Merge branch 'feat/controlnet_backend' of https://github.com/invoke-ai/InvokeAI into feat/controlnet_backend

.github/CODEOWNERS

@@ -2,7 +2,7 @@
 /.github/workflows/  @lstein @blessedcoolant
 
 # documentation
-/docs/ @lstein @blessedcoolant @hipsterusername
+/docs/ @lstein  @tildebyte @blessedcoolant
 /mkdocs.yml @lstein  @blessedcoolant
 
 # nodes
@@ -18,17 +18,17 @@
 /invokeai/version @lstein @blessedcoolant
 
 # web ui
-/invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp
-/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp
+/invokeai/frontend @blessedcoolant @psychedelicious @lstein
+/invokeai/backend @blessedcoolant @psychedelicious @lstein
 
 # generation, model management, postprocessing
-/invokeai/backend  @damian0815 @lstein @blessedcoolant @jpphoto @gregghelt2 @StAlKeR7779
+/invokeai/backend  @damian0815 @lstein @blessedcoolant @jpphoto @gregghelt2
 
 # front ends
 /invokeai/frontend/CLI @lstein
 /invokeai/frontend/install @lstein @ebr  
-/invokeai/frontend/merge @lstein @blessedcoolant 
-/invokeai/frontend/training @lstein @blessedcoolant 
-/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp 
+/invokeai/frontend/merge @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/training @lstein @blessedcoolant @hipsterusername
+/invokeai/frontend/web @psychedelicious @blessedcoolant
 
 

.github/workflows/mkdocs-material.yml

@@ -2,7 +2,8 @@ name: mkdocs-material
 on:
   push:
     branches:
-      - 'refs/heads/v2.3'
+      - 'main'
+      - 'development'
 
 permissions:
     contents: write
@@ -11,10 +12,6 @@ jobs:
   mkdocs-material:
     if: github.event.pull_request.draft == false
     runs-on: ubuntu-latest
-    env:
-      REPO_URL: '${{ github.server_url }}/${{ github.repository }}'
-      REPO_NAME: '${{ github.repository }}'
-      SITE_URL: 'https://${{ github.repository_owner }}.github.io/InvokeAI'
     steps:
       - name: checkout sources
         uses: actions/checkout@v3
@@ -25,15 +22,11 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: '3.10'
-          cache: pip
-          cache-dependency-path: pyproject.toml
 
       - name: install requirements
-        env:
-          PIP_USE_PEP517: 1
         run: |
           python -m \
-            pip install ".[docs]"
+            pip install -r docs/requirements-mkdocs.txt
 
       - name: confirm buildability
         run: |
@@ -43,7 +36,7 @@ jobs:
             --verbose
 
       - name: deploy to gh-pages
-        if: ${{ github.ref == 'refs/heads/v2.3' }}
+        if: ${{ github.ref == 'refs/heads/main' }}
         run: |
           python -m \
             mkdocs gh-deploy \

.github/workflows/test-invoke-pip.yml

@@ -80,6 +80,11 @@ jobs:
         uses: actions/checkout@v3
 
       - name: set test prompt to main branch validation
+        if: ${{ github.ref == 'refs/heads/main' }}
+        run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> ${{ matrix.github-env }}
+
+      - name: set test prompt to Pull Request validation
+        if: ${{ github.ref != 'refs/heads/main' }}
         run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
 
       - name: setup python
@@ -100,6 +105,12 @@ jobs:
         id: run-pytest
         run: pytest
 
+      - name: set INVOKEAI_OUTDIR
+        run: >
+          python -c
+          "import os;from invokeai.backend.globals import Globals;OUTDIR=os.path.join(Globals.root,str('outputs'));print(f'INVOKEAI_OUTDIR={OUTDIR}')"
+          >> ${{ matrix.github-env }}
+
       - name: run invokeai-configure
         id: run-preload-models
         env:
@@ -118,21 +129,15 @@ jobs:
           HF_HUB_OFFLINE: 1
           HF_DATASETS_OFFLINE: 1
           TRANSFORMERS_OFFLINE: 1
-          INVOKEAI_OUTDIR: ${{ github.workspace }}/results
         run: >
           invokeai
           --no-patchmatch
           --no-nsfw_checker
-          --precision=float32
-          --always_use_cpu
-          --use_memory_db
-          --outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
           --from_file ${{ env.TEST_PROMPTS }}
+          --outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
 
       - name: Archive results
         id: archive-results
-        env:
-          INVOKEAI_OUTDIR: ${{ github.workspace }}/results
         uses: actions/upload-artifact@v3
         with:
           name: results

.gitignore

@@ -201,8 +201,6 @@ checkpoints
 # If it's a Mac
 .DS_Store
 
-invokeai/frontend/web/dist/*
-
 # Let the frontend manage its own gitignore
 !invokeai/frontend/web/*
 

README.md

@@ -33,8 +33,6 @@
 
 </div>
 
-_**Note: The UI is not fully functional on `main`. If you need a stable UI based on `main`, use the `pre-nodes` tag while we [migrate to a new backend](https://github.com/invoke-ai/InvokeAI/discussions/3246).**_
-
 InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products.
 
 **Quick links**: [[How to Install](https://invoke-ai.github.io/InvokeAI/#installation)] [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]

binary_installer/install.bat.in

@@ -0,0 +1,164 @@
+@echo off
+
+@rem This script will install git (if not found on the PATH variable)
+@rem  using micromamba (an 8mb static-linked single-file binary, conda replacement).
+@rem For users who already have git, this step will be skipped.
+
+@rem Next, it'll download the project's source code.
+@rem Then it will download a self-contained, standalone Python and unpack it.
+@rem Finally, it'll create the Python virtual environment and preload the models.
+
+@rem This enables a user to install this project without manually installing git or Python
+
+@rem change to the script's directory
+PUSHD "%~dp0"
+
+set "no_cache_dir=--no-cache-dir"
+if "%1" == "use-cache" (
+    set "no_cache_dir="
+)
+
+echo ***** Installing InvokeAI.. *****
+@rem Config
+set INSTALL_ENV_DIR=%cd%\installer_files\env
+@rem https://mamba.readthedocs.io/en/latest/installation.html
+set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
+set RELEASE_URL=https://github.com/invoke-ai/InvokeAI
+set RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
+set PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
+set PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-x86_64-pc-windows-msvc-shared-install_only.tar.gz
+
+set PACKAGES_TO_INSTALL=
+
+call git --version >.tmp1 2>.tmp2
+if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% git
+
+@rem Cleanup
+del /q .tmp1 .tmp2
+
+@rem (if necessary) install git into a contained environment
+if "%PACKAGES_TO_INSTALL%" NEQ "" (
+    @rem download micromamba
+    echo ***** Downloading micromamba from %MICROMAMBA_DOWNLOAD_URL% to micromamba.exe *****
+
+    call curl -L "%MICROMAMBA_DOWNLOAD_URL%" > micromamba.exe
+
+    @rem test the mamba binary
+    echo ***** Micromamba version:  *****
+    call micromamba.exe --version
+
+    @rem create the installer env
+    if not exist "%INSTALL_ENV_DIR%" (
+        call micromamba.exe create -y --prefix "%INSTALL_ENV_DIR%"
+    )
+
+    echo ***** Packages to install:%PACKAGES_TO_INSTALL% *****
+
+    call micromamba.exe install -y --prefix "%INSTALL_ENV_DIR%" -c conda-forge %PACKAGES_TO_INSTALL%
+
+    if not exist "%INSTALL_ENV_DIR%" (
+        echo ----- There was a problem while installing "%PACKAGES_TO_INSTALL%" using micromamba. Cannot continue. -----
+        pause
+        exit /b
+    )
+)
+
+del /q micromamba.exe
+
+@rem For 'git' only
+set PATH=%INSTALL_ENV_DIR%\Library\bin;%PATH%
+
+@rem Download/unpack/clean up InvokeAI release sourceball
+set err_msg=----- InvokeAI source download failed -----
+echo Trying to download "%RELEASE_URL%%RELEASE_SOURCEBALL%"
+curl -L %RELEASE_URL%%RELEASE_SOURCEBALL% --output InvokeAI.tgz
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- InvokeAI source unpack failed -----
+tar -zxf InvokeAI.tgz
+if %errorlevel% neq 0 goto err_exit
+
+del /q InvokeAI.tgz
+
+set err_msg=----- InvokeAI source copy failed -----
+cd InvokeAI-*
+xcopy . .. /e /h
+if %errorlevel% neq 0 goto err_exit
+cd ..
+
+@rem cleanup
+for /f %%i in ('dir /b InvokeAI-*') do rd /s /q %%i
+rd /s /q .dev_scripts .github docker-build tests
+del /q requirements.in requirements-mkdocs.txt shell.nix
+
+echo ***** Unpacked InvokeAI source *****
+
+@rem Download/unpack/clean up python-build-standalone
+set err_msg=----- Python download failed -----
+curl -L %PYTHON_BUILD_STANDALONE_URL%/%PYTHON_BUILD_STANDALONE% --output python.tgz
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- Python unpack failed -----
+tar -zxf python.tgz
+if %errorlevel% neq 0 goto err_exit
+
+del /q python.tgz
+
+echo ***** Unpacked python-build-standalone *****
+
+@rem create venv
+set err_msg=----- problem creating venv -----
+.\python\python -E -s -m venv .venv
+if %errorlevel% neq 0 goto err_exit
+call .venv\Scripts\activate.bat
+
+echo ***** Created Python virtual environment *****
+
+@rem Print venv's Python version
+set err_msg=----- problem calling venv's python -----
+echo We're running under
+.venv\Scripts\python --version
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- pip update failed -----
+.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location --upgrade pip wheel
+if %errorlevel% neq 0 goto err_exit
+
+echo ***** Updated pip and wheel *****
+
+set err_msg=----- requirements file copy failed -----
+copy binary_installer\py3.10-windows-x86_64-cuda-reqs.txt requirements.txt
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- main pip install failed -----
+.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -r requirements.txt
+if %errorlevel% neq 0 goto err_exit
+
+echo ***** Installed Python dependencies *****
+
+set err_msg=----- InvokeAI setup failed -----
+.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -e .
+if %errorlevel% neq 0 goto err_exit
+
+copy binary_installer\invoke.bat.in .\invoke.bat
+echo ***** Installed invoke launcher script ******
+
+@rem more cleanup
+rd /s /q binary_installer installer_files
+
+@rem preload the models
+call .venv\Scripts\python ldm\invoke\config\invokeai_configure.py
+set err_msg=----- model download clone failed -----
+if %errorlevel% neq 0 goto err_exit
+deactivate
+
+echo ***** Finished downloading models *****
+
+echo All done! Execute the file invoke.bat in this directory to start InvokeAI
+pause
+exit
+
+:err_exit
+    echo %err_msg%
+    pause
+    exit

binary_installer/install.sh.in

@@ -0,0 +1,235 @@
+#!/usr/bin/env bash
+
+# ensure we're in the correct folder in case user's CWD is somewhere else
+scriptdir=$(dirname "$0")
+cd "$scriptdir"
+
+set -euo pipefail
+IFS=$'\n\t'
+
+function _err_exit {
+    if test "$1" -ne 0
+    then
+        echo -e "Error code $1; Error caught was '$2'"
+        read -p "Press any key to exit..."
+        exit
+    fi
+}
+
+# This script will install git (if not found on the PATH variable)
+#  using micromamba (an 8mb static-linked single-file binary, conda replacement).
+# For users who already have git, this step will be skipped.
+
+# Next, it'll download the project's source code.
+# Then it will download a self-contained, standalone Python and unpack it.
+# Finally, it'll create the Python virtual environment and preload the models.
+
+# This enables a user to install this project without manually installing git or Python
+
+echo -e "\n***** Installing InvokeAI into $(pwd)... *****\n"
+
+export no_cache_dir="--no-cache-dir"
+if [ $# -ge 1 ]; then
+    if [ "$1" = "use-cache" ]; then
+        export no_cache_dir=""
+    fi
+fi
+
+
+OS_NAME=$(uname -s)
+case "${OS_NAME}" in
+    Linux*)     OS_NAME="linux";;
+    Darwin*)    OS_NAME="darwin";;
+    *)          echo -e "\n----- Unknown OS: $OS_NAME! This script runs only on Linux or macOS -----\n" && exit
+esac
+
+OS_ARCH=$(uname -m)
+case "${OS_ARCH}" in
+    x86_64*)    ;;
+    arm64*)     ;;
+    *)          echo -e "\n----- Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64 -----\n" && exit
+esac
+
+# https://mamba.readthedocs.io/en/latest/installation.html
+MAMBA_OS_NAME=$OS_NAME
+MAMBA_ARCH=$OS_ARCH
+if [ "$OS_NAME" == "darwin" ]; then
+    MAMBA_OS_NAME="osx"
+fi
+
+if [ "$OS_ARCH" == "linux" ]; then
+    MAMBA_ARCH="aarch64"
+fi
+
+if [ "$OS_ARCH" == "x86_64" ]; then
+    MAMBA_ARCH="64"
+fi
+
+PY_ARCH=$OS_ARCH
+if [ "$OS_ARCH" == "arm64" ]; then
+    PY_ARCH="aarch64"
+fi
+
+# Compute device ('cd' segment of reqs files) detect goes here
+# This needs a ton of work
+# Suggestions:
+#   - lspci
+#   - check $PATH for nvidia-smi, gtt CUDA/GPU version from output
+#   - Surely there's a similar utility for AMD?
+CD="cuda"
+if [ "$OS_NAME" == "darwin" ] && [ "$OS_ARCH" == "arm64" ]; then
+    CD="mps"
+fi
+
+# config
+INSTALL_ENV_DIR="$(pwd)/installer_files/env"
+MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/${MAMBA_OS_NAME}-${MAMBA_ARCH}/latest"
+RELEASE_URL=https://github.com/invoke-ai/InvokeAI
+RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
+PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
+if [ "$OS_NAME" == "darwin" ]; then
+    PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-apple-darwin-install_only.tar.gz
+elif [ "$OS_NAME" == "linux" ]; then
+    PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-unknown-linux-gnu-install_only.tar.gz
+fi
+echo "INSTALLING $RELEASE_SOURCEBALL FROM $RELEASE_URL"
+
+PACKAGES_TO_INSTALL=""
+
+if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
+
+# (if necessary) install git and conda into a contained environment
+if [ "$PACKAGES_TO_INSTALL" != "" ]; then
+    # download micromamba
+    echo -e "\n***** Downloading micromamba from $MICROMAMBA_DOWNLOAD_URL to micromamba *****\n"
+
+    curl -L "$MICROMAMBA_DOWNLOAD_URL" | tar -xvjO bin/micromamba > micromamba
+
+    chmod u+x ./micromamba
+
+    # test the mamba binary
+    echo -e "\n***** Micromamba version: *****\n"
+    ./micromamba --version
+
+    # create the installer env
+    if [ ! -e "$INSTALL_ENV_DIR" ]; then
+        ./micromamba create -y --prefix "$INSTALL_ENV_DIR"
+    fi
+
+    echo -e "\n***** Packages to install:$PACKAGES_TO_INSTALL *****\n"
+
+    ./micromamba install -y --prefix "$INSTALL_ENV_DIR" -c conda-forge "$PACKAGES_TO_INSTALL"
+
+    if [ ! -e "$INSTALL_ENV_DIR" ]; then
+        echo -e "\n----- There was a problem while initializing micromamba. Cannot continue. -----\n"
+        exit
+    fi
+fi
+
+rm -f micromamba.exe
+
+export PATH="$INSTALL_ENV_DIR/bin:$PATH"
+
+# Download/unpack/clean up InvokeAI release sourceball
+_err_msg="\n----- InvokeAI source download failed -----\n"
+curl -L $RELEASE_URL/$RELEASE_SOURCEBALL --output InvokeAI.tgz
+_err_exit $? _err_msg
+_err_msg="\n----- InvokeAI source unpack failed -----\n"
+tar -zxf InvokeAI.tgz
+_err_exit $? _err_msg
+
+rm -f InvokeAI.tgz
+
+_err_msg="\n----- InvokeAI source copy failed -----\n"
+cd InvokeAI-*
+cp -r . ..
+_err_exit $? _err_msg
+cd ..
+
+# cleanup
+rm -rf InvokeAI-*/
+rm -rf .dev_scripts/ .github/ docker-build/ tests/ requirements.in requirements-mkdocs.txt shell.nix
+
+echo -e "\n***** Unpacked InvokeAI source *****\n"
+
+# Download/unpack/clean up python-build-standalone
+_err_msg="\n----- Python download failed -----\n"
+curl -L $PYTHON_BUILD_STANDALONE_URL/$PYTHON_BUILD_STANDALONE --output python.tgz
+_err_exit $? _err_msg
+_err_msg="\n----- Python unpack failed -----\n"
+tar -zxf python.tgz
+_err_exit $? _err_msg
+
+rm -f python.tgz
+
+echo -e "\n***** Unpacked python-build-standalone *****\n"
+
+# create venv
+_err_msg="\n----- problem creating venv -----\n"
+
+if [ "$OS_NAME" == "darwin" ]; then
+    # patch sysconfig so that extensions can build properly
+    # adapted from https://github.com/cashapp/hermit-packages/commit/fcba384663892f4d9cfb35e8639ff7a28166ee43
+    PYTHON_INSTALL_DIR="$(pwd)/python"
+    SYSCONFIG="$(echo python/lib/python*/_sysconfigdata_*.py)"
+    TMPFILE="$(mktemp)"
+    chmod +w "${SYSCONFIG}"
+    cp "${SYSCONFIG}" "${TMPFILE}"
+    sed "s,'/install,'${PYTHON_INSTALL_DIR},g" "${TMPFILE}" > "${SYSCONFIG}"
+    rm -f "${TMPFILE}"
+fi
+
+./python/bin/python3 -E -s -m venv .venv
+_err_exit $? _err_msg
+source .venv/bin/activate
+
+echo -e "\n***** Created Python virtual environment *****\n"
+
+# Print venv's Python version
+_err_msg="\n----- problem calling venv's python -----\n"
+echo -e "We're running under"
+.venv/bin/python3 --version
+_err_exit $? _err_msg
+
+_err_msg="\n----- pip update failed -----\n"
+.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location --upgrade pip
+_err_exit $? _err_msg
+
+echo -e "\n***** Updated pip *****\n"
+
+_err_msg="\n----- requirements file copy failed -----\n"
+cp binary_installer/py3.10-${OS_NAME}-"${OS_ARCH}"-${CD}-reqs.txt requirements.txt
+_err_exit $? _err_msg
+
+_err_msg="\n----- main pip install failed -----\n"
+.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location -r requirements.txt
+_err_exit $? _err_msg
+
+echo -e "\n***** Installed Python dependencies *****\n"
+
+_err_msg="\n----- InvokeAI setup failed -----\n"
+.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location -e .
+_err_exit $? _err_msg
+
+echo -e "\n***** Installed InvokeAI *****\n"
+
+cp binary_installer/invoke.sh.in ./invoke.sh
+chmod a+rx ./invoke.sh
+echo -e "\n***** Installed invoke launcher script ******\n"
+
+# more cleanup
+rm -rf binary_installer/ installer_files/
+
+# preload the models
+.venv/bin/python3 scripts/configure_invokeai.py
+_err_msg="\n----- model download clone failed -----\n"
+_err_exit $? _err_msg
+deactivate
+
+echo -e "\n***** Finished downloading models *****\n"
+
+echo "All done! Run the command"
+echo "  $scriptdir/invoke.sh"
+echo "to start InvokeAI."
+read -p "Press any key to exit..."
+exit

binary_installer/invoke.bat.in

@@ -0,0 +1,36 @@
+@echo off
+
+PUSHD "%~dp0"
+call .venv\Scripts\activate.bat
+
+echo Do you want to generate images using the
+echo 1. command-line
+echo 2. browser-based UI
+echo OR
+echo 3. open the developer console
+set /p choice="Please enter 1, 2 or 3: "
+if /i "%choice%" == "1" (
+    echo Starting the InvokeAI command-line.
+    .venv\Scripts\python scripts\invoke.py %*
+) else if /i "%choice%" == "2" (
+    echo Starting the InvokeAI browser-based UI.
+    .venv\Scripts\python scripts\invoke.py --web %*
+) else if /i "%choice%" == "3" (
+    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 (
+    echo Invalid selection
+    pause
+    exit /b
+)
+
+deactivate

binary_installer/invoke.sh.in

@@ -0,0 +1,46 @@
+#!/usr/bin/env sh
+
+set -eu
+
+. .venv/bin/activate
+
+# set required env var for torch on mac MPS
+ if [ "$(uname -s)" == "Darwin" ]; then
+     export PYTORCH_ENABLE_MPS_FALLBACK=1
+ fi
+
+echo "Do you want to generate images using the"
+echo "1. command-line"
+echo "2. browser-based UI"
+echo "OR"
+echo "3. open the developer console"
+echo "Please enter 1, 2, or 3:"
+read choice
+
+case $choice in
+    1)
+        printf "\nStarting the InvokeAI command-line..\n";
+        .venv/bin/python scripts/invoke.py $*;
+    ;;
+    2)
+        printf "\nStarting the InvokeAI browser-based UI..\n";
+        .venv/bin/python scripts/invoke.py --web $*;
+    ;;
+    3)
+        printf "\nDeveloper Console:\n";
+        printf "Python command is:\n\t";
+        which python;
+        printf "Python version is:\n\t";
+        python --version;
+        echo "*************************"
+        echo "You are now in your user shell ($SHELL) with the local InvokeAI Python virtual environment activated,";
+        echo "so that you can troubleshoot this InvokeAI installation as necessary.";
+        printf "*************************\n"
+        echo "*** Type \`exit\` to quit this shell and deactivate the Python virtual environment *** ";
+        /usr/bin/env "$SHELL";
+    ;;
+    *)
+        echo "Invalid selection";
+        exit
+    ;;
+esac

binary_installer/readme.txt

@@ -0,0 +1,17 @@
+InvokeAI
+
+Project homepage: https://github.com/invoke-ai/InvokeAI
+
+Installation on Windows:
+    NOTE: You might need to enable Windows Long Paths. If you're not sure,
+    then you almost certainly need to. Simply double-click the 'WinLongPathsEnabled.reg'
+    file. Note that you will need to have admin privileges in order to
+    do this.
+
+    Please double-click the 'install.bat' file (while keeping it inside the invokeAI folder).
+
+Installation on Linux and Mac:
+    Please open the terminal, and run './install.sh' (while keeping it inside the invokeAI folder).
+
+After installation, please run the 'invoke.bat' file (on Windows) or 'invoke.sh'
+file (on Linux/Mac) to start InvokeAI.

binary_installer/requirements.in

@@ -0,0 +1,33 @@
+--prefer-binary
+--extra-index-url https://download.pytorch.org/whl/torch_stable.html
+--extra-index-url https://download.pytorch.org/whl/cu116
+--trusted-host https://download.pytorch.org
+accelerate~=0.15
+albumentations
+diffusers[torch]~=0.11
+einops
+eventlet
+flask_cors
+flask_socketio
+flaskwebgui==1.0.3
+getpass_asterisk
+imageio-ffmpeg
+pyreadline3
+realesrgan
+send2trash
+streamlit
+taming-transformers-rom1504
+test-tube
+torch-fidelity
+torch==1.12.1 ; platform_system == 'Darwin'
+torch==1.12.0+cu116 ; platform_system == 'Linux' or platform_system == 'Windows'
+torchvision==0.13.1 ; platform_system == 'Darwin'
+torchvision==0.13.0+cu116 ; platform_system == 'Linux' or platform_system == 'Windows'
+transformers
+picklescan
+https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip
+https://github.com/invoke-ai/clipseg/archive/1f754751c85d7d4255fa681f4491ff5711c1c288.zip
+https://github.com/invoke-ai/GFPGAN/archive/3f5d2397361199bc4a91c08bb7d80f04d7805615.zip ; platform_system=='Windows'
+https://github.com/invoke-ai/GFPGAN/archive/c796277a1cf77954e5fc0b288d7062d162894248.zip ; platform_system=='Linux' or platform_system=='Darwin'
+https://github.com/Birch-san/k-diffusion/archive/363386981fee88620709cf8f6f2eea167bd6cd74.zip
+https://github.com/invoke-ai/PyPatchMatch/archive/129863937a8ab37f6bbcec327c994c0f932abdbc.zip

docs/contributing/INVOCATIONS.md

@@ -19,56 +19,31 @@ An invocation looks like this:
 ```py
 class UpscaleInvocation(BaseInvocation):
     """Upscales an image."""
-
-    # fmt: off
-    type: Literal["upscale"] = "upscale"
+    type: Literal['upscale'] = 'upscale'
 
     # Inputs
-    image: Union[ImageField, None] = Field(description="The input image", default=None)
-    strength: float                = Field(default=0.75, gt=0, le=1, description="The strength")
-    level: Literal[2, 4]           = Field(default=2, description="The upscale level")
-    # fmt: on
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["upscaling", "image"],
-            },
-        }
+    image: Union[ImageField,None] = Field(description="The input image")
+    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
+    level: Literal[2,4]           = Field(default=2, description = "The upscale level")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-        results = context.services.restoration.upscale_and_reconstruct(
-            image_list=[[image, 0]],
-            upscale=(self.level, self.strength),
-            strength=0.0,  # GFPGAN strength
-            save_original=False,
-            image_callback=None,
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        results = context.services.generate.upscale_and_reconstruct(
+            image_list     = [[image, 0]],
+            upscale        = (self.level, self.strength),
+            strength       = 0.0, # GFPGAN strength
+            save_original  = False,
+            image_callback = None,
         )
 
         # Results are image and seed, unwrap for now
         # TODO: can this return multiple results?
-        image_dto = context.services.images.create(
-            image=results[0][0],
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, results[0][0])
         return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+            image = ImageField(image_type = image_type, image_name = image_name)
         )
-
 ```
 
 Each portion is important to implement correctly.
@@ -120,67 +95,25 @@ Finally, note that for all linking, the `type` of the linked fields must match.
 If the `name` also matches, then the field can be **automatically linked** to a
 previous invocation by name and matching.
 
-### Config
-
-```py
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["upscaling", "image"],
-            },
-        }
-```
-
-This is an optional configuration for the invocation. It inherits from
-pydantic's model `Config` class, and it used primarily to customize the
-autogenerated OpenAPI schema.
-
-The UI relies on the OpenAPI schema in two ways:
-
-- An API client & Typescript types are generated from it. This happens at build
-  time.
-- The node editor parses the schema into a template used by the UI to create the
-  node editor UI. This parsing happens at runtime.
-
-In this example, a `ui` key has been added to the `schema_extra` dict to provide
-some tags for the UI, to facilitate filtering nodes.
-
-See the Schema Generation section below for more information.
-
 ### Invoke Function
 
 ```py
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-        results = context.services.restoration.upscale_and_reconstruct(
-            image_list=[[image, 0]],
-            upscale=(self.level, self.strength),
-            strength=0.0,  # GFPGAN strength
-            save_original=False,
-            image_callback=None,
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+        results = context.services.generate.upscale_and_reconstruct(
+            image_list     = [[image, 0]],
+            upscale        = (self.level, self.strength),
+            strength       = 0.0, # GFPGAN strength
+            save_original  = False,
+            image_callback = None,
         )
 
         # Results are image and seed, unwrap for now
-        # TODO: can this return multiple results?
-        image_dto = context.services.images.create(
-            image=results[0][0],
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, results[0][0])
         return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+            image = ImageField(image_type = image_type, image_name = image_name)
         )
 ```
 
@@ -202,16 +135,9 @@ scenarios. If you need functionality, please provide it as a service in the
 ```py
 class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
+    type: Literal['image'] = 'image'
 
-    # fmt: off
-    type: Literal["image_output"] = "image_output"
-    image:      ImageField = Field(default=None, description="The output image")
-    width:             int = Field(description="The width of the image in pixels")
-    height:            int = Field(description="The height of the image in pixels")
-    # fmt: on
-
-    class Config:
-        schema_extra = {"required": ["type", "image", "width", "height"]}
+    image: ImageField = Field(default=None, description="The output image")
 ```
 
 Output classes look like an invocation class without the invoke method. Prefer
@@ -242,36 +168,35 @@ Here's that `ImageOutput` class, without the needed schema customisation:
 class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
 
-    # fmt: off
-    type: Literal["image_output"] = "image_output"
+    type: Literal["image"] = "image"
     image:      ImageField = Field(default=None, description="The output image")
-    width:             int = Field(description="The width of the image in pixels")
-    height:            int = Field(description="The height of the image in pixels")
-    # fmt: on
 ```
 
-The OpenAPI schema that results from this `ImageOutput` will have the `type`,
-`image`, `width` and `height` properties marked as optional, even though we know
-they will always have a value.
+The generated OpenAPI schema, and all clients/types generated from it, will have
+the `type` and `image` properties marked as optional, even though we know they
+will always have a value by the time we can interact with them via the API.
+
+Here's the same class, but with the schema customisation added:
 
 ```python
 class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
 
-    # fmt: off
-    type: Literal["image_output"] = "image_output"
+    type: Literal["image"] = "image"
     image:      ImageField = Field(default=None, description="The output image")
-    width:             int = Field(description="The width of the image in pixels")
-    height:            int = Field(description="The height of the image in pixels")
-    # fmt: on
 
