[nodes] Removed InvokerServices, simplying service model

author Kyle Schouviller <kyle0654@hotmail.com> 1669872800 -0800
committer Kyle Schouviller <kyle0654@hotmail.com> 1676240900 -0800

Adding base node architecture

Fix type annotation errors

Runs and generates, but breaks in saving session

Fix default model value setting. Fix deprecation warning.

Fixed node api

Adding markdown docs

Simplifying Generate construction in apps

[nodes] A few minor changes (#2510)

* Pin api-related requirements

* Remove confusing extra CORS origins list

* Adds response models for HTTP 200

[nodes] Adding graph_execution_state to soon replace session. Adding tests with pytest.

Minor typing fixes

[nodes] Fix some small output query hookups

[node] Fixing some additional typing issues

[nodes] Move and expand graph code. Add base item storage and sqlite implementation.

Update startup to match new code

[nodes] Add callbacks to item storage

[nodes] Adding an InvocationContext object to use for invocations to provide easier extensibility

[nodes] New execution model that handles iteration

[nodes] Fixing the CLI

[nodes] Adding a note to the CLI

[nodes] Split processing thread into separate service

[node] Add error message on node processing failure

Removing old files and duplicated packages

Adding python-multipart

.coveragerc

@@ -0,0 +1,6 @@
+[run]
+omit='.env/*'
+source='.'
+
+[report]
+show_missing = true

.editorconfig

@@ -1,8 +1,5 @@
-root = true
-
 # All files
 [*]
-max_line_length = 80
 charset = utf-8
 end_of_line = lf
 indent_size = 2
@@ -13,18 +10,3 @@ trim_trailing_whitespace = true
 # Python
 [*.py]
 indent_size = 4
-max_line_length = 120
-
-# css
-[*.css]
-indent_size = 4
-
-# flake8
-[.flake8]
-indent_size = 4
-
-# Markdown MkDocs
-[docs/**/*.md]
-max_line_length = 80
-indent_size = 4
-indent_style = unset

.flake8

@@ -1,37 +0,0 @@
-[flake8]
-max-line-length = 120
-extend-ignore =
-    # See https://github.com/PyCQA/pycodestyle/issues/373
-    E203,
-    # use Bugbear's B950 instead
-    E501,
-    # from black repo https://github.com/psf/black/blob/main/.flake8
-    E266, W503, B907
-extend-select =
-    # Bugbear line length
-    B950
-extend-exclude =
-    scripts/orig_scripts/*
-    ldm/models/*
-    ldm/modules/*
-    ldm/data/*
-    ldm/generate.py
-    ldm/util.py
-    ldm/simplet2i.py
-per-file-ignores =
-    # B950 line too long
-    # W605 invalid escape sequence
-    # F841 assigned to but never used
-    # F401 imported but unused
-    tests/test_prompt_parser.py: B950, W605, F401
-    tests/test_textual_inversion.py: F841, B950
-    # B023 Function definition does not bind loop variable
-    scripts/legacy_api.py: F401, B950, B023, F841
-    ldm/invoke/__init__.py: F401
-    # B010 Do not call setattr with a constant attribute value
-    ldm/invoke/server_legacy.py: B010
-# =====================
-# flake-quote settings:
-# =====================
-# Set this to match black style:
-inline-quotes = double

.github/CODEOWNERS

@@ -2,60 +2,50 @@
 /.github/workflows/ @mauwii @lstein @blessedcoolant
 
 # documentation
-/docs/ @lstein @mauwii @blessedcoolant
-mkdocs.yml @mauwii @lstein
+/docs/ @lstein @mauwii @tildebyte @blessedcoolant
+mkdocs.yml @lstein @mauwii @blessedcoolant
 
 # installation and configuration
-/pyproject.toml @mauwii @lstein @ebr
-/docker/ @mauwii
+/pyproject.toml @mauwii @lstein @ebr @blessedcoolant
+/docker/ @mauwii @lstein @blessedcoolant
 /scripts/ @ebr @lstein @blessedcoolant
-/installer/ @ebr @lstein 
-ldm/invoke/config @lstein @ebr
-invokeai/assets @lstein @blessedcoolant
+/installer/ @ebr @lstein @tildebyte  @blessedcoolant
+ldm/invoke/config @lstein @ebr  @blessedcoolant
+invokeai/assets @lstein @ebr  @blessedcoolant
 invokeai/configs @lstein @ebr  @blessedcoolant
 /ldm/invoke/_version.py @lstein @blessedcoolant
 
 # web ui
-/invokeai/frontend @blessedcoolant @psychedelicious
-/invokeai/backend @blessedcoolant @psychedelicious
+/invokeai/frontend @blessedcoolant @psychedelicious @lstein
+/invokeai/backend @blessedcoolant @psychedelicious @lstein
 
 # generation and model management
 /ldm/*.py @lstein @blessedcoolant
-/ldm/generate.py @lstein @keturn
+/ldm/generate.py @lstein @keturn @blessedcoolant
 /ldm/invoke/args.py @lstein @blessedcoolant
 /ldm/invoke/ckpt* @lstein @blessedcoolant
 /ldm/invoke/ckpt_generator @lstein @blessedcoolant
 /ldm/invoke/CLI.py @lstein @blessedcoolant
 /ldm/invoke/config @lstein @ebr @mauwii @blessedcoolant
-/ldm/invoke/generator @keturn @damian0815
+/ldm/invoke/generator @keturn @damian0815 @blessedcoolant
 /ldm/invoke/globals.py @lstein @blessedcoolant 
 /ldm/invoke/merge_diffusers.py @lstein @blessedcoolant
 /ldm/invoke/model_manager.py @lstein @blessedcoolant
 /ldm/invoke/txt2mask.py @lstein @blessedcoolant
-/ldm/invoke/patchmatch.py @Kyle0654 @lstein
+/ldm/invoke/patchmatch.py @Kyle0654 @blessedcoolant @lstein
 /ldm/invoke/restoration @lstein @blessedcoolant
 
 # attention, textual inversion, model configuration
-/ldm/models @damian0815 @keturn @blessedcoolant
-/ldm/modules/textual_inversion_manager.py @lstein @blessedcoolant
-/ldm/modules/attention.py @damian0815 @keturn
-/ldm/modules/diffusionmodules @damian0815 @keturn
-/ldm/modules/distributions @damian0815 @keturn
-/ldm/modules/ema.py  @damian0815 @keturn
-/ldm/modules/embedding_manager.py @lstein
-/ldm/modules/encoders @damian0815 @keturn
-/ldm/modules/image_degradation @damian0815 @keturn
-/ldm/modules/losses  @damian0815 @keturn
-/ldm/modules/x_transformer.py @damian0815 @keturn
+/ldm/models @damian0815 @keturn @lstein @blessedcoolant
+/ldm/modules @damian0815 @keturn @lstein @blessedcoolant
 
 # Nodes
-apps/ @Kyle0654 @jpphoto
+apps/ @Kyle0654 @lstein @blessedcoolant
 
 # legacy REST API
-# these are dead code
-#/ldm/invoke/pngwriter.py @CapableWeb
-#/ldm/invoke/server_legacy.py @CapableWeb
-#/scripts/legacy_api.py @CapableWeb
-#/tests/legacy_tests.sh @CapableWeb
-
+# is CapableWeb still engaged?
+/ldm/invoke/pngwriter.py @CapableWeb @lstein @blessedcoolant
+/ldm/invoke/server_legacy.py @CapableWeb @lstein @blessedcoolant
+/scripts/legacy_api.py @CapableWeb @lstein @blessedcoolant
+/tests/legacy_tests.sh @CapableWeb @lstein @blessedcoolant
 

.github/workflows/mkdocs-material.yml

@@ -9,10 +9,6 @@ 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
@@ -23,15 +19,11 @@ 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: |

.gitignore

@@ -68,6 +68,7 @@ htmlcov/
 .cache
 nosetests.xml
 coverage.xml
+cov.xml
 *.cover
 *.py,cover
 .hypothesis/

.pre-commit-config.yaml

@@ -1,41 +0,0 @@
-# See https://pre-commit.com for more information
-# See https://pre-commit.com/hooks.html for more hooks
-repos:
-  - repo: https://github.com/psf/black
-    rev: 23.1.0
-    hooks:
-      - id: black
-
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
-    hooks:
-      - id: isort
-
-  - repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
-    hooks:
-      - id: flake8
-        additional_dependencies:
-          - flake8-black
-          - flake8-bugbear
-          - flake8-comprehensions
-          - flake8-simplify
-
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: 'v3.0.0-alpha.4'
-    hooks:
-      - id: prettier
-
-  - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
-    hooks:
-      - id: check-added-large-files
-      - id: check-executables-have-shebangs
-      - id: check-shebang-scripts-are-executable
-      - id: check-merge-conflict
-      - id: check-symlinks
-      - id: check-toml
-      - id: end-of-file-fixer
-      - id: no-commit-to-branch
-        args: ['--branch', 'main']
-      - id: trailing-whitespace

.prettierignore

@@ -1,14 +0,0 @@
-invokeai/frontend/.husky
-invokeai/frontend/patches
-
-# Ignore artifacts:
-build
-coverage
-static
-invokeai/frontend/dist
-
-# Ignore all HTML files:
-*.html
-
-# Ignore deprecated docs
-docs/installation/deprecated_documentation

.prettierrc.yaml

@@ -1,9 +1,9 @@
-embeddedLanguageFormatting: auto
 endOfLine: lf
-singleQuote: true
-semi: true
-trailingComma: es5
+tabWidth: 2
 useTabs: false
+singleQuote: true
+quoteProps: as-needed
+embeddedLanguageFormatting: auto
 overrides:
   - files: '*.md'
     options:
@@ -11,9 +11,3 @@ overrides:
       printWidth: 80
       parser: markdown
       cursorOffset: -1
-  - files: docs/**/*.md
-    options:
-      tabWidth: 4
-  - files: 'invokeai/frontend/public/locales/*.json'
-    options:
-      tabWidth: 4

.pytest.ini

@@ -0,0 +1,5 @@
+[pytest]
+DJANGO_SETTINGS_MODULE = webtas.settings
+; python_files = tests.py test_*.py *_tests.py
+
+addopts = --cov=. --cov-config=.coveragerc --cov-report xml:cov.xml

README.md

@@ -145,7 +145,7 @@ not supported.
     _For Linux with an AMD GPU:_
 
     ```sh
-    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.2
     ```
 
     _For Macintoshes, either Intel or M1/M2:_

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/.markdownlint.jsonc

@@ -1,5 +0,0 @@
-{
-  "MD046": false,
-  "MD007": false,
-  "MD030": false
-}

docs/contributing/ARCHITECTURE.md

@@ -0,0 +1,93 @@
+# Invoke.AI Architecture
+
+```mermaid
+flowchart TB
+
+  subgraph apps[Applications]
+    webui[WebUI]
+    cli[CLI]
+
+  subgraph webapi[Web API]
+    api[HTTP API]
+    sio[Socket.IO]
+  end
+
+  end
+
+  subgraph invoke[Invoke]
+    direction LR
+    invoker
+    services
+    sessions
+    invocations
+  end
+
+  subgraph core[AI Core]
+    Generate
+  end
+
+  webui --> webapi
+  webapi --> invoke
+  cli --> invoke
+
+  invoker --> services & sessions
+  invocations --> services
+  sessions --> invocations
+
+  services --> core
+
+  %% Styles
+  classDef sg fill:#5028C8,font-weight:bold,stroke-width:2,color:#fff,stroke:#14141A
+  classDef default stroke-width:2px,stroke:#F6B314,color:#fff,fill:#14141A
+
+  class apps,webapi,invoke,core sg
+
+```
+
+## Applications
+
+Applications are built on top of the invoke framework. They should construct `invoker` and then interact through it. They should avoid interacting directly with core code in order to support a variety of configurations.
+
+### Web UI
+
+The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/frontend` and the backend code is found in `/ldm/invoke/app/api_app.py` and `/ldm/invoke/app/api/`. The code is further organized as such:
+
+| Component | Description |
+| --- | --- |
+| api_app.py | Sets up the API app, annotates the OpenAPI spec with additional data, and runs the API |
+| dependencies | Creates all invoker services and the invoker, and provides them to the API |
+| events | An eventing system that could in the future be adapted to support horizontal scale-out |
+| sockets | The Socket.IO interface - handles listening to and emitting session events (events are defined in the events service module) |
+| routers | API definitions for different areas of API functionality |
+
+### CLI
+
+The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/ldm/invoke/app/cli_app.py`.
+
+## Invoke
+
+The Invoke framework provides the interface to the underlying AI systems and is built with flexibility and extensibility in mind. There are four major concepts: invoker, sessions, invocations, and services.
+
+### Invoker
+
+The invoker (`/ldm/invoke/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
+- **invocation services**, which are used by invocations to interact with core functionality.
+- **invoker services**, which are used by the invoker to manage sessions and manage the invocation queue.
+
+### Sessions
+
+Invocations and links between them form a graph, which is maintained in a session. Sessions can be queued for invocation, which will execute their graph (either the next ready invocation, or all invocations). Sessions also maintain execution history for the graph (including storage of any outputs). An invocation may be added to a session at any time, and there is capability to add and entire graph at once, as well as to automatically link new invocations to previous invocations. Invocations can not be deleted or modified once added.
+
+The session graph does not support looping. This is left as an application problem to prevent additional complexity in the graph.
+
+### Invocations
+
+Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/ldm/invoke/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
+
+### Services
+
+Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/ldm/invoke/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
+
+## AI Core
+
+The AI Core is represented by the rest of the code base (i.e. the code outside of `/ldm/invoke/app/`).

docs/contributing/INVOCATIONS.md

@@ -0,0 +1,105 @@
+# Invocations
+
+Invocations represent a single operation, its inputs, and its outputs. These operations and their outputs can be chained together to generate and modify images.
+
+## Creating a new invocation
+
+To create a new invocation, either find the appropriate module file in `/ldm/invoke/app/invocations` to add your invocation to, or create a new one in that folder. All invocations in that folder will be discovered and made available to the CLI and API automatically. Invocations make use of [typing](https://docs.python.org/3/library/typing.html) and [pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration into the CLI and API.
+
+An invocation looks like this:
+
+```py
+class UpscaleInvocation(BaseInvocation):
+    """Upscales an image."""
+    type: Literal['upscale'] = 'upscale'
+
+    # Inputs
+    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(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_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_type = image_type, image_name = image_name)
+        )
+```
+
+Each portion is important to implement correctly.
+
+### Class definition and type
+```py
+class UpscaleInvocation(BaseInvocation):
+    """Upscales an image."""
+    type: Literal['upscale'] = 'upscale'
+```
+All invocations must derive from `BaseInvocation`. They should have a docstring that declares what they do in a single, short line. They should also have a `type` with a type hint that's `Literal["command_name"]`, where `command_name` is what the user will type on the CLI or use in the API to create this invocation. The `command_name` must be unique. The `type` must be assigned to the value of the literal in the type hint.
+
+### Inputs
+```py
+    # Inputs
+    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")
+```
+Inputs consist of three parts: a name, a type hint, and a `Field` with default, description, and validation information. For example:
+| Part | Value | Description |
+| ---- | ----- | ----------- |
+| Name | `strength` | This field is referred to as `strength` |
+| Type Hint | `float` | This field must be of type `float` |
+| Field | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
+
+Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this field to be parsed with `None` as a value, which enables linking to previous invocations. All fields should either provide a default value or allow `None` as a value, so that they can be overwritten with a linked output from another invocation.
+
+The special type `ImageField` is also used here. All images are passed as `ImageField`, which protects them from pydantic validation errors (since images only ever come from links).
+
+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.
+
+### Invoke Function
+```py
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        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
+        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_type = image_type, image_name = image_name)
+        )
+```
+The `invoke` function is the last portion of an invocation. It is provided an `InvocationContext` which contains services to perform work as well as a `session_id` for use as needed. It should return a class with output values that derives from `BaseInvocationOutput`.
+
+Before being called, the invocation will have all of its fields set from defaults, inputs, and finally links (overriding in that order).
+
+Assume that this invocation may be running simultaneously with other invocations, may be running on another machine, or in other interesting scenarios. If you need functionality, please provide it as a service in the `InvocationServices` class, and make sure it can be overridden.
+
+### Outputs
+```py
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+    type: Literal['image'] = 'image'
+
+    image: ImageField = Field(default=None, description="The output image")
+```
+Output classes look like an invocation class without the invoke method. Prefer to use an existing output class if available, and prefer to name inputs the same as outputs when possible, to promote automatic invocation linking.

docs/features/TEXTUAL_INVERSION.md

@@ -154,11 +154,8 @@ training sets will converge with 2000-3000 steps.
 
 This adjusts how many training images are processed simultaneously in
 each step. Higher values will cause the training process to run more
-quickly, but use more memory. The default size is selected based on
-whether you have the `xformers` memory-efficient attention library
-installed. If `xformers` is available, the batch size will be 8,
-otherwise 3.  These values were chosen to allow training to run with
-GPUs with as little as 12 GB VRAM.
+quickly, but use more memory. The default size will run with GPUs with
+as little as 12 GB.
 
 ### Learning rate
 
@@ -175,10 +172,8 @@ learning rate to improve performance.
 
 ### Use xformers acceleration
 
-This will activate XFormers memory-efficient attention, which will
-reduce memory requirements by half or more and allow you to select a
-higher batch size. You need to have XFormers installed for this to
-have an effect.
+This will activate XFormers memory-efficient attention. You need to
+have XFormers installed for this to have an effect.
 
