source2job[source]
@@ -504,7 +504,7 @@ for source in sources:
   print(f"{source} installed as {model_key}")
  elif job.errored:
      print(f"{source}: {job.error_type}.\nStack trace:\n{job.error}")
-
+ 
 ```
 
 As shown here, the `import_model()` method accepts a variety of
@@ -1364,6 +1364,7 @@ the in-memory loaded model:
 |----------------|-----------------|------------------|
 | `config`       | AnyModelConfig         | A copy of the model's configuration record for retrieving base type, etc. |
 | `model`        | AnyModel               | The instantiated model (details below) |
+| `locker`       | ModelLockerBase        | A context manager that mediates the movement of the model into VRAM |
 
 ### get_model_by_key(key, [submodel]) -> LoadedModel
 

docs/contributing/TESTS.md

@@ -1,6 +1,6 @@
 # InvokeAI Backend Tests
 
-We use `pytest` to run the backend python tests. (See [pyproject.toml](https://github.com/invoke-ai/InvokeAI/blob/main/pyproject.toml) for the default `pytest` options.)
+We use `pytest` to run the backend python tests. (See [pyproject.toml](/pyproject.toml) for the default `pytest` options.)
 
 ## Fast vs. Slow
 All tests are categorized as either 'fast' (no test annotation) or 'slow' (annotated with the `@pytest.mark.slow` decorator).
@@ -33,7 +33,7 @@ pytest tests -m ""
 
 ## Test Organization
 
-All backend tests are in the [`tests/`](https://github.com/invoke-ai/InvokeAI/tree/main/tests) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
+All backend tests are in the [`tests/`](/tests/) directory. This directory mirrors the organization of the `invokeai/` directory. For example, tests for `invokeai/model_management/model_manager.py` would be found in `tests/model_management/test_model_manager.py`.
 
 TODO: The above statement is aspirational. A re-organization of legacy tests is required to make it true.
 

docs/contributing/contribution_guides/development.md

@@ -2,7 +2,7 @@
 
 ## **What do I need to know to help?**
 
-If you are looking to help with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential.
+If you are looking to help with a code contribution, InvokeAI uses several different technologies under the hood: Python (Pydantic, FastAPI, diffusers) and Typescript (React, Redux Toolkit, ChakraUI, Mantine, Konva). Familiarity with StableDiffusion and image generation concepts is helpful, but not essential. 
 
 
 ## **Get Started**
@@ -12,7 +12,7 @@ To get started, take a look at our [new contributors checklist](newContributorCh
 Once you're setup, for more information, you can review the documentation specific to your area of interest:
 
 * #### [InvokeAI Architecure](../ARCHITECTURE.md)
-* #### [Frontend Documentation](../frontend/index.md)
+* #### [Frontend Documentation](https://github.com/invoke-ai/InvokeAI/tree/main/invokeai/frontend/web)
 * #### [Node Documentation](../INVOCATIONS.md)
 * #### [Local Development](../LOCAL_DEVELOPMENT.md)
 
@@ -20,15 +20,15 @@ Once you're setup, for more information, you can review the documentation specif
 
 If you don't feel ready to make a code contribution yet, no problem! You can also help out in other ways, such as [documentation](documentation.md), [translation](translation.md) or helping support other users and triage issues as they're reported in GitHub.
 
-There are two paths to making a development contribution:
+There are two paths to making a development contribution: 
 
 1. Choosing an open issue to address. Open issues can be found in the [Issues](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen) section of the InvokeAI repository. These are tagged by the issue type (bug, enhancement, etc.) along with the “good first issues” tag denoting if they are suitable for first time contributors.
-    1. Additional items can be found on our [roadmap](https://github.com/orgs/invoke-ai/projects/7). The roadmap is organized in terms of priority, and contains features of varying size and complexity. If there is an inflight item you’d like to help with, reach out to the contributor assigned to the item to see how you can help.
+    1. Additional items can be found on our [roadmap](https://github.com/orgs/invoke-ai/projects/7). The roadmap is organized in terms of priority, and contains features of varying size and complexity. If there is an inflight item you’d like to help with, reach out to the contributor assigned to the item to see how you can help. 
 2. Opening a new issue or feature to add. **Please make sure you have searched through existing issues before creating new ones.**
 
 *Regardless of what you choose, please post in the  [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord before you start development in order to confirm that the issue or feature is aligned with the current direction of the project. We value our contributors time and effort and want to ensure that no one’s time is being misspent.*
 
-## Best Practices:
+## Best Practices: 
 * Keep your pull requests small. Smaller pull requests are more likely to be accepted and merged
 * Comments! Commenting your code helps reviewers easily understand your contribution
 * Use Python and Typescript’s typing systems, and consider using an editor with [LSP](https://microsoft.github.io/language-server-protocol/) support to streamline development
@@ -38,7 +38,7 @@ There are two paths to making a development contribution:
 
 If you need help, you can ask questions in the [#dev-chat](https://discord.com/channels/1020123559063990373/1049495067846524939) channel of the Discord.
 
-For frontend related work, **@psychedelicious** is the best person to reach out to.
+For frontend related work, **@psychedelicious** is the best person to reach out to. 
 
 For backend related work, please reach out to **@blessedcoolant**, **@lstein**, **@StAlKeR7779** or **@psychedelicious**.
 

docs/contributing/contribution_guides/newContributorChecklist.md

@@ -22,15 +22,15 @@ Before starting these steps, ensure you have your local environment [configured
 2. Fork the [InvokeAI](https://github.com/invoke-ai/InvokeAI) repository to your GitHub profile. This means that you will have a copy of the repository under **your-GitHub-username/InvokeAI**.
 3. Clone the repository to your local machine using:
 
-    ```bash
-    git clone https://github.com/your-GitHub-username/InvokeAI.git
-    ```
+   ```bash
+   git clone https://github.com/your-GitHub-username/InvokeAI.git
+   ```
 
 If you're unfamiliar with using Git through the commandline, [GitHub Desktop](https://desktop.github.com) is a easy-to-use alternative with a UI. You can do all the same steps listed here, but through the interface. 4. Create a new branch for your fix using:
 
-  ```bash
-  git checkout -b branch-name-here
-  ```
+    ```bash
+    git checkout -b branch-name-here
+    ```
 
 5. Make the appropriate changes for the issue you are trying to address or the feature that you want to add.
 6. Add the file contents of the changed files to the "snapshot" git uses to manage the state of the project, also known as the index:

docs/contributing/dev-environment.md

@@ -1,10 +1,12 @@
 # Dev Environment
 
-To make changes to Invoke's backend, frontend or documentation, you'll need to set up a dev environment.
+To make changes to Invoke's backend, frontend, or documentation, you'll need to set up a dev environment.
 
-If you only want to make changes to the docs site, you can skip the frontend dev environment setup as described in the below guide.
+If you just want to use Invoke, you should use the [installer][installer link].
 
-If you just want to use Invoke, you should use the [launcher][launcher link].
+!!! info "Why do I need the frontend toolchain?"
+
+    The repo doesn't contain a build of the frontend. You'll be responsible for rebuilding it every time you pull in new changes, or run it in dev mode (which incurs a substantial performance penalty).
 
 !!! warning
 
@@ -15,76 +17,84 @@ If you just want to use Invoke, you should use the [launcher][launcher link].
 ## Setup
 
 1. Run through the [requirements][requirements link].
-
 2. [Fork and clone][forking link] the [InvokeAI repo][repo link].
-
-3. This repository uses Git LFS to manage large files. To ensure all assets are downloaded:
-      - Install git-lfs → [Download here](https://git-lfs.com/)
-      - Enable automatic LFS fetching for this repository:
-        ```shell
-        git config lfs.fetchinclude "*"
-        ```
-        - Fetch files from LFS (only needs to be done once; subsequent `git pull` will fetch changes automatically):
-        ```
-        git lfs pull
-        ```
-4. Create an directory for user data (images, models, db, etc). This is typically at `~/invokeai`, but if you already have a non-dev install, you may want to create a separate directory for the dev install.
-
-5. Follow the [manual install][manual install link] guide, with some modifications to the install command:
-
-      - Use `.` instead of `invokeai` to install from the current directory. You don't need to specify the version.
-
-      - Add `-e` after the `install` operation to make this an [editable install][editable install link]. That means your changes to the python code will be reflected when you restart the Invoke server.
-
-      - When installing the `invokeai` package, add the `dev`, `test` and `docs` package options to the package specifier. You may or may not need the `xformers` option - follow the manual install guide to figure that out. So, your package specifier will be either `".[dev,test,docs]"` or `".[dev,test,docs,xformers]"`. Note the quotes!
-
-     With the modifications made, the install command should look something like this:
+3. Create an directory for user data (images, models, db, etc). This is typically at `~/invokeai`, but if you already have a non-dev install, you may want to create a separate directory for the dev install.
+4. Create a python virtual environment inside the directory you just created:
 
       ```sh
-      uv pip install -e ".[dev,test,docs,xformers]" --python 3.12 --python-preference only-managed --index=https://download.pytorch.org/whl/cu124 --reinstall
+      python3 -m venv .venv --prompt InvokeAI-Dev
       ```
 
-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.
+5. Activate the venv (you'll need to do this every time you want to run the app):
 
-      This is because the UI build is not distributed with the source code. You need to build it manually. End the running server instance.
+        ```sh
+        source .venv/bin/activate
+        ```
 
-      If you only want to edit the docs, you can stop here and skip to the **Documentation** section below.
+6. Install the repo as an [editable install][editable install link]:
+
+      ```sh
+      pip install -e ".[dev,test,xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+      ```
+
+      Refer to the [manual installation][manual install link]] instructions for more determining the correct install options. `xformers` is optional, but `dev` and `test` are not.
 
 7. Install the frontend dev toolchain:
 
-      - [`nodejs`](https://nodejs.org/) (v20+)
-
-      - [`pnpm`](https://pnpm.io/8.x/installation) (must be v8 - not v9!)
+   - [`nodejs`](https://nodejs.org/) (recommend v20 LTS)
+   - [`pnpm`](https://pnpm.io/8.x/installation) (must be v8 - not v9!)
 
 8. Do a production build of the frontend:
 
       ```sh
-      cd <PATH_TO_INVOKEAI_REPO>/invokeai/frontend/web
+      cd PATH_TO_INVOKEAI_REPO/invokeai/frontend/web
       pnpm i
       pnpm build
       ```
 
-9. Restart the server and navigate to the URL. You should get a UI. After making changes to the python code, restart the server to see those changes.
+9. Start the application:
+
+      ```sh
+      cd PATH_TO_INVOKEAI_REPO
+      python scripts/invokeai-web.py
+      ```
+
+10. Access the UI at `localhost:9090`.
 
 ## Updating the UI
 
-You'll need to run `pnpm build` every time you pull in new changes.
-
-Another option is to skip the build and instead run the UI in dev mode:
+You'll need to run `pnpm build` every time you pull in new changes. Another option is to skip the build and instead run the app in dev mode:
 
 ```sh
 pnpm dev
 ```
 
-This starts a vite dev server for the UI at `127.0.0.1:5173`, which you will use instead of `127.0.0.1:9090`.
+This starts a dev server at `localhost:5173`, which you will use instead of `localhost:9090`.
 
-The dev mode is substantially slower than the production build but may be more convenient if you just need to test things out. It will hot-reload the UI as you make changes to the frontend code. Sometimes the hot-reload doesn't work, and you need to manually refresh the browser tab.
+The dev mode is substantially slower than the production build but may be more convenient if you just need to test things out.
 
 ## Documentation
 
-The documentation is built with `mkdocs`. It provides a hot-reload dev server for the docs. Start it with `mkdocs serve`.
+The documentation is built with `mkdocs`. To preview it locally, you need a additional set of packages installed.
 
-[launcher link]: ../installation/quick_start.md
+```sh
+# after activating the venv
+pip install -e ".[docs]"
+```
+
+Then, you can start a live docs dev server, which will auto-refresh when you edit the docs:
+
+```sh
+mkdocs serve
+```
+
+On macOS and Linux, there is a `make` target for this:
+
+```sh
+make docs
+```
+
+[installer link]: ../installation/installer.md
 [forking link]: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo
 [requirements link]: ../installation/requirements.md
 [repo link]: https://github.com/invoke-ai/InvokeAI

docs/contributing/index.md

@@ -34,11 +34,11 @@ Please reach out to @hipsterusername on [Discord](https://discord.gg/ZmtBAhwWhy)
 
 ## Contributors
 
-This project is a combined effort of dedicated people from across the world. [Check out the list of all these amazing people](contributors.md). We thank them for their time, hard work and effort.
+This project is a combined effort of dedicated people from across the world. [Check out the list of all these amazing people](https://invoke-ai.github.io/InvokeAI/other/CONTRIBUTORS/). We thank them for their time, hard work and effort.
 
 ## Code of Conduct
 
-The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](../CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
+The InvokeAI community is a welcoming place, and we want your help in maintaining that. Please review our [Code of Conduct](https://github.com/invoke-ai/InvokeAI/blob/main/CODE_OF_CONDUCT.md) to learn more - it's essential to maintaining a respectful and inclusive environment.
 
 By making a contribution to this project, you certify that:
 

docs/faq.md

@@ -1,18 +1,26 @@
 # FAQ
 
+!!! info "How to Reinstall"
+
+    Many issues can be resolved by re-installing the application. You won't lose any data by re-installing. We suggest downloading the [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest) and using it to re-install the application. Consult the [installer guide](./installation/installer.md) for more information.
+
+    When you run the installer, you'll have an option to select the version to install. If you aren't ready to upgrade, you choose the current version to fix a broken install.
+
 If the troubleshooting steps on this page don't get you up and running, please either [create an issue] or hop on [discord] for help.
 
 ## How to Install
 
-Follow the [Quick Start guide](./installation/quick_start.md) to install Invoke.
+You can download the latest installers [here](https://github.com/invoke-ai/InvokeAI/releases).
+
+Note that any releases marked as _pre-release_ are in a beta state. You may experience some issues, but we appreciate your help testing those! For stable/reliable installations, please install the [latest release].
 
 ## Downloading models and using existing models
 
 The Model Manager tab in the UI provides a few ways to install models, including using your already-downloaded models. You'll see a popup directing you there on first startup. For more information, see the [model install docs].
 
-## Missing models after updating from v3
+## Missing models after updating to v4
 
-If you find some models are missing after updating from v3, it's likely they weren't correctly registered before the update and didn't get picked up in the migration.
+If you find some models are missing after updating to v4, it's likely they weren't correctly registered before the update and didn't get picked up in the migration.
 
 You can use the `Scan Folder` tab in the Model Manager UI to fix this. The models will either be in the old, now-unused `autoimport` folder, or your `models` folder.
 
@@ -29,27 +37,115 @@ Follow the same steps to scan and import the missing models.
 ## Slow generation
 
 - Check the [system requirements] to ensure that your system is capable of generating images.
-- Follow the [Low-VRAM mode guide](./features/low-vram.md) to optimize performance.
-- Check that your generations are happening on your GPU (if you have one). Invoke will log what is being used for generation upon startup. If your GPU isn't used, re-install to and ensure you select the appropriate GPU option.
-- If you are on Windows with an Nvidia GPU, you may have exceeded your GPU's VRAM capacity and are triggering Nvidia's "sysmem fallback". There's a guide to opt out of this behaviour in the [Low-VRAM mode guide](./features/low-vram.md).
+- Check the `ram` setting in `invokeai.yaml`. This setting tells Invoke how much of your system RAM can be used to cache models. Having this too high or too low can slow things down. That said, it's generally safest to not set this at all and instead let Invoke manage it.
+- Check the `vram` setting in `invokeai.yaml`. This setting tells Invoke how much of your GPU VRAM can be used to cache models. Counter-intuitively, if this setting is too high, Invoke will need to do a lot of shuffling of models as it juggles the VRAM cache and the currently-loaded model. The default value of 0.25 is generally works well for GPUs without 16GB or more VRAM. Even on a 24GB card, the default works well.
+- Check that your generations are happening on your GPU (if you have one). InvokeAI will log what is being used for generation upon startup. If your GPU isn't used, re-install to ensure the correct versions of torch get installed.
+- If you are on Windows, you may have exceeded your GPU's VRAM capacity and are using slower [shared GPU memory](#shared-gpu-memory-windows). There's a guide to opt out of this behaviour in the linked FAQ entry.
+
+## Shared GPU Memory (Windows)
+
+!!! tip "Nvidia GPUs with driver 536.40"
+
+    This only applies to current Nvidia cards with driver 536.40 or later, released in June 2023.
+
+When the GPU doesn't have enough VRAM for a task, Windows is able to allocate some of its CPU RAM to the GPU. This is much slower than VRAM, but it does allow the system to generate when it otherwise might no have enough VRAM.
+
+When shared GPU memory is used, generation slows down dramatically - but at least it doesn't crash.
+
+If you'd like to opt out of this behavior and instead get an error when you exceed your GPU's VRAM, follow [this guide from Nvidia](https://nvidia.custhelp.com/app/answers/detail/a_id/5490).
+
+Here's how to get the python path required in the linked guide:
+
+- Run `invoke.bat`.
+- Select option 2 for developer console.
+- At least one python path will be printed. Copy the path that includes your invoke installation directory (typically the first).
+
+## Installer cannot find python (Windows)
+
+Ensure that you checked **Add python.exe to PATH** when installing Python. This can be found at the bottom of the Python Installer window. If you already have Python installed, you can re-run the python installer, choose the Modify option and check the box.
 
 ## Triton error on startup
 
-This can be safely ignored. Invoke doesn't use Triton, but if you are on Linux and wish to dismiss the error, you can install Triton.
+This can be safely ignored. InvokeAI doesn't use Triton, but if you are on Linux and wish to dismiss the error, you can install Triton.
 
-## Unable to Copy on Firefox
+## Updated to 3.4.0 and xformers can’t load C++/CUDA
 
-Firefox does not allow Invoke to directly access the clipboard by default. As a result, you may be unable to use certain copy functions. You can fix this by configuring Firefox to allow access to write to the clipboard:
+An issue occurred with your PyTorch update. Follow these steps to fix :
 
-- Go to `about:config` and click the Accept button
-- Search for `dom.events.asyncClipboard.clipboardItem`
-- Set it to `true` by clicking the toggle button
-- Restart Firefox
+1. Launch your invoke.bat / invoke.sh and select the option to open the developer console
+2. Run:`pip install ".[xformers]" --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121`
+   - If you run into an error with `typing_extensions`, re-open the developer console and run: `pip install -U typing-extensions`
+
+Note that v3.4.0 is an old, unsupported version. Please upgrade to the [latest release].
+
+## Install failed and says `pip` is out of date
+
+An out of date `pip` typically won't cause an installation to fail. The cause of the error can likely be found above the message that says `pip` is out of date.
+
+If you saw that warning but the install went well, don't worry about it (but you can update `pip` afterwards if you'd like).
 
 ## Replicate image found online
 
 Most example images with prompts that you'll find on the internet have been generated using different software, so you can't expect to get identical results. In order to reproduce an image, you need to replicate the exact settings and processing steps, including (but not limited to) the model, the positive and negative prompts, the seed, the sampler, the exact image size, any upscaling steps, etc.
 
+## OSErrors on Windows while installing dependencies
+
+During a zip file installation or an update, installation stops with an error like this:
+
+![broken-dependency-screenshot](./assets/troubleshooting/broken-dependency.png){:width="800px"}
+
+To resolve this, re-install the application as described above.
+
+## HuggingFace install failed due to invalid access token
+
+Some HuggingFace models require you to authenticate using an [access token].
+
+Invoke doesn't manage this token for you, but it's easy to set it up:
+
+- Follow the instructions in the link above to create an access token. Copy it.
+- Run the launcher script.
+- Select option 2 (developer console).
+- Paste the following command:
+
+  ```sh
+  python -c "import huggingface_hub; huggingface_hub.login()"
+  ```
+
+- Paste your access token when prompted and press Enter. You won't see anything when you paste it.
+- Type `n` if prompted about git credentials.
+
+If you get an error, try the command again - maybe the token didn't paste correctly.
+
+Once your token is set, start Invoke and try downloading the model again. The installer will automatically use the access token.
+
+If the install still fails, you may not have access to the model.
+
+## Stable Diffusion XL generation fails after trying to load UNet
+
+InvokeAI is working in other respects, but when trying to generate
+images with Stable Diffusion XL you get a "Server Error". The text log
+in the launch window contains this log line above several more lines of
+error messages:
+
+`INFO --> Loading model:D:\LONG\PATH\TO\MODEL, type sdxl:main:unet`
+
+This failure mode occurs when there is a network glitch during
+downloading the very large SDXL model.
+
+To address this, first go to the Model Manager and delete the
+Stable-Diffusion-XL-base-1.X model. Then, click the HuggingFace tab,
+paste the Repo ID stabilityai/stable-diffusion-xl-base-1.0 and install
+the model.
+
+## Package dependency conflicts during installation or update
+
+If you have previously installed InvokeAI or another Stable Diffusion
+package, the installer may occasionally pick up outdated libraries and
+either the installer or `invoke` will fail with complaints about
+library conflicts.
+
+To resolve this, re-install the application as described above.
+
 ## Invalid configuration file
 
 Everything seems to install ok, you get a `ValidationError` when starting up the app.
@@ -58,9 +154,64 @@ This is caused by an invalid setting in the `invokeai.yaml` configuration file.
 
 Check the [configuration docs] for more detail about the settings and how to specify them.
 
-## Out of Memory Errors
+## `ModuleNotFoundError: No module named 'controlnet_aux'`
 
-The models are large, VRAM is expensive, and you may find yourself faced with Out of Memory errors when generating images. Follow our [Low-VRAM mode guide](./features/low-vram.md) to configure Invoke to prevent these.
+`controlnet_aux` is a dependency of Invoke and appears to have been packaged or distributed strangely. Sometimes, it doesn't install correctly. This is outside our control.
+
+If you encounter this error, the solution is to remove the package from the `pip` cache and re-run the Invoke installer so a fresh, working version of `controlnet_aux` can be downloaded and installed:
+
+- Run the Invoke launcher
+- Choose the developer console option
+- Run this command: `pip cache remove controlnet_aux`
+- Close the terminal window
+- Download and run the [installer][latest release], selecting your current install location
+
+## Out of Memory Issues
+
+The models are large, VRAM is expensive, and you may find yourself
+faced with Out of Memory errors when generating images. Here are some
+tips to reduce the problem:
+
+!!! info "Optimizing for GPU VRAM"
+
+    === "4GB VRAM GPU"
+
+        This should be adequate for 512x512 pixel images using Stable Diffusion 1.5
+        and derived models, provided that you do not use the NSFW checker. It won't be loaded unless you go into the UI settings and turn it on.
+
+        If you are on a CUDA-enabled GPU, we will automatically use xformers or torch-sdp to reduce VRAM requirements, though you can explicitly configure this. See the [configuration docs].
+
+    === "6GB VRAM GPU"
+
+        This is a border case. Using the SD 1.5 series you should be able to
+        generate images up to 640x640 with the NSFW checker enabled, and up to
+        1024x1024 with it disabled.
+
+        If you run into persistent memory issues there are a series of
+        environment variables that you can set before launching InvokeAI that
+        alter how the PyTorch machine learning library manages memory. See
+        <https://pytorch.org/docs/stable/notes/cuda.html#memory-management> for
+        a list of these tweaks.
+
+    === "12GB VRAM GPU"
+
+        This should be sufficient to generate larger images up to about 1280x1280.
+  
+## Checkpoint Models Load Slowly or Use Too Much RAM
+
+The difference between diffusers models (a folder containing multiple
+subfolders) and checkpoint models (a file ending with .safetensors or
+.ckpt) is that InvokeAI is able to load diffusers models into memory
+incrementally, while checkpoint models must be loaded all at
+once. With very large models, or systems with limited RAM, you may
+experience slowdowns and other memory-related issues when loading
+checkpoint models.
+
+To solve this, go to the Model Manager tab (the cube), select the
+checkpoint model that's giving you trouble, and press the "Convert"
+button in the upper right of your browser window. This will convert the
+checkpoint into a diffusers model, after which loading should be
+faster and less memory-intensive.
 
 ## Memory Leak (Linux)
 
@@ -102,6 +253,8 @@ Note the differences between memory allocated as chunks in an arena vs. memory a
 
 [model install docs]: ./installation/models.md
 [system requirements]: ./installation/requirements.md
+[latest release]: https://github.com/invoke-ai/InvokeAI/releases/latest
 [create an issue]: https://github.com/invoke-ai/InvokeAI/issues
 [discord]: https://discord.gg/ZmtBAhwWhy
 [configuration docs]: ./configuration.md
+[access token]: https://huggingface.co/docs/hub/security-tokens#how-to-manage-user-access-tokens

docs/features/low-vram.md

@@ -1,176 +0,0 @@
----
-title: Low-VRAM mode
----
-
-As of v5.6.0, Invoke has a low-VRAM mode. It works on systems with dedicated GPUs (Nvidia GPUs on Windows/Linux and AMD GPUs on Linux).
-
-This allows you to generate even if your GPU doesn't have enough VRAM to hold full models. Most users should be able to run even the beefiest models - like the ~24GB unquantised FLUX dev model.
-
-## Enabling Low-VRAM mode
-
-To enable Low-VRAM mode, add this line to your `invokeai.yaml` configuration file, then restart Invoke:
-
-```yaml
-enable_partial_loading: true
-```
-
-**Windows users should also [disable the Nvidia sysmem fallback](#disabling-nvidia-sysmem-fallback-windows-only)**.
-
-It is possible to fine-tune the settings for best performance or if you still get out-of-memory errors (OOMs).
-
-!!! tip "How to find `invokeai.yaml`"
-
-    The `invokeai.yaml` configuration file lives in your install directory. To access it, run the **Invoke Community Edition** launcher and click the install location. This will open your install directory in a file explorer window.
-
-    You'll see `invokeai.yaml` there and can edit it with any text editor. After making changes, restart Invoke.
-
-    If you don't see `invokeai.yaml`, launch Invoke once. It will create the file on its first startup.
-
-## Details and fine-tuning
-
-Low-VRAM mode involves 4 features, each of which can be configured or fine-tuned:
-
-- Partial model loading (`enable_partial_loading`)
-- PyTorch CUDA allocator config (`pytorch_cuda_alloc_conf`)
-- Dynamic RAM and VRAM cache sizes (`max_cache_ram_gb`, `max_cache_vram_gb`)
-- Working memory (`device_working_mem_gb`)
-- Keeping a RAM weight copy (`keep_ram_copy_of_weights`)
-
-Read on to learn about these features and understand how to fine-tune them for your system and use-cases.
-
-### Partial model loading
-
-Invoke's partial model loading works by streaming model "layers" between RAM and VRAM as they are needed.
-
-When an operation needs layers that are not in VRAM, but there isn't enough room to load them, inactive layers are offloaded to RAM to make room.
-
-#### Enabling partial model loading
-
-As described above, you can enable partial model loading by adding this line to `invokeai.yaml`:
-
-```yaml
-enable_partial_loading: true
-```
-
-### PyTorch CUDA allocator config
-
-The PyTorch CUDA allocator's behavior can be configured using the `pytorch_cuda_alloc_conf` config. Tuning the allocator configuration can help to reduce the peak reserved VRAM. The optimal configuration is dependent on many factors (e.g. device type, VRAM, CUDA driver version, etc.), but switching from PyTorch's native allocator to using CUDA's built-in allocator works well on many systems. To try this, add the following line to your `invokeai.yaml` file:
-
-```yaml
-pytorch_cuda_alloc_conf: "backend:cudaMallocAsync"
-```
-
-A more complete explanation of the available configuration options is [here](https://pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf).
-
-### Dynamic RAM and VRAM cache sizes
-
-Loading models from disk is slow and can be a major bottleneck for performance. Invoke uses two model caches - RAM and VRAM - to reduce loading from disk to a minimum.
-
-By default, Invoke manages these caches' sizes dynamically for best performance.
-
-#### Fine-tuning cache sizes
-
-Prior to v5.6.0, the cache sizes were static, and for best performance, many users needed to manually fine-tune the `ram` and `vram` settings in `invokeai.yaml`.
-
-As of v5.6.0, the caches are dynamically sized. The `ram` and `vram` settings are no longer used, and new settings are added to configure the cache.
-
-**Most users will not need to fine-tune the cache sizes.**
-
-But, if your GPU has enough VRAM to hold models fully, you might get a perf boost by manually setting the cache sizes in `invokeai.yaml`:
-
-```yaml
-# The default max cache RAM size is logged on InvokeAI startup. It is determined based on your system RAM / VRAM.
-# You can override the default value by setting `max_cache_ram_gb`.
-# Increasing `max_cache_ram_gb` will increase the amount of RAM used to cache inactive models, resulting in faster model
-# reloads for the cached models.
-# As an example, if your system has 32GB of RAM and no other heavy processes, setting the `max_cache_ram_gb` to 28GB
-# might be a good value to achieve aggressive model caching.
-max_cache_ram_gb: 28
-
-# The default max cache VRAM size is adjusted dynamically based on the amount of available VRAM (taking into
-# consideration the VRAM used by other processes).
-# You can override the default value by setting `max_cache_vram_gb`.
-# CAUTION: Most users should not manually set this value. See warning below.
-max_cache_vram_gb: 16
-```
-
-!!! warning "Max safe value for `max_cache_vram_gb`"
-
-    Most users should not manually configure the `max_cache_vram_gb`. This configuration value takes precedence over the `device_working_mem_gb` and any operations that explicitly reserve additional working memory (e.g. VAE decode). As such, manually configuring it increases the likelihood of encountering out-of-memory errors.
-
-    For users who wish to configure `max_cache_vram_gb`, the max safe value can be determined by subtracting `device_working_mem_gb` from your GPU's VRAM. As described below, the default for `device_working_mem_gb` is 3GB.
-
-    For example, if you have a 12GB GPU, the max safe value for `max_cache_vram_gb` is `12GB - 3GB = 9GB`.
-
-    If you had increased `device_working_mem_gb` to 4GB, then the max safe value for `max_cache_vram_gb` is `12GB - 4GB = 8GB`.
-
-    Most users who override `max_cache_vram_gb` are doing so because they wish to use significantly less VRAM, and should be setting `max_cache_vram_gb` to a value significantly less than the 'max safe value'.
-
-### Working memory
-
-Invoke cannot use _all_ of your VRAM for model caching and loading. It requires some VRAM to use as working memory for various operations.
-
-Invoke reserves 3GB VRAM as working memory by default, which is enough for most use-cases. However, it is possible to fine-tune this setting if you still get OOMs.
-
-#### Fine-tuning working memory
-
-You can increase the working memory size in `invokeai.yaml` to prevent OOMs:
-
-```yaml
-# The default is 3GB - bump it up to 4GB to prevent OOMs.
-device_working_mem_gb: 4
-```
-
-!!! tip "Operations may request more working memory"
-
-    For some operations, we can determine VRAM requirements in advance and allocate additional working memory to prevent OOMs.
-
-    VAE decoding is one such operation. This operation converts the generation process's output into an image. For large image outputs, this might use more than the default working memory size of 3GB.
-
-    During this decoding step, Invoke calculates how much VRAM will be required to decode and requests that much VRAM from the model manager. If the amount exceeds the working memory size, the model manager will offload cached model layers from VRAM until there's enough VRAM to decode.
-
-    Once decoding completes, the model manager "reclaims" the extra VRAM allocated as working memory for future model loading operations.
-
-### Keeping a RAM weight copy
-
-Invoke has the option of keeping a RAM copy of all model weights, even when they are loaded onto the GPU. This optimization is _on_ by default, and enables faster model switching and LoRA patching. Disabling this feature will reduce the average RAM load while running Invoke (peak RAM likely won't change), at the cost of slower model switching and LoRA patching. If you have limited RAM, you can disable this optimization:
-
-```yaml
-# Set to false to reduce the average RAM usage at the cost of slower model switching and LoRA patching.
-keep_ram_copy_of_weights: false
-```
-
-### Disabling Nvidia sysmem fallback (Windows only)
-
-On Windows, Nvidia GPUs are able to use system RAM when their VRAM fills up via **sysmem fallback**. While it sounds like a good idea on the surface, in practice it causes massive slowdowns during generation.
-
-It is strongly suggested to disable this feature:
-
-- Open the **NVIDIA Control Panel** app.
-- Expand **3D Settings** on the left panel.
-- Click **Manage 3D Settings** in the left panel.
-- Find **CUDA - Sysmem Fallback Policy** in the right panel and set it to **Prefer No Sysmem Fallback**.
-
-![cuda-sysmem-fallback](./cuda-sysmem-fallback.png)
-
-!!! tip "Invoke does the same thing, but better"
-
-    If the sysmem fallback feature sounds familiar, that's because Invoke's partial model loading strategy is conceptually very similar - use VRAM when there's room, else fall back to RAM.
-
-    Unfortunately, the Nvidia implementation is not optimized for applications like Invoke and does more harm than good.
-
-## Troubleshooting
-
-### Windows page file
-
-Invoke has high virtual memory (a.k.a. 'committed memory') requirements. This can cause issues on Windows if the page file size limits are hit. (See this issue for the technical details on why this happens: https://github.com/invoke-ai/InvokeAI/issues/7563).
-
-If you run out of page file space, InvokeAI may crash. Often, these crashes will happen with one of the following errors:
-
-- InvokeAI exits with Windows error code `3221225477`
-- InvokeAI crashes without an error, but `eventvwr.msc` reveals an error with code `0xc0000005` (the hex equivalent of `3221225477`)
-
-If you are running out of page file space, try the following solutions:
-
-- Make sure that you have sufficient disk space for the page file to grow. Watch your disk usage as Invoke runs. If it climbs near 100% leading up to the crash, then this is very likely the source of the issue. Clear out some disk space to resolve the issue.
-- Make sure that your page file is set to "System managed size" (this is the default) rather than a custom size. Under the "System managed size" policy, the page file will grow dynamically as needed. 

docs/index.md

@@ -50,9 +50,11 @@ title: Invoke
 
 ## Installation
 
-The [Invoke Launcher](installation/quick_start.md) is the easiest way to install, update and run Invoke on Windows, macOS and Linux.
+The [installer script](installation/installer.md) is the easiest way to install and update the application.
 
