making this a prerelease for zipfile purposes

- Closes #3073

.editorconfig

@@ -1,5 +1,8 @@
+root = true
+
 # All files
 [*]
+max_line_length = 80
 charset = utf-8
 end_of_line = lf
 indent_size = 2
@@ -10,3 +13,18 @@ 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

@@ -0,0 +1,37 @@
+[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

@@ -2,50 +2,60 @@
 /.github/workflows/ @mauwii @lstein @blessedcoolant
 
 # documentation
-/docs/ @lstein @mauwii @tildebyte @blessedcoolant
-mkdocs.yml @lstein @mauwii @blessedcoolant
+/docs/ @lstein @mauwii @blessedcoolant
+mkdocs.yml @mauwii @lstein
 
 # installation and configuration
-/pyproject.toml @mauwii @lstein @ebr @blessedcoolant
-/docker/ @mauwii @lstein @blessedcoolant
+/pyproject.toml @mauwii @lstein @ebr
+/docker/ @mauwii
 /scripts/ @ebr @lstein @blessedcoolant
-/installer/ @ebr @lstein @tildebyte  @blessedcoolant
-ldm/invoke/config @lstein @ebr  @blessedcoolant
-invokeai/assets @lstein @ebr  @blessedcoolant
+/installer/ @ebr @lstein 
+ldm/invoke/config @lstein @ebr
+invokeai/assets @lstein @blessedcoolant
 invokeai/configs @lstein @ebr  @blessedcoolant
 /ldm/invoke/_version.py @lstein @blessedcoolant
 
 # web ui
-/invokeai/frontend @blessedcoolant @psychedelicious @lstein
-/invokeai/backend @blessedcoolant @psychedelicious @lstein
+/invokeai/frontend @blessedcoolant @psychedelicious
+/invokeai/backend @blessedcoolant @psychedelicious
 
 # generation and model management
 /ldm/*.py @lstein @blessedcoolant
-/ldm/generate.py @lstein @keturn @blessedcoolant
+/ldm/generate.py @lstein @keturn
 /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 @blessedcoolant
+/ldm/invoke/generator @keturn @damian0815
 /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 @blessedcoolant @lstein
+/ldm/invoke/patchmatch.py @Kyle0654 @lstein
 /ldm/invoke/restoration @lstein @blessedcoolant
 
 # attention, textual inversion, model configuration
-/ldm/models @damian0815 @keturn @lstein @blessedcoolant
-/ldm/modules @damian0815 @keturn @lstein @blessedcoolant
+/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
 
 # Nodes
-apps/ @Kyle0654 @lstein @blessedcoolant
+apps/ @Kyle0654 @jpphoto
 
 # legacy REST API
-# 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
+# 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
+
 

.github/workflows/mkdocs-material.yml

@@ -9,6 +9,10 @@ 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
@@ -19,11 +23,15 @@ 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 -r docs/requirements-mkdocs.txt
+            pip install ".[docs]"
 
       - name: confirm buildability
         run: |

.pre-commit-config.yaml

@@ -0,0 +1,41 @@
+# 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

@@ -0,0 +1,14 @@
+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 @@
-endOfLine: lf
-tabWidth: 2
-useTabs: false
-singleQuote: true
-quoteProps: as-needed
 embeddedLanguageFormatting: auto
+endOfLine: lf
+singleQuote: true
+semi: true
+trailingComma: es5
+useTabs: false
 overrides:
   - files: '*.md'
     options:
@@ -11,3 +11,9 @@ overrides:
       printWidth: 80
       parser: markdown
       cursorOffset: -1
+  - files: docs/**/*.md
+    options:
+      tabWidth: 4
+  - files: 'invokeai/frontend/public/locales/*.json'
+    options:
+      tabWidth: 4

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/rocm5.2
+    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
     ```
 
     _For Macintoshes, either Intel or M1/M2:_

binary_installer/install.bat.in

@@ -1,164 +0,0 @@
-@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

@@ -1,235 +0,0 @@
-#!/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

@@ -1,36 +0,0 @@
-@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

@@ -1,46 +0,0 @@
-#!/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

@@ -1,17 +0,0 @@
-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

@@ -1,33 +0,0 @@
---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

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

docs/features/CONCEPTS.md

@@ -1,5 +1,5 @@
 ---
-title: Concepts Library
+title: Styles and Subjects
 ---
 
 # :material-library-shelves: The Hugging Face Concepts Library and Importing Textual Inversion files
@@ -109,21 +109,43 @@ For example, TI files generated by the Hugging Face toolkit share the named
 `learned_embedding.bin`. You can use subdirectories to keep them distinct.
 
 At startup time, InvokeAI will scan the `embeddings` directory and load any TI
-files it finds there. At startup you will see a message similar to this one:
+files it finds there. At startup you will see messages similar to these:
 
 ```bash
->> Current embedding manager terms: *, <HOI4-Leader>, <princess-knight>
+>> Loading embeddings from /data/lstein/invokeai-2.3/embeddings
+   | Loading v1 embedding file: style-hamunaptra
+   | Loading v4 embedding file: embeddings/learned_embeds-steps-500.bin
+   | Loading v2 embedding file: lfa
+   | Loading v3 embedding file: easynegative
+   | Loading v1 embedding file: rem_rezero
+   | Loading v2 embedding file: midj-strong
+   | Loading v4 embedding file: anime-background-style-v2/learned_embeds.bin
+   | Loading v4 embedding file: kamon-style/learned_embeds.bin
+   ** Notice: kamon-style/learned_embeds.bin was trained on a model with an incompatible token dimension: 768 vs 1024.
+>> Textual inversion triggers: <anime-background-style-v2>, <easynegative>, <lfa>, <midj-strong>, <milo>, Rem3-2600, Style-Hamunaptra
 ```
 
-Note the `*` trigger term. This is a placeholder term that many early TI
-tutorials taught people to use rather than a more descriptive term.
-Unfortunately, if you have multiple TI files that all use this term, only the
-first one loaded will be triggered by use of the term.
+Textual Inversion embeddings trained on version 1.X stable diffusion
+models are incompatible with version 2.X models and vice-versa.
 
-To avoid this problem, you can use the `merge_embeddings.py` script to merge two
-or more TI files together. If it encounters a collision of terms, the script
-will prompt you to select new terms that do not collide. See
-[Textual Inversion](TEXTUAL_INVERSION.md) for details.
+After the embeddings load, InvokeAI will print out a list of all the
+recognized trigger terms. To trigger the term, include it in the
+prompt exactly as written, including angle brackets if any and
+respecting the capitalization.
+
+There are at least four different embedding file formats, and each uses
+a different convention for the trigger terms. In some cases, the
+trigger term is specified in the file contents and may or may not be
+surrounded by angle brackets. In the example above, `Rem3-2600`,
+`Style-Hamunaptra`, and `<midj-strong>` were specified this way and
+there is no easy way to change the term.
+
+In other cases the trigger term is not contained within the embedding
+file. In this case, InvokeAI constructs a trigger term consisting of
+the base name of the file (without the file extension) surrounded by
+angle brackets. In the example above `<easynegative`> is such a file
+(the filename was `easynegative.safetensors`). In such cases, you can
+change the trigger term simply by renaming the file.
 
 ## Further Reading
 

docs/features/TEXTUAL_INVERSION.md

@@ -154,8 +154,11 @@ 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 will run with GPUs with
-as little as 12 GB.
+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.
 
 ### Learning rate
 
@@ -172,8 +175,10 @@ learning rate to improve performance.
 
 ### Use xformers acceleration
 
-This will activate XFormers memory-efficient attention. You need to
-have XFormers installed for this to have an effect.
+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.
 
 ### Learning rate scheduler
 
@@ -250,6 +255,49 @@ 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

@@ -2,62 +2,84 @@
 title: Overview
 ---
 
-Here you can find the documentation for InvokeAI's various features.
+-   The Basics
 
-## The Basics
-### * The [Web User Interface](WEB.md)
-Guide to the Web interface. Also see the [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
+    -   The [Web User Interface](WEB.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.
+        Guide to the Web interface. Also see the
+        [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
 
-### * The [Command Line Interface (CLI)](CLI.md)
-Scriptable access to InvokeAI's features.
+    -   The [Unified Canvas](UNIFIED_CANVAS.md)
 
-## Image Generation
-### * [Prompt Engineering](PROMPTS.md)
-Get the images you want with the InvokeAI  prompt engineering language.
+        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.
 
-## * [Post-Processing](POSTPROCESS.md)
-Restore mangled faces and make images larger with upscaling. Also see the [Embiggen Upscaling Guide](EMBIGGEN.md).
+    -   The [Command Line Interface (CLI)](CLI.md)
 
-## * The [Concepts Library](CONCEPTS.md)
-Add custom subjects and styles using HuggingFace's repository of embeddings.
+        Scriptable access to InvokeAI's features.
 
-### * [Image-to-Image Guide for the CLI](IMG2IMG.md)
-Use a seed image to build new creations in the CLI.
+    -   [Visual Manual for InvokeAI](https://docs.google.com/presentation/d/e/2PACX-1vSE90aC7bVVg0d9KXVMhy-Wve-wModgPFp7AGVTOCgf4xE03SnV24mjdwldolfCr59D_35oheHe4Cow/pub?start=false&loop=true&delayms=60000) (contributed by Statcomm)
 
-### * [Inpainting Guide for the CLI](INPAINTING.md)
-Selectively erase and replace portions of an existing image in the CLI.
+-   Image Generation
 
-### * [Outpainting Guide for the CLI](OUTPAINTING.md)
-Extend the borders of the image with an "outcrop" function within the CLI.
+    -   [Prompt Engineering](PROMPTS.md)
 
-### * [Generating Variations](VARIATIONS.md)
-Have an image you like and want to generate many more like it? Variations
-are the ticket.
+        Get the images you want with the InvokeAI prompt engineering language.
 
-## Model Management
+    -   [Post-Processing](POSTPROCESS.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.
+        Restore mangled faces and make images larger with upscaling. Also see
+        the [Embiggen Upscaling Guide](EMBIGGEN.md).
 
-## * [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.
+    -   The [Concepts Library](CONCEPTS.md)
 
-## * [Textual Inversion](TEXTUAL_INVERSION.md)
-Personalize models by adding your own style or subjects.
+        Add custom subjects and styles using HuggingFace's repository of
+        embeddings.
 
-# Other Features
+    -   [Image-to-Image Guide for the CLI](IMG2IMG.md)
 
-## * [The NSFW Checker](NSFW.md)
-Prevent InvokeAI from displaying unwanted racy images.
+        Use a seed image to build new creations in the CLI.
 
-## * [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!
+    -   [Inpainting Guide for the CLI](INPAINTING.md)
+
+        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!

docs/help/IDE-Settings/index.md

@@ -0,0 +1,4 @@
+# :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

@@ -0,0 +1,250 @@
+---
+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

@@ -0,0 +1,135 @@
+---
+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

@@ -0,0 +1,26 @@
+---
+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

@@ -0,0 +1,32 @@
+---
+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

@@ -0,0 +1,76 @@
+# 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

@@ -0,0 +1,16 @@
+---
+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

@@ -0,0 +1,12 @@
+# :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

@@ -2,6 +2,8 @@
 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::
 
@@ -29,36 +31,36 @@ 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>
@@ -87,24 +89,24 @@ 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
 
@@ -113,133 +115,406 @@ 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)
-<!-- separator -->
-### The InvokeAI Command Line Interface
-- [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)
+
+-   [WebUI overview](features/WEB.md)
+-   [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)
+-   [WebUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
+-   [Visual Manual for InvokeAI v2.3.1](https://docs.google.com/presentation/d/e/2PACX-1vSE90aC7bVVg0d9KXVMhy-Wve-wModgPFp7AGVTOCgf4xE03SnV24mjdwldolfCr59D_35oheHe4Cow/pub?start=false&loop=true&delayms=60000) (contributed by Statcomm)
 
 <!-- separator -->
+
+<!-- separator -->
+
+### The InvokeAI Command Line Interface
+
+-   [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)
+
+<!-- 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)
+-   [Adding custom styles and subjects via 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
 