 ### Learning rate scheduler
 
@@ -255,49 +250,6 @@ invokeai-ti \
        --only_save_embeds
 ```
 
-## Using Distributed Training
-
-If you have multiple GPUs on one machine, or a cluster of GPU-enabled
-machines, you can activate distributed training. See the [HuggingFace
-Accelerate pages](https://huggingface.co/docs/accelerate/index) for
-full information, but the basic recipe is:
-
-1. Enter the InvokeAI developer's console command line by selecting
-option [8] from the `invoke.sh`/`invoke.bat` script.
-
-2. Configurate Accelerate using `accelerate config`:
-```sh
-accelerate config
-```
-This will guide you through the configuration process, including
-specifying how many machines you will run training on and the number
-of GPUs pe rmachine.
-
-You only need to do this once.
-
-3. Launch training from the command line using `accelerate launch`. Be sure
-that your current working directory is the InvokeAI root directory (usually
-named `invokeai` in your home directory):
-
-```sh
-accelerate launch .venv/bin/invokeai-ti \
-       --model=stable-diffusion-1.5 \
-       --resolution=512 \
-       --learnable_property=object \
-       --initializer_token='*' \
-       --placeholder_token='<shraddha>' \
-       --train_data_dir=/home/lstein/invokeai/text-inversion-training-data/shraddha \
-       --output_dir=/home/lstein/invokeai/text-inversion-training/shraddha \
-       --scale_lr \
-       --train_batch_size=10 \
-       --gradient_accumulation_steps=4 \
-       --max_train_steps=2000 \
-       --learning_rate=0.0005 \
-       --lr_scheduler=constant \
-       --mixed_precision=fp16 \
-       --only_save_embeds
-```
-
 ## Using Embeddings
 
 After training completes, the resultant embeddings will be saved into your `$INVOKEAI_ROOT/embeddings/<trigger word>/learned_embeds.bin`.

docs/features/index.md

@@ -2,82 +2,62 @@
 title: Overview
 ---
 
--   The Basics
+Here you can find the documentation for InvokeAI's various features.
 
-    -   The [Web User Interface](WEB.md)
+## The Basics
+### * The [Web User Interface](WEB.md)
+Guide to the Web interface. Also see the [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
 
-        Guide to the Web interface. Also see the
-        [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
+### * The [Unified Canvas](UNIFIED_CANVAS.md)
+Build complex scenes by combine and modifying multiple images in a stepwise
+fashion. This feature combines img2img, inpainting and outpainting in
+a single convenient digital artist-optimized user interface.
 
-    -   The [Unified Canvas](UNIFIED_CANVAS.md)
+### * The [Command Line Interface (CLI)](CLI.md)
+Scriptable access to InvokeAI's features.
 
-        Build complex scenes by combine and modifying multiple images in a
-        stepwise fashion. This feature combines img2img, inpainting and
-        outpainting in a single convenient digital artist-optimized user
-        interface.
+## Image Generation
+### * [Prompt Engineering](PROMPTS.md)
+Get the images you want with the InvokeAI  prompt engineering language.
 
-    -   The [Command Line Interface (CLI)](CLI.md)
+## * [Post-Processing](POSTPROCESS.md)
+Restore mangled faces and make images larger with upscaling. Also see the [Embiggen Upscaling Guide](EMBIGGEN.md).
 
-        Scriptable access to InvokeAI's features.
+## * The [Concepts Library](CONCEPTS.md)
+Add custom subjects and styles using HuggingFace's repository of embeddings.
 
--   Image Generation
+### * [Image-to-Image Guide for the CLI](IMG2IMG.md)
+Use a seed image to build new creations in the CLI.
 
-    -   [Prompt Engineering](PROMPTS.md)
+### * [Inpainting Guide for the CLI](INPAINTING.md)
+Selectively erase and replace portions of an existing image in the CLI.
 
-        Get the images you want with the InvokeAI prompt engineering language.
+### * [Outpainting Guide for the CLI](OUTPAINTING.md)
+Extend the borders of the image with an "outcrop" function within the CLI.
 
-    -   [Post-Processing](POSTPROCESS.md)
+### * [Generating Variations](VARIATIONS.md)
+Have an image you like and want to generate many more like it? Variations
+are the ticket.
 
-        Restore mangled faces and make images larger with upscaling. Also see
-        the [Embiggen Upscaling Guide](EMBIGGEN.md).
+## Model Management
 
-    -   The [Concepts Library](CONCEPTS.md)
+## * [Model Installation](../installation/050_INSTALLING_MODELS.md)
+Learn how to import third-party models and switch among them. This
+guide also covers optimizing models to load quickly.
 
-        Add custom subjects and styles using HuggingFace's repository of
-        embeddings.
+## * [Merging Models](MODEL_MERGING.md)
+Teach an old model new tricks. Merge 2-3 models together to create a
+new model that combines characteristics of the originals.
 
-    -   [Image-to-Image Guide for the CLI](IMG2IMG.md)
+## * [Textual Inversion](TEXTUAL_INVERSION.md)
+Personalize models by adding your own style or subjects.
 
-        Use a seed image to build new creations in the CLI.
+# Other Features
 
-    -   [Inpainting Guide for the CLI](INPAINTING.md)
+## * [The NSFW Checker](NSFW.md)
+Prevent InvokeAI from displaying unwanted racy images.
 
-        Selectively erase and replace portions of an existing image in the CLI.
-
-    -   [Outpainting Guide for the CLI](OUTPAINTING.md)
-
-        Extend the borders of the image with an "outcrop" function within the
-        CLI.
-
-    -   [Generating Variations](VARIATIONS.md)
-
-        Have an image you like and want to generate many more like it?
-        Variations are the ticket.
-
--   Model Management
-
-    -   [Model Installation](../installation/050_INSTALLING_MODELS.md)
-
-        Learn how to import third-party models and switch among them. This guide
-        also covers optimizing models to load quickly.
-
-    -   [Merging Models](MODEL_MERGING.md)
-
-        Teach an old model new tricks. Merge 2-3 models together to create a new
-        model that combines characteristics of the originals.
-
-    -   [Textual Inversion](TEXTUAL_INVERSION.md)
-
-        Personalize models by adding your own style or subjects.
-
--   Other Features
-
-    -   [The NSFW Checker](NSFW.md)
-
-        Prevent InvokeAI from displaying unwanted racy images.
-
-    -   [Miscellaneous](OTHER.md)
-
-        Run InvokeAI on Google Colab, generate images with repeating patterns,
-        batch process a file of prompts, increase the "creativity" of image
-        generation by adding initial noise, and more!
+## * [Miscellaneous](OTHER.md)
+Run InvokeAI on Google Colab, generate images with repeating patterns,
+batch process a file of prompts, increase the "creativity" of image
+generation by adding initial noise, and more!

docs/help/IDE-Settings/index.md

@@ -1,4 +0,0 @@
-# :octicons-file-code-16: IDE-Settings
-
-Here we will share settings for IDEs used by our developers, maybe you can find
-something interestening which will help to boost your development efficency 🔥

docs/help/IDE-Settings/vs-code.md

@@ -1,250 +0,0 @@
----
-title: Visual Studio Code
----
-
-# :material-microsoft-visual-studio-code:Visual Studio Code
-
-The Workspace Settings are stored in the project (repository) root and get
-higher priorized than your user settings.
-
-This helps to have different settings for different projects, while the user
-settings get used as a default value if no workspace settings are provided.
-
-## tasks.json
-
-First we will create a task configuration which will create a virtual
-environment and update the deps (pip, setuptools and wheel).
-
-Into this venv we will then install the pyproject.toml in editable mode with
-dev, docs and test dependencies.
-
-```json title=".vscode/tasks.json"
-{
-    // See https://go.microsoft.com/fwlink/?LinkId=733558
-    // for the documentation about the tasks.json format
-    "version": "2.0.0",
-    "tasks": [
-        {
-            "label": "Create virtual environment",
-            "detail": "Create .venv and upgrade pip, setuptools and wheel",
-            "command": "python3",
-            "args": [
-                "-m",
-                "venv",
-                ".venv",
-                "--prompt",
-                "InvokeAI",
-                "--upgrade-deps"
-            ],
-            "runOptions": {
-                "instanceLimit": 1,
-                "reevaluateOnRerun": true
-            },
-            "group": {
-                "kind": "build"
-            },
-            "presentation": {
-                "echo": true,
-                "reveal": "always",
-                "focus": false,
-                "panel": "shared",
-                "showReuseMessage": true,
-                "clear": false
-            }
-        },
-        {
-            "label": "build InvokeAI",
-            "detail": "Build pyproject.toml with extras dev, docs and test",
-            "command": "${workspaceFolder}/.venv/bin/python3",
-            "args": [
-                "-m",
-                "pip",
-                "install",
-                "--use-pep517",
-                "--editable",
-                ".[dev,docs,test]"
-            ],
-            "dependsOn": "Create virtual environment",
-            "dependsOrder": "sequence",
-            "group": {
-                "kind": "build",
-                "isDefault": true
-            },
-            "presentation": {
-                "echo": true,
-                "reveal": "always",
-                "focus": false,
-                "panel": "shared",
-                "showReuseMessage": true,
-                "clear": false
-            }
-        }
-    ]
-}
-```
-
-The fastest way to build InvokeAI now is ++cmd+shift+b++
-
-## launch.json
-
-This file is used to define debugger configurations, so that you can one-click
-launch and monitor the application, set halt points to inspect specific states,
-...
-
-```json title=".vscode/launch.json"
-{
-    "version": "0.2.0",
-    "configurations": [
-        {
-            "name": "invokeai web",
-            "type": "python",
-            "request": "launch",
-            "program": ".venv/bin/invokeai",
-            "justMyCode": true
-        },
-        {
-            "name": "invokeai cli",
-            "type": "python",
-            "request": "launch",
-            "program": ".venv/bin/invokeai",
-            "justMyCode": true
-        },
-        {
-            "name": "mkdocs serve",
-            "type": "python",
-            "request": "launch",
-            "program": ".venv/bin/mkdocs",
-            "args": ["serve"],
-            "justMyCode": true
-        }
-    ]
-}
-```
-
-Then you only need to hit ++f5++ and the fun begins :nerd: (It is asumed that
-you have created a virtual environment via the [tasks](#tasksjson) from the
-previous step.)
-
-## extensions.json
-
-A list of recommended vscode-extensions to make your life easier:
-
-```json title=".vscode/extensions.json"
-{
-    "recommendations": [
-        "editorconfig.editorconfig",
-        "github.vscode-pull-request-github",
-        "ms-python.black-formatter",
-        "ms-python.flake8",
-        "ms-python.isort",
-        "ms-python.python",
-        "ms-python.vscode-pylance",
-        "redhat.vscode-yaml",
-        "tamasfe.even-better-toml",
-        "eamodio.gitlens",
-        "foxundermoon.shell-format",
-        "timonwong.shellcheck",
-        "esbenp.prettier-vscode",
-        "davidanson.vscode-markdownlint",
-        "yzhang.markdown-all-in-one",
-        "bierner.github-markdown-preview",
-        "ms-azuretools.vscode-docker",
-        "mads-hartmann.bash-ide-vscode"
-    ]
-}
-```
-
-## settings.json
-
-With bellow settings your files already get formated when you save them (only
-your modifications if available), which will help you to not run into trouble
-with the pre-commit hooks. If the hooks fail, they will prevent you from
-commiting, but most hooks directly add a fixed version, so that you just need to
-stage and commit them:
-
-```json title=".vscode/settings.json"
-{
-    "[json]": {
-        "editor.defaultFormatter": "esbenp.prettier-vscode",
-        "editor.quickSuggestions": {
-            "comments": false,
-            "strings": true,
-            "other": true
-        },
-        "editor.suggest.insertMode": "replace",
-        "gitlens.codeLens.scopes": ["document"]
-    },
-    "[jsonc]": {
-        "editor.defaultFormatter": "esbenp.prettier-vscode",
-        "editor.formatOnSave": true,
-        "editor.formatOnSaveMode": "modificationsIfAvailable"
-    },
-    "[python]": {
-        "editor.defaultFormatter": "ms-python.black-formatter",
-        "editor.formatOnSave": true,
-        "editor.formatOnSaveMode": "file"
-    },
-    "[toml]": {
-        "editor.defaultFormatter": "tamasfe.even-better-toml",
-        "editor.formatOnSave": true,
-        "editor.formatOnSaveMode": "modificationsIfAvailable"
-    },
-    "[yaml]": {
-        "editor.defaultFormatter": "esbenp.prettier-vscode",
-        "editor.formatOnSave": true,
-        "editor.formatOnSaveMode": "modificationsIfAvailable"
-    },
-    "[markdown]": {
-        "editor.defaultFormatter": "esbenp.prettier-vscode",
-        "editor.rulers": [80],
-        "editor.unicodeHighlight.ambiguousCharacters": false,
-        "editor.unicodeHighlight.invisibleCharacters": false,
-        "diffEditor.ignoreTrimWhitespace": false,
-        "editor.wordWrap": "on",
-        "editor.quickSuggestions": {
-            "comments": "off",
-            "strings": "off",
-            "other": "off"
-        },
-        "editor.formatOnSave": true,
-        "editor.formatOnSaveMode": "modificationsIfAvailable"
-    },
-    "[shellscript]": {
-        "editor.defaultFormatter": "foxundermoon.shell-format"
-    },
-    "[ignore]": {
-        "editor.defaultFormatter": "foxundermoon.shell-format"
-    },
-    "editor.rulers": [88],
-    "evenBetterToml.formatter.alignEntries": false,
-    "evenBetterToml.formatter.allowedBlankLines": 1,
-    "evenBetterToml.formatter.arrayAutoExpand": true,
-    "evenBetterToml.formatter.arrayTrailingComma": true,
-    "evenBetterToml.formatter.arrayAutoCollapse": true,
-    "evenBetterToml.formatter.columnWidth": 88,
-    "evenBetterToml.formatter.compactArrays": true,
-    "evenBetterToml.formatter.compactInlineTables": true,
-    "evenBetterToml.formatter.indentEntries": false,
-    "evenBetterToml.formatter.inlineTableExpand": true,
-    "evenBetterToml.formatter.reorderArrays": true,
-    "evenBetterToml.formatter.reorderKeys": true,
-    "evenBetterToml.formatter.compactEntries": false,
-    "evenBetterToml.schema.enabled": true,
-    "python.analysis.typeCheckingMode": "basic",
-    "python.formatting.provider": "black",
-    "python.languageServer": "Pylance",
-    "python.linting.enabled": true,
-    "python.linting.flake8Enabled": true,
-    "python.testing.unittestEnabled": false,
-    "python.testing.pytestEnabled": true,
-    "python.testing.pytestArgs": [
-        "tests",
-        "--cov=ldm",
-        "--cov-branch",
-        "--cov-report=term:skip-covered"
-    ],
-    "yaml.schemas": {
-        "https://json.schemastore.org/prettierrc.json": "${workspaceFolder}/.prettierrc.yaml"
-    }
-}
-```

docs/help/contributing/010_PULL_REQUEST.md

@@ -1,135 +0,0 @@
----
-title: Pull-Request
----
-
-# :octicons-git-pull-request-16: Pull-Request
-
-## pre-requirements
-
-To follow the steps in this tutorial you will need:
-
--   [GitHub](https://github.com) account
--   [git](https://git-scm.com/downloads) source controll
--   Text / Code Editor (personally I preffer
-    [Visual Studio Code](https://code.visualstudio.com/Download))
--   Terminal:
-    -   If you are on Linux/MacOS you can use bash or zsh
-    -   for Windows Users the commands are written for PowerShell
-
-## Fork Repository
-
-The first step to be done if you want to contribute to InvokeAI, is to fork the
-rpeository.
-
-Since you are already reading this doc, the easiest way to do so is by clicking
-[here](https://github.com/invoke-ai/InvokeAI/fork). You could also open
-[InvokeAI](https://github.com/invoke-ai/InvoekAI) and click on the "Fork" Button
-in the top right.
-
-## Clone your fork
-
-After you forked the Repository, you should clone it to your dev machine:
-
-=== ":fontawesome-brands-linux:Linux / :simple-apple:macOS"
-
-    ``` sh
-    git clone https://github.com/<github username>/InvokeAI \
-    && cd InvokeAI
-    ```
-
-=== ":fontawesome-brands-windows:Windows"
-
-    ``` powershell
-    git clone https://github.com/<github username>/InvokeAI `
-    && cd InvokeAI
-    ```
-
-## Install in Editable Mode
-
-To install InvokeAI in editable mode, (as always) we recommend to create and
-activate a venv first. Afterwards you can install the InvokeAI Package,
-including dev and docs extras in editable mode, follwed by the installation of
-the pre-commit hook:
-
-=== ":fontawesome-brands-linux:Linux / :simple-apple:macOS"
-
-    ``` sh
-    python -m venv .venv \
-      --prompt InvokeAI \
-      --upgrade-deps \
-    && source .venv/bin/activate \
-    && pip install \
-      --upgrade-deps \
-      --use-pep517 \
-      --editable=".[dev,docs]" \
-    && pre-commit install
-    ```
-
-=== ":fontawesome-brands-windows:Windows"
-
-    ``` powershell
-    python -m venv .venv `
-      --prompt InvokeAI `
-      --upgrade-deps `
-    && .venv/scripts/activate.ps1 `
-    && pip install `
-      --upgrade `
-      --use-pep517 `
-      --editable=".[dev,docs]" `
-    && pre-commit install
-    ```
-
-## Create a branch
-
-Make sure you are on main branch, from there create your feature branch:
-
-=== ":fontawesome-brands-linux:Linux / :simple-apple:macOS"
-
-    ``` sh
-    git checkout main \
-    && git pull \
-    && git checkout -B <branch name>
-    ```
-
-=== ":fontawesome-brands-windows:Windows"
-
-    ``` powershell
-    git checkout main `
-    && git pull `
-    && git checkout -B <branch name>
-    ```
-
-## Commit your changes
-
-When you are done with adding / updating content, you need to commit those
-changes to your repository before you can actually open an PR:
-
-```{ .sh .annotate }
-git add <files you have changed> # (1)!
-git commit -m "A commit message which describes your change"
-git push
-```
-
-1. Replace this with a space seperated list of the files you changed, like:
-   `README.md foo.sh bar.json baz`
-
-## Create a Pull Request
-
-After pushing your changes, you are ready to create a Pull Request. just head
-over to your fork on [GitHub](https://github.com), which should already show you
-a message that there have been recent changes on your feature branch and a green
-button which you could use to create the PR.
-
-The default target for your PRs would be the main branch of
-[invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)
-
-Another way would be to create it in VS-Code or via the GitHub CLI (or even via
-the GitHub CLI in a VS-Code Terminal Window 🤭):
-
-```sh
-gh pr create
-```
-
-The CLI will inform you if there are still unpushed commits on your branch. It
-will also prompt you for things like the the Title and the Body (Description) if
-you did not already pass them as arguments.

docs/help/contributing/020_ISSUES.md

@@ -1,26 +0,0 @@
----
-title: Issues
----
-
-# :octicons-issue-opened-16: Issues
-
-## :fontawesome-solid-bug: Report a bug
-
-If you stumbled over a bug while using InvokeAI, we would apreciate it a lot if
-you
-[open a issue](https://github.com/invoke-ai/InvokeAI/issues/new?assignees=&labels=bug&template=BUG_REPORT.yml&title=%5Bbug%5D%3A+)
-to inform us about the details so that our developers can look into it.
-
-If you also know how to fix the bug, take a look [here](010_PULL_REQUEST.md) to
-find out how to create a Pull Request.
-
-## Request a feature
-
-If you have a idea for a new feature on your mind which you would like to see in
-InvokeAI, there is a
-[feature request](https://github.com/invoke-ai/InvokeAI/issues/new?assignees=&labels=bug&template=BUG_REPORT.yml&title=%5Bbug%5D%3A+)
-available in the issues section of the repository.
-
-If you are just curious which features already got requested you can find the
-overview of open requests
-[here](https://github.com/invoke-ai/InvokeAI/labels/enhancement)

docs/help/contributing/030_DOCS.md

@@ -1,32 +0,0 @@
----
-title: docs
----
-
-# :simple-readthedocs: MkDocs-Material
-
-If you want to contribute to the docs, there is a easy way to verify the results
-of your changes before commiting them.
-
-Just follow the steps in the [Pull-Requests](010_PULL_REQUEST.md) docs, there we
-already
-[create a venv and install the docs extras](010_PULL_REQUEST.md#install-in-editable-mode).
-When installed it's as simple as:
-
-```sh
-mkdocs serve
-```
-
-This will build the docs locally and serve them on your local host, even
-auto-refresh is included, so you can just update a doc, save it and tab to the
-browser, without the needs of restarting the `mkdocs serve`.
-
-More information about the "mkdocs flavored markdown syntax" can be found
-[here](https://squidfunk.github.io/mkdocs-material/reference/).
-
-## :material-microsoft-visual-studio-code:VS-Code
-
-We also provide a
-[launch configuration for VS-Code](../IDE-Settings/vs-code.md#launchjson) which
-includes a `mkdocs serve` entrypoint as well. You also don't have to worry about
-the formatting since this is automated via prettier, but this is of course not
-limited to VS-Code.

docs/help/contributing/090_NODE_TRANSFORMATION.md

@@ -1,76 +0,0 @@
-# Tranformation to nodes
-
-## Current state
-
-```mermaid
-flowchart TD
-    web[WebUI];
-    cli[CLI];
-    web --> |img2img| generate(generate);
-    web --> |txt2img| generate(generate);
-    cli --> |txt2img| generate(generate);
-    cli --> |img2img| generate(generate);
-    generate --> model_manager;
-    generate --> generators;
-    generate --> ti_manager[TI Manager];
-    generate --> etc;
-```
-
-## Transitional Architecture
-
-### first step
-
-```mermaid
-flowchart TD
-    web[WebUI];
-    cli[CLI];
-    web --> |img2img| img2img_node(Img2img node);
-    web --> |txt2img| generate(generate);
-    img2img_node --> model_manager;
-    img2img_node --> generators;
-    cli --> |txt2img| generate;
-    cli --> |img2img| generate;
-    generate --> model_manager;
-    generate --> generators;
-    generate --> ti_manager[TI Manager];
-    generate --> etc;
-```
-
-### second step
-
-```mermaid
-flowchart TD
-    web[WebUI];
-    cli[CLI];
-    web --> |img2img| img2img_node(img2img node);
-    img2img_node --> model_manager;
-    img2img_node --> generators;
-    web --> |txt2img| txt2img_node(txt2img node);
-    cli --> |txt2img| txt2img_node;
-    cli --> |img2img| generate(generate);
-    generate --> model_manager;
-    generate --> generators;
-    generate --> ti_manager[TI Manager];
-    generate --> etc;
-    txt2img_node --> model_manager;
-    txt2img_node --> generators;
-    txt2img_node --> ti_manager[TI Manager];
-```
-
-## Final Architecture
-
-```mermaid
-flowchart TD
-    web[WebUI];
-    cli[CLI];
-    web --> |img2img|img2img_node(img2img node);
-    cli --> |img2img|img2img_node;
-    web --> |txt2img|txt2img_node(txt2img node);
-    cli --> |txt2img|txt2img_node;
-    img2img_node --> model_manager;
-    txt2img_node --> model_manager;
-    img2img_node --> generators;
-    txt2img_node --> generators;
-    img2img_node --> ti_manager[TI Manager];
-    txt2img_node --> ti_manager[TI Manager];
-```

docs/help/contributing/index.md

@@ -1,16 +0,0 @@
----
-title: Contributing
----
-
-# :fontawesome-solid-code-commit: Contributing
-
-There are different ways how you can contribute to
-[InvokeAI](https://github.com/invoke-ai/InvokeAI), like Translations, opening
-Issues for Bugs or ideas how to improve.
-
-This Section of the docs will explain some of the different ways of how you can
-contribute to make it easier for newcommers as well as advanced users :nerd:
-
-If you want to contribute code, but you do not have an exact idea yet, take a
-look at the currently open
-[:fontawesome-solid-bug: Bug Reports](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen+label%3Abug)

docs/help/index.md

@@ -1,12 +0,0 @@
-# :material-help:Help
-
-If you are looking for help with the installation of InvokeAI, please take a
-look into the [Installation](../installation/index.md) section of the docs.
-
-Here you will find help to topics like
-
--   how to contribute
--   configuration recommendation for IDEs
-
-If you have an Idea about what's missing and aren't scared from contributing,
-just take a look at [DOCS](./contributing/030_DOCS.md) to find out how to do so.

docs/index.md

@@ -2,8 +2,6 @@
 title: Home
 ---
 
-# :octicons-home-16: Home
-
 <!--
   The Docs you find here (/docs/*) are built and deployed via mkdocs. If you want to run a local version to verify your changes, it's as simple as::
 
@@ -31,36 +29,36 @@ title: Home
 [![github open prs badge]][github open prs link]
 
 [ci checks on dev badge]:
-    https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
+  https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/development?label=CI%20status%20on%20dev&cache=900&icon=github
 [ci checks on dev link]:
-    https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
+  https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment
 [ci checks on main badge]:
-    https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
+  https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
 [ci checks on main link]:
-    https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
+  https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
 [discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
 [discord link]: https://discord.gg/ZmtBAhwWhy
 [github forks badge]:
-    https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
+  https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
 [github forks link]:
-    https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion
+  https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion
 [github open issues badge]:
-    https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github
+  https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github
 [github open issues link]:
-    https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen
+  https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen
 [github open prs badge]:
-    https://flat.badgen.net/github/open-prs/invoke-ai/InvokeAI?icon=github
+  https://flat.badgen.net/github/open-prs/invoke-ai/InvokeAI?icon=github
 [github open prs link]:
-    https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
+  https://github.com/invoke-ai/InvokeAI/pulls?q=is%3Apr+is%3Aopen
 [github stars badge]:
-    https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
+  https://flat.badgen.net/github/stars/invoke-ai/InvokeAI?icon=github
 [github stars link]: https://github.com/invoke-ai/InvokeAI/stargazers
 [latest commit to dev badge]:
-    https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
+  https://flat.badgen.net/github/last-commit/invoke-ai/InvokeAI/development?icon=github&color=yellow&label=last%20dev%20commit&cache=900
 [latest commit to dev link]:
-    https://github.com/invoke-ai/InvokeAI/commits/development
+  https://github.com/invoke-ai/InvokeAI/commits/development
 [latest release badge]:
-    https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
+  https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
 [latest release link]: https://github.com/invoke-ai/InvokeAI/releases
 
 </div>
@@ -89,24 +87,24 @@ Q&A</a>]
 
 You wil need one of the following:
 
--   :simple-nvidia: An NVIDIA-based graphics card with 4 GB or more VRAM memory.
--   :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux
-    only)
--   :fontawesome-brands-apple: An Apple computer with an M1 chip.
+- :simple-nvidia: An NVIDIA-based graphics card with 4 GB or more VRAM memory.
+- :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux
+  only)
+- :fontawesome-brands-apple: An Apple computer with an M1 chip.
 
 We do **not recommend** the following video cards due to issues with their
 running in half-precision mode and having insufficient VRAM to render 512x512
 images in full-precision mode:
 
--   NVIDIA 10xx series cards such as the 1080ti
--   GTX 1650 series cards
--   GTX 1660 series cards
+- NVIDIA 10xx series cards such as the 1080ti
+- GTX 1650 series cards
+- GTX 1660 series cards
 
 ### :fontawesome-solid-memory: Memory and Disk
 
--   At least 12 GB Main Memory RAM.
--   At least 18 GB of free disk space for the machine learning model, Python,
-    and all its dependencies.
+- At least 12 GB Main Memory RAM.
+- At least 18 GB of free disk space for the machine learning model, Python, and
+  all its dependencies.
 
 ## :octicons-package-dependencies-24: Installation
 
@@ -115,65 +113,48 @@ either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
 driver).
 
 ### [Installation Getting Started Guide](installation)
-
 #### [Automated Installer](installation/010_INSTALL_AUTOMATED.md)
-
 This method is recommended for 1st time users
-
 #### [Manual Installation](installation/020_INSTALL_MANUAL.md)
-
 This method is recommended for experienced users and developers
-
 #### [Docker Installation](installation/040_INSTALL_DOCKER.md)
-
 This method is recommended for those familiar with running Docker containers
-
 ### Other Installation Guides
-
--   [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
--   [XFormers](installation/070_INSTALL_XFORMERS.md)
--   [CUDA and ROCm Drivers](installation/030_INSTALL_CUDA_AND_ROCM.md)
--   [Installing New Models](installation/050_INSTALLING_MODELS.md)
+  - [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
+  - [XFormers](installation/070_INSTALL_XFORMERS.md)
+  - [CUDA and ROCm Drivers](installation/030_INSTALL_CUDA_AND_ROCM.md)
+  - [Installing New Models](installation/050_INSTALLING_MODELS.md)
 
 ## :octicons-gift-24: InvokeAI Features
 
 ### The InvokeAI Web Interface
-
--   [WebUI overview](features/WEB.md)
--   [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)
--   [WebUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
+- [WebUI overview](features/WEB.md)
+- [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)
+- [WebUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
 <!-- separator -->
-
 ### The InvokeAI Command Line Interface
-
--   [Command Line Interace Reference Guide](features/CLI.md)
+- [Command Line Interace Reference Guide](features/CLI.md)
 <!-- separator -->
-
 ### Image Management
-
--   [Image2Image](features/IMG2IMG.md)
--   [Inpainting](features/INPAINTING.md)
--   [Outpainting](features/OUTPAINTING.md)
--   [Adding custom styles and subjects](features/CONCEPTS.md)
--   [Upscaling and Face Reconstruction](features/POSTPROCESS.md)
--   [Embiggen upscaling](features/EMBIGGEN.md)
--   [Other Features](features/OTHER.md)
+- [Image2Image](features/IMG2IMG.md)
+- [Inpainting](features/INPAINTING.md)
+- [Outpainting](features/OUTPAINTING.md)
+- [Adding custom styles and subjects](features/CONCEPTS.md)
+- [Upscaling and Face Reconstruction](features/POSTPROCESS.md)
+- [Embiggen upscaling](features/EMBIGGEN.md)
+- [Other Features](features/OTHER.md)
 
 <!-- separator -->
-
 ### Model Management
-
--   [Installing](installation/050_INSTALLING_MODELS.md)
--   [Model Merging](features/MODEL_MERGING.md)
--   [Style/Subject Concepts and Embeddings](features/CONCEPTS.md)
--   [Textual Inversion](features/TEXTUAL_INVERSION.md)
--   [Not Safe for Work (NSFW) Checker](features/NSFW.md)
+- [Installing](installation/050_INSTALLING_MODELS.md)
+- [Model Merging](features/MODEL_MERGING.md)
+- [Style/Subject Concepts and Embeddings](features/CONCEPTS.md)
+- [Textual Inversion](features/TEXTUAL_INVERSION.md)
+- [Not Safe for Work (NSFW) Checker](features/NSFW.md)
 <!-- seperator -->
-
 ### Prompt Engineering
-
--   [Prompt Syntax](features/PROMPTS.md)
--   [Generating Variations](features/VARIATIONS.md)
+- [Prompt Syntax](features/PROMPTS.md)
+- [Generating Variations](features/VARIATIONS.md)
 
 ## :octicons-log-16: Latest Changes
 
@@ -181,188 +162,84 @@ This method is recommended for those familiar with running Docker containers
 
 #### Migration to Stable Diffusion `diffusers` models
 
-Previous versions of InvokeAI supported the original model file format
-introduced with Stable Diffusion 1.4. In the original format, known variously as
-"checkpoint", or "legacy" format, there is a single large weights file ending
-with `.ckpt` or `.safetensors`. Though this format has served the community
-well, it has a number of disadvantages, including file size, slow loading times,
-and a variety of non-standard variants that require special-case code to handle.