-You can also install Invoke as [python package](installation/manual.md) or with [docker](installation/docker.md).
+You can also install Invoke as python package [via PyPI](installation/manual.md) or [docker](installation/docker.md).
+
+See the [installation section](./installation/index.md) for more information.
 
 ## Help
 

docs/installation/docker.md

@@ -4,7 +4,7 @@ title: Docker
 
 !!! warning "macOS users"
 
-    Docker can not access the GPU on macOS, so your generation speeds will be slow. Use the [launcher](./quick_start.md) instead.
+    Docker can not access the GPU on macOS, so your generation speeds will be slow. Use the [installer](./installer.md) instead.
 
 !!! tip "Linux and Windows Users"
 

docs/installation/index.md

@@ -0,0 +1,36 @@
+# Installation and Updating Overview
+
+Before installing, review the [installation requirements](./requirements.md) to ensure your system is set up properly.
+
+See the [FAQ](../faq.md) for frequently-encountered installation issues.
+
+If you need more help, join our [discord](https://discord.gg/ZmtBAhwWhy) or [create a GitHub issue](https://github.com/invoke-ai/InvokeAI/issues).
+
+## Automated Installer & Updates
+
+✅ The automated [installer](./installer.md) is the best way to install Invoke.
+
+⬆️ The same installer is also the best way to update Invoke - simply rerun it for the same folder you installed to.
+
+The installation process simply manages installation for the core libraries & application dependencies that run Invoke.
+
+Models, images, or other assets in the Invoke root folder won't be affected by the installation process.
+
+## Manual Install
+
+If you are familiar with python and want more control over the packages that are installed, you can [install Invoke manually via PyPI](./manual.md).
+
+Updates are managed by reinstalling the latest version through PyPi.
+
+## Developer Install
+
+If you want to contribute to InvokeAI, you'll need to set up a [dev environment](../contributing/dev-environment.md).
+
+## Docker
+
+Invoke publishes docker images. See the [docker installation guide](./docker.md) for details.
+
+## Other Installation Guides
+
+- [PyPatchMatch](./patchmatch.md)
+- [Installing Models](./models.md)

docs/installation/installer.md

@@ -1,10 +1,4 @@
-# Legacy Scripts
-
-!!! warning "Legacy Scripts"
-
-    We recommend using the Invoke Launcher to install and update Invoke. It's a desktop application for Windows, macOS and Linux. It takes care of a lot of nitty gritty details for you.
-
-    Follow the [quick start guide](./quick_start.md) to get started.
+# Automatic Install & Updates
 
 !!! tip "Use the installer to update"
 

docs/installation/manual.md

@@ -4,11 +4,11 @@
 
     **Python experience is mandatory.**
 
-    If you want to use Invoke locally, you should probably use the [launcher](./quick_start.md).
+    If you want to use Invoke locally, you should probably use the [installer](./installer.md).
 
-    If you want to contribute to Invoke or run the app on the latest dev branch, instead follow the [dev environment](../contributing/dev-environment.md) guide.
+    If you want to contribute to Invoke, instead follow the [dev environment](../contributing/dev-environment.md) guide.
 
-InvokeAI is distributed as a python package on PyPI, installable with `pip`. There are a few things that are handled by the launcher that you'll need to manage manually, described in this guide.
+InvokeAI is distributed as a python package on PyPI, installable with `pip`. There are a few things that are handled by the installer and launcher that you'll need to manage manually, described in this guide.
 
 ## Requirements
 
@@ -16,39 +16,43 @@ Before you start, go through the [installation requirements](./requirements.md).
 
 ## Walkthrough
 
-We'll use [`uv`](https://github.com/astral-sh/uv) to install python and create a virtual environment, then install the `invokeai` package. `uv` is a modern, very fast alternative to `pip`.
-
-The following commands vary depending on the version of Invoke being installed and the system onto which it is being installed.
-
-1. Install `uv` as described in its [docs](https://docs.astral.sh/uv/getting-started/installation/#standalone-installer). We suggest using the standalone installer method.
-
-    Run `uv --version` to confirm that `uv` is installed and working. After installation, you may need to restart your terminal to get access to `uv`.
-
-2. Create a directory for your installation, typically in your home directory (e.g. `~/invokeai` or `$Home/invokeai`):
+1. Create a directory to contain your InvokeAI library, configuration files, and models. This is known as the "runtime" or "root" directory, and typically lives in your home directory under the name `invokeai`.
 
     === "Linux/macOS"
 
         ```bash
         mkdir ~/invokeai
-        cd ~/invokeai
         ```
 
     === "Windows (PowerShell)"
 
         ```bash
         mkdir $Home/invokeai
-        cd $Home/invokeai
         ```
 
-3. Create a virtual environment in that directory:
+1. Enter the root directory and create a virtual Python environment within it named `.venv`.
 
-    ```sh
-    uv venv --relocatable --prompt invoke --python 3.12 --python-preference only-managed .venv
-    ```
+    !!! warning "Virtual Environment Location"
 
-    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.
+        While you may create the virtual environment anywhere in the file system, we recommend that you create it within the root directory as shown here. This allows the application to automatically detect its data directories.
 
-4. Activate the virtual environment:
+        If you choose a different location for the venv, then you _must_ set the `INVOKEAI_ROOT` environment variable or specify the root directory using the `--root` CLI arg.
+
+    === "Linux/macOS"
+
+        ```bash
+        cd ~/invokeai
+        python3 -m venv .venv --prompt InvokeAI
+        ```
+
+    === "Windows (PowerShell)"
+
+        ```bash
+        cd $Home/invokeai
+        python3 -m venv .venv --prompt InvokeAI
+        ```
+
+1. Activate the new environment:
 
     === "Linux/macOS"
 
@@ -56,48 +60,41 @@ The following commands vary depending on the version of Invoke being installed a
         source .venv/bin/activate
         ```
 
-    === "Windows (PowerShell)"
+    === "Windows"
 
         ```ps
         .venv\Scripts\activate
         ```
 
-5. Choose a version to install. Review the [GitHub releases page](https://github.com/invoke-ai/InvokeAI/releases).
+    !!! info "Permissions Error (Windows)"
 
-6. Determine the package package specifier to use when installing. This is a performance optimization.
+        If you get a permissions error at this point, run this command and try again.
 
-    - If you have an Nvidia 20xx series GPU or older, use `invokeai[xformers]`.
-    - If you have an Nvidia 30xx series GPU or newer, or do not have an Nvidia GPU, use `invokeai`.
+        `Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser`
 
-7. Determine the `PyPI` index URL to use for installation, if any. This is necessary to get the right version of torch installed.
+    The command-line prompt should change to to show `(InvokeAI)`, indicating the venv is active.
 
-    === "Invoke v5 or later"
+1. Make sure that pip is installed in your virtual environment and up to date:
 
-        - If you are on Windows with an Nvidia GPU, use `https://download.pytorch.org/whl/cu124`.
-        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
-        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm6.1`.
-        - **In all other cases, do not use an index.**
-
-    === "Invoke v4"
-
-        - If you are on Windows with an Nvidia GPU, use `https://download.pytorch.org/whl/cu124`.
-        - If you are on Linux with no GPU, use `https://download.pytorch.org/whl/cpu`.
-        - If you are on Linux with an AMD GPU, use `https://download.pytorch.org/whl/rocm5.2`.
-        - **In all other cases, do not use an index.**
-
-8. Install the `invokeai` package. Substitute the package specifier and version.
-
-    ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --force-reinstall
+    ```bash
+    python3 -m pip install --upgrade pip
     ```
 
-    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:
+1. Install the InvokeAI Package. The base command is `pip install InvokeAI --use-pep517`, but you may need to change this depending on your system and the desired features.
 
-    ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
-    ```
+    - You may need to provide an [extra index URL](https://pip.pypa.io/en/stable/cli/pip_install/#cmdoption-extra-index-url). Select your platform configuration using [this tool on the PyTorch website](https://pytorch.org/get-started/locally/). Copy the `--extra-index-url` string from this and append it to your install command.
 
-9. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:
+        ```bash
+        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
+        ```
+
+    - If you have a CUDA GPU and want to install with `xformers`, you need to add an option to the package name. Note that `xformers` is not strictly necessary. PyTorch includes an implementation of the SDP attention algorithm with similar performance for most GPUs.
+
+        ```bash
+        pip install "InvokeAI[xformers]" --use-pep517
+        ```
+
+1. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:
 
     === "Linux/macOS"
 
@@ -105,31 +102,17 @@ The following commands vary depending on the version of Invoke being installed a
         deactivate && source .venv/bin/activate
         ```
 
-    === "Windows (PowerShell)"
+    === "Windows"
 
         ```ps
         deactivate
         .venv\Scripts\activate
         ```
 
-10. Run the application, specifying the directory you created earlier as the root directory:
+1. Run the application:
 
-    === "Linux/macOS"
+    Run `invokeai-web` to start the UI. You must activate the virtual environment before running the app.
 
-        ```bash
-        invokeai-web --root ~/invokeai
-        ```
+    !!! warning
 
-    === "Windows (PowerShell)"
-
-        ```bash
-        invokeai-web --root $Home/invokeai
-        ```
-
-## Headless Install and Launch Scripts
-
-If you run Invoke on a headless server, you might want to install and run Invoke on the command line.
-
-We do not plan to maintain scripts to do this moving forward, instead focusing our dev resources on the GUI [launcher](../installation/quick_start.md).
-
-You can create your own scripts for this by copying the handful of commands in this guide. `uv`'s [`pip` interface docs](https://docs.astral.sh/uv/reference/cli/#uv-pip-install) may be useful.
+        If the virtual environment is _not_ inside the root directory, then you _must_ specify the path to the root directory with `--root \path\to\invokeai` or the `INVOKEAI_ROOT` environment variable.

docs/installation/quick_start.md

@@ -1,128 +0,0 @@
-# Invoke Community Edition Quick Start
-
-Welcome to Invoke! Follow these steps to install, update, and get started creating.
-
-## Step 1: System Requirements
-
-Invoke runs on Windows 10+, macOS 14+ and Linux (Ubuntu 20.04+ is well-tested).
-
-Hardware requirements vary significantly depending on model and image output size. The requirements below are rough guidelines.
-
-- All Apple Silicon (M1, M2, etc) Macs work, but 16GB+ memory is recommended.
-- AMD GPUs are supported on Linux only. The VRAM requirements are the same as Nvidia GPUs.
-
-!!! info "Hardware Requirements (Windows/Linux)"
-
-    === "SD1.5 - 512×512"
-
-        - GPU: Nvidia 10xx series or later, 4GB+ VRAM.
-        - Memory: At least 8GB RAM.
-        - Disk: 10GB for base installation plus 30GB for models.
-
-    === "SDXL - 1024×1024"
-
-        - GPU: Nvidia 20xx series or later, 8GB+ VRAM.
-        - Memory: At least 16GB RAM.
-        - Disk: 10GB for base installation plus 100GB for models.
-
-    === "FLUX - 1024×1024"
-
-        - GPU: Nvidia 20xx series or later, 10GB+ VRAM.
-        - Memory: At least 32GB RAM.
-        - Disk: 10GB for base installation plus 200GB for models.
-
-More detail on system requirements can be found [here](./requirements.md).
-
-## Step 2: Download
-
-Download the most launcher for your operating system:
-
-- [Download for Windows](https://download.invoke.ai/Invoke%20Community%20Edition.exe)
-- [Download for macOS](https://download.invoke.ai/Invoke%20Community%20Edition.dmg)
-- [Download for Linux](https://download.invoke.ai/Invoke%20Community%20Edition.AppImage)
-
-## Step 3: Install or Update
-
-Run the launcher you just downloaded, click **Install** and follow the instructions to get set up.
-
-If you have an existing Invoke installation, you can select it and let the launcher manage the install. You'll be able to update or launch the installation.
-
-!!! warning "Problem running the launcher on macOS"
-
-    macOS may not allow you to run the launcher. We are working to resolve this by signing the launcher executable. Until that is done, you can either use the [legacy scripts](./legacy_scripts.md) to install, or manually flag the launcher as safe:
-
-    - Open the **Invoke-Installer-mac-arm64.dmg** file.
-    - Drag the launcher to **Applications**.
-    - Open a terminal.
-    - Run `xattr -d 'com.apple.quarantine' /Applications/Invoke\ Community\ Edition.app`.
-
-    You should now be able to run the launcher.
-
-## Step 4: Launch
-
-Once installed, click **Finish**, then **Launch** to start Invoke.
-
-The very first run after an installation or update will take a few extra moments to get ready.
-
-!!! tip "Server Mode"
-
-    The launcher runs Invoke as a desktop application. You can enable **Server Mode** in the launcher's settings to disable this and instead access the UI through your web browser.
-
-## Step 5: Install Models
-
-With Invoke started up, you'll need to install some models.
-
-The quickest way to get started is to install a **Starter Model** bundle. If you already have a model collection, Invoke can use it.
-
-!!! info "Install Models"
-
-    === "Install a Starter Model bundle"
-
-        1. Go to the **Models** tab.
-        2. Click **Starter Models** on the right.
-        3. Click one of the bundles to install its models. Refer to the [system requirements](#step-1-confirm-system-requirements) if you're unsure which model architecture will work for your system.
-
-    === "Use my model collection"
-
-        4. Go to the **Models** tab.
-        5. Click **Scan Folder** on the right.
-        6. Paste the path to your models collection and click **Scan Folder**.
-        7. With **In-place install** enabled, Invoke will leave the model files where they are. If you disable this, **Invoke will move the models into its own folders**.
-
-You’re now ready to start creating!
-
-## Step 6: Learn the Basics
-
-We recommend watching our [Getting Started Playlist](https://www.youtube.com/playlist?list=PLvWK1Kc8iXGrQy8r9TYg6QdUuJ5MMx-ZO). It covers essential features and workflows, including:
-
-- Generating your first image.
-- Using control layers and reference guides.
-- Refining images with advanced workflows.
-
-## Troubleshooting
-
-If installation fails, retrying the install in Repair Mode may fix it. There's a checkbox to enable this on the Review step of the install flow.
-
-If that doesn't fix it, [clearing the `uv` cache](https://docs.astral.sh/uv/reference/cli/#uv-cache-clean) might do the trick:
-
-- Open and start the dev console (button at the bottom-left of the launcher).
-- Run `uv cache clean`.
-- Retry the installation. Enable Repair Mode for good measure.
-
-If you are still unable to install, try installing to a different location and see if that works.
-
-If you still have problems, ask for help on the Invoke [discord](https://discord.gg/ZmtBAhwWhy).
-
-## Other Installation Methods
-
-- You can install the Invoke application as a python package. See our [manual install](./manual.md) docs.
-- You can run Invoke with docker. See our [docker install](./docker.md) docs.
-- You can still use our legacy scripts to install and run Invoke. See the [legacy scripts](./legacy_scripts.md) docs.
-
-## Need Help?
-
-- Visit our [Support Portal](https://support.invoke.ai).
-- Watch the [Getting Started Playlist](https://www.youtube.com/playlist?list=PLvWK1Kc8iXGrQy8r9TYg6QdUuJ5MMx-ZO).
-- Join the conversation on [Discord][discord link].
-
-[discord link]: https://discord.gg/ZmtBAhwWhy

docs/installation/requirements.md

@@ -1,35 +1,90 @@
 # Requirements
 
-Invoke runs on Windows 10+, macOS 14+ and Linux (Ubuntu 20.04+ is well-tested).
+## GPU
 
-## Hardware
+!!! warning "Problematic Nvidia GPUs"
 
-Hardware requirements vary significantly depending on model and image output size.
+    We do not recommend these GPUs. They cannot operate with half precision, but have insufficient VRAM to generate 512x512 images at full precision.
 
-The requirements below are rough guidelines for best performance. GPUs with less VRAM typically still work, if a bit slower. Follow the [Low-VRAM mode guide](./features/low-vram.md) to optimize performance.
+    - NVIDIA 10xx series cards such as the 1080 TI
+    - GTX 1650 series cards
+    - GTX 1660 series cards
 
-- All Apple Silicon (M1, M2, etc) Macs work, but 16GB+ memory is recommended.
-- AMD GPUs are supported on Linux only. The VRAM requirements are the same as Nvidia GPUs.
+Invoke runs best with a dedicated GPU, but will fall back to running on CPU, albeit much slower. You'll need a beefier GPU for SDXL.
 
-!!! info "Hardware Requirements (Windows/Linux)"
+!!! example "Stable Diffusion 1.5"
 
-    === "SD1.5 - 512×512"
+    === "Nvidia"
 
-        - GPU: Nvidia 10xx series or later, 4GB+ VRAM.
-        - Memory: At least 8GB RAM.
-        - Disk: 10GB for base installation plus 30GB for models.
+        ```
+        Any GPU with at least 4GB VRAM.
+        ```
 
-    === "SDXL - 1024×1024"
+    === "AMD"
 
-        - GPU: Nvidia 20xx series or later, 8GB+ VRAM.
-        - Memory: At least 16GB RAM.
-        - Disk: 10GB for base installation plus 100GB for models.
+        ```
+        Any GPU with at least 4GB VRAM. Linux only.
+        ```
 
-    === "FLUX - 1024×1024"
+    === "Mac"
 
-        - GPU: Nvidia 20xx series or later, 10GB+ VRAM.
-        - Memory: At least 32GB RAM.
-        - Disk: 10GB for base installation plus 200GB for models.
+        ```
+        Any Apple Silicon Mac with at least 8GB memory.
+        ```
+
+!!! example "Stable Diffusion XL"
+
+    === "Nvidia"
+
+        ```
+        Any GPU with at least 8GB VRAM.
+        ```
+
+    === "AMD"
+
+        ```
+        Any GPU with at least 16GB VRAM. Linux only.
+        ```
+
+    === "Mac"
+
+        ```
+        Any Apple Silicon Mac with at least 16GB memory.
+        ```
+
+## RAM
+
+At least 12GB of RAM.
+
+## Disk
+
+SSDs will, of course, offer the best performance.
+
+The base application disk usage depends on the torch backend.
+
+!!! example "Disk"
+
+    === "Nvidia (CUDA)"
+
+        ```
+        ~6.5GB
+        ```
+
+    === "AMD (ROCm)"
+
+        ```
+        ~12GB
+        ```
+
+    === "Mac (MPS)"
+
+        ```
+        ~3.5GB
+        ```
+
+You'll need to set aside some space for images, depending on how much you generate. A couple GB is enough to get started.
+
+You'll need a good chunk of space for models. Even if you only install the most popular models and the usual support models (ControlNet, IP Adapter ,etc), you will quickly hit 50GB of models.
 
 !!! info "`tmpfs` on Linux"
 
@@ -37,32 +92,26 @@ The requirements below are rough guidelines for best performance. GPUs with less
 
 ## Python
 
-!!! tip "The launcher installs python for you"
+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.
 
-    You don't need to do this if you are installing with the [Invoke Launcher](./quick_start.md).
+Check that your system has an up-to-date Python installed by running `python --version` in the terminal (Linux, macOS) or cmd/powershell (Windows).
 
-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.
+<h3>Installing Python (Windows)</h3>
 
-Check that your system has an up-to-date Python installed by running `python3 --version` in the terminal (Linux, macOS) or cmd/powershell (Windows).
+- Install python 3.11 with [an official installer].
+- The installer includes an option to add python to your PATH. Be sure to enable this. If you missed it, re-run the installer, choose to modify an existing installation, and tick that checkbox.
+- You may need to install [Microsoft Visual C++ Redistributable].
 
-!!! info "Installing Python"
+<h3>Installing Python (macOS)</h3>
 
-    === "Windows"
+- Install python 3.11 with [an official installer].
+- If model installs fail with a certificate error, you may need to run this command (changing the python version to match what you have installed): `/Applications/Python\ 3.10/Install\ Certificates.command`
+- If you haven't already, you will need to install the XCode CLI Tools by running `xcode-select --install` in a terminal.
 
-        - 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].
+<h3>Installing Python (Linux)</h3>
 
-    === "macOS"
-
-        - 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. 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`
+- Follow the [linux install instructions], being sure to install python 3.11.
+- 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
 
@@ -126,4 +175,7 @@ An alternative to installing ROCm locally is to use a [ROCm docker container] to
 [ROCm Documentation]: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html
 [cuDNN support matrix]: https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html
 [Nvidia Container Runtime]: https://developer.nvidia.com/container-runtime
+[linux install instructions]: https://docs.python-guide.org/starting/install3/linux/
+[Microsoft Visual C++ Redistributable]: https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170
+[an official installer]: https://www.python.org/downloads/
 [CUDA Toolkit Downloads]: https://developer.nvidia.com/cuda-downloads

docs/nodes/communityNodes.md

@@ -49,7 +49,6 @@ To use a community workflow, download the `.json` node graph file and load it in
     + [BriaAI Background Remove](#briaai-remove-background)
     + [Remove Background](#remove-background)    
     + [Retroize](#retroize)
-    + [Stereogram](#stereogram-nodes)
     + [Size Stepper Nodes](#size-stepper-nodes)
     + [Simple Skin Detection](#simple-skin-detection)
     + [Text font to Image](#text-font-to-image)
@@ -527,16 +526,6 @@ View:
 
 <img src="https://github.com/Ar7ific1al/InvokeAI_nodes_retroize/assets/2306586/de8b4fa6-324c-4c2d-b36c-297600c73974" width="500" />
 
---------------------------------
-### Stereogram Nodes
-
-**Description:** A set of custom nodes for InvokeAI to create cross-view or parallel-view stereograms. Stereograms are 2D images that, when viewed properly, reveal a 3D scene. Check out [r/crossview](https://www.reddit.com/r/CrossView/) for tutorials.
-
-**Node Link:** https://github.com/simonfuhrmann/invokeai-stereo
-
-**Example Workflow and Output**
-</br><img src="https://raw.githubusercontent.com/simonfuhrmann/invokeai-stereo/refs/heads/main/docs/example_promo_03.jpg" width="600" />
-
 --------------------------------
 ### Simple Skin Detection
 

docs/nodes/defaultNodes.md

@@ -99,6 +99,7 @@ their descriptions.
 | Scale Latents                                                 | Scales latents by a given factor.                                                                                                                    |
 | Segment Anything Processor                                    | Applies segment anything processing to image                                                                                                         |
 | Show Image                                                    | Displays a provided image, and passes it forward in the pipeline.                                                                                    |
+| Step Param Easing                                             | Experimental per-step parameter easing for denoising steps                                                                                           |
 | String Primitive Collection                                   | A collection of string primitive values                                                                                                              |
 | String Primitive                                              | A string primitive value                                                                                                                             |
 | Subtract Integers                                             | Subtracts two numbers                                                                                                                                |

invokeai/app/api/dependencies.py

@@ -36,14 +36,7 @@ from invokeai.app.services.style_preset_images.style_preset_images_disk import S
 from invokeai.app.services.style_preset_records.style_preset_records_sqlite import SqliteStylePresetRecordsStorage
 from invokeai.app.services.urls.urls_default import LocalUrlService
 from invokeai.app.services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
-from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_disk import WorkflowThumbnailFileStorageDisk
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    BasicConditioningInfo,
-    ConditioningFieldData,
-    FLUXConditioningInfo,
-    SD3ConditioningInfo,
-    SDXLConditioningInfo,
-)
+from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -90,7 +83,6 @@ class ApiDependencies:
 
         model_images_folder = config.models_path
         style_presets_folder = config.style_presets_path
-        workflow_thumbnails_folder = config.workflow_thumbnails_path
 
         db = init_db(config=config, logger=logger, image_files=image_files)
 
@@ -107,25 +99,10 @@ class ApiDependencies:
         images = ImageService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
         tensors = ObjectSerializerForwardCache(
-            ObjectSerializerDisk[torch.Tensor](
-                output_folder / "tensors",
-                safe_globals=[torch.Tensor],
-                ephemeral=True,
-            ),
-            max_cache_size=0,
+            ObjectSerializerDisk[torch.Tensor](output_folder / "tensors", ephemeral=True)
         )
         conditioning = ObjectSerializerForwardCache(
-            ObjectSerializerDisk[ConditioningFieldData](
-                output_folder / "conditioning",
-                safe_globals=[
-                    ConditioningFieldData,
-                    BasicConditioningInfo,
-                    SDXLConditioningInfo,
-                    FLUXConditioningInfo,
-                    SD3ConditioningInfo,
-                ],
-                ephemeral=True,
-            ),
+            ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
         )
         download_queue_service = DownloadQueueService(app_config=configuration, event_bus=events)
         model_images_service = ModelImageFileStorageDisk(model_images_folder / "model_images")
@@ -143,7 +120,6 @@ class ApiDependencies:
         workflow_records = SqliteWorkflowRecordsStorage(db=db)
         style_preset_records = SqliteStylePresetRecordsStorage(db=db)
         style_preset_image_files = StylePresetImageFileStorageDisk(style_presets_folder / "images")
-        workflow_thumbnails = WorkflowThumbnailFileStorageDisk(workflow_thumbnails_folder)
 
         services = InvocationServices(
             board_image_records=board_image_records,
@@ -171,7 +147,6 @@ class ApiDependencies:
             conditioning=conditioning,
             style_preset_records=style_preset_records,
             style_preset_image_files=style_preset_image_files,
-            workflow_thumbnails=workflow_thumbnails,
         )
 
         ApiDependencies.invoker = Invoker(services)

invokeai/app/api/extract_metadata_from_image.py

@@ -1,124 +0,0 @@
-import json
-import logging
-from dataclasses import dataclass
-
-from PIL import Image
-
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutIDValidator
-
-
-@dataclass
-class ExtractedMetadata:
-    invokeai_metadata: str | None
-    invokeai_workflow: str | None
-    invokeai_graph: str | None
-
-
-def extract_metadata_from_image(
-    pil_image: Image.Image,
-    invokeai_metadata_override: str | None,
-    invokeai_workflow_override: str | None,
-    invokeai_graph_override: str | None,
-    logger: logging.Logger,
-) -> ExtractedMetadata:
-    """
-    Extracts the "invokeai_metadata", "invokeai_workflow", and "invokeai_graph" data embedded in the PIL Image.
-
-    These items are stored as stringified JSON in the image file's metadata, so we need to do some parsing to validate
-    them. Once parsed, the values are returned as they came (as strings), or None if they are not present or invalid.
-
-    In some situations, we may prefer to override the values extracted from the image file with some other values.
-
-    For example, when uploading an image via API, the client can optionally provide the metadata directly in the request,
-    as opposed to embedding it in the image file. In this case, the client-provided metadata will be used instead of the
-    metadata embedded in the image file.
-
-    Args:
-        pil_image: The PIL Image object.
-        invokeai_metadata_override: The metadata override provided by the client.
-        invokeai_workflow_override: The workflow override provided by the client.
-        invokeai_graph_override: The graph override provided by the client.
-        logger: The logger to use for debug logging.
-
-    Returns:
-        ExtractedMetadata: The extracted metadata, workflow, and graph.
-    """
-
-    # The fallback value for metadata is None.
-    stringified_metadata: str | None = None
-
-    # Use the metadata override if provided, else attempt to extract it from the image file.
-    metadata_raw = invokeai_metadata_override or pil_image.info.get("invokeai_metadata", None)
-
-    # If the metadata is present in the image file, we will attempt to parse it as JSON. When we create images,
-    # we always store metadata as a stringified JSON dict. So, we expect it to be a string here.
-    if isinstance(metadata_raw, str):
-        try:
-            # Must be a JSON string
-            metadata_parsed = json.loads(metadata_raw)
-            # Must be a dict
-            if isinstance(metadata_parsed, dict):
-                # Looks good, overwrite the fallback value
-                stringified_metadata = metadata_raw
-        except Exception as e:
-            logger.debug(f"Failed to parse metadata for uploaded image, {e}")
-            pass
-
-    # We expect the workflow, if embedded in the image, to be a JSON-stringified WorkflowWithoutID. We will store it
-    # as a string.
-    workflow_raw: str | None = invokeai_workflow_override or pil_image.info.get("invokeai_workflow", None)
-
-    # The fallback value for workflow is None.
-    stringified_workflow: str | None = None
-
-    # If the workflow is present in the image file, we will attempt to parse it as JSON. When we create images, we
-    # always store workflows as a stringified JSON WorkflowWithoutID. So, we expect it to be a string here.
-    if isinstance(workflow_raw, str):
-        try:
-            # Validate the workflow JSON before storing it
-            WorkflowWithoutIDValidator.validate_json(workflow_raw)
-            # Looks good, overwrite the fallback value
-            stringified_workflow = workflow_raw
-        except Exception:
-            logger.debug("Failed to parse workflow for uploaded image")
-            pass
-
-    # We expect the workflow, if embedded in the image, to be a JSON-stringified Graph. We will store it as a
-    # string.
-    graph_raw: str | None = invokeai_graph_override or pil_image.info.get("invokeai_graph", None)
-
-    # The fallback value for graph is None.
-    stringified_graph: str | None = None
-
-    # If the graph is present in the image file, we will attempt to parse it as JSON. When we create images, we
-    # always store graphs as a stringified JSON Graph. So, we expect it to be a string here.
-    if isinstance(graph_raw, str):
-        try:
-            # TODO(psyche): Due to pydantic's handling of None values, it is possible for the graph to fail validation,
-            # even if it is a direct dump of a valid graph. Node fields in the graph are allowed to have be unset if
-            # they have incoming connections, but something about the ser/de process cannot adequately handle this.
-            #
-            # In lieu of fixing the graph validation, we will just do a simple check here to see if the graph is dict
-            # with the correct keys. This is not a perfect solution, but it should be good enough for now.
-
-            # FIX ME: Validate the graph JSON before storing it
-            # Graph.model_validate_json(graph_raw)
-
-            # Crappy workaround to validate JSON
-            graph_parsed = json.loads(graph_raw)
-            if not isinstance(graph_parsed, dict):
-                raise ValueError("Not a dict")
-            if not isinstance(graph_parsed.get("nodes", None), dict):
-                raise ValueError("'nodes' is not a dict")
-            if not isinstance(graph_parsed.get("edges", None), list):
-                raise ValueError("'edges' is not a list")
-
-            # Looks good, overwrite the fallback value
-            stringified_graph = graph_raw
-        except Exception as e:
-            logger.debug(f"Failed to parse graph for uploaded image, {e}")
-            pass
-
-    return ExtractedMetadata(
-        invokeai_metadata=stringified_metadata, invokeai_workflow=stringified_workflow, invokeai_graph=stringified_graph
-    )

invokeai/app/api/routers/app_info.py

@@ -12,7 +12,6 @@ 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
@@ -100,7 +99,7 @@ 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")
@@ -122,21 +121,6 @@ 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/boards.py

@@ -7,7 +7,6 @@ from pydantic import BaseModel, Field
 from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.services.board_records.board_records_common import BoardChanges, BoardRecordOrderBy
 from invokeai.app.services.boards.boards_common import BoardDTO
-from invokeai.app.services.image_records.image_records_common import ImageCategory
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_common import SQLiteDirection
 
