build: fix path in build script

This allows it to pull in sentencepiece on its own. In 0.10.0, it didn't have this package listed as a dependency, but in recent releases it does. So we are able to remove sentencepiece as an explicit dep.

.dockerignore

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

.github/workflows/build-container.yml

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

.github/workflows/build-wheel.yml

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

.github/workflows/release.yml

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

.nvmrc

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

Makefile

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

docker/Dockerfile

@@ -1,77 +1,6 @@
 # syntax=docker/dockerfile:1.4
 
-## Builder stage
-
-FROM library/ubuntu:24.04 AS builder
-
-ARG DEBIAN_FRONTEND=noninteractive
-RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt,sharing=locked \
-    apt update && apt-get install -y \
-        build-essential \
-        git
-
-# Install `uv` for package management
-COPY --from=ghcr.io/astral-sh/uv:0.6.0 /uv /uvx /bin/
-
-ENV VIRTUAL_ENV=/opt/venv
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ENV INVOKEAI_SRC=/opt/invokeai
-ENV PYTHON_VERSION=3.11
-ENV UV_PYTHON=3.11
-ENV UV_COMPILE_BYTECODE=1
-ENV UV_LINK_MODE=copy
-ENV UV_PROJECT_ENVIRONMENT="$VIRTUAL_ENV"
-ENV UV_INDEX="https://download.pytorch.org/whl/cu124"
-
-ARG GPU_DRIVER=cuda
-# unused but available
-ARG BUILDPLATFORM
-
-# Switch to the `ubuntu` user to work around dependency issues with uv-installed python
-RUN mkdir -p ${VIRTUAL_ENV} && \
-    mkdir -p ${INVOKEAI_SRC} && \
-    chmod -R a+w /opt && \
-    mkdir ~ubuntu/.cache && chown ubuntu: ~ubuntu/.cache
-USER ubuntu
-
-# Install python
-RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
-  uv python install ${PYTHON_VERSION}
-
-WORKDIR ${INVOKEAI_SRC}
-
-# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
-# bind-mount instead of copy to defer adding sources to the image until next layer.
-#
-# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
-# x86_64/CUDA is the default
-RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    --mount=type=bind,source=invokeai/version,target=invokeai/version \
-    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
-      UV_INDEX="https://download.pytorch.org/whl/cpu"; \
-    elif [ "$GPU_DRIVER" = "rocm" ]; then \
-      UV_INDEX="https://download.pytorch.org/whl/rocm6.1"; \
-    fi && \
-    uv sync --no-install-project
-
-# Now that the bulk of the dependencies have been installed, copy in the project files that change more frequently.
-COPY invokeai invokeai
-COPY pyproject.toml .
-
-RUN --mount=type=cache,target=/home/ubuntu/.cache/uv,uid=1000,gid=1000 \
-    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
-    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
-      UV_INDEX="https://download.pytorch.org/whl/cpu"; \
-    elif [ "$GPU_DRIVER" = "rocm" ]; then \
-      UV_INDEX="https://download.pytorch.org/whl/rocm6.1"; \
-    fi && \
-    uv sync
-
-
-#### Build the Web UI ------------------------------------
+#### Web UI ------------------------------------
 
 FROM docker.io/node:22-slim AS web-builder
 ENV PNPM_HOME="/pnpm"
@@ -85,69 +14,89 @@ RUN --mount=type=cache,target=/pnpm/store \
     pnpm install --frozen-lockfile
 RUN npx vite build
 
-#### Runtime stage ---------------------------------------
+## Backend ---------------------------------------
 
-FROM library/ubuntu:24.04 AS runtime
+FROM library/ubuntu:24.04
 
 ARG DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV PYTHONDONTWRITEBYTECODE=1
+RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
+RUN --mount=type=cache,target=/var/cache/apt \
+    --mount=type=cache,target=/var/lib/apt \
+    apt update && apt install -y --no-install-recommends \
+    ca-certificates \
+    git \
+    gosu \
+    libglib2.0-0 \
+    libgl1 \
+    libglx-mesa0 \
+    build-essential \
+    libopencv-dev \
+    libstdc++-10-dev
 