-In addition, because checkpoint files are actually a bundle of multiple machine
-learning sub-models, it is hard to swap different sub-models in and out, or to
-share common sub-models. A new format, introduced by the StabilityAI company in
-collaboration with HuggingFace, is called `diffusers` and consists of a
-directory of individual models. The most immediate benefit of `diffusers` is
-that they load from disk very quickly. A longer term benefit is that in the near
-future `diffusers` models will be able to share common sub-models, dramatically
-reducing disk space when you have multiple fine-tune models derived from the
-same base.
+Previous versions of InvokeAI supported the original model file format introduced with Stable Diffusion 1.4. In the original format, known variously as "checkpoint", or "legacy" format, there is a single large weights file ending with `.ckpt` or `.safetensors`. Though this format has served the community well, it has a number of disadvantages, including file size, slow loading times, and a variety of non-standard variants that require special-case code to handle. In addition, because checkpoint files are actually a bundle of multiple machine learning sub-models, it is hard to swap different sub-models in and out, or to share common sub-models. A new format, introduced by the StabilityAI company in collaboration with HuggingFace, is called `diffusers` and consists of a directory of individual models. The most immediate benefit of `diffusers` is that they load from disk very quickly. A longer term benefit is that in the near future `diffusers` models will be able to share common sub-models, dramatically reducing disk space when you have multiple fine-tune models derived from the same base.
 
-When you perform a new install of version 2.3.0, you will be offered the option
-to install the `diffusers` versions of a number of popular SD models, including
-Stable Diffusion versions 1.5 and 2.1 (including the 768x768 pixel version of
-2.1). These will act and work just like the checkpoint versions. Do not be
-concerned if you already have a lot of ".ckpt" or ".safetensors" models on disk!
-InvokeAI 2.3.0 can still load these and generate images from them without any
-extra intervention on your part.
+When you perform a new install of version 2.3.0, you will be offered the option to install the `diffusers` versions of a number of popular SD models, including Stable Diffusion versions 1.5 and 2.1 (including the 768x768 pixel version of 2.1). These will act and work just like the checkpoint versions. Do not be concerned if you already have a lot of ".ckpt" or ".safetensors" models on disk! InvokeAI 2.3.0 can still load these and generate images from them without any extra intervention on your part.
 
-To take advantage of the optimized loading times of `diffusers` models, InvokeAI
-offers options to convert legacy checkpoint models into optimized `diffusers`
-models. If you use the `invokeai` command line interface, the relevant commands
-are:
+To take advantage of the optimized loading times of `diffusers` models, InvokeAI offers options to convert legacy checkpoint models into optimized `diffusers` models. If you use the `invokeai` command line interface, the relevant commands are:
 
--   `!convert_model` -- Take the path to a local checkpoint file or a URL that
-    is pointing to one, convert it into a `diffusers` model, and import it into
-    InvokeAI's models registry file.
--   `!optimize_model` -- If you already have a checkpoint model in your InvokeAI
-    models file, this command will accept its short name and convert it into a
-    like-named `diffusers` model, optionally deleting the original checkpoint
-    file.
--   `!import_model` -- Take the local path of either a checkpoint file or a
-    `diffusers` model directory and import it into InvokeAI's registry file. You
-    may also provide the ID of any diffusers model that has been published on
-    the
-    [HuggingFace models repository](https://huggingface.co/models?pipeline_tag=text-to-image&sort=downloads)
-    and it will be downloaded and installed automatically.
+* `!convert_model` -- Take the path to a local checkpoint file or a URL that is pointing to one, convert it into a `diffusers` model, and import it into InvokeAI's models registry file.
+* `!optimize_model` -- If you already have a checkpoint model in your InvokeAI models file, this command will accept its short name and convert it into a like-named `diffusers` model, optionally deleting the original checkpoint file.
+* `!import_model` -- Take the local path of either a checkpoint file or a `diffusers` model directory and import it into InvokeAI's registry file. You may also provide the ID of any diffusers model that has been published on the [HuggingFace models repository](https://huggingface.co/models?pipeline_tag=text-to-image&sort=downloads) and it will be downloaded and installed automatically.
 
 The WebGUI offers similar functionality for model management.
 
-For advanced users, new command-line options provide additional functionality.
-Launching `invokeai` with the argument `--autoconvert <path to directory>` takes
-the path to a directory of checkpoint files, automatically converts them into
-`diffusers` models and imports them. Each time the script is launched, the
-directory will be scanned for new checkpoint files to be loaded. Alternatively,
-the `--ckpt_convert` argument will cause any checkpoint or safetensors model
-that is already registered with InvokeAI to be converted into a `diffusers`
-model on the fly, allowing you to take advantage of future diffusers-only
-features without explicitly converting the model and saving it to disk.
+For advanced users, new command-line options provide additional functionality. Launching `invokeai` with the argument `--autoconvert <path to directory>` takes the path to a directory of checkpoint files, automatically converts them into `diffusers` models and imports them. Each time the script is launched, the directory will be scanned for new checkpoint files to be loaded. Alternatively, the `--ckpt_convert` argument will cause any checkpoint or safetensors model that is already registered with InvokeAI to be converted into a `diffusers` model on the fly, allowing you to take advantage of future diffusers-only features without explicitly converting the model and saving it to disk.
 
-Please see
-[INSTALLING MODELS](https://invoke-ai.github.io/InvokeAI/installation/050_INSTALLING_MODELS/)
-for more information on model management in both the command-line and Web
-interfaces.
+Please see [INSTALLING MODELS](https://invoke-ai.github.io/InvokeAI/installation/050_INSTALLING_MODELS/) for more information on model management in both the command-line and Web interfaces.
 
 #### Support for the `XFormers` Memory-Efficient Crossattention Package
 
-On CUDA (Nvidia) systems, version 2.3.0 supports the `XFormers` library. Once
-installed, the`xformers` package dramatically reduces the memory footprint of
-loaded Stable Diffusion models files and modestly increases image generation
-speed. `xformers` will be installed and activated automatically if you specify a
-CUDA system at install time.
+On CUDA (Nvidia) systems, version 2.3.0 supports the `XFormers` library. Once installed,  the`xformers` package dramatically reduces the memory footprint of loaded Stable Diffusion models files and modestly increases image generation speed. `xformers` will be installed and activated automatically if you specify a CUDA system at install time.
 
-The caveat with using `xformers` is that it introduces slightly
-non-deterministic behavior, and images generated using the same seed and other
-settings will be subtly different between invocations. Generally the changes are
-unnoticeable unless you rapidly shift back and forth between images, but to
-disable `xformers` and restore fully deterministic behavior, you may launch
-InvokeAI using the `--no-xformers` option. This is most conveniently done by
-opening the file `invokeai/invokeai.init` with a text editor, and adding the
-line `--no-xformers` at the bottom.
+The caveat with using `xformers` is that it introduces slightly non-deterministic behavior, and images generated using the same seed and other settings will be subtly different between invocations. Generally the changes are unnoticeable unless you rapidly shift back and forth between images, but to disable `xformers` and restore fully deterministic behavior, you may launch InvokeAI using the `--no-xformers` option. This is most conveniently done by opening the file `invokeai/invokeai.init` with a text editor, and adding the line `--no-xformers` at the bottom.
 
 #### A Negative Prompt Box in the WebUI
 
-There is now a separate text input box for negative prompts in the WebUI. This
-is convenient for stashing frequently-used negative prompts ("mangled limbs, bad
-anatomy"). The `[negative prompt]` syntax continues to work in the main prompt
-box as well.
+There is now a separate text input box for negative prompts in the WebUI. This is convenient for stashing frequently-used negative prompts ("mangled limbs, bad anatomy"). The `[negative prompt]` syntax continues to work in the main prompt box as well.
 
-To see exactly how your prompts are being parsed, launch `invokeai` with the
-`--log_tokenization` option. The console window will then display the
-tokenization process for both positive and negative prompts.
+To see exactly how your prompts are being parsed, launch `invokeai` with the `--log_tokenization` option. The console window will then display the tokenization process for both positive and negative prompts.
 
 #### Model Merging
 
-Version 2.3.0 offers an intuitive user interface for merging up to three Stable
-Diffusion models using an intuitive user interface. Model merging allows you to
-mix the behavior of models to achieve very interesting effects. To use this,
-each of the models must already be imported into InvokeAI and saved in
-`diffusers` format, then launch the merger using a new menu item in the InvokeAI
-launcher script (`invoke.sh`, `invoke.bat`) or directly from the command line
-with `invokeai-merge --gui`. You will be prompted to select the models to merge,
-the proportions in which to mix them, and the mixing algorithm. The script will
-create a new merged `diffusers` model and import it into InvokeAI for your use.
+Version 2.3.0 offers an intuitive user interface for merging up to three Stable Diffusion models using an intuitive user interface. Model merging allows you to mix the behavior of models to achieve very interesting effects. To use this, each of the models must already be imported into InvokeAI and saved in `diffusers` format, then launch the merger using a new menu item in the InvokeAI launcher script (`invoke.sh`, `invoke.bat`) or directly from the command line with `invokeai-merge --gui`. You will be prompted to select the models to merge, the proportions in which to mix them, and the mixing algorithm. The script will create a new merged `diffusers` model and import it into InvokeAI for your use.
 
-See
-[MODEL MERGING](https://invoke-ai.github.io/InvokeAI/features/MODEL_MERGING/)
-for more details.
+See [MODEL MERGING](https://invoke-ai.github.io/InvokeAI/features/MODEL_MERGING/) for more details.
 
 #### Textual Inversion Training
 
-Textual Inversion (TI) is a technique for training a Stable Diffusion model to
-emit a particular subject or style when triggered by a keyword phrase. You can
-perform TI training by placing a small number of images of the subject or style
-in a directory, and choosing a distinctive trigger phrase, such as
-"pointillist-style". After successful training, The subject or style will be
-activated by including `<pointillist-style>` in your prompt.
+Textual Inversion (TI) is a technique for training a Stable Diffusion model to emit a particular subject or style when triggered by a keyword phrase. You can perform TI training by placing a small number of images of the subject or style in a directory, and choosing a distinctive trigger phrase, such as "pointillist-style". After successful training, The subject or style will be activated by including `<pointillist-style>` in your prompt.
 
-Previous versions of InvokeAI were able to perform TI, but it required using a
-command-line script with dozens of obscure command-line arguments. Version 2.3.0
-features an intuitive TI frontend that will build a TI model on top of any
-`diffusers` model. To access training you can launch from a new item in the
-launcher script or from the command line using `invokeai-ti --gui`.
+Previous versions of InvokeAI were able to perform TI, but it required using a command-line script with dozens of obscure command-line arguments. Version 2.3.0 features an intuitive TI frontend that will build a TI model on top of any `diffusers` model. To access training you can launch from a new item in the launcher script or from the command line using `invokeai-ti --gui`.
 
-See
-[TEXTUAL INVERSION](https://invoke-ai.github.io/InvokeAI/features/TEXTUAL_INVERSION/)
-for further details.
+See [TEXTUAL INVERSION](https://invoke-ai.github.io/InvokeAI/features/TEXTUAL_INVERSION/) for further details.
 
 #### A New Installer Experience
 
-The InvokeAI installer has been upgraded in order to provide a smoother and
-hopefully more glitch-free experience. In addition, InvokeAI is now packaged as
-a PyPi project, allowing developers and power-users to install InvokeAI with the
-command `pip install InvokeAI  --use-pep517`. Please see
-[Installation](#installation) for details.
+The InvokeAI installer has been upgraded in order to provide a smoother and hopefully more glitch-free experience. In addition, InvokeAI is now packaged as a PyPi project, allowing developers and power-users to install InvokeAI with the command `pip install InvokeAI  --use-pep517`. Please see [Installation](#installation) for details.
 
-Developers should be aware that the `pip` installation procedure has been
-simplified and that the `conda` method is no longer supported at all.
-Accordingly, the `environments_and_requirements` directory has been deleted from
-the repository.
+Developers should be aware that the `pip` installation procedure has been simplified and that the `conda` method is no longer supported at all. Accordingly, the `environments_and_requirements` directory has been deleted from the repository.
 
 #### Command-line name changes
 
-All of InvokeAI's functionality, including the WebUI, command-line interface,
-textual inversion training and model merging, can all be accessed from the
-`invoke.sh` and `invoke.bat` launcher scripts. The menu of options has been
-expanded to add the new functionality. For the convenience of developers and
-power users, we have normalized the names of the InvokeAI command-line scripts:
+All of InvokeAI's functionality, including the WebUI, command-line interface, textual inversion training and model merging, can all be accessed from the `invoke.sh` and `invoke.bat` launcher scripts. The menu of options has been expanded to add the new functionality. For the convenience of developers and power users, we have normalized the names of the InvokeAI command-line scripts:
 
--   `invokeai` -- Command-line client
--   `invokeai --web` -- Web GUI
--   `invokeai-merge --gui` -- Model merging script with graphical front end
--   `invokeai-ti --gui` -- Textual inversion script with graphical front end
--   `invokeai-configure` -- Configuration tool for initializing the `invokeai`
-    directory and selecting popular starter models.
+* `invokeai` -- Command-line client
+* `invokeai --web` -- Web GUI
+* `invokeai-merge --gui` -- Model merging script with graphical front end
+* `invokeai-ti --gui` -- Textual inversion script with graphical front end
+* `invokeai-configure` -- Configuration tool for initializing the `invokeai` directory and selecting popular starter models.
 
-For backward compatibility, the old command names are also recognized, including
-`invoke.py` and `configure-invokeai.py`. However, these are deprecated and will
-eventually be removed.
+For backward compatibility, the old command names are also recognized, including `invoke.py` and `configure-invokeai.py`. However, these are deprecated and will eventually be removed.
 
-Developers should be aware that the locations of the script's source code has
-been moved. The new locations are:
+Developers should be aware that the locations of the script's source code has been moved. The new locations are:
+* `invokeai` =>  `ldm/invoke/CLI.py`
+* `invokeai-configure` => `ldm/invoke/config/configure_invokeai.py`
+* `invokeai-ti`=> `ldm/invoke/training/textual_inversion.py`
+* `invokeai-merge` => `ldm/invoke/merge_diffusers`
 
--   `invokeai` => `ldm/invoke/CLI.py`
--   `invokeai-configure` => `ldm/invoke/config/configure_invokeai.py`
--   `invokeai-ti`=> `ldm/invoke/training/textual_inversion.py`
--   `invokeai-merge` => `ldm/invoke/merge_diffusers`
+Developers are strongly encouraged to perform an "editable" install of InvokeAI using `pip install -e .  --use-pep517` in the Git repository, and then to call the scripts using their 2.3.0 names, rather than executing the scripts directly. Developers should also be aware that the several important data files have been relocated into a new directory named `invokeai`. This includes the WebGUI's `frontend` and `backend` directories, and the `INITIAL_MODELS.yaml` files used by the installer to select starter models. Eventually all InvokeAI modules will be in subdirectories of `invokeai`.
 
-Developers are strongly encouraged to perform an "editable" install of InvokeAI
-using `pip install -e .  --use-pep517` in the Git repository, and then to call
-the scripts using their 2.3.0 names, rather than executing the scripts directly.
-Developers should also be aware that the several important data files have been
-relocated into a new directory named `invokeai`. This includes the WebGUI's
-`frontend` and `backend` directories, and the `INITIAL_MODELS.yaml` files used
-by the installer to select starter models. Eventually all InvokeAI modules will
-be in subdirectories of `invokeai`.
-
-Please see
-[2.3.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v2.3.0)
-for further details. For older changelogs, please visit the
+Please see [2.3.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v2.3.0) for further details.
+For older changelogs, please visit the
 **[CHANGELOG](CHANGELOG/#v223-2-december-2022)**.
 
 ## :material-target: Troubleshooting
 
-Please check out our
-**[:material-frequently-asked-questions: Troubleshooting Guide](installation/010_INSTALL_AUTOMATED.md#troubleshooting)**
-to get solutions for common installation problems and other issues.
+Please check out our **[:material-frequently-asked-questions:
+Troubleshooting
+Guide](installation/010_INSTALL_AUTOMATED.md#troubleshooting)** to
+get solutions for common installation problems and other issues.
 
 ## :octicons-repo-push-24: Contributing
 
@@ -388,8 +265,8 @@ thank them for their time, hard work and effort.
 For support, please use this repository's GitHub Issues tracking service. Feel
 free to send me an email if you use and like the script.
 
-Original portions of the software are Copyright (c) 2022-23 by
-[The InvokeAI Team](https://github.com/invoke-ai).
+Original portions of the software are Copyright (c) 2022-23
+by [The InvokeAI Team](https://github.com/invoke-ai).
 
 ## :octicons-book-24: Further Reading
 

docs/installation/010_INSTALL_AUTOMATED.md

@@ -417,7 +417,7 @@ Then type the following commands:
 
 === "AMD System"
     ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.2
     ```
 
 ### Corrupted configuration file

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -110,7 +110,7 @@ recipes are available
 
 When installing torch and torchvision manually with `pip`, remember to provide
 the argument `--extra-index-url
-https://download.pytorch.org/whl/rocm5.4.2` as described in the [Manual
+https://download.pytorch.org/whl/rocm5.2` as described in the [Manual
 Installation Guide](020_INSTALL_MANUAL.md).
 
 This will be done automatically for you if you use the installer

docs/installation/050_INSTALLING_MODELS.md

@@ -211,26 +211,6 @@ description for the model, whether to make this the default model that
 is loaded at InvokeAI startup time, and whether to replace its
 VAE. Generally the answer to the latter question is "no".
 
-### Specifying a configuration file for legacy checkpoints
-
-Some checkpoint files come with instructions to use a specific .yaml
-configuration file. For InvokeAI load this file correctly, please put
-the config file in the same directory as the corresponding `.ckpt` or
-`.safetensors` file and make sure the file has the same basename as
-the weights file. Here is an example:
-
-```bash
-wonderful-model-v2.ckpt
-wonderful-model-v2.yaml
-```
-
-Similarly, to use a custom VAE, name the VAE like this:
-
-```bash
-wonderful-model-v2.vae.pt
-```
-
-
 ### Converting legacy models into `diffusers`
 
 The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`

docs/requirements-mkdocs.txt

@@ -0,0 +1,5 @@
+mkdocs
+mkdocs-material>=8, <9
+mkdocs-git-revision-date-localized-plugin
+mkdocs-redirects==1.2.0
+

installer/lib/installer.py

@@ -241,18 +241,14 @@ class InvokeAiInstance:
 
         from plumbum import FG, local
 
-        # Note that we're installing pinned versions of torch and
-        # torchvision here, which may not correspond to what is
-        # in pyproject.toml. This is a hack to prevent torch 2.0 from
-        # being installed and immediately uninstalled and replaced with 1.13
         pip = local[self.pip]
 
         (
             pip[
                 "install",
                 "--require-virtualenv",
-                "torch~=1.13.1",
-                "torchvision>=0.14.1",
+                "torch",
+                "torchvision",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,
@@ -383,9 +379,6 @@ class InvokeAiInstance:
             shutil.copy(src, dest)
             os.chmod(dest, 0o0755)
 
-        if OS == "Linux":
-            shutil.copy(Path(__file__).parent / '..' / "templates" / "dialogrc", self.runtime / '.dialogrc')
-
     def update(self):
         pass
 

installer/templates/dialogrc

@@ -1,27 +0,0 @@
-# Screen 
-use_shadow = OFF
-use_colors = ON
-screen_color = (BLACK, BLACK, ON)
-
-# Box 
-dialog_color = (YELLOW, BLACK , ON) 
-title_color = (YELLOW, BLACK, ON) 
-border_color = (YELLOW, BLACK, OFF) 
-border2_color = (YELLOW, BLACK, OFF) 
-
-# Button 
-button_active_color = (RED, BLACK, OFF) 
-button_inactive_color = (YELLOW, BLACK, OFF) 
-button_label_active_color = (YELLOW,BLACK,ON) 
-button_label_inactive_color = (YELLOW,BLACK,ON)
- 
-# Menu box 
-menubox_color = (BLACK, BLACK, ON) 
-menubox_border_color = (YELLOW, BLACK, OFF) 
-menubox_border2_color = (YELLOW, BLACK, OFF) 
-
-# Menu window 
-item_color = (YELLOW, BLACK, OFF) 
-item_selected_color = (BLACK, YELLOW, OFF) 
-tag_key_color = (YELLOW, BLACK, OFF) 
-tag_key_selected_color = (BLACK, YELLOW, OFF)

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 "Generate images 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=$(dialog --clear \
-        --backtitle "\Zb\Zu\Z3InvokeAI" \
-        --colors \
-        --title "What would you like to run?" \
-        --ok-label "Run" \
-        --cancel-label "Exit" \
-        --help-button \
-        --help-label "CLI Help" \
-        --menu "Select an option:" \
-        0 0 0 \
-        "${options[@]}" \
-        2>&1 >/dev/tty) || clear
-    do_choice "$choice"
-    clear
-}
-
-# Command-line interface for launching Invoke functions
-do_line_input() {
-    clear
-    printf " ** For a more attractive experience, please install the 'dialog' utility using your package manager. **\n\n"
-    printf "Do you want to generate images using the\n"
-    printf "1: Browser-based UI\n"
-    printf "2: Command-line interface\n"
-    printf "3: Run textual inversion training\n"
-    printf "4: Merge models (diffusers type only)\n"
-    printf "5: Download and install models\n"
-    printf "6: Change InvokeAI startup options\n"
-    printf "7: Re-run the configure script to fix a broken install\n"
-    printf "8: Open the developer console\n"
-    printf "9: Update InvokeAI\n"
-    printf "10: Command-line help\n"
-    printf "Q: Quit\n\n"
-    read -p "Please enter 1-10, Q: [1] " yn
-    choice=${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
+while true
+do
 if [ "$0" != "bash" ]; then
-    while true; do
-        if $tui; then
-            # .dialogrc must be located in the same directory as the invoke.sh script
-            export DIALOGRC="./.dialogrc"
-            do_dialog
-        else
-            do_line_input
-        fi
-    done
+    echo "Do you want to generate images using the"
+    echo "1. command-line interface"
+    echo "2. browser-based UI"
+    echo "3. run textual inversion training"
+    echo "4. merge models (diffusers type only)"
+    echo "5. download and install models"
+    echo "6. change InvokeAI startup options"
+    echo "7. re-run the configure script to fix a broken install"
+    echo "8. open the developer console"
+    echo "9. update InvokeAI"
+    echo "10. command-line help"
+    echo "Q - Quit"
+    echo ""
+    read -p "Please enter 1-10, Q: [2] " yn
+    choice=${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
 else # in developer console
     python --version
-    printf "Press ^D to exit\n"
+    echo "Press ^D to exit"
     export PS1="(InvokeAI) \u@\h \w> "
 fi
+done

invokeai/configs/INITIAL_MODELS.yaml

@@ -13,19 +13,14 @@ sd-inpainting-1.5:
   vae:
     repo_id: stabilityai/sd-vae-ft-mse
   recommended: True
-stable-diffusion-2.1-768:
+stable-diffusion-2.1:
    description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
    repo_id: stabilityai/stable-diffusion-2-1
    format: diffusers
    recommended: True
-stable-diffusion-2.1-base:
-   description: Stable Diffusion version 2.1 diffusers model, trained on 512 pixel images (5.21 GB)
-   repo_id: stabilityai/stable-diffusion-2-1-base
-   format: diffusers
-   recommended: False
 sd-inpainting-2.0:
    description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
-   repo_id: stabilityai/stable-diffusion-2-inpainting
+   repo_id: stabilityai/stable-diffusion-2-1
    format: diffusers
    recommended: False
 analog-diffusion-1.0:

invokeai/configs/stable-diffusion/v2-inference.yaml

@@ -1,67 +0,0 @@
-model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        use_checkpoint: True
-        use_fp16: True
-        image_size: 32 # unused
-        in_channels: 4
-        out_channels: 4
-        model_channels: 320
-        attention_resolutions: [ 4, 2, 1 ]
-        num_res_blocks: 2
-        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
-        use_spatial_transformer: True
-        use_linear_in_transformer: True
-        transformer_depth: 1
-        context_dim: 1024
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          #attn_type: "vanilla-xformers"
-          double_z: true
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"

invokeai/frontend/.prettierrc.js

@@ -1,7 +1,6 @@
 module.exports = {
   trailingComma: 'es5',
   tabWidth: 2,
-  endOfLine: 'auto',
   semi: true,
   singleQuote: true,
   overrides: [

invokeai/frontend/dist/index.html

@@ -5,7 +5,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>InvokeAI - A Stable Diffusion Toolkit</title>
     <link rel="shortcut icon" type="icon" href="./assets/favicon-0d253ced.ico" />
-    <script type="module" crossorigin src="./assets/index-c09cf9ca.js"></script>
+    <script type="module" crossorigin src="./assets/index-0e39fbc4.js"></script>
     <link rel="stylesheet" href="./assets/index-14cb2922.css">
   </head>
 

invokeai/frontend/dist/locales/en.json

@@ -63,8 +63,7 @@
         "statusConvertingModel": "Converting Model",
         "statusModelConverted": "Model Converted",
         "statusMergingModels": "Merging Models",
-        "statusMergedModels": "Models Merged",
-        "pinOptionsPanel": "Pin Options Panel"
+        "statusMergedModels": "Models Merged"
     },
     "gallery": {
         "generations": "Generations",
@@ -365,8 +364,7 @@
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
         "convertToDiffusersSaveLocation": "Save Location",
         "v1": "v1",
-        "v2_base": "v2 (512px)",
-        "v2_768": "v2 (768px)",
+        "v2": "v2",
         "inpainting": "v1 Inpainting",
         "customConfig": "Custom Config",
         "pathToCustomConfig": "Path To Custom Config",
@@ -395,9 +393,7 @@
         "modelMergeInterpAddDifferenceHelp": "In this mode, Model 3 is first subtracted from Model 2. The resulting version is blended with Model 1 with the alpha rate set above.",
         "inverseSigmoid": "Inverse Sigmoid",
         "sigmoid": "Sigmoid",
-        "weightedSum": "Weighted Sum",
-        "none": "none",
-        "addDifference": "Add Difference"
+        "weightedSum": "Weighted Sum"
     },
     "parameters": {
         "general": "General",

invokeai/frontend/dist/locales/es.json

@@ -15,7 +15,7 @@
         "langSpanish": "Español",
         "nodesDesc": "Un sistema de generación de imágenes basado en nodos, actualmente se encuentra en desarrollo. Mantente pendiente a nuestras actualizaciones acerca de esta fabulosa funcionalidad.",
         "postProcessing": "Post-procesamiento",
-        "postProcessDesc1": "Invoke AI ofrece una gran variedad de funciones de post-procesamiento, El aumento de tamaño y Restauración de Rostros ya se encuentran disponibles en la interfaz web, puedes acceder desde el menú de Opciones Avanzadas en las pestañas de Texto a Imagen y de Imagen a Imagen. También puedes acceder a estas funciones directamente mediante el botón de acciones en el menú superior de la imagen actual o en el visualizador.",
+        "postProcessDesc1": "Invoke AI ofrece una gran variedad de funciones de post-procesamiento, El aumento de tamaño y Restauración de Rostros ya se encuentran disponibles en la interfaz web, puedes acceder desde el menú de Opciones Avanzadas en las pestañas de Texto a Imagen y de Imagen a Imagen. También puedes acceder a estas funciones directamente mediante el botón de acciones en el menú superior de la imagen actual o en el visualizador",
         "postProcessDesc2": "Una interfaz de usuario dedicada se lanzará pronto para facilitar flujos de trabajo de postprocesamiento más avanzado.",
         "postProcessDesc3": "La Interfaz de Línea de Comandos de Invoke AI ofrece muchas otras características, incluyendo -Embiggen-.",
         "training": "Entrenamiento",
@@ -44,26 +44,7 @@
         "statusUpscaling": "Aumentando Tamaño",
         "statusUpscalingESRGAN": "Restaurando Rostros(ESRGAN)",
         "statusLoadingModel": "Cargando Modelo",
-        "statusModelChanged": "Modelo cambiado",
-        "statusMergedModels": "Modelos combinados",
-        "githubLabel": "Github",
-        "discordLabel": "Discord",
-        "langEnglish": "Inglés",
-        "langDutch": "Holandés",
-        "langFrench": "Francés",
-        "langGerman": "Alemán",
-        "langItalian": "Italiano",
-        "langArabic": "Árabe",
-        "langJapanese": "Japones",
-        "langPolish": "Polaco",
-        "langBrPortuguese": "Portugués brasileño",
-        "langRussian": "Ruso",
-        "langSimplifiedChinese": "Chino simplificado",
-        "langUkranian": "Ucraniano",
-        "back": "Atrás",
-        "statusConvertingModel": "Convertir el modelo",
-        "statusModelConverted": "Modelo adaptado",
-        "statusMergingModels": "Fusionar modelos"
+        "statusModelChanged": "Modelo cambiado"
     },
     "gallery": {
         "generations": "Generaciones",
@@ -303,16 +284,16 @@
         "nameValidationMsg": "Introduce un nombre para tu modelo",
         "description": "Descripción",
         "descriptionValidationMsg": "Introduce una descripción para tu modelo",
-        "config": "Configurar",
-        "configValidationMsg": "Ruta del archivo de configuración del modelo.",
+        "config": "Config",
+        "configValidationMsg": "Ruta del archivo de configuración del modelo",
         "modelLocation": "Ubicación del Modelo",
-        "modelLocationValidationMsg": "Ruta del archivo de modelo.",
+        "modelLocationValidationMsg": "Ruta del archivo de modelo",
         "vaeLocation": "Ubicación VAE",
-        "vaeLocationValidationMsg": "Ruta del archivo VAE.",
+        "vaeLocationValidationMsg": "Ruta del archivo VAE",
         "width": "Ancho",
-        "widthValidationMsg": "Ancho predeterminado de tu modelo.",
+        "widthValidationMsg": "Ancho predeterminado de tu modelo",