@@ -32,7 +31,7 @@ class DeleteBoardResult(BaseModel):
     response_model=BoardDTO,
 )
 async def create_board(
-    board_name: str = Query(description="The name of the board to create", max_length=300),
+    board_name: str = Query(description="The name of the board to create"),
     is_private: bool = Query(default=False, description="Whether the board is private"),
 ) -> BoardDTO:
     """Creates a board"""
@@ -88,9 +87,7 @@ async def delete_board(
     try:
         if include_images is True:
             deleted_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
-                board_id=board_id,
-                categories=None,
-                is_intermediate=None,
+                board_id=board_id
             )
             ApiDependencies.invoker.services.images.delete_images_on_board(board_id=board_id)
             ApiDependencies.invoker.services.boards.delete(board_id=board_id)
@@ -101,9 +98,7 @@ async def delete_board(
             )
         else:
             deleted_board_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
-                board_id=board_id,
-                categories=None,
-                is_intermediate=None,
+                board_id=board_id
             )
             ApiDependencies.invoker.services.boards.delete(board_id=board_id)
             return DeleteBoardResult(
@@ -147,14 +142,10 @@ async def list_boards(
 )
 async def list_all_board_image_names(
     board_id: str = Path(description="The id of the board"),
-    categories: list[ImageCategory] | None = Query(default=None, description="The categories of image to include."),
-    is_intermediate: bool | None = Query(default=None, description="Whether to list intermediate images."),
 ) -> list[str]:
     """Gets a list of images for a board"""
 
     image_names = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
         board_id,
-        categories,
-        is_intermediate,
     )
     return image_names

invokeai/app/api/routers/images.py

@@ -6,10 +6,9 @@ from fastapi import BackgroundTasks, Body, HTTPException, Path, Query, Request,
 from fastapi.responses import FileResponse
 from fastapi.routing import APIRouter
 from PIL import Image
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, JsonValue
 
 from invokeai.app.api.dependencies import ApiDependencies
-from invokeai.app.api.extract_metadata_from_image import extract_metadata_from_image
 from invokeai.app.invocations.fields import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
@@ -46,16 +45,18 @@ async def upload_image(
     board_id: Optional[str] = Query(default=None, description="The board to add this image to, if any"),
     session_id: Optional[str] = Query(default=None, description="The session ID associated with this upload, if any"),
     crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
-    metadata: Optional[str] = Body(
-        default=None,
-        description="The metadata to associate with the image, must be a stringified JSON dict",
-        embed=True,
+    metadata: Optional[JsonValue] = Body(
+        default=None, description="The metadata to associate with the image", embed=True
     ),
 ) -> ImageDTO:
     """Uploads an image"""
     if not file.content_type or not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
 
+    _metadata = None
+    _workflow = None
+    _graph = None
+
     contents = await file.read()
     try:
         pil_image = Image.open(io.BytesIO(contents))
@@ -66,13 +67,30 @@ async def upload_image(
         ApiDependencies.invoker.services.logger.error(traceback.format_exc())
         raise HTTPException(status_code=415, detail="Failed to read image")
 
-    extracted_metadata = extract_metadata_from_image(
-        pil_image=pil_image,
-        invokeai_metadata_override=metadata,
-        invokeai_workflow_override=None,
-        invokeai_graph_override=None,
-        logger=ApiDependencies.invoker.services.logger,
-    )
+    # TODO: retain non-invokeai metadata on upload?
+    # attempt to parse metadata from image
+    metadata_raw = metadata if isinstance(metadata, str) else pil_image.info.get("invokeai_metadata", None)
+    if isinstance(metadata_raw, str):
+        _metadata = metadata_raw
+    else:
+        ApiDependencies.invoker.services.logger.debug("Failed to parse metadata for uploaded image")
+        pass
+
+    # attempt to parse workflow from image
+    workflow_raw = pil_image.info.get("invokeai_workflow", None)
+    if isinstance(workflow_raw, str):
+        _workflow = workflow_raw
+    else:
+        ApiDependencies.invoker.services.logger.debug("Failed to parse workflow for uploaded image")
+        pass
+
+    # attempt to extract graph from image
+    graph_raw = pil_image.info.get("invokeai_graph", None)
+    if isinstance(graph_raw, str):
+        _graph = graph_raw
+    else:
+        ApiDependencies.invoker.services.logger.debug("Failed to parse graph for uploaded image")
+        pass
 
     try:
         image_dto = ApiDependencies.invoker.services.images.create(
@@ -81,9 +99,9 @@ async def upload_image(
             image_category=image_category,
             session_id=session_id,
             board_id=board_id,
-            metadata=extracted_metadata.invokeai_metadata,
-            workflow=extracted_metadata.invokeai_workflow,
-            graph=extracted_metadata.invokeai_graph,
+            metadata=_metadata,
+            workflow=_workflow,
+            graph=_graph,
             is_intermediate=is_intermediate,
         )
 
@@ -96,22 +114,6 @@ 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

@@ -4,6 +4,7 @@
 import contextlib
 import io
 import pathlib
+import shutil
 import traceback
 from copy import deepcopy
 from enum import Enum
@@ -20,6 +21,7 @@ from starlette.exceptions import HTTPException
 from typing_extensions import Annotated
 
 from invokeai.app.api.dependencies import ApiDependencies
+from invokeai.app.services.config import get_config
 from invokeai.app.services.model_images.model_images_common import ModelImageFileNotFoundException
 from invokeai.app.services.model_install.model_install_common import ModelInstallJob
 from invokeai.app.services.model_records import (
@@ -28,12 +30,14 @@ 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.load.model_cache.model_cache_base import CacheStats
 from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
 from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
 from invokeai.backend.model_manager.search import ModelSearch
@@ -844,6 +848,74 @@ async def get_starter_models() -> StarterModelResponse:
     return StarterModelResponse(starter_models=starter_models, starter_bundles=starter_bundles)
 
 
+@model_manager_router.get(
+    "/model_cache",
+    operation_id="get_cache_size",
+    response_model=float,
+    summary="Get maximum size of model manager RAM or VRAM cache.",
+)
+async def get_cache_size(cache_type: CacheType = Query(description="The cache type", default=CacheType.RAM)) -> float:
+    """Return the current RAM or VRAM cache size setting (in GB)."""
+    cache = ApiDependencies.invoker.services.model_manager.load.ram_cache
+    value = 0.0
+    if cache_type == CacheType.RAM:
+        value = cache.max_cache_size
+    elif cache_type == CacheType.VRAM:
+        value = cache.max_vram_cache_size
+    return value
+
+
+@model_manager_router.put(
+    "/model_cache",
+    operation_id="set_cache_size",
+    response_model=float,
+    summary="Set maximum size of model manager RAM or VRAM cache, optionally writing new value out to invokeai.yaml config file.",
+)
+async def set_cache_size(
+    value: float = Query(description="The new value for the maximum cache size"),
+    cache_type: CacheType = Query(description="The cache type", default=CacheType.RAM),
+    persist: bool = Query(description="Write new value out to invokeai.yaml", default=False),
+) -> float:
+    """Set the current RAM or VRAM cache size setting (in GB). ."""
+    cache = ApiDependencies.invoker.services.model_manager.load.ram_cache
+    app_config = get_config()
+    # Record initial state.
+    vram_old = app_config.vram
+    ram_old = app_config.ram
+
+    # Prepare target state.
+    vram_new = vram_old
+    ram_new = ram_old
+    if cache_type == CacheType.RAM:
+        ram_new = value
+    elif cache_type == CacheType.VRAM:
+        vram_new = value
+    else:
+        raise ValueError(f"Unexpected {cache_type=}.")
+
+    config_path = app_config.config_file_path
+    new_config_path = config_path.with_suffix(".yaml.new")
+
+    try:
+        # Try to apply the target state.
+        cache.max_vram_cache_size = vram_new
+        cache.max_cache_size = ram_new
+        app_config.ram = ram_new
+        app_config.vram = vram_new
+        if persist:
+            app_config.write_file(new_config_path)
+            shutil.move(new_config_path, config_path)
+    except Exception as e:
+        # If there was a failure, restore the initial state.
+        cache.max_cache_size = ram_old
+        cache.max_vram_cache_size = vram_old
+        app_config.ram = ram_old
+        app_config.vram = vram_old
+
+        raise RuntimeError("Failed to update cache size") from e
+    return value
+
+
 @model_manager_router.get(
     "/stats",
     operation_id="get_stats",
@@ -856,18 +928,6 @@ async def get_stats() -> Optional[CacheStats]:
     return ApiDependencies.invoker.services.model_manager.load.ram_cache.stats
 
 
-@model_manager_router.post(
-    "/empty_model_cache",
-    operation_id="empty_model_cache",
-    status_code=200,
-)
-async def empty_model_cache() -> None:
-    """Drop all models from the model cache to free RAM/VRAM. 'Locked' models that are in active use will not be dropped."""
-    # Request 1000GB of room in order to force the cache to drop all models.
-    ApiDependencies.invoker.services.logger.info("Emptying model cache.")
-    ApiDependencies.invoker.services.model_manager.load.ram_cache.make_room(1000 * 2**30)
-
-
 class HFTokenStatus(str, Enum):
     VALID = "valid"
     INVALID = "invalid"

invokeai/app/api/routers/session_queue.py

@@ -10,13 +10,11 @@ from invokeai.app.services.session_queue.session_queue_common import (
     QUEUE_ITEM_STATUS,
     Batch,
     BatchStatus,
-    CancelAllExceptCurrentResult,
     CancelByBatchIDsResult,
     CancelByDestinationResult,
     ClearResult,
     EnqueueBatchResult,
     PruneResult,
-    RetryItemsResult,
     SessionQueueCountsByDestination,
     SessionQueueItem,
     SessionQueueItemDTO,
@@ -48,9 +46,7 @@ async def enqueue_batch(
 ) -> EnqueueBatchResult:
     """Processes a batch and enqueues the output graphs for execution."""
 
-    return await ApiDependencies.invoker.services.session_queue.enqueue_batch(
-        queue_id=queue_id, batch=batch, prepend=prepend
-    )
+    return ApiDependencies.invoker.services.session_queue.enqueue_batch(queue_id=queue_id, batch=batch, prepend=prepend)
 
 
 @session_queue_router.get(
@@ -98,18 +94,6 @@ async def Pause(
     return ApiDependencies.invoker.services.session_processor.pause()
 
 
-@session_queue_router.put(
-    "/{queue_id}/cancel_all_except_current",
-    operation_id="cancel_all_except_current",
-    responses={200: {"model": CancelAllExceptCurrentResult}},
-)
-async def cancel_all_except_current(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-) -> CancelAllExceptCurrentResult:
-    """Immediately cancels all queue items except in-processing items"""
-    return ApiDependencies.invoker.services.session_queue.cancel_all_except_current(queue_id=queue_id)
-
-
 @session_queue_router.put(
     "/{queue_id}/cancel_by_batch_ids",
     operation_id="cancel_by_batch_ids",
@@ -126,7 +110,7 @@ async def cancel_by_batch_ids(
 @session_queue_router.put(
     "/{queue_id}/cancel_by_destination",
     operation_id="cancel_by_destination",
-    responses={200: {"model": CancelByDestinationResult}},
+    responses={200: {"model": CancelByBatchIDsResult}},
 )
 async def cancel_by_destination(
     queue_id: str = Path(description="The queue id to perform this operation on"),
@@ -138,19 +122,6 @@ async def cancel_by_destination(
     )
 
 
-@session_queue_router.put(
-    "/{queue_id}/retry_items_by_id",
-    operation_id="retry_items_by_id",
-    responses={200: {"model": RetryItemsResult}},
-)
-async def retry_items_by_id(
-    queue_id: str = Path(description="The queue id to perform this operation on"),
-    item_ids: list[int] = Body(description="The queue item ids to retry"),
-) -> RetryItemsResult:
-    """Immediately cancels all queue items with the given origin"""
-    return ApiDependencies.invoker.services.session_queue.retry_items_by_id(queue_id=queue_id, item_ids=item_ids)
-
-
 @session_queue_router.put(
     "/{queue_id}/clear",
     operation_id="clear",

invokeai/app/api/routers/utilities.py

@@ -25,7 +25,6 @@ async def parse_dynamicprompts(
     prompt: str = Body(description="The prompt to parse with dynamicprompts"),
     max_prompts: int = Body(ge=1, le=10000, default=1000, description="The max number of prompts to generate"),
     combinatorial: bool = Body(default=True, description="Whether to use the combinatorial generator"),
-    seed: int | None = Body(None, description="The seed to use for random generation. Only used if not combinatorial"),
 ) -> DynamicPromptsResponse:
     """Creates a batch process"""
     max_prompts = min(max_prompts, 10000)
@@ -36,7 +35,7 @@ async def parse_dynamicprompts(
             generator = CombinatorialPromptGenerator()
             prompts = generator.generate(prompt, max_prompts=max_prompts)
         else:
-            generator = RandomPromptGenerator(seed=seed)
+            generator = RandomPromptGenerator()
             prompts = generator.generate(prompt, num_images=max_prompts)
     except ParseException as e:
         prompts = [prompt]

invokeai/app/api/routers/workflows.py

@@ -1,10 +1,6 @@
-import io
-import traceback
 from typing import Optional
 
-from fastapi import APIRouter, Body, File, HTTPException, Path, Query, UploadFile
-from fastapi.responses import FileResponse
-from PIL import Image
+from fastapi import APIRouter, Body, HTTPException, Path, Query
 
 from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.services.shared.pagination import PaginatedResults
@@ -14,14 +10,11 @@ from invokeai.app.services.workflow_records.workflow_records_common import (
     WorkflowCategory,
     WorkflowNotFoundError,
     WorkflowRecordDTO,
-    WorkflowRecordListItemWithThumbnailDTO,
+    WorkflowRecordListItemDTO,
     WorkflowRecordOrderBy,
-    WorkflowRecordWithThumbnailDTO,
     WorkflowWithoutID,
 )
-from invokeai.app.services.workflow_thumbnails.workflow_thumbnails_common import WorkflowThumbnailFileNotFoundException
 
-IMAGE_MAX_AGE = 31536000
 workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
 
 
@@ -29,17 +22,15 @@ workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
     "/i/{workflow_id}",
     operation_id="get_workflow",
     responses={
-        200: {"model": WorkflowRecordWithThumbnailDTO},
+        200: {"model": WorkflowRecordDTO},
     },
 )
 async def get_workflow(
     workflow_id: str = Path(description="The workflow to get"),
-) -> WorkflowRecordWithThumbnailDTO:
+) -> WorkflowRecordDTO:
     """Gets a workflow"""
     try:
-        thumbnail_url = ApiDependencies.invoker.services.workflow_thumbnails.get_url(workflow_id)
-        workflow = ApiDependencies.invoker.services.workflow_records.get(workflow_id)
-        return WorkflowRecordWithThumbnailDTO(thumbnail_url=thumbnail_url, **workflow.model_dump())
+        return ApiDependencies.invoker.services.workflow_records.get(workflow_id)
     except WorkflowNotFoundError:
         raise HTTPException(status_code=404, detail="Workflow not found")
 
@@ -66,11 +57,6 @@ async def delete_workflow(
     workflow_id: str = Path(description="The workflow to delete"),
 ) -> None:
     """Deletes a workflow"""
-    try:
-        ApiDependencies.invoker.services.workflow_thumbnails.delete(workflow_id)
-    except WorkflowThumbnailFileNotFoundException:
-        # It's OK if the workflow has no thumbnail file. We can still delete the workflow.
-        pass
     ApiDependencies.invoker.services.workflow_records.delete(workflow_id)
 
 
@@ -92,7 +78,7 @@ async def create_workflow(
     "/",
     operation_id="list_workflows",
     responses={
-        200: {"model": PaginatedResults[WorkflowRecordListItemWithThumbnailDTO]},
+        200: {"model": PaginatedResults[WorkflowRecordListItemDTO]},
     },
 )
 async def list_workflows(
@@ -102,158 +88,10 @@ async def list_workflows(
         default=WorkflowRecordOrderBy.Name, description="The attribute to order by"
     ),
     direction: SQLiteDirection = Query(default=SQLiteDirection.Ascending, description="The direction to order by"),
-    categories: Optional[list[WorkflowCategory]] = Query(default=None, description="The categories of workflow to get"),
-    tags: Optional[list[str]] = Query(default=None, description="The tags of workflow to get"),
+    category: WorkflowCategory = Query(default=WorkflowCategory.User, description="The category of workflow to get"),
     query: Optional[str] = Query(default=None, description="The text to query by (matches name and description)"),
-    has_been_opened: Optional[bool] = Query(default=None, description="Whether to include/exclude recent workflows"),
-) -> PaginatedResults[WorkflowRecordListItemWithThumbnailDTO]:
+) -> PaginatedResults[WorkflowRecordListItemDTO]:
     """Gets a page of workflows"""
-    workflows_with_thumbnails: list[WorkflowRecordListItemWithThumbnailDTO] = []
-    workflows = ApiDependencies.invoker.services.workflow_records.get_many(
-        order_by=order_by,
-        direction=direction,
-        page=page,
-        per_page=per_page,
-        query=query,
-        categories=categories,
-        tags=tags,
-        has_been_opened=has_been_opened,
+    return ApiDependencies.invoker.services.workflow_records.get_many(
+        order_by=order_by, direction=direction, page=page, per_page=per_page, query=query, category=category
     )
-    for workflow in workflows.items:
-        workflows_with_thumbnails.append(
-            WorkflowRecordListItemWithThumbnailDTO(
-                thumbnail_url=ApiDependencies.invoker.services.workflow_thumbnails.get_url(workflow.workflow_id),
-                **workflow.model_dump(),
-            )
-        )
-    return PaginatedResults[WorkflowRecordListItemWithThumbnailDTO](
-        items=workflows_with_thumbnails,
-        total=workflows.total,
-        page=workflows.page,
-        pages=workflows.pages,
-        per_page=workflows.per_page,
-    )
-
-
-@workflows_router.put(
-    "/i/{workflow_id}/thumbnail",
-    operation_id="set_workflow_thumbnail",
-    responses={
-        200: {"model": WorkflowRecordDTO},
-    },
-)
-async def set_workflow_thumbnail(
-    workflow_id: str = Path(description="The workflow to update"),
-    image: UploadFile = File(description="The image file to upload"),
-):
-    """Sets a workflow's thumbnail image"""
-    try:
-        ApiDependencies.invoker.services.workflow_records.get(workflow_id)
-    except WorkflowNotFoundError:
-        raise HTTPException(status_code=404, detail="Workflow not found")
-
-    if not image.content_type or not image.content_type.startswith("image"):
-        raise HTTPException(status_code=415, detail="Not an image")
-
-    contents = await image.read()
-    try:
-        pil_image = Image.open(io.BytesIO(contents))
-
-    except Exception:
-        ApiDependencies.invoker.services.logger.error(traceback.format_exc())
-        raise HTTPException(status_code=415, detail="Failed to read image")
-
-    try:
-        ApiDependencies.invoker.services.workflow_thumbnails.save(workflow_id, pil_image)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@workflows_router.delete(
-    "/i/{workflow_id}/thumbnail",
-    operation_id="delete_workflow_thumbnail",
-    responses={
-        200: {"model": WorkflowRecordDTO},
-    },
-)
-async def delete_workflow_thumbnail(
-    workflow_id: str = Path(description="The workflow to update"),
-):
-    """Removes a workflow's thumbnail image"""
-    try:
-        ApiDependencies.invoker.services.workflow_records.get(workflow_id)
-    except WorkflowNotFoundError:
-        raise HTTPException(status_code=404, detail="Workflow not found")
-
-    try:
-        ApiDependencies.invoker.services.workflow_thumbnails.delete(workflow_id)
-    except ValueError as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@workflows_router.get(
-    "/i/{workflow_id}/thumbnail",
-    operation_id="get_workflow_thumbnail",
-    responses={
-        200: {
-            "description": "The workflow thumbnail was fetched successfully",
-        },
-        400: {"description": "Bad request"},
-        404: {"description": "The workflow thumbnail could not be found"},
-    },
-    status_code=200,
-)
-async def get_workflow_thumbnail(
-    workflow_id: str = Path(description="The id of the workflow thumbnail to get"),
-) -> FileResponse:
-    """Gets a workflow's thumbnail image"""
-
-    try:
-        path = ApiDependencies.invoker.services.workflow_thumbnails.get_path(workflow_id)
-
-        response = FileResponse(
-            path,
-            media_type="image/png",
-            filename=workflow_id + ".png",
-            content_disposition_type="inline",
-        )
-        response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
-        return response
-    except Exception:
-        raise HTTPException(status_code=404)
-
-
-@workflows_router.get("/counts_by_tag", operation_id="get_counts_by_tag")
-async def get_counts_by_tag(
-    tags: list[str] = Query(description="The tags to get counts for"),
-    categories: Optional[list[WorkflowCategory]] = Query(default=None, description="The categories to include"),
-    has_been_opened: Optional[bool] = Query(default=None, description="Whether to include/exclude recent workflows"),
-) -> dict[str, int]:
-    """Counts workflows by tag"""
-
-    return ApiDependencies.invoker.services.workflow_records.counts_by_tag(
-        tags=tags, categories=categories, has_been_opened=has_been_opened
-    )
-
-
-@workflows_router.get("/counts_by_category", operation_id="counts_by_category")
-async def counts_by_category(
-    categories: list[WorkflowCategory] = Query(description="The categories to include"),
-    has_been_opened: Optional[bool] = Query(default=None, description="Whether to include/exclude recent workflows"),
-) -> dict[str, int]:
-    """Counts workflows by category"""
-
-    return ApiDependencies.invoker.services.workflow_records.counts_by_category(
-        categories=categories, has_been_opened=has_been_opened
-    )
-
-
-@workflows_router.put(
-    "/i/{workflow_id}/opened_at",
-    operation_id="update_opened_at",
-)
-async def update_opened_at(
-    workflow_id: str = Path(description="The workflow to update"),
-) -> None:
-    """Updates the opened_at field of a workflow"""
-    ApiDependencies.invoker.services.workflow_records.update_opened_at(workflow_id)

invokeai/app/api_app.py

@@ -1,8 +1,12 @@
 import asyncio
 import logging
+import mimetypes
+import socket
 from contextlib import asynccontextmanager
 from pathlib import Path
 
+import torch
+import uvicorn
 from fastapi import FastAPI, Request
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
@@ -11,7 +15,11 @@ from fastapi.responses import HTMLResponse, RedirectResponse
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.middleware import EventHandlerASGIMiddleware
 from starlette.middleware.base import BaseHTTPMiddleware, RequestResponseEndpoint
+from torch.backends.mps import is_available as is_mps_available
 
+# for PyCharm:
+# noinspection PyUnresolvedReferences
+import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
 import invokeai.frontend.web as web_dir
 from invokeai.app.api.dependencies import ApiDependencies
 from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
@@ -30,10 +38,24 @@ from invokeai.app.api.routers import (
 from invokeai.app.api.sockets import SocketIO
 from invokeai.app.services.config.config_default import get_config
 from invokeai.app.util.custom_openapi import get_openapi_func
+from invokeai.backend.util.devices import TorchDevice
 from invokeai.backend.util.logging import InvokeAILogger
 
 app_config = get_config()
+
+
+if is_mps_available():
+    import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+
+
 logger = InvokeAILogger.get_logger(config=app_config)
+# fix for windows mimetypes registry entries being borked
+# see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
+mimetypes.add_type("application/javascript", ".js")
+mimetypes.add_type("text/css", ".css")
+
+torch_device_name = TorchDevice.get_torch_device_name()
+logger.info(f"Using torch device: {torch_device_name}")
 
 loop = asyncio.new_event_loop()
 
@@ -42,23 +64,6 @@ loop = asyncio.new_event_loop()
 async def lifespan(app: FastAPI):
     # Add startup event to load dependencies
     ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, loop=loop, logger=logger)
-
-    # Log the server address when it starts - in case the network log level is not high enough to see the startup log
-    proto = "https" if app_config.ssl_certfile else "http"
-    msg = f"Invoke running on {proto}://{app_config.host}:{app_config.port} (Press CTRL+C to quit)"
-
-    # Logging this way ignores the logger's log level and _always_ logs the message
-    record = logger.makeRecord(
-        name=logger.name,
-        level=logging.INFO,
-        fn="",
-        lno=0,
-        msg=msg,
-        args=(),
-        exc_info=None,
-    )
-    logger.handle(record)
-
     yield
     # Shut down threads
     ApiDependencies.shutdown()
@@ -160,3 +165,73 @@ except RuntimeError:
 app.mount(
     "/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
 )  # docs favicon is in here
+
+
+def check_cudnn(logger: logging.Logger) -> None:
+    """Check for cuDNN issues that could be causing degraded performance."""
+    if torch.backends.cudnn.is_available():
+        try:
+            # Note: At the time of writing (torch 2.2.1), torch.backends.cudnn.version() only raises an error the first
+            # time it is called. Subsequent calls will return the version number without complaining about a mismatch.
+            cudnn_version = torch.backends.cudnn.version()
+            logger.info(f"cuDNN version: {cudnn_version}")
+        except RuntimeError as e:
+            logger.warning(
+                "Encountered a cuDNN version issue. This may result in degraded performance. This issue is usually "
+                "caused by an incompatible cuDNN version installed in your python environment, or on the host "
+                f"system. Full error message:\n{e}"
+            )
+
+
+def invoke_api() -> None:
+    def find_port(port: int) -> int:
+        """Find a port not in use starting at given port"""
+        # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
+        # https://github.com/WaylonWalker
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+            s.settimeout(1)
+            if s.connect_ex(("localhost", port)) == 0:
+                return find_port(port=port + 1)
+            else:
+                return port
+
+    if app_config.dev_reload:
+        try:
+            import jurigged
+        except ImportError as e:
+            logger.error(
+                'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.',
+                exc_info=e,
+            )
+        else:
+            jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
+
+    port = find_port(app_config.port)
+    if port != app_config.port:
+        logger.warn(f"Port {app_config.port} in use, using port {port}")
+
+    check_cudnn(logger)
+
+    config = uvicorn.Config(
+        app=app,
+        host=app_config.host,
+        port=port,
+        loop="asyncio",
+        log_level=app_config.log_level,
+        ssl_certfile=app_config.ssl_certfile,
+        ssl_keyfile=app_config.ssl_keyfile,
+    )
+    server = uvicorn.Server(config)
+
+    # replace uvicorn's loggers with InvokeAI's for consistent appearance
+    for logname in ["uvicorn.access", "uvicorn"]:
+        log = InvokeAILogger.get_logger(logname)
+        log.handlers.clear()
+        for ch in logger.handlers:
+            log.addHandler(ch)
+
+    loop.run_until_complete(server.serve())
+
+
+if __name__ == "__main__":
+    invoke_api()

invokeai/app/invocations/__init__.py

@@ -1,5 +1,28 @@
+import shutil
+import sys
+from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
 
+from invokeai.app.services.config.config_default import get_config
+
+custom_nodes_path = Path(get_config().custom_nodes_path)
+custom_nodes_path.mkdir(parents=True, exist_ok=True)
+
+custom_nodes_init_path = str(custom_nodes_path / "__init__.py")
+custom_nodes_readme_path = str(custom_nodes_path / "README.md")
+
+# copy our custom nodes __init__.py to the custom nodes directory
+shutil.copy(Path(__file__).parent / "custom_nodes/init.py", custom_nodes_init_path)
+shutil.copy(Path(__file__).parent / "custom_nodes/README.md", custom_nodes_readme_path)
+
+# Import custom nodes, see https://docs.python.org/3/library/importlib.html#importing-programmatically
+spec = spec_from_file_location("custom_nodes", custom_nodes_init_path)
+if spec is None or spec.loader is None:
+    raise RuntimeError(f"Could not load custom nodes from {custom_nodes_init_path}")
+module = module_from_spec(spec)
+sys.modules[spec.name] = module
+spec.loader.exec_module(module)
+
 # add core nodes to __all__
 python_files = filter(lambda f: not f.name.startswith("_"), Path(__file__).parent.glob("*.py"))
 __all__ = [f.stem for f in python_files]  # type: ignore

invokeai/app/invocations/baseinvocation.py

@@ -8,7 +8,6 @@ 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,
@@ -28,6 +27,7 @@ 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,
@@ -44,6 +44,8 @@ if TYPE_CHECKING:
 
 logger = InvokeAILogger.get_logger()
 
+CUSTOM_NODE_PACK_SUFFIX = "__invokeai-custom-node"
+
 
 class InvalidVersionError(ValueError):
     pass
@@ -61,7 +63,6 @@ class Classification(str, Enum, metaclass=MetaEnum):
     - `Prototype`: The invocation is not yet stable and may be removed from the application at any time. Workflows built around this invocation may break, and we are *not* committed to supporting this invocation.
     - `Deprecated`: The invocation is deprecated and may be removed in a future version.
     - `Internal`: The invocation is not intended for use by end-users. It may be changed or removed at any time, but is exposed for users to play with.
-    - `Special`: The invocation is a special case and does not fit into any of the other classifications.
     """
 
     Stable = "stable"
@@ -69,7 +70,6 @@ class Classification(str, Enum, metaclass=MetaEnum):
     Prototype = "prototype"
     Deprecated = "deprecated"
     Internal = "internal"
-    Special = "special"
 
 
 class UIConfigBase(BaseModel):
@@ -100,6 +100,37 @@ 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."""
@@ -142,11 +173,66 @@ 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)."""
@@ -249,105 +335,6 @@ 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",
@@ -425,7 +412,7 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
 
         ui_type = field.json_schema_extra.get("ui_type", None)
         if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
-            logger.warn(f'"UIType.{ui_type.split("_")[-1]}" is deprecated, ignoring')
+            logger.warn(f"\"UIType.{ui_type.split('_')[-1]}\" is deprecated, ignoring")
             field.json_schema_extra.pop("ui_type")
     return None
 
@@ -457,27 +444,8 @@ def invocation(
         if re.compile(r"^\S+$").match(invocation_type) is None:
             raise ValueError(f'"invocation_type" must consist of non-whitespace characters, got "{invocation_type}"')
 
-        # The node pack is the module name - will be "invokeai" for built-in nodes
-        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 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
-
-            clobbered_node_pack = clobbered_invocation.UIConfig.node_pack
-
-            if clobbered_node_pack == "invokeai":
-                # The node being clobbered is a core node
-                raise ValueError(
-                    f'Cannot load node "{invocation_type}" from node pack "{node_pack}" - a core node with the same type already exists'
-                )
-            else:
-                # The node being clobbered is a custom node
-                raise ValueError(
-                    f'Cannot load node "{invocation_type}" from node pack "{node_pack}" - a node with the same type already exists in node pack "{clobbered_node_pack}"'
-                )
+        if invocation_type in BaseInvocation.get_invocation_types():
+            raise ValueError(f'Invocation type "{invocation_type}" already exists')
 
         validate_fields(cls.model_fields, invocation_type)
 
@@ -487,7 +455,8 @@ def invocation(
         uiconfig["tags"] = tags
         uiconfig["category"] = category
         uiconfig["classification"] = classification
-        uiconfig["node_pack"] = node_pack
+        # The node pack is the module name - will be "invokeai" for built-in nodes
+        uiconfig["node_pack"] = cls.__module__.split(".")[0]
 
         if version is not None:
             try:
@@ -547,7 +516,8 @@ def invocation(
         )
         cls.__doc__ = docstring
 
-        InvocationRegistry.register_invocation(cls)
+        # TODO: how to type this correctly? it's typed as ModelMetaclass, a private class in pydantic
+        BaseInvocation.register_invocation(cls)  # type: ignore
 
         return cls
 
@@ -572,7 +542,7 @@ 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 InvocationRegistry.get_output_types():
+        if output_type in BaseInvocationOutput.get_output_types():
             raise ValueError(f'Invocation type "{output_type}" already exists')
 
         validate_fields(cls.model_fields, output_type)
@@ -593,7 +563,7 @@ def invocation_output(
         )
         cls.__doc__ = docstring
 
-        InvocationRegistry.register_output(cls)
+        BaseInvocationOutput.register_output(cls)  # type: ignore # TODO: how to type this correctly?
 