+### v2.3.3 <small>(29 March 2023)</small>
+
+#### Bug Fixes
+1. When using legacy checkpoints with an external VAE, the VAE file is now scanned for malware prior to loading. Previously only the main model weights file was scanned.
+2. Textual inversion will select an appropriate batchsize based on whether `xformers` is active, and will default to `xformers` enabled if the library is detected.
+3. The batch script log file names have been fixed to be compatible with Windows.
+4. Occasional corruption of the `.next_prefix` file (which stores the next output file name in sequence) on Windows systems is now detected and corrected.
+5. An infinite loop when opening the developer's console from within the `invoke.sh` script has been corrected.
+
+#### Enhancements
+1. It is now possible to load and run several community-contributed SD-2.0 based models, including the infamous "Illuminati" model.
+2. The "NegativePrompts" embedding file, and others like it, can now be loaded by placing it in the InvokeAI `embeddings` directory.
+3. If no `--model` is specified at launch time, InvokeAI will remember the last model used and restore it the next time it is launched.
+4. On Linux systems, the `invoke.sh` launcher now uses a prettier console-based interface. To take advantage of it, install the `dialog` package using your package manager (e.g. `sudo apt install dialog`).
+5. When loading legacy models (safetensors/ckpt) you can specify a custom config file and/or a VAE by placing like-named files in the same directory as the model following this example:
+```
+my-favorite-model.ckpt
+my-favorite-model.yaml
+my-favorite-model.vae.pt      # or my-favorite-model.vae.safetensors
+```
+
+### v2.3.2 <small>(13 March 2023)</small>
+
+#### Bugfixes
+
+Since version 2.3.1 the following bugs have been fixed:
+
+1. Black images appearing for potential NSFW images when generating with legacy checkpoint models and both `--no-nsfw_checker` and `--ckpt_convert` turned on.
+2. Black images appearing when generating from models fine-tuned on Stable-Diffusion-2-1-base. When importing V2-derived models, you may be asked to select whether the model was derived from a "base" model (512 pixels) or the 768-pixel SD-2.1 model.
+3. The "Use All" button was not restoring the Hi-Res Fix setting on the WebUI
+4. When using the model installer console app, models failed to import correctly when importing from directories with spaces in their names. A similar issue with the output directory was also fixed.
+5. Crashes that occurred during model merging.
+6. Restore previous naming of Stable Diffusion base and 768 models.
+7. Upgraded to latest versions of `diffusers`, `transformers`, `safetensors` and `accelerate` libraries upstream. We hope that this will fix the `assertion NDArray > 2**32` issue that MacOS users have had when generating images larger than 768x768 pixels. Please report back.
+
+As part of the upgrade to `diffusers`, the location of the diffusers-based models has changed from `models/diffusers` to `models/hub`. When you launch InvokeAI for the first time, it will prompt you to OK a one-time move. This should be quick and harmless, but if you have modified your `models/diffusers` directory in some way, for example using symlinks, you may wish to cancel the migration and make appropriate adjustments.
+
+#### New "Invokeai-batch" script
+
+2.3.2 introduces a new command-line only script called
+`invokeai-batch` that can be used to generate hundreds of images from
+prompts and settings that vary systematically. This can be used to try
+the same prompt across multiple combinations of models, steps, CFG
+settings and so forth. It also allows you to template prompts and
+generate a combinatorial list like: ``` a shack in the mountains,
+photograph a shack in the mountains, watercolor a shack in the
+mountains, oil painting a chalet in the mountains, photograph a chalet
+in the mountains, watercolor a chalet in the mountains, oil painting a
+shack in the desert, photograph ...  ```
+
+If you have a system with multiple GPUs, or a single GPU with lots of
+VRAM, you can parallelize generation across the combinatorial set,
+reducing wait times and using your system's resources efficiently
+(make sure you have good GPU cooling).
+
+To try `invokeai-batch` out. Launch the "developer's console" using
+the `invoke` launcher script, or activate the invokeai virtual
+environment manually. From the console, give the command
+`invokeai-batch --help` in order to learn how the script works and
+create your first template file for dynamic prompt generation.
+
+### v2.3.1 <small>(26 February 2023)</small>
+
+This is primarily a bugfix release, but it does provide several new features that will improve the user experience. 
+
+#### Enhanced support for model management
+
+InvokeAI now makes it convenient to add, remove and modify models. You can individually import models that are stored on your local system, scan an entire folder and its subfolders for models and import them automatically, and even directly import models from the internet by providing their download URLs. You also have the option of designating a local folder to scan for new models each time InvokeAI is restarted.
+
+There are three ways of accessing the model management features:
+
+1. ***From the WebUI***, click on the cube to the right of the model selection menu. This will bring up a form that allows you to import models individually from your local disk or scan a directory for models to import.
+
+![image](https://user-images.githubusercontent.com/111189/220638091-918492cc-0719-4194-b033-3741e8289b30.png)
+
+2. **Using the Model Installer App**
+
+Choose option (5) _download and install models_ from the `invoke` launcher script to start a new console-based application for model management. You can use this to select from a curated set of starter models, or import checkpoint, safetensors, and diffusers models from a local disk or the internet. The example below shows importing two checkpoint URLs from popular SD sites and a HuggingFace diffusers model using its Repository ID. It also shows how to designate a folder to be scanned at startup time for new models to import.
+
+Command-line users can start this app using the command `invokeai-model-install`.
+
+![image](https://user-images.githubusercontent.com/111189/220660363-22ff3a2e-8082-410e-a818-d2b3a0529bac.png)
+
+3. **Using the Command Line Client (CLI)**
+
+The `!install_model` and `!convert_model` commands have been enhanced to allow entering of URLs and local directories to scan and import. The first command installs .ckpt and .safetensors files as-is. The second one converts them into the faster diffusers format before installation.
+
+Internally InvokeAI is able to probe the contents of a .ckpt or .safetensors file to distinguish among v1.x, v2.x and inpainting models. This means that you do **not** need to include "inpaint" in your model names to use an inpainting model. Note that Stable Diffusion v2.x models will be autoconverted into a diffusers model the first time you use it.
+
+Please see [INSTALLING MODELS](https://invoke-ai.github.io/InvokeAI/installation/050_INSTALLING_MODELS/) for more information on model management.
+
+#### An Improved Installer Experience
+
+The installer now launches a console-based UI for setting and changing commonly-used startup options:
+
+![image](https://user-images.githubusercontent.com/111189/220644777-3d3a90ca-f9e2-4e6d-93da-cbdd66bf12f3.png)
+
+After selecting the desired options, the installer installs several support models needed by InvokeAI's face reconstruction and upscaling features and then launches the interface for selecting and installing models shown earlier. At any time,  you can edit the startup options by launching `invoke.sh`/`invoke.bat` and entering option (6) _change InvokeAI startup options_
+
+Command-line users can launch the new configure app using `invokeai-configure`.
+
+This release also comes with a renewed updater. To do an update without going through a whole reinstallation, launch `invoke.sh` or `invoke.bat` and choose option (9) _update InvokeAI_ . This will bring you to a screen that prompts you to update to the latest released version, to the most current development version, or any released or unreleased version you choose by selecting the tag or branch of the desired version.
+
+![image](https://user-images.githubusercontent.com/111189/220650124-30a77137-d9cd-406e-a87d-d8283f99a4b3.png)
+
+Command-line users can run this interface by typing `invokeai-configure`
+
+#### Image Symmetry Options
+
+There are now features to generate horizontal and vertical symmetry during generation. The way these work is to wait until a selected step in the generation process and then to turn on a mirror image effect. In addition to generating some cool images, you can also use this to make side-by-side comparisons of how an image will look with more or fewer steps. Access this option from the WebUI by selecting _Symmetry_ from the image generation settings, or within the CLI by using the options `--h_symmetry_time_pct` and `--v_symmetry_time_pct` (these can be abbreviated to `--h_sym` and `--v_sym` like all other options).
+
+![image](https://user-images.githubusercontent.com/111189/220658687-47fd0f2c-7069-4d95-aec9-7196fceb360d.png)
+
+#### A New Unified Canvas Look
+
+This release introduces a beta version of the WebUI Unified Canvas. To try it out, open up the settings dialogue in the WebUI (gear icon) and select _Use Canvas Beta Layout_:
+
+![image](https://user-images.githubusercontent.com/111189/220646958-b7eca95e-dc39-4cd2-b277-63eac98ed446.png)
+
+Refresh the screen and go to to Unified Canvas (left side of screen, third icon from the top). The new layout is designed to provide more space to work in and to keep the image controls close to the image itself:
+
+![image](https://user-images.githubusercontent.com/111189/220647560-4a9265a1-6926-44f9-9d08-e1ef2ce61ff8.png)
+
+#### Model conversion and merging within the WebUI
+
+The WebUI now has an intuitive interface for model merging, as well as for permanent conversion of models from legacy .ckpt/.safetensors formats into diffusers format. These options are also available directly from the `invoke.sh`/`invoke.bat` scripts.
+
+#### An easier way to contribute translations to the WebUI
+
+We have migrated our translation efforts to [Weblate](https://hosted.weblate.org/engage/invokeai/), a FOSS translation product. Maintaining the growing project's translations is now far simpler for the maintainers and community. Please review our brief [translation guide](https://github.com/invoke-ai/InvokeAI/blob/v2.3.1/docs/other/TRANSLATION.md) for more information on how to contribute.
+
+#### Numerous internal bugfixes and performance issues
+
+This releases quashes multiple bugs that were reported in 2.3.0. Major internal changes include upgrading to `diffusers 0.13.0`, and using the `compel` library for prompt parsing. See [Detailed Change Log](#full-change-log) for a detailed list of bugs caught and squished.
+
+#### Summary of InvokeAI command line scripts (all accessible via the launcher menu)
+
+| Command                  | Description                                                         |
+|--------------------------|---------------------------------------------------------------------|
+| `invokeai`               | Command line interface                                              |
+| `invokeai --web`         | Web interface                                                       |
+| `invokeai-model-install` | Model installer with console forms-based front end                  |
+| `invokeai-ti --gui`      | Textual inversion, with a console forms-based front end             |
+| `invokeai-merge --gui`   | Model merging, with a console forms-based front end                 |
+| `invokeai-configure`     | Startup configuration; can also be used to reinstall support models |
+| `invokeai-update`        | InvokeAI software updater                                           |
+
+
 ### v2.3.0 <small>(9 February 2023)</small>
 
 #### 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:
-* `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 should be aware that the locations of the script's source code has
+been moved. The new locations are:
 
-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`.
+-   `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`
 
-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
+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
 **[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
 
@@ -265,8 +540,8 @@ 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/rocm5.2
+    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
     ```
 
 ### Corrupted configuration file

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -77,7 +77,7 @@ machine. To test, open up a terminal window and issue the following
 command:
 
 ```
-rocm-smi
+rocminfo
 ```
 
 If you get a table labeled "ROCm System Management Interface" the
@@ -95,9 +95,17 @@ recent version of Ubuntu, 22.04. However, this [community-contributed
 recipe](https://novaspirit.github.io/amdgpu-rocm-ubu22/) is reported
 to work well.
 
-After installation, please run `rocm-smi` a second time to confirm
+After installation, please run `rocminfo` a second time to confirm
 that the driver is present and the GPU is recognized. You may need to
-do a reboot in order to load the driver.
+do a reboot in order to load the driver. In addition, if you see
+errors relating to your username not being a member of the `render`
+group, you may fix this by adding yourself to this group with the command:
+
+```
+sudo usermod -a -G render myUserName
+```
+
+(Thanks to @EgoringKosmos for the usermod recipe.)
 
 ### Linux Install with a ROCm-docker Container
 
@@ -110,7 +118,7 @@ recipes are available
 
 When installing torch and torchvision manually with `pip`, remember to provide
 the argument `--extra-index-url
-https://download.pytorch.org/whl/rocm5.2` as described in the [Manual
+https://download.pytorch.org/whl/rocm5.4.2` 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

@@ -11,7 +11,7 @@ The model checkpoint files ('\*.ckpt') are the Stable Diffusion
 captioned images gathered from multiple sources.
 
 Originally there was only a single Stable Diffusion weights file,
-which many people named `model.ckpt`. Now there are dozens or more
+which many people named `model.ckpt`. Now there are hundreds
 that have been fine tuned to provide particulary styles, genres, or
 other features. In addition, there are several new formats that
 improve on the original checkpoint format: a `.safetensors` format
@@ -29,9 +29,10 @@ and performance are being made at a rapid pace. Among other features
 is the ability to download and install a `diffusers` model just by
 providing its HuggingFace repository ID.
 
-While InvokeAI will continue to support `.ckpt` and `.safetensors`
+While InvokeAI will continue to support legacy `.ckpt` and `.safetensors`
 models for the near future, these are deprecated and support will
-likely be withdrawn at some point in the not-too-distant future.
+be withdrawn in version 3.0, after which all legacy models will be
+converted into diffusers at the time they are loaded.
 
 This manual will guide you through installing and configuring model
 weight files and converting legacy `.ckpt` and `.safetensors` files
@@ -89,15 +90,18 @@ aware that CIVITAI hosts many models that generate NSFW content.
 !!! note
 
     InvokeAI 2.3.x does not support directly importing and
-    running Stable Diffusion version 2 checkpoint models. You may instead
-    convert them into `diffusers` models using the conversion methods
-    described below.
+    running Stable Diffusion version 2 checkpoint models. If you
+    try to import them, they will be automatically
+    converted into `diffusers` models on the fly. This adds about 20s
+    to loading time. To avoid this overhead, you are encouraged to
+    use one of the conversion methods described below to convert them
+    permanently.
 