         "height": "Alto",
-        "heightValidationMsg": "Alto predeterminado de tu modelo.",
+        "heightValidationMsg": "Alto predeterminado de tu modelo",
         "addModel": "Añadir Modelo",
         "updateModel": "Actualizar Modelo",
         "availableModels": "Modelos disponibles",
@@ -339,61 +320,7 @@
         "deleteModel": "Eliminar Modelo",
         "deleteConfig": "Eliminar Configuración",
         "deleteMsg1": "¿Estás seguro de querer eliminar esta entrada de modelo de InvokeAI?",
-        "deleteMsg2": "El checkpoint del modelo no se eliminará de tu disco. Puedes volver a añadirlo si lo deseas.",
-        "safetensorModels": "SafeTensors",
-        "addDiffuserModel": "Añadir difusores",
-        "inpainting": "v1 Repintado",
-        "repoIDValidationMsg": "Repositorio en línea de tu modelo",
-        "checkpointModels": "Puntos de control",
-        "convertToDiffusersHelpText4": "Este proceso se realiza una sola vez. Puede tardar entre 30 y 60 segundos dependiendo de las especificaciones de tu ordenador.",
-        "diffusersModels": "Difusores",
-        "addCheckpointModel": "Agregar modelo de punto de control/Modelo Safetensor",
-        "vaeRepoID": "Identificador del repositorio de VAE",
-        "vaeRepoIDValidationMsg": "Repositorio en línea de tú VAE",
-        "formMessageDiffusersModelLocation": "Difusores Modelo Ubicación",
-        "formMessageDiffusersModelLocationDesc": "Por favor, introduzca al menos uno.",
-        "formMessageDiffusersVAELocation": "Ubicación VAE",
-        "formMessageDiffusersVAELocationDesc": "Si no se proporciona, InvokeAI buscará el archivo VAE dentro de la ubicación del modelo indicada anteriormente.",
-        "convert": "Convertir",
-        "convertToDiffusers": "Convertir en difusores",
-        "convertToDiffusersHelpText1": "Este modelo se convertirá al formato 🧨 Difusores.",
-        "convertToDiffusersHelpText2": "Este proceso sustituirá su entrada del Gestor de Modelos por la versión de Difusores del mismo modelo.",
-        "convertToDiffusersHelpText3": "Su archivo de puntos de control en el disco NO será borrado ni modificado de ninguna manera. Puede volver a añadir su punto de control al Gestor de Modelos si lo desea.",
-        "convertToDiffusersHelpText5": "Asegúrese de que dispone de suficiente espacio en disco. Los modelos suelen variar entre 4 GB y 7 GB de tamaño.",
-        "convertToDiffusersHelpText6": "¿Desea transformar este modelo?",
-        "convertToDiffusersSaveLocation": "Guardar ubicación",
-        "v1": "v1",
-        "v2": "v2",
-        "statusConverting": "Adaptar",
-        "modelConverted": "Modelo adaptado",
-        "sameFolder": "La misma carpeta",
-        "invokeRoot": "Carpeta InvokeAI",
-        "custom": "Personalizado",
-        "customSaveLocation": "Ubicación personalizada para guardar",
-        "merge": "Fusión",
-        "modelsMerged": "Modelos fusionados",
-        "mergeModels": "Combinar modelos",
-        "modelOne": "Modelo 1",
-        "modelTwo": "Modelo 2",
-        "modelThree": "Modelo 3",
-        "mergedModelName": "Nombre del modelo combinado",
-        "alpha": "Alfa",
-        "interpolationType": "Tipo de interpolación",
-        "mergedModelSaveLocation": "Guardar ubicación",
-        "mergedModelCustomSaveLocation": "Ruta personalizada",
-        "invokeAIFolder": "Invocar carpeta de la inteligencia artificial",
-        "modelMergeHeaderHelp2": "Sólo se pueden fusionar difusores. Si desea fusionar un modelo de punto de control, conviértalo primero en difusores.",
-        "modelMergeAlphaHelp": "Alfa controla la fuerza de mezcla de los modelos. Los valores alfa más bajos reducen la influencia del segundo modelo.",
-        "modelMergeInterpAddDifferenceHelp": "En este modo, el Modelo 3 se sustrae primero del Modelo 2. La versión resultante se mezcla con el Modelo 1 con la tasa alfa establecida anteriormente. La versión resultante se mezcla con el Modelo 1 con la tasa alfa establecida anteriormente.",
-        "ignoreMismatch": "Ignorar discrepancias entre modelos seleccionados",
-        "modelMergeHeaderHelp1": "Puede combinar hasta tres modelos diferentes para crear una mezcla que se adapte a sus necesidades.",
-        "inverseSigmoid": "Sigmoideo inverso",
-        "weightedSum": "Modelo de suma ponderada",
-        "sigmoid": "Función sigmoide",
-        "allModels": "Todos los modelos",
-        "repo_id": "Identificador del repositorio",
-        "pathToCustomConfig": "Ruta a la configuración personalizada",
-        "customConfig": "Configuración personalizada"
+        "deleteMsg2": "El checkpoint del modelo no se eliminará de tu disco. Puedes volver a añadirlo si lo deseas."
     },
     "parameters": {
         "images": "Imágenes",
@@ -453,22 +380,7 @@
         "info": "Información",
         "deleteImage": "Eliminar Imagen",
         "initialImage": "Imagen Inicial",
-        "showOptionsPanel": "Mostrar panel de opciones",
-        "symmetry": "Simetría",
-        "vSymmetryStep": "Paso de simetría V",
-        "hSymmetryStep": "Paso de simetría H",
-        "cancel": {
-            "immediate": "Cancelar inmediatamente",
-            "schedule": "Cancelar tras la iteración actual",
-            "isScheduled": "Cancelando",
-            "setType": "Tipo de cancelación"
-        },
-        "copyImage": "Copiar la imagen",
-        "general": "General",
-        "negativePrompts": "Preguntas negativas",
-        "imageToImage": "Imagen a imagen",
-        "denoisingStrength": "Intensidad de la eliminación del ruido",
-        "hiresStrength": "Alta resistencia"
+        "showOptionsPanel": "Mostrar panel de opciones"
     },
     "settings": {
         "models": "Modelos",
@@ -481,8 +393,7 @@
         "resetWebUI": "Restablecer interfaz web",
         "resetWebUIDesc1": "Al restablecer la interfaz web, solo se restablece la caché local del navegador de sus imágenes y la configuración guardada. No se elimina ninguna imagen de su disco duro.",
         "resetWebUIDesc2": "Si las imágenes no se muestran en la galería o algo más no funciona, intente restablecer antes de reportar un incidente en GitHub.",
-        "resetComplete": "La interfaz web se ha restablecido. Actualice la página para recargarla.",
-        "useSlidersForAll": "Utilice controles deslizantes para todas las opciones"
+        "resetComplete": "La interfaz web se ha restablecido. Actualice la página para recargarla."
     },
     "toast": {
         "tempFoldersEmptied": "Directorio temporal vaciado",
@@ -520,12 +431,12 @@
         "feature": {
             "prompt": "Este campo tomará todo el texto de entrada, incluidos tanto los términos de contenido como los estilísticos. Si bien se pueden incluir pesos en la solicitud, los comandos/parámetros estándar de línea de comandos no funcionarán.",
             "gallery": "Conforme se generan nuevas invocaciones, los archivos del directorio de salida se mostrarán aquí. Las generaciones tienen opciones adicionales para configurar nuevas generaciones.",
-            "other": "Estas opciones habilitarán modos de procesamiento alternativos para Invoke. 'Seamless mosaico' creará patrones repetitivos en la salida. 'Alta resolución' es la generación en dos pasos con img2img: use esta configuración cuando desee una imagen más grande y más coherente sin artefactos. tomar más tiempo de lo habitual txt2img.",
+            "other": "Estas opciones habilitarán modos de procesamiento alternativos para Invoke. El modo sin costuras funciona para generar patrones repetitivos en la salida. La optimización de alta resolución realiza un ciclo de generación de dos pasos y debe usarse en resoluciones más altas cuando desee una imagen/composición más coherente.",
             "seed": "Los valores de semilla proporcionan un conjunto inicial de ruido que guían el proceso de eliminación de ruido y se pueden aleatorizar o rellenar con una semilla de una invocación anterior. La función Umbral se puede usar para mitigar resultados indeseables a valores CFG más altos (intente entre 0-10), y Perlin se puede usar para agregar ruido Perlin al proceso de eliminación de ruido. Ambos sirven para agregar variación a sus salidas.",
             "variations": "Pruebe una variación con una cantidad entre 0 y 1 para cambiar la imagen de salida para la semilla establecida. Se encuentran variaciones interesantes en la semilla entre 0.1 y 0.3.",
             "upscale": "Usando ESRGAN, puede aumentar la resolución de salida sin requerir un ancho/alto más alto en la generación inicial.",
             "faceCorrection": "Usando GFPGAN o Codeformer, la corrección de rostros intentará identificar rostros en las salidas y corregir cualquier defecto/anormalidad. Los valores de fuerza más altos aplicarán una presión correctiva más fuerte en las salidas, lo que resultará en rostros más atractivos. Con Codeformer, una mayor fidelidad intentará preservar la imagen original, a expensas de la fuerza de corrección de rostros.",
-            "imageToImage": "Imagen a Imagen permite cargar una imagen inicial, que InvokeAI usará para guiar el proceso de generación, junto con una solicitud. Un valor más bajo para esta configuración se parecerá más a la imagen original. Se aceptan valores entre 0-1, y se recomienda un rango de .25-.75",
+            "imageToImage": "Imagen a Imagen permite cargar una imagen inicial, que InvokeAI usará para guiar el proceso de generación, junto con una solicitud. Un valor más bajo para esta configuración se parecerá más a la imagen original. Se aceptan valores entre 0-1, y se recomienda un rango de .25-.75.",
             "boundingBox": "La caja delimitadora es análoga a las configuraciones de Ancho y Alto para Texto a Imagen o Imagen a Imagen. Solo se procesará el área en la caja.",
             "seamCorrection": "Controla el manejo de parches visibles que pueden ocurrir cuando se pega una imagen generada de nuevo en el lienzo.",
             "infillAndScaling": "Administra los métodos de relleno (utilizados en áreas enmascaradas o borradas del lienzo) y la escala (útil para tamaños de caja delimitadora pequeños)."

invokeai/frontend/dist/locales/pt_BR.json

@@ -44,26 +44,7 @@
         "statusUpscaling": "Redimensinando",
         "statusUpscalingESRGAN": "Redimensinando (ESRGAN)",
         "statusLoadingModel": "Carregando Modelo",
-        "statusModelChanged": "Modelo Alterado",
-        "githubLabel": "Github",
-        "discordLabel": "Discord",
-        "langArabic": "Árabe",
-        "langEnglish": "Inglês",
-        "langDutch": "Holandês",
-        "langFrench": "Francês",
-        "langGerman": "Alemão",
-        "langItalian": "Italiano",
-        "langJapanese": "Japonês",
-        "langPolish": "Polonês",
-        "langSimplifiedChinese": "Chinês",
-        "langUkranian": "Ucraniano",
-        "back": "Voltar",
-        "statusConvertingModel": "Convertendo Modelo",
-        "statusModelConverted": "Modelo Convertido",
-        "statusMergingModels": "Mesclando Modelos",
-        "statusMergedModels": "Modelos Mesclados",
-        "langRussian": "Russo",
-        "langSpanish": "Espanhol"
+        "statusModelChanged": "Modelo Alterado"
     },
     "gallery": {
         "generations": "Gerações",
@@ -256,7 +237,7 @@
             "desc": "Salva a tela atual na galeria"
         },
         "copyToClipboard": {
-            "title": "Copiar para a Área de Transferência",
+            "title": "Copiar Para a Área de Transferência ",
             "desc": "Copia a tela atual para a área de transferência"
         },
         "downloadImage": {
@@ -303,7 +284,7 @@
         "nameValidationMsg": "Insira um nome para o seu modelo",
         "description": "Descrição",
         "descriptionValidationMsg": "Adicione uma descrição para o seu modelo",
-        "config": "Configuração",
+        "config": "Config",
         "configValidationMsg": "Caminho para o arquivo de configuração do seu modelo.",
         "modelLocation": "Localização do modelo",
         "modelLocationValidationMsg": "Caminho para onde seu modelo está localizado.",
@@ -336,52 +317,7 @@
         "deleteModel": "Excluir modelo",
         "deleteConfig": "Excluir Config",
         "deleteMsg1": "Tem certeza de que deseja excluir esta entrada do modelo de InvokeAI?",
-        "deleteMsg2": "Isso não vai excluir o arquivo de modelo checkpoint do seu disco. Você pode lê-los, se desejar.",
-        "checkpointModels": "Checkpoints",
-        "diffusersModels": "Diffusers",
-        "safetensorModels": "SafeTensors",
-        "addCheckpointModel": "Adicionar Modelo de Checkpoint/Safetensor",
-        "addDiffuserModel": "Adicionar Diffusers",
-        "repo_id": "Repo ID",
-        "vaeRepoID": "VAE Repo ID",
-        "vaeRepoIDValidationMsg": "Repositório Online do seu VAE",
-        "scanAgain": "Digitalize Novamente",
-        "selectAndAdd": "Selecione e Adicione Modelos Listados Abaixo",
-        "noModelsFound": "Nenhum Modelo Encontrado",
-        "formMessageDiffusersModelLocation": "Localização dos Modelos Diffusers",
-        "formMessageDiffusersModelLocationDesc": "Por favor entre com ao menos um.",
-        "formMessageDiffusersVAELocation": "Localização do VAE",
-        "formMessageDiffusersVAELocationDesc": "Se não provido, InvokeAI irá procurar pelo arquivo VAE dentro do local do modelo.",
-        "convertToDiffusers": "Converter para Diffusers",
-        "convertToDiffusersHelpText1": "Este modelo será convertido para o formato 🧨 Diffusers.",
-        "convertToDiffusersHelpText5": "Por favor, certifique-se de que você tenha espaço suficiente em disco. Os modelos geralmente variam entre 4GB e 7GB de tamanho.",
-        "convertToDiffusersHelpText6": "Você deseja converter este modelo?",
-        "convertToDiffusersSaveLocation": "Local para Salvar",
-        "v1": "v1",
-        "v2": "v2",
-        "inpainting": "v1 Inpainting",
-        "customConfig": "Configuração personalizada",
-        "pathToCustomConfig": "Caminho para configuração personalizada",
-        "convertToDiffusersHelpText3": "Seu arquivo de ponto de verificação no disco NÃO será excluído ou modificado de forma alguma. Você pode adicionar seu ponto de verificação ao Gerenciador de modelos novamente, se desejar.",
-        "convertToDiffusersHelpText4": "Este é um processo único. Pode levar cerca de 30 a 60s, dependendo das especificações do seu computador.",
-        "merge": "Mesclar",
-        "modelsMerged": "Modelos mesclados",
-        "mergeModels": "Mesclar modelos",
-        "modelOne": "Modelo 1",
-        "modelTwo": "Modelo 2",
-        "modelThree": "Modelo 3",
-        "statusConverting": "Convertendo",
-        "modelConverted": "Modelo Convertido",
-        "sameFolder": "Mesma pasta",
-        "invokeRoot": "Pasta do InvokeAI",
-        "custom": "Personalizado",
-        "customSaveLocation": "Local de salvamento personalizado",
-        "mergedModelName": "Nome do modelo mesclado",
-        "alpha": "Alpha",
-        "allModels": "Todos os Modelos",
-        "repoIDValidationMsg": "Repositório Online do seu Modelo",
-        "convert": "Converter",
-        "convertToDiffusersHelpText2": "Este processo irá substituir sua entrada de Gerenciador de Modelos por uma versão Diffusers do mesmo modelo."
+        "deleteMsg2": "Isso não vai excluir o arquivo de modelo checkpoint do seu disco. Você pode lê-los, se desejar."
     },
     "parameters": {
         "images": "Imagems",
@@ -506,14 +442,14 @@
         "move": "Mover",
         "resetView": "Resetar Visualização",
         "mergeVisible": "Fundir Visível",
-        "saveToGallery": "Salvar na Galeria",
+        "saveToGallery": "Save To Gallery",
         "copyToClipboard": "Copiar para a Área de Transferência",
         "downloadAsImage": "Baixar Como Imagem",
         "undo": "Desfazer",
         "redo": "Refazer",
         "clearCanvas": "Limpar Tela",
         "canvasSettings": "Configurações de Tela",
-        "showIntermediates": "Mostrar Intermediários",
+        "showIntermediates": "Show Intermediates",
         "showGrid": "Mostrar Grade",
         "snapToGrid": "Encaixar na Grade",
         "darkenOutsideSelection": "Escurecer Seleção Externa",

invokeai/frontend/dist/locales/ro.json

@@ -1 +0,0 @@
-{}

invokeai/frontend/public/locales/en.json

@@ -63,8 +63,7 @@
         "statusConvertingModel": "Converting Model",
         "statusModelConverted": "Model Converted",
         "statusMergingModels": "Merging Models",
-        "statusMergedModels": "Models Merged",
-        "pinOptionsPanel": "Pin Options Panel"
+        "statusMergedModels": "Models Merged"
     },
     "gallery": {
         "generations": "Generations",
@@ -365,8 +364,7 @@
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
         "convertToDiffusersSaveLocation": "Save Location",
         "v1": "v1",
-        "v2_base": "v2 (512px)",
-        "v2_768": "v2 (768px)",
+        "v2": "v2",
         "inpainting": "v1 Inpainting",
         "customConfig": "Custom Config",
         "pathToCustomConfig": "Path To Custom Config",
@@ -395,9 +393,7 @@
         "modelMergeInterpAddDifferenceHelp": "In this mode, Model 3 is first subtracted from Model 2. The resulting version is blended with Model 1 with the alpha rate set above.",
         "inverseSigmoid": "Inverse Sigmoid",
         "sigmoid": "Sigmoid",
-        "weightedSum": "Weighted Sum",
-        "none": "none",
-        "addDifference": "Add Difference"
+        "weightedSum": "Weighted Sum"
     },
     "parameters": {
         "general": "General",

invokeai/frontend/src/app/socketio/listeners.ts

@@ -392,7 +392,7 @@ const makeSocketIOListeners = (
         addLogEntry({
           timestamp: dateFormat(new Date(), 'isoDateTime'),
           message: `${i18n.t(
-            'modelManager.modelAdded'
+            'modelmanager:modelAdded'
           )}: ${deleted_model_name}`,
           level: 'info',
         })
@@ -400,7 +400,7 @@ const makeSocketIOListeners = (
       dispatch(
         addToast({
           title: `${i18n.t(
-            'modelManager.modelEntryDeleted'
+            'modelmanager:modelEntryDeleted'
           )}: ${deleted_model_name}`,
           status: 'success',
           duration: 2500,
@@ -424,7 +424,7 @@ const makeSocketIOListeners = (
       dispatch(
         addToast({
           title: `${i18n.t(
-            'modelManager.modelConverted'
+            'modelmanager:modelConverted'
           )}: ${converted_model_name}`,
           status: 'success',
           duration: 2500,

invokeai/frontend/src/common/util/parameterTranslation.ts

@@ -144,8 +144,8 @@ export const frontendToBackendParameters = (
     variationAmount,
     width,
     shouldUseSymmetry,
-    horizontalSymmetrySteps,
-    verticalSymmetrySteps,
+    horizontalSymmetryTimePercentage,
+    verticalSymmetryTimePercentage,
   } = generationState;
 
   const {
@@ -185,17 +185,17 @@ export const frontendToBackendParameters = (
 
   // Symmetry Settings
   if (shouldUseSymmetry) {
-    if (horizontalSymmetrySteps > 0) {
+    if (horizontalSymmetryTimePercentage > 0) {
       generationParameters.h_symmetry_time_pct = Math.max(
         0,
-        Math.min(1, horizontalSymmetrySteps / steps)
+        Math.min(1, horizontalSymmetryTimePercentage / steps)
       );
     }
 
-    if (verticalSymmetrySteps > 0) {
+    if (horizontalSymmetryTimePercentage > 0) {
       generationParameters.v_symmetry_time_pct = Math.max(
         0,
-        Math.min(1, verticalSymmetrySteps / steps)
+        Math.min(1, verticalSymmetryTimePercentage / steps)
       );
     }
   }

invokeai/frontend/src/features/canvas/components/IAICanvasStatusText.tsx

@@ -109,7 +109,7 @@ const IAICanvasStatusText = () => {
             color: boundingBoxColor,
           }}
         >{`${t(
-          'unifiedCanvas.boundingBox'
+          'unifiedcanvas:boundingBox'
         )}: ${boundingBoxDimensionsString}`}</div>
       )}
       {shouldShowScaledBoundingBox && (
@@ -118,19 +118,19 @@ const IAICanvasStatusText = () => {
             color: boundingBoxColor,
           }}
         >{`${t(
-          'unifiedCanvas.scaledBoundingBox'
+          'unifiedcanvas:scaledBoundingBox'
         )}: ${scaledBoundingBoxDimensionsString}`}</div>
       )}
       {shouldShowCanvasDebugInfo && (
         <>
           <div>{`${t(
-            'unifiedCanvas.boundingBoxPosition'
+            'unifiedcanvas:boundingBoxPosition'
           )}: ${boundingBoxCoordinatesString}`}</div>
           <div>{`${t(
-            'unifiedCanvas.canvasDimensions'
+            'unifiedcanvas:canvasDimensions'
           )}: ${canvasDimensionsString}`}</div>
           <div>{`${t(
-            'unifiedCanvas.canvasPosition'
+            'unifiedcanvas:canvasPosition'
           )}: ${canvasCoordinatesString}`}</div>
           <IAICanvasStatusTextCursorPos />
         </>

invokeai/frontend/src/features/canvas/components/IAICanvasStatusText/IAICanvasStatusTextCursorPos.tsx

@@ -34,7 +34,7 @@ export default function IAICanvasStatusTextCursorPos() {
 
   return (
     <div>{`${t(
-      'unifiedCanvas.cursorPosition'
+      'unifiedcanvas:cursorPosition'
     )}: ${cursorCoordinatesString}`}</div>
   );
 }

invokeai/frontend/src/features/gallery/components/CurrentImageButtons.tsx

@@ -21,7 +21,6 @@ import {
   setInitialImage,
   setSeed,
 } from 'features/parameters/store/generationSlice';
-import { setAllPostProcessingParameters } from 'features/parameters/store/postprocessingSlice';
 import { postprocessingSelector } from 'features/parameters/store/postprocessingSelectors';
 import { systemSelector } from 'features/system/store/systemSelectors';
 import { SystemState } from 'features/system/store/systemSlice';
@@ -190,12 +189,11 @@ const CurrentImageButtons = () => {
   );
 
   const handleClickUseAllParameters = () => {
-    if (!currentImage?.metadata) return;
-    dispatch(setAllParameters(currentImage.metadata));
-    dispatch(setAllPostProcessingParameters(currentImage.metadata));
-    if (currentImage.metadata.image.type === 'img2img') {
+    if (!currentImage) return;
+    currentImage.metadata && dispatch(setAllParameters(currentImage.metadata));
+    if (currentImage.metadata?.image.type === 'img2img') {
       dispatch(setActiveTab('img2img'));
-    } else if (currentImage.metadata.image.type === 'txt2img') {
+    } else if (currentImage.metadata?.image.type === 'txt2img') {
       dispatch(setActiveTab('txt2img'));
     }
   };

invokeai/frontend/src/features/gallery/components/HoverableImage.tsx

@@ -10,7 +10,6 @@ import {
   setInitialImage,
   setSeed,
 } from 'features/parameters/store/generationSlice';
-import { setAllPostProcessingParameters } from 'features/parameters/store/postprocessingSlice';
 import { DragEvent, memo, useState } from 'react';
 import { FaCheck, FaTrashAlt } from 'react-icons/fa';
 import DeleteImageModal from './DeleteImageModal';
@@ -115,10 +114,7 @@ const HoverableImage = memo((props: HoverableImageProps) => {
   };
 
   const handleUseAllParameters = () => {
-    if (metadata) {
-      dispatch(setAllParameters(metadata));
-      dispatch(setAllPostProcessingParameters(metadata));
-    }
+    metadata && dispatch(setAllParameters(metadata));
     toast({
       title: t('toast.parametersSet'),
       status: 'success',

invokeai/frontend/src/features/parameters/components/AdvancedParameters/Output/SymmetrySettings.tsx

@@ -2,18 +2,18 @@ import { RootState } from 'app/store';
 import { useAppDispatch, useAppSelector } from 'app/storeHooks';
 import IAISlider from 'common/components/IAISlider';
 import {
-  setHorizontalSymmetrySteps,
-  setVerticalSymmetrySteps,
+  setHorizontalSymmetryTimePercentage,
+  setVerticalSymmetryTimePercentage,
 } from 'features/parameters/store/generationSlice';
 import { useTranslation } from 'react-i18next';
 
 export default function SymmetrySettings() {
-  const horizontalSymmetrySteps = useAppSelector(
-    (state: RootState) => state.generation.horizontalSymmetrySteps
+  const horizontalSymmetryTimePercentage = useAppSelector(
+    (state: RootState) => state.generation.horizontalSymmetryTimePercentage
   );
 
-  const verticalSymmetrySteps = useAppSelector(
-    (state: RootState) => state.generation.verticalSymmetrySteps
+  const verticalSymmetryTimePercentage = useAppSelector(
+    (state: RootState) => state.generation.verticalSymmetryTimePercentage
   );
 
   const steps = useAppSelector((state: RootState) => state.generation.steps);
@@ -26,28 +26,28 @@ export default function SymmetrySettings() {
     <>
       <IAISlider
         label={t('parameters.hSymmetryStep')}
-        value={horizontalSymmetrySteps}
-        onChange={(v) => dispatch(setHorizontalSymmetrySteps(v))}
+        value={horizontalSymmetryTimePercentage}
+        onChange={(v) => dispatch(setHorizontalSymmetryTimePercentage(v))}
         min={0}
         max={steps}
         step={1}
         withInput
         withSliderMarks
         withReset
-        handleReset={() => dispatch(setHorizontalSymmetrySteps(0))}
+        handleReset={() => dispatch(setHorizontalSymmetryTimePercentage(0))}
         sliderMarkRightOffset={-6}
       ></IAISlider>
       <IAISlider
         label={t('parameters.vSymmetryStep')}
-        value={verticalSymmetrySteps}
-        onChange={(v) => dispatch(setVerticalSymmetrySteps(v))}
+        value={verticalSymmetryTimePercentage}
+        onChange={(v) => dispatch(setVerticalSymmetryTimePercentage(v))}
         min={0}
         max={steps}
         step={1}
         withInput
         withSliderMarks
         withReset
-        handleReset={() => dispatch(setVerticalSymmetrySteps(0))}
+        handleReset={() => dispatch(setVerticalSymmetryTimePercentage(0))}
         sliderMarkRightOffset={-6}
       ></IAISlider>
     </>

invokeai/frontend/src/features/parameters/components/MainParameters/MainSteps.tsx

@@ -3,10 +3,7 @@ import { useAppDispatch, useAppSelector } from 'app/storeHooks';
 import IAINumberInput from 'common/components/IAINumberInput';
 
 import IAISlider from 'common/components/IAISlider';
-import {
-  clampSymmetrySteps,
-  setSteps,
-} from 'features/parameters/store/generationSlice';
+import { setSteps } from 'features/parameters/store/generationSlice';
 import { useTranslation } from 'react-i18next';
 
 export default function MainSteps() {
@@ -17,13 +14,7 @@ export default function MainSteps() {
   );
   const { t } = useTranslation();
 
-  const handleChangeSteps = (v: number) => {
-    dispatch(setSteps(v));
-  };
-
-  const handleBlur = () => {
-    dispatch(clampSymmetrySteps());
-  };
+  const handleChangeSteps = (v: number) => dispatch(setSteps(v));
 
   return shouldUseSliders ? (
     <IAISlider
@@ -50,7 +41,6 @@ export default function MainSteps() {
       width="auto"
       styleClass="main-settings-block"
       textAlign="center"
-      onBlur={handleBlur}
     />
   );
 }

invokeai/frontend/src/features/parameters/components/ProcessButtons/InvokeButton.tsx

@@ -5,7 +5,6 @@ import IAIButton, { IAIButtonProps } from 'common/components/IAIButton';
 import IAIIconButton, {
   IAIIconButtonProps,
 } from 'common/components/IAIIconButton';
-import { clampSymmetrySteps } from 'features/parameters/store/generationSlice';
 import { activeTabNameSelector } from 'features/ui/store/uiSelectors';
 import { useHotkeys } from 'react-hotkeys-hook';
 import { useTranslation } from 'react-i18next';
@@ -31,7 +30,6 @@ export default function InvokeButton(props: InvokeButton) {
   useHotkeys(
     ['ctrl+enter', 'meta+enter'],
     () => {
-      dispatch(clampSymmetrySteps());
       dispatch(generateImage(activeTabName));
     },
     {

invokeai/frontend/src/features/parameters/store/generationSlice.ts

@@ -4,7 +4,6 @@ import * as InvokeAI from 'app/invokeai';
 import { getPromptAndNegative } from 'common/util/getPromptAndNegative';
 import promptToString from 'common/util/promptToString';
 import { seedWeightsToString } from 'common/util/seedWeightPairs';
-import { clamp } from 'lodash';
 
 export interface GenerationState {
   cfgScale: number;
@@ -34,8 +33,8 @@ export interface GenerationState {
   variationAmount: number;
   width: number;
   shouldUseSymmetry: boolean;
-  horizontalSymmetrySteps: number;
-  verticalSymmetrySteps: number;
+  horizontalSymmetryTimePercentage: number;
+  verticalSymmetryTimePercentage: number;
 }
 
 const initialGenerationState: GenerationState = {
@@ -65,8 +64,8 @@ const initialGenerationState: GenerationState = {
   variationAmount: 0.1,
   width: 512,
   shouldUseSymmetry: false,
-  horizontalSymmetrySteps: 0,
-  verticalSymmetrySteps: 0,
+  horizontalSymmetryTimePercentage: 0,
+  verticalSymmetryTimePercentage: 0,
 };
 
 const initialState: GenerationState = initialGenerationState;
@@ -100,18 +99,6 @@ export const generationSlice = createSlice({
     setSteps: (state, action: PayloadAction<number>) => {
       state.steps = action.payload;
     },
-    clampSymmetrySteps: (state) => {
-      state.horizontalSymmetrySteps = clamp(
-        state.horizontalSymmetrySteps,
-        0,
-        state.steps
-      );
-      state.verticalSymmetrySteps = clamp(
-        state.verticalSymmetrySteps,
-        0,
-        state.steps
-      );
-    },
     setCfgScale: (state, action: PayloadAction<number>) => {
       state.cfgScale = action.payload;
     },
@@ -301,6 +288,7 @@ export const generationSlice = createSlice({
         state.perlin = perlin;
       }
       if (typeof seamless === 'boolean') state.seamless = seamless;
+      // if (typeof hires_fix === 'boolean') state.hiresFix = hires_fix; // TODO: Needs to be fixed after reorg
       if (width) state.width = width;
       if (height) state.height = height;
 
@@ -346,17 +334,22 @@ export const generationSlice = createSlice({
     setShouldUseSymmetry: (state, action: PayloadAction<boolean>) => {
       state.shouldUseSymmetry = action.payload;
     },
-    setHorizontalSymmetrySteps: (state, action: PayloadAction<number>) => {
-      state.horizontalSymmetrySteps = action.payload;
+    setHorizontalSymmetryTimePercentage: (
+      state,
+      action: PayloadAction<number>
+    ) => {
+      state.horizontalSymmetryTimePercentage = action.payload;
     },
-    setVerticalSymmetrySteps: (state, action: PayloadAction<number>) => {
-      state.verticalSymmetrySteps = action.payload;
+    setVerticalSymmetryTimePercentage: (
+      state,
+      action: PayloadAction<number>
+    ) => {
+      state.verticalSymmetryTimePercentage = action.payload;
     },
   },
 });
 
 export const {
-  clampSymmetrySteps,
   clearInitialImage,
   resetParametersState,
   resetSeed,
@@ -391,8 +384,8 @@ export const {
   setVariationAmount,
   setWidth,
   setShouldUseSymmetry,
-  setHorizontalSymmetrySteps,
-  setVerticalSymmetrySteps,
+  setHorizontalSymmetryTimePercentage,
+  setVerticalSymmetryTimePercentage,
 } = generationSlice.actions;
 
 export default generationSlice.reducer;

invokeai/frontend/src/features/parameters/store/postprocessingSlice.ts

@@ -1,6 +1,5 @@
 import type { PayloadAction } from '@reduxjs/toolkit';
 import { createSlice } from '@reduxjs/toolkit';
-import * as InvokeAI from 'app/invokeai';
 import { FACETOOL_TYPES } from 'app/constants';
 
 export type UpscalingLevel = 2 | 4;
@@ -41,17 +40,6 @@ export const postprocessingSlice = createSlice({
   name: 'postprocessing',
   initialState,
   reducers: {
-    setAllPostProcessingParameters: (
-      state,
-      action: PayloadAction<InvokeAI.Metadata>
-    ) => {
-      const { type, hires_fix } = action.payload.image;
-
-      if (type === 'txt2img') {
-        state.hiresFix = Boolean(hires_fix);
-        // Strength of img2img used in hires_fix is not currently exposed in the Metadata for the final image.
-      }
-    },
     setFacetoolStrength: (state, action: PayloadAction<number>) => {
       state.facetoolStrength = action.payload;
     },
@@ -95,7 +83,6 @@ export const postprocessingSlice = createSlice({
 });
 
 export const {
-  setAllPostProcessingParameters,
   resetPostprocessingState,
   setCodeformerFidelity,
   setFacetoolStrength,

invokeai/frontend/src/features/system/components/ModelManager/MergeModels.tsx

@@ -57,19 +57,19 @@ export default function MergeModels() {
 
   const [modelMergeForce, setModelMergeForce] = useState<boolean>(false);
 
-  const modelOneList = Object.keys(diffusersModels).filter(
-    (model) => model !== modelTwo && model !== modelThree
-  );
+  const modelOneList = Object.keys(diffusersModels).filter((model) => {
+    if (model !== modelTwo && model !== modelThree) return model;
+  });
 
-  const modelTwoList = Object.keys(diffusersModels).filter(
-    (model) => model !== modelOne && model !== modelThree
-  );