         return cls
 

invokeai/app/invocations/batch.py

@@ -1,274 +0,0 @@
-from typing import Literal
-
-from pydantic import BaseModel
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import (
-    ImageField,
-    Input,
-    InputField,
-    OutputField,
-)
-from invokeai.app.invocations.primitives import (
-    FloatOutput,
-    ImageOutput,
-    IntegerOutput,
-    StringOutput,
-)
-from invokeai.app.services.shared.invocation_context import InvocationContext
-
-BATCH_GROUP_IDS = Literal[
-    "None",
-    "Group 1",
-    "Group 2",
-    "Group 3",
-    "Group 4",
-    "Group 5",
-]
-
-
-class NotExecutableNodeError(Exception):
-    def __init__(self, message: str = "This class should never be executed or instantiated directly."):
-        super().__init__(message)
-
-    pass
-
-
-class BaseBatchInvocation(BaseInvocation):
-    batch_group_id: BATCH_GROUP_IDS = InputField(
-        default="None",
-        description="The ID of this batch node's group. If provided, all batch nodes in with the same ID will be 'zipped' before execution, and all nodes' collections must be of the same size.",
-        input=Input.Direct,
-        title="Batch Group",
-    )
-
-    def __init__(self):
-        raise NotExecutableNodeError()
-
-
-@invocation(
-    "image_batch",
-    title="Image Batch",
-    tags=["primitives", "image", "batch", "special"],
-    category="primitives",
-    version="1.0.0",
-    classification=Classification.Special,
-)
-class ImageBatchInvocation(BaseBatchInvocation):
-    """Create a batched generation, where the workflow is executed once for each image in the batch."""
-
-    images: list[ImageField] = InputField(
-        default=[],
-        min_length=1,
-        description="The images to batch over",
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        raise NotExecutableNodeError()
-
-
-@invocation_output("image_generator_output")
-class ImageGeneratorOutput(BaseInvocationOutput):
-    """Base class for nodes that output a collection of boards"""
-
-    images: list[ImageField] = OutputField(description="The generated images")
-
-
-class ImageGeneratorField(BaseModel):
-    pass
-
-
-@invocation(
-    "image_generator",
-    title="Image Generator",
-    tags=["primitives", "board", "image", "batch", "special"],
-    category="primitives",
-    version="1.0.0",
-    classification=Classification.Special,
-)
-class ImageGenerator(BaseInvocation):
-    """Generated a collection of images for use in a batched generation"""
-
-    generator: ImageGeneratorField = InputField(
-        description="The image generator.",
-        input=Input.Direct,
-        title="Generator Type",
-    )
-
-    def __init__(self):
-        raise NotExecutableNodeError()
-
-    def invoke(self, context: InvocationContext) -> ImageGeneratorOutput:
-        raise NotExecutableNodeError()
-
-
-@invocation(
-    "string_batch",
-    title="String Batch",
-    tags=["primitives", "string", "batch", "special"],
-    category="primitives",
-    version="1.0.0",
-    classification=Classification.Special,
-)
-class StringBatchInvocation(BaseBatchInvocation):
-    """Create a batched generation, where the workflow is executed once for each string in the batch."""
-
-    strings: list[str] = InputField(
-        default=[],
-        min_length=1,
-        description="The strings to batch over",
-    )
-
-    def invoke(self, context: InvocationContext) -> StringOutput:
-        raise NotExecutableNodeError()
-
-
-@invocation_output("string_generator_output")
-class StringGeneratorOutput(BaseInvocationOutput):
-    """Base class for nodes that output a collection of strings"""
-
-    strings: list[str] = OutputField(description="The generated strings")
-
-
-class StringGeneratorField(BaseModel):
-    pass
-
-
-@invocation(
-    "string_generator",
-    title="String Generator",
-    tags=["primitives", "string", "number", "batch", "special"],
-    category="primitives",
-    version="1.0.0",
-    classification=Classification.Special,
-)
-class StringGenerator(BaseInvocation):
-    """Generated a range of strings for use in a batched generation"""
-
-    generator: StringGeneratorField = InputField(
-        description="The string generator.",
-        input=Input.Direct,
-        title="Generator Type",
-    )
-
-    def __init__(self):
-        raise NotExecutableNodeError()
-
-    def invoke(self, context: InvocationContext) -> StringGeneratorOutput:
-        raise NotExecutableNodeError()
-
-
-@invocation(
-    "integer_batch",
-    title="Integer Batch",
-    tags=["primitives", "integer", "number", "batch", "special"],
-    category="primitives",
-    version="1.0.0",
-    classification=Classification.Special,
-)
-class IntegerBatchInvocation(BaseBatchInvocation):
-    """Create a batched generation, where the workflow is executed once for each integer in the batch."""
-
-    integers: list[int] = InputField(
-        default=[],
-        min_length=1,
-        description="The integers to batch over",
-    )
-
-    def invoke(self, context: InvocationContext) -> IntegerOutput:
-        raise NotExecutableNodeError()
-
-
-@invocation_output("integer_generator_output")
-class IntegerGeneratorOutput(BaseInvocationOutput):
-    integers: list[int] = OutputField(description="The generated integers")
-
-
-class IntegerGeneratorField(BaseModel):
-    pass
-
-
-@invocation(
-    "integer_generator",
-    title="Integer Generator",
-    tags=["primitives", "int", "number", "batch", "special"],
-    category="primitives",
-    version="1.0.0",
-    classification=Classification.Special,
-)
-class IntegerGenerator(BaseInvocation):
-    """Generated a range of integers for use in a batched generation"""
-
-    generator: IntegerGeneratorField = InputField(
-        description="The integer generator.",
-        input=Input.Direct,
-        title="Generator Type",
-    )
-
-    def __init__(self):
-        raise NotExecutableNodeError()
-
-    def invoke(self, context: InvocationContext) -> IntegerGeneratorOutput:
-        raise NotExecutableNodeError()
-
-
-@invocation(
-    "float_batch",
-    title="Float Batch",
-    tags=["primitives", "float", "number", "batch", "special"],
-    category="primitives",
-    version="1.0.0",
-    classification=Classification.Special,
-)
-class FloatBatchInvocation(BaseBatchInvocation):
-    """Create a batched generation, where the workflow is executed once for each float in the batch."""
-
-    floats: list[float] = InputField(
-        default=[],
-        min_length=1,
-        description="The floats to batch over",
-    )
-
-    def invoke(self, context: InvocationContext) -> FloatOutput:
-        raise NotExecutableNodeError()
-
-
-@invocation_output("float_generator_output")
-class FloatGeneratorOutput(BaseInvocationOutput):
-    """Base class for nodes that output a collection of floats"""
-
-    floats: list[float] = OutputField(description="The generated floats")
-
-
-class FloatGeneratorField(BaseModel):
-    pass
-
-
-@invocation(
-    "float_generator",
-    title="Float Generator",
-    tags=["primitives", "float", "number", "batch", "special"],
-    category="primitives",
-    version="1.0.0",
-    classification=Classification.Special,
-)
-class FloatGenerator(BaseInvocation):
-    """Generated a range of floats for use in a batched generation"""
-
-    generator: FloatGeneratorField = InputField(
-        description="The float generator.",
-        input=Input.Direct,
-        title="Generator Type",
-    )
-
-    def __init__(self):
-        raise NotExecutableNodeError()
-
-    def invoke(self, context: InvocationContext) -> FloatGeneratorOutput:
-        raise NotExecutableNodeError()

invokeai/app/invocations/blend_latents.py

@@ -1,120 +1,98 @@
-from typing import Optional, Union
+from typing import Any, Union
 
 import numpy as np
+import numpy.typing as npt
 import torch
-import torchvision.transforms as T
-from PIL import Image
-from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, LatentsField
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, LatentsField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.util.devices import TorchDevice
 
 
-def slerp(
-    t: Union[float, np.ndarray],
-    v0: Union[torch.Tensor, np.ndarray],
-    v1: Union[torch.Tensor, np.ndarray],
-    device: torch.device,
-    DOT_THRESHOLD: float = 0.9995,
-):
-    """
-    Spherical linear interpolation
-    Args:
-        t (float/np.ndarray): Float value between 0.0 and 1.0
-        v0 (np.ndarray): Starting vector
-        v1 (np.ndarray): Final vector
-        DOT_THRESHOLD (float): Threshold for considering the two vectors as
-                            colineal. Not recommended to alter this.
-    Returns:
-        v2 (np.ndarray): Interpolation vector between v0 and v1
-    """
-    inputs_are_torch = False
-    if not isinstance(v0, np.ndarray):
-        inputs_are_torch = True
-        v0 = v0.detach().cpu().numpy()
-    if not isinstance(v1, np.ndarray):
-        inputs_are_torch = True
-        v1 = v1.detach().cpu().numpy()
-
-    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
-    if np.abs(dot) > DOT_THRESHOLD:
-        v2 = (1 - t) * v0 + t * v1
-    else:
-        theta_0 = np.arccos(dot)
-        sin_theta_0 = np.sin(theta_0)
-        theta_t = theta_0 * t
-        sin_theta_t = np.sin(theta_t)
-        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
-        s1 = sin_theta_t / sin_theta_0
-        v2 = s0 * v0 + s1 * v1
-
-    if inputs_are_torch:
-        v2 = torch.from_numpy(v2).to(device)
-
-    return v2
-
-
 @invocation(
     "lblend",
     title="Blend Latents",
-    tags=["latents", "blend", "mask"],
+    tags=["latents", "blend"],
     category="latents",
-    version="1.1.0",
+    version="1.0.3",
 )
 class BlendLatentsInvocation(BaseInvocation):
-    """Blend two latents using a given alpha. If a mask is provided, the second latents will be masked before blending.
-    Latents must have same size. Masking functionality added by @dwringer."""
+    """Blend two latents using a given alpha. Latents must have same size."""
 
-    latents_a: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection)
-    latents_b: LatentsField = InputField(description=FieldDescriptions.latents, input=Input.Connection)
-    mask: Optional[ImageField] = InputField(default=None, description="Mask for blending in latents B")
-    alpha: float = InputField(ge=0, default=0.5, description=FieldDescriptions.blend_alpha)
-
-    def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
-        if mask_image.mode != "L":
-            mask_image = mask_image.convert("L")
-        mask_tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
-        if mask_tensor.dim() == 3:
-            mask_tensor = mask_tensor.unsqueeze(0)
-        return mask_tensor
-
-    def replace_tensor_from_masked_tensor(
-        self, tensor: torch.Tensor, other_tensor: torch.Tensor, mask_tensor: torch.Tensor
-    ):
-        output = tensor.clone()
-        mask_tensor = mask_tensor.expand(output.shape)
-        if output.dtype != torch.float16:
-            output = torch.add(output, mask_tensor * torch.sub(other_tensor, tensor))
-        else:
-            output = torch.add(output, mask_tensor.half() * torch.sub(other_tensor, tensor))
-        return output
+    latents_a: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    latents_b: LatentsField = InputField(
+        description=FieldDescriptions.latents,
+        input=Input.Connection,
+    )
+    alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         latents_a = context.tensors.load(self.latents_a.latents_name)
         latents_b = context.tensors.load(self.latents_b.latents_name)
-        if self.mask is None:
-            mask_tensor = torch.zeros(latents_a.shape[-2:])
-        else:
-            mask_tensor = self.prep_mask_tensor(context.images.get_pil(self.mask.image_name))
-            mask_tensor = tv_resize(mask_tensor, latents_a.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
-
-        latents_b = self.replace_tensor_from_masked_tensor(latents_b, latents_a, mask_tensor)
 
         if latents_a.shape != latents_b.shape:
-            raise ValueError("Latents to blend must be the same size.")
+            raise Exception("Latents to blend must be the same size.")
 
         device = TorchDevice.choose_torch_device()
 
+        def slerp(
+            t: Union[float, npt.NDArray[Any]],  # FIXME: maybe use np.float32 here?
+            v0: Union[torch.Tensor, npt.NDArray[Any]],
+            v1: Union[torch.Tensor, npt.NDArray[Any]],
+            DOT_THRESHOLD: float = 0.9995,
+        ) -> Union[torch.Tensor, npt.NDArray[Any]]:
+            """
+            Spherical linear interpolation
+            Args:
+                t (float/np.ndarray): Float value between 0.0 and 1.0
+                v0 (np.ndarray): Starting vector
+                v1 (np.ndarray): Final vector
+                DOT_THRESHOLD (float): Threshold for considering the two vectors as
+                                    colineal. Not recommended to alter this.
+            Returns:
+                v2 (np.ndarray): Interpolation vector between v0 and v1
+            """
+            inputs_are_torch = False
+            if not isinstance(v0, np.ndarray):
+                inputs_are_torch = True
+                v0 = v0.detach().cpu().numpy()
+            if not isinstance(v1, np.ndarray):
+                inputs_are_torch = True
+                v1 = v1.detach().cpu().numpy()
+
+            dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+            if np.abs(dot) > DOT_THRESHOLD:
+                v2 = (1 - t) * v0 + t * v1
+            else:
+                theta_0 = np.arccos(dot)
+                sin_theta_0 = np.sin(theta_0)
+                theta_t = theta_0 * t
+                sin_theta_t = np.sin(theta_t)
+                s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+                s1 = sin_theta_t / sin_theta_0
+                v2 = s0 * v0 + s1 * v1
+
+            if inputs_are_torch:
+                v2_torch: torch.Tensor = torch.from_numpy(v2).to(device)
+                return v2_torch
+            else:
+                assert isinstance(v2, np.ndarray)
+                return v2
+
         # blend
-        blended_latents = slerp(self.alpha, latents_a, latents_b, device)
+        bl = slerp(self.alpha, latents_a, latents_b)
+        assert isinstance(bl, torch.Tensor)
+        blended_latents: torch.Tensor = bl  # for type checking convenience
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         blended_latents = blended_latents.to("cpu")
-        torch.cuda.empty_cache()
+
+        TorchDevice.empty_cache()
 
         name = context.tensors.save(tensor=blended_latents)
-        return LatentsOutput.build(latents_name=name, latents=blended_latents)
+        return LatentsOutput.build(latents_name=name, latents=blended_latents, seed=self.latents_a.seed)

invokeai/app/invocations/compel.py

@@ -19,9 +19,9 @@ from invokeai.app.invocations.model import CLIPField
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.ti_utils import generate_ti_list
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.patches.layer_patcher import LayerPatcher
-from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     ConditioningFieldData,
@@ -40,10 +40,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "compel",
-    title="Prompt - SD1.5",
+    title="Prompt",
     tags=["prompt", "compel"],
     category="conditioning",
-    version="1.2.1",
+    version="1.2.0",
 )
 class CompelInvocation(BaseInvocation):
     """Parse prompt using compel package to conditioning."""
@@ -63,28 +63,29 @@ class CompelInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        def _lora_loader() -> Iterator[Tuple[ModelPatchRaw, float]]:
+        tokenizer_info = context.models.load(self.clip.tokenizer)
+        text_encoder_info = context.models.load(self.clip.text_encoder)
+
+        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.clip.loras:
                 lora_info = context.models.load(lora.lora)
-                assert isinstance(lora_info.model, ModelPatchRaw)
+                assert isinstance(lora_info.model, LoRAModelRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
             return
 
         # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
-        text_encoder_info = context.models.load(self.clip.text_encoder)
         ti_list = generate_ti_list(self.prompt, text_encoder_info.config.base, context)
 
         with (
             # apply all patches while the model is on the target device
             text_encoder_info.model_on_device() as (cached_weights, text_encoder),
-            context.models.load(self.clip.tokenizer) as tokenizer,
-            LayerPatcher.apply_smart_model_patches(
+            tokenizer_info as tokenizer,
+            LoRAPatcher.apply_lora_patches(
                 model=text_encoder,
                 patches=_lora_loader(),
                 prefix="lora_te_",
-                dtype=text_encoder.dtype,
                 cached_weights=cached_weights,
             ),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
@@ -103,7 +104,6 @@ class CompelInvocation(BaseInvocation):
                 textual_inversion_manager=ti_manager,
                 dtype_for_device_getter=TorchDevice.choose_torch_dtype,
                 truncate_long_prompts=False,
-                device=TorchDevice.choose_torch_device(),
             )
 
             conjunction = Compel.parse_prompt_string(self.prompt)
@@ -138,7 +138,9 @@ class SDXLPromptInvocationBase:
         lora_prefix: str,
         zero_on_empty: bool,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        tokenizer_info = context.models.load(clip_field.tokenizer)
         text_encoder_info = context.models.load(clip_field.text_encoder)
+
         # return zero on empty
         if prompt == "" and zero_on_empty:
             cpu_text_encoder = text_encoder_info.model
@@ -160,11 +162,11 @@ class SDXLPromptInvocationBase:
                 c_pooled = None
             return c, c_pooled
 
-        def _lora_loader() -> Iterator[Tuple[ModelPatchRaw, float]]:
+        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in clip_field.loras:
                 lora_info = context.models.load(lora.lora)
                 lora_model = lora_info.model
-                assert isinstance(lora_model, ModelPatchRaw)
+                assert isinstance(lora_model, LoRAModelRaw)
                 yield (lora_model, lora.weight)
                 del lora_info
             return
@@ -176,12 +178,11 @@ class SDXLPromptInvocationBase:
         with (
             # apply all patches while the model is on the target device
             text_encoder_info.model_on_device() as (cached_weights, text_encoder),
-            context.models.load(clip_field.tokenizer) as tokenizer,
-            LayerPatcher.apply_smart_model_patches(
-                model=text_encoder,
+            tokenizer_info as tokenizer,
+            LoRAPatcher.apply_lora_patches(
+                text_encoder,
                 patches=_lora_loader(),
                 prefix=lora_prefix,
-                dtype=text_encoder.dtype,
                 cached_weights=cached_weights,
             ),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
@@ -204,7 +205,6 @@ class SDXLPromptInvocationBase:
                 truncate_long_prompts=False,  # TODO:
                 returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,  # TODO: clip skip
                 requires_pooled=get_pooled,
-                device=TorchDevice.choose_torch_device(),
             )
 
             conjunction = Compel.parse_prompt_string(prompt)
@@ -222,6 +222,7 @@ class SDXLPromptInvocationBase:
 
         del tokenizer
         del text_encoder
+        del tokenizer_info
         del text_encoder_info
 
         c = c.detach().to("cpu")
@@ -233,10 +234,10 @@ class SDXLPromptInvocationBase:
 
 @invocation(
     "sdxl_compel_prompt",
-    title="Prompt - SDXL",
+    title="SDXL Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.2.1",
+    version="1.2.0",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -327,10 +328,10 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
 
 @invocation(
     "sdxl_refiner_compel_prompt",
-    title="Prompt - SDXL Refiner",
+    title="SDXL Refiner Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.1.2",
+    version="1.1.1",
 )
 class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -376,10 +377,10 @@ class CLIPSkipInvocationOutput(BaseInvocationOutput):
 
 @invocation(
     "clip_skip",
-    title="Apply CLIP Skip - SD1.5, SDXL",
+    title="CLIP Skip",
     tags=["clipskip", "clip", "skip"],
     category="conditioning",
-    version="1.1.1",
+    version="1.1.0",
 )
 class CLIPSkipInvocation(BaseInvocation):
     """Skip layers in clip text_encoder model."""
@@ -513,7 +514,7 @@ def log_tokenization_for_text(
             usedTokens += 1
 
     if usedTokens > 0:
-        print(f"\n>> [TOKENLOG] Tokens {display_label or ''} ({usedTokens}):")
+        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
         print(f"{tokenized}\x1b[0m")
 
     if discarded != "":

invokeai/app/invocations/constants.py

@@ -1,5 +1,7 @@
 from typing import Literal
 
+from invokeai.backend.util.devices import TorchDevice
+
 LATENT_SCALE_FACTOR = 8
 """
 HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
@@ -10,3 +12,5 @@ The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
 
 IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
 """A literal type for PIL image modes supported by Invoke"""
+
+DEFAULT_PRECISION = TorchDevice.choose_torch_dtype()

invokeai/app/invocations/controlnet_image_processors.py

@@ -87,7 +87,7 @@ class ControlOutput(BaseInvocationOutput):
     control: ControlField = OutputField(description=FieldDescriptions.control)
 
 
-@invocation("controlnet", title="ControlNet - SD1.5, SDXL", tags=["controlnet"], category="controlnet", version="1.1.3")
+@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.2")
 class ControlNetInvocation(BaseInvocation):
     """Collects ControlNet info to pass to other nodes"""
 

invokeai/app/invocations/create_denoise_mask.py

@@ -6,6 +6,7 @@ from PIL import Image
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.constants import DEFAULT_PRECISION
 from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField
 from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
 from invokeai.app.invocations.model import VAEField
@@ -28,7 +29,11 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
     image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
     mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
-    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32, ui_order=4)
+    fp32: bool = InputField(
+        default=DEFAULT_PRECISION == torch.float32,
+        description=FieldDescriptions.fp32,
+        ui_order=4,
+    )
 
     def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
         if mask_image.mode != "L":

invokeai/app/invocations/create_gradient_mask.py

@@ -7,6 +7,7 @@ from PIL import Image, ImageFilter
 from torchvision.transforms.functional import resize as tv_resize
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from invokeai.app.invocations.constants import DEFAULT_PRECISION
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
     FieldDescriptions,
@@ -19,8 +20,7 @@ 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
-from invokeai.backend.model_manager.taxonomy import ModelVariantType
+from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 
 
@@ -76,7 +76,11 @@ class CreateGradientMaskInvocation(BaseInvocation):
         ui_order=7,
     )
     tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=8)
-    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32, ui_order=9)
+    fp32: bool = InputField(
+        default=DEFAULT_PRECISION == torch.float32,
+        description=FieldDescriptions.fp32,
+        ui_order=9,
+    )
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> GradientMaskOutput:

invokeai/app/invocations/custom_nodes/init.py

@@ -0,0 +1,58 @@
+"""
+Invoke-managed custom node loader. See README.md for more information.
+"""
+
+import sys
+import traceback
+from importlib.util import module_from_spec, spec_from_file_location
+from pathlib import Path
+
+from invokeai.backend.util.logging import InvokeAILogger
+
+logger = InvokeAILogger.get_logger()
+loaded_count = 0
+
+
+for d in Path(__file__).parent.iterdir():
+    # skip files
+    if not d.is_dir():
+        continue
+
+    # skip hidden directories
+    if d.name.startswith("_") or d.name.startswith("."):
+        continue
+
+    # skip directories without an `__init__.py`
+    init = d / "__init__.py"
+    if not init.exists():
+        continue
+
+    module_name = init.parent.stem
+
+    # skip if already imported
+    if module_name in globals():
+        continue
+
+    # load the module, appending adding a suffix to identify it as a custom node pack
+    spec = spec_from_file_location(module_name, init.absolute())
+
+    if spec is None or spec.loader is None:
+        logger.warn(f"Could not load {init}")
+        continue
+
+    logger.info(f"Loading node pack {module_name}")
+
+    try:
+        module = module_from_spec(spec)
+        sys.modules[spec.name] = module
+        spec.loader.exec_module(module)
+
+        loaded_count += 1
+    except Exception:
+        full_error = traceback.format_exc()
+        logger.error(f"Failed to load node pack {module_name}:\n{full_error}")
+
+    del init, module_name
+
+if loaded_count > 0:
+    logger.info(f"Loaded {loaded_count} node packs from {Path(__file__).parent}")

invokeai/app/invocations/denoise_latents.py

@@ -10,9 +10,7 @@ import torchvision.transforms as T
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.models.adapter import T2IAdapter
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
-from diffusers.schedulers.scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
 from diffusers.schedulers.scheduling_dpmsolver_sde import DPMSolverSDEScheduler
-from diffusers.schedulers.scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
 from diffusers.schedulers.scheduling_tcd import TCDScheduler
 from diffusers.schedulers.scheduling_utils import SchedulerMixin as Scheduler
 from PIL import Image
@@ -39,11 +37,10 @@ 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.config import AnyModelConfig
-from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelVariantType
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoRAPatcher
+from invokeai.backend.model_manager 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
 from invokeai.backend.stable_diffusion import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.denoise_context import DenoiseContext, DenoiseInputs
 from invokeai.backend.stable_diffusion.diffusers_pipeline import (
@@ -86,14 +83,12 @@ def get_scheduler(
     scheduler_info: ModelIdentifierField,
     scheduler_name: str,
     seed: int,
-    unet_config: AnyModelConfig,
 ) -> Scheduler:
     """Load a scheduler and apply some scheduler-specific overrides."""
     # TODO(ryand): Silently falling back to ddim seems like a bad idea. Look into why this was added and remove if
     # possible.
     scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
     orig_scheduler_info = context.models.load(scheduler_info)
-
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
 
@@ -105,17 +100,10 @@ def get_scheduler(
         "_backup": scheduler_config,
     }
 
-    if hasattr(unet_config, "prediction_type"):
-        scheduler_config["prediction_type"] = unet_config.prediction_type
-
     # make dpmpp_sde reproducable(seed can be passed only in initializer)
     if scheduler_class is DPMSolverSDEScheduler:
         scheduler_config["noise_sampler_seed"] = seed
 
-    if scheduler_class is DPMSolverMultistepScheduler or scheduler_class is DPMSolverSinglestepScheduler:
-        if scheduler_config["_class_name"] == "DEISMultistepScheduler" and scheduler_config["algorithm_type"] == "deis":
-            scheduler_config["algorithm_type"] = "dpmsolver++"
-
     scheduler = scheduler_class.from_config(scheduler_config)
 
     # hack copied over from generate.py
@@ -127,10 +115,10 @@ def get_scheduler(
 
 @invocation(
     "denoise_latents",
-    title="Denoise - SD1.5, SDXL",
+    title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.4",
+    version="1.5.3",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -423,7 +411,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         context: InvocationContext,
         control_input: ControlField | list[ControlField] | None,
         latents_shape: List[int],
-        device: torch.device,
         exit_stack: ExitStack,
         do_classifier_free_guidance: bool = True,
     ) -> list[ControlNetData] | None:
@@ -465,7 +452,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 height=control_height_resize,
                 # batch_size=batch_size * num_images_per_prompt,
                 # num_images_per_prompt=num_images_per_prompt,
-                device=device,
+                device=control_model.device,
                 dtype=control_model.dtype,
                 control_mode=control_info.control_mode,
                 resize_mode=control_info.resize_mode,
@@ -560,6 +547,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         for single_ip_adapter in ip_adapters:
             with context.models.load(single_ip_adapter.ip_adapter_model) as ip_adapter_model:
                 assert isinstance(ip_adapter_model, IPAdapter)
+                image_encoder_model_info = context.models.load(single_ip_adapter.image_encoder_model)
                 # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
                 single_ipa_image_fields = single_ip_adapter.image
                 if not isinstance(single_ipa_image_fields, list):
@@ -568,7 +556,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 single_ipa_images = [
                     context.images.get_pil(image.image_name, mode="RGB") for image in single_ipa_image_fields
                 ]
-                with context.models.load(single_ip_adapter.image_encoder_model) as image_encoder_model:
+                with image_encoder_model_info as image_encoder_model:
                     assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
                     # Get image embeddings from CLIP and ImageProjModel.
                     image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
@@ -618,7 +606,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         context: InvocationContext,
         t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
         latents_shape: list[int],
-        device: torch.device,
         do_classifier_free_guidance: bool,
     ) -> Optional[list[T2IAdapterData]]:
         if t2i_adapter is None:
@@ -634,6 +621,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         t2i_adapter_data = []
         for t2i_adapter_field in t2i_adapter:
             t2i_adapter_model_config = context.models.get_config(t2i_adapter_field.t2i_adapter_model.key)
+            t2i_adapter_loaded_model = context.models.load(t2i_adapter_field.t2i_adapter_model)
             image = context.images.get_pil(t2i_adapter_field.image.image_name, mode="RGB")
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
@@ -649,7 +637,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 raise ValueError(f"Unexpected T2I-Adapter base model type: '{t2i_adapter_model_config.base}'.")
 
             t2i_adapter_model: T2IAdapter
-            with context.models.load(t2i_adapter_field.t2i_adapter_model) as t2i_adapter_model:
+            with t2i_adapter_loaded_model as t2i_adapter_model:
                 total_downscale_factor = t2i_adapter_model.total_downscale_factor
 
                 # Note: We have hard-coded `do_classifier_free_guidance=False`. This is because we only want to prepare
@@ -669,7 +657,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     width=control_width_resize,
                     height=control_height_resize,
                     num_channels=t2i_adapter_model.config["in_channels"],  # mypy treats this as a FrozenDict
-                    device=device,
+                    device=t2i_adapter_model.device,
                     dtype=t2i_adapter_model.dtype,
                     resize_mode=t2i_adapter_field.resize_mode,
                 )
@@ -834,9 +822,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
         seed, noise, latents = self.prepare_noise_and_latents(context, self.noise, self.latents)
         _, _, latent_height, latent_width = latents.shape
 
-        # get the unet's config so that we can pass the base to sd_step_callback()
-        unet_config = context.models.get_config(self.unet.unet.key)
-
         conditioning_data = self.get_conditioning_data(
             context=context,
             positive_conditioning_field=self.positive_conditioning,
@@ -856,7 +841,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
             scheduler_info=self.unet.scheduler,
             scheduler_name=self.scheduler,
             seed=seed,
-            unet_config=unet_config,
         )
 
         timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
@@ -868,6 +852,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
             denoising_end=self.denoising_end,
         )
 
+        # get the unet's config so that we can pass the base to sd_step_callback()
+        unet_config = context.models.get_config(self.unet.unet.key)
+
         ### preview
         def step_callback(state: PipelineIntermediateState) -> None:
             context.util.sd_step_callback(state, unet_config.base)
@@ -898,7 +885,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         ### inpaint
         mask, masked_latents, is_gradient_mask = self.prep_inpaint_mask(context, latents)
-        # NOTE: We used to identify inpainting models by inspecting the shape of the loaded UNet model weights. Now we
+        # NOTE: We used to identify inpainting models by inpecting the shape of the loaded UNet model weights. Now we
         # use the ModelVariantType config. During testing, there was a report of a user with models that had an
         # incorrect ModelVariantType value. Re-installing the model fixed the issue. If this issue turns out to be
         # prevalent, we will have to revisit how we initialize the inpainting extensions.
@@ -939,8 +926,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
             # ext: t2i/ip adapter
             ext_manager.run_callback(ExtensionCallbackType.SETUP, denoise_ctx)
 
+            unet_info = context.models.load(self.unet.unet)
+            assert isinstance(unet_info.model, UNet2DConditionModel)
             with (
-                context.models.load(self.unet.unet).model_on_device() as (cached_weights, unet),
+                unet_info.model_on_device() as (cached_weights, unet),
                 ModelPatcher.patch_unet_attention_processor(unet, denoise_ctx.inputs.attention_processor_cls),
                 # ext: controlnet
                 ext_manager.patch_extensions(denoise_ctx),
@@ -961,7 +950,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
     @torch.no_grad()
     @SilenceWarnings()  # This quenches the NSFW nag from diffusers.
     def _old_invoke(self, context: InvocationContext) -> LatentsOutput:
-        device = TorchDevice.choose_torch_device()
         seed, noise, latents = self.prepare_noise_and_latents(context, self.noise, self.latents)
 
         mask, masked_latents, gradient_mask = self.prep_inpaint_mask(context, latents)
@@ -976,7 +964,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
             context,
             self.t2i_adapter,
             latents.shape,
-            device=device,
             do_classifier_free_guidance=True,
         )
 
@@ -1000,43 +987,43 @@ class DenoiseLatentsInvocation(BaseInvocation):
         def step_callback(state: PipelineIntermediateState) -> None:
             context.util.sd_step_callback(state, unet_config.base)
 
-        def _lora_loader() -> Iterator[Tuple[ModelPatchRaw, float]]:
+        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.unet.loras:
                 lora_info = context.models.load(lora.lora)
-                assert isinstance(lora_info.model, ModelPatchRaw)
+                assert isinstance(lora_info.model, LoRAModelRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
             return
 
+        unet_info = context.models.load(self.unet.unet)
+        assert isinstance(unet_info.model, UNet2DConditionModel)
         with (
             ExitStack() as exit_stack,
-            context.models.load(self.unet.unet).model_on_device() as (cached_weights, unet),
+            unet_info.model_on_device() as (cached_weights, unet),
             ModelPatcher.apply_freeu(unet, self.unet.freeu_config),
             SeamlessExt.static_patch_model(unet, self.unet.seamless_axes),  # FIXME
             # Apply the LoRA after unet has been moved to its target device for faster patching.
-            LayerPatcher.apply_smart_model_patches(
+            LoRAPatcher.apply_lora_patches(
                 model=unet,
                 patches=_lora_loader(),
                 prefix="lora_unet_",
-                dtype=unet.dtype,
                 cached_weights=cached_weights,
             ),
         ):
             assert isinstance(unet, UNet2DConditionModel)
-            latents = latents.to(device=device, dtype=unet.dtype)
+            latents = latents.to(device=unet.device, dtype=unet.dtype)
             if noise is not None:
-                noise = noise.to(device=device, dtype=unet.dtype)
+                noise = noise.to(device=unet.device, dtype=unet.dtype)
             if mask is not None:
-                mask = mask.to(device=device, dtype=unet.dtype)
+                mask = mask.to(device=unet.device, dtype=unet.dtype)
             if masked_latents is not None:
-                masked_latents = masked_latents.to(device=device, dtype=unet.dtype)
+                masked_latents = masked_latents.to(device=unet.device, dtype=unet.dtype)
 
             scheduler = get_scheduler(
                 context=context,
                 scheduler_info=self.unet.scheduler,
                 scheduler_name=self.scheduler,
                 seed=seed,
-                unet_config=unet_config,
             )
 
             pipeline = self.create_pipeline(unet, scheduler)
@@ -1046,7 +1033,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 context=context,
                 positive_conditioning_field=self.positive_conditioning,
                 negative_conditioning_field=self.negative_conditioning,
-                device=device,
+                device=unet.device,
                 dtype=unet.dtype,
                 latent_height=latent_height,
                 latent_width=latent_width,
@@ -1059,7 +1046,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 context=context,
                 control_input=self.control,
                 latents_shape=latents.shape,
-                device=device,
                 # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
                 do_classifier_free_guidance=True,
                 exit_stack=exit_stack,
@@ -1077,7 +1063,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
             timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
                 scheduler,
-                device=device,
+                device=unet.device,
                 steps=self.steps,
                 denoising_start=self.denoising_start,
                 denoising_end=self.denoising_end,

invokeai/app/invocations/fields.py

@@ -56,10 +56,6 @@ class UIType(str, Enum, metaclass=MetaEnum):
     CLIPLEmbedModel = "CLIPLEmbedModelField"
     CLIPGEmbedModel = "CLIPGEmbedModelField"
     SpandrelImageToImageModel = "SpandrelImageToImageModelField"
-    ControlLoRAModel = "ControlLoRAModelField"
-    SigLipModel = "SigLipModelField"
-    FluxReduxModel = "FluxReduxModelField"
-    LlavaOnevisionModel = "LLaVAModelField"
     # endregion
 