 ## Installation
 
 There are multiple ways to install and manage models:
 
-1. The `invokeai-configure` script which will download and install them for you.
+1. The `invokeai-model-install` script which will download and install them for you.
 
 2. The command-line tool (CLI) has commands that allows you to import, configure and modify
    models files.
@@ -105,14 +109,41 @@ There are multiple ways to install and manage models:
 3. The web interface (WebUI) has a GUI for importing and managing
    models.
 
-### Installation via `invokeai-configure`
+### Installation via `invokeai-model-install`
 
-From the `invoke` launcher, choose option (6) "re-run the configure
-script to download new models." This will launch the same script that
-prompted you to select models at install time. You can use this to add
-models that you skipped the first time around. It is all right to
-specify a model that was previously downloaded; the script will just
-confirm that the files are complete.
+From the `invoke` launcher, choose option (5) "Download and install
+models."  This will launch the same script that prompted you to select
+models at install time. You can use this to add models that you
+skipped the first time around. It is all right to specify a model that
+was previously downloaded; the script will just confirm that the files
+are complete.
+
+This script allows you to load 3d party models. Look for a large text
+entry box labeled "IMPORT LOCAL AND REMOTE MODELS." In this box, you
+can cut and paste one or more of any of the following:
+
+1. A URL that points to a downloadable .ckpt or .safetensors file.
+2. A file path pointing to a .ckpt or .safetensors  file.
+3. A diffusers model repo_id (from HuggingFace) in the format
+   "owner/repo_name".
+4. A directory path pointing to a diffusers model directory.
+5. A directory path pointing to a directory containing a bunch of
+   .ckpt and .safetensors files. All will be imported.
+
+You can enter multiple items into the textbox, each one on a separate
+line. You can paste into the textbox using ctrl-shift-V or by dragging
+and dropping a file/directory from the desktop into the box.
+
+The script also lets you designate a directory that will be scanned
+for new model files each time InvokeAI starts up. These models will be
+added automatically.
+
+Lastly, the script gives you a checkbox option to convert legacy models
+into diffusers, or to run the legacy model directly. If you choose to
+convert, the original .ckpt/.safetensors file will **not** be deleted,
+but a new diffusers directory will be created, using twice your disk
+space. However, the diffusers version will load faster, and will be
+compatible with InvokeAI 3.0.
 
 ### Installation via the CLI
 
@@ -144,19 +175,15 @@ invoke> !import_model https://example.org/sd_models/martians.safetensors
 For this to work, the URL must not be password-protected. Otherwise
 you will receive a 404 error.
 
-When you import a legacy model, the CLI will first ask you what type
-of model this is. You can indicate whether it is a model based on
-Stable Diffusion 1.x (1.4 or 1.5), one based on Stable Diffusion 2.x,
-or a 1.x inpainting model. Be careful to indicate the correct model
-type, or it will not load correctly. You can correct the model type
-after the fact using the `!edit_model` command.
-
-The system will then ask you a few other questions about the model,
-including what size image it was trained on (usually 512x512), what
-name and description you wish to use for it, and whether you would
-like to install a custom VAE (variable autoencoder) file for the
-model. For recent models, the answer to the VAE question is usually
-"no," but it won't hurt to answer "yes".
+When you import a legacy model, the CLI will try to figure out what
+type of model it is and select the correct load configuration file.
+However, one thing it can't do is to distinguish between Stable
+Diffusion 2.x models trained on 512x512 vs 768x768 images. In this
+case, the CLI will pop up a menu of choices, asking you to select
+which type of model it is. Please consult the model documentation to
+identify the correct answer, as loading with the wrong configuration
+will lead to black images. You can correct the model type after the
+fact using the `!edit_model` command.
 
 After importing, the model will load. If this is successful, you will
 be asked if you want to keep the model loaded in memory to start
@@ -211,109 +238,6 @@ 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".
 
-### Converting legacy models into `diffusers`
-
-The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
-models file into `diffusers` and install it.This will enable the model
-to load and run faster without loss of image quality.
-
-The usage is identical to `!import_model`. You may point the command
-to either a downloaded model file on disk, or to a (non-password
-protected) URL:
-
-```bash
-invoke> !convert_model C:/Users/fred/Downloads/martians.safetensors
-```
-
-After a successful conversion, the CLI will offer you the option of
-deleting the original `.ckpt` or `.safetensors` file.
-
-### Optimizing a previously-installed model
-
-Lastly, if you have previously installed a `.ckpt` or `.safetensors`
-file and wish to convert it into a `diffusers` model, you can do this
-without re-downloading and converting the original file using the
-`!optimize_model` command. Simply pass the short name of an existing
-installed model:
-
-```bash
-invoke> !optimize_model martians-v1.0
-```
-
-The model will be converted into `diffusers` format and replace the
-previously installed version. You will again be offered the
-opportunity to delete the original `.ckpt` or `.safetensors` file.
-
-### Related CLI Commands
-
-There are a whole series of additional model management commands in
-the CLI that you can read about in [Command-Line
-Interface](../features/CLI.md). These include:
-
-* `!models` - List all installed models
-* `!switch <model name>` - Switch to the indicated model
-* `!edit_model <model name>` - Edit the indicated model to change its name, description or other properties
-* `!del_model <model name>` - Delete the indicated model
-
-### Manually editing `configs/models.yaml`
-
-
-If you are comfortable with a text editor then you may simply edit `models.yaml`
-directly.
-
-You will need to download the desired `.ckpt/.safetensors` file and
-place it somewhere on your machine's filesystem. Alternatively, for a
-`diffusers` model, record the repo_id or download the whole model
-directory. Then using a **text** editor (e.g. the Windows Notepad
-application), open the file `configs/models.yaml`, and add a new
-stanza that follows this model:
-
-#### A legacy model
-
-A legacy `.ckpt` or `.safetensors` entry will look like this:
-
-```yaml
-arabian-nights-1.0:
-  description: A great fine-tune in Arabian Nights style
-  weights: ./path/to/arabian-nights-1.0.ckpt
-  config: ./configs/stable-diffusion/v1-inference.yaml
-  format: ckpt
-  width: 512
-  height: 512
-  default: false
-```
-
-Note that `format` is `ckpt` for both `.ckpt` and `.safetensors` files.
-
-#### A diffusers model
-
-A stanza for a `diffusers` model will look like this for a HuggingFace
-model with a repository ID:
-
-```yaml
-arabian-nights-1.1:
-  description: An even better fine-tune of the Arabian Nights
-  repo_id: captahab/arabian-nights-1.1
-  format: diffusers
-  default: true
-```
-
-And for a downloaded directory:
-
-```yaml
-arabian-nights-1.1:
-  description: An even better fine-tune of the Arabian Nights
-  path: /path/to/captahab-arabian-nights-1.1
-  format: diffusers
-  default: true
-```
-
-There is additional syntax for indicating an external VAE to use with
-this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
-
-After you save the modified `models.yaml` file relaunch
-`invokeai`. The new model will now be available for your use.
-
 ### Installation via the WebUI
 