+  const modelTwoList = Object.keys(diffusersModels).filter((model) => {
+    if (model !== modelOne && model !== modelThree) return model;
+  });
 
   const modelThreeList = [
-    { key: t('modelManager.none'), value: 'none' },
-    ...Object.keys(diffusersModels)
-      .filter((model) => model !== modelOne && model !== modelTwo)
-      .map((model) => ({ key: model, value: model })),
+    'none',
+    ...Object.keys(diffusersModels).filter((model) => {
+      if (model !== modelOne && model !== modelTwo) return model;
+    }),
   ];
 
   const isProcessing = useAppSelector(
@@ -209,22 +209,18 @@ export default function MergeModels() {
                 <Flex columnGap={4}>
                   {modelThree === 'none' ? (
                     <>
-                      <Radio value="weighted_sum">
-                        {t('modelManager.weightedSum')}
-                      </Radio>
-                      <Radio value="sigmoid">{t('modelManager.sigmoid')}</Radio>
-                      <Radio value="inv_sigmoid">
-                        {t('modelManager.inverseSigmoid')}
-                      </Radio>
+                      <Radio value="weighted_sum">weighted_sum</Radio>
+                      <Radio value="sigmoid">sigmoid</Radio>
+                      <Radio value="inv_sigmoid">inv_sigmoid</Radio>
                     </>
                   ) : (
                     <Radio value="add_difference">
                       <Tooltip
                         label={t(
-                          'modelManager.modelMergeInterpAddDifferenceHelp'
+                          'modelmanager:modelMergeInterpAddDifferenceHelp'
                         )}
                       >
-                        {t('modelManager.addDifference')}
+                        add_difference
                       </Tooltip>
                     </Radio>
                   )}

invokeai/frontend/src/features/system/components/ModelManager/SearchModels.tsx

@@ -181,8 +181,7 @@ export default function SearchModels() {
 
     const configFiles = {
       v1: 'configs/stable-diffusion/v1-inference.yaml',
-      v2_base: 'configs/stable-diffusion/v2-inference-v.yaml',
-      v2_768: 'configs/stable-diffusion/v2-inference-v.yaml',
+      v2: 'configs/stable-diffusion/v2-inference-v.yaml',
       inpainting: 'configs/stable-diffusion/v1-inpainting-inference.yaml',
       custom: pathToConfig,
     };
@@ -386,8 +385,7 @@ export default function SearchModels() {
               >
                 <Flex gap={4}>
                   <Radio value="v1">{t('modelManager.v1')}</Radio>
-                  <Radio value="v2_base">{t('modelManager.v2_base')}</Radio>
-                  <Radio value="v2_768">{t('modelManager.v2_768')}</Radio>
+                  <Radio value="v2">{t('modelManager.v2')}</Radio>
                   <Radio value="inpainting">
                     {t('modelManager.inpainting')}
                   </Radio>

invokeai/frontend/src/features/ui/components/InvokeParametersPanel.tsx

@@ -18,7 +18,6 @@ import { setParametersPanelScrollPosition } from 'features/ui/store/uiSlice';
 import InvokeAILogo from 'assets/images/logo.png';
 import { isEqual } from 'lodash';
 import { uiSelector } from '../store/uiSelectors';
-import { useTranslation } from 'react-i18next';
 
 type Props = { children: ReactNode };
 
@@ -61,8 +60,6 @@ const InvokeOptionsPanel = (props: Props) => {
 
   const { children } = props;
 
-  const { t } = useTranslation();
-
   // Hotkeys
   useHotkeys(
     'o',
@@ -179,7 +176,7 @@ const InvokeOptionsPanel = (props: Props) => {
               }
             }}
           >
-            <Tooltip label={t('common.pinOptionsPanel')}>
+            <Tooltip label="Pin Options Panel">
               <div
                 className="parameters-panel-pin-button"
                 data-selected={shouldPinParametersPanel}

invokeai/frontend/src/i18.d.ts

@@ -1,16 +1,11 @@
 import 'i18next';
 
-import en from '../public/locales/en.json';
-
 declare module 'i18next' {
   // Extend CustomTypeOptions
   interface CustomTypeOptions {
     // Setting Default Namespace As English
     defaultNS: 'en';
     // Custom Types For Resources
-    resources: {
-      en: typeof en;
-    };
     // Never Return Null
     returnNull: false;
   }

ldm/generate.py

@@ -200,8 +200,6 @@ class Generate:
         # it wasn't actually doing anything. This logic could be reinstated.
         self.device = torch.device(choose_torch_device())
         print(f">> Using device_type {self.device.type}")
-        if self.device.type == 'cuda':
-            print(f">> CUDA device '{torch.cuda.get_device_name(torch.cuda.current_device())}' (GPU {os.environ.get('CUDA_VISIBLE_DEVICES') or 0})")
         if full_precision:
             if self.precision != "auto":
                 raise ValueError("Remove --full_precision / -F if using --precision")
@@ -1032,6 +1030,8 @@ class Generate:
         image_callback=None,
         prefix=None,
     ):
+
+        results = []
         for r in image_list:
             image, seed = r
             try:
@@ -1085,6 +1085,10 @@ class Generate:
             else:
                 r[0] = image
 
+            results.append([image, seed])
+
+        return results
+
     def apply_textmask(
         self, image_path: str, prompt: str, callback, threshold: float = 0.5
     ):

ldm/invoke/CLI.py

@@ -4,7 +4,6 @@ import shlex
 import sys
 import traceback
 from argparse import Namespace
-from packaging import version
 from pathlib import Path
 from typing import Union
 
@@ -23,7 +22,7 @@ from ..generate import Generate
 from .args import (Args, dream_cmd_from_png, metadata_dumps,
                              metadata_from_png)
 from .generator.diffusers_pipeline import PipelineIntermediateState
-from .globals import Globals, global_config_dir
+from .globals import Globals
 from .image_util import make_grid
 from .log import write_log
 from .model_manager import ModelManager
@@ -34,6 +33,7 @@ from ..util import url_attachment_name
 # global used in multiple functions (fix)
 infile = None
 
+
 def main():
     """Initialize command-line parsers and the diffusion model"""
     global infile
@@ -66,9 +66,6 @@ def main():
     Globals.sequential_guidance = args.sequential_guidance
     Globals.ckpt_convert = args.ckpt_convert
 
-    # run any post-install patches needed
-    run_patches()
-
     print(f">> Internet connectivity is {Globals.internet_available}")
 
     if not args.conf:
@@ -159,16 +156,10 @@ def main():
     except Exception as e:
         report_model_error(opt, e)
 
-    # completer is the readline object
-    completer = get_completer(opt, models=gen.model_manager.list_models())
-            
     # try to autoconvert new models
     if path := opt.autoimport:
         gen.model_manager.heuristic_import(
-            str(path),
-            convert=False,
-            commit_to_conf=opt.conf,
-            config_file_callback=lambda x: _pick_configuration_file(completer,x),
+            str(path), convert=False, commit_to_conf=opt.conf
         )
 
     if path := opt.autoconvert:
@@ -187,7 +178,7 @@ def main():
         )
 
     try:
-        main_loop(gen, opt, completer)
+        main_loop(gen, opt)
     except KeyboardInterrupt:
         print(
             f'\nGoodbye!\nYou can start InvokeAI again by running the "invoke.bat" (or "invoke.sh") script from {Globals.root}'
@@ -198,7 +189,7 @@ def main():
 
 
 # TODO: main_loop() has gotten busy. Needs to be refactored.
-def main_loop(gen, opt, completer):
+def main_loop(gen, opt):
     """prompt/read/execute loop"""
     global infile
     done = False
@@ -209,6 +200,7 @@ def main_loop(gen, opt, completer):
     # The readline completer reads history from the .dream_history file located in the
     # output directory specified at the time of script launch. We do not currently support
     # changing the history file midstream when the output directory is changed.
+    completer = get_completer(opt, models=gen.model_manager.list_models())
     set_default_output_dir(opt, completer)
     if gen.model:
         add_embedding_terms(gen, completer)
@@ -397,7 +389,6 @@ def main_loop(gen, opt, completer):
                         prior_variations,
                         postprocessed,
                         first_seed,
-                        gen.model_name,
                     )
                     path = file_writer.save_image_and_prompt_to_png(
                         image=image,
@@ -411,7 +402,6 @@ def main_loop(gen, opt, completer):
                                 else first_seed
                             ],
                             model_hash=gen.model_hash,
-                            model_id=gen.model_name,
                         ),
                         name=filename,
                         compress_level=opt.png_compression,
@@ -667,10 +657,10 @@ def import_model(model_path: str, gen, opt, completer, convert=False):
         model_name=model_name,
         description=model_desc,
         convert=convert,
-        config_file_callback=lambda x: _pick_configuration_file(completer,x),
     )
+
     if not imported_name:
-        print("** Aborting import.")
+        print("** Import failed or was skipped")
         return
 
     if not _verify_load(imported_name, gen):
@@ -684,48 +674,6 @@ def import_model(model_path: str, gen, opt, completer, convert=False):
     completer.update_models(gen.model_manager.list_models())
     print(f">> {imported_name} successfully installed")
 
-def _pick_configuration_file(completer, checkpoint_path: Path)->Path:
-    print(
-f"""
-Please select the type of the model at checkpoint {checkpoint_path}:
-[1] A Stable Diffusion v1.x ckpt/safetensors model
-[2] A Stable Diffusion v1.x inpainting ckpt/safetensors model
-[3] A Stable Diffusion v2.x base model (512 pixels; there should be no 'parameterization:' line in its yaml file)
-[4] A Stable Diffusion v2.x v-predictive model (768 pixels; look for a 'parameterization: "v"' line in its yaml file)
-[5] Other (you will be prompted to enter the config file path)
-[Q] I have no idea! Skip the import.
-""")
-    choices = [
-        global_config_dir() / 'stable-diffusion' / x
-        for x in [
-                'v1-inference.yaml',
-                'v1-inpainting-inference.yaml',
-                'v2-inference.yaml',
-                'v2-inference-v.yaml',
-        ]
-    ]
-
-    ok = False
-    while not ok:
-        try:
-            choice = input('select 0-5, Q > ').strip()
-            if choice.startswith(('q','Q')):
-                return
-            if choice == '5':
-                completer.complete_extensions(('.yaml'))
-                choice = Path(input('Select config file for this model> ').strip()).absolute()
-                completer.complete_extensions(None)
-                ok = choice.exists()
-            else:
-                choice = choices[int(choice)-1]
-                ok = True
-        except (ValueError, IndexError):
-            print(f'{choice} is not a valid choice')
-        except EOFError:
-            return
-    return choice
-
-
 def _verify_load(model_name: str, gen) -> bool:
     print(">> Verifying that new model loads...")
     current_model = gen.model_name
@@ -796,8 +744,8 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
         except KeyboardInterrupt:
             return
 
-    manager.commit(opt.conf)
-    if ckpt_path and click.confirm(f"Delete the original .ckpt file at {ckpt_path}?", default=False):
+    manager.commit(opt.conf)    
+    if click.confirm(f"Delete the original .ckpt file at {ckpt_path}?", default=False):
         ckpt_path.unlink(missing_ok=True)
         print(f"{ckpt_path} deleted")
 
@@ -993,14 +941,13 @@ def add_postprocessing_to_metadata(opt, original_file, new_file, tool, command):
 
 
 def prepare_image_metadata(
-        opt,
-        prefix,
-        seed,
-        operation="generate",
-        prior_variations=[],
-        postprocessed=False,
-        first_seed=None,
-        model_id='unknown',
+    opt,
+    prefix,
+    seed,
+    operation="generate",
+    prior_variations=[],
+    postprocessed=False,
+    first_seed=None,
 ):
     if postprocessed and opt.save_original:
         filename = choose_postprocess_name(opt, prefix, seed)
@@ -1008,7 +955,6 @@ def prepare_image_metadata(
         wildcards = dict(opt.__dict__)
         wildcards["prefix"] = prefix
         wildcards["seed"] = seed
-        wildcards["model_id"] = model_id
         try:
             filename = opt.fnformat.format(**wildcards)
         except KeyError as e:
@@ -1026,17 +972,18 @@ def prepare_image_metadata(
         first_seed = first_seed or seed
         this_variation = [[seed, opt.variation_amount]]
         opt.with_variations = prior_variations + this_variation
-        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed,model_id=model_id)
+        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
     elif len(prior_variations) > 0:
-        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed,model_id=model_id)
+        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
     elif operation == "postprocess":
         formatted_dream_prompt = "!fix " + opt.dream_prompt_str(
-            seed=seed, prompt=opt.input_file_path, model_id=model_id,
+            seed=seed, prompt=opt.input_file_path
         )
     else:
-        formatted_dream_prompt = opt.dream_prompt_str(seed=seed,model_id=model_id)
+        formatted_dream_prompt = opt.dream_prompt_str(seed=seed)
     return filename, formatted_dream_prompt
 
+
 def choose_postprocess_name(opt, prefix, seed) -> str:
     match = re.search("postprocess:(\w+)", opt.last_operation)
     if match:
@@ -1287,60 +1234,6 @@ def check_internet() -> bool:
     except:
         return False
 
-# This routine performs any patch-ups needed after installation
-def run_patches():
-    install_missing_config_files()
-    version_file = Path(Globals.root,'.version')
-    if version_file.exists():
-        with open(version_file,'r') as f:
-            root_version = version.parse(f.readline() or 'v2.3.2')
-    else:
-        root_version = version.parse('v2.3.2')
-    app_version = version.parse(ldm.invoke.__version__)
-    if root_version < app_version:
-        try:
-            do_version_update(root_version, ldm.invoke.__version__)
-            with open(version_file,'w') as f:
-                f.write(ldm.invoke.__version__)
-        except:
-            print("** Update failed. Will try again on next launch")
-
-def install_missing_config_files():
-    """
-    install ckpt configuration files that may have been added to the
-    distro after original root directory configuration
-    """
-    import invokeai.configs as conf
-    from shutil import copyfile
-    
-    root_configs = Path(global_config_dir(), 'stable-diffusion')
-    repo_configs = Path(conf.__path__[0], 'stable-diffusion')
-    for src in repo_configs.iterdir():
-        dest = root_configs / src.name
-        if not dest.exists():
-            copyfile(src,dest)
-    
-def do_version_update(root_version: version.Version, app_version: Union[str, version.Version]):
-    """
-    Make any updates to the launcher .sh and .bat scripts that may be needed
-    from release to release. This is not an elegant solution. Instead, the 
-    launcher should be moved into the source tree and installed using pip.
-    """
-    if root_version < version.Version('v2.3.3'):
-        if sys.platform == "linux":
-            print('>> Downloading new version of launcher script and its config file')
-            from ldm.util import download_with_progress_bar
-            url_base = f'https://raw.githubusercontent.com/invoke-ai/InvokeAI/release/v{str(app_version)}/installer/templates/'
-
-            dest = Path(Globals.root,'invoke.sh.in')
-            assert download_with_progress_bar(url_base+'invoke.sh.in',dest)
-            dest.replace(Path(Globals.root,'invoke.sh'))
-            os.chmod(Path(Globals.root,'invoke.sh'), 0o0755)
-
-            dest = Path(Globals.root,'dialogrc')
-            assert download_with_progress_bar(url_base+'dialogrc',dest)
-            dest.replace(Path(Globals.root,'.dialogrc'))
-
 if __name__ == '__main__':
     main()
     

ldm/invoke/_version.py

@@ -1,2 +1 @@
-
-__version__='2.3.3-rc2'
+__version__='2.3.1'

ldm/invoke/app/api/dependencies.py

@@ -0,0 +1,80 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from argparse import Namespace
+import os
+
+from ..services.processor import DefaultInvocationProcessor
+
+from ..services.graph import GraphExecutionState
+from ..services.sqlite import SqliteItemStorage
+
+from ...globals import Globals
+
+from ..services.image_storage import DiskImageStorage
+from ..services.invocation_queue import MemoryInvocationQueue
+from ..services.invocation_services import InvocationServices
+from ..services.invoker import Invoker
+from ..services.generate_initializer import get_generate
+from .events import FastAPIEventService
+
+
+# TODO: is there a better way to achieve this?
+def check_internet()->bool:
+    '''
+    Return true if the internet is reachable.
+    It does this by pinging huggingface.co.
+    '''
+    import urllib.request
+    host = 'http://huggingface.co'
+    try:
+        urllib.request.urlopen(host,timeout=1)
+        return True
+    except:
+        return False
+
+
+class ApiDependencies:
+    """Contains and initializes all dependencies for the API"""
+    invoker: Invoker = None
+
+    @staticmethod
+    def initialize(
+        args,
+        config,
+        event_handler_id: int
+    ):
+        Globals.try_patchmatch = args.patchmatch
+        Globals.always_use_cpu = args.always_use_cpu
+        Globals.internet_available = args.internet_available and check_internet()
+        Globals.disable_xformers = not args.xformers
+        Globals.ckpt_convert = args.ckpt_convert
+
+        # TODO: Use a logger
+        print(f'>> Internet connectivity is {Globals.internet_available}')
+
+        generate = get_generate(args, config)
+
+        events = FastAPIEventService(event_handler_id)
+
+        output_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../../outputs'))
+
+        images = DiskImageStorage(output_folder)
+
+        # TODO: build a file/path manager?
+        db_location = os.path.join(output_folder, 'invokeai.db')
+
+        services = InvocationServices(
+            generate = generate,
+            events   = events,
+            images   = images,
+            queue                   = MemoryInvocationQueue(),
+            graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = db_location, table_name = 'graph_executions'),
+            processor               = DefaultInvocationProcessor()
+        )
+
+        ApiDependencies.invoker = Invoker(services)
+    
+    @staticmethod
+    def shutdown():
+        if ApiDependencies.invoker:
+            ApiDependencies.invoker.stop()

ldm/invoke/app/api/events.py

@@ -0,0 +1,54 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+import asyncio
+from queue import Empty, Queue
+from typing import Any
+from fastapi_events.dispatcher import dispatch
+from ..services.events import EventServiceBase
+import threading
+
+class FastAPIEventService(EventServiceBase):
+    event_handler_id: int
+    __queue: Queue
+    __stop_event: threading.Event
+
+    def __init__(self, event_handler_id: int) -> None:
+        self.event_handler_id = event_handler_id
+        self.__queue = Queue()
+        self.__stop_event = threading.Event()
+        asyncio.create_task(self.__dispatch_from_queue(stop_event = self.__stop_event))
+
+        super().__init__()
+
+
+    def stop(self, *args, **kwargs):
+        self.__stop_event.set()
+        self.__queue.put(None)
+
+
+    def dispatch(self, event_name: str, payload: Any) -> None:
+        self.__queue.put(dict(
+            event_name = event_name,
+            payload = payload
+        ))
+
+
+    async def __dispatch_from_queue(self, stop_event: threading.Event):
+        """Get events on from the queue and dispatch them, from the correct thread"""
+        while not stop_event.is_set():
+            try:
+                event = self.__queue.get(block = False)
+                if not event: # Probably stopping
+                    continue
+
+                dispatch(
+                    event.get('event_name'),
+                    payload       = event.get('payload'),
+                    middleware_id = self.event_handler_id)
+
+            except Empty:
+                await asyncio.sleep(0.001)
+                pass
+
+            except asyncio.CancelledError as e:
+                raise e # Raise a proper error

ldm/invoke/app/api/routers/images.py

@@ -0,0 +1,57 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from datetime import datetime, timezone
+from fastapi import Path, UploadFile, Request
+from fastapi.routing import APIRouter
+from fastapi.responses import FileResponse, Response
+from PIL import Image
+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")
+):
+    """Gets a result"""
+    # TODO: This is not really secure at all. At least make sure only output results are served
+    filename = ApiDependencies.invoker.services.images.get_path(image_type, image_name)
+    return FileResponse(filename)
+
+@images_router.post('/uploads/',
+    operation_id = 'upload_image',
+    responses = {
+        201: {'description': 'The image was uploaded successfully'},
+        404: {'description': 'Session not found'}
+    })
+async def upload_image(
+    file: UploadFile,
+    request: Request
+):
+    if not file.content_type.startswith('image'):
+        return Response(status_code = 415)
+    
+    contents = await file.read()
+    try:
+        im = Image.open(contents)
+    except:
+        # Error opening the image
+        return Response(status_code = 415)
+    
+    filename = f'{str(int(datetime.now(timezone.utc).timestamp()))}.png'
+    ApiDependencies.invoker.services.images.save(ImageType.UPLOAD, filename, im)
+
+    return Response(
+        status_code=201,
+        headers = {
+            'Location': request.url_for('get_image', image_type=ImageType.UPLOAD, image_name=filename)
+        }
+    )

ldm/invoke/app/api/routers/sessions.py

@@ -0,0 +1,232 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from typing import List, Optional, Union, Annotated
+from fastapi import Query, Path, Body
+from fastapi.routing import APIRouter
+from fastapi.responses import Response
+from pydantic.fields import Field
+
+from ...services.item_storage import PaginatedResults
+from ..dependencies import ApiDependencies
+from ...invocations.baseinvocation import BaseInvocation
+from ...services.graph import EdgeConnection, Graph, GraphExecutionState, NodeAlreadyExecutedError
+from ...invocations import *
+
+session_router = APIRouter(
+    prefix = '/v1/sessions',
+    tags = ['sessions']
+)
+
+
+@session_router.post('/',
+    operation_id = 'create_session',
+    responses = {
+        200: {"model": GraphExecutionState},
+        400: {'description': 'Invalid json'}
+    })
+async def create_session(
+    graph: Optional[Graph] = Body(default = None, description = "The graph to initialize the session with")
+) -> GraphExecutionState:
+    """Creates a new session, optionally initializing it with an invocation graph"""
+    session = ApiDependencies.invoker.create_execution_state(graph)
+    return session
+
+
+@session_router.get('/',
+    operation_id = 'list_sessions',
+    responses = {
+        200: {"model": PaginatedResults[GraphExecutionState]}
+    })
+async def list_sessions(
+    page: int     = Query(default = 0, description = "The page of results to get"),
+    per_page: int = Query(default = 10, description = "The number of results per page"),
+    query: str    = Query(default = '', description = "The query string to search for")
+) -> PaginatedResults[GraphExecutionState]:
+    """Gets a list of sessions, optionally searching"""
+    if filter == '':
+        result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page)
+    else:
+        result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page)
+    return result
+
+
+@session_router.get('/{session_id}',
+    operation_id = 'get_session',
+    responses = {
+        200: {"model": GraphExecutionState},
+        404: {'description': 'Session not found'}
+    })
+async def get_session(
+    session_id: str = Path(description = "The id of the session to get")
+) -> GraphExecutionState:
+    """Gets a session"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code = 404)
+    else:
+        return session
+
+
+@session_router.post('/{session_id}/nodes',
+    operation_id = 'add_node',
+    responses = {
+        200: {"model": str},
+        400: {'description': 'Invalid node or link'},
+        404: {'description': 'Session not found'}
+    }
+)
+async def add_node(
+    session_id: str = Path(description = "The id of the session"),
+    node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body(description = "The node to add")
+) -> str:
+    """Adds a node to the graph"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code = 404)
+
+    try:
+        session.add_node(node)
+        ApiDependencies.invoker.services.graph_execution_manager.set(session) # TODO: can this be done automatically, or add node through an API?
+        return session.id
+    except NodeAlreadyExecutedError:
+        return Response(status_code = 400)
+    except IndexError:
+        return Response(status_code = 400)
+        
+
+@session_router.put('/{session_id}/nodes/{node_path}',
+    operation_id = 'update_node',
+    responses = {
+        200: {"model": GraphExecutionState},
+        400: {'description': 'Invalid node or link'},
+        404: {'description': 'Session not found'}
+    }
+)
+async def update_node(
+    session_id: str = Path(description = "The id of the session"),
+    node_path: str = Path(description = "The path to the node in the graph"),
+    node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body(description = "The new node")
+) -> GraphExecutionState:
+    """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:
+        return Response(status_code = 404)
+
+    try:
+        session.update_node(node_path, node)
+        ApiDependencies.invoker.services.graph_execution_manager.set(session) # TODO: can this be done automatically, or add node through an API?
+        return session
+    except NodeAlreadyExecutedError:
+        return Response(status_code = 400)
+    except IndexError:
+        return Response(status_code = 400)
+
+
+@session_router.delete('/{session_id}/nodes/{node_path}',
+    operation_id = 'delete_node',
+    responses = {
+        200: {"model": GraphExecutionState},
+        400: {'description': 'Invalid node or link'},
+        404: {'description': 'Session not found'}
+    }
+)
+async def delete_node(
+    session_id: str = Path(description = "The id of the session"),
+    node_path: str = Path(description = "The path to the node to delete")
+) -> GraphExecutionState:
+    """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:
+        return Response(status_code = 404)
+
+    try:
+        session.delete_node(node_path)
+        ApiDependencies.invoker.services.graph_execution_manager.set(session) # TODO: can this be done automatically, or add node through an API?
+        return session
+    except NodeAlreadyExecutedError:
+        return Response(status_code = 400)
+    except IndexError:
+        return Response(status_code = 400)
+
+
+@session_router.post('/{session_id}/edges',
+    operation_id = 'add_edge',
+    responses = {
+        200: {"model": GraphExecutionState},
+        400: {'description': 'Invalid node or link'},
+        404: {'description': 'Session not found'}
+    }
+)
+async def add_edge(
+    session_id: str = Path(description = "The id of the session"),
+    edge: tuple[EdgeConnection, EdgeConnection] = Body(description = "The edge to add")
+) -> GraphExecutionState:
+    """Adds an edge to the graph"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code = 404)
+
+    try:
+        session.add_edge(edge)
+        ApiDependencies.invoker.services.graph_execution_manager.set(session) # TODO: can this be done automatically, or add node through an API?
+        return session
+    except NodeAlreadyExecutedError:
+        return Response(status_code = 400)
+    except IndexError:
+        return Response(status_code = 400)
+
+
+# TODO: the edge being in the path here is really ugly, find a better solution
+@session_router.delete('/{session_id}/edges/{from_node_id}/{from_field}/{to_node_id}/{to_field}',
+    operation_id = 'delete_edge',
+    responses = {
+        200: {"model": GraphExecutionState},
+        400: {'description': 'Invalid node or link'},
+        404: {'description': 'Session not found'}
+    }
+)
+async def delete_edge(
+    session_id: str = Path(description = "The id of the session"),
+    from_node_id: str = Path(description = "The id of the node the edge is coming from"),
+    from_field: str = Path(description = "The field of the node the edge is coming from"),
+    to_node_id: str = Path(description = "The id of the node the edge is going to"),
+    to_field: str = Path(description = "The field of the node the edge is going to")
+) -> GraphExecutionState:
+    """Deletes an edge from the graph"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code = 404)
+
+    try:
+        edge = (EdgeConnection(node_id = from_node_id, field = from_field), EdgeConnection(node_id = to_node_id, field = to_field))
+        session.delete_edge(edge)
+        ApiDependencies.invoker.services.graph_execution_manager.set(session) # TODO: can this be done automatically, or add node through an API?
+        return session
+    except NodeAlreadyExecutedError:
+        return Response(status_code = 400)
+    except IndexError:
+        return Response(status_code = 400)
+        
+
+@session_router.put('/{session_id}/invoke',
+    operation_id = 'invoke_session',
+    responses = {
+        200: {"model": None},
+        202: {'description': 'The invocation is queued'},
+        400: {'description': 'The session has no invocations ready to invoke'},
+        404: {'description': 'Session not found'}
+    })
+async def invoke_session(
+    session_id: str = Path(description = "The id of the session to invoke"),
+    all: bool       = Query(default = False, description = "Whether or not to invoke all remaining invocations")
+) -> None:
+    """Invokes a session"""
+    session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
+    if session is None:
+        return Response(status_code = 404)
+    
+    if session.is_complete():
+        return Response(status_code = 400)
+    
+    ApiDependencies.invoker.invoke(session, invoke_all = all)
+    return Response(status_code=202)

ldm/invoke/app/api/sockets.py

@@ -0,0 +1,36 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from fastapi import FastAPI
+from fastapi_socketio import SocketManager
+from fastapi_events.handlers.local import local_handler
+from fastapi_events.typing import Event
+from ..services.events import EventServiceBase
+
+class SocketIO:
+    __sio: SocketManager
+
+    def __init__(self, app: FastAPI):
+        self.__sio = SocketManager(app = app)
+        self.__sio.on('subscribe', handler=self._handle_sub)
+        self.__sio.on('unsubscribe', handler=self._handle_unsub)
+        
+        local_handler.register(
+            event_name = EventServiceBase.session_event,
+            _func=self._handle_session_event
+        )
+
+    async def _handle_session_event(self, event: Event):
+        await self.__sio.emit(
+            event = event[1]['event'],
+            data = event[1]['data'],
+            room = event[1]['data']['graph_execution_state_id']
+        )
+
+    async def _handle_sub(self, sid, data, *args, **kwargs):
+        if 'session' in data:
+            self.__sio.enter_room(sid, data['session'])
+        
+        # @app.sio.on('unsubscribe')
+    async def _handle_unsub(self, sid, data, *args, **kwargs):
+        if 'session' in data:
+            self.__sio.leave_room(sid, data['session'])

ldm/invoke/app/api_app.py

@@ -0,0 +1,164 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+import asyncio
+from inspect import signature
+from fastapi import FastAPI
+from fastapi.openapi.utils import get_openapi
+from fastapi.openapi.docs import get_swagger_ui_html, get_redoc_html
+from fastapi.staticfiles import StaticFiles
+from fastapi_events.middleware import EventHandlerASGIMiddleware
+from fastapi_events.handlers.local import local_handler
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic.schema import schema