     # region Misc Field Types
@@ -147,7 +143,6 @@ class FieldDescriptions:
     controlnet_model = "ControlNet model to load"
     vae_model = "VAE model to load"
     lora_model = "LoRA model to load"
-    control_lora_model = "Control LoRA model to load"
     main_model = "Main model (UNet, VAE, CLIP) to load"
     flux_model = "Flux model (Transformer) to load"
     sd3_model = "SD3 model (MMDiTX) to load"
@@ -155,7 +150,6 @@ class FieldDescriptions:
     sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
     onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
     spandrel_image_to_image_model = "Image-to-Image model"
-    vllm_model = "VLLM model"
     lora_weight = "The weight at which the LoRA is applied to each model"
     compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
     raw_prompt = "Raw prompt text (no parsing)"
@@ -205,9 +199,6 @@ class FieldDescriptions:
     freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
     freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
     instantx_control_mode = "The control mode for InstantX ControlNet union models. Ignored for other ControlNet models. The standard mapping is: canny (0), tile (1), depth (2), blur (3), pose (4), gray (5), low quality (6). Negative values will be treated as 'None'."
-    flux_redux_conditioning = "FLUX Redux conditioning tensor"
-    vllm_model = "The VLLM model to use"
-    flux_fill_conditioning = "FLUX Fill conditioning tensor"
 
 
 class ImageField(BaseModel):
@@ -259,29 +250,6 @@ class FluxConditioningField(BaseModel):
     """A conditioning tensor primitive value"""
 
     conditioning_name: str = Field(description="The name of conditioning tensor")
-    mask: Optional[TensorField] = Field(
-        default=None,
-        description="The mask associated with this conditioning tensor. Excluded regions should be set to False, "
-        "included regions should be set to True.",
-    )
-
-
-class FluxReduxConditioningField(BaseModel):
-    """A FLUX Redux conditioning tensor primitive value"""
-
-    conditioning: TensorField = Field(description="The Redux image conditioning tensor.")
-    mask: Optional[TensorField] = Field(
-        default=None,
-        description="The mask associated with this conditioning tensor. Excluded regions should be set to False, "
-        "included regions should be set to True.",
-    )
-
-
-class FluxFillConditioningField(BaseModel):
-    """A FLUX Fill conditioning field."""
-
-    image: ImageField = Field(description="The FLUX Fill reference image.")
-    mask: TensorField = Field(description="The FLUX Fill inpaint mask.")
 
 
 class SD3ConditioningField(BaseModel):
@@ -325,13 +293,6 @@ class BoundingBoxField(BaseModel):
             raise ValueError(f"y_min ({self.y_min}) is greater than y_max ({self.y_max}).")
         return self
 
-    def tuple(self) -> Tuple[int, int, int, int]:
-        """
-        Returns the bounding box as a tuple suitable for use with PIL's `Image.crop()` method.
-        This method returns a tuple of the form (left, upper, right, lower) == (x_min, y_min, x_max, y_max).
-        """
-        return (self.x_min, self.y_min, self.x_max, self.y_max)
-
 
 class MetadataField(RootModel[dict[str, Any]]):
     """

invokeai/app/invocations/flux_control_lora_loader.py

@@ -1,47 +0,0 @@
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import FieldDescriptions, ImageField, InputField, OutputField, UIType
-from invokeai.app.invocations.model import ControlLoRAField, ModelIdentifierField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-
-
-@invocation_output("flux_control_lora_loader_output")
-class FluxControlLoRALoaderOutput(BaseInvocationOutput):
-    """Flux Control LoRA Loader Output"""
-
-    control_lora: ControlLoRAField = OutputField(
-        title="Flux Control LoRA", description="Control LoRAs to apply on model loading", default=None
-    )
-
-
-@invocation(
-    "flux_control_lora_loader",
-    title="Control LoRA - FLUX",
-    tags=["lora", "model", "flux"],
-    category="model",
-    version="1.1.1",
-)
-class FluxControlLoRALoaderInvocation(BaseInvocation):
-    """LoRA model and Image to use with FLUX transformer generation."""
-
-    lora: ModelIdentifierField = InputField(
-        description=FieldDescriptions.control_lora_model, title="Control LoRA", ui_type=UIType.ControlLoRAModel
-    )
-    image: ImageField = InputField(description="The image to encode.")
-    weight: float = InputField(description="The weight of the LoRA.", default=1.0)
-
-    def invoke(self, context: InvocationContext) -> FluxControlLoRALoaderOutput:
-        if not context.models.exists(self.lora.key):
-            raise ValueError(f"Unknown lora: {self.lora.key}!")
-
-        return FluxControlLoRALoaderOutput(
-            control_lora=ControlLoRAField(
-                lora=self.lora,
-                img=self.image,
-                weight=self.weight,
-            )
-        )

invokeai/app/invocations/flux_controlnet.py

@@ -3,6 +3,7 @@ from pydantic import BaseModel, Field, field_validator, model_validator
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Classification,
     invocation,
     invocation_output,
 )
@@ -51,6 +52,7 @@ 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

@@ -1,22 +1,18 @@
 from contextlib import ExitStack
-from typing import Callable, Iterator, Optional, Tuple, Union
+from typing import Callable, Iterator, Optional, Tuple
 
-import einops
 import numpy as np
 import numpy.typing as npt
 import torch
 import torchvision.transforms as tv_transforms
-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, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.fields import (
     DenoiseMaskField,
     FieldDescriptions,
     FluxConditioningField,
-    FluxFillConditioningField,
-    FluxReduxConditioningField,
     ImageField,
     Input,
     InputField,
@@ -25,9 +21,8 @@ from invokeai.app.invocations.fields import (
     WithMetadata,
 )
 from invokeai.app.invocations.flux_controlnet import FluxControlNetField
-from invokeai.app.invocations.flux_vae_encode import FluxVaeEncodeInvocation
 from invokeai.app.invocations.ip_adapter import IPAdapterField
-from invokeai.app.invocations.model import ControlLoRAField, LoRAField, TransformerField, VAEField
+from invokeai.app.invocations.model import TransformerField, VAEField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.flux.controlnet.instantx_controlnet_flux import InstantXControlNetFlux
@@ -35,7 +30,6 @@ from invokeai.backend.flux.controlnet.xlabs_controlnet_flux import XLabsControlN
 from invokeai.backend.flux.denoise import denoise
 from invokeai.backend.flux.extensions.inpaint_extension import InpaintExtension
 from invokeai.backend.flux.extensions.instantx_controlnet_extension import InstantXControlNetExtension
-from invokeai.backend.flux.extensions.regional_prompting_extension import RegionalPromptingExtension
 from invokeai.backend.flux.extensions.xlabs_controlnet_extension import XLabsControlNetExtension
 from invokeai.backend.flux.extensions.xlabs_ip_adapter_extension import XLabsIPAdapterExtension
 from invokeai.backend.flux.ip_adapter.xlabs_ip_adapter_flux import XlabsIpAdapterFlux
@@ -48,11 +42,10 @@ from invokeai.backend.flux.sampling_utils import (
     pack,
     unpack,
 )
-from invokeai.backend.flux.text_conditioning import FluxReduxConditioning, FluxTextConditioning
-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
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_TRANSFORMER_PREFIX
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoRAPatcher
+from invokeai.backend.model_manager.config import ModelFormat
 from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
 from invokeai.backend.util.devices import TorchDevice
@@ -63,7 +56,8 @@ from invokeai.backend.util.devices import TorchDevice
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="3.3.0",
+    version="3.2.1",
+    classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Run denoising process with a FLUX transformer model."""
@@ -93,27 +87,14 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
         title="Transformer",
     )
-    control_lora: Optional[ControlLoRAField] = InputField(
-        description=FieldDescriptions.control_lora_model, input=Input.Connection, title="Control LoRA", default=None
-    )
-    positive_text_conditioning: FluxConditioningField | list[FluxConditioningField] = InputField(
+    positive_text_conditioning: FluxConditioningField = InputField(
         description=FieldDescriptions.positive_cond, input=Input.Connection
     )
-    negative_text_conditioning: FluxConditioningField | list[FluxConditioningField] | None = InputField(
+    negative_text_conditioning: FluxConditioningField | None = InputField(
         default=None,
         description="Negative conditioning tensor. Can be None if cfg_scale is 1.0.",
         input=Input.Connection,
     )
-    redux_conditioning: FluxReduxConditioningField | list[FluxReduxConditioningField] | None = InputField(
-        default=None,
-        description="FLUX Redux conditioning tensor.",
-        input=Input.Connection,
-    )
-    fill_conditioning: FluxFillConditioningField | None = InputField(
-        default=None,
-        description="FLUX Fill conditioning.",
-        input=Input.Connection,
-    )
     cfg_scale: float | list[float] = InputField(default=1.0, description=FieldDescriptions.cfg_scale, title="CFG Scale")
     cfg_scale_start_step: int = InputField(
         default=0,
@@ -158,12 +139,36 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         name = context.tensors.save(tensor=latents)
         return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
 
+    def _load_text_conditioning(
+        self, context: InvocationContext, conditioning_name: str, dtype: torch.dtype
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Load the conditioning data.
+        cond_data = context.conditioning.load(conditioning_name)
+        assert len(cond_data.conditionings) == 1
+        flux_conditioning = cond_data.conditionings[0]
+        assert isinstance(flux_conditioning, FLUXConditioningInfo)
+        flux_conditioning = flux_conditioning.to(dtype=dtype)
+        t5_embeddings = flux_conditioning.t5_embeds
+        clip_embeddings = flux_conditioning.clip_embeds
+        return t5_embeddings, clip_embeddings
+
     def _run_diffusion(
         self,
         context: InvocationContext,
     ):
         inference_dtype = torch.bfloat16
 
+        # Load the conditioning data.
+        pos_t5_embeddings, pos_clip_embeddings = self._load_text_conditioning(
+            context, self.positive_text_conditioning.conditioning_name, inference_dtype
+        )
+        neg_t5_embeddings: torch.Tensor | None = None
+        neg_clip_embeddings: torch.Tensor | None = None
+        if self.negative_text_conditioning is not None:
+            neg_t5_embeddings, neg_clip_embeddings = self._load_text_conditioning(
+                context, self.negative_text_conditioning.conditioning_name, inference_dtype
+            )
+
         # Load the input latents, if provided.
         init_latents = context.tensors.load(self.latents.latents_name) if self.latents else None
         if init_latents is not None:
@@ -178,57 +183,15 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             dtype=inference_dtype,
             seed=self.seed,
         )
-        b, _c, latent_h, latent_w = noise.shape
-        packed_h = latent_h // 2
-        packed_w = latent_w // 2
 
-        # Load the conditioning data.
-        pos_text_conditionings = self._load_text_conditioning(
-            context=context,
-            cond_field=self.positive_text_conditioning,
-            packed_height=packed_h,
-            packed_width=packed_w,
-            dtype=inference_dtype,
-            device=TorchDevice.choose_torch_device(),
-        )
-        neg_text_conditionings: list[FluxTextConditioning] | None = None
-        if self.negative_text_conditioning is not None:
-            neg_text_conditionings = self._load_text_conditioning(
-                context=context,
-                cond_field=self.negative_text_conditioning,
-                packed_height=packed_h,
-                packed_width=packed_w,
-                dtype=inference_dtype,
-                device=TorchDevice.choose_torch_device(),
-            )
-        redux_conditionings: list[FluxReduxConditioning] = self._load_redux_conditioning(
-            context=context,
-            redux_cond_field=self.redux_conditioning,
-            packed_height=packed_h,
-            packed_width=packed_w,
-            device=TorchDevice.choose_torch_device(),
-            dtype=inference_dtype,
-        )
-        pos_regional_prompting_extension = RegionalPromptingExtension.from_text_conditioning(
-            text_conditioning=pos_text_conditionings,
-            redux_conditioning=redux_conditionings,
-            img_seq_len=packed_h * packed_w,
-        )
-        neg_regional_prompting_extension = (
-            RegionalPromptingExtension.from_text_conditioning(
-                text_conditioning=neg_text_conditionings, redux_conditioning=[], img_seq_len=packed_h * packed_w
-            )
-            if neg_text_conditionings
-            else None
-        )
-
-        transformer_config = context.models.get_config(self.transformer.transformer)
-        is_schnell = "schnell" in getattr(transformer_config, "config_path", "")
+        transformer_info = context.models.load(self.transformer.transformer)
+        is_schnell = "schnell" in transformer_info.config.config_path
 
         # Calculate the timestep schedule.
+        image_seq_len = noise.shape[-1] * noise.shape[-2] // 4
         timesteps = get_schedule(
             num_steps=self.num_steps,
-            image_seq_len=packed_h * packed_w,
+            image_seq_len=image_seq_len,
             shift=not is_schnell,
         )
 
@@ -263,36 +226,30 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         if len(timesteps) <= 1:
             return x
 
-        if is_schnell and self.control_lora:
-            raise ValueError("Control LoRAs cannot be used with FLUX Schnell")
-
-        # Prepare the extra image conditioning tensor (img_cond) for either FLUX structural control or FLUX Fill.
-        img_cond: torch.Tensor | None = None
-        is_flux_fill = transformer_config.variant == ModelVariantType.Inpaint  # type: ignore
-        if is_flux_fill:
-            img_cond = self._prep_flux_fill_img_cond(
-                context, device=TorchDevice.choose_torch_device(), dtype=inference_dtype
-            )
-        else:
-            if self.fill_conditioning is not None:
-                raise ValueError("fill_conditioning was provided, but the model is not a FLUX Fill model.")
-
-            if self.control_lora is not None:
-                img_cond = self._prep_structural_control_img_cond(context)
-
         inpaint_mask = self._prep_inpaint_mask(context, x)
 
+        b, _c, latent_h, latent_w = x.shape
         img_ids = generate_img_ids(h=latent_h, w=latent_w, batch_size=b, device=x.device, dtype=x.dtype)
 
+        pos_bs, pos_t5_seq_len, _ = pos_t5_embeddings.shape
+        pos_txt_ids = torch.zeros(
+            pos_bs, pos_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
+        )
+        neg_txt_ids: torch.Tensor | None = None
+        if neg_t5_embeddings is not None:
+            neg_bs, neg_t5_seq_len, _ = neg_t5_embeddings.shape
+            neg_txt_ids = torch.zeros(
+                neg_bs, neg_t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device()
+            )
+
         # Pack all latent tensors.
         init_latents = pack(init_latents) if init_latents is not None else None
         inpaint_mask = pack(inpaint_mask) if inpaint_mask is not None else None
         noise = pack(noise)
         x = pack(x)
 
-        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len, packed_h, and
-        # packed_w correctly.
-        assert packed_h * packed_w == x.shape[1]
+        # Now that we have 'packed' the latent tensors, verify that we calculated the image_seq_len correctly.
+        assert image_seq_len == x.shape[1]
 
         # Prepare inpaint extension.
         inpaint_extension: InpaintExtension | None = None
@@ -309,7 +266,7 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         # TODO(ryand): We should really do this in a separate invocation to benefit from caching.
         ip_adapter_fields = self._normalize_ip_adapter_fields()
         pos_image_prompt_clip_embeds, neg_image_prompt_clip_embeds = self._prep_ip_adapter_image_prompt_clip_embeds(
-            ip_adapter_fields, context, device=x.device
+            ip_adapter_fields, context
         )
 
         cfg_scale = self.prep_cfg_scale(
@@ -332,40 +289,41 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
             )
 
             # Load the transformer model.
-            (cached_weights, transformer) = exit_stack.enter_context(
-                context.models.load(self.transformer.transformer).model_on_device()
-            )
+            (cached_weights, transformer) = exit_stack.enter_context(transformer_info.model_on_device())
             assert isinstance(transformer, Flux)
-            config = transformer_config
+            config = transformer_info.config
             assert config is not None
 
-            # Determine if the model is quantized.
-            # If the model is quantized, then we need to apply the LoRA weights as sidecar layers. This results in
-            # slower inference than direct patching, but is agnostic to the quantization format.
+            # Apply LoRA models to the transformer.
+            # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
             if config.format in [ModelFormat.Checkpoint]:
-                model_is_quantized = False
+                # The model is non-quantized, so we can apply the LoRA weights directly into the model.
+                exit_stack.enter_context(
+                    LoRAPatcher.apply_lora_patches(
+                        model=transformer,
+                        patches=self._lora_iterator(context),
+                        prefix=FLUX_LORA_TRANSFORMER_PREFIX,
+                        cached_weights=cached_weights,
+                    )
+                )
             elif config.format in [
                 ModelFormat.BnbQuantizedLlmInt8b,
                 ModelFormat.BnbQuantizednf4b,
                 ModelFormat.GGUFQuantized,
             ]:
-                model_is_quantized = True
+                # The model is quantized, so apply the LoRA weights as sidecar layers. This results in slower inference,
+                # than directly patching the weights, but is agnostic to the quantization format.
+                exit_stack.enter_context(
+                    LoRAPatcher.apply_lora_sidecar_patches(
+                        model=transformer,
+                        patches=self._lora_iterator(context),
+                        prefix=FLUX_LORA_TRANSFORMER_PREFIX,
+                        dtype=inference_dtype,
+                    )
+                )
             else:
                 raise ValueError(f"Unsupported model format: {config.format}")
 
-            # Apply LoRA models to the transformer.
-            # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
-            exit_stack.enter_context(
-                LayerPatcher.apply_smart_model_patches(
-                    model=transformer,
-                    patches=self._lora_iterator(context),
-                    prefix=FLUX_LORA_TRANSFORMER_PREFIX,
-                    dtype=inference_dtype,
-                    cached_weights=cached_weights,
-                    force_sidecar_patching=model_is_quantized,
-                )
-            )
-
             # Prepare IP-Adapter extensions.
             pos_ip_adapter_extensions, neg_ip_adapter_extensions = self._prep_ip_adapter_extensions(
                 pos_image_prompt_clip_embeds=pos_image_prompt_clip_embeds,
@@ -380,8 +338,12 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 model=transformer,
                 img=x,
                 img_ids=img_ids,
-                pos_regional_prompting_extension=pos_regional_prompting_extension,
-                neg_regional_prompting_extension=neg_regional_prompting_extension,
+                txt=pos_t5_embeddings,
+                txt_ids=pos_txt_ids,
+                vec=pos_clip_embeddings,
+                neg_txt=neg_t5_embeddings,
+                neg_txt_ids=neg_txt_ids,
+                neg_vec=neg_clip_embeddings,
                 timesteps=timesteps,
                 step_callback=self._build_step_callback(context),
                 guidance=self.guidance,
@@ -390,85 +352,11 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 controlnet_extensions=controlnet_extensions,
                 pos_ip_adapter_extensions=pos_ip_adapter_extensions,
                 neg_ip_adapter_extensions=neg_ip_adapter_extensions,
-                img_cond=img_cond,
             )
 
         x = unpack(x.float(), self.height, self.width)
         return x
 
-    def _load_text_conditioning(
-        self,
-        context: InvocationContext,
-        cond_field: FluxConditioningField | list[FluxConditioningField],
-        packed_height: int,
-        packed_width: int,
-        dtype: torch.dtype,
-        device: torch.device,
-    ) -> list[FluxTextConditioning]:
-        """Load text conditioning data from a FluxConditioningField or a list of FluxConditioningFields."""
-        # Normalize to a list of FluxConditioningFields.
-        cond_list = [cond_field] if isinstance(cond_field, FluxConditioningField) else cond_field
-
-        text_conditionings: list[FluxTextConditioning] = []
-        for cond_field in cond_list:
-            # Load the text embeddings.
-            cond_data = context.conditioning.load(cond_field.conditioning_name)
-            assert len(cond_data.conditionings) == 1
-            flux_conditioning = cond_data.conditionings[0]
-            assert isinstance(flux_conditioning, FLUXConditioningInfo)
-            flux_conditioning = flux_conditioning.to(dtype=dtype, device=device)
-            t5_embeddings = flux_conditioning.t5_embeds
-            clip_embeddings = flux_conditioning.clip_embeds
-
-            # Load the mask, if provided.
-            mask: Optional[torch.Tensor] = None
-            if cond_field.mask is not None:
-                mask = context.tensors.load(cond_field.mask.tensor_name)
-                mask = mask.to(device=device)
-                mask = RegionalPromptingExtension.preprocess_regional_prompt_mask(
-                    mask, packed_height, packed_width, dtype, device
-                )
-
-            text_conditionings.append(FluxTextConditioning(t5_embeddings, clip_embeddings, mask))
-
-        return text_conditionings
-
-    def _load_redux_conditioning(
-        self,
-        context: InvocationContext,
-        redux_cond_field: FluxReduxConditioningField | list[FluxReduxConditioningField] | None,
-        packed_height: int,
-        packed_width: int,
-        device: torch.device,
-        dtype: torch.dtype,
-    ) -> list[FluxReduxConditioning]:
-        # Normalize to a list of FluxReduxConditioningFields.
-        if redux_cond_field is None:
-            return []
-
-        redux_cond_list = (
-            [redux_cond_field] if isinstance(redux_cond_field, FluxReduxConditioningField) else redux_cond_field
-        )
-
-        redux_conditionings: list[FluxReduxConditioning] = []
-        for redux_cond_field in redux_cond_list:
-            # Load the Redux conditioning tensor.
-            redux_cond_data = context.tensors.load(redux_cond_field.conditioning.tensor_name)
-            redux_cond_data.to(device=device, dtype=dtype)
-
-            # Load the mask, if provided.
-            mask: Optional[torch.Tensor] = None
-            if redux_cond_field.mask is not None:
-                mask = context.tensors.load(redux_cond_field.mask.tensor_name)
-                mask = mask.to(device=device)
-                mask = RegionalPromptingExtension.preprocess_regional_prompt_mask(
-                    mask, packed_height, packed_width, dtype, device
-                )
-
-            redux_conditionings.append(FluxReduxConditioning(redux_embeddings=redux_cond_data, mask=mask))
-
-        return redux_conditionings
-
     @classmethod
     def prep_cfg_scale(
         cls, cfg_scale: float | list[float], timesteps: list[float], cfg_scale_start_step: int, cfg_scale_end_step: int
@@ -583,18 +471,15 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         # before loading the models. Then make sure that all VAE encoding is done before loading the ControlNets to
         # minimize peak memory.
 
+        # First, load the ControlNet models so that we can determine the ControlNet types.
+        controlnet_models = [context.models.load(controlnet.control_model) for controlnet in controlnets]
+
         # Calculate the controlnet conditioning tensors.
         # We do this before loading the ControlNet models because it may require running the VAE, and we are trying to
         # keep peak memory down.
         controlnet_conds: list[torch.Tensor] = []
-        for controlnet in controlnets:
+        for controlnet, controlnet_model in zip(controlnets, controlnet_models, strict=True):
             image = context.images.get_pil(controlnet.image.image_name)
-
-            # HACK(ryand): We have to load the ControlNet model to determine whether the VAE needs to be run. We really
-            # shouldn't have to load the model here. There's a risk that the model will be dropped from the model cache
-            # before we load it into VRAM and thus we'll have to load it again (context:
-            # https://github.com/invoke-ai/InvokeAI/issues/7513).
-            controlnet_model = context.models.load(controlnet.control_model)
             if isinstance(controlnet_model.model, InstantXControlNetFlux):
                 if self.controlnet_vae is None:
                     raise ValueError("A ControlNet VAE is required when using an InstantX FLUX ControlNet.")
@@ -624,8 +509,10 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         # Finally, load the ControlNet models and initialize the ControlNet extensions.
         controlnet_extensions: list[XLabsControlNetExtension | InstantXControlNetExtension] = []
-        for controlnet, controlnet_cond in zip(controlnets, controlnet_conds, strict=True):
-            model = exit_stack.enter_context(context.models.load(controlnet.control_model))
+        for controlnet, controlnet_cond, controlnet_model in zip(
+            controlnets, controlnet_conds, controlnet_models, strict=True
+        ):
+            model = exit_stack.enter_context(controlnet_model)
 
             if isinstance(model, XLabsControlNetFlux):
                 controlnet_extensions.append(
@@ -658,92 +545,6 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         return controlnet_extensions
 
-    def _prep_structural_control_img_cond(self, context: InvocationContext) -> torch.Tensor | None:
-        if self.control_lora is None:
-            return None
-
-        if not self.controlnet_vae:
-            raise ValueError("controlnet_vae must be set when using a FLUX Control LoRA.")
-
-        # Load the conditioning image and resize it to the target image size.
-        cond_img = context.images.get_pil(self.control_lora.img.image_name)
-        cond_img = cond_img.convert("RGB")
-        cond_img = cond_img.resize((self.width, self.height), Image.Resampling.BICUBIC)
-        cond_img = np.array(cond_img)
-
-        # Normalize the conditioning image to the range [-1, 1].
-        # This normalization is based on the original implementations here:
-        # https://github.com/black-forest-labs/flux/blob/805da8571a0b49b6d4043950bd266a65328c243b/src/flux/modules/image_embedders.py#L34
-        # https://github.com/black-forest-labs/flux/blob/805da8571a0b49b6d4043950bd266a65328c243b/src/flux/modules/image_embedders.py#L60
-        img_cond = torch.from_numpy(cond_img).float() / 127.5 - 1.0
-        img_cond = einops.rearrange(img_cond, "h w c -> 1 c h w")
-
-        vae_info = context.models.load(self.controlnet_vae.vae)
-        img_cond = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=img_cond)
-
-        return pack(img_cond)
-
-    def _prep_flux_fill_img_cond(
-        self, context: InvocationContext, device: torch.device, dtype: torch.dtype
-    ) -> torch.Tensor:
-        """Prepare the FLUX Fill conditioning. This method should be called iff the model is a FLUX Fill model.
-
-        This logic is based on:
-        https://github.com/black-forest-labs/flux/blob/716724eb276d94397be99710a0a54d352664e23b/src/flux/sampling.py#L107-L157
-        """
-        # Validate inputs.
-        if self.fill_conditioning is None:
-            raise ValueError("A FLUX Fill model is being used without fill_conditioning.")
-        # TODO(ryand): We should probable rename controlnet_vae. It's used for more than just ControlNets.
-        if self.controlnet_vae is None:
-            raise ValueError("A FLUX Fill model is being used without controlnet_vae.")
-        if self.control_lora is not None:
-            raise ValueError(
-                "A FLUX Fill model is being used, but a control_lora was provided. Control LoRAs are not compatible with FLUX Fill models."
-            )
-
-        # Log input warnings related to FLUX Fill usage.
-        if self.denoise_mask is not None:
-            context.logger.warning(
-                "Both fill_conditioning and a denoise_mask were provided. You probably meant to use one or the other."
-            )
-        if self.guidance < 25.0:
-            context.logger.warning("A guidance value of ~30.0 is recommended for FLUX Fill models.")
-
-        # Load the conditioning image and resize it to the target image size.
-        cond_img = context.images.get_pil(self.fill_conditioning.image.image_name, mode="RGB")
-        cond_img = cond_img.resize((self.width, self.height), Image.Resampling.BICUBIC)
-        cond_img = np.array(cond_img)
-        cond_img = torch.from_numpy(cond_img).float() / 127.5 - 1.0
-        cond_img = einops.rearrange(cond_img, "h w c -> 1 c h w")
-        cond_img = cond_img.to(device=device, dtype=dtype)
-
-        # Load the mask and resize it to the target image size.
-        mask = context.tensors.load(self.fill_conditioning.mask.tensor_name)
-        # We expect mask to be a bool tensor with shape [1, H, W].
-        assert mask.dtype == torch.bool
-        assert mask.dim() == 3
-        assert mask.shape[0] == 1
-        mask = tv_resize(mask, size=[self.height, self.width], interpolation=tv_transforms.InterpolationMode.NEAREST)
-        mask = mask.to(device=device, dtype=dtype)
-        mask = einops.rearrange(mask, "1 h w -> 1 1 h w")
-
-        # Prepare image conditioning.
-        cond_img = cond_img * (1 - mask)
-        vae_info = context.models.load(self.controlnet_vae.vae)
-        cond_img = FluxVaeEncodeInvocation.vae_encode(vae_info=vae_info, image_tensor=cond_img)
-        cond_img = pack(cond_img)
-
-        # Prepare mask conditioning.
-        mask = mask[:, 0, :, :]
-        # Rearrange mask to a 16-channel representation that matches the shape of the VAE-encoded latent space.
-        mask = einops.rearrange(mask, "b (h ph) (w pw) -> b (ph pw) h w", ph=8, pw=8)
-        mask = pack(mask)
-
-        # Merge image and mask conditioning.
-        img_cond = torch.cat((cond_img, mask), dim=-1)
-        return img_cond
-
     def _normalize_ip_adapter_fields(self) -> list[IPAdapterField]:
         if self.ip_adapter is None:
             return []
@@ -758,7 +559,6 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
         self,
         ip_adapter_fields: list[IPAdapterField],
         context: InvocationContext,
-        device: torch.device,
     ) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
         """Run the IPAdapter CLIPVisionModel, returning image prompt embeddings."""
         clip_image_processor = CLIPImageProcessor()
@@ -798,11 +598,11 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
                 assert isinstance(image_encoder_model, CLIPVisionModelWithProjection)
 
                 clip_image: torch.Tensor = clip_image_processor(images=pos_images, return_tensors="pt").pixel_values
-                clip_image = clip_image.to(device=device, dtype=image_encoder_model.dtype)
+                clip_image = clip_image.to(device=image_encoder_model.device, dtype=image_encoder_model.dtype)
                 pos_clip_image_embeds = image_encoder_model(clip_image).image_embeds
 
                 clip_image = clip_image_processor(images=neg_images, return_tensors="pt").pixel_values
-                clip_image = clip_image.to(device=device, dtype=image_encoder_model.dtype)
+                clip_image = clip_image.to(device=image_encoder_model.device, dtype=image_encoder_model.dtype)
                 neg_clip_image_embeds = image_encoder_model(clip_image).image_embeds
 
             pos_image_prompt_clip_embeds.append(pos_clip_image_embeds)
@@ -851,15 +651,10 @@ class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         return pos_ip_adapter_extensions, neg_ip_adapter_extensions
 
-    def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[ModelPatchRaw, float]]:
-        loras: list[Union[LoRAField, ControlLoRAField]] = [*self.transformer.loras]
-        if self.control_lora:
-            # Note: Since FLUX structural control LoRAs modify the shape of some weights, it is important that they are
-            # applied last.
-            loras.append(self.control_lora)
-        for lora in loras:
+    def _lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
+        for lora in self.transformer.loras:
             lora_info = context.models.load(lora.lora)
-            assert isinstance(lora_info.model, ModelPatchRaw)
+            assert isinstance(lora_info.model, LoRAModelRaw)
             yield (lora_info.model, lora.weight)
             del lora_info
 

invokeai/app/invocations/flux_fill.py

@@ -1,46 +0,0 @@
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    FluxFillConditioningField,
-    InputField,
-    OutputField,
-    TensorField,
-)
-from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-
-
-@invocation_output("flux_fill_output")
-class FluxFillOutput(BaseInvocationOutput):
-    """The conditioning output of a FLUX Fill invocation."""
-
-    fill_cond: FluxFillConditioningField = OutputField(
-        description=FieldDescriptions.flux_redux_conditioning, title="Conditioning"
-    )
-
-
-@invocation(
-    "flux_fill",
-    title="FLUX Fill Conditioning",
-    tags=["inpaint"],
-    category="inpaint",
-    version="1.0.0",
-    classification=Classification.Beta,
-)
-class FluxFillInvocation(BaseInvocation):
-    """Prepare the FLUX Fill conditioning data."""
-
-    image: ImageField = InputField(description="The FLUX Fill reference image.")
-    mask: TensorField = InputField(
-        description="The bool inpainting mask. Excluded regions should be set to "
-        "False, included regions should be set to True.",
-    )
-
-    def invoke(self, context: InvocationContext) -> FluxFillOutput:
-        return FluxFillOutput(fill_cond=FluxFillConditioningField(image=self.image, mask=self.mask))

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, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.fields import InputField, UIType
 from invokeai.app.invocations.ip_adapter import (
     CLIP_VISION_MODEL_MAP,
@@ -28,6 +28,7 @@ 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

@@ -3,13 +3,14 @@ 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.invocations.model import CLIPField, LoRAField, ModelIdentifierField, TransformerField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.taxonomy import BaseModelType
+from invokeai.backend.model_manager.config import BaseModelType
 
 
 @invocation_output("flux_lora_loader_output")
@@ -20,17 +21,15 @@ class FluxLoRALoaderOutput(BaseInvocationOutput):
         default=None, description=FieldDescriptions.transformer, title="FLUX Transformer"
     )
     clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
-    t5_encoder: Optional[T5EncoderField] = OutputField(
-        default=None, description=FieldDescriptions.t5_encoder, title="T5 Encoder"
-    )
 
 
 @invocation(
     "flux_lora_loader",
-    title="Apply LoRA - FLUX",
+    title="FLUX LoRA",
     tags=["lora", "model", "flux"],
     category="model",
-    version="1.2.1",
+    version="1.1.0",
+    classification=Classification.Prototype,
 )
 class FluxLoRALoaderInvocation(BaseInvocation):
     """Apply a LoRA model to a FLUX transformer and/or text encoder."""
@@ -51,12 +50,6 @@ class FluxLoRALoaderInvocation(BaseInvocation):
         description=FieldDescriptions.clip,
         input=Input.Connection,
     )
-    t5_encoder: T5EncoderField | None = InputField(
-        default=None,
-        title="T5 Encoder",
-        description=FieldDescriptions.t5_encoder,
-        input=Input.Connection,
-    )
 
     def invoke(self, context: InvocationContext) -> FluxLoRALoaderOutput:
         lora_key = self.lora.key
@@ -69,8 +62,6 @@ class FluxLoRALoaderInvocation(BaseInvocation):
             raise ValueError(f'LoRA "{lora_key}" already applied to transformer.')
         if self.clip and any(lora.lora.key == lora_key for lora in self.clip.loras):
             raise ValueError(f'LoRA "{lora_key}" already applied to CLIP encoder.')
-        if self.t5_encoder and any(lora.lora.key == lora_key for lora in self.t5_encoder.loras):
-            raise ValueError(f'LoRA "{lora_key}" already applied to T5 encoder.')
 
         output = FluxLoRALoaderOutput()
 
@@ -91,30 +82,23 @@ class FluxLoRALoaderInvocation(BaseInvocation):
                     weight=self.weight,
                 )
             )
-        if self.t5_encoder is not None:
-            output.t5_encoder = self.t5_encoder.model_copy(deep=True)
-            output.t5_encoder.loras.append(
-                LoRAField(
-                    lora=self.lora,
-                    weight=self.weight,
-                )
-            )
 
         return output
 
 
 @invocation(
     "flux_lora_collection_loader",
-    title="Apply LoRA Collection - FLUX",
+    title="FLUX LoRA Collection Loader",
     tags=["lora", "model", "flux"],
     category="model",
-    version="1.3.1",
+    version="1.1.0",
+    classification=Classification.Prototype,
 )
 class FLUXLoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to a FLUX transformer."""
 