 To access the WebUI Model Manager, click on the button that looks like
@@ -393,3 +317,143 @@ And here is what the same argument looks like in `invokeai.init`:
 --no-nsfw_checker
 --autoconvert /home/fred/stable-diffusion-checkpoints
 ```
+
+### 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 model file. Here is an example:
+
+```bash
+wonderful-model-v2.ckpt
+wonderful-model-v2.yaml
+```
+
+This is not needed for `diffusers` models, which come with their own
+pre-packaged configuration.
+
+### Specifying a custom VAE file for legacy checkpoints
+
+To associate a custom VAE with a legacy file, place the VAE file in
+the same directory as the corresponding `.ckpt` or
+`.safetensors` file and make sure the file has the same basename as
+the model file. Use the suffix `.vae.pt` for VAE checkpoint files, and
+`.vae.safetensors` for VAE safetensors files. There is no requirement
+that both the model and the VAE follow the same format.
+
+Example:
+
+```bash
+wonderful-model-v2.pt
+wonderful-model-v2.vae.safetensors
+```
+
+### Converting legacy models into `diffusers`
+
+The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
+models file into `diffusers` and install it.This will enable the model
+to load and run faster without loss of image quality.
+
+The usage is identical to `!import_model`. You may point the command
+to either a downloaded model file on disk, or to a (non-password
+protected) URL:
+
+```bash
+invoke> !convert_model C:/Users/fred/Downloads/martians.safetensors
+```
+
+After a successful conversion, the CLI will offer you the option of
+deleting the original `.ckpt` or `.safetensors` file.
+
+### Optimizing a previously-installed model
+
+Lastly, if you have previously installed a `.ckpt` or `.safetensors`
+file and wish to convert it into a `diffusers` model, you can do this
+without re-downloading and converting the original file using the
+`!optimize_model` command. Simply pass the short name of an existing
+installed model:
+
+```bash
+invoke> !optimize_model martians-v1.0
+```
+
+The model will be converted into `diffusers` format and replace the
+previously installed version. You will again be offered the
+opportunity to delete the original `.ckpt` or `.safetensors` file.
+
+Alternatively you can use the WebUI's model manager to handle diffusers
+optimization. Select the legacy model you wish to convert, and then
+look for a button labeled "Convert to Diffusers" in the upper right of
+the window.
+
+### Related CLI Commands
+
+There are a whole series of additional model management commands in
+the CLI that you can read about in [Command-Line
+Interface](../features/CLI.md). These include:
+
+* `!models` - List all installed models
+* `!switch <model name>` - Switch to the indicated model
+* `!edit_model <model name>` - Edit the indicated model to change its name, description or other properties
+* `!del_model <model name>` - Delete the indicated model
+
+### Manually editing `configs/models.yaml`
+
+If you are comfortable with a text editor then you may simply edit `models.yaml`
+directly.
+
+You will need to download the desired `.ckpt/.safetensors` file and
+place it somewhere on your machine's filesystem. Alternatively, for a
+`diffusers` model, record the repo_id or download the whole model
+directory. Then using a **text** editor (e.g. the Windows Notepad
+application), open the file `configs/models.yaml`, and add a new
+stanza that follows this model:
+
+#### A legacy model
+
+A legacy `.ckpt` or `.safetensors` entry will look like this:
+
+```yaml
+arabian-nights-1.0:
+  description: A great fine-tune in Arabian Nights style
+  weights: ./path/to/arabian-nights-1.0.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  format: ckpt
+  width: 512
+  height: 512
+  default: false
+```
+
+Note that `format` is `ckpt` for both `.ckpt` and `.safetensors` files.
+
+#### A diffusers model
+
+A stanza for a `diffusers` model will look like this for a HuggingFace
+model with a repository ID:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  repo_id: captahab/arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+And for a downloaded directory:
+
+```yaml
+arabian-nights-1.1:
+  description: An even better fine-tune of the Arabian Nights
+  path: /path/to/captahab-arabian-nights-1.1
+  format: diffusers
+  default: true
+```
+
+There is additional syntax for indicating an external VAE to use with
+this model. See `INITIAL_MODELS.yaml` and `models.yaml` for examples.
+
+After you save the modified `models.yaml` file relaunch
+`invokeai`. The new model will now be available for your use.
+

docs/other/CONTRIBUTORS.md

@@ -23,14 +23,16 @@ We thank them for all of their time and hard work.
 * @damian0815 - Attention Systems and Gameplay Engineer
 * @mauwii (Matthias Wild) - Continuous integration and product maintenance engineer
 * @Netsvetaev (Artur Netsvetaev) - UI/UX Developer
-* @tildebyte - General gadfly and resident (self-appointed) know-it-all
 * @keturn - Lead for Diffusers port
 * @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
 * @jpphoto (Jonathan Pollack) - Inference and rendering engine optimization
 * @genomancer (Gregg Helt) - Model training and merging
+* @gogurtenjoyer - User support and testing
+* @whosawwhatsis - User support and testing
 
 ## **Contributions by**
 
+- [tildebyte](https://github.com/tildebyte)
 - [Sean McLellan](https://github.com/Oceanswave)
 - [Kevin Gibbons](https://github.com/bakkot)
 - [Tesseract Cat](https://github.com/TesseractCat)
@@ -78,6 +80,7 @@ We thank them for all of their time and hard work.
 - [psychedelicious](https://github.com/psychedelicious)
 - [damian0815](https://github.com/damian0815)
 - [Eugene Brodsky](https://github.com/ebr)
+- [Statcomm](https://github.com/statcomm)
 
 ## **Original CompVis Authors**
 

docs/requirements-mkdocs.txt

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

installer/lib/installer.py

@@ -241,14 +241,18 @@ 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",
-                "torchvision",
+                "torch~=1.13.1",
+                "torchvision>=0.14.1",
                 "--force-reinstall",
                 "--find-links" if find_links is not None else None,
                 find_links,
@@ -379,6 +383,9 @@ class InvokeAiInstance:
             shutil.copy(src, dest)
             os.chmod(dest, 0o0755)
 
+        if OS == "Linux":
+            shutil.copy(Path(__file__).parents[1] / "templates" / "dialogrc", self.runtime / '.dialogrc')
+
     def update(self):
         pass
 

installer/templates/dialogrc

@@ -0,0 +1,27 @@
+# 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

@@ -1,5 +1,10 @@
 #!/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,
@@ -11,85 +16,168 @@
 
 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="$scriptdir"
+PARAMS=$@
 
-# set required env var for torch on mac MPS
+# 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 "$PARAMS" | sed 's/--no-tui//')
+            break
+        fi
+    done
+else
+    tui=false
+fi
+
+# Set required env var for torch on mac MPS
 if [ "$(uname -s)" == "Darwin" ]; then
     export PYTORCH_ENABLE_MPS_FALLBACK=1
 fi
 
-while true
-do
-if [ "$0" != "bash" ]; then
-    echo "Do you want to generate images using the"
-    echo "1. command-line interface"
-    echo "2. browser-based UI"
-    echo "3. run textual inversion training"
-    echo "4. merge models (diffusers type only)"
-    echo "5. download and install models"
-    echo "6. change InvokeAI startup options"
-    echo "7. re-run the configure script to fix a broken install"
-    echo "8. open the developer console"
-    echo "9. update InvokeAI"
-    echo "10. command-line help"
-    echo "Q - Quit"
-    echo ""
-    read -p "Please enter 1-10, Q: [2] " yn
-    choice=${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;;
+# 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
+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
 else # in developer console
     python --version
-    echo "Press ^D to exit"
+    printf "Press ^D to exit\n"
     export PS1="(InvokeAI) \u@\h \w> "
 fi
-done

invokeai/configs/INITIAL_MODELS.yaml

@@ -13,14 +13,19 @@ sd-inpainting-1.5:
   vae:
     repo_id: stabilityai/sd-vae-ft-mse
   recommended: True
-stable-diffusion-2.1:
+stable-diffusion-2.1-768:
    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-2-1
+   repo_id: stabilityai/stable-diffusion-2-inpainting
    format: diffusers
    recommended: False
 analog-diffusion-1.0:

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

@@ -0,0 +1,67 @@
+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

@@ -1,6 +1,7 @@
 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-0e39fbc4.js"></script>
+    <script type="module" crossorigin src="./assets/index-c09cf9ca.js"></script>
     <link rel="stylesheet" href="./assets/index-14cb2922.css">
   </head>
 

invokeai/frontend/dist/locales/en.json

@@ -63,7 +63,8 @@
         "statusConvertingModel": "Converting Model",
         "statusModelConverted": "Model Converted",
         "statusMergingModels": "Merging Models",
-        "statusMergedModels": "Models Merged"
+        "statusMergedModels": "Models Merged",
+        "pinOptionsPanel": "Pin Options Panel"
     },
     "gallery": {
         "generations": "Generations",
@@ -364,7 +365,8 @@
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
         "convertToDiffusersSaveLocation": "Save Location",
         "v1": "v1",
-        "v2": "v2",
+        "v2_base": "v2 (512px)",
+        "v2_768": "v2 (768px)",
         "inpainting": "v1 Inpainting",
         "customConfig": "Custom Config",
         "pathToCustomConfig": "Path To Custom Config",
@@ -393,7 +395,9 @@
         "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"
+        "weightedSum": "Weighted Sum",
+        "none": "none",
+        "addDifference": "Add Difference"
     },
     "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",
@@ -44,7 +44,26 @@
         "statusUpscaling": "Aumentando Tamaño",
         "statusUpscalingESRGAN": "Restaurando Rostros(ESRGAN)",
         "statusLoadingModel": "Cargando Modelo",
-        "statusModelChanged": "Modelo cambiado"
+        "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"
     },
     "gallery": {
         "generations": "Generaciones",
@@ -284,16 +303,16 @@
         "nameValidationMsg": "Introduce un nombre para tu modelo",
         "description": "Descripción",
         "descriptionValidationMsg": "Introduce una descripción para tu modelo",
-        "config": "Config",
-        "configValidationMsg": "Ruta del archivo de configuración del modelo",
+        "config": "Configurar",
+        "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",
@@ -320,7 +339,61 @@
         "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."
+        "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"
     },
     "parameters": {
         "images": "Imágenes",
@@ -380,7 +453,22 @@
         "info": "Información",
         "deleteImage": "Eliminar Imagen",
         "initialImage": "Imagen Inicial",
-        "showOptionsPanel": "Mostrar panel de opciones"
+        "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"
     },
     "settings": {
         "models": "Modelos",
@@ -393,7 +481,8 @@
         "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."
+        "resetComplete": "La interfaz web se ha restablecido. Actualice la página para recargarla.",
+        "useSlidersForAll": "Utilice controles deslizantes para todas las opciones"
     },
     "toast": {
         "tempFoldersEmptied": "Directorio temporal vaciado",
@@ -431,12 +520,12 @@
         "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. 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.",
+            "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.",
             "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

@@ -44,7 +44,26 @@
         "statusUpscaling": "Redimensinando",
         "statusUpscalingESRGAN": "Redimensinando (ESRGAN)",
         "statusLoadingModel": "Carregando Modelo",
-        "statusModelChanged": "Modelo Alterado"
+        "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"
     },
     "gallery": {
         "generations": "Gerações",
@@ -237,7 +256,7 @@
             "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": {
@@ -284,7 +303,7 @@
         "nameValidationMsg": "Insira um nome para o seu modelo",
         "description": "Descrição",
         "descriptionValidationMsg": "Adicione uma descrição para o seu modelo",
-        "config": "Config",
+        "config": "Configuração",
         "configValidationMsg": "Caminho para o arquivo de configuração do seu modelo.",
         "modelLocation": "Localização do modelo",
         "modelLocationValidationMsg": "Caminho para onde seu modelo está localizado.",
@@ -317,7 +336,52 @@
         "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."
+        "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."
     },
     "parameters": {
         "images": "Imagems",
@@ -442,14 +506,14 @@
         "move": "Mover",
         "resetView": "Resetar Visualização",
         "mergeVisible": "Fundir Visível",
-        "saveToGallery": "Save To Gallery",
+        "saveToGallery": "Salvar na Galeria",
         "copyToClipboard": "Copiar para a Área de Transferência",
         "downloadAsImage": "Baixar Como Imagem",
         "undo": "Desfazer",
         "redo": "Refazer",
         "clearCanvas": "Limpar Tela",
         "canvasSettings": "Configurações de Tela",
-        "showIntermediates": "Show Intermediates",
+        "showIntermediates": "Mostrar Intermediários",
         "showGrid": "Mostrar Grade",
         "snapToGrid": "Encaixar na Grade",
         "darkenOutsideSelection": "Escurecer Seleção Externa",

invokeai/frontend/dist/locales/ro.json

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

invokeai/frontend/public/locales/en.json

@@ -63,7 +63,8 @@
         "statusConvertingModel": "Converting Model",
         "statusModelConverted": "Model Converted",
         "statusMergingModels": "Merging Models",
-        "statusMergedModels": "Models Merged"
+        "statusMergedModels": "Models Merged",
+        "pinOptionsPanel": "Pin Options Panel"
     },
     "gallery": {
         "generations": "Generations",
@@ -364,7 +365,8 @@
         "convertToDiffusersHelpText6": "Do you wish to convert this model?",
         "convertToDiffusersSaveLocation": "Save Location",
         "v1": "v1",
-        "v2": "v2",
+        "v2_base": "v2 (512px)",
+        "v2_768": "v2 (768px)",
         "inpainting": "v1 Inpainting",
         "customConfig": "Custom Config",
         "pathToCustomConfig": "Path To Custom Config",
@@ -393,7 +395,9 @@
         "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"