-    # Add schema customization
     class Config:
-        schema_extra = {"required": ["type", "image", "width", "height"]}
+        schema_extra = {
+            'required': [
+                'type',
+                'image',
+            ]
+        }
 ```
 
-With the customization in place, the schema will now show these properties as
-required, obviating the need for extensive null checks in client code.
+The resultant schema (and any API client or types generated from it) will now
+have see `type` as string literal `"image"` and `image` as an `ImageField`
+object.
 
 See this `pydantic` issue for discussion on this solution:
 <https://github.com/pydantic/pydantic/discussions/4577>

docs/features/LOGGING.md

@@ -1,171 +0,0 @@
----
-title: Controlling Logging
----
-
-# :material-image-off: Controlling Logging
-
-## Controlling How InvokeAI Logs Status Messages
-
-InvokeAI logs status messages using a configurable logging system. You
-can log to the terminal window, to a designated file on the local
-machine, to the syslog facility on a Linux or Mac, or to a properly
-configured web server. You can configure several logs at the same
-time, and control the level of message logged and the logging format
-(to a limited extent).
-
-Three command-line options control logging:
-
-### `--log_handlers <handler1> <handler2> ...`
-
-This option activates one or more log handlers. Options are "console",
-"file", "syslog" and "http". To specify more than one, separate them
-by spaces:
-
-```bash
-invokeai-web --log_handlers console syslog=/dev/log file=C:\Users\fred\invokeai.log
-```
-
-The format of these options is described below.
-
-### `--log_format {plain|color|legacy|syslog}`
-
-This controls the format of log messages written to the console. Only
-the "console" log handler is currently affected by this setting.
-
-* "plain" provides formatted messages like this:
-
-```bash
-
-[2023-05-24 23:18:2[2023-05-24 23:18:50,352]::[InvokeAI]::DEBUG --> this is a debug message
-[2023-05-24 23:18:50,352]::[InvokeAI]::INFO --> this is an informational messages
-[2023-05-24 23:18:50,352]::[InvokeAI]::WARNING --> this is a warning
-[2023-05-24 23:18:50,352]::[InvokeAI]::ERROR --> this is an error
-[2023-05-24 23:18:50,352]::[InvokeAI]::CRITICAL --> this is a critical error
-```
-
-* "color" produces similar output, but the text will be color coded to
-indicate the severity of the message.
-
-* "legacy" produces output similar to InvokeAI versions 2.3 and earlier:
-
-```bash
-### this is a critical error
-*** this is an error
-** this is a warning
->> this is an informational messages
-   | this is a debug message
-```
-
-* "syslog" produces messages suitable for syslog entries:
-
-```bash
-InvokeAI [2691178] <CRITICAL> this is a critical error
-InvokeAI [2691178] <ERROR> this is an error
-InvokeAI [2691178] <WARNING> this is a warning
-InvokeAI [2691178] <INFO> this is an informational messages
-InvokeAI [2691178] <DEBUG> this is a debug message
-```
-
-(note that the date, time and hostname will be added by the syslog
-system)
-
-### `--log_level {debug|info|warning|error|critical}`
-
-Providing this command-line option will cause only messages at the
-specified level or above to be emitted.
-
-## Console logging
-
-When "console" is provided to `--log_handlers`, messages will be
-written to the command line window in which InvokeAI was launched. By
-default, the color formatter will be used unless overridden by
-`--log_format`.
-
-## File logging
-
-When "file" is provided to `--log_handlers`, entries will be written
-to the file indicated in the path argument. By default, the "plain"
-format will be used:
-
-```bash
-invokeai-web --log_handlers file=/var/log/invokeai.log
-```
-
-## Syslog logging
-
-When "syslog" is requested, entries will be sent to the syslog
-system. There are a variety of ways to control where the log message
-is sent:
-
-* Send to the local machine using the `/dev/log` socket:
-
-```
-invokeai-web --log_handlers syslog=/dev/log
-```
-
-* Send to the local machine using a UDP message:
-
-```
-invokeai-web --log_handlers syslog=localhost
-```
-
-* Send to the local machine using a UDP message on a nonstandard
-  port:
-
-```
-invokeai-web --log_handlers syslog=localhost:512
-```
-
-* Send to a remote machine named "loghost" on the local LAN using
-  facility LOG_USER and UDP packets:
-
-```
-invokeai-web --log_handlers syslog=loghost,facility=LOG_USER,socktype=SOCK_DGRAM
-```
-
-This can be abbreviated `syslog=loghost`, as LOG_USER and SOCK_DGRAM
-are defaults.
-
-* Send to a remote machine named "loghost" using the facility LOCAL0
-  and using a TCP socket:
-
-```
-invokeai-web --log_handlers syslog=loghost,facility=LOG_LOCAL0,socktype=SOCK_STREAM
-```
-
-If no arguments are specified (just a bare "syslog"), then the logging
-system will look for a UNIX socket named `/dev/log`, and if not found
-try to send a UDP message to `localhost`. The Macintosh OS used to
-support logging to a socket named `/var/run/syslog`, but this feature
-has since been disabled.
-
-## Web logging
-
-If you have access to a web server that is configured to log messages
-when a particular URL is requested, you can log using the "http"
-method:
-
-```
-invokeai-web --log_handlers http=http://my.server/path/to/logger,method=POST
-```
-
-The optional [,method=] part can be used to specify whether the URL
-accepts GET (default) or POST messages.
-
-Currently password authentication and SSL are not supported.
-
-## Using the configuration file
-
-You can set and forget logging options by adding a "Logging" section
-to `invokeai.yaml`:
-
-```
-InvokeAI:
-  [... other settings...]
-  Logging:
-    log_handlers:
-       - console
-       - syslog=/dev/log
-    log_level: info
-    log_format: color
-```

docs/features/index.md

@@ -57,9 +57,6 @@ Personalize models by adding your own style or subjects.
 ## * [The NSFW Checker](NSFW.md)
 Prevent InvokeAI from displaying unwanted racy images.
 
-## * [Controlling Logging](LOGGING.md)
-Control how InvokeAI logs status messages.
-
 ## * [Miscellaneous](OTHER.md)
 Run InvokeAI on Google Colab, generate images with repeating patterns,
 batch process a file of prompts, increase the "creativity" of image

docs/installation/010_INSTALL_AUTOMATED.md

@@ -89,7 +89,7 @@ experimental versions later.
          sudo apt update
          sudo apt install -y software-properties-common
          sudo add-apt-repository -y ppa:deadsnakes/ppa
-         sudo apt install -y python3.10 python3-pip python3.10-venv
+         sudo apt install python3.10 python3-pip python3.10-venv
          sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
          ```
 

docs/installation/020_INSTALL_MANUAL.md

@@ -216,7 +216,7 @@ manager, please follow these steps:
 9.  Run the command-line- or the web- interface:
 
     From within INVOKEAI_ROOT, activate the environment
-    (with `source .venv/bin/activate` or `.venv\scripts\activate`), and then run
+    (with `source .venv/bin/activate` or `.venv\scripts\activate), and then run
     the script `invokeai`. If the virtual environment you selected is NOT inside
     INVOKEAI_ROOT, then you must specify the path to the root directory by adding
     `--root_dir \path\to\invokeai` to the commands below:

installer/lib/installer.py

@@ -247,8 +247,8 @@ class InvokeAiInstance:
             pip[
                 "install",
                 "--require-virtualenv",
-                "torch~=2.0.0",
-                "torchvision>=0.14.1",
+                "torch",
+                "torchvision",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,

installer/templates/invoke.bat.in

@@ -7,42 +7,42 @@ call .venv\Scripts\activate.bat
 set INVOKEAI_ROOT=.
 
 :start
-echo Desired action:
-echo 1. Generate images with the browser-based interface
-echo 2. Explore InvokeAI nodes using a command-line interface
-echo 3. Run textual inversion training
-echo 4. Merge models (diffusers type only)
-echo 5. Download and install models
-echo 6. Change InvokeAI startup options
-echo 7. Re-run the configure script to fix a broken install
-echo 8. Open the developer console
-echo 9. Update InvokeAI
-echo 10. Command-line help
-echo Q - Quit
-set /P choice="Please enter 1-10, Q: [2] "
-if not defined choice set choice=2
-IF /I "%choice%" == "1" (
-    echo Starting the InvokeAI browser-based UI..
-    python .venv\Scripts\invokeai-web.exe %*
-) ELSE IF /I "%choice%" == "2" (
+echo Do you want to generate images using the
+echo 1. command-line interface
+echo 2. browser-based UI
+echo 3. run textual inversion training
+echo 4. merge models (diffusers type only)
+echo 5. download and install models
+echo 6. change InvokeAI startup options
+echo 7. re-run the configure script to fix a broken install
+echo 8. open the developer console
+echo 9. update InvokeAI
+echo 10. command-line help
+echo Q - quit
+set /P restore="Please enter 1-10, Q: [2] "
+if not defined restore set restore=2
+IF /I "%restore%" == "1" (
     echo Starting the InvokeAI command-line..
     python .venv\Scripts\invokeai.exe %*
-) ELSE IF /I "%choice%" == "3" (
+) ELSE IF /I "%restore%" == "2" (
+    echo Starting the InvokeAI browser-based UI..
+    python .venv\Scripts\invokeai.exe --web %*
+) ELSE IF /I "%restore%" == "3" (
     echo Starting textual inversion training..
     python .venv\Scripts\invokeai-ti.exe --gui
-) ELSE IF /I "%choice%" == "4" (
+) ELSE IF /I "%restore%" == "4" (
     echo Starting model merging script..
     python .venv\Scripts\invokeai-merge.exe --gui
-) ELSE IF /I "%choice%" == "5" (
+) ELSE IF /I "%restore%" == "5" (
     echo Running invokeai-model-install...
     python .venv\Scripts\invokeai-model-install.exe
-) ELSE IF /I "%choice%" == "6" (
+) ELSE IF /I "%restore%" == "6" (
     echo Running invokeai-configure...
     python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models
-) ELSE IF /I "%choice%" == "7" (
+) ELSE IF /I "%restore%" == "7" (
     echo Running invokeai-configure...
     python .venv\Scripts\invokeai-configure.exe --yes --default_only
-) ELSE IF /I "%choice%" == "8" (
+) ELSE IF /I "%restore%" == "8" (
     echo Developer Console
     echo Python command is:
     where python
@@ -54,15 +54,15 @@ IF /I "%choice%" == "1" (
     echo *************************
     echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
     call cmd /k
-) ELSE IF /I "%choice%" == "9" (
+) ELSE IF /I "%restore%" == "9" (
    echo Running invokeai-update...
    python .venv\Scripts\invokeai-update.exe %*
-) ELSE IF /I "%choice%" == "10" (
+) ELSE IF /I "%restore%" == "10" (
     echo Displaying command line help...
     python .venv\Scripts\invokeai.exe --help %*
     pause
     exit /b
-) ELSE IF /I "%choice%" == "q" (
+) ELSE IF /I "%restore%" == "q" (
     echo Goodbye!
     goto ending
 ) ELSE (

installer/templates/invoke.sh.in

@@ -1,10 +1,5 @@
 #!/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,
@@ -16,168 +11,85 @@
 
 set -eu
 
-# Ensure we're in the correct folder in case user's CWD is somewhere else
+# ensure we're in the correct folder in case user's CWD is somewhere else
 scriptdir=$(dirname "$0")
 cd "$scriptdir"
 
 . .venv/bin/activate
 
 export INVOKEAI_ROOT="$scriptdir"
-PARAMS=$@
 
-# Check to see if dialog is installed (it seems to be fairly standard, but good to check regardless) and if the user has passed the --no-tui argument to disable the dialog TUI
-tui=true
-if command -v dialog &>/dev/null; then
-    # This must use $@ to properly loop through the arguments passed by the user
-    for arg in "$@"; do
-        if [ "$arg" == "--no-tui" ]; then
-            tui=false
-            # Remove the --no-tui argument to avoid errors later on when passing arguments to InvokeAI
-            PARAMS=$(echo "$PARAMS" | sed 's/--no-tui//')
-            break
-        fi
-    done
-else
-    tui=false
-fi
-
-# Set required env var for torch on mac MPS
+# set required env var for torch on mac MPS
 if [ "$(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 "Explore InvokeAI nodes using a command-line interface\n"
-        invokeai $PARAMS
-        ;;
-    3)
-        clear
-        printf "Textual inversion training\n"
-        invokeai-ti --gui $PARAMS
-        ;;
-    4)
-        clear
-        printf "Merge models (diffusers type only)\n"
-        invokeai-merge --gui $PARAMS
-        ;;
-    5)
-        clear
-        printf "Download and install models\n"
-        invokeai-model-install --root ${INVOKEAI_ROOT}
-        ;;
-    6)
-        clear
-        printf "Change InvokeAI startup options\n"
-        invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
-        ;;
-    7)
-        clear
-        printf "Re-run the configure script to fix a broken install\n"
-        invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
-        ;;
-    8)
-        clear
-        printf "Open the developer console\n"
-        file_name=$(basename "${BASH_SOURCE[0]}")
-        bash --init-file "$file_name"
-        ;;
-    9)
-        clear
-        printf "Update InvokeAI\n"
-        invokeai-update
-        ;;
-    10)
-        clear
-        printf "Command-line help\n"
-        invokeai --help
-        ;;
-    "HELP 1")
-        clear
-        printf "Command-line help\n"
-        invokeai --help
-        ;;
-    *)
-        clear
-        printf "Exiting...\n"
-        exit
-        ;;
-    esac
-    clear
-}
-
-# Dialog-based TUI for launcing Invoke functions
-do_dialog() {
-    options=(
-        1 "Generate images with a browser-based interface"
-        2 "Generate images using a command-line interface"
-        3 "Textual inversion training"
-        4 "Merge models (diffusers type only)"
-        5 "Download and install models"
-        6 "Change InvokeAI startup options"
-        7 "Re-run the configure script to fix a broken install"
-        8 "Open the developer console"
-        9 "Update InvokeAI")
-
-    choice=$(dialog --clear \
-        --backtitle "\Zb\Zu\Z3InvokeAI" \
-        --colors \
-        --title "What would you like to do?" \
-        --ok-label "Run" \
-        --cancel-label "Exit" \
-        --help-button \
-        --help-label "CLI Help" \
-        --menu "Select an option:" \
-        0 0 0 \
-        "${options[@]}" \
-        2>&1 >/dev/tty) || clear
-    do_choice "$choice"
-    clear
-}
-
-# Command-line interface for launching Invoke functions
-do_line_input() {
-    clear
-    printf " ** For a more attractive experience, please install the 'dialog' utility using your package manager. **\n\n"
-    printf "What would you like to do?\n"
-    printf "1: Generate images using the browser-based interface\n"
-    printf "2: Explore InvokeAI nodes using the command-line interface\n"
-    printf "3: Run textual inversion training\n"
-    printf "4: Merge models (diffusers type only)\n"
-    printf "5: Download and install models\n"
-    printf "6: Change InvokeAI startup options\n"
-    printf "7: Re-run the configure script to fix a broken install\n"
-    printf "8: Open the developer console\n"
-    printf "9: Update InvokeAI\n"
-    printf "10: Command-line help\n"
-    printf "Q: Quit\n\n"
-    read -p "Please enter 1-10, Q: [1] " yn
-    choice=${yn:='1'}
-    do_choice $choice
-    clear
-}
-
-# Main IF statement for launching Invoke with either the TUI or CLI, and for checking if the user is in the developer console
 if [ "$0" != "bash" ]; then
-    while true; do
-        if $tui; then
-            # .dialogrc must be located in the same directory as the invoke.sh script
-            export DIALOGRC="./.dialogrc"
-            do_dialog
-        else
-            do_line_input
-        fi
-    done
+  while true
+  do
+    echo "Do you want to generate images using the"
+    echo "1. command-line interface"
+    echo "2. browser-based UI"
+    echo "3. run textual inversion training"
+    echo "4. merge models (diffusers type only)"
+    echo "5. download and install models"
+    echo "6. change InvokeAI startup options"
+    echo "7. re-run the configure script to fix a broken install"
+    echo "8. open the developer console"
+    echo "9. update InvokeAI"
+    echo "10. command-line help"
+    echo "Q - Quit"
+    echo ""
+    read -p "Please enter 1-10, Q: [2] " yn
+    choice=${yn:='2'}
+    case $choice in
+        1)
+            echo "Starting the InvokeAI command-line..."
+            invokeai $@
+            ;;
+        2)
+            echo "Starting the InvokeAI browser-based UI..."
+            invokeai --web $@
+            ;;
+        3)
+            echo "Starting Textual Inversion:"
+            invokeai-ti --gui $@
+            ;;
+        4)
+            echo "Merging Models:"
+            invokeai-merge --gui $@
+            ;;
+        5)
+            invokeai-model-install --root ${INVOKEAI_ROOT}
+            ;;
+        6)
+            invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
+            ;;
+        7)
+            invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
+            ;;
+        8)
+            echo "Developer Console:"
+            file_name=$(basename "${BASH_SOURCE[0]}")
+            bash --init-file "$file_name"
+            ;;
+        9)
+            echo "Update:"
+            invokeai-update
+            ;;
+        10)
+            invokeai --help
+            ;;
+        [qQ])
+            exit 0
+            ;;
+        *)
+            echo "Invalid selection"
+            exit;;
+    esac
+ done
 else # in developer console
     python --version
-    printf "Press ^D to exit\n"
+    echo "Press ^D to exit"
     export PS1="(InvokeAI) \u@\h \w> "
 fi

invokeai/app/api/dependencies.py

@@ -1,20 +1,19 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from logging import Logger
 import os
-from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
-from invokeai.app.services.images import ImageService
-from invokeai.app.services.metadata import CoreMetadataService
-from invokeai.app.services.resource_name import SimpleNameService
-from invokeai.app.services.urls import LocalUrlService
-from invokeai.backend.util.logging import InvokeAILogger
+from argparse import Namespace
+
+from invokeai.app.services.metadata import PngMetadataService, MetadataServiceBase
 
 from ..services.default_graphs import create_system_graphs
+
 from ..services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
+
+from ...backend import Globals
 from ..services.model_manager_initializer import get_model_manager
 from ..services.restoration_services import RestorationServices
 from ..services.graph import GraphExecutionState, LibraryGraph
-from ..services.image_file_storage import DiskImageFileStorage
+from ..services.image_storage import DiskImageStorage
 from ..services.invocation_queue import MemoryInvocationQueue
 from ..services.invocation_services import InvocationServices
 from ..services.invoker import Invoker
@@ -39,63 +38,52 @@ def check_internet() -> bool:
         return False
 
 
-logger = InvokeAILogger.getLogger()
-
-
 class ApiDependencies:
     """Contains and initializes all dependencies for the API"""
 
     invoker: Invoker = None
 
     @staticmethod
-    def initialize(config, event_handler_id: int, logger: Logger = logger):
-        logger.info(f"Internet connectivity is {config.internet_available}")
+    def initialize(config, event_handler_id: int):
+        Globals.try_patchmatch = config.patchmatch
+        Globals.always_use_cpu = config.always_use_cpu
+        Globals.internet_available = config.internet_available and check_internet()
+        Globals.disable_xformers = not config.xformers
+        Globals.ckpt_convert = config.ckpt_convert
+
+        # TODO: Use a logger
+        print(f">> Internet connectivity is {Globals.internet_available}")
 
         events = FastAPIEventService(event_handler_id)
 
-        output_folder = config.output_path
+        output_folder = os.path.abspath(
+            os.path.join(os.path.dirname(__file__), "../../../../outputs")
+        )
+
+        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents'))
+
+        metadata = PngMetadataService()
+
+        images = DiskImageStorage(f'{output_folder}/images', metadata_service=metadata)
 
         # TODO: build a file/path manager?
-        db_location = config.db_path
-        db_location.parent.mkdir(parents=True,exist_ok=True)
-
-        graph_execution_manager = SqliteItemStorage[GraphExecutionState](
-            filename=db_location, table_name="graph_executions"
-        )
-
-        urls = LocalUrlService()
-        metadata = CoreMetadataService()
-        image_record_storage = SqliteImageRecordStorage(db_location)
-        image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
-        names = SimpleNameService()
-        latents = ForwardCacheLatentsStorage(
-            DiskLatentsStorage(f"{output_folder}/latents")
-        )
-
-        images = ImageService(
-            image_record_storage=image_record_storage,
-            image_file_storage=image_file_storage,
-            metadata=metadata,
-            url=urls,
-            logger=logger,
-            names=names,
-            graph_execution_manager=graph_execution_manager,
-        )
+        db_location = os.path.join(output_folder, "invokeai.db")
 
         services = InvocationServices(
-            model_manager=get_model_manager(config, logger),
+            model_manager=get_model_manager(config),
             events=events,
             latents=latents,
             images=images,
+            metadata=metadata,
             queue=MemoryInvocationQueue(),
             graph_library=SqliteItemStorage[LibraryGraph](
                 filename=db_location, table_name="graphs"
             ),
-            graph_execution_manager=graph_execution_manager,
+            graph_execution_manager=SqliteItemStorage[GraphExecutionState](
+                filename=db_location, table_name="graph_executions"
+            ),
             processor=DefaultInvocationProcessor(),
-            restoration=RestorationServices(config, logger),
-            configuration=config,
-            logger=logger,
+            restoration=RestorationServices(config),
         )
 
         create_system_graphs(services.graph_library)

invokeai/app/api/models/images.py

@@ -0,0 +1,34 @@
+from typing import Optional
+from pydantic import BaseModel, Field
+
+from invokeai.app.models.image import ImageType
+from invokeai.app.services.metadata import InvokeAIMetadata
+
+
+class ImageResponseMetadata(BaseModel):
+    """An image's metadata. Used only in HTTP responses."""
+
+    created: int = Field(description="The creation timestamp of the image")
+    width: int = Field(description="The width of the image in pixels")
+    height: int = Field(description="The height of the image in pixels")
+    invokeai: Optional[InvokeAIMetadata] = Field(
+        description="The image's InvokeAI-specific metadata"
+    )
+
+
+class ImageResponse(BaseModel):
+    """The response type for images"""
+
+    image_type: ImageType = Field(description="The type of the image")
+    image_name: str = Field(description="The name of the image")
+    image_url: str = Field(description="The url of the image")
+    thumbnail_url: str = Field(description="The url of the image's thumbnail")
+    metadata: ImageResponseMetadata = Field(description="The image's metadata")
+
+
+class ProgressImage(BaseModel):
+    """The progress image sent intermittently during processing"""
+
+    width: int = Field(description="The effective width of the image in pixels")
+    height: int = Field(description="The effective height of the image in pixels")
+    dataURL: str = Field(description="The image data as a b64 data URL")

invokeai/app/api/routers/images.py

@@ -1,250 +1,128 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 import io
-from typing import Optional
-from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
+from datetime import datetime, timezone
+import json
+import os
+from typing import Any
+import uuid
+
+from fastapi import HTTPException, Path, Query, Request, UploadFile
+from fastapi.responses import FileResponse, Response
 from fastapi.routing import APIRouter
-from fastapi.responses import FileResponse
 from PIL import Image
-from invokeai.app.models.image import (
-    ImageCategory,
-    ResourceOrigin,
-)
-from invokeai.app.services.image_record_storage import OffsetPaginatedResults
-from invokeai.app.services.models.image_record import (
-    ImageDTO,
-    ImageRecordChanges,
-    ImageUrlsDTO,
-)
+from invokeai.app.api.models.images import ImageResponse, ImageResponseMetadata
+from invokeai.app.services.metadata import InvokeAIMetadata
 from invokeai.app.services.item_storage import PaginatedResults
 
+from ...services.image_storage import ImageType
 from ..dependencies import ApiDependencies
 
 images_router = APIRouter(prefix="/v1/images", tags=["images"])
 
 
+@images_router.get("/{image_type}/{image_name}", operation_id="get_image")
+async def get_image(
+    image_type: ImageType = Path(description="The type of image to get"),
+    image_name: str = Path(description="The name of the image to get"),
+) -> FileResponse | Response:
+    """Gets a result"""
+
+    path = ApiDependencies.invoker.services.images.get_path(
+        image_type=image_type, image_name=image_name
+    )
+
+    if ApiDependencies.invoker.services.images.validate_path(path):
+        return FileResponse(path)
+    else:
+        raise HTTPException(status_code=404)
+
+
+@images_router.get(
+    "/{image_type}/thumbnails/{image_name}", operation_id="get_thumbnail"
+)
+async def get_thumbnail(
+    image_type: ImageType = Path(description="The type of image to get"),
+    image_name: str = Path(description="The name of the image to get"),
+) -> FileResponse | Response:
+    """Gets a thumbnail"""
+
+    path = ApiDependencies.invoker.services.images.get_path(
+        image_type=image_type, image_name=image_name, is_thumbnail=True
+    )
+
+    if ApiDependencies.invoker.services.images.validate_path(path):
+        return FileResponse(path)
+    else:
+        raise HTTPException(status_code=404)
+
+
 @images_router.post(
-    "/",
+    "/uploads/",
     operation_id="upload_image",
     responses={
-        201: {"description": "The image was uploaded successfully"},
+        201: {
+            "description": "The image was uploaded successfully",
+            "model": ImageResponse,
+        },
         415: {"description": "Image upload failed"},
     },
     status_code=201,
-    response_model=ImageDTO,
 )
 async def upload_image(
-    file: UploadFile,
-    request: Request,
-    response: Response,
-    image_category: ImageCategory = Query(description="The category of the image"),
-    is_intermediate: bool = Query(description="Whether this is an intermediate image"),
-    session_id: Optional[str] = Query(
-        default=None, description="The session ID associated with this upload, if any"
-    ),
-) -> ImageDTO:
-    """Uploads an image"""
+    file: UploadFile, request: Request, response: Response
+) -> ImageResponse:
     if not file.content_type.startswith("image"):
         raise HTTPException(status_code=415, detail="Not an image")
 
     contents = await file.read()
 
     try:
-        pil_image = Image.open(io.BytesIO(contents))
+        img = Image.open(io.BytesIO(contents))
     except:
         # Error opening the image
         raise HTTPException(status_code=415, detail="Failed to read image")
 
-    try:
-        image_dto = ApiDependencies.invoker.services.images.create(
-            image=pil_image,
-            image_origin=ResourceOrigin.EXTERNAL,
-            image_category=image_category,
-            session_id=session_id,
-            is_intermediate=is_intermediate,
-        )
+    filename = f"{uuid.uuid4()}_{str(int(datetime.now(timezone.utc).timestamp()))}.png"
 
-        response.status_code = 201
-        response.headers["Location"] = image_dto.image_url
+    (image_path, thumbnail_path, ctime) = ApiDependencies.invoker.services.images.save(
+        ImageType.UPLOAD, filename, img
+    )
 
-        return image_dto
-    except Exception as e:
-        raise HTTPException(status_code=500, detail="Failed to create image")
+    invokeai_metadata = ApiDependencies.invoker.services.metadata.get_metadata(img)
 
+    res = ImageResponse(
+        image_type=ImageType.UPLOAD,
+        image_name=filename,
+        image_url=f"api/v1/images/{ImageType.UPLOAD.value}/{filename}",
+        thumbnail_url=f"api/v1/images/{ImageType.UPLOAD.value}/thumbnails/{os.path.splitext(filename)[0]}.webp",
+        metadata=ImageResponseMetadata(
+            created=ctime,
+            width=img.width,
+            height=img.height,
+            invokeai=invokeai_metadata,
+        ),
+    )
 
-@images_router.delete("/{image_origin}/{image_name}", operation_id="delete_image")
-async def delete_image(
-    image_origin: ResourceOrigin = Path(description="The origin of image to delete"),
-    image_name: str = Path(description="The name of the image to delete"),
-) -> None:
-    """Deletes an image"""
+    response.status_code = 201
+    response.headers["Location"] = request.url_for(
+        "get_image", image_type=ImageType.UPLOAD.value, image_name=filename
+    )
 
-    try:
-        ApiDependencies.invoker.services.images.delete(image_origin, image_name)
-    except Exception as e:
-        # TODO: Does this need any exception handling at all?
-        pass
-
-
-@images_router.patch(
-    "/{image_origin}/{image_name}",
-    operation_id="update_image",
-    response_model=ImageDTO,
-)
-async def update_image(
-    image_origin: ResourceOrigin = Path(description="The origin of image to update"),
-    image_name: str = Path(description="The name of the image to update"),
-    image_changes: ImageRecordChanges = Body(
-        description="The changes to apply to the image"
-    ),
-) -> ImageDTO:
-    """Updates an image"""
-
-    try:
-        return ApiDependencies.invoker.services.images.update(
-            image_origin, image_name, image_changes
-        )
-    except Exception as e:
-        raise HTTPException(status_code=400, detail="Failed to update image")
-
-
-@images_router.get(
-    "/{image_origin}/{image_name}/metadata",
-    operation_id="get_image_metadata",
-    response_model=ImageDTO,
-)
-async def get_image_metadata(
-    image_origin: ResourceOrigin = Path(description="The origin of image to get"),
-    image_name: str = Path(description="The name of image to get"),
-) -> ImageDTO:
-    """Gets an image's metadata"""
-
-    try:
-        return ApiDependencies.invoker.services.images.get_dto(image_origin, image_name)
-    except Exception as e:
-        raise HTTPException(status_code=404)
-
-
-@images_router.get(
-    "/{image_origin}/{image_name}",
-    operation_id="get_image_full",
-    response_class=Response,
-    responses={
-        200: {
-            "description": "Return the full-resolution image",
-            "content": {"image/png": {}},
-        },
-        404: {"description": "Image not found"},
-    },
-)
-async def get_image_full(
-    image_origin: ResourceOrigin = Path(
-        description="The type of full-resolution image file to get"
-    ),
-    image_name: str = Path(description="The name of full-resolution image file to get"),
-) -> FileResponse:
-    """Gets a full-resolution image file"""
-
-    try:
-        path = ApiDependencies.invoker.services.images.get_path(image_origin, image_name)
-
-        if not ApiDependencies.invoker.services.images.validate_path(path):
-            raise HTTPException(status_code=404)
-
-        return FileResponse(
-            path,
-            media_type="image/png",
-            filename=image_name,
-            content_disposition_type="inline",
-        )
-    except Exception as e:
-        raise HTTPException(status_code=404)
-
-
-@images_router.get(
-    "/{image_origin}/{image_name}/thumbnail",
-    operation_id="get_image_thumbnail",
-    response_class=Response,
-    responses={
-        200: {
-            "description": "Return the image thumbnail",
-            "content": {"image/webp": {}},
-        },
-        404: {"description": "Image not found"},
-    },
-)
-async def get_image_thumbnail(
-    image_origin: ResourceOrigin = Path(description="The origin of thumbnail image file to get"),
-    image_name: str = Path(description="The name of thumbnail image file to get"),
-) -> FileResponse:
-    """Gets a thumbnail image file"""
-
-    try:
-        path = ApiDependencies.invoker.services.images.get_path(
-            image_origin, image_name, thumbnail=True
-        )
-        if not ApiDependencies.invoker.services.images.validate_path(path):
-            raise HTTPException(status_code=404)
-
-        return FileResponse(
-            path, media_type="image/webp", content_disposition_type="inline"
-        )
-    except Exception as e:
-        raise HTTPException(status_code=404)
-
-
-@images_router.get(
-    "/{image_origin}/{image_name}/urls",
-    operation_id="get_image_urls",
-    response_model=ImageUrlsDTO,
-)
-async def get_image_urls(
-    image_origin: ResourceOrigin = Path(description="The origin of the image whose URL to get"),
-    image_name: str = Path(description="The name of the image whose URL to get"),
-) -> ImageUrlsDTO:
-    """Gets an image and thumbnail URL"""
-
-    try:
-        image_url = ApiDependencies.invoker.services.images.get_url(
-            image_origin, image_name
-        )
-        thumbnail_url = ApiDependencies.invoker.services.images.get_url(
-            image_origin, image_name, thumbnail=True
-        )
-        return ImageUrlsDTO(
-            image_origin=image_origin,
-            image_name=image_name,
-            image_url=image_url,
-            thumbnail_url=thumbnail_url,
-        )
-    except Exception as e:
-        raise HTTPException(status_code=404)
+    return res
 
 
 @images_router.get(
     "/",
-    operation_id="list_images_with_metadata",
-    response_model=OffsetPaginatedResults[ImageDTO],
+    operation_id="list_images",
+    responses={200: {"model": PaginatedResults[ImageResponse]}},
 )
-async def list_images_with_metadata(
-    image_origin: Optional[ResourceOrigin] = Query(
-        default=None, description="The origin of images to list"
+async def list_images(
+    image_type: ImageType = Query(
+        default=ImageType.RESULT, description="The type of images to get"
     ),
-    categories: Optional[list[ImageCategory]] = Query(
-        default=None, description="The categories of image to include"
-    ),
-    is_intermediate: Optional[bool] = Query(
-        default=None, description="Whether to list intermediate images"
-    ),
-    offset: int = Query(default=0, description="The page offset"),
-    limit: int = Query(default=10, description="The number of images per page"),
-) -> OffsetPaginatedResults[ImageDTO]:
+    page: int = Query(default=0, description="The page of images to get"),
+    per_page: int = Query(default=10, description="The number of images per page"),
+) -> PaginatedResults[ImageResponse]:
     """Gets a list of images"""
-
-    image_dtos = ApiDependencies.invoker.services.images.get_many(
-        offset,
-        limit,
-        image_origin,
-        categories,
-        is_intermediate,
-    )
-
-    return image_dtos
+    result = ApiDependencies.invoker.services.images.list(image_type, page, per_page)
+    return result

invokeai/app/api/routers/models.py

@@ -8,6 +8,10 @@ from fastapi.routing import APIRouter, HTTPException
 from pydantic import BaseModel, Field, parse_obj_as
 from pathlib import Path
 from ..dependencies import ApiDependencies
+from invokeai.backend.globals import Globals, global_converted_ckpts_dir
+from invokeai.backend.args import Args
+
+
 
 models_router = APIRouter(prefix="/v1/models", tags=["models"])
 
@@ -108,20 +112,19 @@ async def update_model(
 async def delete_model(model_name: str) -> None:
     """Delete Model"""
     model_names = ApiDependencies.invoker.services.model_manager.model_names()
-    logger = ApiDependencies.invoker.services.logger
     model_exists = model_name in model_names
 
     # check if model exists
-    logger.info(f"Checking for model {model_name}...")
+    print(f">> Checking for model {model_name}...")
            
     if model_exists:
-        logger.info(f"Deleting Model: {model_name}")
+        print(f">> Deleting Model: {model_name}")
         ApiDependencies.invoker.services.model_manager.del_model(model_name, delete_files=True)
-        logger.info(f"Model Deleted: {model_name}")
+        print(f">> Model Deleted: {model_name}")
         raise HTTPException(status_code=204, detail=f"Model '{model_name}' deleted successfully")
     
     else:
-        logger.error(f"Model not found")
+        print(f">> Model not found")
         raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
     
 
@@ -245,4 +248,4 @@ async def delete_model(model_name: str) -> None:
         #         )
         #         print(f">> Models Merged: {models_to_merge}")
         #         print(f">> New Model Added: {model_merge_info['merged_model_name']}")
-        #     except Exception as e:
+        #     except Exception as e:

invokeai/app/api/routers/sessions.py

@@ -2,7 +2,8 @@
 
 from typing import Annotated, List, Optional, Union
 
-from fastapi import Body, HTTPException, Path, Query, Response
+from fastapi import Body, Path, Query
+from fastapi.responses import Response
 from fastapi.routing import APIRouter
 from pydantic.fields import Field
 
@@ -75,7 +76,7 @@ async def get_session(
     """Gets a session"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
     else:
         return session
 
@@ -98,7 +99,7 @@ async def add_node(
     """Adds a node to the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.add_node(node)
@@ -107,9 +108,9 @@ async def add_node(
         )  # TODO: can this be done automatically, or add node through an API?
         return session.id
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.put(
@@ -131,7 +132,7 @@ async def update_node(
     """Updates a node in the graph and removes all linked edges"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.update_node(node_path, node)
@@ -140,9 +141,9 @@ async def update_node(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.delete(
@@ -161,7 +162,7 @@ async def delete_node(
     """Deletes a node in the graph and removes all linked edges"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.delete_node(node_path)
@@ -170,9 +171,9 @@ async def delete_node(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.post(
@@ -191,7 +192,7 @@ async def add_edge(
     """Adds an edge to the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         session.add_edge(edge)
@@ -200,9 +201,9 @@ async def add_edge(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 # TODO: the edge being in the path here is really ugly, find a better solution
@@ -225,7 +226,7 @@ async def delete_edge(
     """Deletes an edge from the graph"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     try:
         edge = Edge(
@@ -238,9 +239,9 @@ async def delete_edge(
         )  # TODO: can this be done automatically, or add node through an API?
         return session
     except NodeAlreadyExecutedError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
     except IndexError:
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
 
 @session_router.put(
@@ -258,14 +259,14 @@ async def invoke_session(
     all: bool = Query(
         default=False, description="Whether or not to invoke all remaining invocations"
     ),
-) -> Response:
+) -> None:
     """Invokes a session"""
     session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
     if session is None:
-        raise HTTPException(status_code=404)
+        return Response(status_code=404)
 
     if session.is_complete():
-        raise HTTPException(status_code=400)
+        return Response(status_code=400)
 
     ApiDependencies.invoker.invoke(session, invoke_all=all)
     return Response(status_code=202)
@@ -280,7 +281,7 @@ async def invoke_session(
 )
 async def cancel_session_invoke(
     session_id: str = Path(description="The id of the session to cancel"),
-) -> Response:
+) -> None:
     """Invokes a session"""
     ApiDependencies.invoker.cancel(session_id)
     return Response(status_code=202)

invokeai/app/api_app.py

@@ -3,7 +3,6 @@ import asyncio
 from inspect import signature
 
 import uvicorn
-
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
@@ -11,21 +10,13 @@ from fastapi.openapi.utils import get_openapi
 from fastapi.staticfiles import StaticFiles
 from fastapi_events.handlers.local import local_handler
 from fastapi_events.middleware import EventHandlerASGIMiddleware
-from pathlib import Path
 from pydantic.schema import schema
 
-#This should come early so that modules can log their initialization properly
-from .services.config import InvokeAIAppConfig
-from ..backend.util.logging import InvokeAILogger
-app_config = InvokeAIAppConfig.get_config()
-app_config.parse_args()
-logger = InvokeAILogger.getLogger(config=app_config)
-
-import invokeai.frontend.web as web_dir
-
+from ..backend import Args
 from .api.dependencies import ApiDependencies
-from .api.routers import sessions, models, images
+from .api.routers import images, sessions, models
 from .api.sockets import SocketIO
+from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
 
 # Create the app
@@ -42,21 +33,30 @@ app.add_middleware(
     middleware_id=event_handler_id,
 )
 
+# Add CORS
+# TODO: use configuration for this
+origins = []
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 socket_io = SocketIO(app)
 
+config = {}
+
+
 # Add startup event to load dependencies
 @app.on_event("startup")
 async def startup_event():
-    app.add_middleware(
-        CORSMiddleware,
-        allow_origins=app_config.allow_origins,
-        allow_credentials=app_config.allow_credentials,
-        allow_methods=app_config.allow_methods,
-        allow_headers=app_config.allow_headers,
-    )
+    config = Args()
+    config.parse_args()
 
     ApiDependencies.initialize(
-        config=app_config, event_handler_id=event_handler_id, logger=logger
+        config=config, event_handler_id=event_handler_id
     )
 
 
@@ -74,9 +74,10 @@ async def shutdown_event():
 
 app.include_router(sessions.session_router, prefix="/api")
 
+app.include_router(images.images_router, prefix="/api")
+
 app.include_router(models.models_router, prefix="/api")
 
-app.include_router(images.images_router, prefix="/api")
 
 # Build a custom OpenAPI to include all outputs
 # TODO: can outputs be included on metadata of invocation schemas somehow?
@@ -123,7 +124,8 @@ def custom_openapi():
 app.openapi = custom_openapi
 
 # Override API doc favicons
-app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], 'static/dream_web')), name="static")
+app.mount("/static", StaticFiles(directory="static/dream_web"), name="static")
+
 
 @app.get("/docs", include_in_schema=False)
 def overridden_swagger():
@@ -143,20 +145,16 @@ def overridden_redoc():
     )
 
 
-# Must mount *after* the other routes else it borks em
-app.mount("/", 
-          StaticFiles(directory=Path(web_dir.__path__[0],"dist"), 
-                      html=True
-                     ), name="ui"
-         )
-
 def invoke_api():
     # Start our own event loop for eventing usage
+    # TODO: determine if there's a better way to do this
     loop = asyncio.new_event_loop()
-    config = uvicorn.Config(app=app, host=app_config.host, port=app_config.port, loop=loop)
+    config = uvicorn.Config(app=app, host="0.0.0.0", port=9090, loop=loop)
     # Use access_log to turn off logging
+
     server = uvicorn.Server(config)
     loop.run_until_complete(server.serve())
 
+
 if __name__ == "__main__":
     invoke_api()

invokeai/app/cli/commands.py

@@ -2,15 +2,14 @@
 
 from abc import ABC, abstractmethod
 import argparse
-from typing import Any, Callable, Iterable, Literal, Union, get_args, get_origin, get_type_hints
+from typing import Any, Callable, Iterable, Literal, get_args, get_origin, get_type_hints
 from pydantic import BaseModel, Field
 import networkx as nx
 import matplotlib.pyplot as plt
 
-import invokeai.backend.util.logging as logger
 from ..invocations.baseinvocation import BaseInvocation
 from ..invocations.image import ImageField
-from ..services.graph import GraphExecutionState, LibraryGraph, Edge
+from ..services.graph import GraphExecutionState, LibraryGraph, GraphInvocation, Edge
 from ..services.invoker import Invoker
 
 
@@ -230,7 +229,7 @@ class HistoryCommand(BaseCommand):
         for i in range(min(self.count, len(history))):
             entry_id = history[-1 - i]
             entry = context.get_session().graph.get_node(entry_id)
-            logger.info(f"{entry_id}: {get_invocation_command(entry)}")
+            print(f"{entry_id}: {get_invocation_command(entry)}")
 
 
 class SetDefaultCommand(BaseCommand):
@@ -285,19 +284,3 @@ class DrawExecutionGraphCommand(BaseCommand):
         nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
         plt.axis("off")
         plt.show()
-
-class SortedHelpFormatter(argparse.HelpFormatter):
-    def _iter_indented_subactions(self, action):
-        try:
-            get_subactions = action._get_subactions
-        except AttributeError:
-            pass
-        else:
-            self._indent()
-            if isinstance(action, argparse._SubParsersAction):
-                for subaction in sorted(get_subactions(), key=lambda x: x.dest):
-                    yield subaction
-            else:
-                for subaction in get_subactions():
-                    yield subaction
-                self._dedent()

invokeai/app/cli/completer.py

@@ -10,11 +10,9 @@ import shlex
 from pathlib import Path
 from typing import List, Dict, Literal, get_args, get_type_hints, get_origin
 
-import invokeai.backend.util.logging as logger
-from ...backend import ModelManager
+from ...backend import ModelManager, Globals
 from ..invocations.baseinvocation import BaseInvocation
 from .commands import BaseCommand
-from ..services.invocation_services import InvocationServices
 
 # singleton object, class variable
 completer = None
@@ -132,13 +130,13 @@ class Completer(object):
             readline.redisplay()
             self.linebuffer = None
     
-def set_autocompleter(services: InvocationServices) -> Completer:
+def set_autocompleter(model_manager: ModelManager) -> Completer:
     global completer
     
     if completer:
         return completer
     
-    completer = Completer(services.model_manager)
+    completer = Completer(model_manager)
 
     readline.set_completer(completer.complete)
     # pyreadline3 does not have a set_auto_history() method
@@ -154,7 +152,7 @@ def set_autocompleter(services: InvocationServices) -> Completer:
     readline.parse_and_bind("set skip-completed-text on")
     readline.parse_and_bind("set show-all-if-ambiguous on")
 
-    histfile = Path(services.configuration.root_dir / ".invoke_history")
+    histfile = Path(Globals.root, ".invoke_history")
     try:
         readline.read_history_file(histfile)
         readline.set_history_length(1000)
@@ -162,8 +160,8 @@ def set_autocompleter(services: InvocationServices) -> Completer:
         pass
     except OSError:  # file likely corrupted
         newname = f"{histfile}.old"
-        logger.error(
-            f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
+        print(
+            f"## Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
         )
         histfile.replace(Path(newname))
     atexit.register(readline.write_history_file, histfile)

invokeai/app/cli_app.py

@@ -4,41 +4,32 @@ import argparse
 import os
 import re
 import shlex
-import sys
 import time
 from typing import (
     Union,
     get_type_hints,
 )
 
-from pydantic import BaseModel, ValidationError
+from pydantic import BaseModel
 from pydantic.fields import Field
 
-# This should come early so that the logger can pick up its configuration options
-from .services.config import InvokeAIAppConfig
-from invokeai.backend.util.logging import InvokeAILogger
-config = InvokeAIAppConfig.get_config()
-config.parse_args()
-logger = InvokeAILogger().getLogger(config=config)
-
-from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
-from invokeai.app.services.images import ImageService
-from invokeai.app.services.metadata import CoreMetadataService
-from invokeai.app.services.resource_name import SimpleNameService
-from invokeai.app.services.urls import LocalUrlService
+from invokeai.app.services.metadata import PngMetadataService
 
 from .services.default_graphs import create_system_graphs
+
 from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
 
-from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers, SortedHelpFormatter
+from ..backend import Args
+from .cli.commands import BaseCommand, CliContext, ExitCli, add_graph_parsers, add_parsers, get_graph_execution_history
 from .cli.completer import set_autocompleter
+from .invocations import *
 from .invocations.baseinvocation import BaseInvocation
 from .services.events import EventServiceBase
 from .services.model_manager_initializer import get_model_manager
 from .services.restoration_services import RestorationServices
-from .services.graph import Edge, EdgeConnection, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible
+from .services.graph import Edge, EdgeConnection, ExposedNodeInput, GraphExecutionState, GraphInvocation, LibraryGraph, are_connection_types_compatible
 from .services.default_graphs import default_text_to_image_graph_id
-from .services.image_file_storage import DiskImageFileStorage
+from .services.image_storage import DiskImageStorage
 from .services.invocation_queue import MemoryInvocationQueue
 from .services.invocation_services import InvocationServices
 from .services.invoker import Invoker
@@ -53,6 +44,7 @@ class CliCommand(BaseModel):
 class InvalidArgs(Exception):
     pass
 
+
 def add_invocation_args(command_parser):
     # Add linking capability
     command_parser.add_argument(
@@ -73,7 +65,7 @@ def add_invocation_args(command_parser):
 
 def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
     # Create invocation parser
-    parser = argparse.ArgumentParser(formatter_class=SortedHelpFormatter)
+    parser = argparse.ArgumentParser()
 
     def exit(*args, **kwargs):
         raise InvalidArgs
@@ -190,72 +182,50 @@ def invoke_all(context: CliContext):
     # Print any errors
     if context.session.has_error():
         for n in context.session.errors:
-            context.invoker.services.logger.error(
+            print(
                 f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
             )
         
         raise SessionError()
 
-def invoke_cli():
-    
-    # get the optional list of invocations to execute on the command line
-    parser = config.get_parser()
-    parser.add_argument('commands',nargs='*')
-    invocation_commands = parser.parse_args().commands
 
-    # get the optional file to read commands from.
-    # Simplest is to use it for STDIN
-    if infile := config.from_file:
-        sys.stdin = open(infile,"r")
-    
-    model_manager = get_model_manager(config,logger=logger)
-    
+def invoke_cli():
+    config = Args()
+    config.parse_args()
+    model_manager = get_model_manager(config)
+
+    # This initializes the autocompleter and returns it.
+    # Currently nothing is done with the returned Completer
+    # object, but the object can be used to change autocompletion
+    # behavior on the fly, if desired.
+    completer = set_autocompleter(model_manager)
+
     events = EventServiceBase()
-    output_folder = config.output_path
+
+    metadata = PngMetadataService()
+
+    output_folder = os.path.abspath(
+        os.path.join(os.path.dirname(__file__), "../../../outputs")
+    )
 
     # TODO: build a file/path manager?
-    if config.use_memory_db:
-        db_location = ":memory:"
-    else:
-        db_location = config.db_path
-        db_location.parent.mkdir(parents=True,exist_ok=True)
-
-    logger.info(f'InvokeAI database location is "{db_location}"')
-
-    graph_execution_manager = SqliteItemStorage[GraphExecutionState](
-            filename=db_location, table_name="graph_executions"
-        )
-
-    urls = LocalUrlService()
-    metadata = CoreMetadataService()
-    image_record_storage = SqliteImageRecordStorage(db_location)
-    image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
-    names = SimpleNameService()
-
-    images = ImageService(
-        image_record_storage=image_record_storage,
-        image_file_storage=image_file_storage,
-        metadata=metadata,
-        url=urls,
-        logger=logger,
-        names=names,
-        graph_execution_manager=graph_execution_manager,
-    )
+    db_location = os.path.join(output_folder, "invokeai.db")
 
     services = InvocationServices(
         model_manager=model_manager,
         events=events,
         latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents')),
-        images=images,
+        images=DiskImageStorage(f'{output_folder}/images', metadata_service=metadata),
+        metadata=metadata,
         queue=MemoryInvocationQueue(),
         graph_library=SqliteItemStorage[LibraryGraph](
             filename=db_location, table_name="graphs"
         ),
-        graph_execution_manager=graph_execution_manager,
+        graph_execution_manager=SqliteItemStorage[GraphExecutionState](
+            filename=db_location, table_name="graph_executions"
+        ),
         processor=DefaultInvocationProcessor(),
-        restoration=RestorationServices(config,logger=logger),
-        logger=logger,
-        configuration=config,
+        restoration=RestorationServices(config),
     )
 
     system_graphs = create_system_graphs(services.graph_library)
@@ -271,18 +241,10 @@ def invoke_cli():
     # print(services.session_manager.list())
 
     context = CliContext(invoker, session, parser)
-    set_autocompleter(services)
 
-    command_line_args_exist = len(invocation_commands) > 0
-    done = False
-    
-    while not done:
+    while True:
         try:
-            if command_line_args_exist:
-                cmd_input = invocation_commands.pop(0)
-                done = len(invocation_commands) == 0
-            else:
-                cmd_input = input("invoke> ")
+            cmd_input = input("invoke> ")
         except (KeyboardInterrupt, EOFError):
             # Ctrl-c exits
             break
@@ -403,15 +365,12 @@ def invoke_cli():
             invoke_all(context)
 
         except InvalidArgs:
-            invoker.services.logger.warning('Invalid command, use "help" to list commands')
+            print('Invalid command, use "help" to list commands')
             continue
 
-        except ValidationError:
-            invoker.services.logger.warning('Invalid command arguments, run "<command> --help" for summary')
-
         except SessionError:
             # Start a new session
-            invoker.services.logger.warning("Session error: creating a new session")
+            print("Session error: creating a new session")
             context.reset()
 
         except ExitCli:

invokeai/app/invocations/baseinvocation.py

@@ -1,15 +1,12 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-from __future__ import annotations
-
 from abc import ABC, abstractmethod
 from inspect import signature
-from typing import get_args, get_type_hints, Dict, List, Literal, TypedDict, TYPE_CHECKING
+from typing import get_args, get_type_hints, Dict, List, Literal, TypedDict
 
 from pydantic import BaseModel, Field
 
-if TYPE_CHECKING:
-    from ..services.invocation_services import InvocationServices
+from ..services.invocation_services import InvocationServices
 
 
 class InvocationContext:
@@ -78,7 +75,6 @@ class BaseInvocation(ABC, BaseModel):
     
     #fmt: off
     id: str = Field(description="The id of this node. Must be unique among all nodes.")
-    is_intermediate: bool = Field(default=False, description="Whether or not this node is an intermediate node.")
     #fmt: on
 
 
@@ -96,7 +92,6 @@ class UIConfig(TypedDict, total=False):
             "image",
             "latents",
             "model",
-            "control",
         ],
     ]
     tags: List[str]

invokeai/app/invocations/collections.py

@@ -1,14 +1,14 @@
-# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Literal
+from typing import Literal, Optional
 
 import numpy as np
-from pydantic import Field, validator
-
-from invokeai.app.util.misc import SEED_MAX, get_random_seed
+import numpy.random
+from pydantic import Field
 
 from .baseinvocation import (
     BaseInvocation,
+    InvocationConfig,
     InvocationContext,
     BaseInvocationOutput,
 )
@@ -22,17 +22,9 @@ class IntCollectionOutput(BaseInvocationOutput):
     # Outputs
     collection: list[int] = Field(default=[], description="The int collection")
 
-class FloatCollectionOutput(BaseInvocationOutput):
-    """A collection of floats"""
-
-    type: Literal["float_collection"] = "float_collection"
-
-    # Outputs
-    collection: list[float] = Field(default=[], description="The float collection")
-
 
 class RangeInvocation(BaseInvocation):
-    """Creates a range of numbers from start to stop with step"""
+    """Creates a range"""
 
     type: Literal["range"] = "range"
 
@@ -41,34 +33,12 @@ class RangeInvocation(BaseInvocation):
     stop: int = Field(default=10, description="The stop of the range")
     step: int = Field(default=1, description="The step of the range")
 
-    @validator("stop")
-    def stop_gt_start(cls, v, values):
-        if "start" in values and v <= values["start"]:
-            raise ValueError("stop must be greater than start")
-        return v
-
     def invoke(self, context: InvocationContext) -> IntCollectionOutput:
         return IntCollectionOutput(
             collection=list(range(self.start, self.stop, self.step))
         )
 
 
-class RangeOfSizeInvocation(BaseInvocation):
-    """Creates a range from start to start + size with step"""
-
-    type: Literal["range_of_size"] = "range_of_size"
-
-    # Inputs
-    start: int = Field(default=0, description="The start of the range")
-    size: int = Field(default=1, description="The number of values")
-    step: int = Field(default=1, description="The step of the range")
-
-    def invoke(self, context: InvocationContext) -> IntCollectionOutput:
-        return IntCollectionOutput(
-            collection=list(range(self.start, self.start + self.size, self.step))
-        )
-
-
 class RandomRangeInvocation(BaseInvocation):
     """Creates a collection of random numbers"""
 
@@ -80,11 +50,11 @@ class RandomRangeInvocation(BaseInvocation):
         default=np.iinfo(np.int32).max, description="The exclusive high value"
     )
     size: int = Field(default=1, description="The number of values to generate")
-    seed: int = Field(
+    seed: Optional[int] = Field(
         ge=0,
-        le=SEED_MAX,
-        description="The seed for the RNG (omit for random)",
-        default_factory=get_random_seed,
+        le=np.iinfo(np.int32).max,
+        description="The seed for the RNG",
+        default_factory=lambda: numpy.random.randint(0, np.iinfo(np.int32).max),
     )
 
     def invoke(self, context: InvocationContext) -> IntCollectionOutput:

invokeai/app/invocations/compel.py

@@ -1,266 +0,0 @@
-from typing import Literal, Optional, Union
-from pydantic import BaseModel, Field
-
-from invokeai.app.invocations.util.choose_model import choose_model
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
-from ...backend.prompting.conditioning import try_parse_legacy_blend
-
-from ...backend.util.devices import choose_torch_device, torch_dtype
-from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
-from ...backend.stable_diffusion.textual_inversion_manager import TextualInversionManager
-
-from compel import Compel
-from compel.prompt_parser import (
-    Blend,
-    CrossAttentionControlSubstitute,
-    FlattenedPrompt,
-    Fragment, Conjunction,
-)
-
-
-class ConditioningField(BaseModel):
-    conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
-    class Config:
-        schema_extra = {"required": ["conditioning_name"]}
-
-
-class CompelOutput(BaseInvocationOutput):
-    """Compel parser output"""
-
-    #fmt: off
-    type: Literal["compel_output"] = "compel_output"
-
-    conditioning: ConditioningField = Field(default=None, description="Conditioning")
-    #fmt: on
-
-
-class CompelInvocation(BaseInvocation):
-    """Parse prompt using compel package to conditioning."""
-
-    type: Literal["compel"] = "compel"
-
-    prompt: str = Field(default="", description="Prompt")
-    model: str = Field(default="", description="Model to use")
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "title": "Prompt (Compel)",
-                "tags": ["prompt", "compel"],
-                "type_hints": {
-                  "model": "model"
-                }
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> CompelOutput:
-
-        # TODO: load without model
-        model = choose_model(context.services.model_manager, self.model)
-        pipeline = model["model"]
-        tokenizer = pipeline.tokenizer
-        text_encoder = pipeline.text_encoder
-
-        # TODO: global? input?
-        #use_full_precision = precision == "float32" or precision == "autocast"
-        #use_full_precision = False
-
-        # TODO: redo TI when separate model loding implemented
-        #textual_inversion_manager = TextualInversionManager(
-        #    tokenizer=tokenizer,
-        #    text_encoder=text_encoder,
-        #    full_precision=use_full_precision,
-        #)
-
-        def load_huggingface_concepts(concepts: list[str]):
-            pipeline.textual_inversion_manager.load_huggingface_concepts(concepts)
-
-        # apply the concepts library to the prompt
-        prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers(
-            self.prompt,
-            lambda concepts: load_huggingface_concepts(concepts),
-            pipeline.textual_inversion_manager.get_all_trigger_strings(),
-        )
-
-        # lazy-load any deferred textual inversions.
-        # this might take a couple of seconds the first time a textual inversion is used.
-        pipeline.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
-            prompt_str
-        )
-
-        compel = Compel(
-            tokenizer=tokenizer,
-            text_encoder=text_encoder,
-            textual_inversion_manager=pipeline.textual_inversion_manager,
-            dtype_for_device_getter=torch_dtype,
-            truncate_long_prompts=False,
-        )
-
-        legacy_blend = try_parse_legacy_blend(prompt_str, skip_normalize=False)
-        if legacy_blend is not None:
-            conjunction = legacy_blend
-        else:
-            conjunction = Compel.parse_prompt_string(prompt_str)
-
-        if context.services.configuration.log_tokenization:
-            log_tokenization_for_conjunction(conjunction, tokenizer)
-
-        c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
-
-        ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
-            tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
-            cross_attention_control_args=options.get("cross_attention_control", None),
-        )
-
-        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
-
-        # TODO: hacky but works ;D maybe rename latents somehow?
-        context.services.latents.save(conditioning_name, (c, ec))
-
-        return CompelOutput(
-            conditioning=ConditioningField(
-                conditioning_name=conditioning_name,
-            ),
-        )
-
-
-def get_max_token_count(
-    tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
-) -> int:
-    if type(prompt) is Blend:
-        blend: Blend = prompt
-        return max(
-            [
-                get_max_token_count(tokenizer, p, truncate_if_too_long)
-                for p in blend.prompts
-            ]
-        )
-    elif type(prompt) is Conjunction:
-        conjunction: Conjunction = prompt
-        return sum(
-            [
-                get_max_token_count(tokenizer, p, truncate_if_too_long)
-                for p in conjunction.prompts
-            ]
-        )
-    else:
-        return len(
-            get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long)
-        )
-
-
-def get_tokens_for_prompt_object(
-    tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
-) -> [str]:
-    if type(parsed_prompt) is Blend:
-        raise ValueError(
-            "Blend is not supported here - you need to get tokens for each of its .children"
-        )
-
-    text_fragments = [
-        x.text
-        if type(x) is Fragment
-        else (
-            " ".join([f.text for f in x.original])
-            if type(x) is CrossAttentionControlSubstitute
-            else str(x)
-        )
-        for x in parsed_prompt.children
-    ]
-    text = " ".join(text_fragments)
-    tokens = tokenizer.tokenize(text)
-    if truncate_if_too_long:
-        max_tokens_length = tokenizer.model_max_length - 2  # typically 75
-        tokens = tokens[0:max_tokens_length]
-    return tokens
-
-
-def log_tokenization_for_conjunction(
-    c: Conjunction, tokenizer, display_label_prefix=None
-):
-    display_label_prefix = display_label_prefix or ""
-    for i, p in enumerate(c.prompts):
-        if len(c.prompts)>1:
-            this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
-        else:
-            this_display_label_prefix = display_label_prefix
-        log_tokenization_for_prompt_object(
-            p,
-            tokenizer,
-            display_label_prefix=this_display_label_prefix
-        )
-
-
-def log_tokenization_for_prompt_object(
-    p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
-):
-    display_label_prefix = display_label_prefix or ""
-    if type(p) is Blend:
-        blend: Blend = p
-        for i, c in enumerate(blend.prompts):
-            log_tokenization_for_prompt_object(
-                c,
-                tokenizer,
-                display_label_prefix=f"{display_label_prefix}(blend part {i + 1}, weight={blend.weights[i]})",
-            )
-    elif type(p) is FlattenedPrompt:
-        flattened_prompt: FlattenedPrompt = p
-        if flattened_prompt.wants_cross_attention_control:
-            original_fragments = []
-            edited_fragments = []
-            for f in flattened_prompt.children:
-                if type(f) is CrossAttentionControlSubstitute:
-                    original_fragments += f.original
-                    edited_fragments += f.edited
-                else:
-                    original_fragments.append(f)
-                    edited_fragments.append(f)
-
-            original_text = " ".join([x.text for x in original_fragments])
-            log_tokenization_for_text(