+import uvicorn
+from .api.sockets import SocketIO
+from .invocations import *
+from .invocations.baseinvocation import BaseInvocation
+from .api.routers import images, sessions
+from .api.dependencies import ApiDependencies
+from ..args import Args
+
+# Create the app
+# TODO: create this all in a method so configuration/etc. can be passed in?
+app = FastAPI(
+    title     = "Invoke AI",
+    docs_url  = None,
+    redoc_url = None
+)
+
+# Add event handler
+event_handler_id: int = id(app)
+app.add_middleware(
+    EventHandlerASGIMiddleware,
+    handlers      = [local_handler], # TODO: consider doing this in services to support different configurations
+    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():
+    args = Args()
+    config = args.parse_args()
+
+    ApiDependencies.initialize(
+        args             = args,
+        config           = config,
+        event_handler_id = event_handler_id
+    )
+
+# Shut down threads
+@app.on_event('shutdown')
+async def shutdown_event():
+    ApiDependencies.shutdown()
+
+# Include all routers
+# TODO: REMOVE
+# app.include_router(
+#     invocation.invocation_router,
+#     prefix = '/api')
+
+app.include_router(
+    sessions.session_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?
+def custom_openapi():
+    if app.openapi_schema:
+        return app.openapi_schema
+    openapi_schema = get_openapi(
+        title       = app.title,
+        description = "An API for invoking AI image operations",
+        version     = "1.0.0",
+        routes      = app.routes
+    )
+
+    # Add all outputs
+    all_invocations = BaseInvocation.get_invocations()
+    output_types = set()
+    output_type_titles = dict()
+    for invoker in all_invocations:
+        output_type = signature(invoker.invoke).return_annotation
+        output_types.add(output_type)
+
+    output_schemas = schema(output_types, ref_prefix="#/components/schemas/")
+    for schema_key, output_schema in output_schemas['definitions'].items():
+        openapi_schema["components"]["schemas"][schema_key] = output_schema
+
+        # TODO: note that we assume the schema_key here is the TYPE.__name__
+        # This could break in some cases, figure out a better way to do it
+        output_type_titles[schema_key] = output_schema['title']
+
+    # Add a reference to the output type to additionalProperties of the invoker schema
+    for invoker in all_invocations:
+        invoker_name = invoker.__name__
+        output_type = signature(invoker.invoke).return_annotation
+        output_type_title = output_type_titles[output_type.__name__]
+        invoker_schema = openapi_schema["components"]["schemas"][invoker_name]
+        outputs_ref = { '$ref': f'#/components/schemas/{output_type_title}' }
+        if 'additionalProperties' not in invoker_schema:
+            invoker_schema['additionalProperties'] = {}
+
+        invoker_schema['additionalProperties']['outputs'] = outputs_ref
+    
+    app.openapi_schema = openapi_schema
+    return app.openapi_schema
+
+app.openapi = custom_openapi
+
+# Override API doc favicons
+app.mount('/static', StaticFiles(directory='static/dream_web'), name='static')
+
+@app.get("/docs", include_in_schema=False)
+def overridden_swagger():
+	return get_swagger_ui_html(
+        openapi_url=app.openapi_url,
+        title=app.title,
+        swagger_favicon_url="/static/favicon.ico"
+    )
+
+@app.get("/redoc", include_in_schema=False)
+def overridden_redoc():
+	return get_redoc_html(
+        openapi_url=app.openapi_url,
+        title=app.title,
+        redoc_favicon_url="/static/favicon.ico"
+    )
+
+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 = "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()

ldm/invoke/app/cli_app.py

@@ -0,0 +1,303 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+import argparse
+import shlex
+import os
+import time
+from typing import Any, Dict, Iterable, Literal, Union, get_args, get_origin, get_type_hints
+from pydantic import BaseModel
+from pydantic.fields import Field
+
+from .services.processor import DefaultInvocationProcessor
+
+from .services.graph import EdgeConnection, GraphExecutionState
+
+from .services.sqlite import SqliteItemStorage
+
+from .invocations.image import ImageField
+from .services.generate_initializer import get_generate
+from .services.image_storage import DiskImageStorage
+from .services.invocation_queue import MemoryInvocationQueue
+from .invocations.baseinvocation import BaseInvocation
+from .services.invocation_services import InvocationServices
+from .services.invoker import Invoker 
+from .invocations import *
+from ..args import Args
+from .services.events import EventServiceBase
+
+
+class InvocationCommand(BaseModel):
+    invocation: Union[BaseInvocation.get_invocations()] = Field(discriminator="type")
+
+
+class InvalidArgs(Exception):
+    pass
+
+
+def get_invocation_parser() -> argparse.ArgumentParser:
+
+    # Create invocation parser
+    parser = argparse.ArgumentParser()
+    def exit(*args, **kwargs):
+        raise InvalidArgs
+    parser.exit = exit
+
+    subparsers = parser.add_subparsers(dest='type')
+    invocation_parsers = dict()
+
+    # Add history parser
+    history_parser = subparsers.add_parser('history', help="Shows the invocation history")
+    history_parser.add_argument('count', nargs='?', default=5, type=int, help="The number of history entries to show")
+
+    # Add default parser
+    default_parser = subparsers.add_parser('default', help="Define a default value for all inputs with a specified name")
+    default_parser.add_argument('input', type=str, help="The input field")
+    default_parser.add_argument('value', help="The default value")
+    
+    default_parser = subparsers.add_parser('reset_default', help="Resets a default value")
+    default_parser.add_argument('input', type=str, help="The input field")
+
+    # Create subparsers for each invocation
+    invocations = BaseInvocation.get_all_subclasses()
+    for invocation in invocations:
+        hints = get_type_hints(invocation)
+        cmd_name = get_args(hints['type'])[0]
+        command_parser = subparsers.add_parser(cmd_name, help=invocation.__doc__)
+        invocation_parsers[cmd_name] = command_parser
+
+        # Add linking capability
+        command_parser.add_argument('--link', '-l', action='append', nargs=3,
+            help="A link in the format 'dest_field source_node source_field'. source_node can be relative to history (e.g. -1)")
+
+        command_parser.add_argument('--link_node', '-ln', action='append',
+            help="A link from all fields in the specified node. Node can be relative to history (e.g. -1)")
+
+        # Convert all fields to arguments
+        fields = invocation.__fields__
+        for name, field in fields.items():
+            if name in ['id', 'type']:
+                continue
+            
+            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]
+
+                command_parser.add_argument(
+                    f"--{name}",
+                    dest=name,
+                    type=field_type,
+                    default=field.default,
+                    choices = allowed_values,                    
+                    help=field.field_info.description
+                )
+            else:
+                command_parser.add_argument(
+                    f"--{name}",
+                    dest=name,
+                    type=field.type_,
+                    default=field.default,   
+                    help=field.field_info.description
+                )
+
+    return parser
+
+
+def get_invocation_command(invocation) -> str:
+    fields = invocation.__fields__.items()
+    type_hints = get_type_hints(type(invocation))
+    command = [invocation.type]
+    for name,field in fields:
+        if name in ['id', 'type']:
+            continue
+
+        # TODO: add links
+
+        # Skip image fields when serializing command
+        type_hint = type_hints.get(name) or None
+        if type_hint is ImageField or ImageField in get_args(type_hint):
+            continue
+
+        field_value = getattr(invocation, name)
+        field_default = field.default
+        if field_value != field_default:
+            if type_hint is str or str in get_args(type_hint):
+                command.append(f'--{name} "{field_value}"')
+            else:
+                command.append(f'--{name} {field_value}')
+        
+    return ' '.join(command)
+
+
+def get_graph_execution_history(graph_execution_state: GraphExecutionState) -> Iterable[str]:
+    """Gets the history of fully-executed invocations for a graph execution"""
+    return (n for n in reversed(graph_execution_state.executed_history) if n in graph_execution_state.graph.nodes)
+
+
+def generate_matching_edges(a: BaseInvocation, b: BaseInvocation) -> list[tuple[EdgeConnection, EdgeConnection]]:
+    """Generates all possible edges between two invocations"""
+    atype = type(a)
+    btype = type(b)
+
+    aoutputtype = atype.get_output_type()
+
+    afields = get_type_hints(aoutputtype)
+    bfields = get_type_hints(btype)
+
+    matching_fields = set(afields.keys()).intersection(bfields.keys())
+    
+    # Remove invalid fields
+    invalid_fields = set(['type', 'id'])
+    matching_fields = matching_fields.difference(invalid_fields)
+
+    edges = [(EdgeConnection(node_id = a.id, field = field), EdgeConnection(node_id = b.id, field = field)) for field in matching_fields]
+    return edges
+
+
+def invoke_cli():
+    args = Args()
+    config = args.parse_args()
+
+    generate = get_generate(args, config)
+
+    # NOTE: load model on first use, uncomment to load at startup
+    # TODO: Make this a config option?
+    #generate.load_model()
+
+    events = EventServiceBase()
+
+    output_folder = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../outputs'))
+
+    # TODO: build a file/path manager?
+    db_location = os.path.join(output_folder, 'invokeai.db')
+
+    services = InvocationServices(
+        generate                = generate,
+        events                  = events,
+        images                  = DiskImageStorage(output_folder),
+        queue                   = MemoryInvocationQueue(),
+        graph_execution_manager = SqliteItemStorage[GraphExecutionState](filename = db_location, table_name = 'graph_executions'),
+        processor               = DefaultInvocationProcessor()
+    )
+
+    invoker = Invoker(services)
+    session = invoker.create_execution_state()
+    
+    parser = get_invocation_parser()
+
+    # Uncomment to print out previous sessions at startup
+    # print(services.session_manager.list())
+
+    # Defaults storage
+    defaults: Dict[str, Any] = dict()
+
+    while True:
+        try:
+            cmd_input = input("> ")
+        except KeyboardInterrupt:
+            # Ctrl-c exits
+            break
+
+        if cmd_input in ['exit','q']:
+            break;
+
+        if cmd_input in ['--help','help','h','?']:
+            parser.print_help()
+            continue
+
+        try:
+            # Refresh the state of the session
+            session = invoker.services.graph_execution_manager.get(session.id)
+            history = list(get_graph_execution_history(session))
+
+            # Split the command for piping
+            cmds = cmd_input.split('|')
+            start_id = len(history)
+            current_id = start_id
+            new_invocations = list()
+            for cmd in cmds:
+                # Parse args to create invocation
+                args = vars(parser.parse_args(shlex.split(cmd.strip())))
+
+                # Check for special commands
+                # TODO: These might be better as Pydantic models, similar to the invocations
+                if args['type'] == 'history':
+                    history_count = args['count'] or 5
+                    for i in range(min(history_count, len(history))):
+                        entry_id = history[-1 - i]
+                        entry = session.graph.get_node(entry_id)
+                        print(f'{entry_id}: {get_invocation_command(entry.invocation)}')
+                    continue
+
+                if args['type'] == 'reset_default':
+                    if args['input'] in defaults:
+                        del defaults[args['input']]
+                    continue
+
+                if args['type'] == 'default':
+                    field = args['input']
+                    field_value = args['value']
+                    defaults[field] = field_value
+                    continue
+
+                # Override defaults
+                for field_name,field_default in defaults.items():
+                    if field_name in args:
+                        args[field_name] = field_default
+
+                # Parse invocation
+                args['id'] = current_id
+                command = InvocationCommand(invocation = args)
+
+                # Pipe previous command output (if there was a previous command)
+                edges = []
+                if len(history) > 0 or current_id != start_id:
+                    from_id = history[0] if current_id == start_id else str(current_id - 1)
+                    from_node = next(filter(lambda n: n[0].id == from_id, new_invocations))[0] if current_id != start_id else session.graph.get_node(from_id)
+                    matching_edges = generate_matching_edges(from_node, command.invocation)
+                    edges.extend(matching_edges)
+                
+                # Parse provided links
+                if 'link_node' in args and args['link_node']:
+                    for link in args['link_node']:
+                        link_node = session.graph.get_node(link)
+                        matching_edges = generate_matching_edges(link_node, command.invocation)
+                        edges.extend(matching_edges)
+                
+                if 'link' in args and args['link']:
+                    for link in args['link']:
+                        edges.append((EdgeConnection(node_id = link[1], field = link[0]), EdgeConnection(node_id = command.invocation.id, field = link[2])))
+
+                new_invocations.append((command.invocation, edges))
+
+                current_id = current_id + 1
+
+            # Command line was parsed successfully
+            # Add the invocations to the session
+            for invocation in new_invocations:
+                session.add_node(invocation[0])
+                for edge in invocation[1]:
+                    session.add_edge(edge)
+
+            # Execute all available invocations
+            invoker.invoke(session, invoke_all = True)
+            while not session.is_complete():
+                # Wait some time
+                session = invoker.services.graph_execution_manager.get(session.id)
+                time.sleep(0.1)
+
+        except InvalidArgs:
+            print('Invalid command, use "help" to list commands')
+            continue
+
+        except SystemExit:
+            continue
+    
+    invoker.stop()
+
+
+if __name__ == "__main__":
+    invoke_cli()

ldm/invoke/app/invocations/__init__.py

@@ -0,0 +1,8 @@
+import os
+
+__all__ = []
+
+dirname = os.path.dirname(os.path.abspath(__file__))
+for f in os.listdir(dirname):
+    if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py":
+        __all__.append(f[:-3])

ldm/invoke/app/invocations/baseinvocation.py

@@ -0,0 +1,74 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from abc import ABC, abstractmethod
+from inspect import signature
+from typing import get_args, get_type_hints
+from pydantic import BaseModel, Field
+from ..services.invocation_services import InvocationServices
+
+
+class InvocationContext:
+    services: InvocationServices
+    graph_execution_state_id: str
+
+    def __init__(self, services: InvocationServices, graph_execution_state_id: str):
+        self.services = services
+        self.graph_execution_state_id = graph_execution_state_id
+
+
+class BaseInvocationOutput(BaseModel):
+    """Base class for all invocation outputs"""
+
+    # All outputs must include a type name like this:
+    # type: Literal['your_output_name']
+
+    @classmethod
+    def get_all_subclasses_tuple(cls):
+        subclasses = []
+        toprocess = [cls]
+        while len(toprocess) > 0:
+            next = toprocess.pop(0)
+            next_subclasses = next.__subclasses__()
+            subclasses.extend(next_subclasses)
+            toprocess.extend(next_subclasses)
+        return tuple(subclasses)
+
+
+class BaseInvocation(ABC, BaseModel):
+    """A node to process inputs and produce outputs.
+    May use dependency injection in __init__ to receive providers.
+    """
+
+    # All invocations must include a type name like this:
+    # type: Literal['your_output_name']
+
+    @classmethod
+    def get_all_subclasses(cls):
+        subclasses = []
+        toprocess = [cls]
+        while len(toprocess) > 0:
+            next = toprocess.pop(0)
+            next_subclasses = next.__subclasses__()
+            subclasses.extend(next_subclasses)
+            toprocess.extend(next_subclasses)
+        return subclasses
+
+    @classmethod
+    def get_invocations(cls):
+        return tuple(BaseInvocation.get_all_subclasses())
+
+    @classmethod
+    def get_invocations_map(cls):
+        # Get the type strings out of the literals and into a dictionary
+        return dict(map(lambda t: (get_args(get_type_hints(t)['type'])[0], t),BaseInvocation.get_all_subclasses()))
+    
+    @classmethod
+    def get_output_type(cls):
+        return signature(cls.invoke).return_annotation
+
+    @abstractmethod
+    def invoke(self, context: InvocationContext) -> BaseInvocationOutput:
+        """Invoke with provided context and return outputs."""
+        pass
+
+    id: str = Field(description="The id of this node. Must be unique among all nodes.")

ldm/invoke/app/invocations/cv.py

@@ -0,0 +1,42 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from typing import Literal
+import numpy
+from pydantic import Field
+from PIL import Image, ImageOps
+import cv2 as cv
+from .image import ImageField, ImageOutput
+from .baseinvocation import BaseInvocation, InvocationContext
+from ..services.image_storage import ImageType
+
+
+class CvInpaintInvocation(BaseInvocation):
+    """Simple inpaint using opencv."""
+    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")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        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))
+
+        # Inpaint
+        cv_inpainted = cv.inpaint(cv_image, cv_mask, 3, cv.INPAINT_TELEA)
+
+        # Convert back to Pillow
+        # TODO: consider making a utility function
+        image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
+
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, image_inpainted)
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )

ldm/invoke/app/invocations/generate.py

@@ -0,0 +1,160 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from datetime import datetime, timezone
+from typing import Any, Literal, Optional, Union
+import numpy as np
+from pydantic import Field
+from PIL import Image
+from skimage.exposure.histogram_matching import match_histograms
+from .image import ImageField, ImageOutput
+from .baseinvocation import BaseInvocation, InvocationContext
+from ..services.image_storage import ImageType
+from ..services.invocation_services import InvocationServices
+
+
+SAMPLER_NAME_VALUES = Literal["ddim","plms","k_lms","k_dpm_2","k_dpm_2_a","k_euler","k_euler_a","k_heun"]
+
+# Text to image
+class TextToImageInvocation(BaseInvocation):
+    """Generates an image using text2img."""
+    type: Literal['txt2img'] = 'txt2img'
+
+    # Inputs
+    # TODO: consider making prompt optional to enable providing prompt through a link
+    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)")
+    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")
+    sampler_name: SAMPLER_NAME_VALUES = Field(default="k_lms", description="The sampler 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")
+
+    # TODO: pass this an emitter method or something? or a session for dispatching?
+    def dispatch_progress(self, context: InvocationContext, sample: Any = None, step: int = 0) -> None:
+        context.services.events.emit_generator_progress(
+            context.graph_execution_state_id, self.id, step, float(step) / float(self.steps)
+        )
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+
+        def step_callback(sample, step = 0):
+            self.dispatch_progress(context, sample, step)
+
+        # Handle invalid model parameter
+        # TODO: figure out if this can be done via a validator that uses the model_cache
+        # TODO: How to get the default model name now?
+        if self.model is None or self.model == '':
+            self.model = context.services.generate.model_name
+
+        # Set the model (if already cached, this does nothing)
+        context.services.generate.set_model(self.model)
+
+        results = context.services.generate.prompt2image(
+            prompt = self.prompt,
+            step_callback = step_callback,
+            **self.dict(exclude = {'prompt'}) # Shorthand for passing all of the parameters above manually
+        )
+
+        # 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)
+        context.services.images.save(image_type, image_name, results[0][0])
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+
+
+class ImageToImageInvocation(TextToImageInvocation):
+    """Generates an image using img2img."""
+    type: Literal['img2img'] = 'img2img'
+
+    # 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 original image")
+    fit: bool                     = Field(default=True, description="Whether or not the result should be fit to the aspect ratio of the input image")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = None if self.image is None else context.services.images.get(self.image.image_type, self.image.image_name)
+        mask  = None
+
+        def step_callback(sample, step = 0):
+            self.dispatch_progress(context, sample, step)
+
+        # Handle invalid model parameter
+        # TODO: figure out if this can be done via a validator that uses the model_cache
+        # TODO: How to get the default model name now?
+        if self.model is None or self.model == '':
+            self.model = context.services.generate.model_name
+
+        # Set the model (if already cached, this does nothing)
+        context.services.generate.set_model(self.model)
+
+        results = context.services.generate.prompt2image(
+            prompt    = self.prompt,
+            init_img  = image,
+            init_mask = mask,
+            step_callback = step_callback,
+            **self.dict(exclude = {'prompt','image','mask'}) # Shorthand for passing all of the parameters above manually
+        )
+
+        result_image = results[0][0]
+
+        # 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)
+        context.services.images.save(image_type, image_name, result_image)
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+
+
+class InpaintInvocation(ImageToImageInvocation):
+    """Generates an image using inpaint."""
+    type: Literal['inpaint'] = 'inpaint'
+
+    # Inputs
+    mask: Union[ImageField,None]  = Field(description="The mask")
+    inpaint_replace: float        = Field(default=0.0, ge=0.0, le=1.0, description="The amount by which to replace masked areas with latent noise")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = None if self.image is None 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(self.mask.image_type, self.mask.image_name)
+
+        def step_callback(sample, step = 0):
+            self.dispatch_progress(context, sample, step)
+
+        # Handle invalid model parameter
+        # TODO: figure out if this can be done via a validator that uses the model_cache
+        # TODO: How to get the default model name now?
+        if self.model is None or self.model == '':
+            self.model = context.services.generate.model_name
+
+        # Set the model (if already cached, this does nothing)
+        context.services.generate.set_model(self.model)
+
+        results = context.services.generate.prompt2image(
+            prompt    = self.prompt,
+            init_img  = image,
+            init_mask = mask,
+            step_callback = step_callback,
+            **self.dict(exclude = {'prompt','image','mask'}) # Shorthand for passing all of the parameters above manually
+        )
+
+        result_image = results[0][0]
+
+        # 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)
+        context.services.images.save(image_type, image_name, result_image)
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )

ldm/invoke/app/invocations/image.py

@@ -0,0 +1,219 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from datetime import datetime, timezone
+from typing import Literal, Optional
+import numpy
+from pydantic import Field, BaseModel
+from PIL import Image, ImageOps, ImageFilter
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
+from ..services.image_storage import ImageType
+from ..services.invocation_services import InvocationServices
+
+
+class ImageField(BaseModel):
+    """An image field used for passing image objects between invocations"""
+    image_type: str = Field(default=ImageType.RESULT, description="The type of the image")
+    image_name: Optional[str] = Field(default=None, description="The name of the image")
+
+
+class ImageOutput(BaseInvocationOutput):
+    """Base class for invocations that output an image"""
+    type: Literal['image'] = 'image'
+
+    image: ImageField = Field(default=None, description="The output image")
+
+
+class MaskOutput(BaseInvocationOutput):
+    """Base class for invocations that output a mask"""
+    type: Literal['mask'] = 'mask'
+
+    mask: ImageField = Field(default=None, description="The output mask")
+
+
+# TODO: this isn't really necessary anymore
+class LoadImageInvocation(BaseInvocation):
+    """Load an image from a filename and provide it as output."""
+    type: Literal['load_image'] = 'load_image'
+
+    # Inputs
+    image_type: ImageType = Field(description="The type of the image")
+    image_name: str = Field(description="The name of the image")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        return ImageOutput(
+            image = ImageField(image_type = self.image_type, image_name = self.image_name)
+        )
+
+
+class ShowImageInvocation(BaseInvocation):
+    """Displays a provided image, and passes it forward in the pipeline."""
+    type: Literal['show_image'] = 'show_image'
+
+    # Inputs
+    image: ImageField = Field(default=None, description="The image to show")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        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_type = self.image.image_type, image_name = self.image.image_name)
+        )
+
+
+class CropImageInvocation(BaseInvocation):
+    """Crops an image to a specified box. The box can be outside of the image."""
+    type: Literal['crop'] = 'crop'
+
+    # Inputs
+    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")
+    height: int = Field(default=512, gt=0, description="The height of the crop rectangle")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+
+        image_crop = Image.new(mode = 'RGBA', size = (self.width, self.height), color = (0, 0, 0, 0))
+        image_crop.paste(image, (-self.x, -self.y))
+
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, image_crop)
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+
+
+class PasteImageInvocation(BaseInvocation):
+    """Pastes an image into another image."""
+    type: Literal['paste'] = 'paste'
+
+    # Inputs
+    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")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        base_image = context.services.images.get(self.base_image.image_type, self.base_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(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?
+
+        min_x = min(0, self.x)
+        min_y = min(0, self.y)
+        max_x = max(base_image.width, image.width + self.x)
+        max_y = max(base_image.height, image.height + self.y)
+
+        new_image = Image.new(mode = 'RGBA', size = (max_x - min_x, max_y - min_y), color = (0, 0, 0, 0))
+        new_image.paste(base_image, (abs(min_x), abs(min_y)))
+        new_image.paste(image, (max(0, self.x), max(0, self.y)), mask = mask)
+
+        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, new_image)
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+
+
+class MaskFromAlphaInvocation(BaseInvocation):
+    """Extracts the alpha channel of an image as a mask."""
+    type: Literal['tomask'] = 'tomask'
+
+    # Inputs
+    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")
+
+    def invoke(self, context: InvocationContext) -> MaskOutput:
+        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_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, image_mask)
+        return MaskOutput(
+            mask = ImageField(image_type = image_type, image_name = image_name)
+        )
+
+
+class BlurInvocation(BaseInvocation):
+    """Blurs an image"""
+    type: Literal['blur'] = 'blur'
+
+    # Inputs
+    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")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+
+        blur = ImageFilter.GaussianBlur(self.radius) if self.blur_type == 'gaussian' else ImageFilter.BoxBlur(self.radius)
+        blur_image = image.filter(blur)
+
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, blur_image)
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+
+
+class LerpInvocation(BaseInvocation):
+    """Linear interpolation of all pixels of an image"""
+    type: Literal['lerp'] = 'lerp'
+
+    # Inputs
+    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")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+
+        image_arr = numpy.asarray(image, dtype=numpy.float32) / 255
+        image_arr = image_arr * (self.max - self.min) + self.max
+
+        lerp_image = Image.fromarray(numpy.uint8(image_arr))
+
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, lerp_image)
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )
+
+
+class InverseLerpInvocation(BaseInvocation):
+    """Inverse linear interpolation of all pixels of an image"""
+    type: Literal['ilerp'] = 'ilerp'
+
+    # Inputs
+    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")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        image = context.services.images.get(self.image.image_type, self.image.image_name)
+
+        image_arr = numpy.asarray(image, dtype=numpy.float32)
+        image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255
+
+        ilerp_image = Image.fromarray(numpy.uint8(image_arr))
+
+        image_type = ImageType.INTERMEDIATE
+        image_name = context.services.images.create_name(context.graph_execution_state_id, self.id)
+        context.services.images.save(image_type, image_name, ilerp_image)
+        return ImageOutput(
+            image = ImageField(image_type = image_type, image_name = image_name)
+        )

ldm/invoke/app/invocations/prompt.py

@@ -0,0 +1,9 @@
+from typing import Literal
+from pydantic.fields import Field
+from .baseinvocation import BaseInvocationOutput
+
+class PromptOutput(BaseInvocationOutput):
+    """Base class for invocations that output a prompt"""
+    type: Literal['prompt'] = 'prompt'
+
+    prompt: str = Field(default=None, description="The output prompt")

ldm/invoke/app/invocations/reconstruct.py

@@ -0,0 +1,36 @@
+from datetime import datetime, timezone
+from typing import Literal, Union
+from pydantic import Field
+from .image import ImageField, ImageOutput
+from .baseinvocation import BaseInvocation, InvocationContext
+from ..services.image_storage import ImageType
+from ..services.invocation_services import InvocationServices
+
+
+class RestoreFaceInvocation(BaseInvocation):
+    """Restores faces in an image."""
+    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")
+
+
+    def invoke(self, context: InvocationContext) -> ImageOutput: 
+        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        = None,
+            strength       = self.strength, # GFPGAN strength
+            save_original  = False,
+            image_callback = None,
+        )
+
+        # Results are image and seed, unwrap for now
+        # TODO: can this return multiple results?
+        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_type = image_type, image_name = image_name)