-RUN apt update && apt install -y --no-install-recommends \
-        git \
-        curl \
-        vim \
-        tmux \
-        ncdu \
-        iotop \
-        bzip2 \
-        gosu \
-        magic-wormhole \
-        libglib2.0-0 \
-        libgl1 \
-        libglx-mesa0 \
-        build-essential \
-        libopencv-dev \
-        libstdc++-10-dev &&\
-    apt-get clean && apt-get autoclean
+ENV \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    VIRTUAL_ENV=/opt/venv \
+    INVOKEAI_SRC=/opt/invokeai \
+    PYTHON_VERSION=3.12 \
+    UV_PYTHON=3.12 \
+    UV_COMPILE_BYTECODE=1 \
+    UV_MANAGED_PYTHON=1 \
+    UV_LINK_MODE=copy \
+    UV_PROJECT_ENVIRONMENT=/opt/venv \
+    UV_INDEX="https://download.pytorch.org/whl/cu124" \
+    INVOKEAI_ROOT=/invokeai \
+    INVOKEAI_HOST=0.0.0.0 \
+    INVOKEAI_PORT=9090 \
+    PATH="/opt/venv/bin:$PATH" \
+    CONTAINER_UID=${CONTAINER_UID:-1000} \
+    CONTAINER_GID=${CONTAINER_GID:-1000}
 
-ENV INVOKEAI_SRC=/opt/invokeai
-ENV VIRTUAL_ENV=/opt/venv
-ENV UV_PROJECT_ENVIRONMENT="$VIRTUAL_ENV"
-ENV PYTHON_VERSION=3.11
-ENV INVOKEAI_ROOT=/invokeai
-ENV INVOKEAI_HOST=0.0.0.0
-ENV INVOKEAI_PORT=9090
-ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
-ENV CONTAINER_UID=${CONTAINER_UID:-1000}
-ENV CONTAINER_GID=${CONTAINER_GID:-1000}
+ARG GPU_DRIVER=cuda
 
 # Install `uv` for package management
-# and install python for the ubuntu user (expected to exist on ubuntu >=24.x)
-# this is too tiny to optimize with multi-stage builds, but maybe we'll come back to it
-COPY --from=ghcr.io/astral-sh/uv:0.6.0 /uv /uvx /bin/
-USER ubuntu
-RUN uv python install ${PYTHON_VERSION}
-USER root
+COPY --from=ghcr.io/astral-sh/uv:0.6.9 /uv /uvx /bin/
 
-# --link requires buldkit w/ dockerfile syntax 1.4
-COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
-COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
-COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
-
-# Link amdgpu.ids for ROCm builds
-# contributed by https://github.com/Rubonnek
-RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
-  ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
+# Install python & allow non-root user to use it by traversing the /root dir without read permissions
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv python install ${PYTHON_VERSION} && \
+    # chmod --recursive a+rX /root/.local/share/uv/python
+    chmod 711 /root
 
 WORKDIR ${INVOKEAI_SRC}
 
+# Install project's dependencies as a separate layer so they aren't rebuilt every commit.
+# bind-mount instead of copy to defer adding sources to the image until next layer.
+#
+# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
+# x86_64/CUDA is the default
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
+    --mount=type=bind,source=uv.lock,target=uv.lock \
+    # this is just to get the package manager to recognize that the project exists, without making changes to the docker layer
+    --mount=type=bind,source=invokeai/version,target=invokeai/version \
+    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then UV_INDEX="https://download.pytorch.org/whl/cpu"; \
+    elif [ "$GPU_DRIVER" = "rocm" ]; then UV_INDEX="https://download.pytorch.org/whl/rocm6.2"; \
+    fi && \
+    uv sync --frozen
+
 # build patchmatch
 RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
 RUN python -c "from patchmatch import patch_match"
 
+# Link amdgpu.ids for ROCm builds
+# contributed by https://github.com/Rubonnek
+RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
+    ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
+
 RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${INVOKEAI_ROOT}
 
 COPY docker/docker-entrypoint.sh ./
 ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
 CMD ["invokeai-web"]
+
+# --link requires buldkit w/ dockerfile syntax 1.4, does not work with podman
+COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
+
+# add sources last to minimize image changes on code changes
+COPY invokeai ${INVOKEAI_SRC}/invokeai

docs/contributing/dev-environment.md

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

docs/installation/manual.md

@@ -43,10 +43,10 @@ The following commands vary depending on the version of Invoke being installed a
 3. Create a virtual environment in that directory:
 
     ```sh
-    uv venv --relocatable --prompt invoke --python 3.11 --python-preference only-managed .venv
+    uv venv --relocatable --prompt invoke --python 3.12 --python-preference only-managed .venv
     ```
 
-    This command creates a portable virtual environment at `.venv` complete with a portable python 3.11. It doesn't matter if your system has no python installed, or has a different version - `uv` will handle everything.
+    This command creates a portable virtual environment at `.venv` complete with a portable python 3.12. It doesn't matter if your system has no python installed, or has a different version - `uv` will handle everything.
 