-                original_text,
-                tokenizer,
-                display_label=f"{display_label_prefix}(.swap originals)",
-            )
-            edited_text = " ".join([x.text for x in edited_fragments])
-            log_tokenization_for_text(
-                edited_text,
-                tokenizer,
-                display_label=f"{display_label_prefix}(.swap replacements)",
-            )
-        else:
-            text = " ".join([x.text for x in flattened_prompt.children])
-            log_tokenization_for_text(
-                text, tokenizer, display_label=display_label_prefix
-            )
-
-
-def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
-    """shows how the prompt is tokenized
-    # usually tokens have '</w>' to indicate end-of-word,
-    # but for readability it has been replaced with ' '
-    """
-    tokens = tokenizer.tokenize(text)
-    tokenized = ""
-    discarded = ""
-    usedTokens = 0
-    totalTokens = len(tokens)
-
-    for i in range(0, totalTokens):
-        token = tokens[i].replace("</w>", " ")
-        # alternate color
-        s = (usedTokens % 6) + 1
-        if truncate_if_too_long and i >= tokenizer.model_max_length:
-            discarded = discarded + f"\x1b[0;3{s};40m{token}"
-        else:
-            tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
-            usedTokens += 1
-
-    if usedTokens > 0:
-        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
-        print(f"{tokenized}\x1b[0m")
-
-    if discarded != "":
-        print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
-        print(f"{discarded}\x1b[0m")

invokeai/app/invocations/controlnet_image_processors.py

@@ -1,459 +0,0 @@
-# InvokeAI nodes for ControlNet image preprocessors
-# initial implementation by Gregg Helt, 2023
-# heavily leverages controlnet_aux package: https://github.com/patrickvonplaten/controlnet_aux
-from builtins import float
-
-import numpy as np
-from typing import Literal, Optional, Union, List
-from PIL import Image, ImageFilter, ImageOps
-from pydantic import BaseModel, Field, validator
-
-from ..models.image import ImageField, ImageCategory, ResourceOrigin
-from .baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    InvocationContext,
-    InvocationConfig,
-)
-
-from controlnet_aux import (
-    CannyDetector,
-    HEDdetector,
-    LineartDetector,
-    LineartAnimeDetector,
-    MidasDetector,
-    MLSDdetector,
-    NormalBaeDetector,
-    OpenposeDetector,
-    PidiNetDetector,
-    ContentShuffleDetector,
-    ZoeDetector,
-    MediapipeFaceDetector,
-)
-
-from .image import ImageOutput, PILInvocationConfig
-
-CONTROLNET_DEFAULT_MODELS = [
-    ###########################################
-    # lllyasviel sd v1.5, ControlNet v1.0 models
-    ##############################################
-    "lllyasviel/sd-controlnet-canny",
-    "lllyasviel/sd-controlnet-depth",
-    "lllyasviel/sd-controlnet-hed",
-    "lllyasviel/sd-controlnet-seg",
-    "lllyasviel/sd-controlnet-openpose",
-    "lllyasviel/sd-controlnet-scribble",
-    "lllyasviel/sd-controlnet-normal",
-    "lllyasviel/sd-controlnet-mlsd",
-
-    #############################################
-    # lllyasviel sd v1.5, ControlNet v1.1 models
-    #############################################
-    "lllyasviel/control_v11p_sd15_canny",
-    "lllyasviel/control_v11p_sd15_openpose",
-    "lllyasviel/control_v11p_sd15_seg",
-    # "lllyasviel/control_v11p_sd15_depth",  # broken
-    "lllyasviel/control_v11f1p_sd15_depth",
-    "lllyasviel/control_v11p_sd15_normalbae",
-    "lllyasviel/control_v11p_sd15_scribble",
-    "lllyasviel/control_v11p_sd15_mlsd",
-    "lllyasviel/control_v11p_sd15_softedge",
-    "lllyasviel/control_v11p_sd15s2_lineart_anime",
-    "lllyasviel/control_v11p_sd15_lineart",
-    "lllyasviel/control_v11p_sd15_inpaint",
-    # "lllyasviel/control_v11u_sd15_tile",
-    # problem (temporary?) with huffingface "lllyasviel/control_v11u_sd15_tile",
-    # so for now replace  "lllyasviel/control_v11f1e_sd15_tile",
-    "lllyasviel/control_v11e_sd15_shuffle",
-    "lllyasviel/control_v11e_sd15_ip2p",
-    "lllyasviel/control_v11f1e_sd15_tile",
-
-     #################################################
-     #  thibaud sd v2.1 models (ControlNet v1.0? or v1.1?
-     ##################################################
-     "thibaud/controlnet-sd21-openpose-diffusers",
-     "thibaud/controlnet-sd21-canny-diffusers",
-     "thibaud/controlnet-sd21-depth-diffusers",
-     "thibaud/controlnet-sd21-scribble-diffusers",
-     "thibaud/controlnet-sd21-hed-diffusers",
-     "thibaud/controlnet-sd21-zoedepth-diffusers",
-     "thibaud/controlnet-sd21-color-diffusers",
-     "thibaud/controlnet-sd21-openposev2-diffusers",
-     "thibaud/controlnet-sd21-lineart-diffusers",
-     "thibaud/controlnet-sd21-normalbae-diffusers",
-     "thibaud/controlnet-sd21-ade20k-diffusers",
-
-     ##############################################
-     #  ControlNetMediaPipeface, ControlNet v1.1
-     ##############################################
-     # ["CrucibleAI/ControlNetMediaPipeFace", "diffusion_sd15"],  # SD 1.5
-     #    diffusion_sd15 needs to be passed to from_pretrained() as subfolder arg
-     #    hacked t2l to split to model & subfolder if format is "model,subfolder"
-     "CrucibleAI/ControlNetMediaPipeFace,diffusion_sd15",  # SD 1.5
-     "CrucibleAI/ControlNetMediaPipeFace",  # SD 2.1?
-]
-
-CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
-
-class ControlField(BaseModel):
-    image: ImageField = Field(default=None, description="The control image")
-    control_model: Optional[str] = Field(default=None, description="The ControlNet model to use")
-    # control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
-    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)")
-    @validator("control_weight")
-    def abs_le_one(cls, v):
-        """validate that all abs(values) are <=1"""
-        if isinstance(v, list):
-            for i in v:
-                if abs(i) > 1:
-                    raise ValueError('all abs(control_weight) must be <= 1')
-        else:
-            if abs(v) > 1:
-                raise ValueError('abs(control_weight) must be <= 1')
-        return v
-    class Config:
-        schema_extra = {
-            "required": ["image", "control_model", "control_weight", "begin_step_percent", "end_step_percent"],
-            "ui": {
-                "type_hints": {
-                    "control_weight": "float",
-                    # "control_weight": "number",
-                }
-            }
-        }
-
-
-class ControlOutput(BaseInvocationOutput):
-    """node output for ControlNet info"""
-    # fmt: off
-    type: Literal["control_output"] = "control_output"
-    control: ControlField = Field(default=None, description="The control info")
-    # fmt: on
-
-
-class ControlNetInvocation(BaseInvocation):
-    """Collects ControlNet info to pass to other nodes"""
-    # fmt: off
-    type: Literal["controlnet"] = "controlnet"
-    # Inputs
-    image: ImageField = Field(default=None, description="The control image")
-    control_model: CONTROLNET_NAME_VALUES = Field(default="lllyasviel/sd-controlnet-canny",
-                                                  description="control model used")
-    control_weight: Union[float, List[float]] = Field(default=1.0, description="The weight given to the ControlNet")
-    # TODO: add support in backend core for begin_step_percent, end_step_percent, guess_mode
-    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)")
-    # fmt: on
-
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents"],
-                "type_hints": {
-                  "model": "model",
-                  "control": "control",
-                  # "cfg_scale": "float",
-                  "cfg_scale": "number",
-                  "control_weight": "float",
-                }
-            },
-        }
-
-    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,
-            ),
-        )
-
-# TODO: move image processors to separate file (image_analysis.py
-class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
-    """Base class for invocations that preprocess images for ControlNet"""
-
-    # fmt: off
-    type: Literal["image_processor"] = "image_processor"
-    # Inputs
-    image: ImageField = Field(default=None, description="The image to process")
-    # fmt: on
-
-
-    def run_processor(self, image):
-        # superclass just passes through image without processing
-        return image
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        raw_image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-        # image type should be PIL.PngImagePlugin.PngImageFile ?
-        processed_image = self.run_processor(raw_image)
-
-        # FIXME: what happened to image metadata?
-        # metadata = context.services.metadata.build_metadata(
-        #     session_id=context.graph_execution_state_id, node=self
-        # )
-
-        # 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.services.images.create(
-            image=processed_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.CONTROL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate
-        )
-
-        """Builds an ImageOutput and its ImageField"""
-        processed_image_field = ImageField(
-            image_name=image_dto.image_name,
-            image_origin=image_dto.image_origin,
-        )
-        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,
-        )
-
-
-class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Canny edge detection for ControlNet"""
-    # fmt: off
-    type: Literal["canny_image_processor"] = "canny_image_processor"
-    # Input
-    low_threshold: int = Field(default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)")
-    high_threshold: int = Field(default=200, ge=0, le=255, description="The high threshold of the Canny pixel gradient (0-255)")
-    # fmt: on
-
-    def run_processor(self, image):
-        canny_processor = CannyDetector()
-        processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
-        return processed_image
-
-
-class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies HED edge detection to image"""
-    # fmt: off
-    type: Literal["hed_image_processor"] = "hed_image_processor"
-    # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
-    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
-    # safe not supported in controlnet_aux v0.0.3
-    # safe: bool = Field(default=False, description="whether to use safe mode")
-    scribble: bool = Field(default=False, description="Whether to use scribble mode")
-    # fmt: on
-
-    def run_processor(self, image):
-        hed_processor = HEDdetector.from_pretrained("lllyasviel/Annotators")
-        processed_image = hed_processor(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
-
-
-class LineartImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies line art processing to image"""
-    # fmt: off
-    type: Literal["lineart_image_processor"] = "lineart_image_processor"
-    # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
-    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
-    coarse: bool = Field(default=False, description="Whether to use coarse mode")
-    # fmt: on
-
-    def run_processor(self, image):
-        lineart_processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
-        processed_image = lineart_processor(image,
-                                            detect_resolution=self.detect_resolution,
-                                            image_resolution=self.image_resolution,
-                                            coarse=self.coarse)
-        return processed_image
-
-
-class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies line art anime processing to image"""
-    # fmt: off
-    type: Literal["lineart_anime_image_processor"] = "lineart_anime_image_processor"
-    # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
-    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
-    # fmt: on
-
-    def run_processor(self, image):
-        processor = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
-        processed_image = processor(image,
-                                    detect_resolution=self.detect_resolution,
-                                    image_resolution=self.image_resolution,
-                                    )
-        return processed_image
-
-
-class OpenposeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies Openpose processing to image"""
-    # fmt: off
-    type: Literal["openpose_image_processor"] = "openpose_image_processor"
-    # Inputs
-    hand_and_face: bool = Field(default=False, description="Whether to use hands and face mode")
-    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
-    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
-    # fmt: on
-
-    def run_processor(self, image):
-        openpose_processor = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
-        processed_image = openpose_processor(image,
-                                             detect_resolution=self.detect_resolution,
-                                             image_resolution=self.image_resolution,
-                                             hand_and_face=self.hand_and_face,
-                                             )
-        return processed_image
-
-
-class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies Midas depth processing to image"""
-    # fmt: off
-    type: Literal["midas_depth_image_processor"] = "midas_depth_image_processor"
-    # Inputs
-    a_mult: float = Field(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
-    bg_th: float = Field(default=0.1, ge=0, description="Midas parameter `bg_th`")
-    # depth_and_normal not supported in controlnet_aux v0.0.3
-    # depth_and_normal: bool = Field(default=False, description="whether to use depth and normal mode")
-    # fmt: on
-
-    def run_processor(self, image):
-        midas_processor = MidasDetector.from_pretrained("lllyasviel/Annotators")
-        processed_image = midas_processor(image,
-                                          a=np.pi * self.a_mult,
-                                          bg_th=self.bg_th,
-                                          # dept_and_normal not supported in controlnet_aux v0.0.3
-                                          # depth_and_normal=self.depth_and_normal,
-                                          )
-        return processed_image
-
-
-class NormalbaeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies NormalBae processing to image"""
-    # fmt: off
-    type: Literal["normalbae_image_processor"] = "normalbae_image_processor"
-    # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
-    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
-    # fmt: on
-
-    def run_processor(self, 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
-
-
-class MlsdImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies MLSD processing to image"""
-    # fmt: off
-    type: Literal["mlsd_image_processor"] = "mlsd_image_processor"
-    # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
-    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
-    thr_v: float = Field(default=0.1, ge=0, description="MLSD parameter `thr_v`")
-    thr_d: float = Field(default=0.1, ge=0, description="MLSD parameter `thr_d`")
-    # fmt: on
-
-    def run_processor(self, 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
-
-
-class PidiImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies PIDI processing to image"""
-    # fmt: off
-    type: Literal["pidi_image_processor"] = "pidi_image_processor"
-    # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
-    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
-    safe: bool = Field(default=False, description="Whether to use safe mode")
-    scribble: bool = Field(default=False, description="Whether to use scribble mode")
-    # fmt: on
-
-    def run_processor(self, 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
-
-
-class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies content shuffle processing to image"""
-    # fmt: off
-    type: Literal["content_shuffle_image_processor"] = "content_shuffle_image_processor"
-    # Inputs
-    detect_resolution: int = Field(default=512, ge=0, description="The pixel resolution for detection")
-    image_resolution: int = Field(default=512, ge=0, description="The pixel resolution for the output image")
-    h: Union[int, None] = Field(default=512, ge=0, description="Content shuffle `h` parameter")
-    w: Union[int, None] = Field(default=512, ge=0, description="Content shuffle `w` parameter")
-    f: Union[int, None] = Field(default=256, ge=0, description="Content shuffle `f` parameter")
-    # fmt: on
-
-    def run_processor(self, 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
-class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies Zoe depth processing to image"""
-    # fmt: off
-    type: Literal["zoe_depth_image_processor"] = "zoe_depth_image_processor"
-    # fmt: on
-
-    def run_processor(self, image):
-        zoe_depth_processor = ZoeDetector.from_pretrained("lllyasviel/Annotators")
-        processed_image = zoe_depth_processor(image)
-        return processed_image
-
-
-class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
-    """Applies mediapipe face processing to image"""
-    # fmt: off
-    type: Literal["mediapipe_face_processor"] = "mediapipe_face_processor"
-    # Inputs
-    max_faces: int = Field(default=1, ge=1, description="Maximum number of faces to detect")
-    min_confidence: float = Field(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
-    # fmt: on
-
-    def run_processor(self, image):
-        mediapipe_face_processor = MediapipeFaceDetector()
-        processed_image = mediapipe_face_processor(image, max_faces=self.max_faces, min_confidence=self.min_confidence)
-        return processed_image

invokeai/app/invocations/cv.py

@@ -7,9 +7,9 @@ import numpy
 from PIL import Image, ImageOps
 from pydantic import BaseModel, Field
 
-from invokeai.app.models.image import ImageCategory, ImageField, ResourceOrigin
+from invokeai.app.models.image import ImageField, ImageType
 from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput
+from .image import ImageOutput, build_image_output
 
 
 class CvInvocationConfig(BaseModel):
@@ -26,27 +26,24 @@ class CvInvocationConfig(BaseModel):
 
 class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
     """Simple inpaint using opencv."""
-
-    # fmt: off
+    #fmt: off
     type: Literal["cv_inpaint"] = "cv_inpaint"
 
     # Inputs
     image: ImageField = Field(default=None, description="The image to inpaint")
     mask: ImageField = Field(default=None, description="The mask to use when inpainting")
-    # fmt: on
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-        mask = context.services.images.get_pil_image(
-            self.mask.image_origin, self.mask.image_name
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
         )
+        mask = context.services.images.get(self.mask.image_type, self.mask.image_name)
 
         # Convert to cv image/mask
         # TODO: consider making these utility functions
         cv_image = cv.cvtColor(numpy.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
-        cv_mask = numpy.array(ImageOps.invert(mask.convert("L")))
+        cv_mask = numpy.array(ImageOps.invert(mask))
 
         # Inpaint
         cv_inpainted = cv.inpaint(cv_image, cv_mask, 3, cv.INPAINT_TELEA)
@@ -55,20 +52,18 @@ class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
         # TODO: consider making a utility function
         image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
 
-        image_dto = context.services.images.create(
-            image=image_inpainted,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
         )
+        
+        context.services.images.save(image_type, image_name, image_inpainted, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=image_inpainted,
+        )

invokeai/app/invocations/generate.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from functools import partial
-from typing import Literal, Optional, Union, get_args
+from typing import Literal, Optional, Union
 
 import numpy as np
 from diffusers import ControlNetModel
@@ -10,22 +10,15 @@ import torch
 
 from pydantic import BaseModel, Field
 
-from invokeai.app.models.image import ColorField, ImageField, ResourceOrigin
+from invokeai.app.models.image import ImageField, ImageType
 from invokeai.app.invocations.util.choose_model import choose_model
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
-from invokeai.app.util.misc import SEED_MAX, get_random_seed
-from invokeai.backend.generator.inpaint import infill_methods
 from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput
+from .image import ImageOutput, build_image_output
 from ...backend.generator import Txt2Img, Img2Img, Inpaint, InvokeAIGenerator
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ..util.step_callback import stable_diffusion_step_callback
 
 SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
-INFILL_METHODS = Literal[tuple(infill_methods())]
-DEFAULT_INFILL_METHOD = (
-    "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
-)
 
 
 class SDImageInvocation(BaseModel):
@@ -53,16 +46,18 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
     # TODO: consider making prompt optional to enable providing prompt through a link
     # fmt: off
     prompt: Optional[str] = Field(description="The prompt to generate an image from")
-    seed:        int = Field(ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed)
-    steps:       int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
-    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
-    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
-    cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
+    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
+    steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
+    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
+    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image", )
+    cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
+    seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
     model:       str = Field(default="", description="The model to use (currently ignored)")
     progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
     control_model: Optional[str] = Field(default=None, description="The control model to use")
     control_image: Optional[ImageField] = Field(default=None, description="The processed control image")
+    # control_strength: Optional[float] = Field(default=1.0, ge=0, le=1, description="The strength of the controlnet")
     # fmt: on
 
     # TODO: pass this an emitter method or something? or a session for dispatching?
@@ -80,14 +75,13 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
         )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Handle invalid model parameter
         model = choose_model(context.services.model_manager, self.model)
 
         # loading controlnet image (currently requires pre-processed image)
         control_image = (
             None if self.control_image is None
-            else context.services.images.get_pil_image(
-                self.control_image.image_origin, self.control_image.image_name
+            else context.services.images.get(
+                self.control_image.image_type, self.control_image.image_name
             )
         )
         # loading controlnet model
@@ -95,7 +89,6 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
             control_model = None
         else:
             # FIXME: change this to dropdown menu?
-            # FIXME: generalize so don't have to hardcode torch_dtype and device
             control_model = ControlNetModel.from_pretrained(self.control_model,
                                                             torch_dtype=torch.float16).to("cuda")
 
@@ -118,22 +111,25 @@ class TextToImageInvocation(BaseInvocation, SDImageInvocation):
         # each time it is called. We only need the first one.
         generate_output = next(outputs)
 
-        image_dto = context.services.images.create(
-            image=generate_output.image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
+        # Results are image and seed, unwrap for now and ignore the seed
+        # TODO: pre-seed?
+        # TODO: can this return multiple results? Should it?
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(
+            image_type, image_name, generate_output.image, metadata
+        )
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=generate_output.image,
         )
 
 
@@ -169,13 +165,11 @@ class ImageToImageInvocation(TextToImageInvocation):
         image = (
             None
             if self.image is None
-            else context.services.images.get_pil_image(
-                self.image.image_origin, self.image.image_name
+            else context.services.images.get(
+                self.image.image_type, self.image.image_name
             )
         )
-
-        if self.fit:
-            image = image.resize((self.width, self.height))
+        mask = None
 
         # Handle invalid model parameter
         model = choose_model(context.services.model_manager, self.model)
@@ -189,6 +183,7 @@ class ImageToImageInvocation(TextToImageInvocation):
         outputs = Img2Img(model).generate(
             prompt=self.prompt,
             init_image=image,
+            init_mask=mask,
             step_callback=partial(self.dispatch_progress, context, source_node_id),
             **self.dict(
                 exclude={"prompt", "image", "mask"}
@@ -199,22 +194,25 @@ class ImageToImageInvocation(TextToImageInvocation):
         # each time it is called. We only need the first one.
         generator_output = next(outputs)
 
-        image_dto = context.services.images.create(
-            image=generator_output.image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
+        result_image = generator_output.image
+
+        # Results are image and seed, unwrap for now and ignore the seed
+        # TODO: pre-seed?
+        # TODO: can this return multiple results? Should it?
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, result_image, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=result_image,
         )
 
 
@@ -225,39 +223,6 @@ class InpaintInvocation(ImageToImageInvocation):
 
     # Inputs
     mask: Union[ImageField, None] = Field(description="The mask")
-    seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)")
-    seam_blur: int = Field(
-        default=16, ge=0, description="The seam inpaint blur radius (px)"
-    )
-    seam_strength: float = Field(
-        default=0.75, gt=0, le=1, description="The seam inpaint strength"
-    )
-    seam_steps: int = Field(
-        default=30, ge=1, description="The number of steps to use for seam inpaint"
-    )
-    tile_size: int = Field(
-        default=32, ge=1, description="The tile infill method size (px)"
-    )
-    infill_method: INFILL_METHODS = Field(
-        default=DEFAULT_INFILL_METHOD,
-        description="The method used to infill empty regions (px)",
-    )
-    inpaint_width: Optional[int] = Field(
-        default=None,
-        multiple_of=8,
-        gt=0,
-        description="The width of the inpaint region (px)",
-    )
-    inpaint_height: Optional[int] = Field(
-        default=None,
-        multiple_of=8,
-        gt=0,
-        description="The height of the inpaint region (px)",
-    )
-    inpaint_fill: Optional[ColorField] = Field(
-        default=ColorField(r=127, g=127, b=127, a=255),
-        description="The solid infill method color",
-    )
     inpaint_replace: float = Field(
         default=0.0,
         ge=0.0,
@@ -282,14 +247,14 @@ class InpaintInvocation(ImageToImageInvocation):
         image = (
             None
             if self.image is None
-            else context.services.images.get_pil_image(
-                self.image.image_origin, self.image.image_name
+            else context.services.images.get(
+                self.image.image_type, self.image.image_name
             )
         )
         mask = (
             None
             if self.mask is None
-            else context.services.images.get_pil_image(self.mask.image_origin, self.mask.image_name)
+            else context.services.images.get(self.mask.image_type, self.mask.image_name)
         )
 
         # Handle invalid model parameter
@@ -303,8 +268,8 @@ class InpaintInvocation(ImageToImageInvocation):
 
         outputs = Inpaint(model).generate(
             prompt=self.prompt,
-            init_image=image,
-            mask_image=mask,
+            init_img=image,
+            init_mask=mask,
             step_callback=partial(self.dispatch_progress, context, source_node_id),
             **self.dict(
                 exclude={"prompt", "image", "mask"}
@@ -315,20 +280,23 @@ class InpaintInvocation(ImageToImageInvocation):
         # each time it is called. We only need the first one.
         generator_output = next(outputs)
 
-        image_dto = context.services.images.create(
-            image=generator_output.image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            session_id=context.graph_execution_state_id,
-            node_id=self.id,
-            is_intermediate=self.is_intermediate,
+        result_image = generator_output.image
+
+        # Results are image and seed, unwrap for now and ignore the seed
+        # TODO: pre-seed?
+        # TODO: can this return multiple results? Should it?
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, result_image, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=result_image,
         )

invokeai/app/invocations/image.py

@@ -1,13 +1,12 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
-import io
-from typing import Literal, Optional, Union
+from typing import Literal, Optional
 
 import numpy
-from PIL import Image, ImageFilter, ImageOps, ImageChops
+from PIL import Image, ImageFilter, ImageOps
 from pydantic import BaseModel, Field
 
-from ..models.image import ImageCategory, ImageField, ResourceOrigin
+from ..models.image import ImageField, ImageType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
@@ -31,14 +30,32 @@ class ImageOutput(BaseInvocationOutput):
     """Base class for invocations that output an image"""
 
     # fmt: off
-    type: Literal["image_output"] = "image_output"
+    type: Literal["image"] = "image"
     image:      ImageField = Field(default=None, description="The output image")
-    width:             int = Field(description="The width of the image in pixels")
-    height:            int = Field(description="The height of the image in pixels")
+    width:   Optional[int] = Field(default=None, description="The width of the image in pixels")
+    height:  Optional[int] = Field(default=None, description="The height of the image in pixels")
     # fmt: on
 
     class Config:
-        schema_extra = {"required": ["type", "image", "width", "height"]}
+        schema_extra = {
+            "required": ["type", "image", "width", "height", "mode"]
+        }
+
+
+def build_image_output(
+    image_type: ImageType, image_name: str, image: Image.Image
+) -> ImageOutput:
+    """Builds an ImageOutput and its ImageField"""
+    image_field = ImageField(
+        image_name=image_name,
+        image_type=image_type,
+    )
+    return ImageOutput(
+        image=image_field,
+        width=image.width,
+        height=image.height,
+        mode=image.mode,
+    )
 
 
 class MaskOutput(BaseInvocationOutput):
@@ -47,8 +64,6 @@ class MaskOutput(BaseInvocationOutput):
     # fmt: off
     type: Literal["mask"] = "mask"
     mask:      ImageField = Field(default=None, description="The output mask")
-    width:            int = Field(description="The width of the mask in pixels")
-    height:           int = Field(description="The height of the mask in pixels")
     # fmt: on
 
     class Config:
@@ -67,20 +82,16 @@ class LoadImageInvocation(BaseInvocation):
     type: Literal["load_image"] = "load_image"
 
     # Inputs
-    image: Union[ImageField, None] = Field(
-        default=None, description="The image to load"
-    )
+    image_type: ImageType = Field(description="The type of the image")
+    image_name:       str = Field(description="The name of the image")
     # fmt: on
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(self.image.image_origin, self.image.image_name)
+        image = context.services.images.get(self.image_type, self.image_name)
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=self.image.image_name,
-                image_origin=self.image.image_origin,
-            ),
-            width=image.width,
-            height=image.height,
+        return build_image_output(
+            image_type=self.image_type,
+            image_name=self.image_name,
+            image=image,
         )
 
 
@@ -90,37 +101,32 @@ class ShowImageInvocation(BaseInvocation):
     type: Literal["show_image"] = "show_image"
 
     # Inputs
-    image: Union[ImageField, None] = Field(
-        default=None, description="The image to show"
-    )
+    image: ImageField = Field(default=None, description="The image to show")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
         )
         if image:
             image.show()
 
         # TODO: how to handle failure?
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=self.image.image_name,
-                image_origin=self.image.image_origin,
-            ),
-            width=image.width,
-            height=image.height,
+        return build_image_output(
+            image_type=self.image.image_type,
+            image_name=self.image.image_name,
+            image=image,
         )
 
 
-class ImageCropInvocation(BaseInvocation, PILInvocationConfig):
+class CropImageInvocation(BaseInvocation, PILInvocationConfig):
     """Crops an image to a specified box. The box can be outside of the image."""
 
     # fmt: off
-    type: Literal["img_crop"] = "img_crop"
+    type: Literal["crop"] = "crop"
 
     # Inputs
-    image: Union[ImageField, None]  = Field(default=None, description="The image to crop")
+    image: ImageField = Field(default=None, description="The image to crop")
     x:      int = Field(default=0, description="The left x coordinate of the crop rectangle")
     y:      int = Field(default=0, description="The top y coordinate of the crop rectangle")
     width:  int = Field(default=512, gt=0, description="The width of the crop rectangle")
@@ -128,8 +134,8 @@ class ImageCropInvocation(BaseInvocation, PILInvocationConfig):
     # fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
         )
 
         image_crop = Image.new(
@@ -137,53 +143,49 @@ class ImageCropInvocation(BaseInvocation, PILInvocationConfig):
         )
         image_crop.paste(image, (-self.x, -self.y))
 
-        image_dto = context.services.images.create(
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
+        )
+
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+        
+        context.services.images.save(image_type, image_name, image_crop, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
             image=image_crop,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
         )
 
 
-class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
+class PasteImageInvocation(BaseInvocation, PILInvocationConfig):
     """Pastes an image into another image."""
 
     # fmt: off
-    type: Literal["img_paste"] = "img_paste"
+    type: Literal["paste"] = "paste"
 
     # Inputs
-    base_image:     Union[ImageField, None]  = Field(default=None, description="The base image")
-    image:          Union[ImageField, None]  = Field(default=None, description="The image to paste")
+    base_image:     ImageField = Field(default=None, description="The base image")
+    image:          ImageField = Field(default=None, description="The image to paste")
     mask: Optional[ImageField] = Field(default=None, description="The mask to use when pasting")
     x:                     int = Field(default=0, description="The left x coordinate at which to paste the image")
     y:                     int = Field(default=0, description="The top y coordinate at which to paste the image")
     # fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        base_image = context.services.images.get_pil_image(
-            self.base_image.image_origin, self.base_image.image_name
+        base_image = context.services.images.get(
+            self.base_image.image_type, self.base_image.image_name
         )
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
         )
         mask = (
             None
             if self.mask is None
             else ImageOps.invert(
-                context.services.images.get_pil_image(
-                    self.mask.image_origin, self.mask.image_name
-                )
+                context.services.images.get(self.mask.image_type, self.mask.image_name)
             )
         )
         # TODO: probably shouldn't invert mask here... should user be required to do it?
@@ -199,22 +201,20 @@ class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
         new_image.paste(base_image, (abs(min_x), abs(min_y)))
         new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
 
-        image_dto = context.services.images.create(
-            image=new_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+        
+        context.services.images.save(image_type, image_name, new_image, metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=new_image,
         )
 
 
@@ -225,169 +225,47 @@ class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
     type: Literal["tomask"] = "tomask"
 
     # Inputs
-    image: Union[ImageField, None]  = Field(default=None, description="The image to create the mask from")
+    image: ImageField = Field(default=None, description="The image to create the mask from")
     invert:      bool = Field(default=False, description="Whether or not to invert the mask")
     # fmt: on
 
     def invoke(self, context: InvocationContext) -> MaskOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
         )
 
         image_mask = image.split()[-1]
         if self.invert:
             image_mask = ImageOps.invert(image_mask)
 
-        image_dto = context.services.images.create(
-            image=image_mask,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.MASK,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return MaskOutput(
-            mask=ImageField(
-                image_origin=image_dto.image_origin, image_name=image_dto.image_name
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
         )
 
-
-class ImageMultiplyInvocation(BaseInvocation, PILInvocationConfig):
-    """Multiplies two images together using `PIL.ImageChops.multiply()`."""
-
-    # fmt: off
-    type: Literal["img_mul"] = "img_mul"
-
-    # Inputs
-    image1: Union[ImageField, None]  = Field(default=None, description="The first image to multiply")
-    image2: Union[ImageField, None]  = Field(default=None, description="The second image to multiply")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image1 = context.services.images.get_pil_image(
-            self.image1.image_origin, self.image1.image_name
-        )
-        image2 = context.services.images.get_pil_image(
-            self.image2.image_origin, self.image2.image_name
-        )
-
-        multiply_image = ImageChops.multiply(image1, image2)
-
-        image_dto = context.services.images.create(
-            image=multiply_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_origin=image_dto.image_origin, image_name=image_dto.image_name
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
+        context.services.images.save(image_type, image_name, image_mask, metadata)
+        return MaskOutput(mask=ImageField(image_type=image_type, image_name=image_name))
 
 
-IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
-
-
-class ImageChannelInvocation(BaseInvocation, PILInvocationConfig):
-    """Gets a channel from an image."""
-
-    # fmt: off
-    type: Literal["img_chan"] = "img_chan"
-
-    # Inputs
-    image: Union[ImageField, None]  = Field(default=None, description="The image to get the channel from")
-    channel: IMAGE_CHANNELS  = Field(default="A", description="The channel to get")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-
-        channel_image = image.getchannel(self.channel)
-
-        image_dto = context.services.images.create(
-            image=channel_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_origin=image_dto.image_origin, image_name=image_dto.image_name
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
-
-
-class ImageConvertInvocation(BaseInvocation, PILInvocationConfig):
-    """Converts an image to a different mode."""
-
-    # fmt: off
-    type: Literal["img_conv"] = "img_conv"
-
-    # Inputs
-    image: Union[ImageField, None]  = Field(default=None, description="The image to convert")
-    mode: IMAGE_MODES  = Field(default="L", description="The mode to convert to")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-
-        converted_image = image.convert(self.mode)
-
-        image_dto = context.services.images.create(
-            image=converted_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_origin=image_dto.image_origin, image_name=image_dto.image_name
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
+class BlurInvocation(BaseInvocation, PILInvocationConfig):
     """Blurs an image"""
 
     # fmt: off
-    type: Literal["img_blur"] = "img_blur"
+    type: Literal["blur"] = "blur"
 
     # Inputs
-    image: Union[ImageField, None]  = Field(default=None, description="The image to blur")
+    image: ImageField = Field(default=None, description="The image to blur")
     radius:     float = Field(default=8.0, ge=0, description="The blur radius")
     blur_type: Literal["gaussian", "box"] = Field(default="gaussian", description="The type of blur")
     # fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
         )
 
         blur = (
@@ -397,149 +275,36 @@ class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
         )
         blur_image = image.filter(blur)
 
-        image_dto = context.services.images.create(
-            image=blur_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, blur_image, metadata)
+        return build_image_output(
+            image_type=image_type, image_name=image_name, image=blur_image
         )
 
 
-PIL_RESAMPLING_MODES = Literal[
-    "nearest",
-    "box",
-    "bilinear",
-    "hamming",
-    "bicubic",
-    "lanczos",
-]
-
-
-PIL_RESAMPLING_MAP = {
-    "nearest": Image.Resampling.NEAREST,
-    "box": Image.Resampling.BOX,
-    "bilinear": Image.Resampling.BILINEAR,
-    "hamming": Image.Resampling.HAMMING,
-    "bicubic": Image.Resampling.BICUBIC,
-    "lanczos": Image.Resampling.LANCZOS,
-}
-
-
-class ImageResizeInvocation(BaseInvocation, PILInvocationConfig):
-    """Resizes an image to specific dimensions"""
-
-    # fmt: off
-    type: Literal["img_resize"] = "img_resize"
-
-    # Inputs
-    image: Union[ImageField, None]  = Field(default=None, description="The image to resize")
-    width:                         int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
-    height:                        int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
-    resample_mode:  PIL_RESAMPLING_MODES = Field(default="bicubic", description="The resampling mode")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-
-        resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
-
-        resize_image = image.resize(
-            (self.width, self.height),
-            resample=resample_mode,
-        )
-
-        image_dto = context.services.images.create(
-            image=resize_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-class ImageScaleInvocation(BaseInvocation, PILInvocationConfig):
-    """Scales an image by a factor"""
-
-    # fmt: off
-    type: Literal["img_scale"] = "img_scale"
-
-    # Inputs
-    image:       Union[ImageField, None] = Field(default=None, description="The image to scale")
-    scale_factor:                  float = Field(gt=0, description="The factor by which to scale the image")
-    resample_mode:  PIL_RESAMPLING_MODES = Field(default="bicubic", description="The resampling mode")
-    # fmt: on
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-
-        resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
-        width = int(image.width * self.scale_factor)
-        height = int(image.height * self.scale_factor)
-
-        resize_image = image.resize(
-            (width, height),
-            resample=resample_mode,
-        )
-
-        image_dto = context.services.images.create(
-            image=resize_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
+class LerpInvocation(BaseInvocation, PILInvocationConfig):
     """Linear interpolation of all pixels of an image"""
 
     # fmt: off
-    type: Literal["img_lerp"] = "img_lerp"
+    type: Literal["lerp"] = "lerp"
 
     # Inputs
-    image: Union[ImageField, None]  = Field(default=None, description="The image to lerp")
+    image: ImageField = Field(default=None, description="The image to lerp")
     min: int = Field(default=0, ge=0, le=255, description="The minimum output value")
     max: int = Field(default=255, ge=0, le=255, description="The maximum output value")
     # fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
         )
 
         image_arr = numpy.asarray(image, dtype=numpy.float32) / 255
@@ -547,40 +312,36 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
 
         lerp_image = Image.fromarray(numpy.uint8(image_arr))
 
-        image_dto = context.services.images.create(
-            image=lerp_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, lerp_image, metadata)
+        return build_image_output(
+            image_type=image_type, image_name=image_name, image=lerp_image
         )
 
 
-class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
+class InverseLerpInvocation(BaseInvocation, PILInvocationConfig):
     """Inverse linear interpolation of all pixels of an image"""
 
     # fmt: off
-    type: Literal["img_ilerp"] = "img_ilerp"
+    type: Literal["ilerp"] = "ilerp"
 
     # Inputs
-    image: Union[ImageField, None]  = Field(default=None, description="The image to lerp")
+    image: ImageField = Field(default=None, description="The image to lerp")
     min: int = Field(default=0, ge=0, le=255, description="The minimum input value")
     max: int = Field(default=255, ge=0, le=255, description="The maximum input value")
     # fmt: on
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
         )
 
         image_arr = numpy.asarray(image, dtype=numpy.float32)
@@ -593,20 +354,16 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
 
         ilerp_image = Image.fromarray(numpy.uint8(image_arr))
 
-        image_dto = context.services.images.create(
-            image=ilerp_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
+        )
+
+        context.services.images.save(image_type, image_name, ilerp_image, metadata)
+        return build_image_output(
+            image_type=image_type, image_name=image_name, image=ilerp_image
         )

invokeai/app/invocations/infill.py

@@ -1,245 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
-
-from typing import Literal, Union, get_args
-
-import numpy as np
-import math
-from PIL import Image, ImageOps
-from pydantic import Field
-
-from invokeai.app.invocations.image import ImageOutput
-from invokeai.app.util.misc import SEED_MAX, get_random_seed
-from invokeai.backend.image_util.patchmatch import PatchMatch
-
-from ..models.image import ColorField, ImageCategory, ImageField, ResourceOrigin
-from .baseinvocation import (
-    BaseInvocation,
-    InvocationContext,
-)
-
-
-def infill_methods() -> list[str]:
-    methods = [
-        "tile",
-        "solid",
-    ]
-    if PatchMatch.patchmatch_available():
-        methods.insert(0, "patchmatch")
-    return methods
-
-
-INFILL_METHODS = Literal[tuple(infill_methods())]
-DEFAULT_INFILL_METHOD = (
-    "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
-)
-
-
-def infill_patchmatch(im: Image.Image) -> Image.Image:
-    if im.mode != "RGBA":
-        return im
-
-    # Skip patchmatch if patchmatch isn't available
-    if not PatchMatch.patchmatch_available():
-        return im
-
-    # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
-    im_patched_np = PatchMatch.inpaint(
-        im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3
-    )
-    im_patched = Image.fromarray(im_patched_np, mode="RGB")
-    return im_patched
-
-
-def get_tile_images(image: np.ndarray, width=8, height=8):
-    _nrows, _ncols, depth = image.shape
-    _strides = image.strides
-
-    nrows, _m = divmod(_nrows, height)
-    ncols, _n = divmod(_ncols, width)
-    if _m != 0 or _n != 0:
-        return None
-
-    return np.lib.stride_tricks.as_strided(
-        np.ravel(image),
-        shape=(nrows, ncols, height, width, depth),
-        strides=(height * _strides[0], width * _strides[1], *_strides),
-        writeable=False,
-    )
-
-
-def tile_fill_missing(
-    im: Image.Image, tile_size: int = 16, seed: Union[int, None] = None
-) -> Image.Image:
-    # Only fill if there's an alpha layer
-    if im.mode != "RGBA":
-        return im
-
-    a = np.asarray(im, dtype=np.uint8)
-
-    tile_size_tuple = (tile_size, tile_size)
-
-    # Get the image as tiles of a specified size
-    tiles = get_tile_images(a, *tile_size_tuple).copy()
-
-    # Get the mask as tiles
-    tiles_mask = tiles[:, :, :, :, 3]
-
-    # Find any mask tiles with any fully transparent pixels (we will be replacing these later)
-    tmask_shape = tiles_mask.shape
-    tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
-    n, ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
-    tiles_mask = tiles_mask > 0
-    tiles_mask = tiles_mask.reshape((n, ny)).all(axis=1)
-
-    # Get RGB tiles in single array and filter by the mask
-    tshape = tiles.shape
-    tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), *tiles.shape[2:]))
-    filtered_tiles = tiles_all[tiles_mask]
-
-    if len(filtered_tiles) == 0:
-        return im
-
-    # Find all invalid tiles and replace with a random valid tile
-    replace_count = (tiles_mask == False).sum()
-    rng = np.random.default_rng(seed=seed)
-    tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[
-        rng.choice(filtered_tiles.shape[0], replace_count), :, :, :
-    ]
-
-    # Convert back to an image
-    tiles_all = tiles_all.reshape(tshape)
-    tiles_all = tiles_all.swapaxes(1, 2)
-    st = tiles_all.reshape(
-        (
-            math.prod(tiles_all.shape[0:2]),
-            math.prod(tiles_all.shape[2:4]),
-            tiles_all.shape[4],
-        )
-    )
-    si = Image.fromarray(st, mode="RGBA")
-
-    return si
-
-
-class InfillColorInvocation(BaseInvocation):
-    """Infills transparent areas of an image with a solid color"""
-
-    type: Literal["infill_rgba"] = "infill_rgba"
-    image: Union[ImageField, None] = Field(
-        default=None, description="The image to infill"
-    )
-    color: ColorField = Field(
-        default=ColorField(r=127, g=127, b=127, a=255),
-        description="The color to use to infill",
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-
-        solid_bg = Image.new("RGBA", image.size, self.color.tuple())
-        infilled = Image.alpha_composite(solid_bg, image.convert("RGBA"))
-
-        infilled.paste(image, (0, 0), image.split()[-1])
-
-        image_dto = context.services.images.create(
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-class InfillTileInvocation(BaseInvocation):
-    """Infills transparent areas of an image with tiles of the image"""
-
-    type: Literal["infill_tile"] = "infill_tile"
-
-    image: Union[ImageField, None] = Field(
-        default=None, description="The image to infill"
-    )
-    tile_size: int = Field(default=32, ge=1, description="The tile size (px)")
-    seed: int = Field(
-        ge=0,
-        le=SEED_MAX,
-        description="The seed to use for tile generation (omit for random)",
-        default_factory=get_random_seed,
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-
-        infilled = tile_fill_missing(
-            image.copy(), seed=self.seed, tile_size=self.tile_size
-        )
-        infilled.paste(image, (0, 0), image.split()[-1])
-
-        image_dto = context.services.images.create(
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-
-class InfillPatchMatchInvocation(BaseInvocation):
-    """Infills transparent areas of an image using the PatchMatch algorithm"""
-
-    type: Literal["infill_patchmatch"] = "infill_patchmatch"
-
-    image: Union[ImageField, None] = Field(
-        default=None, description="The image to infill"
-    )
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-
-        if PatchMatch.patchmatch_available():
-            infilled = infill_patchmatch(image.copy())
-        else:
-            raise ValueError("PatchMatch is not available on this system")
-
-        image_dto = context.services.images.create(
-            image=infilled,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )

invokeai/app/invocations/latent.py

@@ -1,42 +1,29 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
 import random
-import einops
-from typing import Literal, Optional, Union, List
-
-from compel import Compel
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
-
-from pydantic import BaseModel, Field, validator
+from typing import Literal, Optional
+from pydantic import BaseModel, Field
 import torch
 
 from invokeai.app.invocations.util.choose_model import choose_model
-from invokeai.app.models.image import ImageCategory
-from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
-from .controlnet_image_processors import ControlField
 
 from ...backend.model_management.model_manager import ModelManager
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.image_util.seamless import configure_model_padding
 from ...backend.prompting.conditioning import get_uc_and_c_and_ec
-
-from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline, image_resized_to_grid_as_tensor
-from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
-from ...backend.stable_diffusion.diffusers_pipeline import ControlNetData
-
+from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 import numpy as np
-from ..services.image_file_storage import ResourceOrigin
+from ..services.image_storage import ImageType
 from .baseinvocation import BaseInvocation, InvocationContext
-from .image import ImageField, ImageOutput
-from .compel import ConditioningField
+from .image import ImageField, ImageOutput, build_image_output
 from ...backend.stable_diffusion import PipelineIntermediateState
 from diffusers.schedulers import SchedulerMixin as Scheduler
 import diffusers
-from diffusers import DiffusionPipeline, ControlNetModel
+from diffusers import DiffusionPipeline
 
 
 class LatentsField(BaseModel):
@@ -50,55 +37,41 @@ class LatentsField(BaseModel):
 class LatentsOutput(BaseInvocationOutput):
     """Base class for invocations that output latents"""
     #fmt: off
-    type: Literal["latents_output"] = "latents_output"
-
-    # Inputs
-    latents: LatentsField          = Field(default=None, description="The output latents")
-    width:                     int = Field(description="The width of the latents in pixels")
-    height:                    int = Field(description="The height of the latents in pixels")
+    type: Literal["latent_output"] = "latent_output"
+    latents: LatentsField            = Field(default=None, description="The output latents")
     #fmt: on
 
-
-def build_latents_output(latents_name: str, latents: torch.Tensor):
-      return LatentsOutput(
-          latents=LatentsField(latents_name=latents_name),
-          width=latents.size()[3] * 8,
-          height=latents.size()[2] * 8,
-      )
-
 class NoiseOutput(BaseInvocationOutput):
     """Invocation noise output"""
     #fmt: off
-    type:  Literal["noise_output"] = "noise_output"
-
-    # Inputs
+    type: Literal["noise_output"] = "noise_output"
     noise: LatentsField            = Field(default=None, description="The output noise")
-    width:                     int = Field(description="The width of the noise in pixels")
-    height:                    int = Field(description="The height of the noise in pixels")
     #fmt: on
 
-def build_noise_output(latents_name: str, latents: torch.Tensor):
-      return NoiseOutput(
-          noise=LatentsField(latents_name=latents_name),
-          width=latents.size()[3] * 8,
-          height=latents.size()[2] * 8,
-      )
+
+# TODO: this seems like a hack
+scheduler_map = dict(
+    ddim=diffusers.DDIMScheduler,
+    dpmpp_2=diffusers.DPMSolverMultistepScheduler,
+    k_dpm_2=diffusers.KDPM2DiscreteScheduler,
+    k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
+    k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
+    k_euler=diffusers.EulerDiscreteScheduler,
+    k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
+    k_heun=diffusers.HeunDiscreteScheduler,
+    k_lms=diffusers.LMSDiscreteScheduler,
+    plms=diffusers.PNDMScheduler,
+)
 
 
 SAMPLER_NAME_VALUES = Literal[
-    tuple(list(SCHEDULER_MAP.keys()))
+    tuple(list(scheduler_map.keys()))
 ]
 
 
 def get_scheduler(scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-    scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
-
-    scheduler_config = model.scheduler.config
-    if "_backup" in scheduler_config:
-        scheduler_config = scheduler_config["_backup"]
-    scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
-    scheduler = scheduler_class.from_config(scheduler_config)
-
+    scheduler_class = scheduler_map.get(scheduler_name,'ddim')
+    scheduler = scheduler_class.from_config(model.scheduler.config)
     # hack copied over from generate.py
     if not hasattr(scheduler, 'uses_inpainting_model'):
         scheduler.uses_inpainting_model = lambda: False
@@ -129,15 +102,19 @@ def get_noise(width:int, height:int, device:torch.device, seed:int = 0, latent_c
     return x
 
 
+def random_seed():
+    return random.randint(0, np.iinfo(np.uint32).max)
+
+
 class NoiseInvocation(BaseInvocation):
     """Generates latent noise."""
 
     type: Literal["noise"] = "noise"
 
     # Inputs
-    seed:       int = Field(ge=0, le=SEED_MAX, description="The seed to use", default_factory=get_random_seed)
-    width:       int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting noise", )
-    height:      int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting noise", )
+    seed:        int = Field(ge=0, le=np.iinfo(np.uint32).max, description="The seed to use", default_factory=random_seed)
+    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting noise", )
+    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting noise", )
 
 
     # Schema customisation
@@ -148,62 +125,47 @@ class NoiseInvocation(BaseInvocation):
             },
         }
 
-    @validator("seed", pre=True)
-    def modulo_seed(cls, v):
-        """Returns the seed modulo SEED_MAX to ensure it is within the valid range."""
-        return v % SEED_MAX
-
     def invoke(self, context: InvocationContext) -> NoiseOutput:
         device = torch.device(choose_torch_device())
         noise = get_noise(self.width, self.height, device, self.seed)
 
         name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.save(name, noise)
-        return build_noise_output(latents_name=name, latents=noise)
+        context.services.latents.set(name, noise)
+        return NoiseOutput(
+            noise=LatentsField(latents_name=name)
+        )
 
 
 # Text to image
 class TextToLatentsInvocation(BaseInvocation):
-    """Generates latents from conditionings."""
+    """Generates latents from a prompt."""
 
     type: Literal["t2l"] = "t2l"
 
     # Inputs
+    # TODO: consider making prompt optional to enable providing prompt through a link
     # fmt: off
-    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
-    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
+    prompt: Optional[str] = Field(description="The prompt to generate an image from")
+    seed:        int = Field(default=-1,ge=-1, le=np.iinfo(np.uint32).max, description="The seed to use (-1 for a random seed)", )
     noise: Optional[LatentsField] = Field(description="The noise to use")
     steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
-    cfg_scale: Union[float, List[float]] = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
-    scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
+    width:       int = Field(default=512, multiple_of=64, gt=0, description="The width of the resulting image", )
+    height:      int = Field(default=512, multiple_of=64, gt=0, description="The height of the resulting image", )
+    cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
+    scheduler: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The scheduler to use" )
+    seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
+    seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
     model:       str = Field(default="", description="The model to use (currently ignored)")
-    control: Union[ControlField, List[ControlField]] = Field(default=None, description="The control to use")
-    # seamless:   bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
-    # seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
+    progress_images: bool = Field(default=False, description="Whether or not to produce progress images during generation",  )
     # fmt: on
 
-    @validator("cfg_scale")
-    def ge_one(cls, v):
-        """validate that all cfg_scale values are >= 1"""
-        if isinstance(v, list):
-            for i in v:
-                if i < 1:
-                    raise ValueError('cfg_scale must be greater than 1')
-        else:
-            if v < 1:
-                raise ValueError('cfg_scale must be greater than 1')
-        return v
-
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
             "ui": {
-                "tags": ["latents"],
+                "tags": ["latents", "image"],
                 "type_hints": {
-                  "model": "model",
-                  "control": "control",
-                  # "cfg_scale": "float",
-                  "cfg_scale": "number"
+                  "model": "model"
                 }
             },
         }
@@ -229,123 +191,37 @@ class TextToLatentsInvocation(BaseInvocation):
             scheduler_name=self.scheduler
         )
 
-        # if isinstance(model, DiffusionPipeline):
-        #     for component in [model.unet, model.vae]:
-        #         configure_model_padding(component,
-        #                                 self.seamless,
-        #                                 self.seamless_axes
-        #                                 )
-        # else:
-        #     configure_model_padding(model,
-        #                             self.seamless,
-        #                             self.seamless_axes
-        #                             )
+        if isinstance(model, DiffusionPipeline):
+            for component in [model.unet, model.vae]:
+                configure_model_padding(component,
+                                        self.seamless,
+                                        self.seamless_axes
+                                        )
+        else:
+            configure_model_padding(model,
+                                    self.seamless,
+                                    self.seamless_axes
+                                    )
 
         return model
 
 
-    def get_conditioning_data(self, context: InvocationContext, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
-        c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
-        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
-
-        compel = Compel(
-            tokenizer=model.tokenizer,
-            text_encoder=model.text_encoder,
-            textual_inversion_manager=model.textual_inversion_manager,
-            dtype_for_device_getter=torch_dtype,
-            truncate_long_prompts=False,
-        )
-        [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
-
+    def get_conditioning_data(self, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
+        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(self.prompt, model=model)
         conditioning_data = ConditioningData(
-            unconditioned_embeddings=uc,
-            text_embeddings=c,
-            guidance_scale=self.cfg_scale,
-            extra=extra_conditioning_info,
+            uc,
+            c,
+            self.cfg_scale,
+            extra_conditioning_info,
             postprocessing_settings=PostprocessingSettings(
                 threshold=0.0,#threshold,
                 warmup=0.2,#warmup,
                 h_symmetry_time_pct=None,#h_symmetry_time_pct,
                 v_symmetry_time_pct=None#v_symmetry_time_pct,
             ),
-        ).add_scheduler_args_if_applicable(model.scheduler, eta=0.0)#ddim_eta)
+        ).add_scheduler_args_if_applicable(model.scheduler, eta=None)#ddim_eta)
         return conditioning_data
 
-    def prep_control_data(self,
-                          context: InvocationContext,
-                          model: StableDiffusionGeneratorPipeline, # really only need model for dtype and device
-                          control_input: List[ControlField],
-                          latents_shape: List[int],
-                          do_classifier_free_guidance: bool = True,
-                          ) -> List[ControlNetData]:
-        # assuming fixed dimensional scaling of 8:1 for image:latents
-        control_height_resize = latents_shape[2] * 8
-        control_width_resize = latents_shape[3] * 8
-        if control_input is None:
-            # print("control input is None")
-            control_list = None
-        elif isinstance(control_input, list) and len(control_input) == 0:
-            # print("control input is empty list")
-            control_list = None
-        elif isinstance(control_input, ControlField):
-            # print("control input is ControlField")
-            control_list = [control_input]
-        elif isinstance(control_input, list) and len(control_input) > 0 and isinstance(control_input[0], ControlField):
-            # print("control input is list[ControlField]")
-            control_list = control_input
-        else:
-            # print("input control is unrecognized:", type(self.control))