-    loras: Optional[LoRAField | list[LoRAField]] = InputField(
-        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    loras: LoRAField | list[LoRAField] = InputField(
+        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
 
     transformer: Optional[TransformerField] = InputField(
@@ -129,30 +113,13 @@ class FLUXLoRACollectionLoader(BaseInvocation):
         description=FieldDescriptions.clip,
         input=Input.Connection,
     )
-    t5_encoder: T5EncoderField | None = InputField(
-        default=None,
-        title="T5 Encoder",
-        description=FieldDescriptions.t5_encoder,
-        input=Input.Connection,
-    )
 
     def invoke(self, context: InvocationContext) -> FluxLoRALoaderOutput:
         output = FluxLoRALoaderOutput()
         loras = self.loras if isinstance(self.loras, list) else [self.loras]
         added_loras: list[str] = []
 
-        if self.transformer is not None:
-            output.transformer = self.transformer.model_copy(deep=True)
-
-        if self.clip is not None:
-            output.clip = self.clip.model_copy(deep=True)
-
-        if self.t5_encoder is not None:
-            output.t5_encoder = self.t5_encoder.model_copy(deep=True)
-
         for lora in loras:
-            if lora is None:
-                continue
             if lora.lora.key in added_loras:
                 continue
 
@@ -163,13 +130,14 @@ class FLUXLoRACollectionLoader(BaseInvocation):
 
             added_loras.append(lora.lora.key)
 
-            if self.transformer is not None and output.transformer is not None:
+            if self.transformer is not None:
+                if output.transformer is None:
+                    output.transformer = self.transformer.model_copy(deep=True)
                 output.transformer.loras.append(lora)
 
-            if self.clip is not None and output.clip is not None:
+            if self.clip is not None:
+                if output.clip is None:
+                    output.clip = self.clip.model_copy(deep=True)
                 output.clip.loras.append(lora)
 
-            if self.t5_encoder is not None and output.t5_encoder is not None:
-                output.t5_encoder.loras.append(lora)
-
         return output

invokeai/app/invocations/flux_model_loader.py

@@ -3,21 +3,18 @@ from typing import Literal
 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, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.t5_model_identifier import (
-    preprocess_t5_encoder_model_identifier,
-    preprocess_t5_tokenizer_model_identifier,
-)
 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")
@@ -36,10 +33,11 @@ class FluxModelLoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "flux_model_loader",
-    title="Main Model - FLUX",
+    title="Flux Main Model",
     tags=["model", "flux"],
     category="model",
-    version="1.0.6",
+    version="1.0.4",
+    classification=Classification.Prototype,
 )
 class FluxModelLoaderInvocation(BaseInvocation):
     """Loads a flux base model, outputting its submodels."""
@@ -76,8 +74,8 @@ class FluxModelLoaderInvocation(BaseInvocation):
         tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
         clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
 
-        tokenizer2 = preprocess_t5_tokenizer_model_identifier(self.t5_encoder_model)
-        t5_encoder = preprocess_t5_encoder_model_identifier(self.t5_encoder_model)
+        tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
+        t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
 
         transformer_config = context.models.get_config(transformer)
         assert isinstance(transformer_config, CheckpointConfigBase)
@@ -85,7 +83,7 @@ class FluxModelLoaderInvocation(BaseInvocation):
         return FluxModelLoaderOutput(
             transformer=TransformerField(transformer=transformer, loras=[]),
             clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder, loras=[]),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
             vae=VAEField(vae=vae),
             max_seq_len=max_seq_lengths[transformer_config.config_path],
         )

invokeai/app/invocations/flux_redux.py

@@ -1,120 +0,0 @@
-from typing import Optional
-
-import torch
-from PIL import Image
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    FluxReduxConditioningField,
-    InputField,
-    OutputField,
-    TensorField,
-    UIType,
-)
-from invokeai.app.invocations.model import ModelIdentifierField
-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 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
-
-
-@invocation_output("flux_redux_output")
-class FluxReduxOutput(BaseInvocationOutput):
-    """The conditioning output of a FLUX Redux invocation."""
-
-    redux_cond: FluxReduxConditioningField = OutputField(
-        description=FieldDescriptions.flux_redux_conditioning, title="Conditioning"
-    )
-
-
-@invocation(
-    "flux_redux",
-    title="FLUX Redux",
-    tags=["ip_adapter", "control"],
-    category="ip_adapter",
-    version="2.0.0",
-    classification=Classification.Beta,
-)
-class FluxReduxInvocation(BaseInvocation):
-    """Runs a FLUX Redux model to generate a conditioning tensor."""
-
-    image: ImageField = InputField(description="The FLUX Redux image prompt.")
-    mask: Optional[TensorField] = InputField(
-        default=None,
-        description="The bool mask associated with this FLUX Redux image prompt. Excluded regions should be set to "
-        "False, included regions should be set to True.",
-    )
-    redux_model: ModelIdentifierField = InputField(
-        description="The FLUX Redux model to use.",
-        title="FLUX Redux Model",
-        ui_type=UIType.FluxReduxModel,
-    )
-
-    def invoke(self, context: InvocationContext) -> FluxReduxOutput:
-        image = context.images.get_pil(self.image.image_name, "RGB")
-
-        encoded_x = self._siglip_encode(context, image)
-        redux_conditioning = self._flux_redux_encode(context, encoded_x)
-
-        tensor_name = context.tensors.save(redux_conditioning)
-        return FluxReduxOutput(
-            redux_cond=FluxReduxConditioningField(conditioning=TensorField(tensor_name=tensor_name), mask=self.mask)
-        )
-
-    @torch.no_grad()
-    def _siglip_encode(self, context: InvocationContext, image: Image.Image) -> torch.Tensor:
-        siglip_model_config = self._get_siglip_model(context)
-        with context.models.load(siglip_model_config.key).model_on_device() as (_, siglip_pipeline):
-            assert isinstance(siglip_pipeline, SigLipPipeline)
-            return siglip_pipeline.encode_image(
-                x=image, device=TorchDevice.choose_torch_device(), dtype=TorchDevice.choose_torch_dtype()
-            )
-
-    @torch.no_grad()
-    def _flux_redux_encode(self, context: InvocationContext, encoded_x: torch.Tensor) -> torch.Tensor:
-        with context.models.load(self.redux_model).model_on_device() as (_, flux_redux):
-            assert isinstance(flux_redux, FluxReduxModel)
-            dtype = next(flux_redux.parameters()).dtype
-            encoded_x = encoded_x.to(dtype=dtype)
-            return flux_redux(encoded_x)
-
-    def _get_siglip_model(self, context: InvocationContext) -> AnyModelConfig:
-        siglip_models = context.models.search_by_attrs(name=siglip.name, base=BaseModelType.Any, type=ModelType.SigLIP)
-
-        if not len(siglip_models) > 0:
-            context.logger.warning(
-                f"The SigLIP model required by FLUX Redux ({siglip.name}) is not installed. Downloading and installing now. This may take a while."
-            )
-
-            # TODO(psyche): Can the probe reliably determine the type of the model? Just hardcoding it bc I don't want to experiment now
-            config_overrides = ModelRecordChanges(name=siglip.name, type=ModelType.SigLIP)
-
-            # Queue the job
-            job = context._services.model_manager.install.heuristic_import(siglip.source, config=config_overrides)
-
-            # Wait for up to 10 minutes - model is ~3.5GB
-            context._services.model_manager.install.wait_for_job(job, timeout=600)
-
-            siglip_models = context.models.search_by_attrs(
-                name=siglip.name,
-                base=BaseModelType.Any,
-                type=ModelType.SigLIP,
-            )
-
-            if len(siglip_models) == 0:
-                context.logger.error("Error while fetching SigLIP for FLUX Redux")
-                assert len(siglip_models) == 1
-
-        return siglip_models[0]

invokeai/app/invocations/flux_text_encoder.py

@@ -1,35 +1,29 @@
 from contextlib import ExitStack
-from typing import Iterator, Literal, Optional, Tuple
+from typing import Iterator, Literal, Tuple
 
 import torch
-from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer, T5TokenizerFast
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    FluxConditioningField,
-    Input,
-    InputField,
-    TensorField,
-    UIComponent,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
 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 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
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoRAPatcher
+from invokeai.backend.model_manager.config import ModelFormat
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
 
 
 @invocation(
     "flux_text_encoder",
-    title="Prompt - FLUX",
+    title="FLUX Text Encoding",
     tags=["prompt", "conditioning", "flux"],
     category="conditioning",
-    version="1.1.2",
+    version="1.1.0",
+    classification=Classification.Prototype,
 )
 class FluxTextEncoderInvocation(BaseInvocation):
     """Encodes and preps a prompt for a flux image."""
@@ -47,10 +41,7 @@ class FluxTextEncoderInvocation(BaseInvocation):
     t5_max_seq_len: Literal[256, 512] = InputField(
         description="Max sequence length for the T5 encoder. Expected to be 256 for FLUX schnell models and 512 for FLUX dev models."
     )
-    prompt: str = InputField(description="Text prompt to encode.", ui_component=UIComponent.Textarea)
-    mask: Optional[TensorField] = InputField(
-        default=None, description="A mask defining the region that this conditioning prompt applies to."
-    )
+    prompt: str = InputField(description="Text prompt to encode.")
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> FluxConditioningOutput:
@@ -63,51 +54,20 @@ class FluxTextEncoderInvocation(BaseInvocation):
         )
 
         conditioning_name = context.conditioning.save(conditioning_data)
-        return FluxConditioningOutput(
-            conditioning=FluxConditioningField(conditioning_name=conditioning_name, mask=self.mask)
-        )
+        return FluxConditioningOutput.build(conditioning_name)
 
     def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
+        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
+        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
+
         prompt = [self.prompt]
 
-        t5_encoder_info = context.models.load(self.t5_encoder.text_encoder)
-        t5_encoder_config = t5_encoder_info.config
-        assert t5_encoder_config is not None
-
         with (
-            t5_encoder_info.model_on_device() as (cached_weights, t5_text_encoder),
-            context.models.load(self.t5_encoder.tokenizer) as t5_tokenizer,
-            ExitStack() as exit_stack,
+            t5_text_encoder_info as t5_text_encoder,
+            t5_tokenizer_info as t5_tokenizer,
         ):
             assert isinstance(t5_text_encoder, T5EncoderModel)
-            assert isinstance(t5_tokenizer, (T5Tokenizer, T5TokenizerFast))
-
-            # Determine if the model is quantized.
-            # If the model is quantized, then we need to apply the LoRA weights as sidecar layers. This results in
-            # slower inference than direct patching, but is agnostic to the quantization format.
-            if t5_encoder_config.format in [ModelFormat.T5Encoder, ModelFormat.Diffusers]:
-                model_is_quantized = False
-            elif t5_encoder_config.format in [
-                ModelFormat.BnbQuantizedLlmInt8b,
-                ModelFormat.BnbQuantizednf4b,
-                ModelFormat.GGUFQuantized,
-            ]:
-                model_is_quantized = True
-            else:
-                raise ValueError(f"Unsupported model format: {t5_encoder_config.format}")
-
-            # Apply LoRA models to the T5 encoder.
-            # Note: We apply the LoRA after the encoder has been moved to its target device for faster patching.
-            exit_stack.enter_context(
-                LayerPatcher.apply_smart_model_patches(
-                    model=t5_text_encoder,
-                    patches=self._t5_lora_iterator(context),
-                    prefix=FLUX_LORA_T5_PREFIX,
-                    dtype=t5_text_encoder.dtype,
-                    cached_weights=cached_weights,
-                    force_sidecar_patching=model_is_quantized,
-                )
-            )
+            assert isinstance(t5_tokenizer, T5Tokenizer)
 
             t5_encoder = HFEncoder(t5_text_encoder, t5_tokenizer, False, self.t5_max_seq_len)
 
@@ -118,30 +78,31 @@ class FluxTextEncoderInvocation(BaseInvocation):
         return prompt_embeds
 
     def _clip_encode(self, context: InvocationContext) -> torch.Tensor:
-        prompt = [self.prompt]
-
+        clip_tokenizer_info = context.models.load(self.clip.tokenizer)
         clip_text_encoder_info = context.models.load(self.clip.text_encoder)
-        clip_text_encoder_config = clip_text_encoder_info.config
-        assert clip_text_encoder_config is not None
+
+        prompt = [self.prompt]
 
         with (
             clip_text_encoder_info.model_on_device() as (cached_weights, clip_text_encoder),
-            context.models.load(self.clip.tokenizer) as clip_tokenizer,
+            clip_tokenizer_info as clip_tokenizer,
             ExitStack() as exit_stack,
         ):
             assert isinstance(clip_text_encoder, CLIPTextModel)
             assert isinstance(clip_tokenizer, CLIPTokenizer)
 
+            clip_text_encoder_config = clip_text_encoder_info.config
+            assert clip_text_encoder_config is not None
+
             # Apply LoRA models to the CLIP encoder.
             # Note: We apply the LoRA after the transformer has been moved to its target device for faster patching.
             if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
                 # The model is non-quantized, so we can apply the LoRA weights directly into the model.
                 exit_stack.enter_context(
-                    LayerPatcher.apply_smart_model_patches(
+                    LoRAPatcher.apply_lora_patches(
                         model=clip_text_encoder,
                         patches=self._clip_lora_iterator(context),
                         prefix=FLUX_LORA_CLIP_PREFIX,
-                        dtype=clip_text_encoder.dtype,
                         cached_weights=cached_weights,
                     )
                 )
@@ -157,16 +118,9 @@ class FluxTextEncoderInvocation(BaseInvocation):
         assert isinstance(pooled_prompt_embeds, torch.Tensor)
         return pooled_prompt_embeds
 
-    def _clip_lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[ModelPatchRaw, float]]:
+    def _clip_lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[LoRAModelRaw, float]]:
         for lora in self.clip.loras:
             lora_info = context.models.load(lora.lora)
-            assert isinstance(lora_info.model, ModelPatchRaw)
-            yield (lora_info.model, lora.weight)
-            del lora_info
-
-    def _t5_lora_iterator(self, context: InvocationContext) -> Iterator[Tuple[ModelPatchRaw, float]]:
-        for lora in self.t5_encoder.loras:
-            lora_info = context.models.load(lora.lora)
-            assert isinstance(lora_info.model, ModelPatchRaw)
+            assert isinstance(lora_info.model, LoRAModelRaw)
             yield (lora_info.model, lora.weight)
             del lora_info

invokeai/app/invocations/flux_vae_decode.py

@@ -3,7 +3,6 @@ from einops import rearrange
 from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -22,10 +21,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "flux_vae_decode",
-    title="Latents to Image - FLUX",
+    title="FLUX Latents to Image",
     tags=["latents", "image", "vae", "l2i", "flux"],
     category="latents",
-    version="1.0.2",
+    version="1.0.0",
 )
 class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""
@@ -39,18 +38,8 @@ class FluxVaeDecodeInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
     )
 
-    def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoEncoder) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
-        out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
-        element_size = next(vae.parameters()).element_size()
-        scaling_constant = 2200  # Determined experimentally.
-        working_memory = out_h * out_w * element_size * scaling_constant
-        return int(working_memory)
-
     def _vae_decode(self, vae_info: LoadedModel, latents: torch.Tensor) -> Image.Image:
-        estimated_working_memory = self._estimate_working_memory(latents, vae_info.model)
-        with vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae):
+        with vae_info as vae:
             assert isinstance(vae, AutoEncoder)
             vae_dtype = next(iter(vae.parameters())).dtype
             latents = latents.to(device=TorchDevice.choose_torch_device(), dtype=vae_dtype)

invokeai/app/invocations/flux_vae_encode.py

@@ -19,10 +19,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "flux_vae_encode",
-    title="Image to Latents - FLUX",
+    title="FLUX Image to Latents",
     tags=["latents", "image", "vae", "i2l", "flux"],
     category="latents",
-    version="1.0.1",
+    version="1.0.0",
 )
 class FluxVaeEncodeInvocation(BaseInvocation):
     """Encodes an image into latents."""

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.taxonomy import BaseModelType
+from invokeai.backend.model_manager.config import BaseModelType
 
 
 @invocation_output("ideal_size_output")
@@ -19,9 +19,9 @@ class IdealSizeOutput(BaseInvocationOutput):
 
 @invocation(
     "ideal_size",
-    title="Ideal Size - SD1.5, SDXL",
+    title="Ideal Size",
     tags=["latents", "math", "ideal_size"],
-    version="1.0.5",
+    version="1.0.3",
 )
 class IdealSizeInvocation(BaseInvocation):
     """Calculates the ideal size for generation to avoid duplication"""
@@ -41,16 +41,11 @@ class IdealSizeInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> IdealSizeOutput:
         unet_config = context.models.get_config(self.unet.unet.key)
         aspect = self.width / self.height
-
-        if unet_config.base == BaseModelType.StableDiffusion1:
-            dimension = 512
-        elif unet_config.base == BaseModelType.StableDiffusion2:
+        dimension: float = 512
+        if unet_config.base == BaseModelType.StableDiffusion2:
             dimension = 768
-        elif unet_config.base in (BaseModelType.StableDiffusionXL, BaseModelType.Flux, BaseModelType.StableDiffusion3):
+        elif unet_config.base == BaseModelType.StableDiffusionXL:
             dimension = 1024
-        else:
-            raise ValueError(f"Unsupported model type: {unet_config.base}")
-
         dimension = dimension * self.multiplier
         min_dimension = math.floor(dimension * 0.5)
         model_area = dimension * dimension  # hardcoded for now since all models are trained on square images

invokeai/app/invocations/image.py

@@ -13,7 +13,6 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.constants import IMAGE_MODES
 from invokeai.app.invocations.fields import (
-    BoundingBoxField,
     ColorField,
     FieldDescriptions,
     ImageField,
@@ -24,7 +23,6 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.misc import SEED_MAX
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
@@ -163,12 +161,12 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata, WithBoard):
     crop: bool = InputField(default=False, description="Crop to base image dimensions")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        base_image = context.images.get_pil(self.base_image.image_name, mode="RGBA")
-        image = context.images.get_pil(self.image.image_name, mode="RGBA")
+        base_image = context.images.get_pil(self.base_image.image_name)
+        image = context.images.get_pil(self.image.image_name)
         mask = None
         if self.mask is not None:
-            mask = context.images.get_pil(self.mask.image_name, mode="L")
-            mask = ImageOps.invert(mask)
+            mask = context.images.get_pil(self.mask.image_name)
+            mask = ImageOps.invert(mask.convert("L"))
         # TODO: probably shouldn't invert mask here... should user be required to do it?
 
         min_x = min(0, self.x)
@@ -178,11 +176,7 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         new_image = Image.new(mode="RGBA", size=(max_x - min_x, max_y - min_y), color=(0, 0, 0, 0))
         new_image.paste(base_image, (abs(min_x), abs(min_y)))
-
-        # Create a temporary image to paste the image with transparency
-        temp_image = Image.new("RGBA", new_image.size)
-        temp_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
-        new_image = Image.alpha_composite(new_image, temp_image)
+        new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
 
         if self.crop:
             base_w, base_h = base_image.size
@@ -307,44 +301,14 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
     blur_type: Literal["gaussian", "box"] = InputField(default="gaussian", description="The type of blur")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name, mode="RGBA")
+        image = context.images.get_pil(self.image.image_name)
 
-        # Split the image into RGBA channels
-        r, g, b, a = image.split()
-
-        # Premultiply RGB channels by alpha
-        premultiplied_image = ImageChops.multiply(image, a.convert("RGBA"))
-        premultiplied_image.putalpha(a)
-
-        # Apply the blur
         blur = (
             ImageFilter.GaussianBlur(self.radius) if self.blur_type == "gaussian" else ImageFilter.BoxBlur(self.radius)
         )
-        blurred_image = premultiplied_image.filter(blur)
+        blur_image = image.filter(blur)
 
-        # Split the blurred image into RGBA channels
-        r, g, b, a_orig = blurred_image.split()
-
-        # Convert to float using NumPy. float 32/64 division are much faster than float 16
-        r = numpy.array(r, dtype=numpy.float32)
-        g = numpy.array(g, dtype=numpy.float32)
-        b = numpy.array(b, dtype=numpy.float32)
-        a = numpy.array(a_orig, dtype=numpy.float32) / 255.0  # Normalize alpha to [0, 1]
-
-        # Unpremultiply RGB channels by alpha
-        r /= a + 1e-6  # Add a small epsilon to avoid division by zero
-        g /= a + 1e-6
-        b /= a + 1e-6
-
-        # Convert back to PIL images
-        r = Image.fromarray(numpy.uint8(numpy.clip(r, 0, 255)))
-        g = Image.fromarray(numpy.uint8(numpy.clip(g, 0, 255)))
-        b = Image.fromarray(numpy.uint8(numpy.clip(b, 0, 255)))
-
-        # Merge back into a single image
-        result_image = Image.merge("RGBA", (r, g, b, a_orig))
-
-        image_dto = context.images.save(image=result_image)
+        image_dto = context.images.save(image=blur_image)
 
         return ImageOutput.build(image_dto)
 
@@ -355,6 +319,7 @@ 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"""
@@ -842,7 +807,7 @@ CHANNEL_FORMATS = {
         "value",
     ],
     category="image",
-    version="1.2.3",
+    version="1.2.2",
 )
 class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Add or subtract a value from a specific color channel of an image."""
@@ -852,22 +817,18 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata, WithBoard):
     offset: int = InputField(default=0, ge=-255, le=255, description="The amount to adjust the channel by")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name, "RGBA")
+        pil_image = context.images.get_pil(self.image.image_name)
 
         # extract the channel and mode from the input and reference tuple
         mode = CHANNEL_FORMATS[self.channel][0]
         channel_number = CHANNEL_FORMATS[self.channel][1]
 
         # Convert PIL image to new format
-        converted_image = numpy.array(image.convert(mode)).astype(int)
+        converted_image = numpy.array(pil_image.convert(mode)).astype(int)
         image_channel = converted_image[:, :, channel_number]
 
-        if self.channel == "Hue (HSV)":
-            # loop around the values because hue is special
-            image_channel = (image_channel + self.offset) % 256
-        else:
-            # Adjust the value, clipping to 0..255
-            image_channel = numpy.clip(image_channel + self.offset, 0, 255)
+        # Adjust the value, clipping to 0..255
+        image_channel = numpy.clip(image_channel + self.offset, 0, 255)
 
         # Put the channel back into the image
         converted_image[:, :, channel_number] = image_channel
@@ -875,10 +836,6 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata, WithBoard):
         # Convert back to RGBA format and output
         pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")
 
-        # restore the alpha channel
-        if self.channel != "Alpha (RGBA)":
-            pil_image.putalpha(image.getchannel("A"))
-
         image_dto = context.images.save(image=pil_image)
 
         return ImageOutput.build(image_dto)
@@ -906,7 +863,7 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata, WithBoard):
         "value",
     ],
     category="image",
-    version="1.2.3",
+    version="1.2.2",
 )
 class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Scale a specific color channel of an image."""
@@ -917,14 +874,14 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata, WithBoard):
     invert_channel: bool = InputField(default=False, description="Invert the channel after scaling")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name, "RGBA")
+        pil_image = context.images.get_pil(self.image.image_name)
 
         # extract the channel and mode from the input and reference tuple
         mode = CHANNEL_FORMATS[self.channel][0]
         channel_number = CHANNEL_FORMATS[self.channel][1]
 
         # Convert PIL image to new format
-        converted_image = numpy.array(image.convert(mode)).astype(float)
+        converted_image = numpy.array(pil_image.convert(mode)).astype(float)
         image_channel = converted_image[:, :, channel_number]
 
         # Adjust the value, clipping to 0..255
@@ -940,10 +897,6 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata, WithBoard):
         # Convert back to RGBA format and output
         pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")
 
-        # restore the alpha channel
-        if self.channel != "Alpha (RGBA)":
-            pil_image.putalpha(image.getchannel("A"))
-
         image_dto = context.images.save(image=pil_image)
 
         return ImageOutput.build(image_dto)
@@ -1009,10 +962,10 @@ class CanvasPasteBackInvocation(BaseInvocation, WithMetadata, WithBoard):
 
 @invocation(
     "mask_from_id",
-    title="Mask from Segmented Image",
+    title="Mask from ID",
     tags=["image", "mask", "id"],
     category="image",
-    version="1.0.1",
+    version="1.0.0",
 )
 class MaskFromIDInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generate a mask for a particular color in an ID Map"""
@@ -1022,24 +975,40 @@ class MaskFromIDInvocation(BaseInvocation, WithMetadata, WithBoard):
     threshold: int = InputField(default=100, description="Threshold for color detection")
     invert: bool = InputField(default=False, description="Whether or not to invert the mask")
 
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name, mode="RGBA")
+    def rgba_to_hex(self, rgba_color: tuple[int, int, int, int]):
+        r, g, b, a = rgba_color
+        hex_code = "#{:02X}{:02X}{:02X}{:02X}".format(r, g, b, int(a * 255))
+        return hex_code
 
-        np_color = numpy.array(self.color.tuple())
+    def id_to_mask(self, id_mask: Image.Image, color: tuple[int, int, int, int], threshold: int = 100):
+        if id_mask.mode != "RGB":
+            id_mask = id_mask.convert("RGB")
+
+        # Can directly just use the tuple but I'll leave this rgba_to_hex here
+        # incase anyone prefers using hex codes directly instead of the color picker
+        hex_color_str = self.rgba_to_hex(color)
+        rgb_color = numpy.array([int(hex_color_str[i : i + 2], 16) for i in (1, 3, 5)])
 
         # Maybe there's a faster way to calculate this distance but I can't think of any right now.
-        color_distance = numpy.linalg.norm(image - np_color, axis=-1)
+        color_distance = numpy.linalg.norm(id_mask - rgb_color, axis=-1)
 
         # Create a mask based on the threshold and the distance calculated above
-        binary_mask = (color_distance < self.threshold).astype(numpy.uint8) * 255
+        binary_mask = (color_distance < threshold).astype(numpy.uint8) * 255
 
         # Convert the mask back to PIL
         binary_mask_pil = Image.fromarray(binary_mask)
 
-        if self.invert:
-            binary_mask_pil = ImageOps.invert(binary_mask_pil)
+        return binary_mask_pil
 
-        image_dto = context.images.save(image=binary_mask_pil, image_category=ImageCategory.MASK)
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.images.get_pil(self.image.image_name)
+
+        mask = self.id_to_mask(image, self.color.tuple(), self.threshold)
+
+        if self.invert:
+            mask = ImageOps.invert(mask)
+
+        image_dto = context.images.save(image=mask, image_category=ImageCategory.MASK)
 
         return ImageOutput.build(image_dto)
 