 4. Activate the virtual environment:
 
@@ -88,13 +88,13 @@ The following commands vary depending on the version of Invoke being installed a
 8. Install the `invokeai` package. Substitute the package specifier and version.
 
     ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.11 --python-preference only-managed --force-reinstall
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --force-reinstall
     ```
 
     If you determined you needed to use a `PyPI` index URL in the previous step, you'll need to add `--index=<INDEX_URL>` like this:
 
     ```sh
-    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.11 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
+    uv pip install <PACKAGE_SPECIFIER>==<VERSION> --python 3.12 --python-preference only-managed --index=<INDEX_URL> --force-reinstall
     ```
 
 9. Deactivate and reactivate your venv so that the invokeai-specific commands become available in the environment:

docs/installation/requirements.md

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

installer/install.bat.in

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

installer/install.sh.in

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

installer/lib/installer.py

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

installer/lib/main.py

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

installer/lib/messages.py

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

installer/readme.txt

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

installer/templates/invoke.bat.in

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

installer/templates/invoke.sh.in

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

invokeai/app/api/dependencies.py

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

invokeai/app/invocations/controlnet.py

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

invokeai/app/invocations/controlnet_image_processors.py

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

invokeai/app/invocations/denoise_latents.py

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

invokeai/app/invocations/dw_openpose.py

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

invokeai/app/invocations/metadata_linked.py

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

invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py

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

invokeai/app/services/object_serializer/object_serializer_disk.py

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

invokeai/app/util/startup_utils.py

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

invokeai/backend/image_util/dw_openpose/__init__.py

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

invokeai/backend/image_util/dw_openpose/utils.py

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

invokeai/backend/image_util/dw_openpose/wholebody.py

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

invokeai/backend/stable_diffusion/diffusion/conditioning_data.py

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

invokeai/backend/util/mps_fixes.py

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

invokeai/frontend/web/package.json

@@ -162,5 +162,6 @@
   },
   "engines": {
     "pnpm": "8"
-  }
+  },
+  "packageManager": "pnpm@8.15.9+sha512.499434c9d8fdd1a2794ebf4552b3b25c0a633abcee5bb15e7b5de90f32f47b513aca98cd5cfd001c31f0db454bc3804edccd578501e4ca293a6816166bbd9f81"
 }

invokeai/frontend/web/src/features/nodes/types/openapi.ts

@@ -79,4 +79,4 @@ export const isInvocationOutputSchemaObject = (
 
 export const isInvocationFieldSchema = (
   obj: OpenAPIV3_1.ReferenceObject | OpenAPIV3_1.SchemaObject
-): obj is InvocationFieldSchema => !('$ref' in obj);
+): obj is InvocationFieldSchema => 'field_kind' in obj;

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "5.9.1"
+__version__ = "5.10.0dev4"

pins.json

@@ -0,0 +1,14 @@
+{
+  "python": "3.12",
+  "torchIndexUrl": {
+    "win32": {
+      "cuda": "https://download.pytorch.org/whl/cu126"
+    },
+    "linux": {
+      "cpu": "https://download.pytorch.org/whl/cpu",
+      "rocm": "https://download.pytorch.org/whl/rocm6.2.4",
+      "cuda": "https://download.pytorch.org/whl/cu126"
+    },
+    "darwin": {}
+  }
+}