-            control_list = None
-        if (control_list is None):
-            control_data = None
-            # from above handling, any control that is not None should now be of type list[ControlField]
-        else:
-            # FIXME: add checks to skip entry if model or image is None
-            #        and if weight is None, populate with default 1.0?
-            control_data = []
-            control_models = []
-            for control_info in control_list:
-                # handle control models
-                if ("," in control_info.control_model):
-                    control_model_split = control_info.control_model.split(",")
-                    control_name = control_model_split[0]
-                    control_subfolder = control_model_split[1]
-                    print("Using HF model subfolders")
-                    print("    control_name: ", control_name)
-                    print("    control_subfolder: ", control_subfolder)
-                    control_model = ControlNetModel.from_pretrained(control_name,
-                                                                    subfolder=control_subfolder,
-                                                                    torch_dtype=model.unet.dtype).to(model.device)
-                else:
-                    control_model = ControlNetModel.from_pretrained(control_info.control_model,
-                                                                    torch_dtype=model.unet.dtype).to(model.device)
-                control_models.append(control_model)
-                control_image_field = control_info.image
-                input_image = context.services.images.get_pil_image(control_image_field.image_origin,
-                                                                    control_image_field.image_name)
-                # self.image.image_type, self.image.image_name
-                # FIXME: still need to test with different widths, heights, devices, dtypes
-                #        and add in batch_size, num_images_per_prompt?
-                #        and do real check for classifier_free_guidance?
-                # prepare_control_image should return torch.Tensor of shape(batch_size, 3, height, width)
-                control_image = model.prepare_control_image(
-                    image=input_image,
-                    do_classifier_free_guidance=do_classifier_free_guidance,
-                    width=control_width_resize,
-                    height=control_height_resize,
-                    # batch_size=batch_size * num_images_per_prompt,
-                    # num_images_per_prompt=num_images_per_prompt,
-                    device=control_model.device,
-                    dtype=control_model.dtype,
-                )
-                control_item = ControlNetData(model=control_model,
-                                              image_tensor=control_image,
-                                              weight=control_info.control_weight,
-                                              begin_step_percent=control_info.begin_step_percent,
-                                              end_step_percent=control_info.end_step_percent)
-                control_data.append(control_item)
-                # MultiControlNetModel has been refactored out, just need list[ControlNetData]
-        return control_data
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         noise = context.services.latents.get(self.noise.latents_name)
@@ -358,29 +234,26 @@ class TextToLatentsInvocation(BaseInvocation):
             self.dispatch_progress(context, source_node_id, state)
 
         model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(context, model)
-
-        control_data = self.prep_control_data(model=model, context=context, control_input=self.control,
-                                              latents_shape=noise.shape,
-                                              # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
-                                              do_classifier_free_guidance=True,)
+        conditioning_data = self.get_conditioning_data(model)
 
         # TODO: Verify the noise is the right size
+
         result_latents, result_attention_map_saver = model.latents_from_embeddings(
             latents=torch.zeros_like(noise, dtype=torch_dtype(model.device)),
             noise=noise,
             num_inference_steps=self.steps,
             conditioning_data=conditioning_data,
-            control_data=control_data,  # list[ControlNetData]
-            callback=step_callback,
+            callback=step_callback
         )
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
 
         name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.save(name, result_latents)
-        return build_latents_output(latents_name=name, latents=result_latents)
+        context.services.latents.set(name, result_latents)
+        return LatentsOutput(
+            latents=LatentsField(latents_name=name)
+        )
 
 
 class LatentsToLatentsInvocation(TextToLatentsInvocation):
@@ -388,23 +261,21 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
 
     type: Literal["l2l"] = "l2l"
 
-    # Inputs
-    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
-    strength: float = Field(default=0.7, ge=0, le=1, description="The strength of the latents to use")
-
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
             "ui": {
                 "tags": ["latents"],
                 "type_hints": {
-                    "model": "model",
-                    "control": "control",
-                    "cfg_scale": "number",
+                    "model": "model"
                 }
             },
         }
 
+    # Inputs
+    latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
+    strength: float = Field(default=0.5, description="The strength of the latents to use")
+
     def invoke(self, context: InvocationContext) -> LatentsOutput:
         noise = context.services.latents.get(self.noise.latents_name)
         latent = context.services.latents.get(self.latents.latents_name)
@@ -417,13 +288,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             self.dispatch_progress(context, source_node_id, state)
 
         model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(context, model)
-
-        control_data = self.prep_control_data(model=model, context=context, control_input=self.control,
-                                              latents_shape=noise.shape,
-                                              # do_classifier_free_guidance=(self.cfg_scale >= 1.0))
-                                              do_classifier_free_guidance=True,
-                                              )
+        conditioning_data = self.get_conditioning_data(model)
 
         # TODO: Verify the noise is the right size
 
@@ -431,7 +296,11 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             latent, device=model.device, dtype=latent.dtype
         )
 
-        timesteps, _ = model.get_img2img_timesteps(self.steps, self.strength)
+        timesteps, _ = model.get_img2img_timesteps(
+            self.steps,
+            self.strength,
+            device=model.device,
+        )
 
         result_latents, result_attention_map_saver = model.latents_from_embeddings(
             latents=initial_latents,
@@ -439,7 +308,6 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
             noise=noise,
             num_inference_steps=self.steps,
             conditioning_data=conditioning_data,
-            control_data=control_data,  # list[ControlNetData]
             callback=step_callback
         )
 
@@ -447,8 +315,10 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
         torch.cuda.empty_cache()
 
         name = f'{context.graph_execution_state_id}__{self.id}'
-        context.services.latents.save(name, result_latents)
-        return build_latents_output(latents_name=name, latents=result_latents)
+        context.services.latents.set(name, result_latents)
+        return LatentsOutput(
+            latents=LatentsField(latents_name=name)
+        )
 
 
 # Latent to image
@@ -484,143 +354,18 @@ class LatentsToImageInvocation(BaseInvocation):
             np_image = model.decode_latents(latents)
             image = model.numpy_to_pil(np_image)[0]
 
-            # what happened to metadata?
-            # metadata = context.services.metadata.build_metadata(
-            #     session_id=context.graph_execution_state_id, node=self
-
-            torch.cuda.empty_cache()
-
-            # new (post Image service refactor) way of using services to save image
-            #     and gnenerate unique image_name
-            image_dto = context.services.images.create(
-                image=image,
-                image_origin=ResourceOrigin.INTERNAL,
-                image_category=ImageCategory.GENERAL,
-                session_id=context.graph_execution_state_id,
-                node_id=self.id,
-                is_intermediate=self.is_intermediate
+            image_type = ImageType.RESULT
+            image_name = context.services.images.create_name(
+                context.graph_execution_state_id, self.id
             )
 
-            return ImageOutput(
-                image=ImageField(
-                    image_name=image_dto.image_name,
-                    image_origin=image_dto.image_origin,
-                ),
-                width=image_dto.width,
-                height=image_dto.height,
+            metadata = context.services.metadata.build_metadata(
+                session_id=context.graph_execution_state_id, node=self
             )
 
-
-LATENTS_INTERPOLATION_MODE = Literal[
-    "nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"
-]
-
-
-class ResizeLatentsInvocation(BaseInvocation):
-    """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
-
-    type: Literal["lresize"] = "lresize"
-
-    # Inputs
-    latents:    Optional[LatentsField] = Field(description="The latents to resize")
-    width:                         int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
-    height:                        int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
-    mode:   LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
-    antialias:                    bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        resized_latents = torch.nn.functional.interpolate(
-            latents,
-            size=(self.height // 8, self.width // 8),
-            mode=self.mode,
-            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        # context.services.latents.set(name, resized_latents)
-        context.services.latents.save(name, resized_latents)
-        return build_latents_output(latents_name=name, latents=resized_latents)
-
-
-class ScaleLatentsInvocation(BaseInvocation):
-    """Scales latents by a given factor."""
-
-    type: Literal["lscale"] = "lscale"
-
-    # Inputs
-    latents:   Optional[LatentsField] = Field(description="The latents to scale")
-    scale_factor:               float = Field(gt=0, description="The factor by which to scale the latents")
-    mode:  LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
-    antialias:                   bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
-
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.services.latents.get(self.latents.latents_name)
-
-        # resizing
-        resized_latents = torch.nn.functional.interpolate(
-            latents,
-            scale_factor=self.scale_factor,
-            mode=self.mode,
-            antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
-        )
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        # context.services.latents.set(name, resized_latents)
-        context.services.latents.save(name, resized_latents)
-        return build_latents_output(latents_name=name, latents=resized_latents)
-
-
-class ImageToLatentsInvocation(BaseInvocation):
-    """Encodes an image into latents."""
-
-    type: Literal["i2l"] = "i2l"
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The image to encode")
-    model: str = Field(default="", description="The model to use")
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["latents", "image"],
-                "type_hints": {"model": "model"},
-            },
-        }
-
-    @torch.no_grad()
-    def invoke(self, context: InvocationContext) -> LatentsOutput:
-        # image = context.services.images.get(
-        #     self.image.image_type, self.image.image_name
-        # )
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-
-        # TODO: this only really needs the vae
-        model_info = choose_model(context.services.model_manager, self.model)
-        model: StableDiffusionGeneratorPipeline = model_info["model"]
-
-        image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
-
-        if image_tensor.dim() == 3:
-            image_tensor = einops.rearrange(image_tensor, "c h w -> 1 c h w")
-
-        latents = model.non_noised_latents_from_image(
-            image_tensor,
-            device=model._model_group.device_for(model.unet),
-            dtype=model.unet.dtype,
-        )
-
-        name = f"{context.graph_execution_state_id}__{self.id}"
-        # context.services.latents.set(name, latents)
-        context.services.latents.save(name, latents)
-        return build_latents_output(latents_name=name, latents=latents)
+            context.services.images.save(image_type, image_name, image, metadata)
+            return build_image_output(
+                image_type=image_type,
+                image_name=image_name,
+                image=image
+            )

invokeai/app/invocations/math.py

@@ -3,14 +3,8 @@
 from typing import Literal
 
 from pydantic import BaseModel, Field
-import numpy as np
 
-from .baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    InvocationContext,
-    InvocationConfig,
-)
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 
 
 class MathInvocationConfig(BaseModel):
@@ -27,30 +21,19 @@ class MathInvocationConfig(BaseModel):
 
 class IntOutput(BaseInvocationOutput):
     """An integer output"""
-
-    # fmt: off
+    #fmt: off
     type: Literal["int_output"] = "int_output"
     a: int = Field(default=None, description="The output integer")
-    # fmt: on
-
-
-class FloatOutput(BaseInvocationOutput):
-    """A float output"""
-
-    # fmt: off
-    type: Literal["float_output"] = "float_output"
-    param: float = Field(default=None, description="The output float")
-    # fmt: on
+    #fmt: on
 
 
 class AddInvocation(BaseInvocation, MathInvocationConfig):
     """Adds two numbers"""
-
-    # fmt: off
+    #fmt: off
     type: Literal["add"] = "add"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    # fmt: on
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a + self.b)
@@ -58,12 +41,11 @@ class AddInvocation(BaseInvocation, MathInvocationConfig):
 
 class SubtractInvocation(BaseInvocation, MathInvocationConfig):
     """Subtracts two numbers"""
-
-    # fmt: off
+    #fmt: off
     type: Literal["sub"] = "sub"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    # fmt: on
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a - self.b)
@@ -71,12 +53,11 @@ class SubtractInvocation(BaseInvocation, MathInvocationConfig):
 
 class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
     """Multiplies two numbers"""
-
-    # fmt: off
+    #fmt: off
     type: Literal["mul"] = "mul"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    # fmt: on
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a * self.b)
@@ -84,26 +65,11 @@ class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
 
 class DivideInvocation(BaseInvocation, MathInvocationConfig):
     """Divides two numbers"""
-
-    # fmt: off
+    #fmt: off
     type: Literal["div"] = "div"
     a: int = Field(default=0, description="The first number")
     b: int = Field(default=0, description="The second number")
-    # fmt: on
+    #fmt: on
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=int(self.a / self.b))
-
-
-class RandomIntInvocation(BaseInvocation):
-    """Outputs a single random integer."""
-
-    # fmt: off
-    type: Literal["rand_int"] = "rand_int"
-    low: int = Field(default=0, description="The inclusive low value")
-    high: int = Field(
-        default=np.iinfo(np.int32).max, description="The exclusive high value"
-    )
-    # fmt: on
-    def invoke(self, context: InvocationContext) -> IntOutput:
-        return IntOutput(a=np.random.randint(self.low, self.high))

invokeai/app/invocations/param_easing.py

@@ -1,237 +0,0 @@
-import io
-from typing import Literal, Optional, Any
-
-# from PIL.Image import Image
-import PIL.Image
-from matplotlib.ticker import MaxNLocator
-from matplotlib.figure import Figure
-
-from pydantic import BaseModel, Field
-import numpy as np
-import matplotlib.pyplot as plt
-
-from easing_functions import (
-    LinearInOut,
-    QuadEaseInOut, QuadEaseIn, QuadEaseOut,
-    CubicEaseInOut, CubicEaseIn, CubicEaseOut,
-    QuarticEaseInOut, QuarticEaseIn, QuarticEaseOut,
-    QuinticEaseInOut, QuinticEaseIn, QuinticEaseOut,
-    SineEaseInOut, SineEaseIn, SineEaseOut,
-    CircularEaseIn, CircularEaseInOut, CircularEaseOut,
-    ExponentialEaseInOut, ExponentialEaseIn, ExponentialEaseOut,
-    ElasticEaseIn, ElasticEaseInOut, ElasticEaseOut,
-    BackEaseIn, BackEaseInOut, BackEaseOut,
-    BounceEaseIn, BounceEaseInOut, BounceEaseOut)
-
-from .baseinvocation import (
-    BaseInvocation,
-    BaseInvocationOutput,
-    InvocationContext,
-    InvocationConfig,
-)
-from ...backend.util.logging import InvokeAILogger
-from .collections import FloatCollectionOutput
-
-
-class FloatLinearRangeInvocation(BaseInvocation):
-    """Creates a range"""
-
-    type: Literal["float_range"] = "float_range"
-
-    # Inputs
-    start: float = Field(default=5, description="The first value of the range")
-    stop: float = Field(default=10, description="The last value of the range")
-    steps: int = Field(default=30, description="number of values to interpolate over (including start and stop)")
-
-    def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
-        param_list = list(np.linspace(self.start, self.stop, self.steps))
-        return FloatCollectionOutput(
-            collection=param_list
-        )
-
-
-EASING_FUNCTIONS_MAP = {
-    "Linear": LinearInOut,
-    "QuadIn": QuadEaseIn,
-    "QuadOut": QuadEaseOut,
-    "QuadInOut": QuadEaseInOut,
-    "CubicIn": CubicEaseIn,
-    "CubicOut": CubicEaseOut,
-    "CubicInOut": CubicEaseInOut,
-    "QuarticIn": QuarticEaseIn,
-    "QuarticOut": QuarticEaseOut,
-    "QuarticInOut": QuarticEaseInOut,
-    "QuinticIn": QuinticEaseIn,
-    "QuinticOut": QuinticEaseOut,
-    "QuinticInOut": QuinticEaseInOut,
-    "SineIn": SineEaseIn,
-    "SineOut": SineEaseOut,
-    "SineInOut": SineEaseInOut,
-    "CircularIn": CircularEaseIn,
-    "CircularOut": CircularEaseOut,
-    "CircularInOut": CircularEaseInOut,
-    "ExponentialIn": ExponentialEaseIn,
-    "ExponentialOut": ExponentialEaseOut,
-    "ExponentialInOut": ExponentialEaseInOut,
-    "ElasticIn": ElasticEaseIn,
-    "ElasticOut": ElasticEaseOut,
-    "ElasticInOut": ElasticEaseInOut,
-    "BackIn": BackEaseIn,
-    "BackOut": BackEaseOut,
-    "BackInOut": BackEaseInOut,
-    "BounceIn": BounceEaseIn,
-    "BounceOut": BounceEaseOut,
-    "BounceInOut": BounceEaseInOut,
-}
-
-EASING_FUNCTION_KEYS: Any = Literal[
-    tuple(list(EASING_FUNCTIONS_MAP.keys()))
-]
-
-
-# actually I think for now could just use CollectionOutput (which is list[Any]
-class StepParamEasingInvocation(BaseInvocation):
-    """Experimental per-step parameter easing for denoising steps"""
-
-    type: Literal["step_param_easing"] = "step_param_easing"
-
-    # Inputs
-    # fmt: off
-    easing: EASING_FUNCTION_KEYS = Field(default="Linear", description="The easing function to use")
-    num_steps: int = Field(default=20, description="number of denoising steps")
-    start_value: float = Field(default=0.0, description="easing starting value")
-    end_value: float = Field(default=1.0, description="easing ending value")
-    start_step_percent: float = Field(default=0.0, description="fraction of steps at which to start easing")
-    end_step_percent: float = Field(default=1.0, description="fraction of steps after which to end easing")
-    # if None, then start_value is used prior to easing start
-    pre_start_value: Optional[float] = Field(default=None, description="value before easing start")
-    # if None, then end value is used prior to easing end
-    post_end_value: Optional[float] = Field(default=None, description="value after easing end")
-    mirror: bool = Field(default=False, description="include mirror of easing function")
-    # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
-    # alt_mirror: bool = Field(default=False, description="alternative mirroring by dual easing")
-    show_easing_plot: bool = Field(default=False, description="show easing plot")
-    # fmt: on
-
-
-    def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
-        log_diagnostics = False
-        # convert from start_step_percent to nearest step <= (steps * start_step_percent)
-        # start_step = int(np.floor(self.num_steps * self.start_step_percent))
-        start_step = int(np.round(self.num_steps * self.start_step_percent))
-        # convert from end_step_percent to nearest step >= (steps * end_step_percent)
-        # end_step = int(np.ceil((self.num_steps - 1) * self.end_step_percent))
-        end_step = int(np.round((self.num_steps - 1) * self.end_step_percent))
-
-        # end_step = int(np.ceil(self.num_steps * self.end_step_percent))
-        num_easing_steps = end_step - start_step + 1
-
-        # num_presteps = max(start_step - 1, 0)
-        num_presteps = start_step
-        num_poststeps = self.num_steps - (num_presteps + num_easing_steps)
-        prelist = list(num_presteps * [self.pre_start_value])
-        postlist = list(num_poststeps * [self.post_end_value])
-
-        if log_diagnostics:
-            logger = InvokeAILogger.getLogger(name="StepParamEasing")
-            logger.debug("start_step: " + str(start_step))
-            logger.debug("end_step: " + str(end_step))
-            logger.debug("num_easing_steps: " + str(num_easing_steps))
-            logger.debug("num_presteps: " + str(num_presteps))
-            logger.debug("num_poststeps: " + str(num_poststeps))
-            logger.debug("prelist size: " + str(len(prelist)))
-            logger.debug("postlist size: " + str(len(postlist)))
-            logger.debug("prelist: " + str(prelist))
-            logger.debug("postlist: " + str(postlist))
-
-        easing_class = EASING_FUNCTIONS_MAP[self.easing]
-        if log_diagnostics:
-            logger.debug("easing class: " + str(easing_class))
-        easing_list = list()
-        if self.mirror:  # "expected" mirroring
-            # if number of steps is even, squeeze duration down to (number_of_steps)/2
-            # and create reverse copy of list to append
-            # if number of steps is odd, squeeze duration down to ceil(number_of_steps/2)
-            # and create reverse copy of list[1:end-1]
-            # but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always
-
-            base_easing_duration = int(np.ceil(num_easing_steps/2.0))
-            if log_diagnostics: logger.debug("base easing duration: " + str(base_easing_duration))
-            even_num_steps = (num_easing_steps % 2 == 0)  # even number of steps
-            easing_function = easing_class(start=self.start_value,
-                                           end=self.end_value,
-                                           duration=base_easing_duration - 1)
-            base_easing_vals = list()
-            for step_index in range(base_easing_duration):
-                easing_val = easing_function.ease(step_index)
-                base_easing_vals.append(easing_val)
-                if log_diagnostics:
-                    logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
-            if even_num_steps:
-                mirror_easing_vals = list(reversed(base_easing_vals))
-            else:
-                mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
-            if log_diagnostics:
-                logger.debug("base easing vals: " + str(base_easing_vals))
-                logger.debug("mirror easing vals: " + str(mirror_easing_vals))
-            easing_list = base_easing_vals + mirror_easing_vals
-
-        # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
-        # elif self.alt_mirror:  # function mirroring (unintuitive behavior (at least to me))
-        #     # half_ease_duration = round(num_easing_steps - 1 / 2)
-        #     half_ease_duration = round((num_easing_steps - 1) / 2)
-        #     easing_function = easing_class(start=self.start_value,
-        #                                    end=self.end_value,
-        #                                    duration=half_ease_duration,
-        #                                    )
-        #
-        #     mirror_function = easing_class(start=self.end_value,
-        #                                    end=self.start_value,
-        #                                    duration=half_ease_duration,
-        #                                    )
-        #     for step_index in range(num_easing_steps):
-        #         if step_index <= half_ease_duration:
-        #             step_val = easing_function.ease(step_index)
-        #         else:
-        #             step_val = mirror_function.ease(step_index - half_ease_duration)
-        #         easing_list.append(step_val)
-        #         if log_diagnostics: logger.debug(step_index, step_val)
-        #
-
-        else:  # no mirroring (default)
-            easing_function = easing_class(start=self.start_value,
-                                           end=self.end_value,
-                                           duration=num_easing_steps - 1)
-            for step_index in range(num_easing_steps):
-                step_val = easing_function.ease(step_index)
-                easing_list.append(step_val)
-                if log_diagnostics:
-                    logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
-
-        if log_diagnostics:
-            logger.debug("prelist size: " + str(len(prelist)))
-            logger.debug("easing_list size: " + str(len(easing_list)))
-            logger.debug("postlist size: " + str(len(postlist)))
-
-        param_list = prelist + easing_list + postlist
-
-        if self.show_easing_plot:
-            plt.figure()
-            plt.xlabel("Step")
-            plt.ylabel("Param Value")
-            plt.title("Per-Step Values Based On Easing: " + self.easing)
-            plt.bar(range(len(param_list)), param_list)
-            # plt.plot(param_list)
-            ax = plt.gca()
-            ax.xaxis.set_major_locator(MaxNLocator(integer=True))
-            buf = io.BytesIO()
-            plt.savefig(buf, format='png')
-            buf.seek(0)
-            im = PIL.Image.open(buf)
-            im.show()
-            buf.close()
-
-        # output array of size steps, each entry list[i] is param value for step i
-        return FloatCollectionOutput(
-            collection=param_list
-        )

invokeai/app/invocations/params.py

@@ -3,7 +3,7 @@
 from typing import Literal
 from pydantic import Field
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
-from .math import IntOutput, FloatOutput
+from .math import IntOutput
 
 # Pass-through parameter nodes - used by subgraphs
 
@@ -16,13 +16,3 @@ class ParamIntInvocation(BaseInvocation):
 
     def invoke(self, context: InvocationContext) -> IntOutput:
         return IntOutput(a=self.a)
-
-class ParamFloatInvocation(BaseInvocation):
-    """A float parameter"""
-    #fmt: off
-    type: Literal["param_float"] = "param_float"
-    param: float = Field(default=0.0, description="The float value")
-    #fmt: on
-
-    def invoke(self, context: InvocationContext) -> FloatOutput:
-        return FloatOutput(param=self.param)

invokeai/app/invocations/reconstruct.py

@@ -2,23 +2,21 @@ from typing import Literal, Union
 
 from pydantic import Field
 
-from invokeai.app.models.image import ImageCategory, ImageField, ResourceOrigin
+from invokeai.app.models.image import ImageField, ImageType
 
 from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput
-
+from .image import ImageOutput, build_image_output
 
 class RestoreFaceInvocation(BaseInvocation):
     """Restores faces in an image."""
-
-    # fmt: off
+    #fmt: off
     type:  Literal["restore_face"] = "restore_face"
 
     # Inputs
     image: Union[ImageField, None] = Field(description="The input image")
     strength:                float = Field(default=0.75, gt=0, le=1, description="The strength of the restoration"  )
-    # fmt: on
-
+    #fmt: on
+    
     # Schema customisation
     class Config(InvocationConfig):
         schema_extra = {
@@ -28,8 +26,8 @@ class RestoreFaceInvocation(BaseInvocation):
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
         )
         results = context.services.restoration.upscale_and_reconstruct(
             image_list=[[image, 0]],
@@ -41,20 +39,18 @@ class RestoreFaceInvocation(BaseInvocation):
 
         # Results are image and seed, unwrap for now
         # TODO: can this return multiple results?
-        image_dto = context.services.images.create(
-            image=results[0][0],
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
         )
+
+        context.services.images.save(image_type, image_name, results[0][0], metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=results[0][0]
+        )

invokeai/app/invocations/upscale.py

@@ -4,22 +4,22 @@ from typing import Literal, Union
 
 from pydantic import Field
 
-from invokeai.app.models.image import ImageCategory, ImageField, ResourceOrigin
+from invokeai.app.models.image import ImageField, ImageType
 from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from .image import ImageOutput
+from .image import ImageOutput, build_image_output
 
 
 class UpscaleInvocation(BaseInvocation):
     """Upscales an image."""
-
-    # fmt: off
+    #fmt: off
     type: Literal["upscale"] = "upscale"
 
     # Inputs
     image: Union[ImageField, None] = Field(description="The input image", default=None)
     strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
     level: Literal[2, 4] = Field(default=2, description="The upscale level")
-    # fmt: on
+    #fmt: on
+
 
     # Schema customisation
     class Config(InvocationConfig):
@@ -30,8 +30,8 @@ class UpscaleInvocation(BaseInvocation):
         }
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
+        image = context.services.images.get(
+            self.image.image_type, self.image.image_name
         )
         results = context.services.restoration.upscale_and_reconstruct(
             image_list=[[image, 0]],
@@ -43,20 +43,18 @@ class UpscaleInvocation(BaseInvocation):
 
         # Results are image and seed, unwrap for now
         # TODO: can this return multiple results?
-        image_dto = context.services.images.create(
-            image=results[0][0],
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
+        image_type = ImageType.RESULT
+        image_name = context.services.images.create_name(
+            context.graph_execution_state_id, self.id
         )
 
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
+        metadata = context.services.metadata.build_metadata(
+            session_id=context.graph_execution_state_id, node=self
         )
+
+        context.services.images.save(image_type, image_name, results[0][0], metadata)
+        return build_image_output(
+            image_type=image_type,
+            image_name=image_name,
+            image=results[0][0]
+        )

invokeai/app/invocations/util/choose_model.py

@@ -3,12 +3,12 @@ from invokeai.backend.model_management.model_manager import ModelManager
 
 def choose_model(model_manager: ModelManager, model_name: str):
     """Returns the default model if the `model_name` not a valid model, else returns the selected model."""
-    logger = model_manager.logger
-    if model_name and not model_manager.valid_model(model_name):
-        default_model_name = model_manager.default_model()
-        logger.warning(f"\'{model_name}\' is not a valid model name. Using default model \'{default_model_name}\' instead.")
-        model = model_manager.get_model()
-    else:
+    if model_manager.valid_model(model_name):
         model = model_manager.get_model(model_name)
+    else:
+        model = model_manager.get_model()
+        print(
+            f"* Warning: '{model_name}' is not a valid model name. Using default model \'{model['model_name']}\' instead."
+        )
 
     return model

invokeai/app/models/image.py

@@ -1,93 +1,29 @@
 from enum import Enum
-from typing import Optional, Tuple
+from typing import Optional
 from pydantic import BaseModel, Field
 
-from invokeai.app.util.metaenum import MetaEnum
+
+class ImageType(str, Enum):
+    RESULT = "results"
+    INTERMEDIATE = "intermediates"
+    UPLOAD = "uploads"
 
 
-class ResourceOrigin(str, Enum, metaclass=MetaEnum):
-    """The origin of a resource (eg image).
-
-    - INTERNAL: The resource was created by the application.
-    - EXTERNAL: The resource was not created by the application.
-    This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
-    """
-
-    INTERNAL = "internal"
-    """The resource was created by the application."""
-    EXTERNAL = "external"
-    """The resource was not created by the application.
-    This may be a user-initiated upload, or an internal application upload (eg Canvas init image).
-    """
-
-
-class InvalidOriginException(ValueError):
-    """Raised when a provided value is not a valid ResourceOrigin.
-
-    Subclasses `ValueError`.
-    """
-
-    def __init__(self, message="Invalid resource origin."):
-        super().__init__(message)
-
-
-class ImageCategory(str, Enum, metaclass=MetaEnum):
-    """The category of an image.
-
-    - GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose.
-    - MASK: The image is a mask image.
-    - CONTROL: The image is a ControlNet control image.
-    - USER: The image is a user-provide image.
-    - OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes.
-    """
-
-    GENERAL = "general"
-    """GENERAL: The image is an output, init image, or otherwise an image without a specialized purpose."""
-    MASK = "mask"
-    """MASK: The image is a mask image."""
-    CONTROL = "control"
-    """CONTROL: The image is a ControlNet control image."""
-    USER = "user"
-    """USER: The image is a user-provide image."""
-    OTHER = "other"
-    """OTHER: The image is some other type of image with a specialized purpose. To be used by external nodes."""
-
-
-class InvalidImageCategoryException(ValueError):
-    """Raised when a provided value is not a valid ImageCategory.
-
-    Subclasses `ValueError`.
-    """
-
-    def __init__(self, message="Invalid image category."):
-        super().__init__(message)
+def is_image_type(obj):
+    try:
+        ImageType(obj)
+    except ValueError:
+        return False
+    return True
 
 
 class ImageField(BaseModel):
     """An image field used for passing image objects between invocations"""
 
-    image_origin: ResourceOrigin = Field(
-        default=ResourceOrigin.INTERNAL, description="The type of the image"
+    image_type: ImageType = Field(
+        default=ImageType.RESULT, description="The type of the image"
     )
     image_name: Optional[str] = Field(default=None, description="The name of the image")
 
     class Config:
-        schema_extra = {"required": ["image_origin", "image_name"]}
-
-
-class ColorField(BaseModel):
-    r: int = Field(ge=0, le=255, description="The red component")
-    g: int = Field(ge=0, le=255, description="The green component")
-    b: int = Field(ge=0, le=255, description="The blue component")
-    a: int = Field(ge=0, le=255, description="The alpha component")
-
-    def tuple(self) -> Tuple[int, int, int, int]:
-        return (self.r, self.g, self.b, self.a)
-
-
-class ProgressImage(BaseModel):
-    """The progress image sent intermittently during processing"""
-
-    width: int = Field(description="The effective width of the image in pixels")
-    height: int = Field(description="The effective height of the image in pixels")
-    dataURL: str = Field(description="The image data as a b64 data URL")
+        schema_extra = {"required": ["image_type", "image_name"]}

invokeai/app/models/metadata.py

@@ -1,93 +0,0 @@
-from typing import Optional, Union, List
-from pydantic import BaseModel, Extra, Field, StrictFloat, StrictInt, StrictStr
-
-
-class ImageMetadata(BaseModel):
-    """
-    Core generation metadata for an image/tensor generated in InvokeAI.
-
-    Also includes any metadata from the image's PNG tEXt chunks.
-
-    Generated by traversing the execution graph, collecting the parameters of the nearest ancestors
-    of a given node.
-
-    Full metadata may be accessed by querying for the session in the `graph_executions` table.
-    """
-
-    class Config:
-        extra = Extra.allow
-        """
-        This lets the ImageMetadata class accept arbitrary additional fields. The CoreMetadataService
-        won't add any fields that are not already defined, but other a different metadata service
-        implementation might.
-        """
-
-    type: Optional[StrictStr] = Field(
-        default=None,
-        description="The type of the ancestor node of the image output node.",
-    )
-    """The type of the ancestor node of the image output node."""
-    positive_conditioning: Optional[StrictStr] = Field(
-        default=None, description="The positive conditioning."
-    )
-    """The positive conditioning"""
-    negative_conditioning: Optional[StrictStr] = Field(
-        default=None, description="The negative conditioning."
-    )
-    """The negative conditioning"""
-    width: Optional[StrictInt] = Field(
-        default=None, description="Width of the image/latents in pixels."
-    )
-    """Width of the image/latents in pixels"""
-    height: Optional[StrictInt] = Field(
-        default=None, description="Height of the image/latents in pixels."
-    )
-    """Height of the image/latents in pixels"""
-    seed: Optional[StrictInt] = Field(
-        default=None, description="The seed used for noise generation."
-    )
-    """The seed used for noise generation"""
-    # cfg_scale: Optional[StrictFloat] = Field(
-    # cfg_scale: Union[float, list[float]] = Field(
-    cfg_scale: Union[StrictFloat, List[StrictFloat]] = Field(
-        default=None, description="The classifier-free guidance scale."
-    )
-    """The classifier-free guidance scale"""
-    steps: Optional[StrictInt] = Field(
-        default=None, description="The number of steps used for inference."
-    )
-    """The number of steps used for inference"""
-    scheduler: Optional[StrictStr] = Field(
-        default=None, description="The scheduler used for inference."
-    )
-    """The scheduler used for inference"""
-    model: Optional[StrictStr] = Field(
-        default=None, description="The model used for inference."
-    )
-    """The model used for inference"""
-    strength: Optional[StrictFloat] = Field(
-        default=None,
-        description="The strength used for image-to-image/latents-to-latents.",
-    )
-    """The strength used for image-to-image/latents-to-latents."""
-    latents: Optional[StrictStr] = Field(
-        default=None, description="The ID of the initial latents."
-    )
-    """The ID of the initial latents"""
-    vae: Optional[StrictStr] = Field(
-        default=None, description="The VAE used for decoding."
-    )
-    """The VAE used for decoding"""
-    unet: Optional[StrictStr] = Field(
-        default=None, description="The UNet used dor inference."
-    )
-    """The UNet used dor inference"""
-    clip: Optional[StrictStr] = Field(
-        default=None, description="The CLIP Encoder used for conditioning."
-    )
-    """The CLIP Encoder used for conditioning"""
-    extra: Optional[StrictStr] = Field(
-        default=None,
-        description="Uploaded image metadata, extracted from the PNG tEXt chunk.",
-    )
-    """Uploaded image metadata, extracted from the PNG tEXt chunk."""

invokeai/app/services/config.py

@@ -1,581 +0,0 @@
-# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
-
-'''Invokeai configuration system.
-
-Arguments and fields are taken from the pydantic definition of the
-model.  Defaults can be set by creating a yaml configuration file that
-has a top-level key of "InvokeAI" and subheadings for each of the
-categories returned by `invokeai --help`. The file looks like this:
-
-[file: invokeai.yaml]
-
-InvokeAI:
-  Paths:
-    root: /home/lstein/invokeai-main
-    conf_path: configs/models.yaml
-    legacy_conf_dir: configs/stable-diffusion
-    outdir: outputs
-    embedding_dir: embeddings
-    lora_dir: loras
-    autoconvert_dir: null
-    gfpgan_model_dir: models/gfpgan/GFPGANv1.4.pth
-  Models:
-    model: stable-diffusion-1.5
-    embeddings: true
-  Memory/Performance:
-    xformers_enabled: false
-    sequential_guidance: false
-    precision: float16
-    max_loaded_models: 4
-    always_use_cpu: false
-    free_gpu_mem: false
-  Features:
-    nsfw_checker: true
-    restore: true
-    esrgan: true
-    patchmatch: true
-    internet_available: true
-    log_tokenization: false
-  Web Server:
-    host: 127.0.0.1
-    port: 8081
-    allow_origins: []
-    allow_credentials: true
-    allow_methods:
-    - '*'
-    allow_headers:
-    - '*'
-
-The default name of the configuration file is `invokeai.yaml`, located
-in INVOKEAI_ROOT. You can replace supersede this by providing any
-OmegaConf dictionary object initialization time:
-
- omegaconf = OmegaConf.load('/tmp/init.yaml')
- conf = InvokeAIAppConfig()
- conf.parse_args(conf=omegaconf)
-
-InvokeAIAppConfig.parse_args() will parse the contents of `sys.argv`
-at initialization time. You may pass a list of strings in the optional
-`argv` argument to use instead of the system argv:
-
- conf.parse_args(argv=['--xformers_enabled'])
-
-It is also possible to set a value at initialization time. However, if
-you call parse_args() it may be overwritten.
-
- conf = InvokeAIAppConfig(xformers_enabled=True)
- conf.parse_args(argv=['--no-xformers'])
- conf.xformers_enabled
- # False
-
-
-To avoid this, use `get_config()` to retrieve the application-wide
-configuration object. This will retain any properties set at object
-creation time:
-
- conf = InvokeAIAppConfig.get_config(xformers_enabled=True)
- conf.parse_args(argv=['--no-xformers'])
- conf.xformers_enabled
- # True
-
-Any setting can be overwritten by setting an environment variable of
-form: "INVOKEAI_<setting>", as in:
-
-  export INVOKEAI_port=8080
-
-Order of precedence (from highest):
-   1) initialization options
-   2) command line options
-   3) environment variable options
-   4) config file options
-   5) pydantic defaults
-
-Typical usage at the top level file:
-
- from invokeai.app.services.config import InvokeAIAppConfig
-
- # get global configuration and print its nsfw_checker value
- conf = InvokeAIAppConfig.get_config()
- conf.parse_args()
- print(conf.nsfw_checker)
-
-Typical usage in a backend module:
-
- from invokeai.app.services.config import InvokeAIAppConfig
-
- # get global configuration and print its nsfw_checker value
- conf = InvokeAIAppConfig.get_config()
- print(conf.nsfw_checker)
-
-
-Computed properties:
-
-The InvokeAIAppConfig object has a series of properties that
-resolve paths relative to the runtime root directory. They each return
-a Path object:
-
- root_path          - path to InvokeAI root
- output_path        - path to default outputs directory
- model_conf_path    - path to models.yaml
- conf               - alias for the above
- embedding_path     - path to the embeddings directory
- lora_path          - path to the LoRA directory
-
-In most cases, you will want to create a single InvokeAIAppConfig
-object for the entire application. The InvokeAIAppConfig.get_config() function
-does this:
-
-  config = InvokeAIAppConfig.get_config()
-  config.parse_args()   # read values from the command line/config file
-  print(config.root)
-
-# Subclassing
-
-If you wish to create a similar class, please subclass the
-`InvokeAISettings` class and define a Literal field named "type",
-which is set to the desired top-level name.  For example, to create a
-"InvokeBatch" configuration, define like this:
-
-  class InvokeBatch(InvokeAISettings):
-     type: Literal["InvokeBatch"] = "InvokeBatch"
-     node_count : int = Field(default=1, description="Number of nodes to run on", category='Resources')
-     cpu_count  : int = Field(default=8, description="Number of GPUs to run on per node", category='Resources')
-
-This will now read and write from the "InvokeBatch" section of the
-config file, look for environment variables named INVOKEBATCH_*, and
-accept the command-line arguments `--node_count` and `--cpu_count`. The
-two configs are kept in separate sections of the config file:
-
-  # invokeai.yaml
-
-  InvokeBatch:
-     Resources:
-        node_count: 1
-        cpu_count: 8
-
-  InvokeAI:
-     Paths:
-        root: /home/lstein/invokeai-main
-        conf_path: configs/models.yaml
-        legacy_conf_dir: configs/stable-diffusion
-        outdir: outputs
-     ...
-
-'''
-from __future__ import annotations
-import argparse
-import pydoc
-import os
-import sys
-from argparse import ArgumentParser
-from omegaconf import OmegaConf, DictConfig
-from pathlib import Path
-from pydantic import BaseSettings, Field, parse_obj_as
-from typing import ClassVar, Dict, List, Literal, Type, Union, get_origin, get_type_hints, get_args
-
-INIT_FILE = Path('invokeai.yaml')
-DB_FILE   = Path('invokeai.db')
-LEGACY_INIT_FILE = Path('invokeai.init')
-
-class InvokeAISettings(BaseSettings):
-    '''
-    Runtime configuration settings in which default values are
-    read from an omegaconf .yaml file.
-    '''
-    initconf             : ClassVar[DictConfig] = None
-    argparse_groups      : ClassVar[Dict] = {}
-
-    def parse_args(self, argv: list=sys.argv[1:]):
-        parser = self.get_parser()
-        opt = parser.parse_args(argv)
-        for name in self.__fields__:
-            if name not in self._excluded():
-                setattr(self, name, getattr(opt,name))
-
-    def to_yaml(self)->str:
-        """
-        Return a YAML string representing our settings. This can be used
-        as the contents of `invokeai.yaml` to restore settings later.
-        """
-        cls = self.__class__
-        type = get_args(get_type_hints(cls)['type'])[0]
-        field_dict = dict({type:dict()})
-        for name,field in self.__fields__.items():
-            if name in cls._excluded():
-                continue
-            category = field.field_info.extra.get("category") or "Uncategorized"
-            value = getattr(self,name)
-            if category not in field_dict[type]:
-                field_dict[type][category] = dict()
-            # keep paths as strings to make it easier to read
-            field_dict[type][category][name] = str(value) if isinstance(value,Path) else value
-        conf = OmegaConf.create(field_dict)
-        return OmegaConf.to_yaml(conf)
-
-    @classmethod
-    def add_parser_arguments(cls, parser):
-        if 'type' in get_type_hints(cls):
-            settings_stanza = get_args(get_type_hints(cls)['type'])[0]
-        else:
-            settings_stanza = "Uncategorized"
-
-        env_prefix = cls.Config.env_prefix if hasattr(cls.Config,'env_prefix') else settings_stanza.upper()
-
-        initconf = cls.initconf.get(settings_stanza) \
-            if cls.initconf and settings_stanza in cls.initconf \
-               else OmegaConf.create()
-
-        # create an upcase version of the environment in
-        # order to achieve case-insensitive environment
-        # variables (the way Windows does)
-        upcase_environ = dict()
-        for key,value in os.environ.items():
-            upcase_environ[key.upper()] = value
-        
-        fields = cls.__fields__
-        cls.argparse_groups = {}
-        
-        for name, field in fields.items():
-            if name not in cls._excluded():
-                current_default = field.default
-
-                category = field.field_info.extra.get("category","Uncategorized")
-                env_name = env_prefix + '_' + name
-                if category in initconf and name in initconf.get(category):
-                    field.default = initconf.get(category).get(name)
-                if env_name.upper() in upcase_environ:
-                    field.default = upcase_environ[env_name.upper()]
-                cls.add_field_argument(parser, name, field)
-
-                field.default = current_default
-
-    @classmethod
-    def cmd_name(self, command_field: str='type')->str:
-        hints = get_type_hints(self)
-        if command_field in hints:
-            return get_args(hints[command_field])[0]
-        else:
-            return 'Uncategorized'
-
-    @classmethod
-    def get_parser(cls)->ArgumentParser:
-        parser = PagingArgumentParser(
-            prog=cls.cmd_name(),
-            description=cls.__doc__,
-        )
-        cls.add_parser_arguments(parser)
-        return parser
-
-    @classmethod
-    def add_subparser(cls, parser: argparse.ArgumentParser):
-        parser.add_parser(cls.cmd_name(), help=cls.__doc__)
-
-    @classmethod
-    def _excluded(self)->List[str]:
-        return ['type','initconf']
-
-    class Config:
-        env_file_encoding = 'utf-8'
-        arbitrary_types_allowed = True
-        case_sensitive = True
-
-    @classmethod
-    def add_field_argument(cls, command_parser, name: str, field, default_override = None):
-        field_type = get_type_hints(cls).get(name)
-        default = default_override if default_override is not None else field.default if field.default_factory is None else field.default_factory()
-        if category := field.field_info.extra.get("category"):
-            if category not in cls.argparse_groups:
-                cls.argparse_groups[category] = command_parser.add_argument_group(category)
-            argparse_group = cls.argparse_groups[category]
-        else:
-            argparse_group = command_parser
-
-        if get_origin(field_type) == Literal:
-            allowed_values = get_args(field.type_)
-            allowed_types = set()
-            for val in allowed_values:
-                allowed_types.add(type(val))
-            allowed_types_list = list(allowed_types)
-            field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list]  # type: ignore
-
-            argparse_group.add_argument(
-                f"--{name}",
-                dest=name,
-                type=field_type,
-                default=default,
-                choices=allowed_values,
-                help=field.field_info.description,
-            )
-
-        elif get_origin(field_type) == list:
-            argparse_group.add_argument(
-                f"--{name}",
-                dest=name,
-                nargs='*',
-                type=field.type_,
-                default=default,
-                action=argparse.BooleanOptionalAction if field.type_==bool else 'store',
-                help=field.field_info.description,
-            )
-        else:
-            argparse_group.add_argument(
-                f"--{name}",
-                dest=name,
-                type=field.type_,
-                default=default,
-                action=argparse.BooleanOptionalAction if field.type_==bool else 'store',
-                help=field.field_info.description,
-            )
-def _find_root()->Path:
-    if os.environ.get("INVOKEAI_ROOT"):
-        root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
-    elif (
-            os.environ.get("VIRTUAL_ENV")
-            and (Path(os.environ.get("VIRTUAL_ENV"), "..", INIT_FILE).exists()
-                 or
-                 Path(os.environ.get("VIRTUAL_ENV"), "..", LEGACY_INIT_FILE).exists()
-                 )
-    ):
-        root = Path(os.environ.get("VIRTUAL_ENV"), "..").resolve()
-    else:
-        root = Path("~/invokeai").expanduser().resolve()
-    return root
-
-class InvokeAIAppConfig(InvokeAISettings):
-    '''
-Generate images using Stable Diffusion. Use "invokeai" to launch
-the command-line client (recommended for experts only), or
-"invokeai-web" to launch the web server. Global options
-can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
-setting environment variables INVOKEAI_<setting>.
-     '''
-    singleton_config: ClassVar[InvokeAIAppConfig] = None
-    singleton_init: ClassVar[Dict] = None
-    
-    #fmt: off
-    type: Literal["InvokeAI"] = "InvokeAI"
-    host                : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
-    port                : int = Field(default=9090, description="Port to bind to", category='Web Server')
-    allow_origins       : List[str] = Field(default=[], description="Allowed CORS origins", category='Web Server')
-    allow_credentials   : bool = Field(default=True, description="Allow CORS credentials", category='Web Server')
-    allow_methods       : List[str] = Field(default=["*"], description="Methods allowed for CORS", category='Web Server')
-    allow_headers       : List[str] = Field(default=["*"], description="Headers allowed for CORS", category='Web Server')
-
-    esrgan              : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
-    internet_available  : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
-    log_tokenization    : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
-    nsfw_checker        : bool = Field(default=True, description="Enable/disable the NSFW checker", category='Features')
-    patchmatch          : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
-    restore             : bool = Field(default=True, description="Enable/disable face restoration code", category='Features')
-
-    always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
-    free_gpu_mem        : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
-    max_loaded_models   : int = Field(default=2, gt=0, description="Maximum number of models to keep in memory for rapid switching", category='Memory/Performance')
-    precision           : Literal[tuple(['auto','float16','float32','autocast'])] = Field(default='float16',description='Floating point precision', category='Memory/Performance')
-    sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
-    xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
-
-
-    root                : Path = Field(default=_find_root(), description='InvokeAI runtime root directory', category='Paths')
-    autoconvert_dir     : Path = Field(default=None, description='Path to a directory of ckpt files to be converted into diffusers and imported on startup.', category='Paths')
-    conf_path           : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
-    embedding_dir       : Path = Field(default='embeddings', description='Path to InvokeAI textual inversion aembeddings directory', category='Paths')
-    gfpgan_model_dir    : Path = Field(default="./models/gfpgan/GFPGANv1.4.pth", description='Path to GFPGAN models directory.', category='Paths')
-    controlnet_dir      : Path = Field(default="controlnets", description='Path to directory of ControlNet models.', category='Paths')
-    legacy_conf_dir     : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
-    lora_dir            : Path = Field(default='loras', description='Path to InvokeAI LoRA model directory', category='Paths')
-    db_dir              : Path = Field(default='databases', description='Path to InvokeAI databases directory', category='Paths')
-    outdir              : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
-    from_file           : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
-    use_memory_db       : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
-
-    model               : str = Field(default='stable-diffusion-1.5', description='Initial model name', category='Models')
-    embeddings          : bool = Field(default=True, description='Load contents of embeddings directory', category='Models')
-
-    log_handlers        : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
-    # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
-    log_format          : Literal[tuple(['plain','color','syslog','legacy'])] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
-    log_level           : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="debug", description="Emit logging messages at this level or  higher", category="Logging")
-    #fmt: on
-
-    def parse_args(self, argv: List[str]=None, conf: DictConfig = None, clobber=False):
-        '''
-        Update settings with contents of init file, environment, and 
-        command-line settings.
-        :param conf: alternate Omegaconf dictionary object
-        :param argv: aternate sys.argv list
-        :param clobber: ovewrite any initialization parameters passed during initialization
-        '''
-        # Set the runtime root directory. We parse command-line switches here
-        # in order to pick up the --root_dir option.
-        super().parse_args(argv)
-        if conf is None:
-            try:
-                conf = OmegaConf.load(self.root_dir / INIT_FILE)
-            except:
-                pass
-        InvokeAISettings.initconf = conf
-        
-        # parse args again in order to pick up settings in configuration file
-        super().parse_args(argv)
-
-        if self.singleton_init and not clobber:
-            hints = get_type_hints(self.__class__)
-            for k in self.singleton_init:
-                setattr(self,k,parse_obj_as(hints[k],self.singleton_init[k]))
-
-    @classmethod
-    def get_config(cls,**kwargs)->InvokeAIAppConfig:
-        '''
-        This returns a singleton InvokeAIAppConfig configuration object.
-        '''
-        if cls.singleton_config is None \
-           or type(cls.singleton_config)!=cls \
-           or (kwargs and cls.singleton_init != kwargs):
-            cls.singleton_config = cls(**kwargs)
-            cls.singleton_init = kwargs
-        return cls.singleton_config
-        
-    @property
-    def root_path(self)->Path:
-        '''
-        Path to the runtime root directory
-        '''
-        if self.root:
-            return Path(self.root).expanduser()
-        else:
-            return self.find_root()
-
-    @property
-    def root_dir(self)->Path:
-        '''
-        Alias for above.
-        '''
-        return self.root_path
-
-    def _resolve(self,partial_path:Path)->Path:
-        return (self.root_path / partial_path).resolve()
-
-    @property
-    def init_file_path(self)->Path:
-        '''
-        Path to invokeai.yaml
-        '''
-        return self._resolve(INIT_FILE)
-
-    @property
-    def output_path(self)->Path:
-        '''
-        Path to defaults outputs directory.
-        '''
-        return self._resolve(self.outdir)
-
-    @property
-    def db_path(self)->Path:
-        '''
-        Path to the invokeai.db file.
-        '''
-        return self._resolve(self.db_dir) / DB_FILE
-
-    @property
-    def model_conf_path(self)->Path:
-        '''
-        Path to models configuration file.
-        '''
-        return self._resolve(self.conf_path)
-
-    @property
-    def legacy_conf_path(self)->Path:
-        '''
-        Path to directory of legacy configuration files (e.g. v1-inference.yaml)
-        '''
-        return self._resolve(self.legacy_conf_dir)
-
-    @property
-    def cache_dir(self)->Path:
-        '''
-        Path to the global cache directory for HuggingFace hub-managed models
-        '''
-        return self.models_dir / "hub"
-
-    @property
-    def models_dir(self)->Path:
-        '''
-        Path to the models directory
-        '''
-        return self._resolve("models")
-
-    @property
-    def embedding_path(self)->Path:
-        '''
-        Path to the textual inversion embeddings directory.
-        '''
-        return self._resolve(self.embedding_dir) if self.embedding_dir else None
-
-    @property
-    def lora_path(self)->Path:
-        '''
-        Path to the LoRA models directory.
-        '''
-        return self._resolve(self.lora_dir) if self.lora_dir else None
-
-    @property
-    def controlnet_path(self)->Path:
-        '''
-        Path to the controlnet models directory.
-        '''
-        return self._resolve(self.controlnet_dir) if self.controlnet_dir else None
-
-    @property
-    def autoconvert_path(self)->Path:
-        '''
-        Path to the directory containing models to be imported automatically at startup.
-        '''
-        return self._resolve(self.autoconvert_dir) if self.autoconvert_dir else None
-
-    @property
-    def gfpgan_model_path(self)->Path:
-        '''
-        Path to the GFPGAN model.
-        '''
-        return self._resolve(self.gfpgan_model_dir) if self.gfpgan_model_dir else None
-
-    # the following methods support legacy calls leftover from the Globals era
-    @property
-    def full_precision(self)->bool:
-        """Return true if precision set to float32"""
-        return self.precision=='float32'
-
-    @property
-    def disable_xformers(self)->bool:
-        """Return true if xformers_enabled is false"""
-        return not self.xformers_enabled
-
-    @property
-    def try_patchmatch(self)->bool:
-        """Return true if patchmatch true"""
-        return self.patchmatch
-
-    @staticmethod
-    def find_root()->Path:
-        '''
-        Choose the runtime root directory when not specified on command line or
-        init file.
-        '''
-        return _find_root()
-
-
-class PagingArgumentParser(argparse.ArgumentParser):
-    '''
-    A custom ArgumentParser that uses pydoc to page its output.
-    It also supports reading defaults from an init file.
-    '''
-    def print_help(self, file=None):
-        text = self.format_help()
-        pydoc.pager(text)
-
-def get_invokeai_config(**kwargs)->InvokeAIAppConfig:
-    '''
-    Legacy function which returns InvokeAIAppConfig.get_config()
-    '''
-    return InvokeAIAppConfig.get_config(**kwargs)

invokeai/app/services/default_graphs.py

@@ -1,5 +1,4 @@
 from ..invocations.latent import LatentsToImageInvocation, NoiseInvocation, TextToLatentsInvocation
-from ..invocations.compel import CompelInvocation
 from ..invocations.params import ParamIntInvocation
 from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
 from .item_storage import ItemStorageABC
@@ -17,45 +16,38 @@ def create_text_to_image() -> LibraryGraph:
             nodes={
                 'width': ParamIntInvocation(id='width', a=512),
                 'height': ParamIntInvocation(id='height', a=512),
-                'seed': ParamIntInvocation(id='seed', a=-1),
                 '3': NoiseInvocation(id='3'),
-                '4': CompelInvocation(id='4'),
-                '5': CompelInvocation(id='5'),
-                '6': TextToLatentsInvocation(id='6'),
-                '7': LatentsToImageInvocation(id='7'),
+                '4': TextToLatentsInvocation(id='4'),
+                '5': LatentsToImageInvocation(id='5')
             },
             edges=[
                 Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
                 Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
-                Edge(source=EdgeConnection(node_id='seed', field='a'), destination=EdgeConnection(node_id='3', field='seed')),
-                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='6', field='noise')),
-                Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')),
-                Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')),
-                Edge(source=EdgeConnection(node_id='5', field='conditioning'), destination=EdgeConnection(node_id='6', field='negative_conditioning')),
+                Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='4', field='width')),
+                Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='4', field='height')),
+                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='4', field='noise')),
+                Edge(source=EdgeConnection(node_id='4', field='latents'), destination=EdgeConnection(node_id='5', field='latents')),
             ]
         ),
         exposed_inputs=[
-            ExposedNodeInput(node_path='4', field='prompt', alias='positive_prompt'),
-            ExposedNodeInput(node_path='5', field='prompt', alias='negative_prompt'),
+            ExposedNodeInput(node_path='4', field='prompt', alias='prompt'),
             ExposedNodeInput(node_path='width', field='a', alias='width'),
-            ExposedNodeInput(node_path='height', field='a', alias='height'),
-            ExposedNodeInput(node_path='seed', field='a', alias='seed'),
+            ExposedNodeInput(node_path='height', field='a', alias='height')
         ],
         exposed_outputs=[
-            ExposedNodeOutput(node_path='7', field='image', alias='image')
+            ExposedNodeOutput(node_path='5', field='image', alias='image')
         ])
 
 
 def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[LibraryGraph]:
     """Creates the default system graphs, or adds new versions if the old ones don't match"""
-    
-    # TODO: Uncomment this when we are ready to fix this up to prevent breaking changes
+
     graphs: list[LibraryGraph] = list()
 
-    # text_to_image = graph_library.get(default_text_to_image_graph_id)
+    text_to_image = graph_library.get(default_text_to_image_graph_id)
     
-    # # TODO: Check if the graph is the same as the default one, and if not, update it
-    # #if text_to_image is None:
+    # TODO: Check if the graph is the same as the default one, and if not, update it
+    #if text_to_image is None:
     text_to_image = create_text_to_image()
     graph_library.set(text_to_image)
 

invokeai/app/services/events.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
 
 from typing import Any
-from invokeai.app.models.image import ProgressImage
+from invokeai.app.api.models.images import ProgressImage
 from invokeai.app.util.misc import get_timestamp
 
 

invokeai/app/services/graph.py

@@ -60,33 +60,6 @@ def get_input_field(node: BaseInvocation, field: str) -> Any:
     node_input_field = node_inputs.get(field) or None
     return node_input_field
 
-from typing import Optional, Union, List, get_args
-
-def is_union_subtype(t1, t2):
-    t1_args = get_args(t1)
-    t2_args = get_args(t2)