+        )

ldm/invoke/app/invocations/upscale.py

@@ -0,0 +1,38 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from datetime import datetime, timezone
+from typing import Literal, Union
+from pydantic import Field
+from .image import ImageField, ImageOutput
+from .baseinvocation import BaseInvocation, InvocationContext
+from ..services.image_storage import ImageType
+from ..services.invocation_services import InvocationServices
+
+
+class UpscaleInvocation(BaseInvocation):
+    """Upscales an image."""
+    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")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        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_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_type = image_type, image_name = image_name)
+        )

ldm/invoke/app/services/events.py

@@ -0,0 +1,78 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from typing import Any, Dict
+
+
+class EventServiceBase:
+    session_event: str = 'session_event'
+
+    """Basic event bus, to have an empty stand-in when not needed"""
+    def dispatch(self, event_name: str, payload: Any) -> None:
+        pass
+
+    def __emit_session_event(self,
+        event_name: str,
+        payload: Dict) -> None:
+        self.dispatch(
+            event_name = EventServiceBase.session_event,
+            payload = dict(
+                event = event_name,
+                data = payload
+            )
+        )
+
+    # Define events here for every event in the system.
+    # This will make them easier to integrate until we find a schema generator.
+    def emit_generator_progress(self,
+        graph_execution_state_id: str,
+        invocation_id: str,
+        step: int,
+        percent: float
+    ) -> None:
+        """Emitted when there is generation progress"""
+        self.__emit_session_event(
+            event_name = 'generator_progress',
+            payload = dict(
+                graph_execution_state_id = graph_execution_state_id,
+                invocation_id = invocation_id,
+                step = step,
+                percent = percent
+            )
+        )
+
+    def emit_invocation_complete(self,
+        graph_execution_state_id: str,
+        invocation_id: str,
+        result: Dict
+    ) -> None:
+        """Emitted when an invocation has completed"""
+        self.__emit_session_event(
+            event_name = 'invocation_complete',
+            payload = dict(
+                graph_execution_state_id = graph_execution_state_id,
+                invocation_id = invocation_id,
+                result = result
+            )
+        )
+
+    def emit_invocation_started(self,
+        graph_execution_state_id: str,
+        invocation_id: str
+    ) -> None:
+        """Emitted when an invocation has started"""
+        self.__emit_session_event(
+            event_name = 'invocation_started',
+            payload = dict(
+                graph_execution_state_id = graph_execution_state_id,
+                invocation_id = invocation_id
+            )
+        )
+
+    def emit_graph_execution_complete(self, graph_execution_state_id: str) -> None:
+        """Emitted when a session has completed all invocations"""
+        self.__emit_session_event(
+            event_name = 'graph_execution_state_complete',
+            payload = dict(
+                graph_execution_state_id = graph_execution_state_id
+            )
+        )

ldm/invoke/app/services/generate_initializer.py

@@ -0,0 +1,233 @@
+from argparse import Namespace
+import os
+import sys
+import traceback
+
+from ...model_manager import ModelManager
+
+from ...globals import Globals
+from ....generate import Generate
+import ldm.invoke
+
+
+# TODO: most of this code should be split into individual services as the Generate.py code is deprecated
+def get_generate(args, config) -> Generate:
+    if not args.conf:
+        config_file = os.path.join(Globals.root,'configs','models.yaml')
+        if not os.path.exists(config_file):
+            report_model_error(args, FileNotFoundError(f"The file {config_file} could not be found."))
+
+    print(f'>> {ldm.invoke.__app_name__}, version {ldm.invoke.__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
+    import transformers  # type: ignore
+    transformers.logging.set_verbosity_error()
+    import diffusers
+    diffusers.logging.set_verbosity_error()
+
+    # Loading Face Restoration and ESRGAN Modules
+    gfpgan,codeformer,esrgan = load_face_restoration(args)
+
+    # normalize the config directory relative to root
+    if not os.path.isabs(args.conf):
+        args.conf = os.path.normpath(os.path.join(Globals.root,args.conf))
+
+    if args.embeddings:
+        if not os.path.isabs(args.embedding_path):
+            embedding_path = os.path.normpath(os.path.join(Globals.root,args.embedding_path))
+        else:
+            embedding_path = args.embedding_path
+    else:
+        embedding_path = None
+
+    # migrate legacy models
+    ModelManager.migrate_models()
+
+    # load the infile as a list of lines
+    if args.infile:
+        try:
+            if os.path.isfile(args.infile):
+                infile = open(args.infile, 'r', encoding='utf-8')
+            elif args.infile == '-':  # stdin
+                infile = sys.stdin
+            else:
+                raise FileNotFoundError(f'{args.infile} not found.')
+        except (FileNotFoundError, IOError) as e:
+            print(f'{e}. Aborting.')
+            sys.exit(-1)
+
+    # creating a Generate object:
+    try:
+        gen = Generate(
+            conf              = args.conf,
+            model             = args.model,
+            sampler_name      = args.sampler_name,
+            embedding_path    = embedding_path,
+            full_precision    = args.full_precision,
+            precision         = args.precision,
+            gfpgan            = gfpgan,
+            codeformer        = codeformer,
+            esrgan            = esrgan,
+            free_gpu_mem      = args.free_gpu_mem,
+            safety_checker    = args.safety_checker,
+            max_loaded_models = args.max_loaded_models,
+            )
+    except (FileNotFoundError, TypeError, AssertionError) as e:
+        report_model_error(opt,e)
+    except (IOError, KeyError) as e:
+        print(f'{e}. Aborting.')
+        sys.exit(-1)
+
+    if args.seamless:
+        print(">> changed to seamless tiling mode")
+
+    # preload the model
+    try:
+        gen.load_model()
+    except KeyError:
+        pass
+    except Exception as e:
+        report_model_error(args, e)
+
+    # try to autoconvert new models
+    # autoimport new .ckpt files
+    if path := args.autoconvert:
+        gen.model_manager.autoconvert_weights(
+            conf_path=args.conf,
+            weights_directory=path,
+        )
+    
+    return gen
+
+
+def load_face_restoration(opt):
+    try:
+        gfpgan, codeformer, esrgan = None, None, None
+        if opt.restore or opt.esrgan:
+            from ldm.invoke.restoration import Restoration
+            restoration = Restoration()
+            if opt.restore:
+                gfpgan, codeformer = restoration.load_face_restore_models(opt.gfpgan_model_path)
+            else:
+                print('>> Face restoration disabled')
+            if opt.esrgan:
+                esrgan = restoration.load_esrgan(opt.esrgan_bg_tile)
+            else:
+                print('>> Upscaling disabled')
+        else:
+            print('>> Face restoration and upscaling disabled')
+    except (ModuleNotFoundError, ImportError):
+        print(traceback.format_exc(), file=sys.stderr)
+        print('>> You may need to install the ESRGAN and/or GFPGAN modules')
+    return gfpgan,codeformer,esrgan
+
+
+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:
+        print('** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE')
+    else:
+        response = input('Do you want to run invokeai-configure script to select and/or reinstall models? [y] ')
+        if response.startswith(('n', 'N')):
+            return
+
+    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_args = sys.argv
+    sys.argv = [ 'invokeai-configure' ]
+    sys.argv.extend(root_dir)
+    sys.argv.extend(config)
+    if yes_to_all is not None:
+        for arg in yes_to_all.split():
+            sys.argv.append(arg)
+
+    from ldm.invoke.config import invokeai_configure
+    invokeai_configure.main()
+    # TODO: Figure out how to restart
+    # print('** InvokeAI will now restart')
+    # sys.argv = previous_args
+    # main() # would rather do a os.exec(), but doesn't exist?
+    # sys.exit(0)
+
+
+# Temporary initializer for Generate until we migrate off of it
+def old_get_generate(args, config) -> Generate:
+    # TODO: Remove the need for globals
+    from ldm.invoke.globals import Globals
+
+    # alert - setting globals here
+    Globals.root = os.path.expanduser(args.root_dir or os.environ.get('INVOKEAI_ROOT') or os.path.abspath('.'))
+    Globals.try_patchmatch = args.patchmatch
+    
+    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
+    import transformers
+    transformers.logging.set_verbosity_error()
+
+    # Loading Face Restoration and ESRGAN Modules
+    gfpgan, codeformer, esrgan = None, None, None
+    try:
+        if config.restore or config.esrgan:
+            from ldm.invoke.restoration import Restoration
+            restoration = Restoration()
+            if config.restore:
+                gfpgan, codeformer = restoration.load_face_restore_models(config.gfpgan_model_path)
+            else:
+                print('>> Face restoration disabled')
+            if config.esrgan:
+                esrgan = restoration.load_esrgan(config.esrgan_bg_tile)
+            else:
+                print('>> Upscaling disabled')
+        else:
+            print('>> Face restoration and upscaling disabled')
+    except (ModuleNotFoundError, ImportError):
+        print(traceback.format_exc(), file=sys.stderr)
+        print('>> You may need to install the ESRGAN and/or GFPGAN modules')
+
+    # 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 = None
+
+
+    # TODO: lazy-initialize this by wrapping it
+    try:
+        generate = Generate(
+            conf              = config.conf,
+            model             = config.model,
+            sampler_name      = config.sampler_name,
+            embedding_path    = embedding_path,
+            full_precision    = config.full_precision,
+            precision         = config.precision,
+            gfpgan            = gfpgan,
+            codeformer        = codeformer,
+            esrgan            = esrgan,
+            free_gpu_mem      = config.free_gpu_mem,
+            safety_checker    = config.safety_checker,
+            max_loaded_models = config.max_loaded_models,
+        )
+    except (FileNotFoundError, TypeError, AssertionError):
+        #emergency_model_reconfigure() # TODO?
+        sys.exit(-1)
+    except (IOError, KeyError) as e:
+        print(f'{e}. Aborting.')
+        sys.exit(-1)
+
+    generate.free_gpu_mem = config.free_gpu_mem
+
+    return generate

ldm/invoke/app/services/graph.py

@@ -0,0 +1,797 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+import copy
+import itertools
+from types import NoneType
+import uuid
+import networkx as nx
+from pydantic import BaseModel, validator
+from pydantic.fields import Field
+from typing import Any, Literal, Optional, Union, get_args, get_origin, get_type_hints, Annotated
+
+from .invocation_services import InvocationServices
+from ..invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
+from ..invocations import *
+
+
+class EdgeConnection(BaseModel):
+    node_id: str  = Field(description="The id of the node for this edge connection")
+    field: str = Field(description="The field for this connection")
+
+    def __eq__(self, other):
+        return (isinstance(other, self.__class__) and
+            getattr(other, 'node_id', None) == self.node_id and
+            getattr(other, 'field', None) == self.field)
+    
+    def __hash__(self):
+        return hash(f'{self.node_id}.{self.field}')
+
+
+def get_output_field(node: BaseInvocation, field: str) -> Any:
+    node_type = type(node)
+    node_outputs = get_type_hints(node_type.get_output_type())
+    node_output_field = node_outputs.get(field) or None
+    return node_output_field
+
+    
+def get_input_field(node: BaseInvocation, field: str) -> Any:
+    node_type = type(node)
+    node_inputs = get_type_hints(node_type)
+    node_input_field = node_inputs.get(field) or None
+    return node_input_field
+
+
+def are_connection_types_compatible(from_type: Any, to_type: Any) -> bool:
+    if not from_type:
+        return False
+    if not to_type:
+        return False
+    
+    # TODO: this is pretty forgiving on generic types. Clean that up (need to handle optionals and such)
+    if from_type and to_type:
+        # Ports are compatible
+        if (from_type == to_type or
+            from_type == Any or
+            to_type == Any or
+            Any in get_args(from_type) or
+            Any in get_args(to_type)):
+            return True
+
+        if from_type in get_args(to_type):
+            return True
+
+        if to_type in get_args(from_type):
+            return True
+
+        if not issubclass(from_type, to_type):
+            return False
+    else:
+        return False
+    
+    return True
+
+
+def are_connections_compatible(
+    from_node: BaseInvocation,
+    from_field: str,
+    to_node: BaseInvocation,
+    to_field: str) -> bool:
+    """Determines if a connection between fields of two nodes is compatible."""
+
+    # TODO: handle iterators and collectors
+    from_node_field = get_output_field(from_node, from_field)
+    to_node_field = get_input_field(to_node, to_field)
+
+    return are_connection_types_compatible(from_node_field, to_node_field)
+
+
+class NodeAlreadyInGraphError(Exception):
+    pass
+
+
+class InvalidEdgeError(Exception):
+    pass
+
+class NodeNotFoundError(Exception):
+    pass
+
+class NodeAlreadyExecutedError(Exception):
+    pass
+
+
+# TODO: Create and use an Empty output?
+class GraphInvocationOutput(BaseInvocationOutput):
+    type: Literal['graph_output'] = 'graph_output'
+
+
+# TODO: Fill this out and move to invocations
+class GraphInvocation(BaseInvocation):
+    type: Literal['graph'] = 'graph'
+
+    # TODO: figure out how to create a default here
+    graph: 'Graph' = Field(description="The graph to run", default=None)
+
+    def invoke(self, context: InvocationContext) -> GraphInvocationOutput:
+        """Invoke with provided services and return outputs."""
+        return GraphInvocationOutput()
+
+
+class IterateInvocationOutput(BaseInvocationOutput):
+    """Used to connect iteration outputs. Will be expanded to a specific output."""
+    type: Literal['iterate_output'] = 'iterate_output'
+
+    item: Any = Field(description="The item being iterated over")
+
+
+# TODO: Fill this out and move to invocations
+class IterateInvocation(BaseInvocation):
+    type: Literal['iterate'] = 'iterate'
+
+    collection: list[Any] = Field(description="The list of items to iterate over", default_factory=list)
+    index: int = Field(description="The index, will be provided on executed iterators", default=0)
+
+    def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
+        """Produces the outputs as values"""
+        return IterateInvocationOutput(item = self.collection[self.index])
+
+
+class CollectInvocationOutput(BaseInvocationOutput):
+    type: Literal['collect_output'] = 'collect_output'
+
+    collection: list[Any] = Field(description="The collection of input items")
+
+
+class CollectInvocation(BaseInvocation):
+    """Collects values into a collection"""
+    type: Literal['collect'] = 'collect'
+
+    item: Any = Field(description="The item to collect (all inputs must be of the same type)", default=None)
+    collection: list[Any] = Field(description="The collection, will be provided on execution", default_factory=list)
+
+    def invoke(self, context: InvocationContext) -> CollectInvocationOutput:
+        """Invoke with provided services and return outputs."""
+        return CollectInvocationOutput(collection = copy.copy(self.collection))
+
+
+InvocationsUnion = Union[BaseInvocation.get_invocations()]
+InvocationOutputsUnion = Union[BaseInvocationOutput.get_all_subclasses_tuple()]
+
+
+class Graph(BaseModel):
+    id: str = Field(description="The id of this graph", default_factory=uuid.uuid4)
+    # TODO: use a list (and never use dict in a BaseModel) because pydantic/fastapi hates me
+    nodes: dict[str, Annotated[InvocationsUnion, Field(discriminator="type")]] = Field(description="The nodes in this graph", default_factory=dict)
+    edges: list[tuple[EdgeConnection,EdgeConnection]] = Field(description="The connections between nodes and their fields in this graph", default_factory=list)
+
+    def add_node(self, node: BaseInvocation) -> None:
+        """Adds a node to a graph
+        
+        :raises NodeAlreadyInGraphError: the node is already present in the graph.
+        """
+
+        if node.id in self.nodes:
+            raise NodeAlreadyInGraphError()
+
+        self.nodes[node.id] = node
+    
+
+    def _get_graph_and_node(self, node_path: str) -> tuple['Graph', str]:
+        """Returns the graph and node id for a node path."""
+        # Materialized graphs may have nodes at the top level
+        if node_path in self.nodes:
+            return (self, node_path)
+
+        node_id = node_path if '.' not in node_path else node_path[:node_path.index('.')]
+        if node_id not in self.nodes:
+            raise NodeNotFoundError(f'Node {node_path} not found in graph')
+
+        node = self.nodes[node_id]
+
+        if not isinstance(node, GraphInvocation):
+            # There's more node path left but this isn't a graph - failure
+            raise NodeNotFoundError('Node path terminated early at a non-graph node')
+
+        return node.graph._get_graph_and_node(node_path[node_path.index('.')+1:])
+
+
+    def delete_node(self, node_path: str) -> None:
+        """Deletes a node from a graph"""
+
+        try:
+            graph, node_id = self._get_graph_and_node(node_path)
+
+            # Delete edges for this node
+            input_edges = self._get_input_edges_and_graphs(node_path)
+            output_edges = self._get_output_edges_and_graphs(node_path)
+
+            for edge_graph,_,edge in input_edges:
+                edge_graph.delete_edge(edge)
+
+            for edge_graph,_,edge in output_edges:
+                edge_graph.delete_edge(edge)
+
+            del graph.nodes[node_id]
+
+        except NodeNotFoundError:
+            pass # Ignore, not doesn't exist (should this throw?)
+
+
+    def add_edge(self, edge: tuple[EdgeConnection, EdgeConnection]) -> None:
+        """Adds an edge to a graph
+        
+        :raises InvalidEdgeError: the provided edge is invalid.
+        """
+
+        if self._is_edge_valid(edge) and edge not in self.edges:
+            self.edges.append(edge)
+        else:
+            raise InvalidEdgeError()
+    
+    
+    def delete_edge(self, edge: tuple[EdgeConnection, EdgeConnection]) -> None:
+        """Deletes an edge from a graph"""
+
+        try:
+            self.edges.remove(edge)
+        except KeyError:
+            pass
+
+
+    def is_valid(self) -> bool:
+        """Validates the graph."""
+
+        # Validate all subgraphs
+        for gn in (n for n in self.nodes.values() if isinstance(n, GraphInvocation)):
+            if not gn.graph.is_valid():
+                return False
+
+        # Validate all edges reference nodes in the graph
+        node_ids = set([e[0].node_id for e in self.edges]+[e[1].node_id for e in self.edges])
+        if not all((self.has_node(node_id) for node_id in node_ids)):
+            return False
+
+        # Validate there are no cycles
+        g = self.nx_graph_flat()
+        if not nx.is_directed_acyclic_graph(g):
+            return False
+        
+        # Validate all edge connections are valid
+        if not all((are_connections_compatible(
+                self.get_node(e[0].node_id), e[0].field,
+                self.get_node(e[1].node_id), e[1].field
+            ) for e in self.edges)):
+            return False
+        
+        # Validate all iterators
+        # TODO: may need to validate all iterators in subgraphs so edge connections in parent graphs will be available
+        if not all((self._is_iterator_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, IterateInvocation))):
+            return False
+
+        # Validate all collectors
+        # TODO: may need to validate all collectors in subgraphs so edge connections in parent graphs will be available
+        if not all((self._is_collector_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, CollectInvocation))):
+            return False
+        
+        return True
+        
+    def _is_edge_valid(self, edge: tuple[EdgeConnection, EdgeConnection]) -> bool:
+        """Validates that a new edge doesn't create a cycle in the graph"""
+
+        # Validate that the nodes exist (edges may contain node paths, so we can't just check for nodes directly)
+        try:
+            from_node = self.get_node(edge[0].node_id)
+            to_node = self.get_node(edge[1].node_id)
+        except NodeNotFoundError:
+            return False
+
+        # Validate that an edge to this node+field doesn't already exist
+        input_edges = self._get_input_edges(edge[1].node_id, edge[1].field)
+        if len(input_edges) > 0 and not isinstance(to_node, CollectInvocation):
+            return False
+
+        # Validate that no cycles would be created
+        g = self.nx_graph_flat()
+        g.add_edge(edge[0].node_id, edge[1].node_id)
+        if not nx.is_directed_acyclic_graph(g):
+            return False
+        
+        # Validate that the field types are compatible
+        if not are_connections_compatible(from_node, edge[0].field, to_node, edge[1].field):
+            return False
+
+        # Validate if iterator output type matches iterator input type (if this edge results in both being set)
+        if isinstance(to_node, IterateInvocation) and edge[1].field == 'collection':
+            if not self._is_iterator_connection_valid(edge[1].node_id, new_input = edge[0]):
+                return False
+
+        # Validate if iterator input type matches output type (if this edge results in both being set)
+        if isinstance(from_node, IterateInvocation) and edge[0].field == 'item':
+            if not self._is_iterator_connection_valid(edge[0].node_id, new_output = edge[1]):
+                return False
+
+        # Validate if collector input type matches output type (if this edge results in both being set)
+        if isinstance(to_node, CollectInvocation) and edge[1].field == 'item':
+            if not self._is_collector_connection_valid(edge[1].node_id, new_input = edge[0]):
+                return False
+
+        # Validate if collector output type matches input type (if this edge results in both being set)
+        if isinstance(from_node, CollectInvocation) and edge[0].field == 'collection':
+            if not self._is_collector_connection_valid(edge[0].node_id, new_output = edge[1]):
+                return False
+
+        return True
+
+    def has_node(self, node_path: str) -> bool:
+        """Determines whether or not a node exists in the graph."""
+        try:
+            n = self.get_node(node_path)
+            if n is not None:
+                return True
+            else:
+                return False
+        except NodeNotFoundError:
+            return False
+
+    def get_node(self, node_path: str) -> InvocationsUnion:
+        """Gets a node from the graph using a node path."""
+        # Materialized graphs may have nodes at the top level
+        graph, node_id = self._get_graph_and_node(node_path)
+        return graph.nodes[node_id]
+
+
+    def _get_node_path(self, node_id: str, prefix: Optional[str] = None) -> str:
+        return node_id if prefix is None or prefix == '' else f'{prefix}.{node_id}'
+
+
+    def update_node(self, node_path: str, new_node: BaseInvocation) -> None:
+        """Updates a node in the graph."""
+        graph, node_id = self._get_graph_and_node(node_path)
+        node = graph.nodes[node_id]
+        
+        # Ensure the node type matches the new node
+        if type(node) != type(new_node):
+            raise TypeError(f'Node {node_path} is type {type(node)} but new node is type {type(new_node)}')
+
+        # Ensure the new id is either the same or is not in the graph
+        prefix = None if '.' not in node_path else node_path[:node_path.rindex('.')]
+        new_path = self._get_node_path(new_node.id, prefix = prefix)        
+        if new_node.id != node.id and self.has_node(new_path):
+            raise NodeAlreadyInGraphError('Node with id {new_node.id} already exists in graph')
+
+        # Set the new node in the graph
+        graph.nodes[new_node.id] = new_node
+        if new_node.id != node.id:
+            input_edges = self._get_input_edges_and_graphs(node_path)
+            output_edges = self._get_output_edges_and_graphs(node_path)
+
+            # Delete node and all edges
+            graph.delete_node(node_path)
+
+            # Create new edges for each input and output
+            for graph,_,edge in input_edges:
+                # Remove the graph prefix from the node path
+                new_graph_node_path = new_node.id if '.' not in edge[1].node_id else f'{edge[1].node_id[edge[1].node_id.rindex("."):]}.{new_node.id}'
+                graph.add_edge((edge[0], EdgeConnection(node_id = new_graph_node_path, field = edge[1].field)))
+
+            for graph,_,edge in output_edges:
+                # Remove the graph prefix from the node path
+                new_graph_node_path = new_node.id if '.' not in edge[0].node_id else f'{edge[0].node_id[edge[0].node_id.rindex("."):]}.{new_node.id}'
+                graph.add_edge((EdgeConnection(node_id = new_graph_node_path, field = edge[0].field), edge[1]))
+        
+
+    def _get_input_edges(self, node_path: str, field: Optional[str] = None) -> list[tuple[EdgeConnection,EdgeConnection]]:
+        """Gets all input edges for a node"""
+        edges = self._get_input_edges_and_graphs(node_path)
+        
+        # Filter to edges that match the field
+        filtered_edges = (e for e in edges if field is None or e[2][1].field == field)
+
+        # Create full node paths for each edge
+        return [(EdgeConnection(node_id = self._get_node_path(e[0].node_id, prefix = prefix), field=e[0].field), EdgeConnection(node_id = self._get_node_path(e[1].node_id, prefix = prefix), field=e[1].field)) for _,prefix,e in filtered_edges]
+
+
+    def _get_input_edges_and_graphs(self, node_path: str, prefix: Optional[str] = None) -> list[tuple['Graph', str, tuple[EdgeConnection,EdgeConnection]]]:
+        """Gets all input edges for a node along with the graph they are in and the graph's path"""
+        edges = list()
+
+        # Return any input edges that appear in this graph
+        edges.extend([(self, prefix, e) for e in self.edges if e[1].node_id == node_path])
+
+        node_id = node_path if '.' not in node_path else node_path[:node_path.index('.')]
+        node = self.nodes[node_id]
+
+        if isinstance(node, GraphInvocation):
+            graph = node.graph
+            graph_path = node.id if prefix is None or prefix == '' else self._get_node_path(node.id, prefix = prefix)
+            graph_edges = graph._get_input_edges_and_graphs(node_path[(len(node_id)+1):], prefix=graph_path)
+            edges.extend(graph_edges)
+        
+        return edges
+
+
+    def _get_output_edges(self, node_path: str, field: str) -> list[tuple[EdgeConnection,EdgeConnection]]:
+        """Gets all output edges for a node"""
+        edges = self._get_output_edges_and_graphs(node_path)
+        
+        # Filter to edges that match the field
+        filtered_edges = (e for e in edges if e[2][0].field == field)
+
+        # Create full node paths for each edge
+        return [(EdgeConnection(node_id = self._get_node_path(e[0].node_id, prefix = prefix), field=e[0].field), EdgeConnection(node_id = self._get_node_path(e[1].node_id, prefix = prefix), field=e[1].field)) for _,prefix,e in filtered_edges]
+
+
+    def _get_output_edges_and_graphs(self, node_path: str, prefix: Optional[str] = None) -> list[tuple['Graph', str, tuple[EdgeConnection,EdgeConnection]]]:
+        """Gets all output edges for a node along with the graph they are in and the graph's path"""
+        edges = list()
+
+        # Return any input edges that appear in this graph
+        edges.extend([(self, prefix, e) for e in self.edges if e[0].node_id == node_path])
+
+        node_id = node_path if '.' not in node_path else node_path[:node_path.index('.')]
+        node = self.nodes[node_id]
+
+        if isinstance(node, GraphInvocation):
+            graph = node.graph
+            graph_path = node.id if prefix is None or prefix == '' else self._get_node_path(node.id, prefix = prefix)
+            graph_edges = graph._get_output_edges_and_graphs(node_path[(len(node_id)+1):], prefix=graph_path)
+            edges.extend(graph_edges)
+        
+        return edges
+
+
+    def _is_iterator_connection_valid(self, node_path: str, new_input: Optional[EdgeConnection] = None, new_output: Optional[EdgeConnection] = None) -> bool:
+        inputs = list([e[0] for e in self._get_input_edges(node_path, 'collection')])
+        outputs = list([e[1] for e in self._get_output_edges(node_path, 'item')])
+
+        if new_input is not None:
+            inputs.append(new_input)
+        if new_output is not None:
+            outputs.append(new_output)
+        
+        # Only one input is allowed for iterators
+        if len(inputs) > 1:
+            return False
+
+        # Get input and output fields (the fields linked to the iterator's input/output)
+        input_field = get_output_field(self.get_node(inputs[0].node_id), inputs[0].field)
+        output_fields = list([get_input_field(self.get_node(e.node_id), e.field) for e in outputs])
+
+        # Input type must be a list
+        if get_origin(input_field) != list:
+            return False
+        
+        # Validate that all outputs match the input type
+        input_field_item_type = get_args(input_field)[0]
+        if not all((are_connection_types_compatible(input_field_item_type, f) for f in output_fields)):
+            return False
+
+        return True
+
+    def _is_collector_connection_valid(self, node_path: str, new_input: Optional[EdgeConnection] = None, new_output: Optional[EdgeConnection] = None) -> bool:
+        inputs = list([e[0] for e in self._get_input_edges(node_path, 'item')])
+        outputs = list([e[1] for e in self._get_output_edges(node_path, 'collection')])
+
+        if new_input is not None:
+            inputs.append(new_input)
+        if new_output is not None:
+            outputs.append(new_output)
+        
+        # Get input and output fields (the fields linked to the iterator's input/output)
+        input_fields = list([get_output_field(self.get_node(e.node_id), e.field) for e in inputs])
+        output_fields = list([get_input_field(self.get_node(e.node_id), e.field) for e in outputs])
+        
+        # Validate that all inputs are derived from or match a single type
+        input_field_types = set([t for input_field in input_fields for t in ([input_field] if get_origin(input_field) == None else get_args(input_field)) if t != NoneType]) # Get unique types
+        type_tree = nx.DiGraph()
+        type_tree.add_nodes_from(input_field_types)
+        type_tree.add_edges_from([e for e in itertools.permutations(input_field_types, 2) if issubclass(e[1], e[0])])
+        type_degrees = type_tree.in_degree(type_tree.nodes)
+        if sum((t[1] == 0 for t in type_degrees)) != 1:
+            return False # There is more than one root type
+
+        # Get the input root type
+        input_root_type = next(t[0] for t in type_degrees if t[1] == 0)
+
+        # Verify that all outputs are lists
+        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)
+        if not all((issubclass(input_root_type, get_args(f)[0]) for f in output_fields)):
+            return False
+
+        return True
+
+    def nx_graph(self) -> nx.DiGraph:
+        """Returns a NetworkX DiGraph representing the layout of this graph"""
+        # TODO: Cache this?
+        g = nx.DiGraph()