+        "weightedSum": "Weighted Sum",
+        "none": "none",
+        "addDifference": "Add Difference"
     },
     "parameters": {
         "general": "General",

invokeai/frontend/public/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",
@@ -44,7 +44,26 @@
         "statusUpscaling": "Aumentando Tamaño",
         "statusUpscalingESRGAN": "Restaurando Rostros(ESRGAN)",
         "statusLoadingModel": "Cargando Modelo",
-        "statusModelChanged": "Modelo cambiado"
+        "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"
     },
     "gallery": {
         "generations": "Generaciones",
@@ -284,16 +303,16 @@
         "nameValidationMsg": "Introduce un nombre para tu modelo",
         "description": "Descripción",
         "descriptionValidationMsg": "Introduce una descripción para tu modelo",
-        "config": "Config",
-        "configValidationMsg": "Ruta del archivo de configuración del modelo",
+        "config": "Configurar",
+        "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",
@@ -320,7 +339,61 @@
         "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."
+        "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"
     },
     "parameters": {
         "images": "Imágenes",
@@ -380,7 +453,22 @@
         "info": "Información",
         "deleteImage": "Eliminar Imagen",
         "initialImage": "Imagen Inicial",
-        "showOptionsPanel": "Mostrar panel de opciones"
+        "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"
     },
     "settings": {
         "models": "Modelos",
@@ -393,7 +481,8 @@
         "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."
+        "resetComplete": "La interfaz web se ha restablecido. Actualice la página para recargarla.",
+        "useSlidersForAll": "Utilice controles deslizantes para todas las opciones"
     },
     "toast": {
         "tempFoldersEmptied": "Directorio temporal vaciado",
@@ -431,12 +520,12 @@
         "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. 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.",
+            "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.",
             "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/public/locales/pt_BR.json

@@ -44,7 +44,26 @@
         "statusUpscaling": "Redimensinando",
         "statusUpscalingESRGAN": "Redimensinando (ESRGAN)",
         "statusLoadingModel": "Carregando Modelo",
-        "statusModelChanged": "Modelo Alterado"
+        "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"
     },
     "gallery": {
         "generations": "Gerações",
@@ -237,7 +256,7 @@
             "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": {
@@ -284,7 +303,7 @@
         "nameValidationMsg": "Insira um nome para o seu modelo",
         "description": "Descrição",
         "descriptionValidationMsg": "Adicione uma descrição para o seu modelo",
-        "config": "Config",
+        "config": "Configuração",
         "configValidationMsg": "Caminho para o arquivo de configuração do seu modelo.",
         "modelLocation": "Localização do modelo",
         "modelLocationValidationMsg": "Caminho para onde seu modelo está localizado.",
@@ -317,7 +336,52 @@
         "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."
+        "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."
     },
     "parameters": {
         "images": "Imagems",
@@ -442,14 +506,14 @@
         "move": "Mover",
         "resetView": "Resetar Visualização",
         "mergeVisible": "Fundir Visível",
-        "saveToGallery": "Save To Gallery",
+        "saveToGallery": "Salvar na Galeria",
         "copyToClipboard": "Copiar para a Área de Transferência",
         "downloadAsImage": "Baixar Como Imagem",
         "undo": "Desfazer",
         "redo": "Refazer",
         "clearCanvas": "Limpar Tela",
         "canvasSettings": "Configurações de Tela",
-        "showIntermediates": "Show Intermediates",
+        "showIntermediates": "Mostrar Intermediários",
         "showGrid": "Mostrar Grade",
         "snapToGrid": "Encaixar na Grade",
         "darkenOutsideSelection": "Escurecer Seleção Externa",

invokeai/frontend/public/locales/ro.json

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

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

@@ -21,6 +21,7 @@ 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';
@@ -189,11 +190,12 @@ const CurrentImageButtons = () => {
   );
 
   const handleClickUseAllParameters = () => {
-    if (!currentImage) return;
-    currentImage.metadata && dispatch(setAllParameters(currentImage.metadata));
-    if (currentImage.metadata?.image.type === 'img2img') {
+    if (!currentImage?.metadata) return;
+    dispatch(setAllParameters(currentImage.metadata));
+    dispatch(setAllPostProcessingParameters(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

@@ -10,6 +10,7 @@ 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';
@@ -114,7 +115,10 @@ const HoverableImage = memo((props: HoverableImageProps) => {
   };
 
   const handleUseAllParameters = () => {
-    metadata && dispatch(setAllParameters(metadata));
+    if (metadata) {
+      dispatch(setAllParameters(metadata));
+      dispatch(setAllPostProcessingParameters(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 {
-  setHorizontalSymmetryTimePercentage,
-  setVerticalSymmetryTimePercentage,
+  setHorizontalSymmetrySteps,
+  setVerticalSymmetrySteps,
 } from 'features/parameters/store/generationSlice';
 import { useTranslation } from 'react-i18next';
 
 export default function SymmetrySettings() {
-  const horizontalSymmetryTimePercentage = useAppSelector(
-    (state: RootState) => state.generation.horizontalSymmetryTimePercentage
+  const horizontalSymmetrySteps = useAppSelector(
+    (state: RootState) => state.generation.horizontalSymmetrySteps
   );
 
-  const verticalSymmetryTimePercentage = useAppSelector(
-    (state: RootState) => state.generation.verticalSymmetryTimePercentage
+  const verticalSymmetrySteps = useAppSelector(
+    (state: RootState) => state.generation.verticalSymmetrySteps
   );
 
   const steps = useAppSelector((state: RootState) => state.generation.steps);
@@ -26,28 +26,28 @@ export default function SymmetrySettings() {
     <>
       <IAISlider
         label={t('parameters.hSymmetryStep')}
-        value={horizontalSymmetryTimePercentage}
-        onChange={(v) => dispatch(setHorizontalSymmetryTimePercentage(v))}
+        value={horizontalSymmetrySteps}
+        onChange={(v) => dispatch(setHorizontalSymmetrySteps(v))}
         min={0}
         max={steps}
         step={1}
         withInput
         withSliderMarks
         withReset
-        handleReset={() => dispatch(setHorizontalSymmetryTimePercentage(0))}
+        handleReset={() => dispatch(setHorizontalSymmetrySteps(0))}
         sliderMarkRightOffset={-6}
       ></IAISlider>
       <IAISlider
         label={t('parameters.vSymmetryStep')}
-        value={verticalSymmetryTimePercentage}
-        onChange={(v) => dispatch(setVerticalSymmetryTimePercentage(v))}
+        value={verticalSymmetrySteps}
+        onChange={(v) => dispatch(setVerticalSymmetrySteps(v))}
         min={0}
         max={steps}
         step={1}
         withInput
         withSliderMarks
         withReset
-        handleReset={() => dispatch(setVerticalSymmetryTimePercentage(0))}
+        handleReset={() => dispatch(setVerticalSymmetrySteps(0))}
         sliderMarkRightOffset={-6}
       ></IAISlider>
     </>

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

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

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

@@ -5,6 +5,7 @@ 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';
@@ -30,6 +31,7 @@ export default function InvokeButton(props: InvokeButton) {
   useHotkeys(
     ['ctrl+enter', 'meta+enter'],
     () => {
+      dispatch(clampSymmetrySteps());
       dispatch(generateImage(activeTabName));
     },
     {

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

@@ -4,6 +4,7 @@ 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;
@@ -33,8 +34,8 @@ export interface GenerationState {
   variationAmount: number;
   width: number;
   shouldUseSymmetry: boolean;
-  horizontalSymmetryTimePercentage: number;
-  verticalSymmetryTimePercentage: number;
+  horizontalSymmetrySteps: number;
+  verticalSymmetrySteps: number;
 }
 
 const initialGenerationState: GenerationState = {
@@ -64,8 +65,8 @@ const initialGenerationState: GenerationState = {
   variationAmount: 0.1,
   width: 512,
   shouldUseSymmetry: false,
-  horizontalSymmetryTimePercentage: 0,
-  verticalSymmetryTimePercentage: 0,
+  horizontalSymmetrySteps: 0,
+  verticalSymmetrySteps: 0,
 };
 
 const initialState: GenerationState = initialGenerationState;
@@ -99,6 +100,18 @@ 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;
     },
@@ -288,7 +301,6 @@ 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;
 
@@ -334,22 +346,17 @@ export const generationSlice = createSlice({
     setShouldUseSymmetry: (state, action: PayloadAction<boolean>) => {
       state.shouldUseSymmetry = action.payload;
     },
-    setHorizontalSymmetryTimePercentage: (
-      state,
-      action: PayloadAction<number>
-    ) => {
-      state.horizontalSymmetryTimePercentage = action.payload;
+    setHorizontalSymmetrySteps: (state, action: PayloadAction<number>) => {
+      state.horizontalSymmetrySteps = action.payload;
     },
-    setVerticalSymmetryTimePercentage: (
-      state,
-      action: PayloadAction<number>
-    ) => {
-      state.verticalSymmetryTimePercentage = action.payload;
+    setVerticalSymmetrySteps: (state, action: PayloadAction<number>) => {
+      state.verticalSymmetrySteps = action.payload;
     },
   },
 });
 
 export const {
+  clampSymmetrySteps,
   clearInitialImage,
   resetParametersState,
   resetSeed,
@@ -384,8 +391,8 @@ export const {
   setVariationAmount,
   setWidth,
   setShouldUseSymmetry,
-  setHorizontalSymmetryTimePercentage,
-  setVerticalSymmetryTimePercentage,
+  setHorizontalSymmetrySteps,
+  setVerticalSymmetrySteps,
 } = generationSlice.actions;
 
 export default generationSlice.reducer;

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

@@ -1,5 +1,6 @@
 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;
@@ -40,6 +41,17 @@ 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;
     },
@@ -83,6 +95,7 @@ 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) => {
-    if (model !== modelTwo && model !== modelThree) return model;
-  });
+  const modelOneList = Object.keys(diffusersModels).filter(
+    (model) => model !== modelTwo && model !== modelThree
+  );
 
-  const modelTwoList = Object.keys(diffusersModels).filter((model) => {
-    if (model !== modelOne && model !== modelThree) return model;
-  });
+  const modelTwoList = Object.keys(diffusersModels).filter(
+    (model) => model !== modelOne && model !== modelThree
+  );
 
   const modelThreeList = [
-    'none',
-    ...Object.keys(diffusersModels).filter((model) => {
-      if (model !== modelOne && model !== modelTwo) return model;
-    }),
+    { key: t('modelManager.none'), value: 'none' },
+    ...Object.keys(diffusersModels)
+      .filter((model) => model !== modelOne && model !== modelTwo)
+      .map((model) => ({ key: model, value: model })),
   ];
 
   const isProcessing = useAppSelector(
@@ -209,18 +209,22 @@ export default function MergeModels() {
                 <Flex columnGap={4}>
                   {modelThree === 'none' ? (
                     <>
-                      <Radio value="weighted_sum">weighted_sum</Radio>
-                      <Radio value="sigmoid">sigmoid</Radio>
-                      <Radio value="inv_sigmoid">inv_sigmoid</Radio>
+                      <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="add_difference">
                       <Tooltip
                         label={t(
-                          'modelmanager:modelMergeInterpAddDifferenceHelp'
+                          'modelManager.modelMergeInterpAddDifferenceHelp'
                         )}
                       >
-                        add_difference
+                        {t('modelManager.addDifference')}
                       </Tooltip>
                     </Radio>
                   )}

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

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

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

@@ -18,6 +18,7 @@ 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 };
 
@@ -60,6 +61,8 @@ const InvokeOptionsPanel = (props: Props) => {
 
   const { children } = props;
 
+  const { t } = useTranslation();
+
   // Hotkeys
   useHotkeys(
     'o',
@@ -176,7 +179,7 @@ const InvokeOptionsPanel = (props: Props) => {
               }
             }}
           >
-            <Tooltip label="Pin Options Panel">
+            <Tooltip label={t('common.pinOptionsPanel')}>
               <div
                 className="parameters-panel-pin-button"
                 data-selected={shouldPinParametersPanel}

invokeai/frontend/src/i18.d.ts

@@ -1,11 +1,16 @@
 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

@@ -200,6 +200,8 @@ 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")
@@ -974,7 +976,7 @@ class Generate:
         self.generators = {}
 
         seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
-        if self.embedding_path is not None:
+        if self.embedding_path and not model_data.get("ti_embeddings_loaded"):
             print(f'>> Loading embeddings from {self.embedding_path}')
             for root, _, files in os.walk(self.embedding_path):
                 for name in files:
@@ -982,9 +984,10 @@ class Generate:
                     self.model.textual_inversion_manager.load_textual_inversion(
                         ti_path, defer_injecting_tokens=True
                     )
-            print(
-                f'>> Textual inversion triggers: {", ".join(sorted(self.model.textual_inversion_manager.get_all_trigger_strings()))}'
-            )
+            model_data["ti_embeddings_loaded"] = True
+        print(
+            f'>> Textual inversion triggers: {", ".join(sorted(self.model.textual_inversion_manager.get_all_trigger_strings()))}'
+        )
 
         self.model_name = model_name
         self._set_sampler()  # requires self.model_name to be set first

ldm/invoke/CLI.py

@@ -4,6 +4,7 @@ import shlex
 import sys
 import traceback
 from argparse import Namespace
+from packaging import version
 from pathlib import Path
 from typing import Union
 
@@ -22,7 +23,7 @@ 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
+from .globals import Globals, global_config_dir
 from .image_util import make_grid
 from .log import write_log
 from .model_manager import ModelManager
@@ -33,7 +34,6 @@ 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