-    if not t1_args:
-        # t1 is a single type
-        return t1 in t2_args
-    else:
-        # t1 is a Union, check that all of its types are in t2_args
-        return all(arg in t2_args for arg in t1_args)
-
-def is_list_or_contains_list(t):
-    t_args = get_args(t)
-
-    # If the type is a List
-    if get_origin(t) is list:
-        return True
-
-    # If the type is a Union
-    elif t_args:
-        # Check if any of the types in the Union is a List
-        for arg in t_args:
-            if get_origin(arg) is list:
-                return True
-    return False
-
 
 def are_connection_types_compatible(from_type: Any, to_type: Any) -> bool:
     if not from_type:
@@ -112,8 +85,7 @@ def are_connection_types_compatible(from_type: Any, to_type: Any) -> bool:
         if to_type in get_args(from_type):
             return True
 
-        # if not issubclass(from_type, to_type):
-        if not is_union_subtype(from_type, to_type):
+        if not issubclass(from_type, to_type):
             return False
     else:
         return False
@@ -163,7 +135,6 @@ class GraphInvocationOutput(BaseInvocationOutput):
 
 # TODO: Fill this out and move to invocations
 class GraphInvocation(BaseInvocation):
-    """Execute a graph"""
     type: Literal["graph"] = "graph"
 
     # TODO: figure out how to create a default here
@@ -191,7 +162,6 @@ class IterateInvocationOutput(BaseInvocationOutput):
 
 # TODO: Fill this out and move to invocations
 class IterateInvocation(BaseInvocation):
-    """Iterates over a list of items"""
     type: Literal["iterate"] = "iterate"
 
     collection: list[Any] = Field(
@@ -391,7 +361,7 @@ class Graph(BaseModel):
             from_node = self.get_node(edge.source.node_id)
             to_node = self.get_node(edge.destination.node_id)
         except NodeNotFoundError:
-            raise InvalidEdgeError("One or both nodes don't exist: {edge.source.node_id} -> {edge.destination.node_id}")
+            raise InvalidEdgeError("One or both nodes don't exist")
 
         # Validate that an edge to this node+field doesn't already exist
         input_edges = self._get_input_edges(edge.destination.node_id, edge.destination.field)
@@ -402,41 +372,41 @@ class Graph(BaseModel):
         g = self.nx_graph_flat()
         g.add_edge(edge.source.node_id, edge.destination.node_id)
         if not nx.is_directed_acyclic_graph(g):
-            raise InvalidEdgeError(f'Edge creates a cycle in the graph: {edge.source.node_id} -> {edge.destination.node_id}')
+            raise InvalidEdgeError(f'Edge creates a cycle in the graph')
 
         # Validate that the field types are compatible
         if not are_connections_compatible(
             from_node, edge.source.field, to_node, edge.destination.field
         ):
-            raise InvalidEdgeError(f'Fields are incompatible: cannot connect {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
+            raise InvalidEdgeError(f'Fields are incompatible')
 
         # Validate if iterator output type matches iterator input type (if this edge results in both being set)
         if isinstance(to_node, IterateInvocation) and edge.destination.field == "collection":
             if not self._is_iterator_connection_valid(
                 edge.destination.node_id, new_input=edge.source
             ):
-                raise InvalidEdgeError(f'Iterator input type does not match iterator output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
+                raise InvalidEdgeError(f'Iterator input type does not match iterator output type')
 
         # Validate if iterator input type matches output type (if this edge results in both being set)
         if isinstance(from_node, IterateInvocation) and edge.source.field == "item":
             if not self._is_iterator_connection_valid(
                 edge.source.node_id, new_output=edge.destination
             ):
-                raise InvalidEdgeError(f'Iterator output type does not match iterator input type:, {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
+                raise InvalidEdgeError(f'Iterator output type does not match iterator input type')
 
         # Validate if collector input type matches output type (if this edge results in both being set)
         if isinstance(to_node, CollectInvocation) and edge.destination.field == "item":
             if not self._is_collector_connection_valid(
                 edge.destination.node_id, new_input=edge.source
             ):
-                raise InvalidEdgeError(f'Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
+                raise InvalidEdgeError(f'Collector output type does not match collector input type')
 
         # Validate if collector output type matches input type (if this edge results in both being set)
         if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
             if not self._is_collector_connection_valid(
                 edge.source.node_id, new_output=edge.destination
             ):
-                raise InvalidEdgeError(f'Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
+                raise InvalidEdgeError(f'Collector input type does not match collector output type')
 
 
     def has_node(self, node_path: str) -> bool:
@@ -722,11 +692,7 @@ class Graph(BaseModel):
         input_root_type = next(t[0] for t in type_degrees if t[1] == 0)  # type: ignore
 
         # Verify that all outputs are lists
-        # if not all((get_origin(f) == list for f in output_fields)):
-        #     return False
-
-        # Verify that all outputs are lists
-        if not all(is_list_or_contains_list(f) for f in output_fields):
+        if not all((get_origin(f) == list for f in output_fields)):
             return False
 
         # Verify that all outputs match the input type (are a base class or the same class)
@@ -745,13 +711,6 @@ class Graph(BaseModel):
         g.add_edges_from(set([(e.source.node_id, e.destination.node_id) for e in self.edges]))
         return g
 
-    def nx_graph_with_data(self) -> nx.DiGraph:
-        """Returns a NetworkX DiGraph representing the data and layout of this graph"""
-        g = nx.DiGraph()
-        g.add_nodes_from([n for n in self.nodes.items()])
-        g.add_edges_from(set([(e.source.node_id, e.destination.node_id) for e in self.edges]))
-        return g
-
     def nx_graph_flat(
         self, nx_graph: Optional[nx.DiGraph] = None, prefix: Optional[str] = None
     ) -> nx.DiGraph:
@@ -857,9 +816,11 @@ class GraphExecutionState(BaseModel):
         if next_node is None:
             prepared_id = self._prepare()
 
-            # Prepare as many nodes as we can
-            while prepared_id is not None:
-                prepared_id = self._prepare()
+            # TODO: prepare multiple nodes at once?
+            # while prepared_id is not None and not isinstance(self.graph.nodes[prepared_id], IterateInvocation):
+            #     prepared_id = self._prepare()
+
+            if prepared_id is not None:
                 next_node = self._get_next_node()
 
         # Get values from edges
@@ -1006,30 +967,14 @@ class GraphExecutionState(BaseModel):
         # Get flattened source graph
         g = self.graph.nx_graph_flat()
 
-        # Find next node that:
-        # - was not already prepared
-        # - is not an iterate node whose inputs have not been executed
-        # - does not have an unexecuted iterate ancestor
+        # Find next unprepared node where all source nodes are executed
         sorted_nodes = nx.topological_sort(g)
         next_node_id = next(
             (
                 n
                 for n in sorted_nodes
-                # exclude nodes that have already been prepared
                 if n not in self.source_prepared_mapping
-                # exclude iterate nodes whose inputs have not been executed
-                and not (
-                    isinstance(self.graph.get_node(n), IterateInvocation)  # `n` is an iterate node...
-                    and not all((e[0] in self.executed for e in g.in_edges(n)))  # ...that has unexecuted inputs
-                )
-                # exclude nodes who have unexecuted iterate ancestors
-                and not any(
-                    (
-                        isinstance(self.graph.get_node(a), IterateInvocation)  # `a` is an iterate ancestor of `n`...
-                        and a not in self.executed  # ...that is not executed
-                        for a in nx.ancestors(g, n)  # for all ancestors `a` of node `n`
-                    )
-                )
+                and all((e[0] in self.executed for e in g.in_edges(n)))
             ),
             None,
         )
@@ -1126,22 +1071,9 @@ class GraphExecutionState(BaseModel):
         )
 
     def _get_next_node(self) -> Optional[BaseInvocation]:
-        """Gets the deepest node that is ready to be executed"""
         g = self.execution_graph.nx_graph()
-
-        # Depth-first search with pre-order traversal is a depth-first topological sort
-        sorted_nodes = nx.dfs_preorder_nodes(g)
-        
-        next_node = next(
-            (
-                n
-                for n in sorted_nodes
-                if n not in self.executed # the node must not already be executed...
-                and all((e[0] in self.executed for e in g.in_edges(n))) # ...and all its inputs must be executed
-            ),
-            None,
-        )
-
+        sorted_nodes = nx.topological_sort(g)
+        next_node = next((n for n in sorted_nodes if n not in self.executed), None)
         if next_node is None:
             return None
 

invokeai/app/services/image_file_storage.py

@@ -1,204 +0,0 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
-import os
-from abc import ABC, abstractmethod
-from pathlib import Path
-from queue import Queue
-from typing import Dict, Optional
-
-from PIL.Image import Image as PILImageType
-from PIL import Image, PngImagePlugin
-from send2trash import send2trash
-
-from invokeai.app.models.image import ResourceOrigin
-from invokeai.app.models.metadata import ImageMetadata
-from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
-
-
-# TODO: Should these excpetions subclass existing python exceptions?
-class ImageFileNotFoundException(Exception):
-    """Raised when an image file is not found in storage."""
-
-    def __init__(self, message="Image file not found"):
-        super().__init__(message)
-
-
-class ImageFileSaveException(Exception):
-    """Raised when an image cannot be saved."""
-
-    def __init__(self, message="Image file not saved"):
-        super().__init__(message)
-
-
-class ImageFileDeleteException(Exception):
-    """Raised when an image cannot be deleted."""
-
-    def __init__(self, message="Image file not deleted"):
-        super().__init__(message)
-
-
-class ImageFileStorageBase(ABC):
-    """Low-level service responsible for storing and retrieving image files."""
-
-    @abstractmethod
-    def get(self, image_origin: ResourceOrigin, image_name: str) -> PILImageType:
-        """Retrieves an image as PIL Image."""
-        pass
-
-    @abstractmethod
-    def get_path(
-        self, image_origin: ResourceOrigin, image_name: str, thumbnail: bool = False
-    ) -> str:
-        """Gets the internal path to an image or thumbnail."""
-        pass
-
-    # TODO: We need to validate paths before starlette makes the FileResponse, else we get a
-    # 500 internal server error. I don't like having this method on the service.
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates the path given for an image or thumbnail."""
-        pass
-
-    @abstractmethod
-    def save(
-        self,
-        image: PILImageType,
-        image_origin: ResourceOrigin,
-        image_name: str,
-        metadata: Optional[ImageMetadata] = None,
-        thumbnail_size: int = 256,
-    ) -> None:
-        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
-        pass
-
-    @abstractmethod
-    def delete(self, image_origin: ResourceOrigin, image_name: str) -> None:
-        """Deletes an image and its thumbnail (if one exists)."""
-        pass
-
-
-class DiskImageFileStorage(ImageFileStorageBase):
-    """Stores images on disk"""
-
-    __output_folder: str
-    __cache_ids: Queue  # TODO: this is an incredibly naive cache
-    __cache: Dict[str, PILImageType]
-    __max_cache_size: int
-
-    def __init__(self, output_folder: str):
-        self.__output_folder = output_folder
-        self.__cache = dict()
-        self.__cache_ids = Queue()
-        self.__max_cache_size = 10  # TODO: get this from config
-
-        Path(output_folder).mkdir(parents=True, exist_ok=True)
-
-        # TODO: don't hard-code. get/save/delete should maybe take subpath?
-        for image_origin in ResourceOrigin:
-            Path(os.path.join(output_folder, image_origin)).mkdir(
-                parents=True, exist_ok=True
-            )
-            Path(os.path.join(output_folder, image_origin, "thumbnails")).mkdir(
-                parents=True, exist_ok=True
-            )
-
-    def get(self, image_origin: ResourceOrigin, image_name: str) -> PILImageType:
-        try:
-            image_path = self.get_path(image_origin, image_name)
-            cache_item = self.__get_cache(image_path)
-            if cache_item:
-                return cache_item
-
-            image = Image.open(image_path)
-            self.__set_cache(image_path, image)
-            return image
-        except FileNotFoundError as e:
-            raise ImageFileNotFoundException from e
-
-    def save(
-        self,
-        image: PILImageType,
-        image_origin: ResourceOrigin,
-        image_name: str,
-        metadata: Optional[ImageMetadata] = None,
-        thumbnail_size: int = 256,
-    ) -> None:
-        try:
-            image_path = self.get_path(image_origin, image_name)
-
-            if metadata is not None:
-                pnginfo = PngImagePlugin.PngInfo()
-                pnginfo.add_text("invokeai", metadata.json())
-                image.save(image_path, "PNG", pnginfo=pnginfo)
-            else:
-                image.save(image_path, "PNG")
-
-            thumbnail_name = get_thumbnail_name(image_name)
-            thumbnail_path = self.get_path(image_origin, thumbnail_name, thumbnail=True)
-            thumbnail_image = make_thumbnail(image, thumbnail_size)
-            thumbnail_image.save(thumbnail_path)
-
-            self.__set_cache(image_path, image)
-            self.__set_cache(thumbnail_path, thumbnail_image)
-        except Exception as e:
-            raise ImageFileSaveException from e
-
-    def delete(self, image_origin: ResourceOrigin, image_name: str) -> None:
-        try:
-            basename = os.path.basename(image_name)
-            image_path = self.get_path(image_origin, basename)
-
-            if os.path.exists(image_path):
-                send2trash(image_path)
-            if image_path in self.__cache:
-                del self.__cache[image_path]
-
-            thumbnail_name = get_thumbnail_name(image_name)
-            thumbnail_path = self.get_path(image_origin, thumbnail_name, True)
-
-            if os.path.exists(thumbnail_path):
-                send2trash(thumbnail_path)
-            if thumbnail_path in self.__cache:
-                del self.__cache[thumbnail_path]
-        except Exception as e:
-            raise ImageFileDeleteException from e
-
-    # TODO: make this a bit more flexible for e.g. cloud storage
-    def get_path(
-        self, image_origin: ResourceOrigin, image_name: str, thumbnail: bool = False
-    ) -> str:
-        # strip out any relative path shenanigans
-        basename = os.path.basename(image_name)
-
-        if thumbnail:
-            thumbnail_name = get_thumbnail_name(basename)
-            path = os.path.join(
-                self.__output_folder, image_origin, "thumbnails", thumbnail_name
-            )
-        else:
-            path = os.path.join(self.__output_folder, image_origin, basename)
-
-        abspath = os.path.abspath(path)
-
-        return abspath
-
-    def validate_path(self, path: str) -> bool:
-        """Validates the path given for an image or thumbnail."""
-        try:
-            os.stat(path)
-            return True
-        except:
-            return False
-
-    def __get_cache(self, image_name: str) -> PILImageType | None:
-        return None if image_name not in self.__cache else self.__cache[image_name]
-
-    def __set_cache(self, image_name: str, image: PILImageType):
-        if not image_name in self.__cache:
-            self.__cache[image_name] = image
-            self.__cache_ids.put(
-                image_name
-            )  # TODO: this should refresh position for LRU cache
-            if len(self.__cache) > self.__max_cache_size:
-                cache_id = self.__cache_ids.get()
-                if cache_id in self.__cache:
-                    del self.__cache[cache_id]

invokeai/app/services/image_record_storage.py

@@ -1,419 +0,0 @@
-from abc import ABC, abstractmethod
-from datetime import datetime
-from typing import Generic, Optional, TypeVar, cast
-import sqlite3
-import threading
-from typing import Optional, Union
-
-from pydantic import BaseModel, Field
-from pydantic.generics import GenericModel
-
-from invokeai.app.models.metadata import ImageMetadata
-from invokeai.app.models.image import (
-    ImageCategory,
-    ResourceOrigin,
-)
-from invokeai.app.services.models.image_record import (
-    ImageRecord,
-    ImageRecordChanges,
-    deserialize_image_record,
-)
-
-T = TypeVar("T", bound=BaseModel)
-
-class OffsetPaginatedResults(GenericModel, Generic[T]):
-    """Offset-paginated results"""
-
-    # fmt: off
-    items: list[T] = Field(description="Items")
-    offset: int = Field(description="Offset from which to retrieve items")
-    limit: int = Field(description="Limit of items to get")
-    total: int = Field(description="Total number of items in result")
-    # fmt: on
-
-
-# TODO: Should these excpetions subclass existing python exceptions?
-class ImageRecordNotFoundException(Exception):
-    """Raised when an image record is not found."""
-
-    def __init__(self, message="Image record not found"):
-        super().__init__(message)
-
-
-class ImageRecordSaveException(Exception):
-    """Raised when an image record cannot be saved."""
-
-    def __init__(self, message="Image record not saved"):
-        super().__init__(message)
-
-
-class ImageRecordDeleteException(Exception):
-    """Raised when an image record cannot be deleted."""
-
-    def __init__(self, message="Image record not deleted"):
-        super().__init__(message)
-
-
-class ImageRecordStorageBase(ABC):
-    """Low-level service responsible for interfacing with the image record store."""
-
-    # TODO: Implement an `update()` method
-
-    @abstractmethod
-    def get(self, image_origin: ResourceOrigin, image_name: str) -> ImageRecord:
-        """Gets an image record."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        image_name: str,
-        image_origin: ResourceOrigin,
-        changes: ImageRecordChanges,
-    ) -> None:
-        """Updates an image record."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-    ) -> OffsetPaginatedResults[ImageRecord]:
-        """Gets a page of image records."""
-        pass
-
-    # TODO: The database has a nullable `deleted_at` column, currently unused.
-    # Should we implement soft deletes? Would need coordination with ImageFileStorage.
-    @abstractmethod
-    def delete(self, image_origin: ResourceOrigin, image_name: str) -> None:
-        """Deletes an image record."""
-        pass
-
-    @abstractmethod
-    def save(
-        self,
-        image_name: str,
-        image_origin: ResourceOrigin,
-        image_category: ImageCategory,
-        width: int,
-        height: int,
-        session_id: Optional[str],
-        node_id: Optional[str],
-        metadata: Optional[ImageMetadata],
-        is_intermediate: bool = False,
-    ) -> datetime:
-        """Saves an image record."""
-        pass
-
-
-class SqliteImageRecordStorage(ImageRecordStorageBase):
-    _filename: str
-    _conn: sqlite3.Connection
-    _cursor: sqlite3.Cursor
-    _lock: threading.Lock
-
-    def __init__(self, filename: str) -> None:
-        super().__init__()
-        self._filename = filename
-        self._conn = sqlite3.connect(filename, check_same_thread=False)
-        # Enable row factory to get rows as dictionaries (must be done before making the cursor!)
-        self._conn.row_factory = sqlite3.Row
-        self._cursor = self._conn.cursor()
-        self._lock = threading.Lock()
-
-        try:
-            self._lock.acquire()
-            # Enable foreign keys
-            self._conn.execute("PRAGMA foreign_keys = ON;")
-            self._create_tables()
-            self._conn.commit()
-        finally:
-            self._lock.release()
-
-    def _create_tables(self) -> None:
-        """Creates the tables for the `images` database."""
-
-        # Create the `images` table.
-        self._cursor.execute(
-            """--sql
-            CREATE TABLE IF NOT EXISTS images (
-                image_name TEXT NOT NULL PRIMARY KEY,
-                -- This is an enum in python, unrestricted string here for flexibility
-                image_origin TEXT NOT NULL,
-                -- This is an enum in python, unrestricted string here for flexibility
-                image_category TEXT NOT NULL,
-                width INTEGER NOT NULL,
-                height INTEGER NOT NULL,
-                session_id TEXT,
-                node_id TEXT,
-                metadata TEXT,
-                is_intermediate BOOLEAN DEFAULT FALSE,
-                created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Updated via trigger
-                updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
-                -- Soft delete, currently unused
-                deleted_at DATETIME
-            );
-            """
-        )
-
-        # Create the `images` table indices.
-        self._cursor.execute(
-            """--sql
-            CREATE UNIQUE INDEX IF NOT EXISTS idx_images_image_name ON images(image_name);
-            """
-        )
-        self._cursor.execute(
-            """--sql
-            CREATE INDEX IF NOT EXISTS idx_images_image_origin ON images(image_origin);
-            """
-        )
-        self._cursor.execute(
-            """--sql
-            CREATE INDEX IF NOT EXISTS idx_images_image_category ON images(image_category);
-            """
-        )
-        self._cursor.execute(
-            """--sql
-            CREATE INDEX IF NOT EXISTS idx_images_created_at ON images(created_at);
-            """
-        )
-
-        # Add trigger for `updated_at`.
-        self._cursor.execute(
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS tg_images_updated_at
-            AFTER UPDATE
-            ON images FOR EACH ROW
-            BEGIN
-                UPDATE images SET updated_at = current_timestamp
-                    WHERE image_name = old.image_name;
-            END;
-            """
-        )
-
-    def get(
-        self, image_origin: ResourceOrigin, image_name: str
-    ) -> Union[ImageRecord, None]:
-        try:
-            self._lock.acquire()
-
-            self._cursor.execute(
-                f"""--sql
-                SELECT * FROM images
-                WHERE image_name = ?;
-                """,
-                (image_name,),
-            )
-
-            result = cast(Union[sqlite3.Row, None], self._cursor.fetchone())
-        except sqlite3.Error as e:
-            self._conn.rollback()
-            raise ImageRecordNotFoundException from e
-        finally:
-            self._lock.release()
-
-        if not result:
-            raise ImageRecordNotFoundException
-
-        return deserialize_image_record(dict(result))
-
-    def update(
-        self,
-        image_name: str,
-        image_origin: ResourceOrigin,
-        changes: ImageRecordChanges,
-    ) -> None:
-        try:
-            self._lock.acquire()
-            # Change the category of the image
-            if changes.image_category is not None:
-                self._cursor.execute(
-                    f"""--sql
-                    UPDATE images
-                    SET image_category = ?
-                    WHERE image_name = ?;
-                    """,
-                    (changes.image_category, image_name),
-                )
-
-            # Change the session associated with the image
-            if changes.session_id is not None:
-                self._cursor.execute(
-                    f"""--sql
-                    UPDATE images
-                    SET session_id = ?
-                    WHERE image_name = ?;
-                    """,
-                    (changes.session_id, image_name),
-                )
-
-            # Change the image's `is_intermediate`` flag
-            if changes.is_intermediate is not None:
-                self._cursor.execute(
-                    f"""--sql
-                    UPDATE images
-                    SET is_intermediate = ?
-                    WHERE image_name = ?;
-                    """,
-                    (changes.is_intermediate, image_name),
-                )
-            self._conn.commit()
-        except sqlite3.Error as e:
-            self._conn.rollback()
-            raise ImageRecordSaveException from e
-        finally:
-            self._lock.release()
-
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-    ) -> OffsetPaginatedResults[ImageRecord]:
-        try:
-            self._lock.acquire()
-
-            # Manually build two queries - one for the count, one for the records
-
-            count_query = f"""SELECT COUNT(*) FROM images WHERE 1=1\n"""
-            images_query = f"""SELECT * FROM images WHERE 1=1\n"""
-
-            query_conditions = ""
-            query_params = []
-
-            if image_origin is not None:
-                query_conditions += f"""AND image_origin = ?\n"""
-                query_params.append(image_origin.value)
-
-            if categories is not None:
-                ## Convert the enum values to unique list of strings
-                category_strings = list(
-                    map(lambda c: c.value, set(categories))
-                )
-                # Create the correct length of placeholders
-                placeholders = ",".join("?" * len(category_strings))
-                query_conditions += f"AND image_category IN ( {placeholders} )\n"
-
-                # Unpack the included categories into the query params
-                for c in category_strings:
-                    query_params.append(c)
-
-            if is_intermediate is not None:
-                query_conditions += f"""AND is_intermediate = ?\n"""
-                query_params.append(is_intermediate)
-
-            query_pagination = f"""ORDER BY created_at DESC LIMIT ? OFFSET ?\n"""
-
-            # Final images query with pagination
-            images_query += query_conditions + query_pagination + ";"
-            # Add all the parameters
-            images_params = query_params.copy()
-            images_params.append(limit)
-            images_params.append(offset)
-            # Build the list of images, deserializing each row
-            self._cursor.execute(images_query, images_params)
-            result = cast(list[sqlite3.Row], self._cursor.fetchall())
-            images = list(map(lambda r: deserialize_image_record(dict(r)), result))
-
-            # Set up and execute the count query, without pagination
-            count_query += query_conditions + ";"
-            count_params = query_params.copy()
-            self._cursor.execute(count_query, count_params)
-            count = self._cursor.fetchone()[0]
-        except sqlite3.Error as e:
-            self._conn.rollback()
-            raise e
-        finally:
-            self._lock.release()
-
-        return OffsetPaginatedResults(
-            items=images, offset=offset, limit=limit, total=count
-        )
-
-    def delete(self, image_origin: ResourceOrigin, image_name: str) -> None:
-        try:
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                DELETE FROM images
-                WHERE image_name = ?;
-                """,
-                (image_name,),
-            )
-            self._conn.commit()
-        except sqlite3.Error as e:
-            self._conn.rollback()
-            raise ImageRecordDeleteException from e
-        finally:
-            self._lock.release()
-
-    def save(
-        self,
-        image_name: str,
-        image_origin: ResourceOrigin,
-        image_category: ImageCategory,
-        session_id: Optional[str],
-        width: int,
-        height: int,
-        node_id: Optional[str],
-        metadata: Optional[ImageMetadata],
-        is_intermediate: bool = False,
-    ) -> datetime:
-        try:
-            metadata_json = (
-                None if metadata is None else metadata.json(exclude_none=True)
-            )
-            self._lock.acquire()
-            self._cursor.execute(
-                """--sql
-                INSERT OR IGNORE INTO images (
-                    image_name,
-                    image_origin,
-                    image_category,
-                    width,
-                    height,
-                    node_id,
-                    session_id,
-                    metadata,
-                    is_intermediate
-                    )
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?);
-                """,
-                (
-                    image_name,
-                    image_origin.value,
-                    image_category.value,
-                    width,
-                    height,
-                    node_id,
-                    session_id,
-                    metadata_json,
-                    is_intermediate,
-                ),
-            )
-            self._conn.commit()
-
-            self._cursor.execute(
-                """--sql
-                SELECT created_at
-                FROM images
-                WHERE image_name = ?;
-                """,
-                (image_name,),
-            )
-
-            created_at = datetime.fromisoformat(self._cursor.fetchone()[0])
-
-            return created_at
-        except sqlite3.Error as e:
-            self._conn.rollback()
-            raise ImageRecordSaveException from e
-        finally:
-            self._lock.release()

invokeai/app/services/image_storage.py

@@ -0,0 +1,238 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+import os
+from glob import glob
+from abc import ABC, abstractmethod
+from pathlib import Path
+from queue import Queue
+from typing import Dict, List, Tuple
+
+from PIL.Image import Image
+import PIL.Image as PILImage
+from invokeai.app.api.models.images import ImageResponse, ImageResponseMetadata
+from invokeai.app.models.image import ImageType
+from invokeai.app.services.metadata import (
+    InvokeAIMetadata,
+    MetadataServiceBase,
+    build_invokeai_metadata_pnginfo,
+)
+from invokeai.app.services.item_storage import PaginatedResults
+from invokeai.app.util.misc import get_timestamp
+from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
+
+
+class ImageStorageBase(ABC):
+    """Responsible for storing and retrieving images."""
+
+    @abstractmethod
+    def get(self, image_type: ImageType, image_name: str) -> Image:
+        """Retrieves an image as PIL Image."""
+        pass
+
+    @abstractmethod
+    def list(
+        self, image_type: ImageType, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[ImageResponse]:
+        """Gets a paginated list of images."""
+        pass
+
+    # TODO: make this a bit more flexible for e.g. cloud storage
+    @abstractmethod
+    def get_path(
+        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
+    ) -> str:
+        """Gets the path to an image or its thumbnail."""
+        pass
+
+    # TODO: make this a bit more flexible for e.g. cloud storage
+    @abstractmethod
+    def validate_path(self, path: str) -> bool:
+        """Validates an image path."""
+        pass
+
+    @abstractmethod
+    def save(
+        self,
+        image_type: ImageType,
+        image_name: str,
+        image: Image,
+        metadata: InvokeAIMetadata | None = None,
+    ) -> Tuple[str, str, int]:
+        """Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image path, thumbnail path, and created timestamp."""
+        pass
+
+    @abstractmethod
+    def delete(self, image_type: ImageType, image_name: str) -> None:
+        """Deletes an image and its thumbnail (if one exists)."""
+        pass
+
+    def create_name(self, context_id: str, node_id: str) -> str:
+        """Creates a unique contextual image filename."""
+        return f"{context_id}_{node_id}_{str(get_timestamp())}.png"
+
+
+class DiskImageStorage(ImageStorageBase):
+    """Stores images on disk"""
+
+    __output_folder: str
+    __cache_ids: Queue  # TODO: this is an incredibly naive cache
+    __cache: Dict[str, Image]
+    __max_cache_size: int
+    __metadata_service: MetadataServiceBase
+
+    def __init__(self, output_folder: str, metadata_service: MetadataServiceBase):
+        self.__output_folder = output_folder
+        self.__cache = dict()
+        self.__cache_ids = Queue()
+        self.__max_cache_size = 10  # TODO: get this from config
+        self.__metadata_service = metadata_service
+
+        Path(output_folder).mkdir(parents=True, exist_ok=True)
+
+        # TODO: don't hard-code. get/save/delete should maybe take subpath?
+        for image_type in ImageType:
+            Path(os.path.join(output_folder, image_type)).mkdir(
+                parents=True, exist_ok=True
+            )
+            Path(os.path.join(output_folder, image_type, "thumbnails")).mkdir(
+                parents=True, exist_ok=True
+            )
+
+    def list(
+        self, image_type: ImageType, page: int = 0, per_page: int = 10
+    ) -> PaginatedResults[ImageResponse]:
+        dir_path = os.path.join(self.__output_folder, image_type)
+        image_paths = glob(f"{dir_path}/*.png")
+        count = len(image_paths)
+
+        sorted_image_paths = sorted(
+            glob(f"{dir_path}/*.png"), key=os.path.getctime, reverse=True
+        )
+
+        page_of_image_paths = sorted_image_paths[
+            page * per_page : (page + 1) * per_page
+        ]
+
+        page_of_images: List[ImageResponse] = []
+
+        for path in page_of_image_paths:
+            filename = os.path.basename(path)
+            img = PILImage.open(path)
+
+            invokeai_metadata = self.__metadata_service.get_metadata(img)
+
+            page_of_images.append(
+                ImageResponse(
+                    image_type=image_type.value,
+                    image_name=filename,
+                    # TODO: DiskImageStorage should not be building URLs...?
+                    image_url=f"api/v1/images/{image_type.value}/{filename}",
+                    thumbnail_url=f"api/v1/images/{image_type.value}/thumbnails/{os.path.splitext(filename)[0]}.webp",
+                    # TODO: Creation of this object should happen elsewhere (?), just making it fit here so it works
+                    metadata=ImageResponseMetadata(
+                        created=int(os.path.getctime(path)),
+                        width=img.width,
+                        height=img.height,
+                        invokeai=invokeai_metadata,
+                    ),
+                )
+            )
+
+        page_count_trunc = int(count / per_page)
+        page_count_mod = count % per_page
+        page_count = page_count_trunc if page_count_mod == 0 else page_count_trunc + 1
+
+        return PaginatedResults[ImageResponse](
+            items=page_of_images,
+            page=page,
+            pages=page_count,
+            per_page=per_page,
+            total=count,
+        )
+
+    def get(self, image_type: ImageType, image_name: str) -> Image:
+        image_path = self.get_path(image_type, image_name)
+        cache_item = self.__get_cache(image_path)
+        if cache_item:
+            return cache_item
+
+        image = PILImage.open(image_path)
+        self.__set_cache(image_path, image)
+        return image
+
+    # TODO: make this a bit more flexible for e.g. cloud storage
+    def get_path(
+        self, image_type: ImageType, image_name: str, is_thumbnail: bool = False
+    ) -> str:
+        # strip out any relative path shenanigans
+        basename = os.path.basename(image_name)
+
+        if is_thumbnail:
+            path = os.path.join(
+                self.__output_folder, image_type, "thumbnails", basename
+            )
+        else:
+            path = os.path.join(self.__output_folder, image_type, basename)
+
+        return path
+
+    def validate_path(self, path: str) -> bool:
+        try:
+            os.stat(path)
+            return True
+        except Exception:
+            return False
+
+    def save(
+        self,
+        image_type: ImageType,
+        image_name: str,
+        image: Image,
+        metadata: InvokeAIMetadata | None = None,
+    ) -> Tuple[str, str, int]:
+        image_path = self.get_path(image_type, image_name)
+
+        # TODO: Reading the image and then saving it strips the metadata...
+        if metadata:
+            pnginfo = build_invokeai_metadata_pnginfo(metadata=metadata)
+            image.save(image_path, "PNG", pnginfo=pnginfo)
+        else:
+            image.save(image_path) # this saved image has an empty info
+
+        thumbnail_name = get_thumbnail_name(image_name)
+        thumbnail_path = self.get_path(image_type, thumbnail_name, is_thumbnail=True)
+        thumbnail_image = make_thumbnail(image)
+        thumbnail_image.save(thumbnail_path)
+
+        self.__set_cache(image_path, image)
+        self.__set_cache(thumbnail_path, thumbnail_image)
+
+        return (image_path, thumbnail_path, int(os.path.getctime(image_path)))
+
+    def delete(self, image_type: ImageType, image_name: str) -> None:
+        image_path = self.get_path(image_type, image_name)
+        thumbnail_path = self.get_path(image_type, image_name, True)
+        if os.path.exists(image_path):
+            os.remove(image_path)
+
+        if image_path in self.__cache:
+            del self.__cache[image_path]
+
+        if os.path.exists(thumbnail_path):
+            os.remove(thumbnail_path)
+
+        if thumbnail_path in self.__cache:
+            del self.__cache[thumbnail_path]
+
+    def __get_cache(self, image_name: str) -> Image:
+        return None if image_name not in self.__cache else self.__cache[image_name]
+
+    def __set_cache(self, image_name: str, image: Image):
+        if not image_name in self.__cache:
+            self.__cache[image_name] = image
+            self.__cache_ids.put(
+                image_name
+            )  # TODO: this should refresh position for LRU cache
+            if len(self.__cache) > self.__max_cache_size:
+                cache_id = self.__cache_ids.get()
+                del self.__cache[cache_id]

invokeai/app/services/images.py

@@ -1,393 +0,0 @@
-from abc import ABC, abstractmethod
-from logging import Logger
-from typing import Optional, TYPE_CHECKING, Union
-from PIL.Image import Image as PILImageType
-
-from invokeai.app.models.image import (
-    ImageCategory,
-    ResourceOrigin,
-    InvalidImageCategoryException,
-    InvalidOriginException,
-)
-from invokeai.app.models.metadata import ImageMetadata
-from invokeai.app.services.image_record_storage import (
-    ImageRecordDeleteException,
-    ImageRecordNotFoundException,
-    ImageRecordSaveException,
-    ImageRecordStorageBase,
-    OffsetPaginatedResults,
-)
-from invokeai.app.services.models.image_record import (
-    ImageRecord,
-    ImageDTO,
-    ImageRecordChanges,
-    image_record_to_dto,
-)
-from invokeai.app.services.image_file_storage import (
-    ImageFileDeleteException,
-    ImageFileNotFoundException,
-    ImageFileSaveException,
-    ImageFileStorageBase,
-)
-from invokeai.app.services.item_storage import ItemStorageABC, PaginatedResults
-from invokeai.app.services.metadata import MetadataServiceBase
-from invokeai.app.services.resource_name import NameServiceBase
-from invokeai.app.services.urls import UrlServiceBase
-
-if TYPE_CHECKING:
-    from invokeai.app.services.graph import GraphExecutionState
-
-
-class ImageServiceABC(ABC):
-    """High-level service for image management."""
-
-    @abstractmethod
-    def create(
-        self,
-        image: PILImageType,
-        image_origin: ResourceOrigin,
-        image_category: ImageCategory,
-        node_id: Optional[str] = None,
-        session_id: Optional[str] = None,
-        intermediate: bool = False,
-    ) -> ImageDTO:
-        """Creates an image, storing the file and its metadata."""
-        pass
-
-    @abstractmethod
-    def update(
-        self,
-        image_origin: ResourceOrigin,
-        image_name: str,
-        changes: ImageRecordChanges,
-    ) -> ImageDTO:
-        """Updates an image."""
-        pass
-
-    @abstractmethod
-    def get_pil_image(self, image_origin: ResourceOrigin, image_name: str) -> PILImageType:
-        """Gets an image as a PIL image."""
-        pass
-
-    @abstractmethod
-    def get_record(self, image_origin: ResourceOrigin, image_name: str) -> ImageRecord:
-        """Gets an image record."""
-        pass
-
-    @abstractmethod
-    def get_dto(self, image_origin: ResourceOrigin, image_name: str) -> ImageDTO:
-        """Gets an image DTO."""
-        pass
-
-    @abstractmethod
-    def get_path(self, image_origin: ResourceOrigin, image_name: str) -> str:
-        """Gets an image's path."""
-        pass
-
-    @abstractmethod
-    def validate_path(self, path: str) -> bool:
-        """Validates an image's path."""
-        pass
-
-    @abstractmethod
-    def get_url(
-        self, image_origin: ResourceOrigin, image_name: str, thumbnail: bool = False
-    ) -> str:
-        """Gets an image's or thumbnail's URL."""
-        pass
-
-    @abstractmethod
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-    ) -> OffsetPaginatedResults[ImageDTO]:
-        """Gets a paginated list of image DTOs."""
-        pass
-
-    @abstractmethod
-    def delete(self, image_origin: ResourceOrigin, image_name: str):
-        """Deletes an image."""
-        pass
-
-
-class ImageServiceDependencies:
-    """Service dependencies for the ImageService."""
-
-    records: ImageRecordStorageBase
-    files: ImageFileStorageBase
-    metadata: MetadataServiceBase
-    urls: UrlServiceBase
-    logger: Logger
-    names: NameServiceBase
-    graph_execution_manager: ItemStorageABC["GraphExecutionState"]
-
-    def __init__(
-        self,
-        image_record_storage: ImageRecordStorageBase,
-        image_file_storage: ImageFileStorageBase,
-        metadata: MetadataServiceBase,
-        url: UrlServiceBase,
-        logger: Logger,
-        names: NameServiceBase,
-        graph_execution_manager: ItemStorageABC["GraphExecutionState"],
-    ):
-        self.records = image_record_storage
-        self.files = image_file_storage
-        self.metadata = metadata
-        self.urls = url
-        self.logger = logger
-        self.names = names
-        self.graph_execution_manager = graph_execution_manager
-
-
-class ImageService(ImageServiceABC):
-    _services: ImageServiceDependencies
-
-    def __init__(
-        self,
-        image_record_storage: ImageRecordStorageBase,
-        image_file_storage: ImageFileStorageBase,
-        metadata: MetadataServiceBase,
-        url: UrlServiceBase,
-        logger: Logger,
-        names: NameServiceBase,
-        graph_execution_manager: ItemStorageABC["GraphExecutionState"],
-    ):
-        self._services = ImageServiceDependencies(
-            image_record_storage=image_record_storage,
-            image_file_storage=image_file_storage,
-            metadata=metadata,
-            url=url,
-            logger=logger,
-            names=names,
-            graph_execution_manager=graph_execution_manager,
-        )
-
-    def create(
-        self,
-        image: PILImageType,
-        image_origin: ResourceOrigin,
-        image_category: ImageCategory,
-        node_id: Optional[str] = None,
-        session_id: Optional[str] = None,
-        is_intermediate: bool = False,
-    ) -> ImageDTO:
-        if image_origin not in ResourceOrigin:
-            raise InvalidOriginException
-
-        if image_category not in ImageCategory:
-            raise InvalidImageCategoryException
-
-        image_name = self._services.names.create_image_name()
-
-        metadata = self._get_metadata(session_id, node_id)
-
-        (width, height) = image.size
-
-        try:
-            # TODO: Consider using a transaction here to ensure consistency between storage and database
-            created_at = self._services.records.save(
-                # Non-nullable fields
-                image_name=image_name,
-                image_origin=image_origin,
-                image_category=image_category,
-                width=width,
-                height=height,
-                # Meta fields
-                is_intermediate=is_intermediate,
-                # Nullable fields
-                node_id=node_id,
-                session_id=session_id,
-                metadata=metadata,
-            )
-
-            self._services.files.save(
-                image_origin=image_origin,
-                image_name=image_name,
-                image=image,
-                metadata=metadata,
-            )
-
-            image_url = self._services.urls.get_image_url(image_origin, image_name)
-            thumbnail_url = self._services.urls.get_image_url(
-                image_origin, image_name, True
-            )
-
-            return ImageDTO(
-                # Non-nullable fields
-                image_name=image_name,
-                image_origin=image_origin,
-                image_category=image_category,
-                width=width,
-                height=height,
-                # Nullable fields
-                node_id=node_id,
-                session_id=session_id,
-                metadata=metadata,
-                # Meta fields
-                created_at=created_at,
-                updated_at=created_at,  # this is always the same as the created_at at this time
-                deleted_at=None,
-                is_intermediate=is_intermediate,
-                # Extra non-nullable fields for DTO
-                image_url=image_url,
-                thumbnail_url=thumbnail_url,
-            )
-        except ImageRecordSaveException:
-            self._services.logger.error("Failed to save image record")
-            raise
-        except ImageFileSaveException:
-            self._services.logger.error("Failed to save image file")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem saving image record and file")
-            raise e
-
-    def update(
-        self,
-        image_origin: ResourceOrigin,
-        image_name: str,
-        changes: ImageRecordChanges,
-    ) -> ImageDTO:
-        try:
-            self._services.records.update(image_name, image_origin, changes)
-            return self.get_dto(image_origin, image_name)
-        except ImageRecordSaveException:
-            self._services.logger.error("Failed to update image record")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem updating image record")
-            raise e
-
-    def get_pil_image(self, image_origin: ResourceOrigin, image_name: str) -> PILImageType:
-        try:
-            return self._services.files.get(image_origin, image_name)
-        except ImageFileNotFoundException:
-            self._services.logger.error("Failed to get image file")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem getting image file")
-            raise e
-
-    def get_record(self, image_origin: ResourceOrigin, image_name: str) -> ImageRecord:
-        try:
-            return self._services.records.get(image_origin, image_name)
-        except ImageRecordNotFoundException:
-            self._services.logger.error("Image record not found")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem getting image record")
-            raise e
-
-    def get_dto(self, image_origin: ResourceOrigin, image_name: str) -> ImageDTO:
-        try:
-            image_record = self._services.records.get(image_origin, image_name)
-
-            image_dto = image_record_to_dto(
-                image_record,
-                self._services.urls.get_image_url(image_origin, image_name),
-                self._services.urls.get_image_url(image_origin, image_name, True),
-            )
-
-            return image_dto
-        except ImageRecordNotFoundException:
-            self._services.logger.error("Image record not found")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem getting image DTO")
-            raise e
-
-    def get_path(
-        self, image_origin: ResourceOrigin, image_name: str, thumbnail: bool = False
-    ) -> str:
-        try:
-            return self._services.files.get_path(image_origin, image_name, thumbnail)
-        except Exception as e:
-            self._services.logger.error("Problem getting image path")
-            raise e
-
-    def validate_path(self, path: str) -> bool:
-        try:
-            return self._services.files.validate_path(path)
-        except Exception as e:
-            self._services.logger.error("Problem validating image path")
-            raise e
-
-    def get_url(
-        self, image_origin: ResourceOrigin, image_name: str, thumbnail: bool = False
-    ) -> str:
-        try:
-            return self._services.urls.get_image_url(image_origin, image_name, thumbnail)
-        except Exception as e:
-            self._services.logger.error("Problem getting image path")
-            raise e
-
-    def get_many(
-        self,
-        offset: int = 0,
-        limit: int = 10,
-        image_origin: Optional[ResourceOrigin] = None,
-        categories: Optional[list[ImageCategory]] = None,
-        is_intermediate: Optional[bool] = None,
-    ) -> OffsetPaginatedResults[ImageDTO]:
-        try:
-            results = self._services.records.get_many(
-                offset,
-                limit,
-                image_origin,
-                categories,
-                is_intermediate,
-            )
-
-            image_dtos = list(
-                map(
-                    lambda r: image_record_to_dto(
-                        r,
-                        self._services.urls.get_image_url(r.image_origin, r.image_name),
-                        self._services.urls.get_image_url(
-                            r.image_origin, r.image_name, True
-                        ),
-                    ),
-                    results.items,
-                )
-            )
-
-            return OffsetPaginatedResults[ImageDTO](
-                items=image_dtos,
-                offset=results.offset,
-                limit=results.limit,
-                total=results.total,
-            )
-        except Exception as e:
-            self._services.logger.error("Problem getting paginated image DTOs")
-            raise e
-
-    def delete(self, image_origin: ResourceOrigin, image_name: str):
-        try:
-            self._services.files.delete(image_origin, image_name)
-            self._services.records.delete(image_origin, image_name)
-        except ImageRecordDeleteException:
-            self._services.logger.error(f"Failed to delete image record")
-            raise
-        except ImageFileDeleteException:
-            self._services.logger.error(f"Failed to delete image file")
-            raise
-        except Exception as e:
-            self._services.logger.error("Problem deleting image record and file")
-            raise e
-
-    def _get_metadata(
-        self, session_id: Optional[str] = None, node_id: Optional[str] = None
-    ) -> Union[ImageMetadata, None]:
-        """Get the metadata for a node."""
-        metadata = None
-
-        if node_id is not None and session_id is not None:
-            session = self._services.graph_execution_manager.get(session_id)
-            metadata = self._services.metadata.create_image_metadata(session, node_id)
-
-        return metadata

invokeai/app/services/invocation_services.py

@@ -1,60 +1,50 @@
-# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
-from __future__ import annotations
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from logging import Logger
-    from invokeai.app.services.images import ImageService
-    from invokeai.backend import ModelManager
-    from invokeai.app.services.events import EventServiceBase
-    from invokeai.app.services.latent_storage import LatentsStorageBase
-    from invokeai.app.services.restoration_services import RestorationServices
-    from invokeai.app.services.invocation_queue import InvocationQueueABC
-    from invokeai.app.services.item_storage import ItemStorageABC
-    from invokeai.app.services.config import InvokeAISettings
-    from invokeai.app.services.graph import GraphExecutionState, LibraryGraph
-    from invokeai.app.services.invoker import InvocationProcessorABC
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+from invokeai.app.services.metadata import MetadataServiceBase
+from invokeai.backend import ModelManager
 
+from .events import EventServiceBase
+from .latent_storage import LatentsStorageBase
+from .image_storage import ImageStorageBase
+from .restoration_services import RestorationServices
+from .invocation_queue import InvocationQueueABC
+from .item_storage import ItemStorageABC
 
 class InvocationServices:
     """Services that can be used by invocations"""
 
-    # TODO: Just forward-declared everything due to circular dependencies. Fix structure.
-    events: "EventServiceBase"
-    latents: "LatentsStorageBase"
-    queue: "InvocationQueueABC"
-    model_manager: "ModelManager"
-    restoration: "RestorationServices"
-    configuration: "InvokeAISettings"
-    images: "ImageService"
+    events: EventServiceBase
+    latents: LatentsStorageBase
+    images: ImageStorageBase
+    metadata: MetadataServiceBase
+    queue: InvocationQueueABC
+    model_manager: ModelManager
+    restoration: RestorationServices
 
     # NOTE: we must forward-declare any types that include invocations, since invocations can use services
-    graph_library: "ItemStorageABC"["LibraryGraph"]
-    graph_execution_manager: "ItemStorageABC"["GraphExecutionState"]
+    graph_library: ItemStorageABC["LibraryGraph"]
+    graph_execution_manager: ItemStorageABC["GraphExecutionState"]
     processor: "InvocationProcessorABC"
 
     def __init__(
-        self,
-        model_manager: "ModelManager",
-        events: "EventServiceBase",
-        logger: "Logger",
-        latents: "LatentsStorageBase",
-        images: "ImageService",
-        queue: "InvocationQueueABC",
-        graph_library: "ItemStorageABC"["LibraryGraph"],
-        graph_execution_manager: "ItemStorageABC"["GraphExecutionState"],
-        processor: "InvocationProcessorABC",
-        restoration: "RestorationServices",
-        configuration: "InvokeAISettings",
+            self,
+            model_manager: ModelManager,
+            events: EventServiceBase,
+            latents: LatentsStorageBase,
+            images: ImageStorageBase,
+            metadata: MetadataServiceBase,
+            queue: InvocationQueueABC,
+            graph_library: ItemStorageABC["LibraryGraph"],
+            graph_execution_manager: ItemStorageABC["GraphExecutionState"],
+            processor: "InvocationProcessorABC",
+            restoration: RestorationServices,
     ):
         self.model_manager = model_manager
         self.events = events
-        self.logger = logger
         self.latents = latents
         self.images = images
+        self.metadata = metadata
         self.queue = queue
         self.graph_library = graph_library
         self.graph_execution_manager = graph_execution_manager
         self.processor = processor
         self.restoration = restoration
-        self.configuration = configuration

invokeai/app/services/invoker.py

@@ -22,8 +22,7 @@ class Invoker:
     def invoke(
         self, graph_execution_state: GraphExecutionState, invoke_all: bool = False
     ) -> str | None:
-        """Determines the next node to invoke and enqueues it, preparing if needed.
-        Returns the id of the queued node, or `None` if there are no nodes left to enqueue."""
+        """Determines the next node to invoke and returns the id of the invoked node, or None if there are no nodes to execute"""
 
         # Get the next invocation
         invocation = graph_execution_state.next()
@@ -50,7 +49,7 @@ class Invoker:
         new_state = GraphExecutionState(graph=Graph() if graph is None else graph)
         self.services.graph_execution_manager.set(new_state)
         return new_state
-
+    
     def cancel(self, graph_execution_state_id: str) -> None:
         """Cancels the given execution state"""
         self.services.queue.cancel(graph_execution_state_id)
@@ -72,12 +71,18 @@ class Invoker:
         for service in vars(self.services):
             self.__start_service(getattr(self.services, service))
 
+        for service in vars(self.services):
+            self.__start_service(getattr(self.services, service))
+
     def stop(self) -> None:
         """Stops the invoker. A new invoker will have to be created to execute further."""
         # First stop all services
         for service in vars(self.services):
             self.__stop_service(getattr(self.services, service))
 
+        for service in vars(self.services):
+            self.__stop_service(getattr(self.services, service))
+
         self.services.queue.put(None)
 
 

invokeai/app/services/latent_storage.py

@@ -16,7 +16,7 @@ class LatentsStorageBase(ABC):
         pass
 
     @abstractmethod
-    def save(self, name: str, data: torch.Tensor) -> None:
+    def set(self, name: str, data: torch.Tensor) -> None:
         pass
 
     @abstractmethod
@@ -47,8 +47,8 @@ class ForwardCacheLatentsStorage(LatentsStorageBase):
         self.__set_cache(name, latent)
         return latent
 
-    def save(self, name: str, data: torch.Tensor) -> None:
-        self.__underlying_storage.save(name, data)
+    def set(self, name: str, data: torch.Tensor) -> None:
+        self.__underlying_storage.set(name, data)
         self.__set_cache(name, data)
 
     def delete(self, name: str) -> None:
@@ -80,7 +80,7 @@ class DiskLatentsStorage(LatentsStorageBase):
         latent_path = self.get_path(name)
         return torch.load(latent_path)
 
-    def save(self, name: str, data: torch.Tensor) -> None:
+    def set(self, name: str, data: torch.Tensor) -> None:
         latent_path = self.get_path(name)
         torch.save(data, latent_path)
 

invokeai/app/services/metadata.py

@@ -1,142 +1,96 @@
+import json
 from abc import ABC, abstractmethod
-from typing import Any, Union
-import networkx as nx
+from typing import Any, Dict, Optional, TypedDict
+from PIL import Image, PngImagePlugin
+from pydantic import BaseModel
 
-from invokeai.app.models.metadata import ImageMetadata
-from invokeai.app.services.graph import Graph, GraphExecutionState
+from invokeai.app.models.image import ImageType, is_image_type
+
+
+class MetadataImageField(TypedDict):
+    """Pydantic-less ImageField, used for metadata parsing."""
+
+    image_type: ImageType
+    image_name: str
+
+
+class MetadataLatentsField(TypedDict):
+    """Pydantic-less LatentsField, used for metadata parsing."""
+
+    latents_name: str
+
+
+# TODO: This is a placeholder for `InvocationsUnion` pending resolution of circular imports
+NodeMetadata = Dict[
+    str, str | int | float | bool | MetadataImageField | MetadataLatentsField
+]
+
+
+class InvokeAIMetadata(TypedDict, total=False):
+    """InvokeAI-specific metadata format."""
+
+    session_id: Optional[str]
+    node: Optional[NodeMetadata]
+
+
+def build_invokeai_metadata_pnginfo(
+    metadata: InvokeAIMetadata | None,
+) -> PngImagePlugin.PngInfo:
+    """Builds a PngInfo object with key `"invokeai"` and value `metadata`"""
+    pnginfo = PngImagePlugin.PngInfo()
+
+    if metadata is not None:
+        pnginfo.add_text("invokeai", json.dumps(metadata))
+
+    return pnginfo
 
 
 class MetadataServiceBase(ABC):
-    """Handles building metadata for nodes, images, and outputs."""
+    @abstractmethod
+    def get_metadata(self, image: Image.Image) -> InvokeAIMetadata | None:
+        """Gets the InvokeAI metadata from a PIL Image, skipping invalid values"""
+        pass
 
     @abstractmethod
-    def create_image_metadata(
-        self, session: GraphExecutionState, node_id: str
-    ) -> ImageMetadata:
-        """Builds an ImageMetadata object for a node."""
+    def build_metadata(
+        self, session_id: str, node: BaseModel
+    ) -> InvokeAIMetadata | None:
+        """Builds an InvokeAIMetadata object"""
         pass
 
 
-class CoreMetadataService(MetadataServiceBase):
-    _ANCESTOR_TYPES = ["t2l", "l2l"]
-    """The ancestor types that contain the core metadata"""
+class PngMetadataService(MetadataServiceBase):
+    """Handles loading and building metadata for images."""
 
-    _ANCESTOR_PARAMS = ["type", "steps", "model", "cfg_scale", "scheduler", "strength"]
-    """The core metadata parameters in the ancestor types"""
+    # TODO: Use `InvocationsUnion` to **validate** metadata as representing a fully-functioning node
+    def _load_metadata(self, image: Image.Image) -> dict | None:
+        """Loads a specific info entry from a PIL Image."""
 
-    _NOISE_FIELDS = ["seed", "width", "height"]
-    """The core metadata parameters in the noise node"""
+        try:
+            info = image.info.get("invokeai")
 
-    def create_image_metadata(
-        self, session: GraphExecutionState, node_id: str
-    ) -> ImageMetadata:
-        metadata = self._build_metadata_from_graph(session, node_id)
+            if type(info) is not str:
+                return None
+
+            loaded_metadata = json.loads(info)
+
+            if type(loaded_metadata) is not dict:
+                return None
+
+            if len(loaded_metadata.items()) == 0:
+                return None
+
+            return loaded_metadata
+        except:
+            return None
+
+    def get_metadata(self, image: Image.Image) -> dict | None:
+        """Retrieves an image's metadata as a dict"""
+        loaded_metadata = self._load_metadata(image)
+
+        return loaded_metadata
+
+    def build_metadata(self, session_id: str, node: BaseModel) -> InvokeAIMetadata:
+        metadata = InvokeAIMetadata(session_id=session_id, node=node.dict())
 
         return metadata
-
-    def _find_nearest_ancestor(self, G: nx.DiGraph, node_id: str) -> Union[str, None]:
-        """
-        Finds the id of the nearest ancestor (of a valid type) of a given node.
-
-        Parameters:
-        G (nx.DiGraph): The execution graph, converted in to a networkx DiGraph. Its nodes must
-        have the same data as the execution graph.
-        node_id (str): The ID of the node.
-
-        Returns:
-        str | None: The ID of the nearest ancestor, or None if there are no valid ancestors.
-        """
-
-        # Retrieve the node from the graph
-        node = G.nodes[node_id]
-
-        # If the node type is one of the core metadata node types, return its id
-        if node.get("type") in self._ANCESTOR_TYPES:
-            return node.get("id")
-
-        # Else, look for the ancestor in the predecessor nodes
-        for predecessor in G.predecessors(node_id):
-            result = self._find_nearest_ancestor(G, predecessor)
-            if result:
-                return result
-
-        # If there are no valid ancestors, return None
-        return None
-
-    def _get_additional_metadata(
-        self, graph: Graph, node_id: str
-    ) -> Union[dict[str, Any], None]:
-        """
-        Returns additional metadata for a given node.
-
-        Parameters:
-        graph (Graph): The execution graph.
-        node_id (str): The ID of the node.
-
-        Returns:
-        dict[str, Any] | None: A dictionary of additional metadata.
-        """
-
-        metadata = {}
-
-        # Iterate over all edges in the graph
-        for edge in graph.edges:
-            dest_node_id = edge.destination.node_id
-            dest_field = edge.destination.field
-            source_node_dict = graph.nodes[edge.source.node_id].dict()
-
-            # If the destination node ID matches the given node ID, gather necessary metadata
-            if dest_node_id == node_id:
-                # Prompt
-                if dest_field == "positive_conditioning":
-                    metadata["positive_conditioning"] = source_node_dict.get("prompt")
-                # Negative prompt
-                if dest_field == "negative_conditioning":
-                    metadata["negative_conditioning"] = source_node_dict.get("prompt")
-                # Seed, width and height
-                if dest_field == "noise":
-                    for field in self._NOISE_FIELDS:
-                        metadata[field] = source_node_dict.get(field)
-        return metadata
-
-    def _build_metadata_from_graph(
-        self, session: GraphExecutionState, node_id: str
-    ) -> ImageMetadata:
-        """
-        Builds an ImageMetadata object for a node.
-
-        Parameters:
-        session (GraphExecutionState): The session.
-        node_id (str): The ID of the node.
-
-        Returns:
-        ImageMetadata: The metadata for the node.
-        """
-
-        # We need to do all the traversal on the execution graph
-        graph = session.execution_graph
-
-        # Find the nearest `t2l`/`l2l` ancestor of the given node
-        ancestor_id = self._find_nearest_ancestor(graph.nx_graph_with_data(), node_id)
-
-        # If no ancestor was found, return an empty ImageMetadata object
-        if ancestor_id is None:
-            return ImageMetadata()
-
-        ancestor_node = graph.get_node(ancestor_id)
-
-        # Grab all the core metadata from the ancestor node
-        ancestor_metadata = {
-            param: val
-            for param, val in ancestor_node.dict().items()
-            if param in self._ANCESTOR_PARAMS
-        }
-
-        # Get this image's prompts and noise parameters
-        addl_metadata = self._get_additional_metadata(graph, ancestor_id)
-
-        # If additional metadata was found, add it to the main metadata
-        if addl_metadata is not None:
-            ancestor_metadata.update(addl_metadata)
-
-        return ImageMetadata(**ancestor_metadata)

invokeai/app/services/model_manager_initializer.py