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "InvokeAI"
 description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
-requires-python = ">=3.10, <3.12"
+requires-python = ">=3.10, <3.13"
 readme = { content-type = "text/markdown", file = "README.md" }
 keywords = ["stable-diffusion", "AI"]
 dynamic = ["version"]
@@ -33,69 +33,46 @@ classifiers = [
 ]
 dependencies = [
   # Core generation dependencies, pinned for reproducible builds.
-  "accelerate==1.0.1",
-  "bitsandbytes==0.45.0; sys_platform!='darwin'",
-  "clip_anytorch==2.6.0",       # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
+  "accelerate",
+  "bitsandbytes; sys_platform!='darwin'",
   "compel==2.0.2",
-  "controlnet-aux",
-  "diffusers[torch]==0.31.0",
-  "gguf==0.10.0",
-  "invisible-watermark==0.2.0", # needed to install SDXL base and refiner using their repo_ids
-  "mediapipe==0.10.14",         # needed for "mediapipeface" controlnet model
+  "diffusers[torch]",
+  "gguf",
+  "invisible-watermark==0.2.0",           # needed to install SDXL base and refiner using their repo_ids
+  "mediapipe==0.10.14",                   # needed for "mediapipeface" controlnet model
   "numpy<2.0.0",
   "onnx==1.16.1",
   "onnxruntime==1.19.2",
   "opencv-python==4.9.0.80",
-  "pytorch-lightning==2.1.3",
-  "safetensors==0.4.3",
-  # sentencepiece is required to load T5TokenizerFast (used by FLUX).
-  "sentencepiece==0.2.0",
-  "spandrel==0.3.4",
-  "timm~=1.0.0",
-  "torch<2.5.0",                # torch and related dependencies are loosely pinned, will respect requirement of `diffusers[torch]`
-  "torchmetrics",
-  "torchsde",
+  "safetensors",
+  "spandrel",
+  "torch~=2.6.0",                         # torch and related dependencies are loosely pinned, will respect requirement of `diffusers[torch]`
+  "torchsde",                             # diffusers needs this for SDE solvers, but it is not an explicit dep of diffusers
   "torchvision",
-  "transformers==4.46.3",
+  "transformers",
 
   # Core application dependencies, pinned for reproducible builds.
-  "fastapi-events==0.11.1",
-  "fastapi==0.111.0",
-  "huggingface-hub==0.26.1",
-  "pydantic-settings==2.2.1",
-  "pydantic==2.7.2",
-  "python-socketio==5.11.1",
-  "uvicorn[standard]==0.28.0",
+  "fastapi-events",
+  "fastapi",
+  "huggingface-hub",
+  "pydantic-settings",
+  "pydantic",
+  "python-socketio",
+  "uvicorn[standard]",
 
   # Auxiliary dependencies, pinned only if necessary.
-  "albumentations",
   "blake3",
-  "click",
-  "datasets",
   "Deprecated",
   "dnspython",
   "dynamicprompts",
   "einops",
-  "facexlib",
-  # Exclude 3.9.1 which has a problem on windows, see https://github.com/matplotlib/matplotlib/issues/28551
-  "matplotlib!=3.9.1",
-  "npyscreen",
-  "omegaconf",
   "picklescan",
   "pillow",
   "prompt-toolkit",
-  "pympler",
   "pypatchmatch",
-  "pyperclip",
-  "pyreadline3",
   "python-multipart",
   "requests",
-  "rich~=13.3",
-  "scikit-image",
   "semver~=3.0.1",
-  "test-tube",
-  "windows-curses; sys_platform=='win32'",
-  "humanize==4.12.1",
 ]
 
 [project.optional-dependencies]
@@ -127,7 +104,8 @@ dependencies = [
   "pytest-datadir",
   "requests_testadapter",
   "httpx",
-  "polyfactory==2.19.0"
+  "polyfactory==2.19.0",
+  "humanize==4.12.1",
 ]
 
 [project.scripts]
@@ -207,9 +185,9 @@ exclude = [
   ".venv*",
   "*.ipynb",
   "invokeai/backend/image_util/mediapipe_face/", # External code
-  "invokeai/backend/image_util/mlsd/", # External code
-  "invokeai/backend/image_util/normal_bae/", # External code
-  "invokeai/backend/image_util/pidi/", # External code
+  "invokeai/backend/image_util/mlsd/",           # External code
+  "invokeai/backend/image_util/normal_bae/",     # External code
+  "invokeai/backend/image_util/pidi/",           # External code
 ]
 
 [tool.ruff.lint]

scripts/build_wheel.sh

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

tests/test_object_serializer_disk.py

@@ -21,16 +21,18 @@ def count_files(path: Path):
 
 @pytest.fixture
 def obj_serializer(tmp_path: Path):
-    return ObjectSerializerDisk[MockDataclass](tmp_path)
+    return ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass])
 
 
 @pytest.fixture
 def fwd_cache(tmp_path: Path):
-    return ObjectSerializerForwardCache(ObjectSerializerDisk[MockDataclass](tmp_path), max_cache_size=2)
+    return ObjectSerializerForwardCache(
+        ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass]), max_cache_size=2
+    )
 
 
 def test_obj_serializer_disk_initializes(tmp_path: Path):
-    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path)
+    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass])
     assert obj_serializer._output_dir == tmp_path
 