+        g.add_nodes_from([n for n in self.nodes.keys()])
+        g.add_edges_from(set([(e[0].node_id, e[1].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:
+        """Returns a flattened NetworkX DiGraph, including all subgraphs (but not with iterations expanded)"""
+        g = nx_graph or nx.DiGraph()
+
+        # Add all nodes from this graph except graph/iteration nodes
+        g.add_nodes_from([self._get_node_path(n.id, prefix) for n in self.nodes.values() if not isinstance(n, GraphInvocation) and not isinstance(n, IterateInvocation)])
+
+        # Expand graph nodes
+        for sgn in (gn for gn in self.nodes.values() if isinstance(gn, GraphInvocation)):
+            sgn.graph.nx_graph_flat(g, self._get_node_path(sgn.id, prefix))
+
+        # TODO: figure out if iteration nodes need to be expanded
+
+        unique_edges = set([(e[0].node_id, e[1].node_id) for e in self.edges])
+        g.add_edges_from([(self._get_node_path(e[0], prefix), self._get_node_path(e[1], prefix)) for e in unique_edges])
+        return g
+
+
+class GraphExecutionState(BaseModel):
+    """Tracks the state of a graph execution"""
+    id: str = Field(description="The id of the execution state", default_factory=uuid.uuid4)
+
+    # TODO: Store a reference to the graph instead of the actual graph?
+    graph: Graph = Field(description="The graph being executed")
+
+    # The graph of materialized nodes
+    execution_graph: Graph = Field(description="The expanded graph of activated and executed nodes", default_factory=Graph)
+
+    # Nodes that have been executed
+    executed: set[str] = Field(description="The set of node ids that have been executed", default_factory=set)
+    executed_history: list[str] = Field(description="The list of node ids that have been executed, in order of execution", default_factory=list)
+
+    # The results of executed nodes
+    results: dict[str, Annotated[InvocationOutputsUnion, Field(discriminator="type")]] = Field(description="The results of node executions", default_factory=dict)
+
+    # Map of prepared/executed nodes to their original nodes
+    prepared_source_mapping: dict[str, str] = Field(description="The map of prepared nodes to original graph nodes", default_factory=dict)
+
+    # Map of original nodes to prepared nodes
+    source_prepared_mapping: dict[str, set[str]] = Field(description="The map of original graph nodes to prepared nodes", default_factory=dict)
+
+    def next(self) -> BaseInvocation | None:
+        """Gets the next node ready to execute."""
+
+        # TODO: enable multiple nodes to execute simultaneously by tracking currently executing nodes
+        #       possibly with a timeout?
+
+        # If there are no prepared nodes, prepare some nodes
+        next_node = self._get_next_node()
+        if next_node is 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
+        if next_node is not None:
+            self._prepare_inputs(next_node)
+
+        # If next is still none, there's no next node, return None
+        return next_node
+
+    def complete(self, node_id: str, output: InvocationOutputsUnion):
+        """Marks a node as complete"""
+
+        if node_id not in self.execution_graph.nodes:
+            return # TODO: log error?
+
+        # Mark node as executed
+        self.executed.add(node_id)
+        self.results[node_id] = output
+
+        # Check if source node is complete (all prepared nodes are complete)
+        source_node = self.prepared_source_mapping[node_id]
+        prepared_nodes = self.source_prepared_mapping[source_node]
+
+        if all([n in self.executed for n in prepared_nodes]):
+            self.executed.add(source_node)
+            self.executed_history.append(source_node)
+
+    def is_complete(self) -> bool:
+        """Returns true if the graph is complete"""
+        return all((k in self.executed for k in self.graph.nodes))
+
+    def _create_execution_node(self, node_path: str, iteration_node_map: list[tuple[str, str]]) -> list[str]:
+        """Prepares an iteration node and connects all edges, returning the new node id"""
+
+        node = self.graph.get_node(node_path)
+
+        self_iteration_count = -1
+
+        # If this is an iterator node, we must create a copy for each iteration
+        if isinstance(node, IterateInvocation):
+            # Get input collection edge (should error if there are no inputs)
+            input_collection_edge = next(iter(self.graph._get_input_edges(node_path, 'collection')))
+            input_collection_prepared_node_id = next(n[1] for n in iteration_node_map if n[0] == input_collection_edge[0].node_id)
+            input_collection_prepared_node_output = self.results[input_collection_prepared_node_id]
+            input_collection = getattr(input_collection_prepared_node_output, input_collection_edge[0].field)
+            self_iteration_count = len(input_collection)
+
+        new_nodes = list()
+        if self_iteration_count == 0:
+            # TODO: should this raise a warning? It might just happen if an empty collection is input, and should be valid.
+            return new_nodes
+
+        # Get all input edges
+        input_edges = self.graph._get_input_edges(node_path)
+
+        # Create new edges for this iteration
+        # For collect nodes, this may contain multiple inputs to the same field
+        new_edges = list()
+        for edge in input_edges:
+            for input_node_id in (n[1] for n in iteration_node_map if n[0] == edge[0].node_id):
+                new_edge = (EdgeConnection(node_id = input_node_id, field = edge[0].field), EdgeConnection(node_id = '', field = edge[1].field))
+                new_edges.append(new_edge)
+        
+        # Create a new node (or one for each iteration of this iterator)
+        for i in (range(self_iteration_count) if self_iteration_count > 0 else [-1]):
+            # Create a new node
+            new_node = copy.deepcopy(node)
+
+            # Create the node id (use a random uuid)
+            new_node.id = str(uuid.uuid4())
+
+            # Set the iteration index for iteration invocations
+            if isinstance(new_node, IterateInvocation):
+                new_node.index = i
+
+            # Add to execution graph
+            self.execution_graph.add_node(new_node)
+            self.prepared_source_mapping[new_node.id] = node_path
+            if node_path not in self.source_prepared_mapping:
+                self.source_prepared_mapping[node_path] = set()
+            self.source_prepared_mapping[node_path].add(new_node.id)
+
+            # Add new edges to execution graph
+            for edge in new_edges:
+                new_edge = (edge[0], EdgeConnection(node_id = new_node.id, field = edge[1].field))
+                self.execution_graph.add_edge(new_edge)
+
+            new_nodes.append(new_node.id)
+
+        return new_nodes
+
+    def _iterator_graph(self) -> nx.DiGraph:
+        """Gets a DiGraph with edges to collectors removed so an ancestor search produces all active iterators for any node"""
+        g = self.graph.nx_graph()
+        collectors = (n for n in self.graph.nodes if isinstance(self.graph.nodes[n], CollectInvocation))
+        for c in collectors:
+            g.remove_edges_from(list(g.in_edges(c)))
+        return g
+
+
+    def _get_node_iterators(self, node_id: str) -> list[str]:
+        """Gets iterators for a node"""
+        g = self._iterator_graph()
+        iterators = [n for n in nx.ancestors(g, node_id) if isinstance(self.graph.nodes[n], IterateInvocation)]
+        return iterators
+
+
+    def _prepare(self) -> Optional[str]:
+        # Get flattened source graph
+        g = self.graph.nx_graph_flat()
+
+        # 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 if n not in self.source_prepared_mapping and all((e[0] in self.executed for e in g.in_edges(n)))), None)
+
+        if next_node_id == None:
+            return None
+
+        # Get all parents of the next node
+        next_node_parents = [e[0] for e in g.in_edges(next_node_id)]
+
+        # Create execution nodes
+        next_node = self.graph.get_node(next_node_id)
+        new_node_ids = list()
+        if isinstance(next_node, CollectInvocation):
+            # Collapse all iterator input mappings and create a single execution node for the collect invocation
+            all_iteration_mappings = list(itertools.chain(*(((s,p) for p in self.source_prepared_mapping[s]) for s in next_node_parents)))
+            #all_iteration_mappings = list(set(itertools.chain(*prepared_parent_mappings)))
+            create_results = self._create_execution_node(next_node_id, all_iteration_mappings)
+            if create_results is not None:
+                new_node_ids.extend(create_results)
+        else: # Iterators or normal nodes
+            # Get all iterator combinations for this node
+            # Will produce a list of lists of prepared iterator nodes, from which results can be iterated
+            iterator_nodes = self._get_node_iterators(next_node_id)
+            iterator_nodes_prepared = [list(self.source_prepared_mapping[n]) for n in iterator_nodes]
+            iterator_node_prepared_combinations = list(itertools.product(*iterator_nodes_prepared))
+
+            # Select the correct prepared parents for each iteration
+            # For every iterator, the parent must either not be a child of that iterator, or must match the prepared iteration for that iterator
+            # TODO: Handle a node mapping to none
+            eg = self.execution_graph.nx_graph_flat()
+            prepared_parent_mappings = [[(n,self._get_iteration_node(n, g, eg, it)) for n in next_node_parents] for it in iterator_node_prepared_combinations]
+        
+            # Create execution node for each iteration
+            for iteration_mappings in prepared_parent_mappings:
+                create_results = self._create_execution_node(next_node_id, iteration_mappings)
+                if create_results is not None:
+                    new_node_ids.extend(create_results)
+
+        return next(iter(new_node_ids), None)
+        
+    def _get_iteration_node(self, source_node_path: str, graph: nx.DiGraph, execution_graph: nx.DiGraph, prepared_iterator_nodes: list[str]) -> Optional[str]:
+        """Gets the prepared version of the specified source node that matches every iteration specified"""
+        prepared_nodes = self.source_prepared_mapping[source_node_path]
+        if len(prepared_nodes) == 1:
+            return next(iter(prepared_nodes))
+        
+        # Check if the requested node is an iterator
+        prepared_iterator = next((n for n in prepared_nodes if n in prepared_iterator_nodes), None)
+        if prepared_iterator is not None:
+            return prepared_iterator
+
+        # Filter to only iterator nodes that are a parent of the specified node, in tuple format (prepared, source)
+        iterator_source_node_mapping = [(n, self.prepared_source_mapping[n]) for n in prepared_iterator_nodes]
+        parent_iterators = [itn for itn in iterator_source_node_mapping if nx.has_path(graph, itn[1], source_node_path)]
+
+        return next((n for n in prepared_nodes if all(pit for pit in parent_iterators if nx.has_path(execution_graph, pit[0], n))), None)
+
+    def _get_next_node(self) -> Optional[BaseInvocation]:
+        g = self.execution_graph.nx_graph()
+        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
+
+        return self.execution_graph.nodes[next_node]
+
+    def _prepare_inputs(self, node: BaseInvocation):
+        input_edges = [e for e in self.execution_graph.edges if e[1].node_id == node.id]
+        if isinstance(node, CollectInvocation):
+            output_collection = [getattr(self.results[edge[0].node_id], edge[0].field) for edge in input_edges if edge[1].field == 'item']
+            setattr(node, 'collection', output_collection)
+        else:
+            for edge in input_edges:
+                output_value = getattr(self.results[edge[0].node_id], edge[0].field)
+                setattr(node, edge[1].field, output_value)
+
+    # TODO: Add API for modifying underlying graph that checks if the change will be valid given the current execution state
+    def _is_edge_valid(self, edge: tuple[EdgeConnection, EdgeConnection]) -> bool:
+        if not self._is_edge_valid(edge):
+            return False
+        
+        # Invalid if destination has already been prepared or executed
+        if edge[1].node_id in self.source_prepared_mapping:
+            return False
+
+        # Otherwise, the edge is valid
+        return True
+
+    def _is_node_updatable(self, node_id: str) -> bool:
+        # The node is updatable as long as it hasn't been prepared or executed
+        return node_id not in self.source_prepared_mapping
+
+    def add_node(self, node: BaseInvocation) -> None:
+        self.graph.add_node(node)
+    
+    def update_node(self, node_path: str, new_node: BaseInvocation) -> None:
+        if not self._is_node_updatable(node_path):
+            raise NodeAlreadyExecutedError(f'Node {node_path} has already been prepared or executed and cannot be updated')
+        self.graph.update_node(node_path, new_node)
+
+    def delete_node(self, node_path: str) -> None:
+        if not self._is_node_updatable(node_path):
+            raise NodeAlreadyExecutedError(f'Node {node_path} has already been prepared or executed and cannot be deleted')
+        self.graph.delete_node(node_path)
+
+    def add_edge(self, edge: tuple[EdgeConnection, EdgeConnection]) -> None:
+        if not self._is_node_updatable(edge[1].node_id):
+            raise NodeAlreadyExecutedError(f'Destination node {edge[1].node_id} has already been prepared or executed and cannot be linked to')
+        self.graph.add_edge(edge)
+    
+    def delete_edge(self, edge: tuple[EdgeConnection, EdgeConnection]) -> None:
+        if not self._is_node_updatable(edge[1].node_id):
+            raise NodeAlreadyExecutedError(f'Destination node {edge[1].node_id} has already been prepared or executed and cannot have a source edge deleted')
+        self.graph.delete_edge(edge)
+
+GraphInvocation.update_forward_refs()

ldm/invoke/app/services/image_storage.py

@@ -0,0 +1,104 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from abc import ABC, abstractmethod
+from enum import Enum
+import datetime
+import os
+from pathlib import Path
+from queue import Queue
+from typing import Dict
+from PIL.Image import Image
+from ...pngwriter import PngWriter
+
+
+class ImageType(str, Enum):
+    RESULT = 'results'
+    INTERMEDIATE = 'intermediates'
+    UPLOAD = 'uploads'
+
+
+class ImageStorageBase(ABC):
+    """Responsible for storing and retrieving images."""
+
+    @abstractmethod
+    def get(self, image_type: ImageType, image_name: str) -> Image:
+        pass
+
+    # TODO: make this a bit more flexible for e.g. cloud storage
+    @abstractmethod
+    def get_path(self, image_type: ImageType, image_name: str) -> str:
+        pass
+
+    @abstractmethod
+    def save(self, image_type: ImageType, image_name: str, image: Image) -> None:
+        pass
+
+    @abstractmethod
+    def delete(self, image_type: ImageType, image_name: str) -> None:
+        pass
+
+    def create_name(self, context_id: str, node_id: str) -> str:
+        return f'{context_id}_{node_id}_{str(int(datetime.datetime.now(datetime.timezone.utc).timestamp()))}.png'
+
+
+class DiskImageStorage(ImageStorageBase):
+    """Stores images on disk"""
+    __output_folder: str
+    __pngWriter: PngWriter
+    __cache_ids: Queue # TODO: this is an incredibly naive cache
+    __cache: Dict[str, Image]
+    __max_cache_size: int
+
+    def __init__(self, output_folder: str):
+        self.__output_folder = output_folder
+        self.__pngWriter = PngWriter(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_type in ImageType:
+            Path(os.path.join(output_folder, image_type)).mkdir(parents=True, exist_ok=True)
+
+    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 = Image.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) -> str:
+        path = os.path.join(self.__output_folder, image_type, image_name)
+        return path
+
+    def save(self, image_type: ImageType, image_name: str, image: Image) -> None:
+        image_subpath = os.path.join(image_type, image_name)
+        self.__pngWriter.save_image_and_prompt_to_png(image, "", image_subpath, None) # TODO: just pass full path to png writer
+
+        image_path = self.get_path(image_type, image_name)
+        self.__set_cache(image_path, image)
+
+    def delete(self, image_type: ImageType, image_name: str) -> None:
+        image_path = self.get_path(image_type, image_name)
+        if os.path.exists(image_path):
+            os.remove(image_path)
+        
+        if image_path in self.__cache:
+            del self.__cache[image_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]

ldm/invoke/app/services/invocation_queue.py

@@ -0,0 +1,46 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from abc import ABC, abstractmethod
+from queue import Queue
+
+
+# TODO: make this serializable
+class InvocationQueueItem:
+    #session_id: str
+    graph_execution_state_id: str
+    invocation_id: str
+    invoke_all: bool
+
+    def __init__(self,
+        #session_id: str,
+        graph_execution_state_id: str,
+        invocation_id: str,
+        invoke_all: bool = False):
+        #self.session_id = session_id
+        self.graph_execution_state_id = graph_execution_state_id
+        self.invocation_id = invocation_id
+        self.invoke_all = invoke_all
+
+
+class InvocationQueueABC(ABC):
+    """Abstract base class for all invocation queues"""
+    @abstractmethod
+    def get(self) -> InvocationQueueItem:
+        pass
+    
+    @abstractmethod
+    def put(self, item: InvocationQueueItem|None) -> None:
+        pass
+
+
+class MemoryInvocationQueue(InvocationQueueABC):
+    __queue: Queue
+
+    def __init__(self):
+        self.__queue = Queue()
+    
+    def get(self) -> InvocationQueueItem:
+        return self.__queue.get()
+    
+    def put(self, item: InvocationQueueItem|None) -> None:
+        self.__queue.put(item)

ldm/invoke/app/services/invocation_services.py

@@ -0,0 +1,33 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+from .invocation_queue import InvocationQueueABC
+from .item_storage import ItemStorageABC
+from .image_storage import ImageStorageBase
+from .events import EventServiceBase
+from ....generate import Generate
+
+
+class InvocationServices():
+    """Services that can be used by invocations"""
+    generate: Generate # TODO: wrap Generate, or split it up from model?
+    events: EventServiceBase
+    images: ImageStorageBase
+    queue: InvocationQueueABC
+
+    # NOTE: we must forward-declare any types that include invocations, since invocations can use services
+    graph_execution_manager: ItemStorageABC['GraphExecutionState']
+    processor: 'InvocationProcessorABC'
+
+    def __init__(self,
+        generate: Generate,
+        events: EventServiceBase,
+        images: ImageStorageBase,
+        queue: InvocationQueueABC,
+        graph_execution_manager: ItemStorageABC['GraphExecutionState'],
+        processor: 'InvocationProcessorABC'
+    ):
+        self.generate = generate
+        self.events = events
+        self.images = images
+        self.queue = queue
+        self.graph_execution_manager = graph_execution_manager
+        self.processor = processor

ldm/invoke/app/services/invoker.py

@@ -0,0 +1,90 @@
+# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
+
+from abc import ABC
+from threading import Event, Thread
+from .graph import Graph, GraphExecutionState
+from .item_storage import ItemStorageABC
+from ..invocations.baseinvocation import InvocationContext
+from .invocation_services import InvocationServices
+from .invocation_queue import InvocationQueueABC, InvocationQueueItem
+
+
+class Invoker:
+    """The invoker, used to execute invocations"""
+
+    services: InvocationServices
+
+    def __init__(self,
+        services: InvocationServices
+    ):
+        self.services = services
+        self._start()
+
+
+    def invoke(self, graph_execution_state: GraphExecutionState, invoke_all: bool = False) -> str|None:
+        """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()
+        if not invocation:
+            return None
+
+        # Save the execution state
+        self.services.graph_execution_manager.set(graph_execution_state)
+
+        # Queue the invocation
+        print(f'queueing item {invocation.id}')
+        self.services.queue.put(InvocationQueueItem(
+            #session_id    = session.id,
+            graph_execution_state_id = graph_execution_state.id,
+            invocation_id = invocation.id,
+            invoke_all    = invoke_all
+        ))
+
+        return invocation.id
+
+
+    def create_execution_state(self, graph: Graph|None = None) -> GraphExecutionState:
+        """Creates a new execution state for the given graph"""
+        new_state = GraphExecutionState(graph = Graph() if graph is None else graph)
+        self.services.graph_execution_manager.set(new_state)
+        return new_state
+
+
+    def __start_service(self, service) -> None:
+        # Call start() method on any services that have it
+        start_op = getattr(service, 'start', None)
+        if callable(start_op):
+            start_op(self)
+
+
+    def __stop_service(self, service) -> None:
+        # Call stop() method on any services that have it
+        stop_op = getattr(service, 'stop', None)
+        if callable(stop_op):
+            stop_op(self)
+
+    
+    def _start(self) -> None:
+        """Starts the invoker. This is called automatically when the invoker is created."""
+        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)
+
+
+class InvocationProcessorABC(ABC):
+    pass

ldm/invoke/app/services/item_storage.py

@@ -0,0 +1,57 @@
+
+from typing import Callable, TypeVar, Generic
+from pydantic import BaseModel, Field
+from pydantic.generics import GenericModel
+from abc import ABC, abstractmethod
+
+T = TypeVar('T', bound=BaseModel)
+
+class PaginatedResults(GenericModel, Generic[T]):
+    """Paginated results"""
+    items: list[T]   = Field(description = "Items")
+    page: int        = Field(description = "Current Page")
+    pages: int       = Field(description = "Total number of pages")
+    per_page: int    = Field(description = "Number of items per page")
+    total: int       = Field(description = "Total number of items in result")
+
+
+class ItemStorageABC(ABC, Generic[T]):
+    _on_changed_callbacks: list[Callable[[T], None]]
+    _on_deleted_callbacks: list[Callable[[str], None]]
+
+    def __init__(self) -> None:
+        self._on_changed_callbacks = list()
+        self._on_deleted_callbacks = list()
+
+    """Base item storage class"""
+    @abstractmethod
+    def get(self, item_id: str) -> T:
+        pass
+
+    @abstractmethod
+    def set(self, item: T) -> None:
+        pass
+
+    @abstractmethod
+    def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
+        pass
+
+    @abstractmethod
+    def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
+        pass
+
+    def on_changed(self, on_changed: Callable[[T], None]) -> None:
+        """Register a callback for when an item is changed"""
+        self._on_changed_callbacks.append(on_changed)
+
+    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
+        """Register a callback for when an item is deleted"""
+        self._on_deleted_callbacks.append(on_deleted)
+
+    def _on_changed(self, item: T) -> None:
+        for callback in self._on_changed_callbacks:
+            callback(item)
+    
+    def _on_deleted(self, item_id: str) -> None:
+        for callback in self._on_deleted_callbacks:
+            callback(item_id)

ldm/invoke/app/services/processor.py

@@ -0,0 +1,78 @@
+from threading import Event, Thread
+from ..invocations.baseinvocation import InvocationContext
+from .invocation_queue import InvocationQueueItem
+from .invoker import InvocationProcessorABC, Invoker
+
+
+class DefaultInvocationProcessor(InvocationProcessorABC):
+    __invoker_thread: Thread
+    __stop_event: Event
+    __invoker: Invoker
+
+    def start(self, invoker) -> None:
+        self.__invoker = invoker
+        self.__stop_event = Event()
+        self.__invoker_thread = Thread(
+            name = "invoker_processor",
+            target = self.__process,
+            kwargs = dict(stop_event = self.__stop_event)
+        )
+        self.__invoker_thread.daemon = True # TODO: probably better to just not use threads?
+        self.__invoker_thread.start()
+
+
+    def stop(self, *args, **kwargs) -> None:
+        self.__stop_event.set()
+
+
+    def __process(self, stop_event: Event):
+        try:
+            while not stop_event.is_set():
+                queue_item: InvocationQueueItem = self.__invoker.services.queue.get()
+                if not queue_item: # Probably stopping
+                    continue
+
+                graph_execution_state = self.__invoker.services.graph_execution_manager.get(queue_item.graph_execution_state_id)
+                invocation = graph_execution_state.execution_graph.get_node(queue_item.invocation_id)
+
+                # Send starting event
+                self.__invoker.services.events.emit_invocation_started(
+                    graph_execution_state_id = graph_execution_state.id,
+                    invocation_id = invocation.id
+                )
+
+                # Invoke
+                try:
+                    outputs = invocation.invoke(InvocationContext(
+                        services = self.__invoker.services,
+                        graph_execution_state_id = graph_execution_state.id
+                    ))
+
+                    # Save outputs and history
+                    graph_execution_state.complete(invocation.id, outputs)
+
+                    # Save the state changes
+                    self.__invoker.services.graph_execution_manager.set(graph_execution_state)
+
+                    # Send complete event
+                    self.__invoker.services.events.emit_invocation_complete(
+                        graph_execution_state_id = graph_execution_state.id,
+                        invocation_id = invocation.id,
+                        result = outputs.dict()
+                    )
+
+                    # Queue any further commands if invoking all
+                    is_complete = graph_execution_state.is_complete()
+                    if queue_item.invoke_all and not is_complete:
+                        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
+                except Exception as e:
+                    # TODO: Log the error, mark the invocation as failed, and emit an event
+                    print(f'Error invoking {invocation.id}: {e}')
+                    pass
+
+        except KeyboardInterrupt:
+            ... # Log something?

ldm/invoke/app/services/sqlite.py

@@ -0,0 +1,119 @@
+import sqlite3
+from threading import Lock
+from typing import Generic, TypeVar, Union, get_args
+from pydantic import BaseModel, parse_raw_as
+from .item_storage import ItemStorageABC, PaginatedResults
+
+T = TypeVar('T', bound=BaseModel)
+
+sqlite_memory = ':memory:'
+
+class SqliteItemStorage(ItemStorageABC, Generic[T]):
+    _filename: str
+    _table_name: str
+    _conn: sqlite3.Connection
+    _cursor: sqlite3.Cursor
+    _id_field: str
+    _lock: Lock
+
+    def __init__(self, filename: str, table_name: str, id_field: str = 'id'):
+        super().__init__()
+
+        self._filename = filename
+        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
+        self._cursor = self._conn.cursor()
+
+        self._create_table()
+
+    def _create_table(self):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(f'''CREATE TABLE IF NOT EXISTS {self._table_name} (
+                item TEXT,
+                id TEXT GENERATED ALWAYS AS (json_extract(item, '$.{self._id_field}')) VIRTUAL NOT NULL);''')
+            self._cursor.execute(f'''CREATE UNIQUE INDEX IF NOT EXISTS {self._table_name}_id ON {self._table_name}(id);''')
+        finally:
+            self._lock.release()
+
+    def _parse_item(self, item: str) -> T:
+        item_type = get_args(self.__orig_class__)[0]
+        return parse_raw_as(item_type, item)
+
+    def set(self, item: T):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(f'''INSERT OR REPLACE INTO {self._table_name} (item) VALUES (?);''', (item.json(),))
+        finally:
+            self._lock.release()
+        self._on_changed(item)
+
+    def get(self, id: str) -> Union[T, None]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(f'''SELECT item FROM {self._table_name} WHERE id = ?;''', (str(id),))
+            result = self._cursor.fetchone()
+        finally:
+            self._lock.release()
+
+        if not result:
+            return None
+
+        return self._parse_item(result[0])
+
+    def delete(self, id: str):
+        try:
+            self._lock.acquire()
+            self._cursor.execute(f'''DELETE FROM {self._table_name} WHERE id = ?;''', (str(id),))
+        finally:
+            self._lock.release()
+        self._on_deleted(id)
+
+    def list(self, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(f'''SELECT item FROM {self._table_name} LIMIT ? OFFSET ?;''', (per_page, page * per_page))
+            result = self._cursor.fetchall()
+
+            items = list(map(lambda r: self._parse_item(r[0]), result))
+
+            self._cursor.execute(f'''SELECT count(*) FROM {self._table_name};''')
+            count = self._cursor.fetchone()[0]
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedResults[T](
+            items = items,
+            page = page,
+            pages = pageCount,
+            per_page = per_page,
+            total = count
+        )
+    
+    def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
+        try:
+            self._lock.acquire()
+            self._cursor.execute(f'''SELECT item FROM {self._table_name} WHERE item LIKE ? LIMIT ? OFFSET ?;''', (f'%{query}%', per_page, page * per_page))
+            result = self._cursor.fetchall()
+
+            items = list(map(lambda r: self._parse_item(r[0]), result))
+
+            self._cursor.execute(f'''SELECT count(*) FROM {self._table_name} WHERE item LIKE ?;''', (f'%{query}%',))
+            count = self._cursor.fetchone()[0]
+        finally:
+            self._lock.release()
+
+        pageCount = int(count / per_page) + 1
+
+        return PaginatedResults[T](
+            items = items,
+            page = page,
+            pages = pageCount,
+            per_page = per_page,
+            total = count
+        )

ldm/invoke/args.py

@@ -333,7 +333,7 @@ class Args(object):
             switches.append(f'-V {formatted_variations}')
         if 'variations' in a and len(a['variations'])>0:
             switches.append(f'-V {a["variations"]}')
-        return ' '.join(switches) + f' # model_id={kwargs.get("model_id","unknown model")}'
+        return ' '.join(switches)
 
     def __getattribute__(self,name):
         '''
@@ -878,7 +878,7 @@ class Args(object):
         )
         render_group.add_argument(
             '--fnformat',
-            default=None,
+            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',
         )
@@ -1155,7 +1155,6 @@ def format_metadata(**kwargs):
 def metadata_dumps(opt,
                    seeds=[],
                    model_hash=None,
-                   model_id=None,
                    postprocessing=None):
     '''
     Given an Args object, returns a dict containing the keys and
@@ -1168,7 +1167,7 @@ def metadata_dumps(opt,
     # top-level metadata minus `image` or `images`
     metadata = {
         'model'       : 'stable diffusion',
-        'model_id'    : model_id or opt.model,
+        'model_id'    : opt.model,
         'model_hash'  : model_hash,
         'app_id'      : ldm.invoke.__app_id__,
         'app_version' : ldm.invoke.__version__,
@@ -1181,7 +1180,7 @@ def metadata_dumps(opt,
     )
 
     # remove any image keys not mentioned in RFC #266
-    rfc266_img_fields = ['type','postprocessing','sampler','prompt','seed','variations','steps','hires_fix',
+    rfc266_img_fields = ['type','postprocessing','sampler','prompt','seed','variations','steps',
                          'cfg_scale','threshold','perlin','step_number','width','height','extra','strength','seamless'
                          'init_img','init_mask','facetool','facetool_strength','upscale','h_symmetry_time_pct',
                          'v_symmetry_time_pct']