@@ -2,25 +2,26 @@ import os
 import sys
 import torch
 from argparse import Namespace
+from invokeai.backend import Args
 from omegaconf import OmegaConf
 from pathlib import Path
-from typing import types
 
 import invokeai.version
-from .config import InvokeAISettings
 from ...backend import ModelManager
 from ...backend.util import choose_precision, choose_torch_device
+from ...backend import Globals
 
 # TODO: Replace with an abstract class base ModelManagerBase
-def get_model_manager(config: InvokeAISettings, logger: types.ModuleType) -> ModelManager:
-    model_config = config.model_conf_path
-    if not model_config.exists():
-        report_model_error(
-            config, FileNotFoundError(f"The file {model_config} could not be found."), logger
-        )
+def get_model_manager(config: Args) -> ModelManager:
+    if not config.conf:
+        config_file = os.path.join(Globals.root, "configs", "models.yaml")
+        if not os.path.exists(config_file):
+            report_model_error(
+                config, FileNotFoundError(f"The file {config_file} could not be found.")
+            )
 
-    logger.info(f"{invokeai.version.__app_name__}, version {invokeai.version.__version__}")
-    logger.info(f'InvokeAI runtime directory is "{config.root}"')
+    print(f">> {invokeai.version.__app_name__}, version {invokeai.version.__version__}")
+    print(f'>> InvokeAI runtime directory is "{Globals.root}"')
 
     # these two lines prevent a horrible warning message from appearing
     # when the frozen CLIP tokenizer is imported
@@ -30,7 +31,20 @@ def get_model_manager(config: InvokeAISettings, logger: types.ModuleType) -> Mod
     import diffusers
 
     diffusers.logging.set_verbosity_error()
-    embedding_path = config.embedding_path
+
+    # normalize the config directory relative to root
+    if not os.path.isabs(config.conf):
+        config.conf = os.path.normpath(os.path.join(Globals.root, config.conf))
+
+    if config.embeddings:
+        if not os.path.isabs(config.embedding_path):
+            embedding_path = os.path.normpath(
+                os.path.join(Globals.root, config.embedding_path)
+            )
+        else:
+            embedding_path = config.embedding_path
+    else:
+        embedding_path = None
 
     # migrate legacy models
     ModelManager.migrate_models()
@@ -43,36 +57,37 @@ def get_model_manager(config: InvokeAISettings, logger: types.ModuleType) -> Mod
         else choose_precision(device)
         
         model_manager = ModelManager(
-            OmegaConf.load(config.model_conf_path),
+            OmegaConf.load(config.conf),
             precision=precision,
             device_type=device,
             max_loaded_models=config.max_loaded_models,
-            embedding_path = embedding_path,
-            logger = logger,
+            embedding_path = Path(embedding_path),
         )
     except (FileNotFoundError, TypeError, AssertionError) as e:
-        report_model_error(config, e, logger)
+        report_model_error(config, e)
     except (IOError, KeyError) as e:
-        logger.error(f"{e}. Aborting.")
+        print(f"{e}. Aborting.")
         sys.exit(-1)
 
     # try to autoconvert new models
     # autoimport new .ckpt files
-    if config.autoconvert_path:
-        model_manager.heuristic_import(
-            config.autoconvert_path,
+    if path := config.autoconvert:
+        model_manager.autoconvert_weights(
+            conf_path=config.conf,
+            weights_directory=path,
         )
+
     return model_manager
 
-def report_model_error(opt: Namespace, e: Exception, logger: types.ModuleType):
-    logger.error(f'An error occurred while attempting to initialize the model: "{str(e)}"')
-    logger.error(
-        "This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
+def report_model_error(opt: Namespace, e: Exception):
+    print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
+    print(
+        "** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
     )
     yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE")
     if yes_to_all:
-        logger.warning(
-            "Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
+        print(
+            "** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
         )
     else:
         response = input(
@@ -81,12 +96,13 @@ def report_model_error(opt: Namespace, e: Exception, logger: types.ModuleType):
         if response.startswith(("n", "N")):
             return
 
-    logger.info("invokeai-configure is launching....\n")
+    print("invokeai-configure is launching....\n")
 
     # Match arguments that were set on the CLI
     # only the arguments accepted by the configuration script are parsed
     root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else []
     config = ["--config", opt.conf] if opt.conf is not None else []
+    previous_config = sys.argv
     sys.argv = ["invokeai-configure"]
     sys.argv.extend(root_dir)
     sys.argv.extend(config.to_dict())

invokeai/app/services/models/image_record.py

@@ -1,148 +0,0 @@
-import datetime
-from typing import Optional, Union
-from pydantic import BaseModel, Extra, Field, StrictBool, StrictStr
-from invokeai.app.models.image import ImageCategory, ResourceOrigin
-from invokeai.app.models.metadata import ImageMetadata
-from invokeai.app.util.misc import get_iso_timestamp
-
-
-class ImageRecord(BaseModel):
-    """Deserialized image record."""
-
-    image_name: str = Field(description="The unique name of the image.")
-    """The unique name of the image."""
-    image_origin: ResourceOrigin = Field(description="The type of the image.")
-    """The origin of the image."""
-    image_category: ImageCategory = Field(description="The category of the image.")
-    """The category of the image."""
-    width: int = Field(description="The width of the image in px.")
-    """The actual width of the image in px. This may be different from the width in metadata."""
-    height: int = Field(description="The height of the image in px.")
-    """The actual height of the image in px. This may be different from the height in metadata."""
-    created_at: Union[datetime.datetime, str] = Field(
-        description="The created timestamp of the image."
-    )
-    """The created timestamp of the image."""
-    updated_at: Union[datetime.datetime, str] = Field(
-        description="The updated timestamp of the image."
-    )
-    """The updated timestamp of the image."""
-    deleted_at: Union[datetime.datetime, str, None] = Field(
-        description="The deleted timestamp of the image."
-    )
-    """The deleted timestamp of the image."""
-    is_intermediate: bool = Field(description="Whether this is an intermediate image.")
-    """Whether this is an intermediate image."""
-    session_id: Optional[str] = Field(
-        default=None,
-        description="The session ID that generated this image, if it is a generated image.",
-    )
-    """The session ID that generated this image, if it is a generated image."""
-    node_id: Optional[str] = Field(
-        default=None,
-        description="The node ID that generated this image, if it is a generated image.",
-    )
-    """The node ID that generated this image, if it is a generated image."""
-    metadata: Optional[ImageMetadata] = Field(
-        default=None,
-        description="A limited subset of the image's generation metadata. Retrieve the image's session for full metadata.",
-    )
-    """A limited subset of the image's generation metadata. Retrieve the image's session for full metadata."""
-
-
-class ImageRecordChanges(BaseModel, extra=Extra.forbid):
-    """A set of changes to apply to an image record.
-
-    Only limited changes are valid:
-      - `image_category`: change the category of an image
-      - `session_id`: change the session associated with an image
-      - `is_intermediate`: change the image's `is_intermediate` flag
-    """
-
-    image_category: Optional[ImageCategory] = Field(
-        description="The image's new category."
-    )
-    """The image's new category."""
-    session_id: Optional[StrictStr] = Field(
-        default=None,
-        description="The image's new session ID.",
-    )
-    """The image's new session ID."""
-    is_intermediate: Optional[StrictBool] = Field(
-        default=None, description="The image's new `is_intermediate` flag."
-    )
-    """The image's new `is_intermediate` flag."""
-
-
-class ImageUrlsDTO(BaseModel):
-    """The URLs for an image and its thumbnail."""
-
-    image_name: str = Field(description="The unique name of the image.")
-    """The unique name of the image."""
-    image_origin: ResourceOrigin = Field(description="The type of the image.")
-    """The origin of the image."""
-    image_url: str = Field(description="The URL of the image.")
-    """The URL of the image."""
-    thumbnail_url: str = Field(description="The URL of the image's thumbnail.")
-    """The URL of the image's thumbnail."""
-
-
-class ImageDTO(ImageRecord, ImageUrlsDTO):
-    """Deserialized image record, enriched for the frontend with URLs."""
-
-    pass
-
-
-def image_record_to_dto(
-    image_record: ImageRecord, image_url: str, thumbnail_url: str
-) -> ImageDTO:
-    """Converts an image record to an image DTO."""
-    return ImageDTO(
-        **image_record.dict(),
-        image_url=image_url,
-        thumbnail_url=thumbnail_url,
-    )
-
-
-def deserialize_image_record(image_dict: dict) -> ImageRecord:
-    """Deserializes an image record."""
-
-    # Retrieve all the values, setting "reasonable" defaults if they are not present.
-
-    image_name = image_dict.get("image_name", "unknown")
-    image_origin = ResourceOrigin(
-        image_dict.get("image_origin", ResourceOrigin.INTERNAL.value)
-    )
-    image_category = ImageCategory(
-        image_dict.get("image_category", ImageCategory.GENERAL.value)
-    )
-    width = image_dict.get("width", 0)
-    height = image_dict.get("height", 0)
-    session_id = image_dict.get("session_id", None)
-    node_id = image_dict.get("node_id", None)
-    created_at = image_dict.get("created_at", get_iso_timestamp())
-    updated_at = image_dict.get("updated_at", get_iso_timestamp())
-    deleted_at = image_dict.get("deleted_at", get_iso_timestamp())
-    is_intermediate = image_dict.get("is_intermediate", False)
-
-    raw_metadata = image_dict.get("metadata")
-
-    if raw_metadata is not None:
-        metadata = ImageMetadata.parse_raw(raw_metadata)
-    else:
-        metadata = None
-
-    return ImageRecord(
-        image_name=image_name,
-        image_origin=image_origin,
-        image_category=image_category,
-        width=width,
-        height=height,
-        session_id=session_id,
-        node_id=node_id,
-        metadata=metadata,
-        created_at=created_at,
-        updated_at=updated_at,
-        deleted_at=deleted_at,
-        is_intermediate=is_intermediate,
-    )

invokeai/app/services/processor.py

@@ -1,22 +1,17 @@
-import time
 import traceback
-from threading import Event, Thread, BoundedSemaphore
+from threading import Event, Thread
 
 from ..invocations.baseinvocation import InvocationContext
 from .invocation_queue import InvocationQueueItem
 from .invoker import InvocationProcessorABC, Invoker
 from ..models.exceptions import CanceledException
 
-import invokeai.backend.util.logging as logger
 class DefaultInvocationProcessor(InvocationProcessorABC):
     __invoker_thread: Thread
     __stop_event: Event
     __invoker: Invoker
-    __threadLimit: BoundedSemaphore
 
     def start(self, invoker) -> None:
-        # if we do want multithreading at some point, we could make this configurable
-        self.__threadLimit = BoundedSemaphore(1)
         self.__invoker = invoker
         self.__stop_event = Event()
         self.__invoker_thread = Thread(
@@ -25,7 +20,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
             kwargs=dict(stop_event=self.__stop_event),
         )
         self.__invoker_thread.daemon = (
-            True  # TODO: make async and do not use threads
+            True  # TODO: probably better to just not use threads?
         )
         self.__invoker_thread.start()
 
@@ -34,16 +29,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
 
     def __process(self, stop_event: Event):
         try:
-            self.__threadLimit.acquire()
             while not stop_event.is_set():
-                try:
-                    queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
-                except Exception as e:
-                    logger.debug("Exception while getting from queue: %s" % e)
-
+                queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
                 if not queue_item:  # Probably stopping
-                    # do not hammer the queue
-                    time.sleep(0.5)
                     continue
 
                 graph_execution_state = (
@@ -122,7 +110,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                     )
 
                     pass
-
+                
                 # Check queue to see if this is canceled, and skip if so
                 if self.__invoker.services.queue.is_canceled(
                     graph_execution_state.id
@@ -132,22 +120,11 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                 # Queue any further commands if invoking all
                 is_complete = graph_execution_state.is_complete()
                 if queue_item.invoke_all and not is_complete:
-                    try:
-                        self.__invoker.invoke(graph_execution_state, invoke_all=True)
-                    except Exception as e:
-                        logger.error("Error while invoking: %s" % e)
-                        self.__invoker.services.events.emit_invocation_error(
-                            graph_execution_state_id=graph_execution_state.id,
-                            node=invocation.dict(),
-                            source_node_id=source_node_id,
-                            error=traceback.format_exc()
-                        )
+                    self.__invoker.invoke(graph_execution_state, invoke_all=True)
                 elif is_complete:
                     self.__invoker.services.events.emit_graph_execution_complete(
                         graph_execution_state.id
                     )
 
         except KeyboardInterrupt:
-            pass  # Log something? KeyboardInterrupt is probably not going to be seen by the processor
-        finally:
-            self.__threadLimit.release()
+            ...  # Log something?

invokeai/app/services/resource_name.py

@@ -1,30 +0,0 @@
-from abc import ABC, abstractmethod
-from enum import Enum, EnumMeta
-import uuid
-
-
-class ResourceType(str, Enum, metaclass=EnumMeta):
-    """Enum for resource types."""
-
-    IMAGE = "image"
-    LATENT = "latent"
-
-
-class NameServiceBase(ABC):
-    """Low-level service responsible for naming resources (images, latents, etc)."""
-
-    # TODO: Add customizable naming schemes
-    @abstractmethod
-    def create_image_name(self) -> str:
-        """Creates a name for an image."""
-        pass
-
-
-class SimpleNameService(NameServiceBase):
-    """Creates image names from UUIDs."""
-
-    # TODO: Add customizable naming schemes
-    def create_image_name(self) -> str:
-        uuid_str = str(uuid.uuid4())
-        filename = f"{uuid_str}.png"
-        return filename

invokeai/app/services/restoration_services.py

@@ -1,7 +1,6 @@
 import sys
 import traceback
 import torch
-from typing import types
 from ...backend.restoration import Restoration
 from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
 
@@ -11,7 +10,7 @@ from ...backend.util import choose_torch_device, CPU_DEVICE, MPS_DEVICE
 class RestorationServices:
     '''Face restoration and upscaling'''
     
-    def __init__(self,args,logger:types.ModuleType):
+    def __init__(self,args):
         try:
             gfpgan, codeformer, esrgan = None, None, None
             if args.restore or args.esrgan:
@@ -21,22 +20,20 @@ class RestorationServices:
                         args.gfpgan_model_path
                     )
                 else:
-                    logger.info("Face restoration disabled")
+                    print(">> Face restoration disabled")
                     if args.esrgan:
                         esrgan = restoration.load_esrgan(args.esrgan_bg_tile)
                     else:
-                        logger.info("Upscaling disabled")
+                        print(">> Upscaling disabled")
             else:
-                logger.info("Face restoration and upscaling disabled")
+                print(">> Face restoration and upscaling disabled")
         except (ModuleNotFoundError, ImportError):
             print(traceback.format_exc(), file=sys.stderr)
-            logger.info("You may need to install the ESRGAN and/or GFPGAN modules")
+            print(">> You may need to install the ESRGAN and/or GFPGAN modules")
         self.device = torch.device(choose_torch_device())
         self.gfpgan = gfpgan
         self.codeformer = codeformer
         self.esrgan = esrgan
-        self.logger = logger
-        self.logger.info('Face restoration initialized')
 
     # note that this one method does gfpgan and codepath reconstruction, as well as
     # esrgan upscaling
@@ -61,15 +58,15 @@ class RestorationServices:
                     if self.gfpgan is not None or self.codeformer is not None:
                         if facetool == "gfpgan":
                             if self.gfpgan is None:
-                                self.logger.info(
-                                    "GFPGAN not found. Face restoration is disabled."
+                                print(
+                                    ">> GFPGAN not found. Face restoration is disabled."
                                 )
                             else:
                                 image = self.gfpgan.process(image, strength, seed)
                         if facetool == "codeformer":
                             if self.codeformer is None:
-                                self.logger.info(
-                                    "CodeFormer not found. Face restoration is disabled."
+                                print(
+                                    ">> CodeFormer not found. Face restoration is disabled."
                                 )
                             else:
                                 cf_device = (
@@ -83,7 +80,7 @@ class RestorationServices:
                                     fidelity=codeformer_fidelity,
                                 )
                     else:
-                        self.logger.info("Face Restoration is disabled.")
+                        print(">> Face Restoration is disabled.")
                 if upscale is not None:
                     if self.esrgan is not None:
                         if len(upscale) < 2:
@@ -96,10 +93,10 @@ class RestorationServices:
                             denoise_str=upscale_denoise_str,
                         )
                     else:
-                        self.logger.info("ESRGAN is disabled. Image not upscaled.")
+                        print(">> ESRGAN is disabled. Image not upscaled.")
             except Exception as e:
-                self.logger.info(
-                    f"Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
+                print(
+                    f">> Error running RealESRGAN or GFPGAN. Your image was not upscaled.\n{e}"
                 )
 
             if image_callback is not None:

invokeai/app/services/sqlite.py

@@ -26,6 +26,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
         self._table_name = table_name
         self._id_field = id_field  # TODO: validate that T has this field
         self._lock = Lock()
+
         self._conn = sqlite3.connect(
             self._filename, check_same_thread=False
         )  # TODO: figure out a better threading solution

invokeai/app/services/urls.py

@@ -1,34 +0,0 @@
-import os
-from abc import ABC, abstractmethod
-
-from invokeai.app.models.image import ResourceOrigin
-from invokeai.app.util.thumbnails import get_thumbnail_name
-
-
-class UrlServiceBase(ABC):
-    """Responsible for building URLs for resources."""
-
-    @abstractmethod
-    def get_image_url(
-        self, image_origin: ResourceOrigin, image_name: str, thumbnail: bool = False
-    ) -> str:
-        """Gets the URL for an image or thumbnail."""
-        pass
-
-
-class LocalUrlService(UrlServiceBase):
-    def __init__(self, base_url: str = "api/v1"):
-        self._base_url = base_url
-
-    def get_image_url(
-        self, image_origin: ResourceOrigin, image_name: str, thumbnail: bool = False
-    ) -> str:
-        image_basename = os.path.basename(image_name)
-
-        # These paths are determined by the routes in invokeai/app/api/routers/images.py
-        if thumbnail:
-            return (
-                f"{self._base_url}/images/{image_origin.value}/{image_basename}/thumbnail"
-            )
-
-        return f"{self._base_url}/images/{image_origin.value}/{image_basename}"

invokeai/app/util/metaenum.py

@@ -1,15 +0,0 @@
-from enum import EnumMeta
-
-
-class MetaEnum(EnumMeta):
-    """Metaclass to support additional features in Enums.
-
-    - `in` operator support: `'value' in MyEnum -> bool`
-    """
-
-    def __contains__(cls, item):
-        try:
-            cls(item)
-        except ValueError:
-            return False
-        return True

invokeai/app/util/misc.py

@@ -1,21 +1,5 @@
 import datetime
-import numpy as np
 
 
 def get_timestamp():
     return int(datetime.datetime.now(datetime.timezone.utc).timestamp())
-
-
-def get_iso_timestamp() -> str:
-    return datetime.datetime.utcnow().isoformat()
-
-
-def get_datetime_from_iso_timestamp(iso_timestamp: str) -> datetime.datetime:
-    return datetime.datetime.fromisoformat(iso_timestamp)
-
-
-SEED_MAX = np.iinfo(np.int32).max
-
-
-def get_random_seed():
-    return np.random.randint(0, SEED_MAX)

invokeai/app/util/step_callback.py

@@ -1,5 +1,5 @@
+from invokeai.app.api.models.images import ProgressImage
 from invokeai.app.models.exceptions import CanceledException
-from invokeai.app.models.image import ProgressImage
 from ..invocations.baseinvocation import InvocationContext
 from ...backend.util.util import image_to_dataURL
 from ...backend.generator.base import Generator

invokeai/backend/__init__.py

@@ -1,6 +1,7 @@
 """
 Initialization file for invokeai.backend
 """
+from .generate import Generate
 from .generator import (
     InvokeAIGeneratorBasicParams,
     InvokeAIGenerator,
@@ -11,3 +12,5 @@ from .generator import (
 )
 from .model_management import ModelManager, SDModelComponent
 from .safety_checker import SafetyChecker
+from .args import Args
+from .globals import Globals

invokeai/backend/config/invokeai_configure.py

@@ -12,17 +12,17 @@ print("Loading Python libraries...\n",file=sys.stderr)
 import argparse
 import io
 import os
+import re
 import shutil
-import textwrap
 import traceback
 import warnings
 from argparse import Namespace
 from pathlib import Path
 from shutil import get_terminal_size
-from typing import get_type_hints
 from urllib import request
 
 import npyscreen
+import torch
 import transformers
 from diffusers import AutoencoderKL
 from huggingface_hub import HfFolder
@@ -35,53 +35,57 @@ from transformers import (
     CLIPTextModel,
     CLIPTokenizer,
 )
+
 import invokeai.configs as configs
 
-from invokeai.app.services.config import (
-    InvokeAIAppConfig,
-)
-from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
-from invokeai.frontend.install.widgets import (
+from ...frontend.install.model_install import addModelsForm, process_and_execute
+from ...frontend.install.widgets import (
     CenteredButtonPress,
     IntTitleSlider,
     set_min_terminal_size,
-    CyclingForm,
-    MIN_COLS,
-    MIN_LINES,
 )
-from invokeai.backend.install.legacy_arg_parsing import legacy_parser
-from invokeai.backend.install.model_install_backend import (
+from ..args import PRECISION_CHOICES, Args
+from ..globals import Globals, global_cache_dir, global_config_dir, global_config_file
+from .model_install_backend import (
     default_dataset,
     download_from_hf,
     hf_download_with_resume,
     recommended_datasets,
-    UserSelections,
 )
 
 warnings.filterwarnings("ignore")
+
 transformers.logging.set_verbosity_error()
 
-
 # --------------------------globals-----------------------
-
-config = InvokeAIAppConfig.get_config()
-
 Model_dir = "models"
 Weights_dir = "ldm/stable-diffusion-v1/"
 
-Default_config_file = config.model_conf_path
-SD_Configs = config.legacy_conf_path
+# the initial "configs" dir is now bundled in the `invokeai.configs` package
+Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
 
-PRECISION_CHOICES = ['auto','float16','float32','autocast']
+Default_config_file = Path(global_config_dir()) / "models.yaml"
+SD_Configs = Path(global_config_dir()) / "stable-diffusion"
+
+Datasets = OmegaConf.load(Dataset_path)
+
+# minimum size for the UI
+MIN_COLS = 135
+MIN_LINES = 45
 
 INIT_FILE_PREAMBLE = """# InvokeAI initialization file
 # This is the InvokeAI initialization file, which contains command-line default values.
 # Feel free to edit. If anything goes wrong, you can re-initialize this file by deleting
 # or renaming it and then running invokeai-configure again.
+# Place  frequently-used startup commands here, one or more per line.
+# Examples:
+# --outdir=D:\data\images
+# --no-nsfw_checker
+# --web --host=0.0.0.0
+# --steps=20
+# -Ak_euler_a -C10.0
 """
 
-logger=None
 
 # --------------------------------------------
 def postscript(errors: None):
@@ -92,13 +96,14 @@ If you installed manually from source or with 'pip install': activate the virtua
 then run one of the following commands to start InvokeAI.
 
 Web UI:
-   invokeai-web
+   invokeai --web # (connect to http://localhost:9090)
+   invokeai --web --host 0.0.0.0 # (connect to http://your-lan-ip:9090 from another computer on the local network)
 
-Command-line client:
+Command-line interface:
    invokeai
 
 If you installed using an installation script, run:
-  {config.root_path}/invoke.{"bat" if sys.platform == "win32" else "sh"}
+  {Globals.root}/invoke.{"bat" if sys.platform == "win32" else "sh"}
 
 Add the '--help' argument to see all of the command-line switches available for use.
 """
@@ -210,11 +215,16 @@ def download_realesrgan():
     model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth"
     wdn_model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth"
 
-    model_dest = config.root_path / "models/realesrgan/realesr-general-x4v3.pth"
-    wdn_model_dest = config.root_path / "models/realesrgan/realesr-general-wdn-x4v3.pth"
+    model_dest = os.path.join(
+        Globals.root, "models/realesrgan/realesr-general-x4v3.pth"
+    )
 
-    download_with_progress_bar(model_url, str(model_dest), "RealESRGAN")
-    download_with_progress_bar(wdn_model_url, str(wdn_model_dest), "RealESRGANwdn")
+    wdn_model_dest = os.path.join(
+        Globals.root, "models/realesrgan/realesr-general-wdn-x4v3.pth"
+    )
+
+    download_with_progress_bar(model_url, model_dest, "RealESRGAN")
+    download_with_progress_bar(wdn_model_url, wdn_model_dest, "RealESRGANwdn")
 
 
 def download_gfpgan():
@@ -233,8 +243,8 @@ def download_gfpgan():
             "./models/gfpgan/weights/parsing_parsenet.pth",
         ],
     ):
-        model_url, model_dest = model[0], config.root_path / model[1]
-        download_with_progress_bar(model_url, str(model_dest), "GFPGAN weights")
+        model_url, model_dest = model[0], os.path.join(Globals.root, model[1])
+        download_with_progress_bar(model_url, model_dest, "GFPGAN weights")
 
 
 # ---------------------------------------------
@@ -243,8 +253,8 @@ def download_codeformer():
     model_url = (
         "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
     )
-    model_dest = config.root_path / "models/codeformer/codeformer.pth"
-    download_with_progress_bar(model_url, str(model_dest), "CodeFormer")
+    model_dest = os.path.join(Globals.root, "models/codeformer/codeformer.pth")
+    download_with_progress_bar(model_url, model_dest, "CodeFormer")
 
 
 # ---------------------------------------------
@@ -285,7 +295,7 @@ def download_vaes():
         # first the diffusers version
         repo_id = "stabilityai/sd-vae-ft-mse"
         args = dict(
-            cache_dir=config.cache_dir,
+            cache_dir=global_cache_dir("hub"),
         )
         if not AutoencoderKL.from_pretrained(repo_id, **args):
             raise Exception(f"download of {repo_id} failed")
@@ -296,7 +306,7 @@ def download_vaes():
         if not hf_download_with_resume(
             repo_id=repo_id,
             model_name=model_name,
-            model_dir=str(config.root_path / Model_dir / Weights_dir),
+            model_dir=str(Globals.root / Model_dir / Weights_dir),
         ):
             raise Exception(f"download of {model_name} failed")
     except Exception as e:
@@ -311,24 +321,25 @@ def get_root(root: str = None) -> str:
     elif os.environ.get("INVOKEAI_ROOT"):
         return os.environ.get("INVOKEAI_ROOT")
     else:
-        return str(config.root_path)
+        return Globals.root
+
 
 # -------------------------------------
-class editOptsForm(CyclingForm, npyscreen.FormMultiPage):
+class editOptsForm(npyscreen.FormMultiPage):
     # for responsive resizing - disabled
     # FIX_MINIMUM_SIZE_WHEN_CREATED = False
 
     def create(self):
         program_opts = self.parentApp.program_opts
         old_opts = self.parentApp.invokeai_opts
-        first_time = not (config.root_path / 'invokeai.yaml').exists()
+        first_time = not (Globals.root / Globals.initfile).exists()
         access_token = HfFolder.get_token()
         window_width, window_height = get_terminal_size()
-        label = """Configure startup settings. You can come back and change these later. 
-Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.
-Use cursor arrows to make a checkbox selection, and space to toggle.
-"""
-        for i in textwrap.wrap(label,width=window_width-6):
+        for i in [
+            "Configure startup settings. You can come back and change these later.",
+            "Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.",
+            "Use cursor arrows to make a checkbox selection, and space to toggle.",
+        ]:
             self.add_widget_intelligent(
                 npyscreen.FixedText,
                 value=i,
@@ -355,7 +366,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
         self.outdir = self.add_widget_intelligent(
             npyscreen.TitleFilename,
             name="(<tab> autocompletes, ctrl-N advances):",
-            value=str(default_output_dir()),
+            value=old_opts.outdir or str(default_output_dir()),
             select_dir=True,
             must_exist=False,
             use_two_lines=False,
@@ -370,21 +381,22 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             editable=False,
             color="CONTROL",
         )
-        self.nsfw_checker = self.add_widget_intelligent(
+        self.safety_checker = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="NSFW checker",
-            value=old_opts.nsfw_checker,
+            value=old_opts.safety_checker,
             relx=5,
             scroll_exit=True,
         )
         self.nextrely += 1
-        label = """If you have an account at HuggingFace you may optionally paste your access token here
-to allow InvokeAI to download restricted styles & subjects from the "Concept Library". See https://huggingface.co/settings/tokens.
-"""
-        for line in textwrap.wrap(label,width=window_width-6):
+        for i in [
+            "If you have an account at HuggingFace you may paste your access token here",
+            'to allow InvokeAI to download styles & subjects from the "Concept Library".',
+            "See https://huggingface.co/settings/tokens",
+        ]:
             self.add_widget_intelligent(
                 npyscreen.FixedText,
-                value=line,
+                value=i,
                 editable=False,
                 color="CONTROL",
             )
@@ -423,10 +435,17 @@ to allow InvokeAI to download restricted styles & subjects from the "Concept Lib
             relx=5,
             scroll_exit=True,
         )
-        self.xformers_enabled = self.add_widget_intelligent(
+        self.xformers = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="Enable xformers support if available",
-            value=old_opts.xformers_enabled,
+            value=old_opts.xformers,
+            relx=5,
+            scroll_exit=True,
+        )
+        self.ckpt_convert = self.add_widget_intelligent(
+            npyscreen.Checkbox,
+            name="Load legacy checkpoint models into memory as diffusers models",
+            value=old_opts.ckpt_convert,
             relx=5,
             scroll_exit=True,
         )
@@ -461,41 +480,19 @@ to allow InvokeAI to download restricted styles & subjects from the "Concept Lib
         self.nextrely += 1
         self.add_widget_intelligent(
             npyscreen.FixedText,
-            value="Directories containing textual inversion, controlnet and LoRA models (<tab> autocompletes, ctrl-N advances):",
+            value="Directory containing embedding/textual inversion files:",
             editable=False,
             color="CONTROL",
         )
-        self.embedding_dir = self.add_widget_intelligent(
+        self.embedding_path = self.add_widget_intelligent(
             npyscreen.TitleFilename,
-            name=" Textual Inversion Embeddings:",
+            name="(<tab> autocompletes, ctrl-N advances):",
             value=str(default_embedding_dir()),
             select_dir=True,
             must_exist=False,
             use_two_lines=False,
             labelColor="GOOD",
-            begin_entry_at=32,
-            scroll_exit=True,
-        )
-        self.lora_dir = self.add_widget_intelligent(
-            npyscreen.TitleFilename,
-            name="             LoRA and LyCORIS:",
-            value=str(default_lora_dir()),
-            select_dir=True,
-            must_exist=False,
-            use_two_lines=False,
-            labelColor="GOOD",
-            begin_entry_at=32,
-            scroll_exit=True,
-        )
-        self.controlnet_dir = self.add_widget_intelligent(
-            npyscreen.TitleFilename,
-            name="             ControlNets:",
-            value=str(default_controlnet_dir()),
-            select_dir=True,
-            must_exist=False,
-            use_two_lines=False,
-            labelColor="GOOD",
-            begin_entry_at=32,
+            begin_entry_at=40,
             scroll_exit=True,
         )
         self.nextrely += 1
@@ -508,11 +505,11 @@ to allow InvokeAI to download restricted styles & subjects from the "Concept Lib
             scroll_exit=True,
         )
         self.nextrely -= 1
-        label = """BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ
-AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSE LOCATED AT
-https://huggingface.co/spaces/CompVis/stable-diffusion-license
-"""
-        for i in textwrap.wrap(label,width=window_width-6):
+        for i in [
+            "BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ",
+            "AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSE LOCATED AT",
+            "https://huggingface.co/spaces/CompVis/stable-diffusion-license",
+        ]:
             self.add_widget_intelligent(
                 npyscreen.FixedText,
                 value=i,
@@ -551,7 +548,7 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
             self.editing = False
         else:
             self.editing = True
-            
+
     def validate_field_values(self, opt: Namespace) -> bool:
         bad_fields = []
         if not opt.license_acceptance:
@@ -562,9 +559,9 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
             bad_fields.append(
                 f"The output directory does not seem to be valid. Please check that {str(Path(opt.outdir).parent)} is an existing directory."
             )
-        if not Path(opt.embedding_dir).parent.exists():
+        if not Path(opt.embedding_path).parent.exists():
             bad_fields.append(
-                f"The embedding directory does not seem to be valid. Please check that {str(Path(opt.embedding_dir).parent)} is an existing directory."
+                f"The embedding directory does not seem to be valid. Please check that {str(Path(opt.embedding_path).parent)} is an existing directory."
             )
         if len(bad_fields) > 0:
             message = "The following problems were detected and must be corrected:\n"
@@ -579,24 +576,20 @@ https://huggingface.co/spaces/CompVis/stable-diffusion-license
         new_opts = Namespace()
 
         for attr in [
-                "outdir",
-                "nsfw_checker",
-                "free_gpu_mem",
-                "max_loaded_models",
-                "xformers_enabled",
-                "always_use_cpu",
-                "embedding_dir",
-                "lora_dir",
-                "controlnet_dir",
+            "outdir",
+            "safety_checker",
+            "free_gpu_mem",
+            "max_loaded_models",
+            "xformers",
+            "always_use_cpu",
+            "embedding_path",
+            "ckpt_convert",
         ]:
             setattr(new_opts, attr, getattr(self, attr).value)
 
         new_opts.hf_token = self.hf_token.value
         new_opts.license_acceptance = self.license_acceptance.value
         new_opts.precision = PRECISION_CHOICES[self.precision.value[0]]
-        
-        # widget library workaround to make max_loaded_models an int rather than a float
-        new_opts.max_loaded_models = int(new_opts.max_loaded_models)
 
         return new_opts
 
@@ -615,7 +608,6 @@ class EditOptApplication(npyscreen.NPSAppManaged):
             "MAIN",
             editOptsForm,
             name="InvokeAI Startup Options",
-            cycle_widgets=True,
         )
         if not (self.program_opts.skip_sd_weights or self.program_opts.default_only):
             self.model_select = self.addForm(
@@ -623,7 +615,6 @@ class EditOptApplication(npyscreen.NPSAppManaged):
                 addModelsForm,
                 name="Install Stable Diffusion Models",
                 multipage=True,
-                cycle_widgets=True,
             )
 
     def new_opts(self):
@@ -637,14 +628,18 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
 
 
 def default_startup_options(init_file: Path) -> Namespace:
-    opts = InvokeAIAppConfig.get_config()
+    opts = Args().parse_args([])
+    outdir = Path(opts.outdir)
+    if not outdir.is_absolute():
+        opts.outdir = str(Globals.root / opts.outdir)
     if not init_file.exists():
-        opts.nsfw_checker = True
+        opts.safety_checker = True
     return opts
 
-def default_user_selections(program_opts: Namespace) -> UserSelections:
-    return UserSelections(
-        install_models=default_dataset()
+
+def default_user_selections(program_opts: Namespace) -> Namespace:
+    return Namespace(
+        starter_models=default_dataset()
         if program_opts.default_only
         else recommended_datasets()
         if program_opts.yes_to_all
@@ -652,27 +647,26 @@ def default_user_selections(program_opts: Namespace) -> UserSelections:
         purge_deleted_models=False,
         scan_directory=None,
         autoscan_on_startup=None,
+        import_model_paths=None,
+        convert_to_diffusers=None,
     )
 
 
 # -------------------------------------
-def initialize_rootdir(root: Path, yes_to_all: bool = False):
+def initialize_rootdir(root: str, yes_to_all: bool = False):
     print("** INITIALIZING INVOKEAI RUNTIME DIRECTORY **")
 
     for name in (
-            "models",
-            "configs",
-            "embeddings",
-            "databases",
-            "loras",
-            "controlnets",
-            "text-inversion-output",
-            "text-inversion-training-data",
+        "models",
+        "configs",
+        "embeddings",
+        "text-inversion-output",
+        "text-inversion-training-data",
     ):
         os.makedirs(os.path.join(root, name), exist_ok=True)
 
     configs_src = Path(configs.__path__[0])
-    configs_dest = root / "configs"
+    configs_dest = Path(root) / "configs"
     if not os.path.samefile(configs_src, configs_dest):
         shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
 
@@ -683,17 +677,8 @@ def run_console_ui(
 ) -> (Namespace, Namespace):
     # parse_args() will read from init file if present
     invokeai_opts = default_startup_options(initfile)
-    invokeai_opts.root = program_opts.root
 
-    # The third argument is needed in the Windows 11 environment to
-    # launch a console window running this program.
-    set_min_terminal_size(MIN_COLS, MIN_LINES,'invokeai-configure')
-
-    # the install-models application spawns a subprocess to install
-    # models, and will crash unless this is set before running.
-    import torch
-    torch.multiprocessing.set_start_method("spawn")
-    
+    set_min_terminal_size(MIN_COLS, MIN_LINES)
     editApp = EditOptApplication(program_opts, invokeai_opts)
     editApp.run()
     if editApp.user_cancelled:
@@ -705,66 +690,70 @@ def run_console_ui(
 # -------------------------------------
 def write_opts(opts: Namespace, init_file: Path):
     """
-    Update the invokeai.yaml file with values from current settings.
+    Update the invokeai.init file with values from opts Namespace
     """
-    # this will load current settings
-    new_config = InvokeAIAppConfig.get_config()
-    new_config.root = config.root
-    
-    for key,value in opts.__dict__.items():
-        if hasattr(new_config,key):
-            setattr(new_config,key,value)
+    # touch file if it doesn't exist
+    if not init_file.exists():
+        with open(init_file, "w") as f:
+            f.write(INIT_FILE_PREAMBLE)
+
+    # We want to write in the changed arguments without clobbering
+    # any other initialization values the user has entered. There is
+    # no good way to do this because of the one-way nature of
+    # argparse: i.e. --outdir could be --outdir, --out, or -o
+    # initfile needs to be replaced with a fully structured format
+    # such as yaml; this is a hack that will work much of the time
+    args_to_skip = re.compile(
+        "^--?(o|out|no-xformer|xformer|no-ckpt|ckpt|free|no-nsfw|nsfw|prec|max_load|embed|always|ckpt|free_gpu)"
+    )
+    # fix windows paths
+    opts.outdir = opts.outdir.replace("\\", "/")
+    opts.embedding_path = opts.embedding_path.replace("\\", "/")
+    new_file = f"{init_file}.new"
+    try:
+        lines = [x.strip() for x in open(init_file, "r").readlines()]
+        with open(new_file, "w") as out_file:
+            for line in lines:
+                if len(line) > 0 and not args_to_skip.match(line):
+                    out_file.write(line + "\n")
+            out_file.write(
+                f"""
+--outdir={opts.outdir}
+--embedding_path={opts.embedding_path}
+--precision={opts.precision}
+--max_loaded_models={int(opts.max_loaded_models)}
+--{'no-' if not opts.safety_checker else ''}nsfw_checker
+--{'no-' if not opts.xformers else ''}xformers
+--{'no-' if not opts.ckpt_convert else ''}ckpt_convert
+{'--free_gpu_mem' if opts.free_gpu_mem else ''}
+{'--always_use_cpu' if opts.always_use_cpu else ''}
+"""
+            )
+    except OSError as e:
+        print(f"** An error occurred while writing the init file: {str(e)}")
+
+    os.replace(new_file, init_file)
+
+    if opts.hf_token:
+        HfLogin(opts.hf_token)
 
-    with open(init_file,'w', encoding='utf-8') as file:
-        file.write(new_config.to_yaml())
 
 # -------------------------------------
 def default_output_dir() -> Path:
-    return config.root_path / "outputs"
+    return Globals.root / "outputs"
+
 
 # -------------------------------------
 def default_embedding_dir() -> Path:
-    return config.root_path / "embeddings"
+    return Globals.root / "embeddings"
 
-# -------------------------------------
-def default_lora_dir() -> Path:
-    return config.root_path / "loras"
-
-# -------------------------------------
-def default_controlnet_dir() -> Path:
-    return config.root_path / "controlnets"
 
 # -------------------------------------
 def write_default_options(program_opts: Namespace, initfile: Path):
     opt = default_startup_options(initfile)
+    opt.hf_token = HfFolder.get_token()
     write_opts(opt, initfile)
 
-# -------------------------------------
-# Here we bring in
-# the legacy Args object in order to parse
-# the old init file and write out the new
-# yaml format.
-def migrate_init_file(legacy_format:Path):
-    old = legacy_parser.parse_args([f'@{str(legacy_format)}'])
-    new = InvokeAIAppConfig.get_config()
-
-    fields = list(get_type_hints(InvokeAIAppConfig).keys())
-    for attr in fields:
-        if hasattr(old,attr):
-            setattr(new,attr,getattr(old,attr))
-
-    # a few places where the field names have changed and we have to
-    # manually add in the new names/values
-    new.nsfw_checker = old.safety_checker
-    new.xformers_enabled = old.xformers
-    new.conf_path = old.conf
-    new.embedding_dir = old.embedding_path
-
-    invokeai_yaml = legacy_format.parent / 'invokeai.yaml'
-    with open(invokeai_yaml,"w", encoding="utf-8") as outfile:
-        outfile.write(new.to_yaml())
-
-    legacy_format.replace(legacy_format.parent / 'invokeai.init.old')
 
 # -------------------------------------
 def main():
@@ -820,13 +809,8 @@ def main():
     )
     opt = parser.parse_args()
 
-    invoke_args = []
-    if opt.root:
-        invoke_args.extend(['--root',opt.root])
-    if opt.full_precision:
-        invoke_args.extend(['--precision','float32'])
-    config.parse_args(invoke_args)
-    logger = InvokeAILogger().getLogger(config=config)
+    # setting a global here
+    Globals.root = Path(os.path.expanduser(get_root(opt.root) or ""))
 
     errors = set()
 
@@ -834,26 +818,19 @@ def main():
         models_to_download = default_user_selections(opt)
 
         # We check for to see if the runtime directory is correctly initialized.
-        old_init_file = config.root_path / 'invokeai.init'
-        new_init_file = config.root_path / 'invokeai.yaml'
-        if old_init_file.exists() and not new_init_file.exists():
-            print('** Migrating invokeai.init to invokeai.yaml')
-            migrate_init_file(old_init_file)
-            # Load new init file into config
-            config.parse_args(argv=[],conf=OmegaConf.load(new_init_file))
-
-        if not config.model_conf_path.exists():
-            initialize_rootdir(config.root_path, opt.yes_to_all)
+        init_file = Path(Globals.root, Globals.initfile)
+        if not init_file.exists() or not global_config_file().exists():
+            initialize_rootdir(Globals.root, opt.yes_to_all)
 
         if opt.yes_to_all:
-            write_default_options(opt, new_init_file)
+            write_default_options(opt, init_file)
             init_options = Namespace(
                 precision="float32" if opt.full_precision else "float16"
             )
         else:
-            init_options, models_to_download = run_console_ui(opt, new_init_file)
+            init_options, models_to_download = run_console_ui(opt, init_file)
             if init_options:
-                write_opts(init_options, new_init_file)
+                write_opts(init_options, init_file)
             else:
                 print(
                     '\n** CANCELLED AT USER\'S REQUEST. USE THE "invoke.sh" LAUNCHER TO RUN LATER **\n'
@@ -863,7 +840,7 @@ def main():
         if opt.skip_support_models:
             print("\n** SKIPPING SUPPORT MODEL DOWNLOADS PER USER REQUEST **")
         else:
-            print("\n** CHECKING/UPDATING SUPPORT MODELS **")
+            print("\n** DOWNLOADING SUPPORT MODELS **")
             download_bert()
             download_sd1_clip()
             download_sd2_clip()
@@ -881,8 +858,6 @@ def main():
             process_and_execute(opt, models_to_download)
 
         postscript(errors=errors)
-        if not opt.yes_to_all:
-            input('Press any key to continue...')
     except KeyboardInterrupt:
         print("\nGoodbye! Come back soon.")
 

invokeai/backend/config/model_install_backend.py

@@ -6,30 +6,26 @@ import re
 import shutil
 import sys
 import warnings
-from dataclasses import dataclass,field
 from pathlib import Path
 from tempfile import TemporaryFile
-from typing import List, Dict, Callable
+from typing import List
 
 import requests
 from diffusers import AutoencoderKL
-from huggingface_hub import hf_hub_url, HfFolder
+from huggingface_hub import hf_hub_url
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
 from tqdm import tqdm
 
 import invokeai.configs as configs
 
-
-from invokeai.app.services.config import InvokeAIAppConfig
+from ..globals import Globals, global_cache_dir, global_config_dir
+from ..model_management import ModelManager
 from ..stable_diffusion import StableDiffusionGeneratorPipeline
-from ..util.logging import InvokeAILogger
 
 warnings.filterwarnings("ignore")
 
 # --------------------------globals-----------------------
-config = InvokeAIAppConfig.get_config()
-
 Model_dir = "models"
 Weights_dir = "ldm/stable-diffusion-v1/"
 
@@ -39,9 +35,6 @@ Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
 # initial models omegaconf
 Datasets = None
 
-# logger
-logger = InvokeAILogger.getLogger(name='InvokeAI')
-
 Config_preamble = """
 # This file describes the alternative machine learning models
 # available to InvokeAI script.
@@ -52,98 +45,58 @@ Config_preamble = """
 # was trained on.
 """
 
-@dataclass
-class ModelInstallList:
-    '''Class for listing models to be installed/removed'''
-    install_models: List[str] = field(default_factory=list)
-    remove_models: List[str] = field(default_factory=list)
 
-@dataclass
-class UserSelections():
-    install_models: List[str]= field(default_factory=list)
-    remove_models: List[str]=field(default_factory=list)
-    purge_deleted_models: bool=field(default_factory=list)
-    install_cn_models: List[str] = field(default_factory=list)
-    remove_cn_models: List[str] = field(default_factory=list)
-    install_lora_models: List[str] = field(default_factory=list)
-    remove_lora_models: List[str] = field(default_factory=list)
-    install_ti_models: List[str] = field(default_factory=list)
-    remove_ti_models: List[str] = field(default_factory=list)
-    scan_directory: Path = None
-    autoscan_on_startup: bool=False
-    import_model_paths: str=None
-        
 def default_config_file():
-    return config.model_conf_path
+    return Path(global_config_dir()) / "models.yaml"
+
 
 def sd_configs():
-    return config.legacy_conf_path
+    return Path(global_config_dir()) / "stable-diffusion"
+
 
 def initial_models():
     global Datasets
     if Datasets:
         return Datasets
-    return (Datasets := OmegaConf.load(Dataset_path)['diffusers'])
+    return (Datasets := OmegaConf.load(Dataset_path))
+
 
 def install_requested_models(
-        diffusers: ModelInstallList = None,
-        controlnet: ModelInstallList = None,
-        lora: ModelInstallList = None,
-        ti: ModelInstallList = None,
-        cn_model_map: Dict[str,str] = None, # temporary - move to model manager
-        scan_directory: Path = None,
-        external_models: List[str] = None,
-        scan_at_startup: bool = False,
-        precision: str = "float16",
-        purge_deleted: bool = False,
-        config_file_path: Path = None,
-        model_config_file_callback:  Callable[[Path],Path] = None
+    install_initial_models: List[str] = None,
+    remove_models: List[str] = None,
+    scan_directory: Path = None,
+    external_models: List[str] = None,
+    scan_at_startup: bool = False,
+    precision: str = "float16",
+    purge_deleted: bool = False,
+    config_file_path: Path = None,
 ):
     """
     Entry point for installing/deleting starter models, or installing external models.
     """
-    access_token = HfFolder.get_token()
     config_file_path = config_file_path or default_config_file()
     if not config_file_path.exists():
         open(config_file_path, "w")
 
-    # prevent circular import here
-    from ..model_management import ModelManager
     model_manager = ModelManager(OmegaConf.load(config_file_path), precision=precision)
-    if controlnet:
-        model_manager.install_controlnet_models(controlnet.install_models, access_token=access_token)
-        model_manager.delete_controlnet_models(controlnet.remove_models)
 
-    if lora:
-        model_manager.install_lora_models(lora.install_models, access_token=access_token)
-        model_manager.delete_lora_models(lora.remove_models)
+    if remove_models and len(remove_models) > 0:
+        print("== DELETING UNCHECKED STARTER MODELS ==")
+        for model in remove_models:
+            print(f"{model}...")
+            model_manager.del_model(model, delete_files=purge_deleted)
+        model_manager.commit(config_file_path)
 
-    if ti:
-        model_manager.install_ti_models(ti.install_models, access_token=access_token)
-        model_manager.delete_ti_models(ti.remove_models)
-
-    if diffusers:
-        # TODO: Replace next three paragraphs with calls into new model manager
-        if diffusers.remove_models and len(diffusers.remove_models) > 0:
-            logger.info("Processing requested deletions")
-            for model in diffusers.remove_models:
-                logger.info(f"{model}...")
-                model_manager.del_model(model, delete_files=purge_deleted)
-            model_manager.commit(config_file_path)
-
-        if diffusers.install_models and len(diffusers.install_models) > 0:
-            logger.info("Installing requested models")
-            downloaded_paths = download_weight_datasets(
-                models=diffusers.install_models,
-                access_token=None,
-                precision=precision,
-            )
-            successful = {x:v for x,v in downloaded_paths.items() if v is not None}
-            if len(successful) > 0:
-                update_config_file(successful, config_file_path)
-            if len(successful) < len(diffusers.install_models):
-                unsuccessful = [x for x in downloaded_paths if downloaded_paths[x] is None]
-                logger.warning(f"Some of the model downloads were not successful: {unsuccessful}")
+    if install_initial_models and len(install_initial_models) > 0:
+        print("== INSTALLING SELECTED STARTER MODELS ==")
+        successfully_downloaded = download_weight_datasets(
+            models=install_initial_models,
+            access_token=None,
+            precision=precision,
+        )  # FIX: for historical reasons, we don't use model manager here
+        update_config_file(successfully_downloaded, config_file_path)
+        if len(successfully_downloaded) < len(install_initial_models):
+            print("** Some of the model downloads were not successful")
 
     # due to above, we have to reload the model manager because conf file
     # was changed behind its back
@@ -154,14 +107,12 @@ def install_requested_models(
         external_models.append(str(scan_directory))
 
     if len(external_models) > 0:
-        logger.info("INSTALLING EXTERNAL MODELS")
+        print("== INSTALLING EXTERNAL MODELS ==")
         for path_url_or_repo in external_models:
             try:
-                logger.debug(f'In install_requested_models; callback = {model_config_file_callback}')
                 model_manager.heuristic_import(
                     path_url_or_repo,
                     commit_to_conf=config_file_path,
-                    config_file_callback = model_config_file_callback,
                 )
             except KeyboardInterrupt:
                 sys.exit(-1)
@@ -169,22 +120,17 @@ def install_requested_models(
                 pass
 
     if scan_at_startup and scan_directory.is_dir():
-        update_autoconvert_dir(scan_directory)
-    else:
-        update_autoconvert_dir(None)
-
-def update_autoconvert_dir(autodir: Path):
-    '''
-    Update the "autoconvert_dir" option in invokeai.yaml
-    '''
-    invokeai_config_path = config.init_file_path
-    conf = OmegaConf.load(invokeai_config_path)
-    conf.InvokeAI.Paths.autoconvert_dir = str(autodir) if autodir else None
-    yaml = OmegaConf.to_yaml(conf)
-    tmpfile = invokeai_config_path.parent / "new_config.tmp"
-    with open(tmpfile, "w", encoding="utf-8") as outfile:
-        outfile.write(yaml)
-    tmpfile.replace(invokeai_config_path)
+        argument = "--autoconvert"
+        initfile = Path(Globals.root, Globals.initfile)
+        replacement = Path(Globals.root, f"{Globals.initfile}.new")
+        directory = str(scan_directory).replace("\\", "/")
+        with open(initfile, "r") as input:
+            with open(replacement, "w") as output:
+                while line := input.readline():
+                    if not line.startswith(argument):
+                        output.writelines([line])
+                output.writelines([f"{argument} {directory}"])
+        os.replace(replacement, initfile)
 
 
 # -------------------------------------
@@ -196,21 +142,33 @@ def yes_or_no(prompt: str, default_yes=True):
     else:
         return response[0] in ("y", "Y")
 
+
+# -------------------------------------
+def get_root(root: str = None) -> str:
+    if root:
+        return root
+    elif os.environ.get("INVOKEAI_ROOT"):
+        return os.environ.get("INVOKEAI_ROOT")
+    else:
+        return Globals.root
+
+
 # ---------------------------------------------
-def recommended_datasets() -> List['str']:
-    datasets = set()
+def recommended_datasets() -> dict:
+    datasets = dict()
     for ds in initial_models().keys():
         if initial_models()[ds].get("recommended", False):
-            datasets.add(ds)
-    return list(datasets)
+            datasets[ds] = True
+    return datasets
+
 
 # ---------------------------------------------
 def default_dataset() -> dict:
-    datasets = set()
+    datasets = dict()
     for ds in initial_models().keys():
         if initial_models()[ds].get("default", False):
-            datasets.add(ds)
-    return list(datasets)
+            datasets[ds] = True
+    return datasets
 
 
 # ---------------------------------------------
@@ -225,14 +183,14 @@ def all_datasets() -> dict:
 # look for legacy model.ckpt in models directory and offer to
 # normalize its name
 def migrate_models_ckpt():
-    model_path = os.path.join(config.root_dir, Model_dir, Weights_dir)
+    model_path = os.path.join(Globals.root, Model_dir, Weights_dir)
     if not os.path.exists(os.path.join(model_path, "model.ckpt")):
         return
     new_name = initial_models()["stable-diffusion-1.4"]["file"]
-    logger.warning(
+    print(
         'The Stable Diffusion v4.1 "model.ckpt" is already installed. The name will be changed to {new_name} to avoid confusion.'
     )
-    logger.warning(f"model.ckpt => {new_name}")
+    print(f"model.ckpt => {new_name}")
     os.replace(
         os.path.join(model_path, "model.ckpt"), os.path.join(model_path, new_name)
     )
@@ -245,7 +203,7 @@ def download_weight_datasets(
     migrate_models_ckpt()
     successful = dict()
     for mod in models:
-        logger.info(f"Downloading {mod}:")
+        print(f"Downloading {mod}:")
         successful[mod] = _download_repo_or_file(
             initial_models()[mod], access_token, precision=precision
         )
@@ -266,10 +224,11 @@ def _download_repo_or_file(
             )
     return path
 
+
 def _download_ckpt_weights(mconfig: DictConfig, access_token: str) -> Path:
     repo_id = mconfig["repo_id"]
     filename = mconfig["file"]
-    cache_dir = os.path.join(config.root_dir, Model_dir, Weights_dir)
+    cache_dir = os.path.join(Globals.root, Model_dir, Weights_dir)
     return hf_download_with_resume(
         repo_id=repo_id,
         model_dir=cache_dir,
@@ -280,12 +239,9 @@ def _download_ckpt_weights(mconfig: DictConfig, access_token: str) -> Path:
 
 # ---------------------------------------------
 def download_from_hf(
-    model_class: object, model_name: str, **kwargs
+    model_class: object, model_name: str, cache_subdir: Path = Path("hub"), **kwargs
 ):
-    logger = InvokeAILogger.getLogger('InvokeAI')
-    logger.addFilter(lambda x: 'fp16 is not a valid' not in x.getMessage())
-    
-    path = config.cache_dir
+    path = global_cache_dir(cache_subdir)
     model = model_class.from_pretrained(
         model_name,
         cache_dir=path,
@@ -316,10 +272,10 @@ def _download_diffusion_weights(
                 **extra_args,
             )
         except OSError as e:
-            if 'Revision Not Found' in str(e):
+            if str(e).startswith("fp16 is not a valid"):
                 pass
             else:
-                logger.error(str(e))
+                print(f"An unexpected error occurred while downloading the model: {e})")
         if path:
             break
     return path
@@ -327,13 +283,9 @@ def _download_diffusion_weights(
 
 # ---------------------------------------------
 def hf_download_with_resume(
-        repo_id: str,
-        model_dir: str,
-        model_name: str,
-        model_dest: Path = None,
-        access_token: str = None,
+    repo_id: str, model_dir: str, model_name: str, access_token: str = None
 ) -> Path:
-    model_dest = model_dest or Path(os.path.join(model_dir, model_name))
+    model_dest = Path(os.path.join(model_dir, model_name))
     os.makedirs(model_dir, exist_ok=True)
 
     url = hf_hub_url(repo_id, model_name)
@@ -353,19 +305,20 @@ def hf_download_with_resume(
     if (
         resp.status_code == 416
     ):  # "range not satisfiable", which means nothing to return
-        logger.info(f"{model_name}: complete file found. Skipping.")
+        print(f"* {model_name}: complete file found. Skipping.")
         return model_dest
-    elif resp.status_code == 404:
-        logger.warning("File not found")
-        return None
     elif resp.status_code != 200:
-        logger.warning(f"{model_name}: {resp.reason}")
+        print(f"** An error occurred during downloading {model_name}: {resp.reason}")
     elif exist_size > 0:
-        logger.info(f"{model_name}: partial file found. Resuming...")
+        print(f"* {model_name}: partial file found. Resuming...")
     else:
-        logger.info(f"{model_name}: Downloading...")
+        print(f"* {model_name}: Downloading...")
 
     try:
+        if total < 2000:
+            print(f"*** ERROR DOWNLOADING {model_name}: {resp.text}")
+            return None
+
         with open(model_dest, open_mode) as file, tqdm(
             desc=model_name,
             initial=exist_size,
@@ -378,7 +331,7 @@ def hf_download_with_resume(
                 size = file.write(data)
                 bar.update(size)
     except Exception as e:
-        logger.error(f"An error occurred while downloading {model_name}: {str(e)}")
+        print(f"An error occurred while downloading {model_name}: {str(e)}")
         return None
     return model_dest
 
@@ -403,8 +356,8 @@ def update_config_file(successfully_downloaded: dict, config_file: Path):
     try:
         backup = None
         if os.path.exists(config_file):
-            logger.warning(
-                f"{config_file.name} exists. Renaming to {config_file.stem}.yaml.orig"
+            print(
+                f"** {config_file.name} exists. Renaming to {config_file.stem}.yaml.orig"
             )
             backup = config_file.with_suffix(".yaml.orig")
             ## Ugh. Windows is unable to overwrite an existing backup file, raises a WinError 183
@@ -421,16 +374,16 @@ def update_config_file(successfully_downloaded: dict, config_file: Path):
                 new_config.write(tmp.read())
 
     except Exception as e:
-        logger.error(f"Error creating config file {config_file}: {str(e)}")
+        print(f"**Error creating config file {config_file}: {str(e)} **")
         if backup is not None:
-            logger.info("restoring previous config file")
+            print("restoring previous config file")
             ## workaround, for WinError 183, see above
             if sys.platform == "win32" and config_file.is_file():
                 config_file.unlink()
             backup.rename(config_file)
         return
-    
-    logger.info(f"Successfully created new configuration file {config_file}")
+
+    print(f"Successfully created new configuration file {config_file}")
 
 
 # ---------------------------------------------
@@ -464,7 +417,7 @@ def new_config_file_contents(
             stanza["height"] = mod["height"]
         if "file" in mod:
             stanza["weights"] = os.path.relpath(
-                successfully_downloaded[model], start=config.root_dir
+                successfully_downloaded[model], start=Globals.root
             )
             stanza["config"] = os.path.normpath(
                 os.path.join(sd_configs(), mod["config"])
@@ -497,14 +450,14 @@ def delete_weights(model_name: str, conf_stanza: dict):
     if re.match("/VAE/", conf_stanza.get("config")):
         return
 
-    logger.warning(
-        f"\nThe checkpoint version of {model_name} is superseded by the diffusers version. Deleting the original file {weights}?"
+    print(
+        f"\n** The checkpoint version of {model_name} is superseded by the diffusers version. Deleting the original file {weights}?"
     )
 
     weights = Path(weights)
     if not weights.is_absolute():
-        weights = config.root_dir / weights
+        weights = Path(Globals.root) / weights
         try:
             weights.unlink()
         except OSError as e:
-            logger.error(str(e))
+            print(str(e))

invokeai/backend/generator/base.py

@@ -25,13 +25,11 @@ from typing import Callable, List, Iterator, Optional, Type
 from dataclasses import dataclass, field
 from diffusers.schedulers import SchedulerMixin as Scheduler
 
-import invokeai.backend.util.logging as logger
 from ..image_util import configure_model_padding
 from ..util.util import rand_perlin_2d
 from ..safety_checker import SafetyChecker
 from ..prompting.conditioning import get_uc_and_c_and_ec
 from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from ..stable_diffusion.schedulers import SCHEDULER_MAP
 
 downsampling = 8
 
@@ -72,6 +70,19 @@ class InvokeAIGeneratorOutput:
 # we are interposing a wrapper around the original Generator classes so that
 # old code that calls Generate will continue to work.
 class InvokeAIGenerator(metaclass=ABCMeta):
+    scheduler_map = dict(