@@ -1050,7 +1019,7 @@ class MaskFromIDInvocation(BaseInvocation, WithMetadata, WithBoard):
     tags=["image", "mask", "id"],
     category="image",
     version="1.0.0",
-    classification=Classification.Deprecated,
+    classification=Classification.Internal,
 )
 class CanvasV2MaskAndCropInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Handles Canvas V2 image output masking and cropping"""
@@ -1086,246 +1055,3 @@ class CanvasV2MaskAndCropInvocation(BaseInvocation, WithMetadata, WithBoard):
             image_dto = context.images.save(image=generated_image)
 
         return ImageOutput.build(image_dto)
-
-
-@invocation(
-    "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")
-    threshold: int = InputField(default=0, ge=0, le=255, description="The threshold for the binary mask (0-255)")
-    fade_size_px: int = InputField(default=32, ge=0, description="The size of the fade in pixels")
-
-    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
-        # If we don't threshold we can get some weird artifacts
-        np_mask = numpy.where(np_mask > self.threshold, 255, 0).astype(numpy.uint8)
-
-        # Create a mask for the black region (1 where black, 0 otherwise)
-        black_mask = (np_mask == 0).astype(numpy.uint8)
-
-        # Invert the black region
-        bg_mask = 1 - black_mask
-
-        # Create a distance transform of the inverted mask
-        dist = cv2.distanceTransform(bg_mask, cv2.DIST_L2, 5)
-
-        # Normalize distances so that pixels <fade_size_px become a linear gradient (0 to 1)
-        d_norm = numpy.clip(dist / self.fade_size_px, 0, 1)
-
-        # Control points: x values (normalized distance) and corresponding fade pct y values.
-
-        # There are some magic numbers here that are used to create a smooth transition:
-        # - The first point is at 0% of fade size from edge of mask (meaning the edge of the mask), and is 0% fade (black)
-        # - The second point is 1px from the edge of the mask and also has 0% fade, effectively expanding the mask
-        #   by 1px. This fixes an issue where artifacts can occur at the edge of the mask
-        # - The third point is at 20% of the fade size from the edge of the mask and has 20% fade
-        # - The fourth point is at 80% of the fade size from the edge of the mask and has 90% fade
-        # - The last point is at 100% of the fade size from the edge of the mask and has 100% fade (white)
-
-        # x values: 0 = mask edge, 1 = fade_size_px from edge
-        x_control = numpy.array([0.0, 1.0 / self.fade_size_px, 0.2, 0.8, 1.0])
-        # y values: 0 = black, 1 = white
-        y_control = numpy.array([0.0, 0.0, 0.2, 0.9, 1.0])
-
-        # Fit a cubic polynomial that smoothly passes through the control points
-        coeffs = numpy.polyfit(x_control, y_control, 3)
-        poly = numpy.poly1d(coeffs)
-
-        # 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))
-
-        # Convert back to PIL, grayscale
-        pil_result = Image.fromarray(np_result.astype(numpy.uint8), mode="L")
-
-        image_dto = context.images.save(image=pil_result, image_category=ImageCategory.MASK)
-
-        return ImageOutput.build(image_dto)
-
-
-@invocation(
-    "apply_mask_to_image",
-    title="Apply Mask to Image",
-    tags=["image", "mask", "blend"],
-    category="image",
-    version="1.0.0",
-)
-class ApplyMaskToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """
-    Extracts a region from a generated image using a mask and blends it seamlessly onto a source image.
-    The mask uses black to indicate areas to keep from the generated image and white for areas to discard.
-    """
-
-    image: ImageField = InputField(description="The image from which to extract the masked region")
-    mask: ImageField = InputField(description="The mask defining the region (black=keep, white=discard)")
-    invert_mask: bool = InputField(
-        default=False,
-        description="Whether to invert the mask before applying it",
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Load images
-        image = context.images.get_pil(self.image.image_name, mode="RGBA")
-        mask = context.images.get_pil(self.mask.image_name, mode="L")
-
-        if self.invert_mask:
-            # Invert the mask if requested
-            mask = ImageOps.invert(mask.copy())
-
-        # Combine the mask as the alpha channel of the image
-        r, g, b, _ = image.split()  # Split the image into RGB and alpha channels
-        result_image = Image.merge("RGBA", (r, g, b, mask))  # Use the mask as the new alpha channel
-
-        # Save the resulting image
-        image_dto = context.images.save(image=result_image)
-
-        return ImageOutput.build(image_dto)
-
-
-@invocation(
-    "img_noise",
-    title="Add Image Noise",
-    tags=["image", "noise"],
-    category="image",
-    version="1.0.1",
-)
-class ImageNoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Add noise to an image"""
-
-    image: ImageField = InputField(description="The image to add noise to")
-    seed: int = InputField(
-        default=0,
-        ge=0,
-        le=SEED_MAX,
-        description=FieldDescriptions.seed,
-    )
-    noise_type: Literal["gaussian", "salt_and_pepper"] = InputField(
-        default="gaussian",
-        description="The type of noise to add",
-    )
-    amount: float = InputField(default=0.1, ge=0, le=1, description="The amount of noise to add")
-    noise_color: bool = InputField(default=True, description="Whether to add colored noise")
-    size: int = InputField(default=1, ge=1, description="The size of the noise points")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name, mode="RGBA")
-
-        # Save out the alpha channel
-        alpha = image.getchannel("A")
-
-        # Set the seed for numpy random
-        rs = numpy.random.RandomState(numpy.random.MT19937(numpy.random.SeedSequence(self.seed)))
-
-        if self.noise_type == "gaussian":
-            if self.noise_color:
-                noise = rs.normal(0, 1, (image.height // self.size, image.width // self.size, 3)) * 255
-            else:
-                noise = rs.normal(0, 1, (image.height // self.size, image.width // self.size)) * 255
-                noise = numpy.stack([noise] * 3, axis=-1)
-        elif self.noise_type == "salt_and_pepper":
-            if self.noise_color:
-                noise = rs.choice(
-                    [0, 255], (image.height // self.size, image.width // self.size, 3), p=[1 - self.amount, self.amount]
-                )
-            else:
-                noise = rs.choice(
-                    [0, 255], (image.height // self.size, image.width // self.size), p=[1 - self.amount, self.amount]
-                )
-                noise = numpy.stack([noise] * 3, axis=-1)
-
-        noise = Image.fromarray(noise.astype(numpy.uint8), mode="RGB").resize(
-            (image.width, image.height), Image.Resampling.NEAREST
-        )
-        noisy_image = Image.blend(image.convert("RGB"), noise, self.amount).convert("RGBA")
-
-        # Paste back the alpha channel
-        noisy_image.putalpha(alpha)
-
-        image_dto = context.images.save(image=noisy_image)
-
-        return ImageOutput.build(image_dto)
-
-
-@invocation(
-    "crop_image_to_bounding_box",
-    title="Crop Image to Bounding Box",
-    category="image",
-    version="1.0.0",
-    tags=["image", "crop"],
-)
-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."""
-
-    image: ImageField = InputField(description="The image to crop")
-    bounding_box: BoundingBoxField | None = InputField(
-        default=None, description="The bounding box to crop the image to"
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
-
-        bounding_box = self.bounding_box.tuple() if self.bounding_box is not None else image.getbbox()
-
-        cropped_image = image.crop(bounding_box)
-
-        image_dto = context.images.save(image=cropped_image)
-        return ImageOutput.build(image_dto)
-
-
-@invocation(
-    "paste_image_into_bounding_box",
-    title="Paste Image into Bounding Box",
-    category="image",
-    version="1.0.0",
-    tags=["image", "crop"],
-)
-class PasteImageIntoBoundingBoxInvocation(BaseInvocation, WithMetadata, WithBoard):
-    """Paste the source image into the target image at the given bounding box.
-
-    The source image must be the same size as the bounding box, and the bounding box must fit within the target image."""
-
-    source_image: ImageField = InputField(description="The image to paste")
-    target_image: ImageField = InputField(description="The image to paste into")
-    bounding_box: BoundingBoxField = InputField(description="The bounding box to paste the image into")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        source_image = context.images.get_pil(self.source_image.image_name, mode="RGBA")
-        target_image = context.images.get_pil(self.target_image.image_name, mode="RGBA")
-
-        bounding_box = self.bounding_box.tuple()
-
-        target_image.paste(source_image, bounding_box, source_image)
-
-        image_dto = context.images.save(image=target_image)
-        return ImageOutput.build(image_dto)

invokeai/app/invocations/image_panels.py

@@ -1,59 +0,0 @@
-from pydantic import ValidationInfo, field_validator
-
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    Classification,
-    invocation,
-    invocation_output,
-)
-from invokeai.app.invocations.fields import InputField, OutputField
-from invokeai.app.services.shared.invocation_context import InvocationContext
-
-
-@invocation_output("image_panel_coordinate_output")
-class ImagePanelCoordinateOutput(BaseInvocationOutput):
-    x_left: int = OutputField(description="The left x-coordinate of the panel.")
-    y_top: int = OutputField(description="The top y-coordinate of the panel.")
-    width: int = OutputField(description="The width of the panel.")
-    height: int = OutputField(description="The height of the panel.")
-
-
-@invocation(
-    "image_panel_layout",
-    title="Image Panel Layout",
-    tags=["image", "panel", "layout"],
-    category="image",
-    version="1.0.0",
-    classification=Classification.Prototype,
-)
-class ImagePanelLayoutInvocation(BaseInvocation):
-    """Get the coordinates of a single panel in a grid. (If the full image shape cannot be divided evenly into panels,
-    then the grid may not cover the entire image.)
-    """
-
-    width: int = InputField(description="The width of the entire grid.")
-    height: int = InputField(description="The height of the entire grid.")
-    num_cols: int = InputField(ge=1, default=1, description="The number of columns in the grid.")
-    num_rows: int = InputField(ge=1, default=1, description="The number of rows in the grid.")
-    panel_col_idx: int = InputField(ge=0, default=0, description="The column index of the panel to be processed.")
-    panel_row_idx: int = InputField(ge=0, default=0, description="The row index of the panel to be processed.")
-
-    @field_validator("panel_col_idx")
-    def validate_panel_col_idx(cls, v: int, info: ValidationInfo) -> int:
-        if v < 0 or v >= info.data["num_cols"]:
-            raise ValueError(f"panel_col_idx must be between 0 and {info.data['num_cols'] - 1}")
-        return v
-
-    @field_validator("panel_row_idx")
-    def validate_panel_row_idx(cls, v: int, info: ValidationInfo) -> int:
-        if v < 0 or v >= info.data["num_rows"]:
-            raise ValueError(f"panel_row_idx must be between 0 and {info.data['num_rows'] - 1}")
-        return v
-
-    def invoke(self, context: InvocationContext) -> ImagePanelCoordinateOutput:
-        x_left = self.panel_col_idx * (self.width // self.num_cols)
-        y_top = self.panel_row_idx * (self.height // self.num_rows)
-        width = self.width // self.num_cols
-        height = self.height // self.num_rows
-        return ImagePanelCoordinateOutput(x_left=x_left, y_top=y_top, width=width, height=height)

invokeai/app/invocations/image_to_latents.py

@@ -13,7 +13,7 @@ from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -26,15 +26,14 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
 from invokeai.backend.stable_diffusion.vae_tiling import patch_vae_tiling_params
-from invokeai.backend.util.devices import TorchDevice
 
 
 @invocation(
     "i2l",
-    title="Image to Latents - SD1.5, SDXL",
+    title="Image to Latents",
     tags=["latents", "image", "vae", "i2l"],
     category="latents",
-    version="1.1.1",
+    version="1.1.0",
 )
 class ImageToLatentsInvocation(BaseInvocation):
     """Encodes an image into latents."""
@@ -50,7 +49,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     # NOTE: tile_size = 0 is a special value. We use this rather than `int | None`, because the workflow UI does not
     # offer a way to directly set None values.
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
-    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32)
+    fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
 
     @staticmethod
     def vae_encode(
@@ -99,7 +98,7 @@ class ImageToLatentsInvocation(BaseInvocation):
                 )
 
             # non_noised_latents_from_image
-            image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae.dtype)
+            image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
             with torch.inference_mode(), tiling_context:
                 latents = ImageToLatentsInvocation._encode_to_tensor(vae, image_tensor)
 

invokeai/app/invocations/ip_adapter.py

@@ -13,8 +13,10 @@ 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,
@@ -22,7 +24,6 @@ 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):
@@ -68,13 +69,7 @@ CLIP_VISION_MODEL_MAP: dict[Literal["ViT-L", "ViT-H", "ViT-G"], StarterModel] =
 }
 
 
-@invocation(
-    "ip_adapter",
-    title="IP-Adapter - SD1.5, SDXL",
-    tags=["ip_adapter", "control"],
-    category="ip_adapter",
-    version="1.5.1",
-)
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.5.0")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 

invokeai/app/invocations/latents_to_image.py

@@ -12,7 +12,7 @@ from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
+from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -31,10 +31,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "l2i",
-    title="Latents to Image - SD1.5, SDXL",
+    title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.3.2",
+    version="1.3.0",
 )
 class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""
@@ -51,58 +51,18 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     # NOTE: tile_size = 0 is a special value. We use this rather than `int | None`, because the workflow UI does not
     # offer a way to directly set None values.
     tile_size: int = InputField(default=0, multiple_of=8, description=FieldDescriptions.vae_tile_size)
-    fp32: bool = InputField(default=False, description=FieldDescriptions.fp32)
-
-    def _estimate_working_memory(
-        self, latents: torch.Tensor, use_tiling: bool, vae: AutoencoderKL | AutoencoderTiny
-    ) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        # It was found experimentally that the peak working memory scales linearly with the number of pixels and the
-        # element size (precision). This estimate is accurate for both SD1 and SDXL.
-        element_size = 4 if self.fp32 else 2
-        scaling_constant = 2200  # Determined experimentally.
-
-        if use_tiling:
-            tile_size = self.tile_size
-            if tile_size == 0:
-                tile_size = vae.tile_sample_min_size
-                assert isinstance(tile_size, int)
-            out_h = tile_size
-            out_w = tile_size
-            working_memory = out_h * out_w * element_size * scaling_constant
-
-            # We add 25% to the working memory estimate when tiling is enabled to account for factors like tile overlap
-            # and number of tiles. We could make this more precise in the future, but this should be good enough for
-            # most use cases.
-            working_memory = working_memory * 1.25
-        else:
-            out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
-            out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
-            working_memory = out_h * out_w * element_size * scaling_constant
-
-        if self.fp32:
-            # If we are running in FP32, then we should account for the likely increase in model size (~250MB).
-            working_memory += 250 * 2**20
-
-        return int(working_memory)
+    fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
-        use_tiling = self.tiled or context.config.get().force_tiled_decode
-
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL, AutoencoderTiny))
-
-        estimated_working_memory = self._estimate_working_memory(latents, use_tiling, vae_info.model)
-        with (
-            SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes),
-            vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae),
-        ):
+        with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
             context.util.signal_progress("Running VAE decoder")
             assert isinstance(vae, (AutoencoderKL, AutoencoderTiny))
-            latents = latents.to(TorchDevice.choose_torch_device())
+            latents = latents.to(vae.device)
             if self.fp32:
                 vae.to(dtype=torch.float32)
 
@@ -128,7 +88,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 vae.to(dtype=torch.float16)
                 latents = latents.half()
 
-            if use_tiling:
+            if self.tiled or context.config.get().force_tiled_decode:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()

invokeai/app/invocations/llava_onevision_vllm.py

@@ -1,67 +0,0 @@
-from typing import Any
-
-import torch
-from PIL.Image import Image
-from pydantic import field_validator
-
-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
-from invokeai.app.services.shared.invocation_context import InvocationContext
-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",
-    classification=Classification.Beta,
-)
-class LlavaOnevisionVllmInvocation(BaseInvocation):
-    """Run a LLaVA OneVision VLLM model."""
-
-    images: list[ImageField] | ImageField | None = InputField(default=None, max_length=3, description="Input image.")
-    prompt: str = InputField(
-        default="",
-        description="Input text prompt.",
-        ui_component=UIComponent.Textarea,
-    )
-    vllm_model: ModelIdentifierField = InputField(
-        title="LLaVA Model Type",
-        description=FieldDescriptions.vllm_model,
-        ui_type=UIType.LlavaOnevisionModel,
-    )
-
-    @field_validator("images", mode="before")
-    def listify_images(cls, v: Any) -> list:
-        if v is None:
-            return v
-        if not isinstance(v, list):
-            return [v]
-        return v
-
-    def _get_images(self, context: InvocationContext) -> list[Image]:
-        if self.images is None:
-            return []
-
-        image_fields = self.images if isinstance(self.images, list) else [self.images]
-        return [context.images.get_pil(image_field.image_name, "RGB") for image_field in image_fields]
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> StringOutput:
-        images = self._get_images(context)
-
-        with context.models.load(self.vllm_model) as vllm_model:
-            assert isinstance(vllm_model, LlavaOnevisionModel)
-            output = vllm_model.run(
-                prompt=self.prompt,
-                images=images,
-                device=TorchDevice.choose_torch_device(),
-                dtype=TorchDevice.choose_torch_dtype(),
-            )
-
-        return StringOutput(value=output)

invokeai/app/invocations/load_custom_nodes.py

@@ -1,83 +0,0 @@
-import logging
-import shutil
-import sys
-import traceback
-from importlib.util import module_from_spec, spec_from_file_location
-from pathlib import Path
-
-
-def load_custom_nodes(custom_nodes_path: Path, logger: logging.Logger):
-    """
-    Loads all custom nodes from the custom_nodes_path directory.
-
-    If custom_nodes_path does not exist, it creates it.
-
-    It also copies the custom_nodes/README.md file to the custom_nodes_path directory. Because this file may change,
-    it is _always_ copied to the custom_nodes_path directory.
-
-    Then, it crawls the custom_nodes_path directory and imports all top-level directories as python modules.
-
-    If the directory does not contain an __init__.py file or starts with an `_` or `.`, it is skipped.
-    """
-
-    # create the custom nodes directory if it does not exist
-    custom_nodes_path.mkdir(parents=True, exist_ok=True)
-
-    # Copy the README file to the custom nodes directory
-    source_custom_nodes_readme_path = Path(__file__).parent / "custom_nodes/README.md"
-    target_custom_nodes_readme_path = Path(custom_nodes_path) / "README.md"
-
-    # copy our custom nodes README to the custom nodes directory
-    shutil.copy(source_custom_nodes_readme_path, target_custom_nodes_readme_path)
-
-    loaded_packs: list[str] = []
-    failed_packs: list[str] = []
-
-    # Import custom nodes, see https://docs.python.org/3/library/importlib.html#importing-programmatically
-    for d in custom_nodes_path.iterdir():
-        # skip files
-        if not d.is_dir():
-            continue
-
-        # skip hidden directories
-        if d.name.startswith("_") or d.name.startswith("."):
-            continue
-
-        # skip directories without an `__init__.py`
-        init = d / "__init__.py"
-        if not init.exists():
-            continue
-
-        module_name = init.parent.stem
-
-        # skip if already imported
-        if module_name in globals():
-            continue
-
-        # load the module
-        spec = spec_from_file_location(module_name, init.absolute())
-
-        if spec is None or spec.loader is None:
-            logger.warning(f"Could not load {init}")
-            continue
-
-        logger.info(f"Loading node pack {module_name}")
-
-        try:
-            module = module_from_spec(spec)
-            sys.modules[spec.name] = module
-            spec.loader.exec_module(module)
-
-            loaded_packs.append(module_name)
-        except Exception:
-            failed_packs.append(module_name)
-            full_error = traceback.format_exc()
-            logger.error(f"Failed to load node pack {module_name} (may have partially loaded):\n{full_error}")
-
-        del init, module_name
-
-    loaded_count = len(loaded_packs)
-    if loaded_count > 0:
-        logger.info(
-            f"Loaded {loaded_count} node pack{'s' if loaded_count != 1 else ''} from {custom_nodes_path}: {', '.join(loaded_packs)}"
-        )

invokeai/app/invocations/mask.py

@@ -2,21 +2,9 @@ import numpy as np
 import torch
 from PIL import Image
 
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    InvocationContext,
-    invocation,
-)
-from invokeai.app.invocations.fields import (
-    BoundingBoxField,
-    ColorField,
-    ImageField,
-    InputField,
-    TensorField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.primitives import BoundingBoxOutput, ImageOutput, MaskOutput
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, InvocationContext, invocation
+from invokeai.app.invocations.fields import ImageField, InputField, TensorField, WithBoard, WithMetadata
+from invokeai.app.invocations.primitives import ImageOutput, MaskOutput
 from invokeai.backend.image_util.util import pil_to_np
 
 
@@ -57,6 +45,7 @@ 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."""
@@ -65,7 +54,7 @@ class AlphaMaskToTensorInvocation(BaseInvocation):
     invert: bool = InputField(default=False, description="Whether to invert the mask.")
 
     def invoke(self, context: InvocationContext) -> MaskOutput:
-        image = context.images.get_pil(self.image.image_name, mode="RGBA")
+        image = context.images.get_pil(self.image.image_name)
         mask = torch.zeros((1, image.height, image.width), dtype=torch.bool)
         if self.invert:
             mask[0] = torch.tensor(np.array(image)[:, :, 3] == 0, dtype=torch.bool)
@@ -84,7 +73,8 @@ class AlphaMaskToTensorInvocation(BaseInvocation):
     title="Invert Tensor Mask",
     tags=["conditioning"],
     category="conditioning",
-    version="1.1.0",
+    version="1.0.0",
+    classification=Classification.Beta,
 )
 class InvertTensorMaskInvocation(BaseInvocation):
     """Inverts a tensor mask."""
@@ -93,15 +83,6 @@ class InvertTensorMaskInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> MaskOutput:
         mask = context.tensors.load(self.mask.tensor_name)
-
-        # Verify dtype and shape.
-        assert mask.dtype == torch.bool
-        assert mask.dim() in [2, 3]
-
-        # Unsqueeze the channel dimension if it is missing. The MaskOutput type expects a single channel.
-        if mask.dim() == 2:
-            mask = mask.unsqueeze(0)
-
         inverted = ~mask
 
         return MaskOutput(
@@ -220,47 +201,3 @@ class ApplyMaskTensorToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         image_dto = context.images.save(image=masked_image)
 
         return ImageOutput.build(image_dto)
-
-
-WHITE = ColorField(r=255, g=255, b=255, a=255)
-
-
-@invocation(
-    "get_image_mask_bounding_box",
-    title="Get Image Mask Bounding Box",
-    tags=["mask"],
-    category="mask",
-    version="1.0.0",
-)
-class GetMaskBoundingBoxInvocation(BaseInvocation):
-    """Gets the bounding box of the given mask image."""
-
-    mask: ImageField = InputField(description="The mask to crop.")
-    margin: int = InputField(default=0, description="Margin to add to the bounding box.")
-    mask_color: ColorField = InputField(default=WHITE, description="Color of the mask in the image.")
-
-    def invoke(self, context: InvocationContext) -> BoundingBoxOutput:
-        mask = context.images.get_pil(self.mask.image_name, mode="RGBA")
-        mask_np = np.array(mask)
-
-        # Convert mask_color to RGBA tuple
-        mask_color_rgb = self.mask_color.tuple()
-
-        # Find the bounding box of the mask color
-        y, x = np.where(np.all(mask_np == mask_color_rgb, axis=-1))
-
-        if len(x) == 0 or len(y) == 0:
-            # No pixels found with the given color
-            return BoundingBoxOutput(bounding_box=BoundingBoxField(x_min=0, y_min=0, x_max=0, y_max=0))
-
-        left, upper, right, lower = x.min(), y.min(), x.max(), y.max()
-
-        # Add the margin
-        left = max(0, left - self.margin)
-        upper = max(0, upper - self.margin)
-        right = min(mask_np.shape[1], right + self.margin)
-        lower = min(mask_np.shape[0], lower + self.margin)
-
-        bounding_box = BoundingBoxField(x_min=left, y_min=upper, x_max=right, y_max=lower)
-
-        return BoundingBoxOutput(bounding_box=bounding_box)

invokeai/app/invocations/metadata.py

@@ -18,7 +18,6 @@ from invokeai.app.invocations.fields import (
     UIType,
 )
 from invokeai.app.invocations.model import ModelIdentifierField
-from invokeai.app.invocations.primitives import StringOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import CONTROLNET_MODE_VALUES, CONTROLNET_RESIZE_VALUES
 from invokeai.version.invokeai_version import __version__
@@ -276,34 +275,3 @@ class CoreMetadataInvocation(BaseInvocation):
         return MetadataOutput(metadata=MetadataField.model_validate(as_dict))
 
     model_config = ConfigDict(extra="allow")
-
-
-@invocation(
-    "metadata_field_extractor",
-    title="Metadata Field Extractor",
-    tags=["metadata"],
-    category="metadata",
-    version="1.0.0",
-    classification=Classification.Deprecated,
-)
-class MetadataFieldExtractorInvocation(BaseInvocation):
-    """Extracts the text value from an image's metadata given a key.
-    Raises an error if the image has no metadata or if the value is not a string (nesting not permitted)."""
-
-    image: ImageField = InputField(description="The image to extract metadata from")
-    key: str = InputField(description="The key in the image's metadata to extract the value from")
-
-    def invoke(self, context: InvocationContext) -> StringOutput:
-        image_name = self.image.image_name
-
-        metadata = context.images.get_metadata(image_name=image_name)
-        if not metadata:
-            raise ValueError(f"No metadata found on image {image_name}")
-
-        try:
-            val = metadata.root[self.key]
-            if not isinstance(val, str):
-                raise ValueError(f"Metadata at key '{self.key}' must be a string")
-            return StringOutput(value=val)
-        except KeyError as e:
-            raise ValueError(f"No key '{self.key}' found in the metadata for {image_name}") from e

invokeai/app/invocations/model.py

@@ -6,16 +6,19 @@ from pydantic import BaseModel, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Classification,
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField, UIType
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
 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):
@@ -62,10 +65,14 @@ class CLIPField(BaseModel):
     loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
+class TransformerField(BaseModel):
+    transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
+    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
+
+
 class T5EncoderField(BaseModel):
     tokenizer: ModelIdentifierField = Field(description="Info to load tokenizer submodel")
     text_encoder: ModelIdentifierField = Field(description="Info to load text_encoder submodel")
-    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
 
 
 class VAEField(BaseModel):
@@ -73,15 +80,6 @@ class VAEField(BaseModel):
     seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
 
 
-class ControlLoRAField(LoRAField):
-    img: ImageField = Field(description="Image to use in structural conditioning")
-
-
-class TransformerField(BaseModel):
-    transformer: ModelIdentifierField = Field(description="Info to load Transformer submodel")
-    loras: List[LoRAField] = Field(description="LoRAs to apply on model loading")
-
-
 @invocation_output("unet_output")
 class UNetOutput(BaseInvocationOutput):
     """Base class for invocations that output a UNet field."""
@@ -119,10 +117,11 @@ class ModelIdentifierOutput(BaseInvocationOutput):
 
 @invocation(
     "model_identifier",
-    title="Any Model",
+    title="Model identifier",
     tags=["model"],
     category="model",
-    version="1.0.1",
+    version="1.0.0",
+    classification=Classification.Prototype,
 )
 class ModelIdentifierInvocation(BaseInvocation):
     """Selects any model, outputting it its identifier. Be careful with this one! The identifier will be accepted as
@@ -140,10 +139,10 @@ class ModelIdentifierInvocation(BaseInvocation):
 
 @invocation(
     "main_model_loader",
-    title="Main Model - SD1.5",
+    title="Main Model",
     tags=["model"],
     category="model",
-    version="1.0.4",
+    version="1.0.3",
 )
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
@@ -177,7 +176,7 @@ class LoRALoaderOutput(BaseInvocationOutput):
     clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
-@invocation("lora_loader", title="Apply LoRA - SD1.5", tags=["model"], category="model", version="1.0.4")
+@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.3")
 class LoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
@@ -202,7 +201,7 @@ class LoRALoaderInvocation(BaseInvocation):
         lora_key = self.lora.key
 
         if not context.models.exists(lora_key):
-            raise Exception(f"Unknown lora: {lora_key}!")
+            raise Exception(f"Unkown lora: {lora_key}!")
 
         if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras):
             raise Exception(f'LoRA "{lora_key}" already applied to unet')
@@ -240,7 +239,7 @@ class LoRASelectorOutput(BaseInvocationOutput):
     lora: LoRAField = OutputField(description="LoRA model and weight", title="LoRA")
 
 
-@invocation("lora_selector", title="Select LoRA", tags=["model"], category="model", version="1.0.3")
+@invocation("lora_selector", title="LoRA Selector", tags=["model"], category="model", version="1.0.1")
 class LoRASelectorInvocation(BaseInvocation):
     """Selects a LoRA model and weight."""
 
@@ -253,14 +252,12 @@ class LoRASelectorInvocation(BaseInvocation):
         return LoRASelectorOutput(lora=LoRAField(lora=self.lora, weight=self.weight))
 
 
-@invocation(
-    "lora_collection_loader", title="Apply LoRA Collection - SD1.5", tags=["model"], category="model", version="1.1.2"
-)
+@invocation("lora_collection_loader", title="LoRA Collection Loader", tags=["model"], category="model", version="1.0.0")
 class LoRACollectionLoader(BaseInvocation):
     """Applies a collection of LoRAs to the provided UNet and CLIP models."""
 
-    loras: Optional[LoRAField | list[LoRAField]] = InputField(
-        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    loras: LoRAField | list[LoRAField] = InputField(
+        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -280,14 +277,7 @@ class LoRACollectionLoader(BaseInvocation):
         loras = self.loras if isinstance(self.loras, list) else [self.loras]
         added_loras: list[str] = []
 
-        if self.unet is not None:
-            output.unet = self.unet.model_copy(deep=True)
-        if self.clip is not None:
-            output.clip = self.clip.model_copy(deep=True)
-
         for lora in loras:
-            if lora is None:
-                continue
             if lora.lora.key in added_loras:
                 continue
 
@@ -298,10 +288,14 @@ class LoRACollectionLoader(BaseInvocation):
 
             added_loras.append(lora.lora.key)
 
-            if self.unet is not None and output.unet is not None:
+            if self.unet is not None:
+                if output.unet is None:
+                    output.unet = self.unet.model_copy(deep=True)
                 output.unet.loras.append(lora)
 
-            if self.clip is not None and output.clip is not None:
+            if self.clip is not None:
+                if output.clip is None:
+                    output.clip = self.clip.model_copy(deep=True)
                 output.clip.loras.append(lora)
 
         return output
@@ -318,10 +312,10 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "sdxl_lora_loader",
-    title="Apply LoRA - SDXL",
+    title="SDXL LoRA",
     tags=["lora", "model"],
     category="model",
-    version="1.0.5",
+    version="1.0.3",
 )
 class SDXLLoRALoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
@@ -398,16 +392,16 @@ class SDXLLoRALoaderInvocation(BaseInvocation):
 
 @invocation(
     "sdxl_lora_collection_loader",
-    title="Apply LoRA Collection - SDXL",
+    title="SDXL LoRA Collection Loader",
     tags=["model"],
     category="model",
-    version="1.1.2",
+    version="1.0.0",
 )
 class SDXLLoRACollectionLoader(BaseInvocation):
     """Applies a collection of SDXL LoRAs to the provided UNet and CLIP models."""
 
-    loras: Optional[LoRAField | list[LoRAField]] = InputField(
-        default=None, description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
+    loras: LoRAField | list[LoRAField] = InputField(
+        description="LoRA models and weights. May be a single LoRA or collection.", title="LoRAs"
     )
     unet: Optional[UNetField] = InputField(
         default=None,
@@ -433,18 +427,7 @@ class SDXLLoRACollectionLoader(BaseInvocation):
         loras = self.loras if isinstance(self.loras, list) else [self.loras]
         added_loras: list[str] = []
 
-        if self.unet is not None:
-            output.unet = self.unet.model_copy(deep=True)
-
-        if self.clip is not None:
-            output.clip = self.clip.model_copy(deep=True)
-
-        if self.clip2 is not None:
-            output.clip2 = self.clip2.model_copy(deep=True)
-
         for lora in loras:
-            if lora is None:
-                continue
             if lora.lora.key in added_loras:
                 continue
 
@@ -455,21 +438,25 @@ class SDXLLoRACollectionLoader(BaseInvocation):
 
             added_loras.append(lora.lora.key)
 
-            if self.unet is not None and output.unet is not None:
+            if self.unet is not None:
+                if output.unet is None:
+                    output.unet = self.unet.model_copy(deep=True)
                 output.unet.loras.append(lora)
 
-            if self.clip is not None and output.clip is not None:
+            if self.clip is not None:
+                if output.clip is None:
+                    output.clip = self.clip.model_copy(deep=True)
                 output.clip.loras.append(lora)
 
-            if self.clip2 is not None and output.clip2 is not None:
+            if self.clip2 is not None:
+                if output.clip2 is None:
+                    output.clip2 = self.clip2.model_copy(deep=True)
                 output.clip2.loras.append(lora)
 
         return output
 
 
-@invocation(
-    "vae_loader", title="VAE Model - SD1.5, SDXL, SD3, FLUX", tags=["vae", "model"], category="model", version="1.0.4"
-)
+@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.3")
 class VAELoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
@@ -481,7 +468,7 @@ class VAELoaderInvocation(BaseInvocation):
         key = self.vae_model.key
 
         if not context.models.exists(key):
-            raise Exception(f"Unknown vae: {key}!")
+            raise Exception(f"Unkown vae: {key}!")
 
         return VAEOutput(vae=VAEField(vae=self.vae_model))
 
@@ -496,10 +483,10 @@ class SeamlessModeOutput(BaseInvocationOutput):
 
 @invocation(
     "seamless",
-    title="Apply Seamless - SD1.5, SDXL",
+    title="Seamless",
     tags=["seamless", "model"],
     category="model",
-    version="1.0.2",
+    version="1.0.1",
 )
 class SeamlessModeInvocation(BaseInvocation):
     """Applies the seamless transformation to the Model UNet and VAE."""
@@ -539,7 +526,7 @@ class SeamlessModeInvocation(BaseInvocation):
         return SeamlessModeOutput(unet=unet, vae=vae)
 
 
-@invocation("freeu", title="Apply FreeU - SD1.5, SDXL", tags=["freeu"], category="unet", version="1.0.2")
+@invocation("freeu", title="FreeU", tags=["freeu"], category="unet", version="1.0.1")
 class FreeUInvocation(BaseInvocation):
     """
     Applies FreeU to the UNet. Suggested values (b1/b2/s1/s2):

invokeai/app/invocations/noise.py

@@ -72,10 +72,10 @@ class NoiseOutput(BaseInvocationOutput):
 