@@ -66,6 +66,9 @@ 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:
@@ -123,11 +126,13 @@ def main():
             print(f"{e}. Aborting.")
             sys.exit(-1)
 
+    model = opt.model or retrieve_last_used_model()
+
     # creating a Generate object:
     try:
         gen = Generate(
             conf=opt.conf,
-            model=opt.model,
+            model=model,
             sampler_name=opt.sampler_name,
             embedding_path=embedding_path,
             full_precision=opt.full_precision,
@@ -156,10 +161,16 @@ 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
+            str(path),
+            convert=False,
+            commit_to_conf=opt.conf,
+            config_file_callback=lambda x: _pick_configuration_file(completer,x),
         )
 
     if path := opt.autoconvert:
@@ -170,6 +181,7 @@ def main():
     # web server loops forever
     if opt.web or opt.gui:
         invoke_ai_web_server_loop(gen, gfpgan, codeformer, esrgan)
+        save_last_used_model(gen.model_name)
         sys.exit(0)
 
     if not infile:
@@ -178,7 +190,7 @@ def main():
         )
 
     try:
-        main_loop(gen, opt)
+        main_loop(gen, opt, completer)
     except KeyboardInterrupt:
         print(
             f'\nGoodbye!\nYou can start InvokeAI again by running the "invoke.bat" (or "invoke.sh") script from {Globals.root}'
@@ -189,7 +201,7 @@ def main():
 
 
 # TODO: main_loop() has gotten busy. Needs to be refactored.
-def main_loop(gen, opt):
+def main_loop(gen, opt, completer):
     """prompt/read/execute loop"""
     global infile
     done = False
@@ -200,7 +212,6 @@ def main_loop(gen, opt):
     # 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)
@@ -389,6 +400,7 @@ def main_loop(gen, opt):
                         prior_variations,
                         postprocessed,
                         first_seed,
+                        gen.model_name,
                     )
                     path = file_writer.save_image_and_prompt_to_png(
                         image=image,
@@ -402,6 +414,7 @@ def main_loop(gen, opt):
                                 else first_seed
                             ],
                             model_hash=gen.model_hash,
+                            model_id=gen.model_name,
                         ),
                         name=filename,
                         compress_level=opt.png_compression,
@@ -489,6 +502,7 @@ def main_loop(gen, opt):
     print(
         f'\nGoodbye!\nYou can start InvokeAI again by running the "invoke.bat" (or "invoke.sh") script from {Globals.root}'
     )
+    save_last_used_model(gen.model_name)
 
 
 # TO DO: remove repetitive code and the awkward command.replace() trope
@@ -657,10 +671,10 @@ 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("** Import failed or was skipped")
+        print("** Aborting import.")
         return
 
     if not _verify_load(imported_name, gen):
@@ -674,6 +688,48 @@ 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
@@ -720,14 +776,10 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             original_config_file = Path(model_info["config"])
             model_name = model_name_or_path
             model_description = model_info["description"]
-            vae = model_info["vae"]
+            vae_path = model_info.get("vae")
         else:
             print(f"** {model_name_or_path} is not a legacy .ckpt weights file")
             return
-        if vae_repo := ldm.invoke.model_manager.VAE_TO_REPO_ID.get(Path(vae).stem):
-            vae_repo = dict(repo_id=vae_repo)
-        else:
-            vae_repo = None
         model_name = manager.convert_and_import(
             ckpt_path,
             diffusers_path=Path(
@@ -736,7 +788,7 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
             model_name=model_name,
             model_description=model_description,
             original_config_file=original_config_file,
-            vae=vae_repo,
+            vae_path=vae_path,
         )
     else:
         try:
@@ -744,8 +796,8 @@ def convert_model(model_name_or_path: Union[Path, str], gen, opt, completer):
         except KeyboardInterrupt:
             return
 
-    manager.commit(opt.conf)    
-    if click.confirm(f"Delete the original .ckpt file at {ckpt_path}?", default=False):
+    manager.commit(opt.conf)
+    if ckpt_path and click.confirm(f"Delete the original .ckpt file at {ckpt_path}?", default=False):
         ckpt_path.unlink(missing_ok=True)
         print(f"{ckpt_path} deleted")
 
@@ -782,6 +834,7 @@ def edit_model(model_name: str, gen, opt, completer):
     print(f"\n>> Editing model {model_name} from configuration file {opt.conf}")
     new_name = _get_model_name(manager.list_models(), completer, model_name)
 
+    completer.complete_extensions(('.yaml','.ckpt','.safetensors','.pt'))
     for attribute in info.keys():
         if type(info[attribute]) != str:
             continue
@@ -789,6 +842,7 @@ def edit_model(model_name: str, gen, opt, completer):
             continue
         completer.set_line(info[attribute])
         info[attribute] = input(f"{attribute}: ") or info[attribute]
+    completer.complete_extensions(None)
 
     if info["format"] == "diffusers":
         vae = info.get("vae", dict(repo_id=None, path=None, subfolder=None))
@@ -941,13 +995,14 @@ 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,
+        opt,
+        prefix,
+        seed,
+        operation="generate",
+        prior_variations=[],
+        postprocessed=False,
+        first_seed=None,
+        model_id='unknown',
 ):
     if postprocessed and opt.save_original:
         filename = choose_postprocess_name(opt, prefix, seed)
@@ -955,6 +1010,7 @@ 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:
@@ -972,18 +1028,17 @@ 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)
+        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed,model_id=model_id)
     elif len(prior_variations) > 0:
-        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed)
+        formatted_dream_prompt = opt.dream_prompt_str(seed=first_seed,model_id=model_id)
     elif operation == "postprocess":
         formatted_dream_prompt = "!fix " + opt.dream_prompt_str(
-            seed=seed, prompt=opt.input_file_path
+            seed=seed, prompt=opt.input_file_path, model_id=model_id,
         )
     else:
-        formatted_dream_prompt = opt.dream_prompt_str(seed=seed)
+        formatted_dream_prompt = opt.dream_prompt_str(seed=seed,model_id=model_id)
     return filename, formatted_dream_prompt
 
-
 def choose_postprocess_name(opt, prefix, seed) -> str:
     match = re.search("postprocess:(\w+)", opt.last_operation)
     if match:
@@ -1234,6 +1289,79 @@ def check_internet() -> bool:
     except:
         return False
 
+
+def retrieve_last_used_model()->str:
+    """
+    Return name of the last model used.
+    """
+    model_file_path = Path(Globals.root,'.last_model')
+    if not model_file_path.exists():
+        return None
+    with open(model_file_path,'r') as f:
+        return f.readline()
+
+def save_last_used_model(model_name:str):
+    """
+    Save name of the last model used.
+    """
+    model_file_path = Path(Globals.root,'.last_model')
+    with open(model_file_path,'w') as f:
+        f.write(model_name)
+
+# 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/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 +1,2 @@
-__version__='2.3.1'
+
+__version__='2.3.3rc7'

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)
+        return ' '.join(switches) + f' # model_id={kwargs.get("model_id","unknown model")}'
 
     def __getattribute__(self,name):
         '''
@@ -878,7 +878,7 @@ class Args(object):
         )
         render_group.add_argument(
             '--fnformat',
-            default='{prefix}.{seed}.png',
+            default=None,
             type=str,
             help='Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png',
         )
@@ -1155,6 +1155,7 @@ 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
@@ -1167,7 +1168,7 @@ def metadata_dumps(opt,
     # top-level metadata minus `image` or `images`
     metadata = {
         'model'       : 'stable diffusion',
-        'model_id'    : opt.model,
+        'model_id'    : model_id or opt.model,
         'model_hash'  : model_hash,
         'app_id'      : ldm.invoke.__app_id__,
         'app_version' : ldm.invoke.__version__,
@@ -1180,7 +1181,7 @@ def metadata_dumps(opt,
     )
 
     # remove any image keys not mentioned in RFC #266
-    rfc266_img_fields = ['type','postprocessing','sampler','prompt','seed','variations','steps',
+    rfc266_img_fields = ['type','postprocessing','sampler','prompt','seed','variations','steps','hires_fix',
                          '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']

ldm/invoke/conditioning.py

@@ -9,7 +9,7 @@ get_uc_and_c_and_ec()           get the conditioned and unconditioned latent, an
 import re
 from typing import Union, Optional, Any
 
-from transformers import CLIPTokenizer, CLIPTextModel
+from transformers import CLIPTokenizer
 
 from compel import Compel
 from compel.prompt_parser import FlattenedPrompt, Blend, Fragment, CrossAttentionControlSubstitute, PromptParser
@@ -52,6 +52,8 @@ def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_l
                     textual_inversion_manager=model.textual_inversion_manager,
                     dtype_for_device_getter=torch_dtype)
 
+    # get rid of any newline characters
+    prompt_string = prompt_string.replace("\n", " ")
     positive_prompt_string, negative_prompt_string = split_prompt_to_positive_and_negative(prompt_string)
     legacy_blend = try_parse_legacy_blend(positive_prompt_string, skip_normalize_legacy_blend)
     positive_prompt: FlattenedPrompt|Blend
@@ -113,7 +115,7 @@ def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, trun
     return tokens
 
 
-def split_prompt_to_positive_and_negative(prompt_string_uncleaned):
+def split_prompt_to_positive_and_negative(prompt_string_uncleaned: str):
     unconditioned_words = ''
     unconditional_regex = r'\[(.*?)\]'
     unconditionals = re.findall(unconditional_regex, prompt_string_uncleaned)

ldm/invoke/config/invokeai_configure.py

@@ -290,7 +290,7 @@ def download_vaes():
         # first the diffusers version
         repo_id = "stabilityai/sd-vae-ft-mse"
         args = dict(
-            cache_dir=global_cache_dir("diffusers"),
+            cache_dir=global_cache_dir("hub"),
         )
         if not AutoencoderKL.from_pretrained(repo_id, **args):
             raise Exception(f"download of {repo_id} failed")
@@ -712,8 +712,8 @@ def write_opts(opts: Namespace, init_file: Path):
                     out_file.write(line + "\n")
             out_file.write(
                 f"""
---outdir={opts.outdir}
---embedding_path={opts.embedding_path}
+--outdir="{opts.outdir}"
+--embedding_path="{opts.embedding_path}"
 --precision={opts.precision}
 --max_loaded_models={int(opts.max_loaded_models)}
 --{'no-' if not opts.safety_checker else ''}nsfw_checker

ldm/invoke/config/invokeai_update.py

@@ -16,6 +16,8 @@ from rich.text import Text
 from ldm.invoke import __version__
 
 INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive"
+INVOKE_AI_TAG="https://github.com/invoke-ai/InvokeAI/archive/refs/tags"
+INVOKE_AI_BRANCH="https://github.com/invoke-ai/InvokeAI/archive/refs/heads"
 INVOKE_AI_REL="https://api.github.com/repos/invoke-ai/InvokeAI/releases"
 
 OS = platform.uname().system
@@ -41,7 +43,8 @@ def welcome(versions: dict):
         yield '[bold yellow]Options:'
         yield f'''[1] Update to the latest official release ([italic]{versions[0]['tag_name']}[/italic])
 [2] Update to the bleeding-edge development version ([italic]main[/italic])
-[3] Manually enter the tag or branch name you wish to update'''
+[3] Manually enter the [bold]tag name[/bold] for the version you wish to update to
+[4] Manually enter the [bold]branch name[/bold] for the version you wish to update to'''        
 
     console.rule()
     print(
@@ -62,17 +65,26 @@ def main():
     welcome(versions)
 
     tag = None
-    choice = Prompt.ask('Choice:',choices=['1','2','3'],default='1')
+    branch = None
+    release = None
+    choice = Prompt.ask('Choice:',choices=['1','2','3','4'],default='1')
     
     if choice=='1':
-        tag = versions[0]['tag_name']
+        release = versions[0]['tag_name']
     elif choice=='2':
-        tag = 'main'
+        release = 'main'
     elif choice=='3':
-        tag = Prompt.ask('Enter an InvokeAI tag or branch name')
+        tag = Prompt.ask('Enter an InvokeAI tag name')
+    elif choice=='4':
+        branch = Prompt.ask('Enter an InvokeAI branch name')
 
     print(f':crossed_fingers: Upgrading to [yellow]{tag}[/yellow]')
-    cmd = f'pip install {INVOKE_AI_SRC}/{tag}.zip --use-pep517'
+    if release:
+        cmd = f'pip install {INVOKE_AI_SRC}/{release}.zip --use-pep517 --upgrade'
+    elif tag:
+        cmd = f'pip install {INVOKE_AI_TAG}/{tag}.zip --use-pep517 --upgrade'
+    else:
+        cmd = f'pip install {INVOKE_AI_BRANCH}/{branch}.zip --use-pep517 --upgrade'
     print('')
     print('')
     if os.system(cmd)==0:

ldm/invoke/config/model_install_backend.py

@@ -29,7 +29,13 @@ Model_dir = "models"
 Weights_dir = "ldm/stable-diffusion-v1/"
 
 # the initial "configs" dir is now bundled in the `invokeai.configs` package
-Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
+Dataset_path = None
+for path in configs.__path__:
+    file =Path(path, "INITIAL_MODELS.yaml")
+    if file.exists():
+        Dataset_path = file
+        break
+assert Dataset_path,f"Could not find the file INITIAL_MODELS.yaml in {configs.__path__}"
 
 # initial models omegaconf
 Datasets = None
@@ -109,6 +115,7 @@ def install_requested_models(
                 model_manager.heuristic_import(
                     path_url_or_repo,
                     convert=convert_to_diffusers,
+                    config_file_callback=_pick_configuration_file,
                     commit_to_conf=config_file_path
                 )
             except KeyboardInterrupt:
@@ -126,7 +133,7 @@ def install_requested_models(
                 while line := input.readline():
                     if not line.startswith(argument):
                         output.writelines([line])
-                output.writelines([f'{argument} {directory}'])
+                output.writelines([f'{argument} "{directory}"'])
         os.replace(replacement,initfile)
 
 # -------------------------------------
@@ -138,6 +145,45 @@ def yes_or_no(prompt: str, default_yes=True):
     else:
         return response[0] in ("y", "Y")
 
+# -------------------------------------
+def _pick_configuration_file(checkpoint_path: Path)->Path:
+    print(
+"""
+Please select the type of this model:
+[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; no 'parameterization:' in its yaml file)
+[4] A Stable Diffusion v2.x v-predictive model (768 pixels; look for 'parameterization: "v"' 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':
+                choice = Path(input('Select config file for this model> ').strip()).absolute()
+                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 get_root(root: str = None) -> str:
@@ -262,7 +308,6 @@ def _download_diffusion_weights(
             path = download_from_hf(
                 model_class,
                 repo_id,
-                cache_subdir="diffusers",
                 safety_checker=None,
                 **extra_args,
             )

ldm/invoke/dynamic_prompts.py

@@ -0,0 +1,535 @@
+#!/usr/bin/env python
+
+"""
+Simple script to generate a file of InvokeAI prompts and settings
+that scan across steps and other parameters.
+"""
+
+import argparse
+import io
+import json
+import os
+import pydoc
+import re
+import shutil
+import sys
+import time
+from contextlib import redirect_stderr
+from io import TextIOBase
+from itertools import product
+from multiprocessing import Process
+from multiprocessing.connection import Connection, Pipe
+from pathlib import Path
+from tempfile import gettempdir
+from typing import Callable, Iterable, List
+
+import numpy as np
+import yaml
+from omegaconf import OmegaConf, dictconfig, listconfig
+
+
+def expand_prompts(
+    template_file: Path,
+    run_invoke: bool = False,
+    invoke_model: str = None,
+    invoke_outdir: Path = None,
+    processes_per_gpu: int = 1,
+):
+    """
+    :param template_file: A YAML file containing templated prompts and args
+    :param run_invoke: A boolean which if True will pass expanded prompts to invokeai CLI
+    :param invoke_model: Name of the model to load when run_invoke is true; otherwise uses default
+    :param invoke_outdir: Directory for outputs when run_invoke is true; otherwise uses default
+    """
+    if template_file.name.endswith(".json"):
+        with open(template_file, "r") as file:
+            with io.StringIO(yaml.dump(json.load(file))) as fh:
+                conf = OmegaConf.load(fh)
+    else:
+        conf = OmegaConf.load(template_file)
+
+    # loading here to avoid long wait for help message
+    import torch
+
+    torch.multiprocessing.set_start_method("spawn")
+    gpu_count = torch.cuda.device_count() if torch.cuda.is_available() else 1
+    commands = expanded_invokeai_commands(conf, run_invoke)
+    children = list()
+
+    try:
+        if run_invoke:
+            invokeai_args = [shutil.which("invokeai"), "--from_file", "-"]
+            if invoke_model:
+                invokeai_args.extend(("--model", invoke_model))
+            if invoke_outdir:
+                outdir = os.path.expanduser(invoke_outdir)
+                invokeai_args.extend(("--outdir", outdir))
+            else:
+                outdir = gettempdir()
+            logdir = Path(outdir, "invokeai-batch-logs")
+
+            processes_to_launch = gpu_count * processes_per_gpu
+            print(
+                f">> Spawning {processes_to_launch} invokeai processes across {gpu_count} CUDA gpus",
+                file=sys.stderr,
+            )
+            print(
+                f'>> Outputs will be written into {invoke_outdir or "default InvokeAI outputs directory"}, and error logs will be written to {logdir}',
+                file=sys.stderr,
+            )
+            import ldm.invoke.CLI
+
+            parent_conn, child_conn = Pipe()
+            children = set()
+            for i in range(processes_to_launch):
+                p = Process(
+                    target=_run_invoke,
+                    kwargs=dict(
+                        entry_point=ldm.invoke.CLI.main,
+                        conn_in=child_conn,
+                        conn_out=parent_conn,
+                        args=invokeai_args,
+                        gpu=i % gpu_count,
+                        logdir=logdir,
+                    ),
+                )
+                p.start()
+                children.add(p)
+            child_conn.close()
+            sequence = 0
+            for command in commands:
+                sequence += 1
+                parent_conn.send(
+                    command + f' --fnformat="dp.{sequence:04}.{{prompt}}.png"'
+                )
+            parent_conn.close()
+        else:
+            for command in commands:
+                print(command)
+    except KeyboardInterrupt:
+        for p in children:
+            p.terminate()
+
+
+class MessageToStdin(object):
+    def __init__(self, connection: Connection):
+        self.connection = connection
+        self.linebuffer = list()
+
+    def readline(self) -> str:
+        try:
+            if len(self.linebuffer) == 0:
+                message = self.connection.recv()
+                self.linebuffer = message.split("\n")
+            result = self.linebuffer.pop(0)
+            return result
+        except EOFError:
+            return None
+
+
+class FilterStream(object):
+    def __init__(
+        self, stream: TextIOBase, include: re.Pattern = None, exclude: re.Pattern = None
+    ):
+        self.stream = stream
+        self.include = include
+        self.exclude = exclude
+
+    def write(self, data: str):
+        if self.include and self.include.match(data):
+            self.stream.write(data)
+            self.stream.flush()
+        elif self.exclude and not self.exclude.match(data):
+            self.stream.write(data)
+            self.stream.flush()
+
+    def flush(self):
+        self.stream.flush()
+
+
+def _run_invoke(
+    entry_point: Callable,
+    conn_in: Connection,
+    conn_out: Connection,
+    args: List[str],
+    logdir: Path,
+    gpu: int = 0,
+):
+    pid = os.getpid()
+    logdir.mkdir(parents=True, exist_ok=True)
+    logfile = Path(logdir, f'{time.strftime("%Y-%m-%d_%H-%M-%S")}-pid={pid}.txt')
+    print(
+        f">> Process {pid} running on GPU {gpu}; logging to {logfile}", file=sys.stderr
+    )
+    conn_out.close()
+    os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu}"
+    sys.argv = args
+    sys.stdin = MessageToStdin(conn_in)
+    sys.stdout = FilterStream(sys.stdout, include=re.compile("^\[\d+\]"))
+    with open(logfile, "w") as stderr, redirect_stderr(stderr):
+        entry_point()
+
+
+def _filter_output(stream: TextIOBase):
+    while line := stream.readline():
+        if re.match("^\[\d+\]", line):
+            print(line)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=HELP,
+    )
+    parser.add_argument(
+        "template_file",
+        type=Path,
+        nargs="?",
+        help="path to a template file, use --example to generate an example file",
+    )
+    parser.add_argument(
+        "--example",
+        action="store_true",
+        default=False,
+        help=f'Print an example template file in YAML format. Use "{sys.argv[0]} --example > example.yaml" to save output to a file',
+    )
+    parser.add_argument(
+        "--json-example",
+        action="store_true",
+        default=False,
+        help=f'Print an example template file in json format. Use "{sys.argv[0]} --json-example > example.json" to save output to a file',
+    )
+    parser.add_argument(
+        "--instructions",
+        "-i",
+        dest="instructions",
+        action="store_true",
+        default=False,
+        help="Print verbose instructions.",
+    )
+    parser.add_argument(
+        "--invoke",
+        action="store_true",
+        help="Execute invokeai using specified optional --model, --processes_per_gpu and --outdir",
+    )
+    parser.add_argument(
+        "--model",
+        help="Feed the generated prompts to the invokeai CLI using the indicated model. Will be overriden by a model: section in template file.",
+    )
+    parser.add_argument(
+        "--outdir", type=Path, help="Write images and log into indicated directory"
+    )
+    parser.add_argument(
+        "--processes_per_gpu",
+        type=int,
+        default=1,
+        help="When executing invokeai, how many parallel processes to execute per CUDA GPU.",
+    )
+    opt = parser.parse_args()
+
+    if opt.example:
+        print(EXAMPLE_TEMPLATE_FILE)
+        sys.exit(0)
+
+    if opt.json_example:
+        print(_yaml_to_json(EXAMPLE_TEMPLATE_FILE))
+        sys.exit(0)
+
+    if opt.instructions:
+        pydoc.pager(INSTRUCTIONS)
+        sys.exit(0)
+
+    if not opt.template_file:
+        parser.print_help()
+        sys.exit(-1)
+
+    expand_prompts(
+        template_file=opt.template_file,
+        run_invoke=opt.invoke,
+        invoke_model=opt.model,
+        invoke_outdir=opt.outdir,
+        processes_per_gpu=opt.processes_per_gpu,
+    )
+
+
+def expanded_invokeai_commands(
+    conf: OmegaConf, always_switch_models: bool = False
+) -> List[List[str]]:
+    models = expand_values(conf.get("model"))
+    steps = expand_values(conf.get("steps")) or [30]
+    cfgs = expand_values(conf.get("cfg")) or [7.5]
+    samplers = expand_values(conf.get("sampler")) or ["ddim"]
+    seeds = expand_values(conf.get("seed")) or [0]
+    dimensions = expand_values(conf.get("dimensions")) or ["512x512"]
+    init_img = expand_values(conf.get("init_img")) or [""]
+    perlin = expand_values(conf.get("perlin")) or [0]
+    threshold = expand_values(conf.get("threshold")) or [0]
+    strength = expand_values(conf.get("strength")) or [0.75]
+    prompts = expand_prompt(conf.get("prompt")) or ["banana sushi"]
+
+    cross_product = product(
+        *[
+            models,
+            seeds,
+            prompts,
+            samplers,
+            cfgs,
+            steps,
+            perlin,
+            threshold,
+            init_img,
+            strength,
+            dimensions,
+        ]
+    )
+    previous_model = None
+
+    result = list()
+    for p in cross_product:
+        (
+            model,
+            seed,
+            prompt,
+            sampler,
+            cfg,
+            step,
+            perlin,
+            threshold,
+            init_img,
+            strength,
+            dimensions,
+        ) = tuple(p)
+        (width, height) = dimensions.split("x")
+        switch_args = (
+            f"!switch {model}\n"
+            if always_switch_models or previous_model != model
+            else ""
+        )
+        image_args = f"-I{init_img} -f{strength}" if init_img else ""
+        command = f"{switch_args}{prompt} -S{seed} -A{sampler} -C{cfg} -s{step} {image_args} --perlin={perlin} --threshold={threshold} -W{width} -H{height}"
+        result.append(command)
+        previous_model = model
+    return result
+
+
+def expand_prompt(
+    stanza: str | dict | listconfig.ListConfig | dictconfig.DictConfig,
+) -> list | range:
+    if not stanza:
+        return None
+    if isinstance(stanza, listconfig.ListConfig):
+        return stanza
+    if isinstance(stanza, str):
+        return [stanza]
+    if not isinstance(stanza, dictconfig.DictConfig):
+        raise ValueError(f"Unrecognized template: {stanza}")
+
+    if not (template := stanza.get("template")):
+        raise KeyError('"prompt" section must contain a "template" definition')
+
+    fragment_labels = re.findall("{([^{}]+?)}", template)
+    if len(fragment_labels) == 0:
+        return [template]
+    fragments = [[{x: y} for y in stanza.get(x)] for x in fragment_labels]
+    dicts = merge(product(*fragments))
+    return [template.format(**x) for x in dicts]
+
+
+def merge(dicts: Iterable) -> List[dict]:
+    result = list()
+    for x in dicts:
+        to_merge = dict()
+        for item in x:
+            to_merge = to_merge | item
+        result.append(to_merge)
+    return result
+
+
+def expand_values(stanza: str | dict | listconfig.ListConfig) -> list | range:
+    if not stanza:
+        return None
+    if isinstance(stanza, listconfig.ListConfig):
+        return stanza
+    elif match := re.match("^(-?\d+);(-?\d+)(;(\d+))?", str(stanza)):
+        (start, stop, step) = (
+            int(match.group(1)),
+            int(match.group(2)),
+            int(match.group(4)) or 1,
+        )
+        return range(start, stop + step, step)
+    elif match := re.match("^(-?[\d.]+);(-?[\d.]+)(;([\d.]+))?", str(stanza)):
+        (start, stop, step) = (
+            float(match.group(1)),
+            float(match.group(2)),
+            float(match.group(4)) or 1.0,
+        )
+        return np.arange(start, stop + step, step).tolist()
+    else:
+        return [stanza]
+
+
+def _yaml_to_json(yaml_input: str) -> str:
+    """
+    Converts a yaml string into a json string. Used internally
+    to generate the example template file.
+    """
+    with io.StringIO(yaml_input) as yaml_in:
+        data = yaml.safe_load(yaml_in)
+    return json.dumps(data, indent=2)
+
+
+HELP = """
+This script takes a prompt template file that contains multiple
+alternative values for the prompt and its generation arguments (such
+as steps). It then expands out the prompts using all combinations of
+arguments and either prints them to the terminal's standard output, or
+feeds the prompts directly to the invokeai command-line interface.
+
+Call this script again with --instructions (-i) for verbose instructions.
+"""
+
+INSTRUCTIONS = f"""
+== INTRODUCTION ==
+This script takes a prompt template file that contains multiple
+alternative values for the prompt and its generation arguments (such
+as steps). It then expands out the prompts using all combinations of
+arguments and either prints them to the terminal's standard output, or
+feeds the prompts directly to the invokeai command-line interface.
+
+If the optional --invoke argument is provided, then the generated
+prompts will be fed directly to invokeai for image generation. You
+will likely want to add the --outdir option in order to save the image
+files to their own folder.
+
+   {sys.argv[0]} --invoke --outdir=/tmp/outputs my_template.yaml
+
+If --invoke isn't specified, the expanded prompts will be printed to
+output. You can capture them to a file for inspection and editing this
+way:
+
+   {sys.argv[0]} my_template.yaml > prompts.txt
+
+And then feed them to invokeai this way:
+
+  invokeai --outdir=/tmp/outputs < prompts.txt
+
+Note that after invokeai finishes processing the list of prompts, the
+output directory will contain a markdown file named `log.md`
+containing annotated images. You can open this file using an e-book
+reader such as the cross-platform Calibre eBook reader
+(https://calibre-ebook.com/).
+
+== FORMAT OF THE TEMPLATES FILE ==
+
+This will generate an example template file that you can get
+started with:
+