 
@@ -70,7 +72,7 @@ def test_obj_serializer_disk_deletes(obj_serializer: ObjectSerializerDisk[MockDa
 
 
 def test_obj_serializer_ephemeral_creates_tempdir(tmp_path: Path):
-    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=True)
+    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=True)
     assert isinstance(obj_serializer._tempdir, tempfile.TemporaryDirectory)
     assert obj_serializer._base_output_dir == tmp_path
     assert obj_serializer._output_dir != tmp_path
@@ -78,21 +80,21 @@ def test_obj_serializer_ephemeral_creates_tempdir(tmp_path: Path):
 
 
 def test_obj_serializer_ephemeral_deletes_tempdir(tmp_path: Path):
-    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=True)
+    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=True)
     tempdir_path = obj_serializer._output_dir
     del obj_serializer
     assert not tempdir_path.exists()
 
 
 def test_obj_serializer_ephemeral_deletes_tempdir_on_stop(tmp_path: Path):
-    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=True)
+    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=True)
     tempdir_path = obj_serializer._output_dir
     obj_serializer.stop(None)  # pyright: ignore [reportArgumentType]
     assert not tempdir_path.exists()
 
 
 def test_obj_serializer_ephemeral_writes_to_tempdir(tmp_path: Path):
-    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=True)
+    obj_serializer = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=True)
     obj_1 = MockDataclass(foo="bar")
     obj_1_name = obj_serializer.save(obj_1)
     assert Path(obj_serializer._output_dir, obj_1_name).exists()
@@ -102,19 +104,19 @@ def test_obj_serializer_ephemeral_writes_to_tempdir(tmp_path: Path):
 def test_obj_serializer_ephemeral_deletes_dangling_tempdirs_on_init(tmp_path: Path):
     tempdir = tmp_path / "tmpdir"
     tempdir.mkdir()
-    ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=True)
+    ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=True)
     assert not tempdir.exists()
 
 
 def test_obj_serializer_does_not_delete_tempdirs_on_init(tmp_path: Path):
     tempdir = tmp_path / "tmpdir"
     tempdir.mkdir()
-    ObjectSerializerDisk[MockDataclass](tmp_path, ephemeral=False)
+    ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass], ephemeral=False)
     assert tempdir.exists()
 
 
 def test_obj_serializer_disk_different_types(tmp_path: Path):
-    obj_serializer_1 = ObjectSerializerDisk[MockDataclass](tmp_path)
+    obj_serializer_1 = ObjectSerializerDisk[MockDataclass](tmp_path, safe_globals=[MockDataclass])
     obj_1 = MockDataclass(foo="bar")
     obj_1_name = obj_serializer_1.save(obj_1)
     obj_1_loaded = obj_serializer_1.load(obj_1_name)
@@ -123,19 +125,19 @@ def test_obj_serializer_disk_different_types(tmp_path: Path):
     assert obj_1_loaded.foo == "bar"
     assert obj_1_name.startswith("MockDataclass_")
 
-    obj_serializer_2 = ObjectSerializerDisk[int](tmp_path)
+    obj_serializer_2 = ObjectSerializerDisk[int](tmp_path, safe_globals=[int])
     obj_2_name = obj_serializer_2.save(9001)
     assert obj_serializer_2._obj_class_name == "int"
     assert obj_serializer_2.load(obj_2_name) == 9001
     assert obj_2_name.startswith("int_")
 
-    obj_serializer_3 = ObjectSerializerDisk[str](tmp_path)
+    obj_serializer_3 = ObjectSerializerDisk[str](tmp_path, safe_globals=[str])
     obj_3_name = obj_serializer_3.save("foo")
     assert obj_serializer_3._obj_class_name == "str"
     assert obj_serializer_3.load(obj_3_name) == "foo"
     assert obj_3_name.startswith("str_")
 
-    obj_serializer_4 = ObjectSerializerDisk[torch.Tensor](tmp_path)
+    obj_serializer_4 = ObjectSerializerDisk[torch.Tensor](tmp_path, safe_globals=[torch.Tensor])
     obj_4_name = obj_serializer_4.save(torch.tensor([1, 2, 3]))
     obj_4_loaded = obj_serializer_4.load(obj_4_name)
     assert obj_serializer_4._obj_class_name == "Tensor"