 @invocation(
     "noise",
-    title="Create Latent Noise",
+    title="Noise",
     tags=["latents", "noise"],
     category="latents",
-    version="1.0.3",
+    version="1.0.2",
 )
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""

invokeai/app/invocations/param_easing.py

@@ -1,4 +1,43 @@
+import io
+from typing import Literal, Optional
+
+import matplotlib.pyplot as plt
 import numpy as np
+import PIL.Image
+from easing_functions import (
+    BackEaseIn,
+    BackEaseInOut,
+    BackEaseOut,
+    BounceEaseIn,
+    BounceEaseInOut,
+    BounceEaseOut,
+    CircularEaseIn,
+    CircularEaseInOut,
+    CircularEaseOut,
+    CubicEaseIn,
+    CubicEaseInOut,
+    CubicEaseOut,
+    ElasticEaseIn,
+    ElasticEaseInOut,
+    ElasticEaseOut,
+    ExponentialEaseIn,
+    ExponentialEaseInOut,
+    ExponentialEaseOut,
+    LinearInOut,
+    QuadEaseIn,
+    QuadEaseInOut,
+    QuadEaseOut,
+    QuarticEaseIn,
+    QuarticEaseInOut,
+    QuarticEaseOut,
+    QuinticEaseIn,
+    QuinticEaseInOut,
+    QuinticEaseOut,
+    SineEaseIn,
+    SineEaseInOut,
+    SineEaseOut,
+)
+from matplotlib.ticker import MaxNLocator
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
 from invokeai.app.invocations.fields import InputField
@@ -26,3 +65,191 @@ class FloatLinearRangeInvocation(BaseInvocation):
     def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
         param_list = list(np.linspace(self.start, self.stop, self.steps))
         return FloatCollectionOutput(collection=param_list)
+
+
+EASING_FUNCTIONS_MAP = {
+    "Linear": LinearInOut,
+    "QuadIn": QuadEaseIn,
+    "QuadOut": QuadEaseOut,
+    "QuadInOut": QuadEaseInOut,
+    "CubicIn": CubicEaseIn,
+    "CubicOut": CubicEaseOut,
+    "CubicInOut": CubicEaseInOut,
+    "QuarticIn": QuarticEaseIn,
+    "QuarticOut": QuarticEaseOut,
+    "QuarticInOut": QuarticEaseInOut,
+    "QuinticIn": QuinticEaseIn,
+    "QuinticOut": QuinticEaseOut,
+    "QuinticInOut": QuinticEaseInOut,
+    "SineIn": SineEaseIn,
+    "SineOut": SineEaseOut,
+    "SineInOut": SineEaseInOut,
+    "CircularIn": CircularEaseIn,
+    "CircularOut": CircularEaseOut,
+    "CircularInOut": CircularEaseInOut,
+    "ExponentialIn": ExponentialEaseIn,
+    "ExponentialOut": ExponentialEaseOut,
+    "ExponentialInOut": ExponentialEaseInOut,
+    "ElasticIn": ElasticEaseIn,
+    "ElasticOut": ElasticEaseOut,
+    "ElasticInOut": ElasticEaseInOut,
+    "BackIn": BackEaseIn,
+    "BackOut": BackEaseOut,
+    "BackInOut": BackEaseInOut,
+    "BounceIn": BounceEaseIn,
+    "BounceOut": BounceEaseOut,
+    "BounceInOut": BounceEaseInOut,
+}
+
+EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]
+
+
+# actually I think for now could just use CollectionOutput (which is list[Any]
+@invocation(
+    "step_param_easing",
+    title="Step Param Easing",
+    tags=["step", "easing"],
+    category="step",
+    version="1.0.2",
+)
+class StepParamEasingInvocation(BaseInvocation):
+    """Experimental per-step parameter easing for denoising steps"""
+
+    easing: EASING_FUNCTION_KEYS = InputField(default="Linear", description="The easing function to use")
+    num_steps: int = InputField(default=20, description="number of denoising steps")
+    start_value: float = InputField(default=0.0, description="easing starting value")
+    end_value: float = InputField(default=1.0, description="easing ending value")
+    start_step_percent: float = InputField(default=0.0, description="fraction of steps at which to start easing")
+    end_step_percent: float = InputField(default=1.0, description="fraction of steps after which to end easing")
+    # if None, then start_value is used prior to easing start
+    pre_start_value: Optional[float] = InputField(default=None, description="value before easing start")
+    # if None, then end value is used prior to easing end
+    post_end_value: Optional[float] = InputField(default=None, description="value after easing end")
+    mirror: bool = InputField(default=False, description="include mirror of easing function")
+    # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
+    # alt_mirror: bool = InputField(default=False, description="alternative mirroring by dual easing")
+    show_easing_plot: bool = InputField(default=False, description="show easing plot")
+
+    def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
+        log_diagnostics = False
+        # convert from start_step_percent to nearest step <= (steps * start_step_percent)
+        # start_step = int(np.floor(self.num_steps * self.start_step_percent))
+        start_step = int(np.round(self.num_steps * self.start_step_percent))
+        # convert from end_step_percent to nearest step >= (steps * end_step_percent)
+        # end_step = int(np.ceil((self.num_steps - 1) * self.end_step_percent))
+        end_step = int(np.round((self.num_steps - 1) * self.end_step_percent))
+
+        # end_step = int(np.ceil(self.num_steps * self.end_step_percent))
+        num_easing_steps = end_step - start_step + 1
+
+        # num_presteps = max(start_step - 1, 0)
+        num_presteps = start_step
+        num_poststeps = self.num_steps - (num_presteps + num_easing_steps)
+        prelist = list(num_presteps * [self.pre_start_value])
+        postlist = list(num_poststeps * [self.post_end_value])
+
+        if log_diagnostics:
+            context.logger.debug("start_step: " + str(start_step))
+            context.logger.debug("end_step: " + str(end_step))
+            context.logger.debug("num_easing_steps: " + str(num_easing_steps))
+            context.logger.debug("num_presteps: " + str(num_presteps))
+            context.logger.debug("num_poststeps: " + str(num_poststeps))
+            context.logger.debug("prelist size: " + str(len(prelist)))
+            context.logger.debug("postlist size: " + str(len(postlist)))
+            context.logger.debug("prelist: " + str(prelist))
+            context.logger.debug("postlist: " + str(postlist))
+
+        easing_class = EASING_FUNCTIONS_MAP[self.easing]
+        if log_diagnostics:
+            context.logger.debug("easing class: " + str(easing_class))
+        easing_list = []
+        if self.mirror:  # "expected" mirroring
+            # if number of steps is even, squeeze duration down to (number_of_steps)/2
+            # and create reverse copy of list to append
+            # if number of steps is odd, squeeze duration down to ceil(number_of_steps/2)
+            # and create reverse copy of list[1:end-1]
+            # but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always
+
+            base_easing_duration = int(np.ceil(num_easing_steps / 2.0))
+            if log_diagnostics:
+                context.logger.debug("base easing duration: " + str(base_easing_duration))
+            even_num_steps = num_easing_steps % 2 == 0  # even number of steps
+            easing_function = easing_class(
+                start=self.start_value,
+                end=self.end_value,
+                duration=base_easing_duration - 1,
+            )
+            base_easing_vals = []
+            for step_index in range(base_easing_duration):
+                easing_val = easing_function.ease(step_index)
+                base_easing_vals.append(easing_val)
+                if log_diagnostics:
+                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
+            if even_num_steps:
+                mirror_easing_vals = list(reversed(base_easing_vals))
+            else:
+                mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
+            if log_diagnostics:
+                context.logger.debug("base easing vals: " + str(base_easing_vals))
+                context.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
+            easing_list = base_easing_vals + mirror_easing_vals
+
+        # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
+        # elif self.alt_mirror:  # function mirroring (unintuitive behavior (at least to me))
+        #     # half_ease_duration = round(num_easing_steps - 1 / 2)
+        #     half_ease_duration = round((num_easing_steps - 1) / 2)
+        #     easing_function = easing_class(start=self.start_value,
+        #                                    end=self.end_value,
+        #                                    duration=half_ease_duration,
+        #                                    )
+        #
+        #     mirror_function = easing_class(start=self.end_value,
+        #                                    end=self.start_value,
+        #                                    duration=half_ease_duration,
+        #                                    )
+        #     for step_index in range(num_easing_steps):
+        #         if step_index <= half_ease_duration:
+        #             step_val = easing_function.ease(step_index)
+        #         else:
+        #             step_val = mirror_function.ease(step_index - half_ease_duration)
+        #         easing_list.append(step_val)
+        #         if log_diagnostics: logger.debug(step_index, step_val)
+        #
+
+        else:  # no mirroring (default)
+            easing_function = easing_class(
+                start=self.start_value,
+                end=self.end_value,
+                duration=num_easing_steps - 1,
+            )
+            for step_index in range(num_easing_steps):
+                step_val = easing_function.ease(step_index)
+                easing_list.append(step_val)
+                if log_diagnostics:
+                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
+
+        if log_diagnostics:
+            context.logger.debug("prelist size: " + str(len(prelist)))
+            context.logger.debug("easing_list size: " + str(len(easing_list)))
+            context.logger.debug("postlist size: " + str(len(postlist)))
+
+        param_list = prelist + easing_list + postlist
+
+        if self.show_easing_plot:
+            plt.figure()
+            plt.xlabel("Step")
+            plt.ylabel("Param Value")
+            plt.title("Per-Step Values Based On Easing: " + self.easing)
+            plt.bar(range(len(param_list)), param_list)
+            # plt.plot(param_list)
+            ax = plt.gca()
+            ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+            buf = io.BytesIO()
+            plt.savefig(buf, format="png")
+            buf.seek(0)
+            im = PIL.Image.open(buf)
+            im.show()
+            buf.close()
+
+        # output array of size steps, each entry list[i] is param value for step i
+        return FloatCollectionOutput(collection=param_list)

invokeai/app/invocations/primitives.py

@@ -4,12 +4,7 @@ from typing import Optional
 
 import torch
 
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    invocation,
-    invocation_output,
-)
+from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
 from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     BoundingBoxField,
@@ -265,9 +260,13 @@ class ImageInvocation(BaseInvocation):
     image: ImageField = InputField(description="The image to load")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image_dto = context.images.get_dto(self.image.image_name)
+        image = context.images.get_pil(self.image.image_name)
 
-        return ImageOutput.build(image_dto=image_dto)
+        return ImageOutput(
+            image=ImageField(image_name=self.image.image_name),
+            width=image.width,
+            height=image.height,
+        )
 
 
 @invocation(
@@ -412,7 +411,6 @@ class ColorInvocation(BaseInvocation):
 class MaskOutput(BaseInvocationOutput):
     """A torch mask tensor."""
 
-    # shape: [1, H, W], dtype: bool
     mask: TensorField = OutputField(description="The mask.")
     width: int = OutputField(description="The width of the mask in pixels.")
     height: int = OutputField(description="The height of the mask in pixels.")

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, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, 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 import BaseModelType
+from invokeai.backend.model_manager.config 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
@@ -32,10 +32,11 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "sd3_denoise",
-    title="Denoise - SD3",
+    title="SD3 Denoise",
     tags=["image", "sd3"],
     category="image",
-    version="1.1.1",
+    version="1.1.0",
+    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, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     ImageField,
@@ -16,15 +16,15 @@ from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.model_manager.load.load_base import LoadedModel
 from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
-from invokeai.backend.util.devices import TorchDevice
 
 
 @invocation(
     "sd3_i2l",
-    title="Image to Latents - SD3",
+    title="SD3 Image to Latents",
     tags=["image", "latents", "vae", "i2l", "sd3"],
     category="image",
-    version="1.0.1",
+    version="1.0.0",
+    classification=Classification.Prototype,
 )
 class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates latents from an image."""
@@ -39,7 +39,7 @@ class SD3ImageToLatentsInvocation(BaseInvocation, WithMetadata, WithBoard):
 
             vae.disable_tiling()
 
-            image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=vae.dtype)
+            image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
             with torch.inference_mode():
                 image_tensor_dist = vae.encode(image_tensor).latent_dist
                 # TODO: Use seed to make sampling reproducible.

invokeai/app/invocations/sd3_latents_to_image.py

@@ -6,7 +6,6 @@ from einops import rearrange
 from PIL import Image
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -24,10 +23,10 @@ from invokeai.backend.util.devices import TorchDevice
 
 @invocation(
     "sd3_l2i",
-    title="Latents to Image - SD3",
+    title="SD3 Latents to Image",
     tags=["latents", "image", "vae", "l2i", "sd3"],
     category="latents",
-    version="1.3.2",
+    version="1.3.0",
 )
 class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
     """Generates an image from latents."""
@@ -41,29 +40,16 @@ class SD3LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         input=Input.Connection,
     )
 
-    def _estimate_working_memory(self, latents: torch.Tensor, vae: AutoencoderKL) -> int:
-        """Estimate the working memory required by the invocation in bytes."""
-        out_h = LATENT_SCALE_FACTOR * latents.shape[-2]
-        out_w = LATENT_SCALE_FACTOR * latents.shape[-1]
-        element_size = next(vae.parameters()).element_size()
-        scaling_constant = 2200  # Determined experimentally.
-        working_memory = out_h * out_w * element_size * scaling_constant
-        return int(working_memory)
-
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         latents = context.tensors.load(self.latents.latents_name)
 
         vae_info = context.models.load(self.vae.vae)
         assert isinstance(vae_info.model, (AutoencoderKL))
-        estimated_working_memory = self._estimate_working_memory(latents, vae_info.model)
-        with (
-            SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes),
-            vae_info.model_on_device(working_mem_bytes=estimated_working_memory) as (_, vae),
-        ):
+        with SeamlessExt.static_patch_model(vae_info.model, self.vae.seamless_axes), vae_info as vae:
             context.util.signal_progress("Running VAE")
             assert isinstance(vae, (AutoencoderKL))
-            latents = latents.to(TorchDevice.choose_torch_device())
+            latents = latents.to(vae.device)
 
             vae.disable_tiling()
 

invokeai/app/invocations/sd3_model_loader.py

@@ -3,17 +3,14 @@ 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, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.t5_model_identifier import (
-    preprocess_t5_encoder_model_identifier,
-    preprocess_t5_tokenizer_model_identifier,
-)
-from invokeai.backend.model_manager.taxonomy import SubModelType
+from invokeai.backend.model_manager.config import SubModelType
 
 
 @invocation_output("sd3_model_loader_output")
@@ -29,10 +26,11 @@ class Sd3ModelLoaderOutput(BaseInvocationOutput):
 
 @invocation(
     "sd3_model_loader",
-    title="Main Model - SD3",
+    title="SD3 Main Model",
     tags=["model", "sd3"],
     category="model",
-    version="1.0.1",
+    version="1.0.0",
+    classification=Classification.Prototype,
 )
 class Sd3ModelLoaderInvocation(BaseInvocation):
     """Loads a SD3 base model, outputting its submodels."""
@@ -90,13 +88,21 @@ class Sd3ModelLoaderInvocation(BaseInvocation):
             if self.clip_g_model
             else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
         )
-        tokenizer_t5 = preprocess_t5_tokenizer_model_identifier(self.t5_encoder_model or self.model)
-        t5_encoder = preprocess_t5_encoder_model_identifier(self.t5_encoder_model or self.model)
+        tokenizer_t5 = (
+            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
+            if self.t5_encoder_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer3})
+        )
+        t5_encoder = (
+            self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
+            if self.t5_encoder_model
+            else self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder3})
+        )
 
         return Sd3ModelLoaderOutput(
             transformer=TransformerField(transformer=transformer, loras=[]),
             clip_l=CLIPField(tokenizer=tokenizer_l, text_encoder=clip_encoder_l, loras=[], skipped_layers=0),
             clip_g=CLIPField(tokenizer=tokenizer_g, text_encoder=clip_encoder_g, loras=[], skipped_layers=0),
-            t5_encoder=T5EncoderField(tokenizer=tokenizer_t5, text_encoder=t5_encoder, loras=[]),
+            t5_encoder=T5EncoderField(tokenizer=tokenizer_t5, text_encoder=t5_encoder),
             vae=VAEField(vae=vae),
         )

invokeai/app/invocations/sd3_text_encoder.py

@@ -11,17 +11,16 @@ from transformers import (
     T5TokenizerFast,
 )
 
-from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, 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.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
+from invokeai.backend.lora.conversions.flux_lora_constants import FLUX_LORA_CLIP_PREFIX
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoRAPatcher
+from invokeai.backend.model_manager.config import ModelFormat
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, SD3ConditioningInfo
-from invokeai.backend.util.devices import TorchDevice
 
 # The SD3 T5 Max Sequence Length set based on the default in diffusers.
 SD3_T5_MAX_SEQ_LEN = 256
@@ -29,10 +28,11 @@ SD3_T5_MAX_SEQ_LEN = 256
 
 @invocation(
     "sd3_text_encoder",
-    title="Prompt - SD3",
+    title="SD3 Text Encoding",
     tags=["prompt", "conditioning", "sd3"],
     category="conditioning",
-    version="1.0.1",
+    version="1.0.0",
+    classification=Classification.Prototype,
 )
 class Sd3TextEncoderInvocation(BaseInvocation):
     """Encodes and preps a prompt for a SD3 image."""
@@ -86,11 +86,14 @@ class Sd3TextEncoderInvocation(BaseInvocation):
 
     def _t5_encode(self, context: InvocationContext, max_seq_len: int) -> torch.Tensor:
         assert self.t5_encoder is not None
+        t5_tokenizer_info = context.models.load(self.t5_encoder.tokenizer)
+        t5_text_encoder_info = context.models.load(self.t5_encoder.text_encoder)
+
         prompt = [self.prompt]
 
         with (
-            context.models.load(self.t5_encoder.text_encoder) as t5_text_encoder,
-            context.models.load(self.t5_encoder.tokenizer) as t5_tokenizer,
+            t5_text_encoder_info as t5_text_encoder,
+            t5_tokenizer_info as t5_tokenizer,
         ):
             context.util.signal_progress("Running T5 encoder")
             assert isinstance(t5_text_encoder, T5EncoderModel)
@@ -117,7 +120,7 @@ class Sd3TextEncoderInvocation(BaseInvocation):
                     f" {max_seq_len} tokens: {removed_text}"
                 )
 
-            prompt_embeds = t5_text_encoder(text_input_ids.to(TorchDevice.choose_torch_device()))[0]
+            prompt_embeds = t5_text_encoder(text_input_ids.to(t5_text_encoder.device))[0]
 
         assert isinstance(prompt_embeds, torch.Tensor)
         return prompt_embeds
@@ -125,12 +128,14 @@ class Sd3TextEncoderInvocation(BaseInvocation):
     def _clip_encode(
         self, context: InvocationContext, clip_model: CLIPField, tokenizer_max_length: int = 77
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        clip_tokenizer_info = context.models.load(clip_model.tokenizer)
+        clip_text_encoder_info = context.models.load(clip_model.text_encoder)
+
         prompt = [self.prompt]
 
-        clip_text_encoder_info = context.models.load(clip_model.text_encoder)
         with (
             clip_text_encoder_info.model_on_device() as (cached_weights, clip_text_encoder),
-            context.models.load(clip_model.tokenizer) as clip_tokenizer,
+            clip_tokenizer_info as clip_tokenizer,
             ExitStack() as exit_stack,
         ):
             context.util.signal_progress("Running CLIP encoder")
@@ -145,11 +150,10 @@ class Sd3TextEncoderInvocation(BaseInvocation):
             if clip_text_encoder_config.format in [ModelFormat.Diffusers]:
                 # The model is non-quantized, so we can apply the LoRA weights directly into the model.
                 exit_stack.enter_context(
-                    LayerPatcher.apply_smart_model_patches(
+                    LoRAPatcher.apply_lora_patches(
                         model=clip_text_encoder,
                         patches=self._clip_lora_iterator(context, clip_model),
                         prefix=FLUX_LORA_CLIP_PREFIX,
-                        dtype=clip_text_encoder.dtype,
                         cached_weights=cached_weights,
                     )
                 )
@@ -180,7 +184,7 @@ class Sd3TextEncoderInvocation(BaseInvocation):
                     f" {tokenizer_max_length} tokens: {removed_text}"
                 )
             prompt_embeds = clip_text_encoder(
-                input_ids=text_input_ids.to(TorchDevice.choose_torch_device()), output_hidden_states=True
+                input_ids=text_input_ids.to(clip_text_encoder.device), output_hidden_states=True
             )
             pooled_prompt_embeds = prompt_embeds[0]
             prompt_embeds = prompt_embeds.hidden_states[-2]
@@ -189,9 +193,9 @@ class Sd3TextEncoderInvocation(BaseInvocation):
 
     def _clip_lora_iterator(
         self, context: InvocationContext, clip_model: CLIPField
-    ) -> Iterator[Tuple[ModelPatchRaw, float]]:
+    ) -> Iterator[Tuple[LoRAModelRaw, float]]:
         for lora in clip_model.loras:
             lora_info = context.models.load(lora.lora)
-            assert isinstance(lora_info.model, ModelPatchRaw)
+            assert isinstance(lora_info.model, LoRAModelRaw)
             yield (lora_info.model, lora.weight)
             del lora_info

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.taxonomy import SubModelType
+from invokeai.backend.model_manager import SubModelType
 
 
 @invocation_output("sdxl_model_loader_output")
@@ -24,7 +24,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
-@invocation("sdxl_model_loader", title="Main Model - SDXL", tags=["model", "sdxl"], category="model", version="1.0.4")
+@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.3")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
@@ -58,10 +58,10 @@ class SDXLModelLoaderInvocation(BaseInvocation):
 
 @invocation(
     "sdxl_refiner_model_loader",
-    title="Refiner Model - SDXL",
+    title="SDXL Refiner Model",
     tags=["model", "sdxl", "refiner"],
     category="model",
-    version="1.0.4",
+    version="1.0.3",
 )
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""

invokeai/app/invocations/segment_anything.py

@@ -49,7 +49,7 @@ class SAMPointsField(BaseModel):
     title="Segment Anything",
     tags=["prompt", "segmentation"],
     category="segmentation",
-    version="1.2.0",
+    version="1.1.0",
 )
 class SegmentAnythingInvocation(BaseInvocation):
     """Runs a Segment Anything Model."""
@@ -96,10 +96,8 @@ class SegmentAnythingInvocation(BaseInvocation):
             # masks contains bool values, so we merge them via max-reduce.
             combined_mask, _ = torch.stack(masks).max(dim=0)
 
-        # Unsqueeze the channel dimension.
-        combined_mask = combined_mask.unsqueeze(0)
         mask_tensor_name = context.tensors.save(combined_mask)
-        _, height, width = combined_mask.shape
+        height, width = combined_mask.shape
         return MaskOutput(mask=TensorField(tensor_name=mask_tensor_name), width=width, height=height)
 
     @staticmethod
@@ -185,9 +183,9 @@ class SegmentAnythingInvocation(BaseInvocation):
             # Find the largest mask.
             return [max(masks, key=lambda x: float(x.sum()))]
         elif self.mask_filter == "highest_box_score":
-            assert bounding_boxes is not None, (
-                "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
-            )
+            assert (
+                bounding_boxes is not None
+            ), "Bounding boxes must be provided to use the 'highest_box_score' mask filter."
             assert len(masks) == len(bounding_boxes)
             # Find the index of the bounding box with the highest score.
             # Note that we fallback to -1.0 if the score is None. This is mainly to satisfy the type checker. In most

invokeai/app/invocations/spandrel_image_to_image.py

@@ -22,7 +22,6 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.spandrel_image_to_image_model import SpandrelImageToImageModel
 from invokeai.backend.tiles.tiles import calc_tiles_min_overlap
 from invokeai.backend.tiles.utils import TBLR, Tile
-from invokeai.backend.util.devices import TorchDevice
 
 
 @invocation("spandrel_image_to_image", title="Image-to-Image", tags=["upscale"], category="upscale", version="1.3.0")
@@ -103,7 +102,7 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
             (height * scale, width * scale, channels), dtype=torch.uint8, device=torch.device("cpu")
         )
 
-        image_tensor = image_tensor.to(device=TorchDevice.choose_torch_device(), dtype=spandrel_model.dtype)
+        image_tensor = image_tensor.to(device=spandrel_model.device, dtype=spandrel_model.dtype)
 
         # Run the model on each tile.
         pbar = tqdm(list(zip(tiles, scaled_tiles, strict=True)), desc="Upscaling Tiles")
@@ -117,7 +116,9 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 raise CanceledException
 
             # Extract the current tile from the input tensor.
-            input_tile = image_tensor[:, :, tile.coords.top : tile.coords.bottom, tile.coords.left : tile.coords.right]
+            input_tile = image_tensor[
+                :, :, tile.coords.top : tile.coords.bottom, tile.coords.left : tile.coords.right
+            ].to(device=spandrel_model.device, dtype=spandrel_model.dtype)
 
             # Run the model on the tile.
             output_tile = spandrel_model.run(input_tile)
@@ -150,12 +151,15 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
         return pil_image
 
-    @torch.no_grad()
+    @torch.inference_mode()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         # Images are converted to RGB, because most models don't support an alpha channel. In the future, we may want to
         # revisit this.
         image = context.images.get_pil(self.image.image_name, mode="RGB")
 
+        # Load the model.
+        spandrel_model_info = context.models.load(self.image_to_image_model)
+
         def step_callback(step: int, total_steps: int) -> None:
             context.util.signal_progress(
                 message=f"Processing tile {step}/{total_steps}",
@@ -163,7 +167,7 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
             )
 
         # Do the upscaling.
-        with context.models.load(self.image_to_image_model) as spandrel_model:
+        with spandrel_model_info as spandrel_model:
             assert isinstance(spandrel_model, SpandrelImageToImageModel)
 
             # Upscale the image
@@ -196,12 +200,15 @@ class SpandrelImageToImageAutoscaleInvocation(SpandrelImageToImageInvocation):
         description="If true, the output image will be resized to the nearest multiple of 8 in both dimensions.",
     )
 
-    @torch.no_grad()
+    @torch.inference_mode()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         # Images are converted to RGB, because most models don't support an alpha channel. In the future, we may want to
         # revisit this.
         image = context.images.get_pil(self.image.image_name, mode="RGB")
 
+        # Load the model.
+        spandrel_model_info = context.models.load(self.image_to_image_model)
+
         # The target size of the image, determined by the provided scale. We'll run the upscaler until we hit this size.
         # Later, we may mutate this value if the model doesn't upscale the image or if the user requested a multiple of 8.
         target_width = int(image.width * self.scale)
@@ -214,7 +221,7 @@ class SpandrelImageToImageAutoscaleInvocation(SpandrelImageToImageInvocation):
             )
 
         # Do the upscaling.
-        with context.models.load(self.image_to_image_model) as spandrel_model:
+        with spandrel_model_info as spandrel_model:
             assert isinstance(spandrel_model, SpandrelImageToImageModel)
 
             iteration = 1

invokeai/app/invocations/t2i_adapter.py

@@ -45,11 +45,7 @@ class T2IAdapterOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "t2i_adapter",
-    title="T2I-Adapter - SD1.5, SDXL",
-    tags=["t2i_adapter", "control"],
-    category="t2i_adapter",
-    version="1.0.4",
+    "t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.3"
 )
 class T2IAdapterInvocation(BaseInvocation):
     """Collects T2I-Adapter info to pass to other nodes."""

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, invocation
+from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, 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
@@ -22,8 +22,8 @@ from invokeai.app.invocations.fields import (
 from invokeai.app.invocations.model import UNetField
 from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.patches.layer_patcher import LayerPatcher
-from invokeai.backend.patches.model_patch_raw import ModelPatchRaw
+from invokeai.backend.lora.lora_model_raw import LoRAModelRaw
+from invokeai.backend.lora.lora_patcher import LoRAPatcher
 from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, PipelineIntermediateState
 from invokeai.backend.stable_diffusion.multi_diffusion_pipeline import (
     MultiDiffusionPipeline,
@@ -53,10 +53,11 @@ def crop_controlnet_data(control_data: ControlNetData, latent_region: TBLR) -> C
 
 @invocation(
     "tiled_multi_diffusion_denoise_latents",
-    title="Tiled Multi-Diffusion Denoise - SD1.5, SDXL",
+    title="Tiled Multi-Diffusion Denoise Latents",
     tags=["upscale", "denoise"],
     category="latents",
-    version="1.0.1",
+    classification=Classification.Beta,
+    version="1.0.0",
 )
 class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
     """Tiled Multi-Diffusion denoising.
@@ -193,31 +194,30 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
             context.util.sd_step_callback(state, unet_config.base)
 
         # Prepare an iterator that yields the UNet's LoRA models and their weights.
-        def _lora_loader() -> Iterator[Tuple[ModelPatchRaw, float]]:
+        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.unet.loras:
                 lora_info = context.models.load(lora.lora)
-                assert isinstance(lora_info.model, ModelPatchRaw)
+                assert isinstance(lora_info.model, LoRAModelRaw)
                 yield (lora_info.model, lora.weight)
                 del lora_info
 
-        device = TorchDevice.choose_torch_device()
+        # Load the UNet model.
+        unet_info = context.models.load(self.unet.unet)
+
         with (
             ExitStack() as exit_stack,
-            context.models.load(self.unet.unet) as unet,
-            LayerPatcher.apply_smart_model_patches(
-                model=unet, patches=_lora_loader(), prefix="lora_unet_", dtype=unet.dtype
-            ),
+            unet_info as unet,
+            LoRAPatcher.apply_lora_patches(model=unet, patches=_lora_loader(), prefix="lora_unet_"),
         ):
             assert isinstance(unet, UNet2DConditionModel)
-            latents = latents.to(device=device, dtype=unet.dtype)
+            latents = latents.to(device=unet.device, dtype=unet.dtype)
             if noise is not None:
-                noise = noise.to(device=device, dtype=unet.dtype)
+                noise = noise.to(device=unet.device, dtype=unet.dtype)
             scheduler = get_scheduler(
                 context=context,
                 scheduler_info=self.unet.scheduler,
                 scheduler_name=self.scheduler,
                 seed=seed,
-                unet_config=unet_config,
             )
             pipeline = self.create_pipeline(unet=unet, scheduler=scheduler)
 
@@ -226,7 +226,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
                 context=context,
                 positive_conditioning_field=self.positive_conditioning,
                 negative_conditioning_field=self.negative_conditioning,
-                device=device,
+                device=unet.device,
                 dtype=unet.dtype,
                 latent_height=latent_tile_height,
                 latent_width=latent_tile_width,
@@ -239,7 +239,6 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
                 context=context,
                 control_input=self.control,
                 latents_shape=list(latents.shape),
-                device=device,
                 # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
                 do_classifier_free_guidance=True,
                 exit_stack=exit_stack,
@@ -265,7 +264,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
 
             timesteps, init_timestep, scheduler_step_kwargs = DenoiseLatentsInvocation.init_scheduler(
                 scheduler,
-                device=device,
+                device=unet.device,
                 steps=self.steps,
                 denoising_start=self.denoising_start,
                 denoising_end=self.denoising_end,

invokeai/app/invocations/tiles.py

@@ -7,6 +7,7 @@ from pydantic import BaseModel
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Classification,
     invocation,
     invocation_output,
 )
@@ -39,6 +40,7 @@ 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."""
@@ -72,6 +74,7 @@ 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."""
@@ -114,6 +117,7 @@ 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."""
@@ -164,6 +168,7 @@ class TileToPropertiesOutput(BaseInvocationOutput):
     tags=["tiles"],
     category="tiles",
     version="1.0.1",
+    classification=Classification.Beta,
 )
 class TileToPropertiesInvocation(BaseInvocation):
     """Split a Tile into its individual properties."""
@@ -196,6 +201,7 @@ class PairTileImageOutput(BaseInvocationOutput):
     tags=["tiles"],
     category="tiles",
     version="1.0.1",
+    classification=Classification.Beta,
 )
 class PairTileImageInvocation(BaseInvocation):
     """Pair an image with its tile properties."""
@@ -224,6 +230,7 @@ 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/invocations/util.py

@@ -9,6 +9,6 @@ def validate_weights(weights: Union[float, list[float]]) -> None:
 
 
 def validate_begin_end_step(begin_step_percent: float, end_step_percent: float) -> None:
-    """Validate that begin_step_percent is less than or equal to end_step_percent"""
-    if begin_step_percent > end_step_percent:
+    """Validate that begin_step_percent is less than end_step_percent"""
+    if begin_step_percent >= end_step_percent:
         raise ValueError("Begin step percent must be less than or equal to end step percent")

invokeai/app/run_app.py

@@ -1,86 +1,12 @@
-import uvicorn
-
-from invokeai.app.invocations.load_custom_nodes import load_custom_nodes
-from invokeai.app.services.config.config_default import get_config
-from invokeai.app.util.torch_cuda_allocator import configure_torch_cuda_allocator
-from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.frontend.cli.arg_parser import InvokeAIArgs
-
-
-def get_app():
-    """Import the app and event loop. We wrap this in a function to more explicitly control when it happens, because
-    importing from api_app does a bunch of stuff - it's more like calling a function than importing a module.
-    """
-    from invokeai.app.api_app import app, loop
-
-    return app, loop
+"""This is a wrapper around the main app entrypoint, to allow for CLI args to be parsed before running the app."""
 
 
 def run_app() -> None:
-    """The main entrypoint for the app."""
-    # Parse the CLI arguments.
+    # Before doing _anything_, parse CLI args!
+    from invokeai.frontend.cli.arg_parser import InvokeAIArgs
+
     InvokeAIArgs.parse_args()
 
-    # Load config.
-    app_config = get_config()
+    from invokeai.app.api_app import invoke_api
 
-    logger = InvokeAILogger.get_logger(config=app_config)
-
-    # Configure the torch CUDA memory allocator.
-    # NOTE: It is important that this happens before torch is imported.
-    if app_config.pytorch_cuda_alloc_conf:
-        configure_torch_cuda_allocator(app_config.pytorch_cuda_alloc_conf, logger)
-
-    # Import from startup_utils here to avoid importing torch before configure_torch_cuda_allocator() is called.
-    from invokeai.app.util.startup_utils import (
-        apply_monkeypatches,
-        check_cudnn,
-        enable_dev_reload,
-        find_open_port,
-        register_mime_types,
-    )
-
-    # Find an open port, and modify the config accordingly.
-    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()
-    check_cudnn(logger)
-
-    # Initialize the app and event loop.
-    app, loop = get_app()
-
-    # Load custom nodes. This must be done after importing the Graph class, which itself imports all modules from the
-    # invocations module. The ordering here is implicit, but important - we want to load custom nodes after all the
-    # 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,
-        host=app_config.host,
-        port=app_config.port,
-        loop="asyncio",
-        log_level=app_config.log_level_network,
-        ssl_certfile=app_config.ssl_certfile,
-        ssl_keyfile=app_config.ssl_keyfile,
-    )
-    server = uvicorn.Server(config)
-
-    # replace uvicorn's loggers with InvokeAI's for consistent appearance
-    uvicorn_logger = InvokeAILogger.get_logger("uvicorn")
-    uvicorn_logger.handlers.clear()
-    for hdlr in logger.handlers:
-        uvicorn_logger.addHandler(hdlr)
-
-    loop.run_until_complete(server.serve())
+    invoke_api()

invokeai/app/services/board_image_records/board_image_records_base.py

@@ -1,8 +1,6 @@
 from abc import ABC, abstractmethod
 from typing import Optional
 
-from invokeai.app.services.image_records.image_records_common import ImageCategory
-
 
 class BoardImageRecordStorageBase(ABC):
     """Abstract base class for the one-to-many board-image relationship record storage."""
@@ -28,8 +26,6 @@ class BoardImageRecordStorageBase(ABC):
     def get_all_board_image_names_for_board(
         self,
         board_id: str,
-        categories: list[ImageCategory] | None,
-        is_intermediate: bool | None,
     ) -> list[str]:
         """Gets all board images for a board, as a list of the image names."""
         pass