+        ddim=diffusers.DDIMScheduler,
+        dpmpp_2=diffusers.DPMSolverMultistepScheduler,
+        k_dpm_2=diffusers.KDPM2DiscreteScheduler,
+        k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
+        k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
+        k_euler=diffusers.EulerDiscreteScheduler,
+        k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
+        k_heun=diffusers.HeunDiscreteScheduler,
+        k_lms=diffusers.LMSDiscreteScheduler,
+        plms=diffusers.PNDMScheduler,
+    )
+
     def __init__(self,
                  model_info: dict,
                  params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
@@ -168,20 +179,14 @@ class InvokeAIGenerator(metaclass=ABCMeta):
         '''
         Return list of all the schedulers that we currently handle.
         '''
-        return list(SCHEDULER_MAP.keys())
+        return list(self.scheduler_map.keys())
 
     def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
         return generator_class(model, self.params.precision)
 
     def get_scheduler(self, scheduler_name:str, model: StableDiffusionGeneratorPipeline)->Scheduler:
-        scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP['ddim'])
-        
-        scheduler_config = model.scheduler.config
-        if "_backup" in scheduler_config:
-            scheduler_config = scheduler_config["_backup"]
-        scheduler_config = {**scheduler_config, **scheduler_extra_config, "_backup": scheduler_config}
-        scheduler = scheduler_class.from_config(scheduler_config)
-        
+        scheduler_class = self.scheduler_map.get(scheduler_name,'ddim')
+        scheduler = scheduler_class.from_config(model.scheduler.config)
         # hack copied over from generate.py
         if not hasattr(scheduler, 'uses_inpainting_model'):
             scheduler.uses_inpainting_model = lambda: False
@@ -225,10 +230,10 @@ class Inpaint(Img2Img):
     def generate(self,
                  mask_image: Image.Image | torch.FloatTensor,
                  # Seam settings - when 0, doesn't fill seam
-                 seam_size: int = 96,
-                 seam_blur: int = 16,
+                 seam_size: int = 0,
+                 seam_blur: int = 0,
                  seam_strength: float = 0.7,
-                 seam_steps: int = 30,
+                 seam_steps: int = 10,
                  tile_size: int = 32,
                  inpaint_replace=False,
                  infill_method=None,
@@ -354,7 +359,6 @@ class Generator:
         seed = seed if seed is not None and seed >= 0 else self.new_seed()
         first_seed = seed
         seed, initial_noise = self.generate_initial_noise(seed, width, height)
-
         # There used to be an additional self.model.ema_scope() here, but it breaks
         # the inpaint-1.5 model. Not sure what it did.... ?
         with scope(self.model.device.type):
@@ -372,7 +376,7 @@ class Generator:
                     try:
                         x_T = self.get_noise(width, height)
                     except:
-                        logger.error("An error occurred while getting initial noise")
+                        print("** An error occurred while getting initial noise **")
                         print(traceback.format_exc())
 
                 # Pass on the seed in case a layer beneath us needs to generate noise on its own.
@@ -607,7 +611,7 @@ class Generator:
         image = self.sample_to_image(sample)
         dirname = os.path.dirname(filepath) or "."
         if not os.path.exists(dirname):
-            logger.info(f"creating directory {dirname}")
+            print(f"** creating directory {dirname}")
             os.makedirs(dirname, exist_ok=True)
         image.save(filepath, "PNG")
 

invokeai/backend/generator/embiggen.py

@@ -8,11 +8,10 @@ import torch
 from PIL import Image
 from tqdm import trange
 
-import invokeai.backend.util.logging as logger
-
 from .base import Generator
 from .img2img import Img2Img
 
+
 class Embiggen(Generator):
     def __init__(self, model, precision):
         super().__init__(model, precision)
@@ -73,22 +72,22 @@ class Embiggen(Generator):
             embiggen = [1.0]  # If not specified, assume no scaling
         elif embiggen[0] < 0:
             embiggen[0] = 1.0
-            logger.warning(
-                "Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !"
+            print(
+                ">> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !"
             )
         if len(embiggen) < 2:
             embiggen.append(0.75)
         elif embiggen[1] > 1.0 or embiggen[1] < 0:
             embiggen[1] = 0.75
-            logger.warning(
-                "Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !"
+            print(
+                ">> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !"
             )
         if len(embiggen) < 3:
             embiggen.append(0.25)
         elif embiggen[2] < 0:
             embiggen[2] = 0.25
-            logger.warning(
-                "Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !"
+            print(
+                ">> Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !"
             )
 
         # Convert tiles from their user-freindly count-from-one to count-from-zero, because we need to do modulo math
@@ -98,8 +97,8 @@ class Embiggen(Generator):
             embiggen_tiles.sort()
 
         if strength >= 0.5:
-            logger.warning(
-                f"Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
+            print(
+                f"* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45."
             )
 
         # Prep img2img generator, since we wrap over it
@@ -122,8 +121,8 @@ class Embiggen(Generator):
                 from ..restoration.realesrgan import ESRGAN
 
                 esrgan = ESRGAN()
-                logger.info(
-                    f"ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
+                print(
+                    f">> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}"
                 )
                 if embiggen[0] > 2:
                     initsuperimage = esrgan.process(
@@ -313,10 +312,10 @@ class Embiggen(Generator):
         def make_image():
             # Make main tiles -------------------------------------------------
             if embiggen_tiles:
-                logger.info(f"Making {len(embiggen_tiles)} Embiggen tiles...")
+                print(f">> Making {len(embiggen_tiles)} Embiggen tiles...")
             else:
-                logger.info(
-                    f"Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
+                print(
+                    f">> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})..."
                 )
 
             emb_tile_store = []
@@ -362,11 +361,11 @@ class Embiggen(Generator):
                 # newinitimage.save(newinitimagepath)
 
                 if embiggen_tiles:
-                    logger.debug(
+                    print(
                         f"Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)"
                     )
                 else:
-                    logger.debug(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles")
+                    print(f"Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles")
 
                 # create a torch tensor from an Image
                 newinitimage = np.array(newinitimage).astype(np.float32) / 255.0
@@ -548,8 +547,8 @@ class Embiggen(Generator):
                     # Layer tile onto final image
                     outputsuperimage.alpha_composite(intileimage, (left, top))
             else:
-                logger.error(
-                    "Could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
+                print(
+                    "Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation."
                 )
 
             # after internal loops and patching up return Embiggen image

invokeai/backend/generator/inpaint.py

@@ -4,7 +4,6 @@ invokeai.backend.generator.inpaint descends from .generator
 from __future__ import annotations
 
 import math
-from typing import Tuple, Union
 
 import cv2
 import numpy as np
@@ -60,7 +59,7 @@ class Inpaint(Img2Img):
             writeable=False,
         )
 
-    def infill_patchmatch(self, im: Image.Image) -> Image.Image:
+    def infill_patchmatch(self, im: Image.Image) -> Image:
         if im.mode != "RGBA":
             return im
 
@@ -76,18 +75,18 @@ class Inpaint(Img2Img):
         return im_patched
 
     def tile_fill_missing(
-        self, im: Image.Image, tile_size: int = 16, seed: Union[int, None] = None
-    ) -> Image.Image:
+        self, im: Image.Image, tile_size: int = 16, seed: int = None
+    ) -> Image:
         # Only fill if there's an alpha layer
         if im.mode != "RGBA":
             return im
 
         a = np.asarray(im, dtype=np.uint8)
 
-        tile_size_tuple = (tile_size, tile_size)
+        tile_size = (tile_size, tile_size)
 
         # Get the image as tiles of a specified size
-        tiles = self.get_tile_images(a, *tile_size_tuple).copy()
+        tiles = self.get_tile_images(a, *tile_size).copy()
 
         # Get the mask as tiles
         tiles_mask = tiles[:, :, :, :, 3]
@@ -128,9 +127,7 @@ class Inpaint(Img2Img):
 
         return si
 
-    def mask_edge(
-        self, mask: Image.Image, edge_size: int, edge_blur: int
-    ) -> Image.Image:
+    def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
         npimg = np.asarray(mask, dtype=np.uint8)
 
         # Detect any partially transparent regions
@@ -196,7 +193,7 @@ class Inpaint(Img2Img):
 
         seam_noise = self.get_noise(im.width, im.height)
 
-        result = make_image(seam_noise, seed=None)
+        result = make_image(seam_noise, seed)
 
         return result
 
@@ -209,15 +206,15 @@ class Inpaint(Img2Img):
         cfg_scale,
         ddim_eta,
         conditioning,
-        init_image: Image.Image | torch.FloatTensor,
-        mask_image: Image.Image | torch.FloatTensor,
+        init_image: PIL.Image.Image | torch.FloatTensor,
+        mask_image: PIL.Image.Image | torch.FloatTensor,
         strength: float,
         mask_blur_radius: int = 8,
         # Seam settings - when 0, doesn't fill seam
-        seam_size: int = 96,
-        seam_blur: int = 16,
+        seam_size: int = 0,
+        seam_blur: int = 0,
         seam_strength: float = 0.7,
-        seam_steps: int = 30,
+        seam_steps: int = 10,
         tile_size: int = 32,
         step_callback=None,
         inpaint_replace=False,
@@ -225,7 +222,7 @@ class Inpaint(Img2Img):
         infill_method=None,
         inpaint_width=None,
         inpaint_height=None,
-        inpaint_fill: Tuple[int, int, int, int] = (0x7F, 0x7F, 0x7F, 0xFF),
+        inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
         attention_maps_callback=None,
         **kwargs,
     ):
@@ -242,7 +239,7 @@ class Inpaint(Img2Img):
         self.inpaint_width = inpaint_width
         self.inpaint_height = inpaint_height
 
-        if isinstance(init_image, Image.Image):
+        if isinstance(init_image, PIL.Image.Image):
             self.pil_image = init_image.copy()
 
             # Do infill
@@ -253,8 +250,8 @@ class Inpaint(Img2Img):
                     self.pil_image.copy(), seed=self.seed, tile_size=tile_size
                 )
             elif infill_method == "solid":
-                solid_bg = Image.new("RGBA", init_image.size, inpaint_fill)
-                init_filled = Image.alpha_composite(solid_bg, init_image)
+                solid_bg = PIL.Image.new("RGBA", init_image.size, inpaint_fill)
+                init_filled = PIL.Image.alpha_composite(solid_bg, init_image)
             else:
                 raise ValueError(
                     f"Non-supported infill type {infill_method}", infill_method
@@ -272,7 +269,7 @@ class Inpaint(Img2Img):
             # Create init tensor
             init_image = image_resized_to_grid_as_tensor(init_filled.convert("RGB"))
 
-        if isinstance(mask_image, Image.Image):
+        if isinstance(mask_image, PIL.Image.Image):
             self.pil_mask = mask_image.copy()
             debug_image(
                 mask_image,

invokeai/backend/generator/txt2img2img.py

@@ -14,8 +14,6 @@ from ..stable_diffusion.diffusers_pipeline import StableDiffusionGeneratorPipeli
 from ..stable_diffusion.diffusers_pipeline import ConditioningData
 from ..stable_diffusion.diffusers_pipeline import trim_to_multiple_of
 
-import invokeai.backend.util.logging as logger
-
 class Txt2Img2Img(Generator):
     def __init__(self, model, precision):
         super().__init__(model, precision)
@@ -79,8 +77,8 @@ class Txt2Img2Img(Generator):
             # the message below is accurate.
             init_width = first_pass_latent_output.size()[3] * self.downsampling_factor
             init_height = first_pass_latent_output.size()[2] * self.downsampling_factor
-            logger.info(
-                f"Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
+            print(
+                f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
             )
 
             # resizing

invokeai/backend/globals.py

@@ -0,0 +1,122 @@
+"""
+invokeai.backend.globals defines a small number of global variables that would
+otherwise have to be passed through long and complex call chains.
+
+It defines a Namespace object named "Globals" that contains
+the attributes:
+
+  - root           - the root directory under which "models" and "outputs" can be found
+  - initfile       - path to the initialization file
+  - try_patchmatch - option to globally disable loading of 'patchmatch' module
+  - always_use_cpu - force use of CPU even if GPU is available
+"""
+
+import os
+import os.path as osp
+from argparse import Namespace
+from pathlib import Path
+from typing import Union
+
+Globals = Namespace()
+
+# Where to look for the initialization file and other key components
+Globals.initfile = "invokeai.init"
+Globals.models_file = "models.yaml"
+Globals.models_dir = "models"
+Globals.config_dir = "configs"
+Globals.autoscan_dir = "weights"
+Globals.converted_ckpts_dir = "converted_ckpts"
+
+# Set the default root directory. This can be overwritten by explicitly
+# passing the `--root <directory>` argument on the command line.
+# logic is:
+# 1) use INVOKEAI_ROOT environment variable (no check for this being a valid directory)
+# 2) use VIRTUAL_ENV environment variable, with a check for initfile being there
+# 3) use ~/invokeai
+
+if os.environ.get("INVOKEAI_ROOT"):
+    Globals.root = osp.abspath(os.environ.get("INVOKEAI_ROOT"))
+elif (
+    os.environ.get("VIRTUAL_ENV")
+    and Path(os.environ.get("VIRTUAL_ENV"), "..", Globals.initfile).exists()
+):
+    Globals.root = osp.abspath(osp.join(os.environ.get("VIRTUAL_ENV"), ".."))
+else:
+    Globals.root = osp.abspath(osp.expanduser("~/invokeai"))
+
+# Try loading patchmatch
+Globals.try_patchmatch = True
+
+# Use CPU even if GPU is available (main use case is for debugging MPS issues)
+Globals.always_use_cpu = False
+
+# Whether the internet is reachable for dynamic downloads
+# The CLI will test connectivity at startup time.
+Globals.internet_available = True
+
+# Whether to disable xformers
+Globals.disable_xformers = False
+
+# Low-memory tradeoff for guidance calculations.
+Globals.sequential_guidance = False
+
+# whether we are forcing full precision
+Globals.full_precision = False
+
+# whether we should convert ckpt files into diffusers models on the fly
+Globals.ckpt_convert = True
+
+# logging tokenization everywhere
+Globals.log_tokenization = False
+
+
+def global_config_file() -> Path:
+    return Path(Globals.root, Globals.config_dir, Globals.models_file)
+
+
+def global_config_dir() -> Path:
+    return Path(Globals.root, Globals.config_dir)
+
+
+def global_models_dir() -> Path:
+    return Path(Globals.root, Globals.models_dir)
+
+
+def global_autoscan_dir() -> Path:
+    return Path(Globals.root, Globals.autoscan_dir)
+
+
+def global_converted_ckpts_dir() -> Path:
+    return Path(global_models_dir(), Globals.converted_ckpts_dir)
+
+
+def global_set_root(root_dir: Union[str, Path]):
+    Globals.root = root_dir
+
+
+def global_cache_dir(subdir: Union[str, Path] = "") -> Path:
+    """
+    Returns Path to the model cache directory. If a subdirectory
+    is provided, it will be appended to the end of the path, allowing
+    for Hugging Face-style conventions. Currently, Hugging Face has
+    moved all models into the "hub" subfolder, so for any pretrained
+    HF model, use:
+         global_cache_dir('hub')
+
+    The legacy location for transformers used to be global_cache_dir('transformers')
+    and global_cache_dir('diffusers') for diffusers.
+    """
+    home: str = os.getenv("HF_HOME")
+
+    if home is None:
+        home = os.getenv("XDG_CACHE_HOME")
+
+        if home is not None:
+            # Set `home` to $XDG_CACHE_HOME/huggingface, which is the default location mentioned in Hugging Face Hub Client Library.
+            # See: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#xdgcachehome
+            home += os.sep + "huggingface"
+
+    if home is not None:
+        return Path(home, subdir)
+    else:
+        return Path(Globals.root, "models", subdir)

invokeai/backend/image_util/patchmatch.py

@@ -5,9 +5,9 @@ wraps the actual patchmatch object. It respects the global
 be suppressed or deferred
 """
 import numpy as np
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
-config = InvokeAIAppConfig.get_config()
+
+from invokeai.backend.globals import Globals
+
 
 class PatchMatch:
     """
@@ -24,16 +24,16 @@ class PatchMatch:
     def _load_patch_match(self):
         if self.tried_load:
             return
-        if config.try_patchmatch:
+        if Globals.try_patchmatch:
             from patchmatch import patch_match as pm
 
             if pm.patchmatch_available:
-                logger.info("Patchmatch initialized")
+                print(">> Patchmatch initialized")
             else:
-                logger.info("Patchmatch not loaded (nonfatal)")
+                print(">> Patchmatch not loaded (nonfatal)")
             self.patch_match = pm
         else:
-            logger.info("Patchmatch loading disabled")
+            print(">> Patchmatch loading disabled")
         self.tried_load = True
 
     @classmethod

invokeai/backend/image_util/txt2mask.py

@@ -30,14 +30,14 @@ work fine.
 import numpy as np
 import torch
 from PIL import Image, ImageOps
+from torchvision import transforms
 from transformers import AutoProcessor, CLIPSegForImageSegmentation
 
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.globals import global_cache_dir
 
 CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
 CLIPSEG_SIZE = 352
-config = InvokeAIAppConfig.get_config()
+
 
 class SegmentedGrayscale(object):
     def __init__(self, image: Image, heatmap: torch.Tensor):
@@ -83,15 +83,15 @@ class Txt2Mask(object):
     """
 
     def __init__(self, device="cpu", refined=False):
-        logger.info("Initializing clipseg model for text to mask inference")
+        print(">> Initializing clipseg model for text to mask inference")
 
         # BUG: we are not doing anything with the device option at this time
         self.device = device
         self.processor = AutoProcessor.from_pretrained(
-            CLIPSEG_MODEL, cache_dir=config.cache_dir
+            CLIPSEG_MODEL, cache_dir=global_cache_dir("hub")
         )
         self.model = CLIPSegForImageSegmentation.from_pretrained(
-            CLIPSEG_MODEL, cache_dir=config.cache_dir
+            CLIPSEG_MODEL, cache_dir=global_cache_dir("hub")
         )
 
     @torch.no_grad()
@@ -101,6 +101,18 @@ class Txt2Mask(object):
         provided image and returns a SegmentedGrayscale object in which the brighter
         pixels indicate where the object is inferred to be.
         """
+        transform = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize(
+                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+                ),
+                transforms.Resize(
+                    (CLIPSEG_SIZE, CLIPSEG_SIZE)
+                ),  # must be multiple of 64...
+            ]
+        )
+
         if type(image) is str:
             image = Image.open(image).convert("RGB")
 

invokeai/backend/install/legacy_arg_parsing.py

@@ -1,390 +0,0 @@
-# Copyright 2023 Lincoln D. Stein and the InvokeAI Team
-
-import argparse
-import shlex
-from argparse import ArgumentParser
-
-SAMPLER_CHOICES = [
-    "ddim",
-    "ddpm",
-    "deis",
-    "lms",
-    "pndm",
-    "heun",
-    "heun_k",
-    "euler",
-    "euler_k",
-    "euler_a",
-    "kdpm_2",
-    "kdpm_2_a",
-    "dpmpp_2s",
-    "dpmpp_2m",
-    "dpmpp_2m_k",
-    "unipc",
-]
-
-PRECISION_CHOICES = [
-    "auto",
-    "float32",
-    "autocast",
-    "float16",
-]
-
-class FileArgumentParser(ArgumentParser):
-    """
-    Supports reading defaults from an init file.
-    """
-    def convert_arg_line_to_args(self, arg_line):
-        return shlex.split(arg_line, comments=True)
-
-
-legacy_parser = FileArgumentParser(
-    description=
-    """
-Generate images using Stable Diffusion.
-    Use --web to launch the web interface.
-    Use --from_file to load prompts from a file path or standard input ("-").
-    Otherwise you will be dropped into an interactive command prompt (type -h for help.)
-    Other command-line arguments are defaults that can usually be overridden
-    prompt the command prompt.
-    """,
-    fromfile_prefix_chars='@',
-)
-general_group    = legacy_parser.add_argument_group('General')
-model_group      = legacy_parser.add_argument_group('Model selection')
-file_group       = legacy_parser.add_argument_group('Input/output')
-web_server_group = legacy_parser.add_argument_group('Web server')
-render_group     = legacy_parser.add_argument_group('Rendering')
-postprocessing_group     = legacy_parser.add_argument_group('Postprocessing')
-deprecated_group = legacy_parser.add_argument_group('Deprecated options')
-
-deprecated_group.add_argument('--laion400m')
-deprecated_group.add_argument('--weights') # deprecated
-general_group.add_argument(
-    '--version','-V',
-    action='store_true',
-    help='Print InvokeAI version number'
-)
-model_group.add_argument(
-    '--root_dir',
-    default=None,
-    help='Path to directory containing "models", "outputs" and "configs". If not present will read from environment variable INVOKEAI_ROOT. Defaults to ~/invokeai.',
-)
-model_group.add_argument(
-    '--config',
-    '-c',
-    '-config',
-    dest='conf',
-    default='./configs/models.yaml',
-    help='Path to configuration file for alternate models.',
-)
-model_group.add_argument(
-    '--model',
-    help='Indicates which diffusion model to load (defaults to "default" stanza in configs/models.yaml)',
-)
-model_group.add_argument(
-    '--weight_dirs',
-    nargs='+',
-    type=str,
-    help='List of one or more directories that will be auto-scanned for new model weights to import',
-)
-model_group.add_argument(
-    '--png_compression','-z',
-    type=int,
-    default=6,
-    choices=range(0,9),
-    dest='png_compression',
-    help='level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.'
-)
-model_group.add_argument(
-    '-F',
-    '--full_precision',
-    dest='full_precision',
-    action='store_true',
-    help='Deprecated way to set --precision=float32',
-)
-model_group.add_argument(
-    '--max_loaded_models',
-    dest='max_loaded_models',
-    type=int,
-    default=2,
-    help='Maximum number of models to keep in memory for fast switching, including the one in GPU',
-)
-model_group.add_argument(
-    '--free_gpu_mem',
-    dest='free_gpu_mem',
-    action='store_true',
-    help='Force free gpu memory before final decoding',
-)
-model_group.add_argument(
-    '--sequential_guidance',
-    dest='sequential_guidance',
-    action='store_true',
-    help="Calculate guidance in serial instead of in parallel, lowering memory requirement "
-         "at the expense of speed",
-)
-model_group.add_argument(
-    '--xformers',
-    action=argparse.BooleanOptionalAction,
-    default=True,
-    help='Enable/disable xformers support (default enabled if installed)',
-)
-model_group.add_argument(
-    "--always_use_cpu",
-    dest="always_use_cpu",
-    action="store_true",
-    help="Force use of CPU even if GPU is available"
-)
-model_group.add_argument(
-    '--precision',
-    dest='precision',
-    type=str,
-    choices=PRECISION_CHOICES,
-    metavar='PRECISION',
-    help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
-    default='auto',
-)
-model_group.add_argument(
-    '--ckpt_convert',
-    action=argparse.BooleanOptionalAction,
-    dest='ckpt_convert',
-    default=True,
-    help='Deprecated option. Legacy ckpt files are now always converted to diffusers when loaded.'
-)
-model_group.add_argument(
-    '--internet',
-    action=argparse.BooleanOptionalAction,
-    dest='internet_available',
-    default=True,
-    help='Indicate whether internet is available for just-in-time model downloading (default: probe automatically).',
-)
-model_group.add_argument(
-    '--nsfw_checker',
-    '--safety_checker',
-    action=argparse.BooleanOptionalAction,
-    dest='safety_checker',
-    default=False,
-    help='Check for and blur potentially NSFW images. Use --no-nsfw_checker to disable.',
-)
-model_group.add_argument(
-    '--autoimport',
-    default=None,
-    type=str,
-    help='Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly',
-)
-model_group.add_argument(
-    '--autoconvert',
-    default=None,
-    type=str,
-    help='Check the indicated directory for .ckpt/.safetensors weights files at startup and import as optimized diffuser models',
-)
-model_group.add_argument(
-    '--patchmatch',
-    action=argparse.BooleanOptionalAction,
-    default=True,
-    help='Load the patchmatch extension for outpainting. Use --no-patchmatch to disable.',
-)
-file_group.add_argument(
-    '--from_file',
-    dest='infile',
-    type=str,
-    help='If specified, load prompts from this file',
-)
-file_group.add_argument(
-    '--outdir',
-    '-o',
-    type=str,
-    help='Directory to save generated images and a log of prompts and seeds. Default: ROOTDIR/outputs',
-    default='outputs',
-)
-file_group.add_argument(
-    '--prompt_as_dir',
-    '-p',
-    action='store_true',
-    help='Place images in subdirectories named after the prompt.',
-)
-render_group.add_argument(
-    '--fnformat',
-    default='{prefix}.{seed}.png',
-    type=str,
-    help='Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png',
-)
-render_group.add_argument(
-    '-s',
-    '--steps',
-    type=int,
-    default=50,
-    help='Number of steps'
-)
-render_group.add_argument(
-    '-W',
-    '--width',
-    type=int,
-    help='Image width, multiple of 64',
-)
-render_group.add_argument(
-    '-H',
-    '--height',
-    type=int,
-    help='Image height, multiple of 64',
-)
-render_group.add_argument(
-    '-C',
-    '--cfg_scale',
-    default=7.5,
-    type=float,
-    help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
-)
-render_group.add_argument(
-    '--sampler',
-    '-A',
-    '-m',
-    dest='sampler_name',
-    type=str,
-    choices=SAMPLER_CHOICES,
-    metavar='SAMPLER_NAME',
-    help=f'Set the default sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
-    default='k_lms',
-)
-render_group.add_argument(
-    '--log_tokenization',
-    '-t',
-    action='store_true',
-    help='shows how the prompt is split into tokens'
-)
-render_group.add_argument(
-    '-f',
-    '--strength',
-    type=float,
-    help='img2img strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
-)
-render_group.add_argument(
-    '-T',
-    '-fit',
-    '--fit',
-    action=argparse.BooleanOptionalAction,
-    help='If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)',
-)
-
-render_group.add_argument(
-    '--grid',
-    '-g',
-    action=argparse.BooleanOptionalAction,
-    help='generate a grid'
-)
-render_group.add_argument(
-    '--embedding_directory',
-    '--embedding_path',
-    dest='embedding_path',
-    default='embeddings',
-    type=str,
-    help='Path to a directory containing .bin and/or .pt files, or a single .bin/.pt file. You may use subdirectories. (default is ROOTDIR/embeddings)'
-)
-render_group.add_argument(
-    '--lora_directory',
-    dest='lora_path',
-    default='loras',
-    type=str,
-    help='Path to a directory containing LoRA files; subdirectories are not supported. (default is ROOTDIR/loras)'
-)
-render_group.add_argument(
-    '--embeddings',
-    action=argparse.BooleanOptionalAction,
-    default=True,
-    help='Enable embedding directory (default). Use --no-embeddings to disable.',
-)
-render_group.add_argument(
-    '--enable_image_debugging',
-    action='store_true',
-    help='Generates debugging image to display'
-)
-render_group.add_argument(
-    '--karras_max',
-    type=int,
-    default=None,
-    help="control the point at which the K* samplers will shift from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts). Set to 0 to use LatentDiffusion for all step values, and to a high value (e.g. 1000) to use Karras for all step values. [29]."
-)
-# Restoration related args
-postprocessing_group.add_argument(
-    '--no_restore',
-    dest='restore',
-    action='store_false',
-    help='Disable face restoration with GFPGAN or codeformer',
-)
-postprocessing_group.add_argument(
-    '--no_upscale',
-    dest='esrgan',
-    action='store_false',
-    help='Disable upscaling with ESRGAN',
-)
-postprocessing_group.add_argument(
-    '--esrgan_bg_tile',
-    type=int,
-    default=400,
-    help='Tile size for background sampler, 0 for no tile during testing. Default: 400.',
-)
-postprocessing_group.add_argument(
-    '--esrgan_denoise_str',
-    type=float,
-    default=0.75,
-    help='esrgan denoise str. 0 is no denoise, 1 is max denoise.  Default: 0.75',
-)
-postprocessing_group.add_argument(
-    '--gfpgan_model_path',
-    type=str,
-    default='./models/gfpgan/GFPGANv1.4.pth',
-    help='Indicates the path to the GFPGAN model',
-)
-web_server_group.add_argument(
-    '--web',
-    dest='web',
-    action='store_true',
-    help='Start in web server mode.',
-)
-web_server_group.add_argument(
-    '--web_develop',
-    dest='web_develop',
-    action='store_true',
-    help='Start in web server development mode.',
-)
-web_server_group.add_argument(
-    "--web_verbose",
-    action="store_true",
-    help="Enables verbose logging",
-)
-web_server_group.add_argument(
-    "--cors",
-    nargs="*",
-    type=str,
-    help="Additional allowed origins, comma-separated",
-)
-web_server_group.add_argument(
-    '--host',
-    type=str,
-    default='127.0.0.1',
-    help='Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.'
-)
-web_server_group.add_argument(
-    '--port',
-    type=int,
-    default='9090',
-    help='Web server: Port to listen on'
-)
-web_server_group.add_argument(
-    '--certfile',
-    type=str,
-    default=None,
-    help='Web server: Path to certificate file to use for SSL. Use together with --keyfile'
-)
-web_server_group.add_argument(
-    '--keyfile',
-    type=str,
-    default=None,
-    help='Web server: Path to private key file to use for SSL. Use together with --certfile'
-)
-web_server_group.add_argument(
-    '--gui',
-    dest='gui',
-    action='store_true',
-    help='Start InvokeAI GUI',
-)

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -25,8 +25,7 @@ from typing import Union
 import torch
 from safetensors.torch import load_file
 
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.globals import global_cache_dir, global_config_dir
 
 from .model_manager import ModelManager, SDLegacyType
 
@@ -47,7 +46,6 @@ from diffusers import (
     LDMTextToImagePipeline,
     LMSDiscreteScheduler,
     PNDMScheduler,
-    UniPCMultistepScheduler,
     StableDiffusionPipeline,
     UNet2DConditionModel,
 )
@@ -74,6 +72,7 @@ from transformers import (
 
 from ..stable_diffusion import StableDiffusionGeneratorPipeline
 
+
 def shave_segments(path, n_shave_prefix_segments=1):
     """
     Removes segments. Positive values shave the first segments, negative shave the last segments.
@@ -373,9 +372,9 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
     unet_key = "model.diffusion_model."
     # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
     if sum(k.startswith("model_ema") for k in keys) > 100:
-        logger.debug(f"Checkpoint {path} has both EMA and non-EMA weights.")
+        print(f"   | Checkpoint {path} has both EMA and non-EMA weights.")
         if extract_ema:
-            logger.debug("Extracting EMA weights (usually better for inference)")
+            print("   | Extracting EMA weights (usually better for inference)")
             for key in keys:
                 if key.startswith("model.diffusion_model"):
                     flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
@@ -393,8 +392,8 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
                             key
                         )
         else:
-            logger.debug(
-                "Extracting only the non-EMA weights (usually better for fine-tuning)"
+            print(
+                "   | Extracting only the non-EMA weights (usually better for fine-tuning)"
             )
 
     for key in keys:
@@ -842,7 +841,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
 
 def convert_ldm_clip_checkpoint(checkpoint):
     text_model = CLIPTextModel.from_pretrained(
-        "openai/clip-vit-large-patch14", cache_dir=InvokeAIAppConfig.get_config().cache_dir
+        "openai/clip-vit-large-patch14", cache_dir=global_cache_dir("hub")
     )
 
     keys = list(checkpoint.keys())
@@ -897,7 +896,7 @@ textenc_pattern = re.compile("|".join(protected.keys()))
 
 
 def convert_paint_by_example_checkpoint(checkpoint):
-    cache_dir = InvokeAIAppConfig.get_config().cache_dir
+    cache_dir = global_cache_dir("hub")
     config = CLIPVisionConfig.from_pretrained(
         "openai/clip-vit-large-patch14", cache_dir=cache_dir
     )
@@ -969,7 +968,7 @@ def convert_paint_by_example_checkpoint(checkpoint):
 
 
 def convert_open_clip_checkpoint(checkpoint):
-    cache_dir = InvokeAIAppConfig.get_config().cache_dir
+    cache_dir = global_cache_dir("hub")
     text_model = CLIPTextModel.from_pretrained(
         "stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir
     )
@@ -1092,8 +1091,6 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     :param vae: A diffusers VAE to load into the pipeline.
     :param vae_path: Path to a checkpoint VAE that will be converted into diffusers and loaded into the pipeline.
     """
-    config = InvokeAIAppConfig.get_config()
-    cache_dir = config.cache_dir
 
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
@@ -1107,6 +1104,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         else:
             checkpoint = load_file(checkpoint_path)
 
+        cache_dir = global_cache_dir("hub")
         pipeline_class = (
             StableDiffusionGeneratorPipeline
             if return_generator_pipeline
@@ -1117,7 +1115,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         if "global_step" in checkpoint:
             global_step = checkpoint["global_step"]
         else:
-            logger.debug("global_step key not found in model")
+            print("   | global_step key not found in model")
             global_step = None
 
         # sometimes there is a state_dict key and sometimes not
@@ -1130,23 +1128,25 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
 
             if model_type == SDLegacyType.V2_v:
                 original_config_file = (
-                    config.legacy_conf_path / "v2-inference-v.yaml"
+                    global_config_dir() / "stable-diffusion" / "v2-inference-v.yaml"
                 )
                 if global_step == 110000:
                     # v2.1 needs to upcast attention
                     upcast_attention = True
             elif model_type == SDLegacyType.V2_e:
                 original_config_file = (
-                    config.legacy_conf_path / "v2-inference.yaml"
+                    global_config_dir() / "stable-diffusion" / "v2-inference.yaml"
                 )
             elif model_type == SDLegacyType.V1_INPAINT:
                 original_config_file = (
-                    config.legacy_conf_path / "v1-inpainting-inference.yaml"
+                    global_config_dir()
+                    / "stable-diffusion"
+                    / "v1-inpainting-inference.yaml"
                 )
 
             elif model_type == SDLegacyType.V1:
                 original_config_file = (
-                    config.legacy_conf_path / "v1-inference.yaml"
+                    global_config_dir() / "stable-diffusion" / "v1-inference.yaml"
                 )
 
             else:
@@ -1208,8 +1208,6 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
             scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
         elif scheduler_type == "dpm":
             scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
-        elif scheduler_type == 'unipc':
-            scheduler = UniPCMultistepScheduler.from_config(scheduler.config)
         elif scheduler_type == "ddim":
             scheduler = scheduler
         else:
@@ -1231,15 +1229,15 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
         # If a replacement VAE path was specified, we'll incorporate that into
         # the checkpoint model and then convert it
         if vae_path:
-            logger.debug(f"Converting VAE {vae_path}")
+            print(f"   | Converting VAE {vae_path}")
             replace_checkpoint_vae(checkpoint,vae_path)
         # otherwise we use the original VAE, provided that
         # an externally loaded diffusers VAE was not passed
         elif not vae:
-            logger.debug("Using checkpoint model's original VAE")
+            print("   | Using checkpoint model's original VAE")
 
         if vae:
-            logger.debug("Using replacement diffusers VAE")
+            print("   | Using replacement diffusers VAE")
         else:  # convert the original or replacement VAE
             vae_config = create_vae_diffusers_config(
                 original_config, image_size=image_size
@@ -1298,7 +1296,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
             )
             safety_checker = StableDiffusionSafetyChecker.from_pretrained(
                 "CompVis/stable-diffusion-safety-checker",
-                cache_dir=cache_dir,
+                cache_dir=global_cache_dir("hub"),
             )
             feature_extractor = AutoFeatureExtractor.from_pretrained(
                 "CompVis/stable-diffusion-safety-checker", cache_dir=cache_dir

invokeai/backend/model_management/model_manager.py

@@ -11,33 +11,32 @@ import gc
 import hashlib
 import os
 import re
-import shutil
 import sys
 import textwrap
 import time
-import traceback
 import warnings
 from enum import Enum, auto
 from pathlib import Path
 from shutil import move, rmtree
-from typing import Any, Optional, Union, Callable, Dict, List, types
+from typing import Any, Optional, Union, Callable
 
 import safetensors
 import safetensors.torch
 import torch
 import transformers
-import invokeai.backend.util.logging as logger
 from diffusers import (
     AutoencoderKL,
     UNet2DConditionModel,
     SchedulerMixin,
     logging as dlogging,
-)
+)    
 from huggingface_hub import scan_cache_dir
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
 from picklescan.scanner import scan_file_path
 
+from invokeai.backend.globals import Globals, global_cache_dir
+
 from transformers import (
     CLIPTextModel,
     CLIPTokenizer,
@@ -49,13 +48,9 @@ from diffusers.pipelines.stable_diffusion.safety_checker import (
 from ..stable_diffusion import (
     StableDiffusionGeneratorPipeline,
 )
-from invokeai.app.services.config import InvokeAIAppConfig
-from ..install.model_install_backend import (
-    Dataset_path,
-    hf_download_with_resume,
-)
 from ..util import CUDA_DEVICE, ask_user, download_with_resume
 
+
 class SDLegacyType(Enum):
     V1 = auto()
     V1_INPAINT = auto()
@@ -72,7 +67,7 @@ class SDModelComponent(Enum):
     scheduler="scheduler"
     safety_checker="safety_checker"
     feature_extractor="feature_extractor"
-
+    
 DEFAULT_MAX_MODELS = 2
 
 class ModelManager(object):
@@ -80,8 +75,6 @@ class ModelManager(object):
     Model manager handles loading, caching, importing, deleting, converting, and editing models.
     """
 
-    logger: types.ModuleType = logger
-
     def __init__(
         self,
         config: OmegaConf | Path,
@@ -90,7 +83,6 @@ class ModelManager(object):
         max_loaded_models=DEFAULT_MAX_MODELS,
         sequential_offload=False,
         embedding_path: Path = None,
-        logger: types.ModuleType = logger,
     ):
         """
         Initialize with the path to the models.yaml config file or
@@ -104,7 +96,6 @@ class ModelManager(object):
         if not isinstance(config, DictConfig):
             config = OmegaConf.load(config)
         self.config = config
-        self.globals = InvokeAIAppConfig.get_config()
         self.precision = precision
         self.device = torch.device(device_type)
         self.max_loaded_models = max_loaded_models
@@ -113,7 +104,6 @@ class ModelManager(object):
         self.current_model = None
         self.sequential_offload = sequential_offload
         self.embedding_path = embedding_path
-        self.logger = logger
 
     def valid_model(self, model_name: str) -> bool:
         """
@@ -142,8 +132,8 @@ class ModelManager(object):
             )
 
         if not self.valid_model(model_name):
-            self.logger.error(
-                f'"{model_name}" is not a known model name. Please check your models.yaml file'
+            print(
+                f'** "{model_name}" is not a known model name. Please check your models.yaml file'
             )
             return self.current_model
 
@@ -154,7 +144,7 @@ class ModelManager(object):
 
         if model_name in self.models:
             requested_model = self.models[model_name]["model"]
-            self.logger.info(f"Retrieving model {model_name} from system RAM cache")
+            print(f">> Retrieving model {model_name} from system RAM cache")
             requested_model.ready()
             width = self.models[model_name]["width"]
             height = self.models[model_name]["height"]
@@ -187,7 +177,7 @@ class ModelManager(object):
         vae from the model currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.vae)
-
+                                   
     def get_model_tokenizer(self, model_name: str=None)->CLIPTokenizer:
         """Given a model name identified in models.yaml, load the model into
         GPU if necessary and return its assigned CLIPTokenizer. If no
@@ -195,12 +185,12 @@ class ModelManager(object):
         currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.tokenizer)
-
+                                   
     def get_model_unet(self, model_name: str=None)->UNet2DConditionModel:
         """Given a model name identified in models.yaml, load the model into
         GPU if necessary and return its assigned UNet2DConditionModel. If no model
         name is provided, return the UNet from the model
-        currently in the GPU.
+        currently in the GPU.  
         """
         return self._get_sub_model(model_name, SDModelComponent.unet)
 
@@ -227,7 +217,7 @@ class ModelManager(object):
         currently in the GPU.
         """
         return self._get_sub_model(model_name, SDModelComponent.scheduler)
-
+    
     def _get_sub_model(
             self,
             model_name: str=None,
@@ -297,7 +287,7 @@ class ModelManager(object):
         """
         # if we are converting legacy files automatically, then
         # there are no legacy ckpts!
-        if self.globals.ckpt_convert:
+        if Globals.ckpt_convert:
             return False
         info = self.model_info(model_name)
         if "weights" in info and info["weights"].endswith((".ckpt", ".safetensors")):
@@ -320,7 +310,7 @@ class ModelManager(object):
         models = {}
         for name in sorted(self.config, key=str.casefold):
             stanza = self.config[name]
-            
+
             # don't include VAEs in listing (legacy style)
             if "config" in stanza and "/VAE/" in stanza["config"]:
                 continue
@@ -389,7 +379,7 @@ class ModelManager(object):
         """
         omega = self.config
         if model_name not in omega:
-            self.logger.error(f"Unknown model {model_name}")
+            print(f"** Unknown model {model_name}")
             return
         # save these for use in deletion later
         conf = omega[model_name]
@@ -402,13 +392,13 @@ class ModelManager(object):
             self.stack.remove(model_name)
         if delete_files:
             if weights:
-                self.logger.info(f"Deleting file {weights}")
+                print(f"** Deleting file {weights}")
                 Path(weights).unlink(missing_ok=True)
             elif path:
-                self.logger.info(f"Deleting directory {path}")
+                print(f"** Deleting directory {path}")
                 rmtree(path, ignore_errors=True)
             elif repo_id:
-                self.logger.info(f"Deleting the cached model directory for {repo_id}")
+                print(f"** Deleting the cached model directory for {repo_id}")
                 self._delete_model_from_cache(repo_id)
 
     def add_model(
@@ -449,7 +439,7 @@ class ModelManager(object):
     def _load_model(self, model_name: str):
         """Load and initialize the model from configuration variables passed at object creation time"""
         if model_name not in self.config:
-            self.logger.error(
+            print(
                 f'"{model_name}" is not a known model name. Please check your models.yaml file'
             )
             return
@@ -467,7 +457,7 @@ class ModelManager(object):
         model_format = mconfig.get("format", "ckpt")
         if model_format == "ckpt":
             weights = mconfig.weights
-            self.logger.info(f"Loading {model_name} from {weights}")
+            print(f">> Loading {model_name} from {weights}")
             model, width, height, model_hash = self._load_ckpt_model(
                 model_name, mconfig
             )
@@ -483,15 +473,13 @@ class ModelManager(object):
 
         # usage statistics
         toc = time.time()
-        self.logger.info("Model loaded in " + "%4.2fs" % (toc - tic))
+        print(">> Model loaded in", "%4.2fs" % (toc - tic))
         if self._has_cuda():
-            self.logger.info(
-                "Max VRAM used to load the model: "+
-                "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9)
-            )
-            self.logger.info(
-                "Current VRAM usage: "+
-                "%4.2fG" % (torch.cuda.memory_allocated() / 1e9)
+            print(
+                ">> Max VRAM used to load the model:",
+                "%4.2fG" % (torch.cuda.max_memory_allocated() / 1e9),
+                "\n>> Current VRAM usage:"
+                "%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
             )
         return model, width, height, model_hash
 
@@ -499,21 +487,21 @@ class ModelManager(object):
         name_or_path = self.model_name_or_path(mconfig)
         using_fp16 = self.precision == "float16"
 
-        self.logger.info(f"Loading diffusers model from {name_or_path}")
+        print(f">> Loading diffusers model from {name_or_path}")
         if using_fp16:
-            self.logger.debug("Using faster float16 precision")
+            print("   | Using faster float16 precision")
         else:
-            self.logger.debug("Using more accurate float32 precision")
+            print("   | Using more accurate float32 precision")
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
-            safety_checker=None, local_files_only=not self.globals.internet_available
+            safety_checker=None, local_files_only=not Globals.internet_available
         )
         if "vae" in mconfig and mconfig["vae"] is not None:
             if vae := self._load_vae(mconfig["vae"]):
                 pipeline_args.update(vae=vae)
         if not isinstance(name_or_path, Path):
-            pipeline_args.update(cache_dir=self.globals.cache_dir)
+            pipeline_args.update(cache_dir=global_cache_dir("hub"))
         if using_fp16:
             pipeline_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
@@ -532,12 +520,11 @@ class ModelManager(object):
                     **fp_args,
                 )
             except OSError as e:
-                if str(e).startswith("fp16 is not a valid") or \
-                   'Invalid rev id: fp16' in str(e):
+                if str(e).startswith("fp16 is not a valid"):
                     pass
                 else:
-                    self.logger.error(
-                        f"An unexpected error occurred while downloading the model: {e})"
+                    print(
+                        f"** An unexpected error occurred while downloading the model: {e})"
                     )
             if pipeline:
                 break
@@ -555,7 +542,7 @@ class ModelManager(object):
         # square images???
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
-        self.logger.debug(f"Default image dimensions = {width} x {height}")
+        print(f"   | Default image dimensions = {width} x {height}")
 
         return pipeline, width, height, model_hash
 
@@ -566,24 +553,29 @@ class ModelManager(object):
         width = mconfig.width
         height = mconfig.height
 
-        root_dir = self.globals.root_dir
-        config = str(root_dir / config)
-        weights = str(root_dir / weights)
+        if not os.path.isabs(config):
+            config = os.path.join(Globals.root, config)
+        if not os.path.isabs(weights):
+            weights = os.path.normpath(os.path.join(Globals.root, weights))
 
         # Convert to diffusers and return a diffusers pipeline
-        self.logger.info(f"Converting legacy checkpoint {model_name} into a diffusers model...")
+        print(f">> Converting legacy checkpoint {model_name} into a diffusers model...")
 
         from . import load_pipeline_from_original_stable_diffusion_ckpt
 
         try:
             if self.list_models()[self.current_model]["status"] == "active":
                 self.offload_model(self.current_model)
-        except Exception:
+        except Exception as e:
             pass
 
         vae_path = None
         if vae:
-            vae_path = str(root_dir / vae)
+            vae_path = (
+                vae
+                if os.path.isabs(vae)
+                else os.path.normpath(os.path.join(Globals.root, vae))
+            )
         if self._has_cuda():
             torch.cuda.empty_cache()
         pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
@@ -615,7 +607,9 @@ class ModelManager(object):
             )
 
         if "path" in mconfig and mconfig["path"] is not None:
-            path = self.globals.root_dir / Path(mconfig["path"])
+            path = Path(mconfig["path"])
+            if not path.is_absolute():
+                path = Path(Globals.root, path).resolve()
             return path
         elif "repo_id" in mconfig:
             return mconfig["repo_id"]
@@ -630,7 +624,7 @@ class ModelManager(object):
         if model_name not in self.models:
             return
 
-        self.logger.info(f"Offloading {model_name} to CPU")
+        print(f">> Offloading {model_name} to CPU")
         model = self.models[model_name]["model"]
         model.offload_all()
         self.current_model = None
@@ -646,26 +640,30 @@ class ModelManager(object):
         and option to exit if an infected file is identified.
         """
         # scan model
-        self.logger.debug(f"Scanning Model: {model_name}")
+        print(f"   | Scanning Model: {model_name}")
         scan_result = scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
             if scan_result.infected_files == 1:
-                self.logger.critical(f"Issues Found In Model: {scan_result.issues_count}")
-                self.logger.critical("The model you are trying to load seems to be infected.")
-                self.logger.critical("For your safety, InvokeAI will not load this model.")
-                self.logger.critical("Please use checkpoints from trusted sources.")
-                self.logger.critical("Exiting InvokeAI")
+                print(f"\n### Issues Found In Model: {scan_result.issues_count}")
+                print(
+                    "### WARNING: The model you are trying to load seems to be infected."
+                )
+                print("### For your safety, InvokeAI will not load this model.")
+                print("### Please use checkpoints from trusted sources.")
+                print("### Exiting InvokeAI")
                 sys.exit()
             else:
-                self.logger.warning("InvokeAI was unable to scan the model you are using.")
+                print(
+                    "\n### WARNING: InvokeAI was unable to scan the model you are using."
+                )
                 model_safe_check_fail = ask_user(
                     "Do you want to to continue loading the model?", ["y", "n"]
                 )
                 if model_safe_check_fail.lower() != "y":
-                    self.logger.critical("Exiting InvokeAI")
+                    print("### Exiting InvokeAI")
                     sys.exit()
         else:
-            self.logger.debug("Model scanned ok")
+            print("   | Model scanned ok")
 
     def import_diffuser_model(
         self,
@@ -780,26 +778,28 @@ class ModelManager(object):
 
         """
         model_path: Path = None
-        thing = str(path_url_or_repo)  # to save typing
+        thing = path_url_or_repo  # to save typing
 
-        self.logger.info(f"Probing {thing} for import")
+        print(f">> Probing {thing} for import")
 
-        if str(thing).startswith(("http:", "https:", "ftp:")):
-            self.logger.info(f"{thing} appears to be a URL")
+        if thing.startswith(("http:", "https:", "ftp:")):
+            print(f"   | {thing} appears to be a URL")
             model_path = self._resolve_path(
                 thing, "models/ldm/stable-diffusion-v1"
             )  # _resolve_path does a download if needed
 
         elif Path(thing).is_file() and thing.endswith((".ckpt", ".safetensors")):
             if Path(thing).stem in ["model", "diffusion_pytorch_model"]:
-                self.logger.debug(f"{Path(thing).name} appears to be part of a diffusers model. Skipping import")
+                print(
+                    f"   | {Path(thing).name} appears to be part of a diffusers model. Skipping import"
+                )
                 return
             else:
-                self.logger.debug(f"{thing} appears to be a checkpoint file on disk")
+                print(f"   | {thing} appears to be a checkpoint file on disk")
                 model_path = self._resolve_path(thing, "models/ldm/stable-diffusion-v1")
 
         elif Path(thing).is_dir() and Path(thing, "model_index.json").exists():
-            self.logger.debug(f"{thing} appears to be a diffusers file on disk")
+            print(f"   | {thing} appears to be a diffusers file on disk")
             model_name = self.import_diffuser_model(
                 thing,
                 vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
@@ -810,32 +810,34 @@ class ModelManager(object):
 
         elif Path(thing).is_dir():
             if (Path(thing) / "model_index.json").exists():
-                self.logger.debug(f"{thing} appears to be a diffusers model.")
+                print(f"   | {thing} appears to be a diffusers model.")
                 model_name = self.import_diffuser_model(
                     thing, commit_to_conf=commit_to_conf
                 )
             else:
-                self.logger.debug(f"{thing} appears to be a directory. Will scan for models to import")
+                print(
+                    f"   |{thing} appears to be a directory. Will scan for models to import"
+                )
                 for m in list(Path(thing).rglob("*.ckpt")) + list(
                     Path(thing).rglob("*.safetensors")
                 ):
                     if model_name := self.heuristic_import(
-                            str(m),
-                            commit_to_conf=commit_to_conf,
-                            config_file_callback=config_file_callback,
+                        str(m), commit_to_conf=commit_to_conf
                     ):
-                        self.logger.info(f"{model_name} successfully imported")
+                        print(f" >> {model_name} successfully imported")
                 return model_name
 
         elif re.match(r"^[\w.+-]+/[\w.+-]+$", thing):
-            self.logger.debug(f"{thing} appears to be a HuggingFace diffusers repo_id")
+            print(f"   | {thing} appears to be a HuggingFace diffusers repo_id")
             model_name = self.import_diffuser_model(
                 thing, commit_to_conf=commit_to_conf
             )
             pipeline, _, _, _ = self._load_diffusers_model(self.config[model_name])
             return model_name
         else:
-            self.logger.warning(f"{thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id")
+            print(
+                f"** {thing}: Unknown thing. Please provide a URL, file path, directory or HuggingFace repo_id"
+            )
 
         # Model_path is set in the event of a legacy checkpoint file.
         # If not set, we're all done
@@ -843,7 +845,7 @@ class ModelManager(object):
             return
 
         if model_path.stem in self.config:  # already imported
-            self.logger.debug("Already imported. Skipping")
+            print("   | Already imported. Skipping")
             return model_path.stem
 
         # another round of heuristics to guess the correct config file.
@@ -859,30 +861,41 @@ class ModelManager(object):
             # look for a like-named .yaml file in same directory
             if model_path.with_suffix(".yaml").exists():
                 model_config_file = model_path.with_suffix(".yaml")
-                self.logger.debug(f"Using config file {model_config_file.name}")
+                print(f"   | Using config file {model_config_file.name}")
 
             else:
                 model_type = self.probe_model_type(checkpoint)
                 if model_type == SDLegacyType.V1:
-                    self.logger.debug("SD-v1 model detected")
-                    model_config_file = self.globals.legacy_conf_path / "v1-inference.yaml"
+                    print("   | SD-v1 model detected")
+                    model_config_file = Path(
+                        Globals.root, "configs/stable-diffusion/v1-inference.yaml"
+                    )
                 elif model_type == SDLegacyType.V1_INPAINT:
-                    self.logger.debug("SD-v1 inpainting model detected")
-                    model_config_file = self.globals.legacy_conf_path / "v1-inpainting-inference.yaml"
+                    print("   | SD-v1 inpainting model detected")
+                    model_config_file = Path(
+                        Globals.root,
+                        "configs/stable-diffusion/v1-inpainting-inference.yaml",
+                    )
                 elif model_type == SDLegacyType.V2_v:
-                    self.logger.debug("SD-v2-v model detected")
-                    model_config_file = self.globals.legacy_conf_path / "v2-inference-v.yaml"
+                    print("   | SD-v2-v model detected")
+                    model_config_file = Path(
+                        Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
+                    )
                 elif model_type == SDLegacyType.V2_e:
-                    self.logger.debug("SD-v2-e model detected")
-                    model_config_file = self.globals.legacy_conf_path / "v2-inference.yaml"
+                    print("   | SD-v2-e model detected")
+                    model_config_file = Path(
+                        Globals.root, "configs/stable-diffusion/v2-inference.yaml"
+                    )
                 elif model_type == SDLegacyType.V2:
-                    self.logger.warning(
-                        f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined."
+                    print(
+                        f"** {thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
                     )
+                    return
                 else:
-                    self.logger.warning(
-                        f"{thing} is a legacy checkpoint file but not a known Stable Diffusion model."
+                    print(
+                        f"** {thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
                     )
+                    return
 
         if not model_config_file and config_file_callback:
             model_config_file = config_file_callback(model_path)
@@ -896,10 +909,12 @@ class ModelManager(object):
         for suffix in ["pt", "ckpt", "safetensors"]:
             if (model_path.with_suffix(f".vae.{suffix}")).exists():
                 vae_path = model_path.with_suffix(f".vae.{suffix}")
-                self.logger.debug(f"Using VAE file {vae_path.name}")
+                print(f"   | Using VAE file {vae_path.name}")
         vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
 
-        diffuser_path = self.globals.root_dir / "models/converted_ckpts" / model_path.stem
+        diffuser_path = Path(
+            Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
+        )
         model_name = self.convert_and_import(
             model_path,
             diffusers_path=diffuser_path,
@@ -939,36 +954,35 @@ class ModelManager(object):
 
         from . import convert_ckpt_to_diffusers
 
+        if diffusers_path.exists():
+            print(
+                f"ERROR: The path {str(diffusers_path)} already exists. Please move or remove it and try again."
+            )
+            return
+
         model_name = model_name or diffusers_path.name
         model_description = model_description or f"Converted version of {model_name}"
-        
+        print(f"   | Converting {model_name} to diffusers (30-60s)")
         try:
-            if diffusers_path.exists():
-                self.logger.error(
-                    f"The path {str(diffusers_path)} already exists. Installing previously-converted path."
-                )
-            else:
-                self.logger.debug(f"Converting {model_name} to diffusers (30-60s)")
-
-                # By passing the specified VAE to the conversion function, the autoencoder
-                # will be built into the model rather than tacked on afterward via the config file
-                vae_model = None
-                if vae:
-                    vae_model = self._load_vae(vae)
-                    vae_path = None
-                convert_ckpt_to_diffusers(
-                    ckpt_path,
-                    diffusers_path,
-                    extract_ema=True,
-                    original_config_file=original_config_file,
-                    vae=vae_model,
-                    vae_path=vae_path,
-                    scan_needed=scan_needed,
-                )
-                self.logger.debug(
-                    f"Success. Converted model is now located at {str(diffusers_path)}"
-                )
-            self.logger.debug(f"Writing new config file entry for {model_name}")
+            # By passing the specified VAE to the conversion function, the autoencoder
+            # will be built into the model rather than tacked on afterward via the config file
+            vae_model = None
+            if vae:
+                vae_model = self._load_vae(vae)
+                vae_path = None
+            convert_ckpt_to_diffusers(
+                ckpt_path,
+                diffusers_path,
+                extract_ema=True,
+                original_config_file=original_config_file,
+                vae=vae_model,
+                vae_path=vae_path,
+                scan_needed=scan_needed,
+            )
+            print(
+                f"   | Success. Converted model is now located at {str(diffusers_path)}"
+            )
+            print(f"   | Writing new config file entry for {model_name}")
             new_config = dict(
                 path=str(diffusers_path),
                 description=model_description,
@@ -979,18 +993,17 @@ class ModelManager(object):
             self.add_model(model_name, new_config, True)
             if commit_to_conf:
                 self.commit(commit_to_conf)
-            self.logger.debug(f"Model {model_name} installed")
+            print("   | Conversion succeeded")
         except Exception as e:
-            self.logger.warning(f"Conversion failed: {str(e)}")
-            self.logger.warning(traceback.format_exc())
-            self.logger.warning(
-                "If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)"
+            print(f"** Conversion failed: {str(e)}")
+            print(
+                "** If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)"
             )
 
         return model_name
 
     def search_models(self, search_folder):
-        self.logger.info(f"Finding Models In: {search_folder}")
+        print(f">> Finding Models In: {search_folder}")
         models_folder_ckpt = Path(search_folder).glob("**/*.ckpt")
         models_folder_safetensors = Path(search_folder).glob("**/*.safetensors")
 
@@ -1014,8 +1027,8 @@ class ModelManager(object):
         num_loaded_models = len(self.models)
         if num_loaded_models >= self.max_loaded_models:
             least_recent_model = self._pop_oldest_model()
-            self.logger.info(
-                f"Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}"
+            print(
+                f">> Cache limit (max={self.max_loaded_models}) reached. Purging {least_recent_model}"
             )
             if least_recent_model is not None:
                 del self.models[least_recent_model]
@@ -1023,8 +1036,8 @@ class ModelManager(object):
 
     def print_vram_usage(self) -> None:
         if self._has_cuda:
-            self.logger.info(
-                "Current VRAM usage:"+
+            print(
+                ">> Current VRAM usage: ",
                 "%4.2fG" % (torch.cuda.memory_allocated() / 1e9),
             )
 
@@ -1034,7 +1047,9 @@ class ModelManager(object):
         """
         yaml_str = OmegaConf.to_yaml(self.config)
         if not os.path.isabs(config_file_path):
-            config_file_path = self.globals.model_conf_path
+            config_file_path = os.path.normpath(
+                os.path.join(Globals.root, config_file_path)
+            )
         tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp")
         with open(tmpfile, "w", encoding="utf-8") as outfile:
             outfile.write(self.preamble())
@@ -1066,8 +1081,7 @@ class ModelManager(object):
         """
         # Three transformer models to check: bert, clip and safety checker, and
         # the diffusers as well
-        config = InvokeAIAppConfig.get_config()
-        models_dir = config.root_dir / "models"
+        models_dir = Path(Globals.root, "models")
         legacy_locations = [
             Path(
                 models_dir,
@@ -1079,8 +1093,8 @@ class ModelManager(object):
                 "openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14",
             ),
         ]
-        legacy_cache_dir = config.cache_dir / "../diffusers"
-        legacy_locations.extend(list(legacy_cache_dir.glob("*")))
+        legacy_locations.extend(list(global_cache_dir("diffusers").glob("*")))
+
         legacy_layout = False
         for model in legacy_locations:
             legacy_layout = legacy_layout or model.exists()
@@ -1102,7 +1116,7 @@ class ModelManager(object):
 
         # transformer files get moved into the hub directory
         if cls._is_huggingface_hub_directory_present():
-            hub = config.cache_dir
+            hub = global_cache_dir("hub")
         else:
             hub = models_dir / "hub"
 
@@ -1112,10 +1126,10 @@ class ModelManager(object):
             dest = hub / model.stem
             if dest.exists() and not source.exists():
                 continue
-            cls.logger.info(f"{source} => {dest}")
+            print(f"** {source} => {dest}")
             if source.exists():
                 if dest.is_symlink():
-                    logger.warning(f"Found symlink at {dest.name}. Not migrating.")
+                    print(f"** Found symlink at {dest.name}. Not migrating.")
                 elif dest.exists():
                     if source.is_dir():
                         rmtree(source)
@@ -1132,7 +1146,7 @@ class ModelManager(object):
         ]
         for d in empty:
             os.rmdir(d)
-        cls.logger.info("Migration is done. Continuing...")
+        print("** Migration is done. Continuing...")
 
     def _resolve_path(
         self, source: Union[str, Path], dest_directory: str
@@ -1141,12 +1155,13 @@ class ModelManager(object):
         if str(source).startswith(("http:", "https:", "ftp:")):
             dest_directory = Path(dest_directory)
             if not dest_directory.is_absolute():
-                dest_directory = self.globals.root_dir / dest_directory
+                dest_directory = Globals.root / dest_directory
             dest_directory.mkdir(parents=True, exist_ok=True)
             resolved_path = download_with_resume(str(source), dest_directory)
         else:
-            source = self.globals.root_dir / source
-            resolved_path = source
+            if not os.path.isabs(source):
+                source = os.path.join(Globals.root, source)
+            resolved_path = Path(source)
         return resolved_path
 
     def _invalidate_cached_model(self, model_name: str) -> None:
@@ -1174,15 +1189,15 @@ class ModelManager(object):
 
     def _add_embeddings_to_model(self, model: StableDiffusionGeneratorPipeline):
         if self.embedding_path is not None:
-            self.logger.info(f"Loading embeddings from {self.embedding_path}")
+            print(f">> Loading embeddings from {self.embedding_path}")
             for root, _, files in os.walk(self.embedding_path):
                 for name in files:
                     ti_path = os.path.join(root, name)
                     model.textual_inversion_manager.load_textual_inversion(
                         ti_path, defer_injecting_tokens=True
                     )
-            self.logger.info(
-                f'Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
+            print(
+                f'>> Textual inversion triggers: {", ".join(sorted(model.textual_inversion_manager.get_all_trigger_strings()))}'
             )
 
     def _has_cuda(self) -> bool:
@@ -1196,7 +1211,7 @@ class ModelManager(object):
             path = name_or_path
         else:
             owner, repo = name_or_path.split("/")
-            path = self.globals.cache_dir / f"models--{owner}--{repo}"
+            path = Path(global_cache_dir("hub") / f"models--{owner}--{repo}")
         if not path.exists():
             return None
         hashpath = path / "checksum.sha256"
@@ -1204,7 +1219,7 @@ class ModelManager(object):
             with open(hashpath) as f:
                 hash = f.read()
             return hash
-        self.logger.debug("Calculating sha256 hash of model files")
+        print("   | Calculating sha256 hash of model files")
         tic = time.time()
         sha = hashlib.sha256()
         count = 0
@@ -1216,7 +1231,7 @@ class ModelManager(object):
                         sha.update(chunk)
         hash = sha.hexdigest()
         toc = time.time()
-        self.logger.debug(f"sha256 = {hash} ({count} files hashed in {toc - tic:4.2f}s)")
+        print(f"   | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
         with open(hashpath, "w") as f:
             f.write(hash)
         return hash
@@ -1234,13 +1249,13 @@ class ModelManager(object):
                 hash = f.read()
             return hash
 
-        self.logger.debug("Calculating sha256 hash of weights file")
+        print("   | Calculating sha256 hash of weights file")
         tic = time.time()
         sha = hashlib.sha256()
         sha.update(data)
         hash = sha.hexdigest()
         toc = time.time()
-        self.logger.debug(f"sha256 = {hash} "+"(%4.2fs)" % (toc - tic))
+        print(f">> sha256 = {hash}", "(%4.2fs)" % (toc - tic))
 
         with open(hashpath, "w") as f:
             f.write(hash)
@@ -1257,16 +1272,16 @@ class ModelManager(object):
         using_fp16 = self.precision == "float16"
 
         vae_args.update(
-            cache_dir=self.globals.cache_dir,
-            local_files_only=not self.globals.internet_available,
+            cache_dir=global_cache_dir("hub"),
+            local_files_only=not Globals.internet_available,
         )
 
-        self.logger.debug(f"Loading diffusers VAE from {name_or_path}")
+        print(f"   | Loading diffusers VAE from {name_or_path}")
         if using_fp16:
             vae_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
         else:
-            self.logger.debug("Using more accurate float32 precision")
+            print("   | Using more accurate float32 precision")
             fp_args_list = [{}]
 
         vae = None
@@ -1290,13 +1305,13 @@ class ModelManager(object):
                 break
 
         if not vae and deferred_error:
-            self.logger.warning(f"Could not load VAE {name_or_path}: {str(deferred_error)}")
+            print(f"** Could not load VAE {name_or_path}: {str(deferred_error)}")
 
         return vae
 
-    @classmethod
-    def _delete_model_from_cache(cls,repo_id):
-        cache_info = scan_cache_dir(InvokeAIAppConfig.get_config().cache_dir)
+    @staticmethod
+    def _delete_model_from_cache(repo_id):
+        cache_info = scan_cache_dir(global_cache_dir("hub"))
 
         # I'm sure there is a way to do this with comprehensions
         # but the code quickly became incomprehensible!
@@ -1306,202 +1321,19 @@ class ModelManager(object):
                 for revision in repo.revisions:
                     hashes_to_delete.add(revision.commit_hash)
         strategy = cache_info.delete_revisions(*hashes_to_delete)
-        cls.logger.warning(
-            f"Deletion of this model is expected to free {strategy.expected_freed_size_str}"
+        print(
+            f"** Deletion of this model is expected to free {strategy.expected_freed_size_str}"
         )
         strategy.execute()
 
     @staticmethod
     def _abs_path(path: str | Path) -> Path:
-        globals = InvokeAIAppConfig.get_config()
         if path is None or Path(path).is_absolute():
             return path
-        return Path(globals.root_dir, path).resolve()
+        return Path(Globals.root, path).resolve()
 
     @staticmethod
     def _is_huggingface_hub_directory_present() -> bool:
         return (
             os.getenv("HF_HOME") is not None or os.getenv("XDG_CACHE_HOME") is not None
         )
-
-    def list_lora_models(self)->Dict[str,bool]:
-        '''Return a dict of installed lora models; key is either the shortname
-        defined in INITIAL_MODELS, or the basename of the file in the LoRA
-        directory. Value is True if installed'''
-
-        models = OmegaConf.load(Dataset_path).get('lora') or {}
-        installed_models = {x: False for x in models.keys()}
-        
-        dir = self.globals.lora_path
-        installed_models = dict()
-        for root, dirs, files in os.walk(dir):
-            for name in files:
-                if Path(name).suffix not in ['.safetensors','.ckpt','.pt','.bin']:
-                    continue
-                if name == 'pytorch_lora_weights.bin':
-                    name = Path(root,name).parent.stem #Path(root,name).stem
-                else:
-                    name = Path(name).stem
-                installed_models.update({name: True})
-
-        return installed_models
-    
-    def install_lora_models(self, model_names: list[str], access_token:str=None):
-        '''Download list of LoRA/LyCORIS models'''
-        
-        short_names = OmegaConf.load(Dataset_path).get('lora') or {}
-        for name in model_names:
-            name = short_names.get(name) or name
-
-            # HuggingFace style LoRA
-            if re.match(r"^[\w.+-]+/([\w.+-]+)$", name):
-                self.logger.info(f'Downloading LoRA/LyCORIS model {name}')
-                _,dest_dir = name.split("/")
-                
-                hf_download_with_resume(
-                    repo_id = name,
-                    model_dir = self.globals.lora_path / dest_dir,
-                    model_name = 'pytorch_lora_weights.bin',
-                    access_token = access_token,
-                )
-
-            elif name.startswith(("http:", "https:", "ftp:")):
-                download_with_resume(name, self.globals.lora_path)
-
-            else:
-                self.logger.error(f"Unknown repo_id or URL: {name}")
-    
-    def delete_lora_models(self, model_names: List[str]):
-        '''Remove the list of lora models'''
-        for name in model_names:
-            file_or_directory = self.globals.lora_path / name
-            if file_or_directory.is_dir():
-                self.logger.info(f'Purging LoRA/LyCORIS {name}')
-                shutil.rmtree(str(file_or_directory))
-            else:
-                for path in self.globals.lora_path.glob(f'{name}.*'):
-                    self.logger.info(f'Purging LoRA/LyCORIS {name}')
-                    path.unlink()
-                
-    def list_ti_models(self)->Dict[str,bool]:
-        '''Return a dict of installed textual models; key is either the shortname
-        defined in INITIAL_MODELS, or the basename of the file in the LoRA
-        directory. Value is True if installed'''
-
-        models = OmegaConf.load(Dataset_path).get('textual_inversion') or {}
-        installed_models = {x: False for x in models.keys()}
-        
-        dir = self.globals.embedding_path
-        for root, dirs, files in os.walk(dir):
-            for name in files:
-                if not Path(name).suffix in ['.bin','.pt','.ckpt','.safetensors']:
-                    continue
-                if name == 'learned_embeds.bin':
-                    name = Path(root,name).parent.stem #Path(root,name).stem
-                else:
-                    name = Path(name).stem
-                installed_models.update({name: True})
-        return installed_models
-
-    def install_ti_models(self, model_names: list[str], access_token: str=None):
-        '''Download list of textual inversion embeddings'''
-
-        short_names = OmegaConf.load(Dataset_path).get('textual_inversion') or {}
-        for name in model_names:
-            name = short_names.get(name) or name
-            
-            if re.match(r"^[\w.+-]+/([\w.+-]+)$", name):
-                self.logger.info(f'Downloading Textual Inversion embedding {name}')
-                _,dest_dir = name.split("/")
-                hf_download_with_resume(
-                    repo_id = name,
-                    model_dir = self.globals.embedding_path / dest_dir,
-                    model_name = 'learned_embeds.bin',
-                    access_token = access_token
-                )
-            elif name.startswith(('http:','https:','ftp:')):
-                download_with_resume(name, self.globals.embedding_path)
-            else:
-                self.logger.error(f'{name} does not look like either a HuggingFace repo_id or a downloadable URL')
-    
-    def delete_ti_models(self, model_names: list[str]):
-        '''Remove TI embeddings from disk'''
-        for name in model_names:
-            file_or_directory = self.globals.embedding_path / name
-            if file_or_directory.is_dir():
-                self.logger.info(f'Purging textual inversion embedding {name}')
-                shutil.rmtree(str(file_or_directory))
-            else:
-                for path in self.globals.embedding_path.glob(f'{name}.*'):
-                    self.logger.info(f'Purging textual inversion embedding {name}')
-                    path.unlink()
-
-    def list_controlnet_models(self)->Dict[str,bool]:
-        '''Return a dict of installed controlnet models; key is repo_id or short name
-        of model (defined in INITIAL_MODELS), and value is True if installed'''
-
-        cn_models = OmegaConf.load(Dataset_path).get('controlnet') or {}
-        installed_models = {x: False for x in cn_models.keys()}
-        
-        cn_dir = self.globals.controlnet_path
-        for root, dirs, files in os.walk(cn_dir):
-            for name in dirs:
-                if Path(root, name, '.download_complete').exists():
-                    installed_models.update({name.replace('--','/'): True})
-        return installed_models
-
-    def install_controlnet_models(self, model_names: list[str], access_token: str=None):
-        '''Download list of controlnet models; provide either repo_id or short name listed in INITIAL_MODELS.yaml'''
-        short_names = OmegaConf.load(Dataset_path).get('controlnet') or {}
-        dest_dir = self.globals.controlnet_path
-        dest_dir.mkdir(parents=True,exist_ok=True)
-        
-        # The model file may be fp32 or fp16, and may be either a
-        # .bin file or a .safetensors. We try each until we get one,
-        # preferring 'fp16' if using half precision, and preferring
-        # safetensors over over bin.
-        precisions = ['.fp16',''] if self.precision=='float16' else ['']
-        formats = ['.safetensors','.bin']
-        possible_filenames = list()
-        for p in precisions:
-            for f in formats:
-                possible_filenames.append(Path(f'diffusion_pytorch_model{p}{f}'))
-        
-        for directory_name in model_names:
-            repo_id = short_names.get(directory_name) or directory_name
-            safe_name = directory_name.replace('/','--')
-            self.logger.info(f'Downloading ControlNet model {directory_name} ({repo_id})')
-            hf_download_with_resume(
-                repo_id = repo_id,
-                model_dir = dest_dir / safe_name,
-                model_name = 'config.json',
-                access_token = access_token
-            )
-
-            path = None
-            for filename in possible_filenames:
-                suffix = filename.suffix
-                dest_filename = Path(f'diffusion_pytorch_model{suffix}')
-                self.logger.info(f'Checking availability of {directory_name}/{filename}...')
-                path = hf_download_with_resume(
-                    repo_id = repo_id,
-                    model_dir = dest_dir / safe_name,
-                    model_name = str(filename),
-                    access_token = access_token,
-                    model_dest = Path(dest_dir, safe_name, dest_filename),
-                )
-                if path:
-                    (path.parent / '.download_complete').touch()
-                    break
-
-    def delete_controlnet_models(self, model_names: List[str]):
-        '''Remove the list of controlnet models'''
-        for name in model_names:
-            safe_name = name.replace('/','--')
-            directory = self.globals.controlnet_path / safe_name
-            if directory.exists():
-                self.logger.info(f'Purging controlnet model {name}')
-                shutil.rmtree(str(directory))
-
-
-        

invokeai/backend/prompting/conditioning.py

@@ -16,59 +16,66 @@ from compel.prompt_parser import (
     FlattenedPrompt,
     Fragment,
     PromptParser,
-    Conjunction,
 )
 
-import invokeai.backend.util.logging as logger
+from invokeai.backend.globals import Globals
 
-from invokeai.app.services.config import InvokeAIAppConfig
 from ..stable_diffusion import InvokeAIDiffuserComponent
 from ..util import torch_dtype
 
-config = InvokeAIAppConfig.get_config()
 
-def get_uc_and_c_and_ec(prompt_string,
-                        model: InvokeAIDiffuserComponent,
-                        log_tokens=False, skip_normalize_legacy_blend=False):
+def get_uc_and_c_and_ec(
+    prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False
+):
     # lazy-load any deferred textual inversions.
     # this might take a couple of seconds the first time a textual inversion is used.
-    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(prompt_string)
+    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
+        prompt_string
+    )
 
-    compel = Compel(tokenizer=model.tokenizer,
-                    text_encoder=model.text_encoder,
-                    textual_inversion_manager=model.textual_inversion_manager,
-                    dtype_for_device_getter=torch_dtype,
-                    truncate_long_prompts=False,
-                   )
+    tokenizer = model.tokenizer
+    compel = Compel(
+        tokenizer=tokenizer,
+        text_encoder=model.text_encoder,
+        textual_inversion_manager=model.textual_inversion_manager,
+        dtype_for_device_getter=torch_dtype,
+        truncate_long_prompts=False
+    )
 
     # get rid of any newline characters
     prompt_string = prompt_string.replace("\n", " ")
-    positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
-
-    legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
-    positive_conjunction: Conjunction
+    (
+        positive_prompt_string,
+        negative_prompt_string,
+    ) = split_prompt_to_positive_and_negative(prompt_string)
+    legacy_blend = try_parse_legacy_blend(
+        positive_prompt_string, skip_normalize_legacy_blend
+    )
+    positive_prompt: Union[FlattenedPrompt, Blend]
     if legacy_blend is not None:
-        positive_conjunction = legacy_blend
+        positive_prompt = legacy_blend
     else:
-        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
-    positive_prompt = positive_conjunction.prompts[0]
+        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
+    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
+        negative_prompt_string
+    )
 
-    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
-    negative_prompt: FlattenedPrompt | Blend = negative_conjunction.prompts[0]
-
-    tokens_count = get_max_token_count(model.tokenizer, positive_prompt)
-    if log_tokens or config.log_tokenization:
-        log_tokenization(positive_prompt, negative_prompt, tokenizer=model.tokenizer)
+    if log_tokens or getattr(Globals, "log_tokenization", False):
+        log_tokenization(positive_prompt, negative_prompt, tokenizer=tokenizer)
 
     c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
     uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
     [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
 
-    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
-                                                         cross_attention_control_args=options.get(
-                                                             'cross_attention_control', None))
+    tokens_count = get_max_token_count(tokenizer, positive_prompt)
+
+    ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+        tokens_count_including_eos_bos=tokens_count,
+        cross_attention_control_args=options.get("cross_attention_control", None),
+    )
     return uc, c, ec
 
+
 def get_prompt_structure(
     prompt_string, skip_normalize_legacy_blend: bool = False
 ) -> (Union[FlattenedPrompt, Blend], FlattenedPrompt):
@@ -79,17 +86,18 @@ def get_prompt_structure(
     legacy_blend = try_parse_legacy_blend(
         positive_prompt_string, skip_normalize_legacy_blend
     )
-    positive_prompt: Conjunction
+    positive_prompt: Union[FlattenedPrompt, Blend]
     if legacy_blend is not None:
-        positive_conjunction = legacy_blend
+        positive_prompt = legacy_blend
     else:
-        positive_conjunction = Compel.parse_prompt_string(positive_prompt_string)
-    positive_prompt = positive_conjunction.prompts[0]
-    negative_conjunction = Compel.parse_prompt_string(negative_prompt_string)
-    negative_prompt: FlattenedPrompt|Blend = negative_conjunction.prompts[0]
+        positive_prompt = Compel.parse_prompt_string(positive_prompt_string)
+    negative_prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(
+        negative_prompt_string
+    )
 
     return positive_prompt, negative_prompt
 
+
 def get_max_token_count(
     tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
 ) -> int:
@@ -154,8 +162,8 @@ def log_tokenization(
     negative_prompt: Union[Blend, FlattenedPrompt],
     tokenizer,
 ):
-    logger.info(f"[TOKENLOG] Parsed Prompt: {positive_prompt}")
-    logger.info(f"[TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
+    print(f"\n>> [TOKENLOG] Parsed Prompt: {positive_prompt}")
+    print(f"\n>> [TOKENLOG] Parsed Negative Prompt: {negative_prompt}")
 
     log_tokenization_for_prompt_object(positive_prompt, tokenizer)
     log_tokenization_for_prompt_object(
@@ -229,28 +237,29 @@ def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_t
             usedTokens += 1
 
     if usedTokens > 0:
-        logger.info(f'[TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
-        logger.debug(f"{tokenized}\x1b[0m")
+        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
+        print(f"{tokenized}\x1b[0m")
 
     if discarded != "":
-        logger.info(f"[TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
-        logger.debug(f"{discarded}\x1b[0m")
+        print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
+        print(f"{discarded}\x1b[0m")
 
-def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Conjunction]:
+
+def try_parse_legacy_blend(text: str, skip_normalize: bool = False) -> Optional[Blend]:
     weighted_subprompts = split_weighted_subprompts(text, skip_normalize=skip_normalize)
     if len(weighted_subprompts) <= 1:
         return None
     strings = [x[0] for x in weighted_subprompts]
+    weights = [x[1] for x in weighted_subprompts]
 
     pp = PromptParser()
     parsed_conjunctions = [pp.parse_conjunction(x) for x in strings]
-    flattened_prompts = []
-    weights = []
-    for i, x in enumerate(parsed_conjunctions):
-        if len(x.prompts)>0:
-            flattened_prompts.append(x.prompts[0])
-            weights.append(weighted_subprompts[i][1])
-    return Conjunction([Blend(prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize)])
+    flattened_prompts = [x.prompts[0] for x in parsed_conjunctions]
+
+    return Blend(
+        prompts=flattened_prompts, weights=weights, normalize_weights=not skip_normalize
+    )
+
 
 def split_weighted_subprompts(text, skip_normalize=False) -> list:
     """
@@ -282,14 +291,12 @@ def split_weighted_subprompts(text, skip_normalize=False) -> list:
         (match.group("prompt").replace("\\:", ":"), float(match.group("weight") or 1))
         for match in re.finditer(prompt_parser, text)
     ]
-    if len(parsed_prompts) == 0:
-        return []
     if skip_normalize:
         return parsed_prompts
     weight_sum = sum(map(lambda x: x[1], parsed_prompts))
     if weight_sum == 0:
-        logger.warning(
-            "Subprompt weights add up to zero. Discarding and using even weights instead."
+        print(
+            "* Warning: Subprompt weights add up to zero. Discarding and using even weights instead."
         )
         equal_weight = 1 / max(len(parsed_prompts), 1)
         return [(x[0], equal_weight) for x in parsed_prompts]

invokeai/backend/restoration/base.py

@@ -1,5 +1,3 @@
-import invokeai.backend.util.logging as logger
-
 class Restoration:
     def __init__(self) -> None:
         pass
@@ -10,17 +8,17 @@ class Restoration:
         # Load GFPGAN
         gfpgan = self.load_gfpgan(gfpgan_model_path)
         if gfpgan.gfpgan_model_exists:
-            logger.info("GFPGAN Initialized")
+            print(">> GFPGAN Initialized")
         else:
-            logger.info("GFPGAN Disabled")
+            print(">> GFPGAN Disabled")
             gfpgan = None
 
         # Load CodeFormer
         codeformer = self.load_codeformer()
         if codeformer.codeformer_model_exists:
-            logger.info("CodeFormer Initialized")
+            print(">> CodeFormer Initialized")
         else:
-            logger.info("CodeFormer Disabled")
+            print(">> CodeFormer Disabled")
             codeformer = None
 
         return gfpgan, codeformer
@@ -41,5 +39,5 @@ class Restoration:
         from .realesrgan import ESRGAN
 
         esrgan = ESRGAN(esrgan_bg_tile)
-        logger.info("ESRGAN Initialized")
+        print(">> ESRGAN Initialized")
         return esrgan

invokeai/backend/restoration/codeformer.py

@@ -5,8 +5,7 @@ import warnings
 import numpy as np
 import torch
 
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
+from ..globals import Globals
 
 pretrained_model_url = (
     "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
@@ -17,19 +16,19 @@ class CodeFormerRestoration:
     def __init__(
         self, codeformer_dir="models/codeformer", codeformer_model_path="codeformer.pth"
     ) -> None:
+        if not os.path.isabs(codeformer_dir):
+            codeformer_dir = os.path.join(Globals.root, codeformer_dir)
 
-        self.globals = InvokeAIAppConfig.get_config()
-        codeformer_dir = self.globals.root_dir / codeformer_dir
-        self.model_path = codeformer_dir / codeformer_model_path
-        self.codeformer_model_exists = self.model_path.exists()
+        self.model_path = os.path.join(codeformer_dir, codeformer_model_path)
+        self.codeformer_model_exists = os.path.isfile(self.model_path)
 
         if not self.codeformer_model_exists:
-            logger.error("NOT FOUND: CodeFormer model not found at " + self.model_path)
+            print("## NOT FOUND: CodeFormer model not found at " + self.model_path)
         sys.path.append(os.path.abspath(codeformer_dir))
 
     def process(self, image, strength, device, seed=None, fidelity=0.75):
         if seed is not None:
-            logger.info(f"CodeFormer - Restoring Faces for image seed:{seed}")
+            print(f">> CodeFormer - Restoring Faces for image seed:{seed}")
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=UserWarning)
@@ -71,7 +70,9 @@ class CodeFormerRestoration:
                 upscale_factor=1,
                 use_parse=True,
                 device=device,
-                model_rootpath = self.globals.root_dir / "gfpgan" / "weights"
+                model_rootpath=os.path.join(
+                    Globals.root, "models", "gfpgan", "weights"
+                ),
             )
             face_helper.clean_all()
             face_helper.read_image(bgr_image_array)
@@ -96,7 +97,7 @@ class CodeFormerRestoration:
                     del output
                     torch.cuda.empty_cache()
                 except RuntimeError as error:
-                    logger.error(f"Failed inference for CodeFormer: {error}.")
+                    print(f"\tFailed inference for CodeFormer: {error}.")
                     restored_face = cropped_face
 
                 restored_face = restored_face.astype("uint8")

invokeai/backend/restoration/gfpgan.py

@@ -6,19 +6,20 @@ import numpy as np
 import torch
 from PIL import Image
 
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.globals import Globals
+
 
 class GFPGAN:
     def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
-        self.globals = InvokeAIAppConfig.get_config()
         if not os.path.isabs(gfpgan_model_path):
-            gfpgan_model_path = self.globals.root_dir / gfpgan_model_path
+            gfpgan_model_path = os.path.abspath(
+                os.path.join(Globals.root, gfpgan_model_path)
+            )
         self.model_path = gfpgan_model_path
         self.gfpgan_model_exists = os.path.isfile(self.model_path)
 
         if not self.gfpgan_model_exists:
-            logger.error("NOT FOUND: GFPGAN model not found at " + self.model_path)
+            print("## NOT FOUND: GFPGAN model not found at " + self.model_path)
             return None
 
     def model_exists(self):
@@ -26,13 +27,13 @@ class GFPGAN:
 
     def process(self, image, strength: float, seed: str = None):
         if seed is not None:
-            logger.info(f"GFPGAN - Restoring Faces for image seed:{seed}")
+            print(f">> GFPGAN - Restoring Faces for image seed:{seed}")
 
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=UserWarning)
             cwd = os.getcwd()
-            os.chdir(self.globals.root_dir / 'models')
+            os.chdir(os.path.join(Globals.root, "models"))
             try:
                 from gfpgan import GFPGANer
 
@@ -46,14 +47,14 @@ class GFPGAN:
             except Exception:
                 import traceback
 
-                logger.error("Error loading GFPGAN:", file=sys.stderr)
+                print(">> Error loading GFPGAN:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
             os.chdir(cwd)
 
         if self.gfpgan is None:
-            logger.warning("WARNING: GFPGAN not initialized.")
-            logger.warning(
-                f"Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
+            print(f">> WARNING: GFPGAN not initialized.")
+            print(
+                f">> Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
             )
 
         image = image.convert("RGB")

invokeai/backend/restoration/outcrop.py

@@ -1,7 +1,7 @@
 import math
 
 from PIL import Image
-import invokeai.backend.util.logging as logger
+
 
 class Outcrop(object):
     def __init__(
@@ -82,7 +82,7 @@ class Outcrop(object):
             pixels = extents[direction]
             # round pixels up to the nearest 64
             pixels = math.ceil(pixels / 64) * 64
-            logger.info(f"extending image {direction}ward by {pixels} pixels")
+            print(f">> extending image {direction}ward by {pixels} pixels")
             image = self._rotate(image, direction)
             image = self._extend(image, pixels)
             image = self._rotate(image, direction, reverse=True)

invokeai/backend/restoration/realesrgan.py

@@ -1,3 +1,4 @@
+import os
 import warnings
 
 import numpy as np
@@ -5,14 +6,18 @@ import torch
 from PIL import Image
 from PIL.Image import Image as ImageType
 
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
-config = InvokeAIAppConfig.get_config()
+from invokeai.backend.globals import Globals
+
 
 class ESRGAN:
     def __init__(self, bg_tile_size=400) -> None:
         self.bg_tile_size = bg_tile_size
 
+        if not torch.cuda.is_available():  # CPU or MPS on M1
+            use_half_precision = False
+        else:
+            use_half_precision = True
+
     def load_esrgan_bg_upsampler(self, denoise_str):
         if not torch.cuda.is_available():  # CPU or MPS on M1
             use_half_precision = False
@@ -30,8 +35,12 @@ class ESRGAN:
             upscale=4,
             act_type="prelu",
         )
-        model_path = config.root_dir / "models/realesrgan/realesr-general-x4v3.pth"
-        wdn_model_path = config.root_dir / "models/realesrgan/realesr-general-wdn-x4v3.pth"
+        model_path = os.path.join(
+            Globals.root, "models/realesrgan/realesr-general-x4v3.pth"
+        )
+        wdn_model_path = os.path.join(
+            Globals.root, "models/realesrgan/realesr-general-wdn-x4v3.pth"
+        )
         scale = 4
 
         bg_upsampler = RealESRGANer(
@@ -65,16 +74,16 @@ class ESRGAN:
                 import sys
                 import traceback
 
-                logger.error("Error loading Real-ESRGAN:")
+                print(">> Error loading Real-ESRGAN:", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
         if upsampler_scale == 0:
-            logger.warning("Real-ESRGAN: Invalid scaling option. Image not upscaled.")
+            print(">> Real-ESRGAN: Invalid scaling option. Image not upscaled.")
             return image
 
         if seed is not None:
-            logger.info(
-                f"Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
+            print(
+                f">> Real-ESRGAN Upscaling seed:{seed}, scale:{upsampler_scale}x, tile:{self.bg_tile_size}, denoise:{denoise_str}"
             )
         # ESRGAN outputs images with partial transparency if given RGBA images; convert to RGB
         image = image.convert("RGB")

invokeai/backend/safety_checker.py

@@ -14,12 +14,9 @@ from PIL import Image, ImageFilter
 from transformers import AutoFeatureExtractor
 
 import invokeai.assets.web as web_assets
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
+from .globals import global_cache_dir
 from .util import CPU_DEVICE
 
-config = InvokeAIAppConfig.get_config()
-
 class SafetyChecker(object):
     CAUTION_IMG = "caution.png"
     
@@ -28,10 +25,10 @@ class SafetyChecker(object):
         caution = Image.open(path)
         self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
         self.device = device
-
+        
         try:
             safety_model_id = "CompVis/stable-diffusion-safety-checker"
-            safety_model_path = config.cache_dir
+            safety_model_path = global_cache_dir("hub")
             self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(
                 safety_model_id,
                 local_files_only=True,
@@ -43,8 +40,8 @@ class SafetyChecker(object):
                 cache_dir=safety_model_path,
             )
         except Exception:
-            logger.error(
-                "An error was encountered while installing the safety checker:"
+            print(
+                "** An error was encountered while installing the safety checker:"
             )
             print(traceback.format_exc())
 
@@ -68,8 +65,8 @@ class SafetyChecker(object):
         )
         self.safety_checker.to(CPU_DEVICE) # offload
         if has_nsfw_concept[0]:
-            logger.warning(
-                "An image with potential non-safe content has been detected. A blurred image will be returned."
+            print(
+                "** An image with potential non-safe content has been detected. A blurred image will be returned. **"
             )
             return self.blur(image)
         else:

invokeai/backend/stable_diffusion/concepts_lib.py

@@ -17,17 +17,15 @@ from huggingface_hub import (
     hf_hub_url,
 )
 
-from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.app.services.config import InvokeAIAppConfig
-logger = InvokeAILogger.getLogger()
+from invokeai.backend.globals import Globals
+
 
 class HuggingFaceConceptsLibrary(object):
     def __init__(self, root=None):
         """
         Initialize the Concepts object. May optionally pass a root directory.
         """
-        self.config = InvokeAIAppConfig.get_config()
-        self.root = root or self.config.root
+        self.root = root or Globals.root
         self.hf_api = HfApi()
         self.local_concepts = dict()
         self.concept_list = None
@@ -59,7 +57,7 @@ class HuggingFaceConceptsLibrary(object):
                 self.concept_list.extend(list(local_concepts_to_add))
                 return self.concept_list
             return self.concept_list
-        elif self.config.internet_available is True:
+        elif Globals.internet_available is True:
             try:
                 models = self.hf_api.list_models(
                     filter=ModelFilter(model_name="sd-concepts-library/")
@@ -68,11 +66,11 @@ class HuggingFaceConceptsLibrary(object):
                 # when init, add all in dir. when not init, add only concepts added between init and now
                 self.concept_list.extend(list(local_concepts_to_add))
             except Exception as e:
-                logger.warning(
-                    f"Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}."
+                print(
+                    f" ** WARNING: Hugging Face textual inversion concepts libraries could not be loaded. The error was {str(e)}."
                 )
-                logger.warning(
-                    "You may load .bin and .pt file(s) manually using the --embedding_directory argument."
+                print(
+                    " ** You may load .bin and .pt file(s) manually using the --embedding_directory argument."
                 )
             return self.concept_list
         else: 
@@ -85,7 +83,7 @@ class HuggingFaceConceptsLibrary(object):
         be downloaded.
         """
         if not concept_name in self.list_concepts():
-            logger.warning(
+            print(
                 f"{concept_name} is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept."
             )
             return None
@@ -223,7 +221,7 @@ class HuggingFaceConceptsLibrary(object):
             if chunk == 0:
                 bytes += total
 
-        logger.info(f"Downloading {repo_id}...", end="")
+        print(f">> Downloading {repo_id}...", end="")
         try:
             for file in (
                 "README.md",
@@ -237,22 +235,22 @@ class HuggingFaceConceptsLibrary(object):
                 )
         except ul_error.HTTPError as e:
             if e.code == 404:
-                logger.warning(
+                print(
                     f"Concept {concept_name} is not known to the Hugging Face library. Generation will continue without the concept."
                 )
             else:
-                logger.warning(
+                print(
                     f"Failed to download {concept_name}/{file} ({str(e)}. Generation will continue without the concept.)"
                 )
             os.rmdir(dest)
             return False
         except ul_error.URLError as e:
-            logger.error(
-                f"an error occurred while downloading {concept_name}: {str(e)}. This may reflect a network issue. Generation will continue without the concept."
+            print(
+                f"ERROR while downloading {concept_name}: {str(e)}. This may reflect a network issue. Generation will continue without the concept."
             )
             os.rmdir(dest)
             return False
-        logger.info("...{:.2f}Kb".format(bytes / 1024))
+        print("...{:.2f}Kb".format(bytes / 1024))
         return succeeded
 
     def _concept_id(self, concept_name: str) -> str:

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -2,12 +2,10 @@ from __future__ import annotations
 
 import dataclasses
 import inspect
-import math
 import secrets
 from collections.abc import Sequence
 from dataclasses import dataclass, field
 from typing import Any, Callable, Generic, List, Optional, Type, TypeVar, Union
-from pydantic import BaseModel, Field
 
 import einops
 import PIL.Image
@@ -40,7 +38,8 @@ from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from typing_extensions import ParamSpec
 
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.globals import Globals
+
 from ..util import CPU_DEVICE, normalize_device
 from .diffusion import (
     AttentionMapSaver,
@@ -50,6 +49,7 @@ from .diffusion import (
 from .offloading import FullyLoadedModelGroup, LazilyLoadedModelGroup, ModelGroup
 from .textual_inversion_manager import TextualInversionManager
 
+
 @dataclass
 class PipelineIntermediateState:
     run_id: str
@@ -75,10 +75,10 @@ class AddsMaskLatents:
     initial_image_latents: torch.Tensor
 
     def __call__(
-        self, latents: torch.Tensor, t: torch.Tensor, text_embeddings: torch.Tensor, **kwargs,
+        self, latents: torch.Tensor, t: torch.Tensor, text_embeddings: torch.Tensor
     ) -> torch.Tensor:
         model_input = self.add_mask_channels(latents)
-        return self.forward(model_input, t, text_embeddings, **kwargs)
+        return self.forward(model_input, t, text_embeddings)
 
     def add_mask_channels(self, latents):
         batch_size = latents.size(0)
@@ -214,19 +214,12 @@ class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]):
             raise AssertionError("why was that an empty generator?")
         return result
 
-@dataclass
-class ControlNetData:
-    model: ControlNetModel = Field(default=None)
-    image_tensor: torch.Tensor= Field(default=None)
-    weight: Union[float, List[float]]= Field(default=1.0)
-    begin_step_percent: float = Field(default=0.0)
-    end_step_percent: float = Field(default=1.0)
 
 @dataclass(frozen=True)
 class ConditioningData:
     unconditioned_embeddings: torch.Tensor
     text_embeddings: torch.Tensor
-    guidance_scale: Union[float, List[float]]
+    guidance_scale: float
     """
     Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
     `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
@@ -364,11 +357,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         """
         if xformers is available, use it, otherwise use sliced attention.
         """
-        config = InvokeAIAppConfig.get_config()
         if (
             torch.cuda.is_available()
             and is_xformers_available()
-            and not config.disable_xformers
+            and not Globals.disable_xformers
         ):
             self.enable_xformers_memory_efficient_attention()
         else:
@@ -527,16 +519,12 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         additional_guidance: List[Callable] = None,
         run_id=None,
         callback: Callable[[PipelineIntermediateState], None] = None,
-        control_data: List[ControlNetData] = None,
         **kwargs,
     ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
-        if self.scheduler.config.get("cpu_only", False):
-            scheduler_device = torch.device('cpu')
-        else:
-            scheduler_device = self._model_group.device_for(self.unet)
-
         if timesteps is None:
-            self.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
+            self.scheduler.set_timesteps(
+                num_inference_steps, device=self._model_group.device_for(self.unet)
+            )
             timesteps = self.scheduler.timesteps
         infer_latents_from_embeddings = GeneratorToCallbackinator(
             self.generate_latents_from_embeddings, PipelineIntermediateState
@@ -549,7 +537,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             additional_guidance=additional_guidance,
             run_id=run_id,
             callback=callback,
-            control_data=control_data,
             **kwargs,
         )
         return result.latents, result.attention_map_saver
@@ -563,7 +550,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         noise: torch.Tensor,
         run_id: str = None,
         additional_guidance: List[Callable] = None,
-        control_data: List[ControlNetData] = None,
         **kwargs,
     ):
         self._adjust_memory_efficient_attention(latents)
@@ -573,9 +559,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             additional_guidance = []
         extra_conditioning_info = conditioning_data.extra
         with self.invokeai_diffuser.custom_attention_context(
-                self.invokeai_diffuser.model,
-                extra_conditioning_info=extra_conditioning_info,
-                step_count=len(self.scheduler.timesteps),
+            extra_conditioning_info=extra_conditioning_info,
+            step_count=len(self.scheduler.timesteps),
         ):
             yield PipelineIntermediateState(
                 run_id=run_id,
@@ -594,7 +579,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             latents = self.scheduler.add_noise(latents, noise, batched_t)
 
             attention_map_saver: Optional[AttentionMapSaver] = None
-            # print("timesteps:", timesteps)
+
             for i, t in enumerate(self.progress_bar(timesteps)):
                 batched_t.fill_(t)
                 step_output = self.step(
@@ -604,7 +589,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                     step_index=i,
                     total_step_count=len(timesteps),
                     additional_guidance=additional_guidance,
-                    control_data=control_data,
                     **kwargs,
                 )
                 latents = step_output.prev_sample
@@ -646,11 +630,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         step_index: int,
         total_step_count: int,
         additional_guidance: List[Callable] = None,
-        control_data: List[ControlNetData] = None,
         **kwargs,
     ):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
+
         if additional_guidance is None:
             additional_guidance = []
 
@@ -658,55 +642,32 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         #     i.e. before or after passing it to InvokeAIDiffuserComponent
         latent_model_input = self.scheduler.scale_model_input(latents, timestep)
 
-        # default is no controlnet, so set controlnet processing output to None
-        down_block_res_samples, mid_block_res_sample = None, None
-
-        if control_data is not None:
-            # FIXME: make sure guidance_scale < 1.0 is handled correctly if doing per-step guidance setting
-            # if conditioning_data.guidance_scale > 1.0:
-            if conditioning_data.guidance_scale is not None:
+        if (self.control_model is not None) and (kwargs.get("control_image") is not None):
+            control_image = kwargs.get("control_image") # should be a processed tensor derived from the control image(s)
+            control_scale = kwargs.get("control_scale", 1.0)  # control_scale default is 1.0
+            # handling case where using multiple control models but only specifying single control_scale
+            #     so reshape control_scale to match number of control models
+            if isinstance(self.control_model, MultiControlNetModel) and isinstance(control_scale, float):
+                control_scale = [control_scale] * len(self.control_model.nets)
+            if conditioning_data.guidance_scale > 1.0:
                 # expand the latents input to control model if doing classifier free guidance
                 #    (which I think for now is always true, there is conditional elsewhere that stops execution if
                 #     classifier_free_guidance is <= 1.0 ?)
                 latent_control_input = torch.cat([latent_model_input] * 2)
             else:
                 latent_control_input = latent_model_input
-            # control_data should be type List[ControlNetData]
-            # this loop covers both ControlNet (one ControlNetData in list)
-            #      and MultiControlNet (multiple ControlNetData in list)
-            for i, control_datum in enumerate(control_data):
-                # print("controlnet", i, "==>", type(control_datum))
-                first_control_step = math.floor(control_datum.begin_step_percent * total_step_count)
-                last_control_step = math.ceil(control_datum.end_step_percent * total_step_count)
-                # only apply controlnet if current step is within the controlnet's begin/end step range
-                if step_index >= first_control_step and step_index <= last_control_step:
-                    # print("running controlnet", i, "for step", step_index)
-                    if isinstance(control_datum.weight, list):
-                        # if controlnet has multiple weights, use the weight for the current step
-                        controlnet_weight = control_datum.weight[step_index]
-                    else:
-                        # if controlnet has a single weight, use it for all steps
-                        controlnet_weight = control_datum.weight
-                    down_samples, mid_sample = control_datum.model(
-                        sample=latent_control_input,
-                        timestep=timestep,
-                        encoder_hidden_states=torch.cat([conditioning_data.unconditioned_embeddings,
-                                                         conditioning_data.text_embeddings]),
-                        controlnet_cond=control_datum.image_tensor,
-                        conditioning_scale=controlnet_weight,
-                        # cross_attention_kwargs,
-                        guess_mode=False,
-                        return_dict=False,
-                    )
-                    if down_block_res_samples is None and mid_block_res_sample is None:
-                        down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
-                    else:
-                        # add controlnet outputs together if have multiple controlnets
-                        down_block_res_samples = [
-                            samples_prev + samples_curr
-                            for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
-                        ]
-                        mid_block_res_sample += mid_sample
+            # controlnet inference
+            down_block_res_samples, mid_block_res_sample = self.control_model(
+                latent_control_input,
+                timestep,
+                encoder_hidden_states=torch.cat([conditioning_data.unconditioned_embeddings,
+                                                 conditioning_data.text_embeddings]),
+                controlnet_cond=control_image,
+                conditioning_scale=control_scale,
+                return_dict=False,
+            )
+        else:
+            down_block_res_samples, mid_block_res_sample = None, None
 
         # predict the noise residual
         noise_pred = self.invokeai_diffuser.do_diffusion_step(
@@ -717,8 +678,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             conditioning_data.guidance_scale,
             step_index=step_index,
             total_step_count=total_step_count,
-            down_block_additional_residuals=down_block_res_samples,  # from controlnet(s)
-            mid_block_additional_residual=mid_block_res_sample,      # from controlnet(s)
+            down_block_additional_residuals=down_block_res_samples,
+            mid_block_additional_residual=mid_block_res_sample,
         )
 
         # compute the previous noisy sample x_t -> x_t-1
@@ -812,7 +773,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         run_id=None,
         callback=None,
     ) -> InvokeAIStableDiffusionPipelineOutput:
-        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
+        timesteps, _ = self.get_img2img_timesteps(
+            num_inference_steps,
+            strength,
+            device=self._model_group.device_for(self.unet),
+        )
         result_latents, result_attention_maps = self.latents_from_embeddings(
             latents=initial_latents if strength < 1.0 else torch.zeros_like(
                 initial_latents, device=initial_latents.device, dtype=initial_latents.dtype
@@ -838,19 +803,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
     def get_img2img_timesteps(
-        self, num_inference_steps: int, strength: float, device=None
+        self, num_inference_steps: int, strength: float, device
     ) -> (torch.Tensor, int):
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         assert img2img_pipeline.scheduler is self.scheduler
-
-        if self.scheduler.config.get("cpu_only", False):
-            scheduler_device = torch.device('cpu')
-        else:
-            scheduler_device = self._model_group.device_for(self.unet)
-
-        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=scheduler_device)
+        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps, adjusted_steps = img2img_pipeline.get_timesteps(
-            num_inference_steps, strength, device=scheduler_device
+            num_inference_steps, strength, device=device
         )
         # Workaround for low strength resulting in zero timesteps.
         # TODO: submit upstream fix for zero-step img2img
@@ -884,7 +843,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if init_image.dim() == 3:
             init_image = init_image.unsqueeze(0)
 
-        timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
+        timesteps, _ = self.get_img2img_timesteps(
+            num_inference_steps, strength, device=device
+        )
 
         # 6. Prepare latent variables
         # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
@@ -1029,17 +990,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     def prepare_control_image(
         self,
         image,
-        # FIXME: need to fix hardwiring of width and height, change to basing on latents dimensions?
-        # latents,
-        width=512,  # should be 8 * latent.shape[3]
-        height=512, # should be 8 * latent height[2]
+        width=512,
+        height=512,
         batch_size=1,
         num_images_per_prompt=1,
         device="cuda",
         dtype=torch.float16,
         do_classifier_free_guidance=True,
     ):
-
         if not isinstance(image, torch.Tensor):
             if isinstance(image, PIL.Image.Image):
                 image = [image]

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -1,7 +1,6 @@
 # adapted from bloc97's CrossAttentionControl colab
 # https://github.com/bloc97/CrossAttentionControl
 
-
 import enum
 import math
 from typing import Callable, Optional
@@ -10,13 +9,12 @@ import diffusers
 import psutil
 import torch
 from compel.cross_attention_control import Arguments
-from diffusers.models.unet_2d_condition import UNet2DConditionModel
 from diffusers.models.attention_processor import AttentionProcessor
 from torch import nn
 
-import invokeai.backend.util.logging as logger
 from ...util import torch_dtype
 
+
 class CrossAttentionType(enum.Enum):
     SELF = 1
     TOKENS = 2
@@ -353,7 +351,8 @@ def restore_default_cross_attention(
     else:
         remove_attention_function(model)
 
-def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: Context):
+
+def override_cross_attention(model, context: Context, is_running_diffusers=False):
     """
     Inject attention parameters and functions into the passed in model to enable cross attention editing.
 
@@ -372,22 +371,37 @@ def setup_cross_attention_control_attention_processors(unet: UNet2DConditionMode
     indices = torch.arange(max_length, dtype=torch.long)
     for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
         if b0 < max_length:
-            if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
+            if name == "equal":  # or (name == "replace" and a1 - a0 == b1 - b0):
                 # these tokens have not been edited
                 indices[b0:b1] = indices_target[a0:a1]
                 mask[b0:b1] = 1
 
     context.cross_attention_mask = mask.to(device)
     context.cross_attention_index_map = indices.to(device)
-    old_attn_processors = unet.attn_processors
-    if torch.backends.mps.is_available():
-        # see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
-        unet.set_attn_processor(SwapCrossAttnProcessor())
+    if is_running_diffusers:
+        unet = model
+        old_attn_processors = unet.attn_processors
+        if torch.backends.mps.is_available():
+            # see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
+            unet.set_attn_processor(SwapCrossAttnProcessor())
+        else:
+            # try to re-use an existing slice size
+            default_slice_size = 4
+            slice_size = next(
+                (
+                    p.slice_size
+                    for p in old_attn_processors.values()
+                    if type(p) is SlicedAttnProcessor
+                ),
+                default_slice_size,
+            )
+            unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
+        return old_attn_processors
     else:
-        # try to re-use an existing slice size
-        default_slice_size = 4
-        slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
-        unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
+        context.register_cross_attention_modules(model)
+        inject_attention_function(model, context)
+        return None
+
 
 def get_cross_attention_modules(
     model, which: CrossAttentionType
@@ -406,7 +420,7 @@ def get_cross_attention_modules(
     expected_count = 16
     if cross_attention_modules_in_model_count != expected_count:
         # non-fatal error but .swap() won't work.
-        logger.error(
+        print(
             f"Error! CrossAttentionControl found an unexpected number of {cross_attention_class} modules in the model "
             + f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed "
             + "or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, "

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -1,16 +1,14 @@
 from contextlib import contextmanager
 from dataclasses import dataclass
 from math import ceil
-from typing import Any, Callable, Dict, Optional, Union, List
+from typing import Any, Callable, Dict, Optional, Union
 
 import numpy as np
 import torch
-from diffusers import UNet2DConditionModel
 from diffusers.models.attention_processor import AttentionProcessor
 from typing_extensions import TypeAlias
 
-import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.globals import Globals
 
 from .cross_attention_control import (
     Arguments,
@@ -18,8 +16,8 @@ from .cross_attention_control import (
     CrossAttentionType,
     SwapCrossAttnContext,
     get_cross_attention_modules,
+    override_cross_attention,
     restore_default_cross_attention,
-    setup_cross_attention_control_attention_processors,
 )
 from .cross_attention_map_saving import AttentionMapSaver
 
@@ -32,6 +30,7 @@ ModelForwardCallback: TypeAlias = Union[
     Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],
 ]
 
+
 @dataclass(frozen=True)
 class PostprocessingSettings:
     threshold: float
@@ -72,65 +71,51 @@ class InvokeAIDiffuserComponent:
         :param model: the unet model to pass through to cross attention control
         :param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
         """
-        config = InvokeAIAppConfig.get_config()
         self.conditioning = None
         self.model = model
         self.is_running_diffusers = is_running_diffusers
         self.model_forward_callback = model_forward_callback
         self.cross_attention_control_context = None
-        self.sequential_guidance = config.sequential_guidance
+        self.sequential_guidance = Globals.sequential_guidance
 
-    @classmethod
     @contextmanager
     def custom_attention_context(
-        cls,
-        unet: UNet2DConditionModel, # note: also may futz with the text encoder depending on requested LoRAs
-        extra_conditioning_info: Optional[ExtraConditioningInfo],
-        step_count: int
+        self, extra_conditioning_info: Optional[ExtraConditioningInfo], step_count: int
     ):
-        old_attn_processors = None
-        if extra_conditioning_info and (
-            extra_conditioning_info.wants_cross_attention_control
-        ):
-            old_attn_processors = unet.attn_processors
-            # Load lora conditions into the model
-            if extra_conditioning_info.wants_cross_attention_control:
-                cross_attention_control_context = Context(
-                    arguments=extra_conditioning_info.cross_attention_control_args,
-                    step_count=step_count,
-                )
-                setup_cross_attention_control_attention_processors(
-                    unet,
-                    cross_attention_control_context,
-                )
-
+        do_swap = (
+            extra_conditioning_info is not None
+            and extra_conditioning_info.wants_cross_attention_control
+        )
+        old_attn_processor = None
+        if do_swap:
+            old_attn_processor = self.override_cross_attention(
+                extra_conditioning_info, step_count=step_count
+            )
         try:
             yield None
         finally:
-            if old_attn_processors is not None:
-                unet.set_attn_processor(old_attn_processors)
+            if old_attn_processor is not None:
+                self.restore_default_cross_attention(old_attn_processor)
             # TODO resuscitate attention map saving
             # self.remove_attention_map_saving()
 
-    # apparently unused code
-    # TODO: delete
-    # def override_cross_attention(
-    #     self, conditioning: ExtraConditioningInfo, step_count: int
-    # ) -> Dict[str, AttentionProcessor]:
-    #     """
-    #     setup cross attention .swap control. for diffusers this replaces the attention processor, so
-    #     the previous attention processor is returned so that the caller can restore it later.
-    #     """
-    #     self.conditioning = conditioning
-    #     self.cross_attention_control_context = Context(
-    #         arguments=self.conditioning.cross_attention_control_args,
-    #         step_count=step_count,
-    #     )
-    #     return override_cross_attention(
-    #         self.model,
-    #         self.cross_attention_control_context,
-    #         is_running_diffusers=self.is_running_diffusers,
-    #     )
+    def override_cross_attention(
+        self, conditioning: ExtraConditioningInfo, step_count: int
+    ) -> Dict[str, AttentionProcessor]:
+        """
+        setup cross attention .swap control. for diffusers this replaces the attention processor, so
+        the previous attention processor is returned so that the caller can restore it later.
+        """
+        self.conditioning = conditioning
+        self.cross_attention_control_context = Context(
+            arguments=self.conditioning.cross_attention_control_args,
+            step_count=step_count,
+        )
+        return override_cross_attention(
+            self.model,
+            self.cross_attention_control_context,
+            is_running_diffusers=self.is_running_diffusers,
+        )
 
     def restore_default_cross_attention(
         self, restore_attention_processor: Optional["AttentionProcessor"] = None
@@ -180,8 +165,7 @@ class InvokeAIDiffuserComponent:
         sigma: torch.Tensor,
         unconditioning: Union[torch.Tensor, dict],
         conditioning: Union[torch.Tensor, dict],
-        # unconditional_guidance_scale: float,
-        unconditional_guidance_scale: Union[float, List[float]],
+        unconditional_guidance_scale: float,
         step_index: Optional[int] = None,
         total_step_count: Optional[int] = None,
         **kwargs,
@@ -196,11 +180,6 @@ class InvokeAIDiffuserComponent:
         :return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning.
         """
 
-        if isinstance(unconditional_guidance_scale, list):
-            guidance_scale = unconditional_guidance_scale[step_index]
-        else:
-            guidance_scale = unconditional_guidance_scale
-
         cross_attention_control_types_to_do = []
         context: Context = self.cross_attention_control_context
         if self.cross_attention_control_context is not None:
@@ -249,8 +228,7 @@ class InvokeAIDiffuserComponent:
             )
 
         combined_next_x = self._combine(
-            # unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale
-            unconditioned_next_x, conditioned_next_x, guidance_scale
+            unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale
         )
 
         return combined_next_x
@@ -498,14 +476,10 @@ class InvokeAIDiffuserComponent:
             outside = torch.count_nonzero(
                 (latents < -current_threshold) | (latents > current_threshold)
             )
-            logger.info(
-                f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})"
-                )
-            logger.debug(
-                f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}"
-            )
-            logger.debug(
-                f"{outside / latents.numel() * 100:.2f}% values outside threshold"
+            print(
+                f"\nThreshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
+                f"  | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
+                f"  | {outside / latents.numel() * 100:.2f}% values outside threshold"
             )
 
         if maxval < current_threshold and minval > -current_threshold:
@@ -532,11 +506,9 @@ class InvokeAIDiffuserComponent:
             )
 
         if self.debug_thresholding:
-            logger.debug(
-                f"min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})"
-            )
-            logger.debug(
-                f"{num_altered / latents.numel() * 100:.2f}% values altered"
+            print(
+                f"  | min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})\n"
+                f"  | {num_altered / latents.numel() * 100:.2f}% values altered"
             )
 
         return latents

invokeai/backend/stable_diffusion/image_degradation/utils_image.py

@@ -10,7 +10,7 @@ from torchvision.utils import make_grid
 
 # import matplotlib.pyplot as plt   # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
 
-import invokeai.backend.util.logging as logger
+
 os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
 
 
@@ -191,7 +191,7 @@ def mkdirs(paths):
 def mkdir_and_rename(path):
     if os.path.exists(path):
         new_name = path + "_archived_" + get_timestamp()
-        logger.error("Path already exists. Rename it to [{:s}]".format(new_name))
+        print("Path already exists. Rename it to [{:s}]".format(new_name))
         os.replace(path, new_name)
     os.makedirs(path)