+  {sys.argv[0]} --example > example.yaml
+
+An excerpt from the top of this file looks like this:
+
+ model:
+   - stable-diffusion-1.5
+   - stable-diffusion-2.1-base
+ steps: 30;50;1  # start steps at 30 and go up to 50, incrementing by 1 each time
+ seed: 50        # fixed constant, seed=50
+ cfg:            # list of CFG values to try
+   - 7
+   - 8
+   - 12
+ prompt: a walk in the park   # constant value
+
+In more detail, the template file can one or more of the
+following sections:
+ - model:
+ - steps:
+ - seed:
+ - cfg:
+ - sampler:
+ - prompt:
+ - init_img:
+ - perlin:
+ - threshold:
+ - strength
+
+- Each section can have a constant value such as this:
+     steps: 50
+- Or a range of numeric values in the format:
+     steps: <start>;<stop>;<step>      (note semicolon, not colon!)
+- Or a list of values in the format:
+     - value1
+     - value2
+     - value3
+
+The "prompt:" section is special. It can accept a constant value:
+
+   prompt: a walk in the woods in the style of donatello
+
+Or it can accept a list of prompts:
+
+   prompt:
+      - a walk in the woods
+      - a walk on the beach
+
+Or it can accept a templated list of prompts. These allow you to
+define a series of phrases, each of which is a list. You then combine
+them together into a prompt template in this way:
+
+  prompt:
+    style:
+         - oil painting
+         - watercolor
+         - comic book
+         - studio photography
+    subject:
+         - sunny meadow in the mountains
+         - gathering storm in the mountains
+    template: a {{subject}} in the style of {{style}}
+
+In the example above, the phrase names "style" and "subject" are
+examples only. You can use whatever you like. However, the "template:"
+field is required. The output will be:
+
+  "a sunny meadow in the mountains in the style of an oil painting"
+  "a sunny meadow in the mountains in the style of watercolor masterpiece"
+  ...
+  "a gathering storm in the mountains in the style of an ink sketch"
+
+== SUPPORT FOR JSON FORMAT ==
+
+For those who prefer the JSON format, this script will accept JSON
+template files as well. Please run "{sys.argv[0]} --json-example"
+to print out a version of the example template file in json format.
+You may save it to disk and use it as a starting point for your own
+template this way:
+
+   {sys.argv[0]} --json-example > template.json 
+"""
+
+EXAMPLE_TEMPLATE_FILE = """
+model: stable-diffusion-1.5
+steps: 30;50;10
+seed: 50
+dimensions: 512x512
+perlin: 0.0
+threshold: 0
+cfg:
+  - 7
+  - 12
+sampler:
+  - k_euler_a
+  - k_lms
+prompt:
+  style:
+       - oil painting
+       - watercolor
+  location:
+       - the mountains
+       - a desert
+  object:
+       - luxurious dwelling
+       - crude tent
+  template: a {object} in {location}, in the style of {style}
+"""
+
+if __name__ == "__main__":
+    main()

ldm/invoke/globals.py

@@ -88,16 +88,13 @@ def global_cache_dir(subdir:Union[str,Path]='')->Path:
     '''
     Returns Path to the model cache directory. If a subdirectory
     is provided, it will be appended to the end of the path, allowing
-    for huggingface-style conventions:
-         global_cache_dir('diffusers')
+    for Hugging Face-style conventions. Currently, Hugging Face has
+    moved all models into the "hub" subfolder, so for any pretrained
+    HF model, use:
          global_cache_dir('hub')
-    Current HuggingFace documentation (mid-Jan 2023) indicates that
-    transformers models will be cached into a "transformers" subdirectory,
-    but in practice they seem to go into "hub". But if needed:
-         global_cache_dir('transformers')
-    One other caveat is that HuggingFace is moving some diffusers models
-    into the "hub" subdirectory as well, so this will need to be revisited
-    from time to time.
+
+    The legacy location for transformers used to be global_cache_dir('transformers')
+    and global_cache_dir('diffusers') for diffusers.
     '''
     home: str = os.getenv('HF_HOME')
 

ldm/invoke/invokeai_metadata.py

@@ -0,0 +1,29 @@
+#!/usr/bin/env python
+
+import sys
+import json
+from ldm.invoke.pngwriter import retrieve_metadata
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: file2prompt.py <file1.png> <file2.png> <file3.png>...")
+        print("This script opens up the indicated invoke.py-generated PNG file(s) and prints out their metadata.")
+        exit(-1)
+
+    filenames = sys.argv[1:]
+    for f in filenames:
+        try:
+            metadata = retrieve_metadata(f)
+            print(f'{f}:\n',json.dumps(metadata['sd-metadata'], indent=4))
+        except FileNotFoundError:
+            sys.stderr.write(f'{f} not found\n')
+            continue
+        except PermissionError:
+            sys.stderr.write(f'{f} could not be opened due to inadequate permissions\n')
+            continue
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        pass

ldm/invoke/merge_diffusers.py

@@ -437,10 +437,10 @@ def main():
     args = _parse_args()
     global_set_root(args.root_dir)
 
-    cache_dir = str(global_cache_dir("diffusers"))
+    cache_dir = str(global_cache_dir("hub"))
     os.environ[
         "HF_HOME"
-    ] = cache_dir  # because not clear the merge pipeline is honoring cache_dir
+    ] = str(global_cache_dir())  # because not clear the merge pipeline is honoring cache_dir
     args.cache_dir = cache_dir
 
     try:

ldm/invoke/model_manager.py

@@ -19,7 +19,7 @@ import warnings
 from enum import Enum
 from pathlib import Path
 from shutil import move, rmtree
-from typing import Any, Optional, Union
+from typing import Any, Callable, Optional, Union, List
 
 import safetensors
 import safetensors.torch
@@ -35,26 +35,18 @@ from picklescan.scanner import scan_file_path
 from ldm.invoke.devices import CPU_DEVICE
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
 from ldm.invoke.globals import Globals, global_cache_dir
-from ldm.util import (
-    ask_user,
-    download_with_resume,
-    instantiate_from_config,
-    url_attachment_name,
-)
+from ldm.util import ask_user, download_with_resume, instantiate_from_config, url_attachment_name
 
 
 class SDLegacyType(Enum):
     V1 = 1
     V1_INPAINT = 2
     V2 = 3
+    V2_e = 4
+    V2_v = 5
     UNKNOWN = 99
 
-
 DEFAULT_MAX_MODELS = 2
-VAE_TO_REPO_ID = {  # hack, see note in convert_and_import()
-    "vae-ft-mse-840000-ema-pruned": "stabilityai/sd-vae-ft-mse",
-}
-
 
 class ModelManager(object):
     def __init__(
@@ -111,11 +103,7 @@ class ModelManager(object):
             requested_model = self.models[model_name]["model"]
             print(f">> Retrieving model {model_name} from system RAM cache")
             self.models[model_name]["model"] = self._model_from_cpu(requested_model)
-            width = self.models[model_name]["width"]
-            height = self.models[model_name]["height"]
-            hash = self.models[model_name]["hash"]
-
-        else:  # we're about to load a new model, so potentially offload the least recently used one
+        else:
             requested_model, width, height, hash = self._load_model(model_name)
             self.models[model_name] = {
                 "model": requested_model,
@@ -126,13 +114,8 @@ class ModelManager(object):
 
         self.current_model = model_name
         self._push_newest_model(model_name)
-        return {
-            "model": requested_model,
-            "width": width,
-            "height": height,
-            "hash": hash,
-        }
-
+        return self.models[model_name]
+    
     def default_model(self) -> str | None:
         """
         Returns the name of the default model, or None
@@ -175,9 +158,9 @@ class ModelManager(object):
         """
         # if we are converting legacy files automatically, then
         # there are no legacy ckpts!
-        if Globals.ckpt_convert:
-            return False
         info = self.model_info(model_name)
+        if Globals.ckpt_convert or info.format=='diffusers' or self.is_v2_config(info.config):
+            return False
         if "weights" in info and info["weights"].endswith((".ckpt", ".safetensors")):
             return True
         return False
@@ -280,13 +263,13 @@ class ModelManager(object):
             self.stack.remove(model_name)
         if delete_files:
             if weights:
-                print(f"** deleting file {weights}")
+                print(f"** Deleting file {weights}")
                 Path(weights).unlink(missing_ok=True)
             elif path:
-                print(f"** deleting directory {path}")
+                print(f"** Deleting directory {path}")
                 rmtree(path, ignore_errors=True)
             elif repo_id:
-                print(f"** deleting the cached model directory for {repo_id}")
+                print(f"** Deleting the cached model directory for {repo_id}")
                 self._delete_model_from_cache(repo_id)
 
     def add_model(
@@ -382,25 +365,43 @@ class ModelManager(object):
         if not os.path.isabs(weights):
             weights = os.path.normpath(os.path.join(Globals.root, weights))
 
+        # check whether this is a v2 file and force conversion
+        convert = Globals.ckpt_convert or self.is_v2_config(config)
+
+        if matching_config := self._scan_for_matching_file(Path(weights),suffixes=['.yaml']):
+            print(f'   | Using external config file {matching_config}')
+            config = matching_config
+
+        # get the path to the custom vae, if any
+        vae_path = None
+        # first we use whatever is in the config file
+        if vae:
+            path = Path(vae if os.path.isabs(vae) else os.path.normpath(os.path.join(Globals.root, vae)))
+            if path.exists():
+                vae_path = path
+        # then we look for a file with the same basename
+        vae_path = vae_path or self._scan_for_matching_file(Path(weights))
+            
         # if converting automatically to diffusers, then we do the conversion and return
         # a diffusers pipeline
-        if Globals.ckpt_convert:
+        if convert:
             print(
                 f">> Converting legacy checkpoint {model_name} into a diffusers model..."
             )
-            from ldm.invoke.ckpt_to_diffuser import (
-                load_pipeline_from_original_stable_diffusion_ckpt,
-            )
+            from ldm.invoke.ckpt_to_diffuser import load_pipeline_from_original_stable_diffusion_ckpt
 
-            self.offload_model(self.current_model)
-            if vae_config := self._choose_diffusers_vae(model_name):
-                vae = self._load_vae(vae_config)
+            try:
+                if self.list_models()[self.current_model]['status'] == 'active':
+                    self.offload_model(self.current_model)
+            except Exception:
+                pass
+            
             if self._has_cuda():
                 torch.cuda.empty_cache()
             pipeline = load_pipeline_from_original_stable_diffusion_ckpt(
                 checkpoint_path=weights,
                 original_config_file=config,
-                vae=vae,
+                vae_path=vae_path,
                 return_generator_pipeline=True,
                 precision=torch.float16
                 if self.precision == "float16"
@@ -418,11 +419,6 @@ class ModelManager(object):
                 "NOHASH",
             )
 
-        # scan model
-        self.scan_model(model_name, weights)
-
-        print(f">> Loading {model_name} from {weights}")
-
         # for usage statistics
         if self._has_cuda():
             torch.cuda.reset_peak_memory_stats()
@@ -436,10 +432,13 @@ class ModelManager(object):
             weight_bytes = f.read()
         model_hash = self._cached_sha256(weights, weight_bytes)
         sd = None
-        if weights.endswith(".safetensors"):
-            sd = safetensors.torch.load(weight_bytes)
-        else:
+
+        if weights.endswith(".ckpt"):
+            self.scan_model(model_name, weights)
             sd = torch.load(io.BytesIO(weight_bytes), map_location="cpu")
+        else:
+            sd = safetensors.torch.load(weight_bytes)
+
         del weight_bytes
         # merged models from auto11 merge board are flat for some reason
         if "state_dict" in sd:
@@ -457,26 +456,17 @@ class ModelManager(object):
             print("   | Using more accurate float32 precision")
 
         # look and load a matching vae file. Code borrowed from AUTOMATIC1111 modules/sd_models.py
-        if vae:
-            if not os.path.isabs(vae):
-                vae = os.path.normpath(os.path.join(Globals.root, vae))
-            if os.path.exists(vae):
-                print(f"   | Loading VAE weights from: {vae}")
-                vae_ckpt = None
-                vae_dict = None
-                if vae.endswith(".safetensors"):
-                    vae_ckpt = safetensors.torch.load_file(vae)
-                    vae_dict = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss"}
-                else:
-                    vae_ckpt = torch.load(vae, map_location="cpu")
-                    vae_dict = {
-                        k: v
-                        for k, v in vae_ckpt["state_dict"].items()
-                        if k[0:4] != "loss"
-                    }
-                model.first_stage_model.load_state_dict(vae_dict, strict=False)
+        if vae_path:
+            print(f"   | Loading VAE weights from: {vae_path}")
+            if vae_path.suffix in [".ckpt", ".pt"]:
+                self.scan_model(vae_path.name, vae_path)
+                vae_ckpt = torch.load(vae_path, map_location="cpu")
             else:
-                print(f"   | VAE file {vae} not found. Skipping.")
+                vae_ckpt = safetensors.torch.load_file(vae_path)
+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+            model.first_stage_model.load_state_dict(vae_dict, strict=False)
+        else:
+            print("   | Using VAE built into model.")
 
         model.to(self.device)
         # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
@@ -495,9 +485,9 @@ class ModelManager(object):
 
         print(f">> Loading diffusers model from {name_or_path}")
         if using_fp16:
-            print("  | Using faster float16 precision")
+            print("   | Using faster float16 precision")
         else:
-            print("  | Using more accurate float32 precision")
+            print("   | Using more accurate float32 precision")
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
@@ -507,7 +497,7 @@ class ModelManager(object):
             if vae := self._load_vae(mconfig["vae"]):
                 pipeline_args.update(vae=vae)
         if not isinstance(name_or_path, Path):
-            pipeline_args.update(cache_dir=global_cache_dir("diffusers"))
+            pipeline_args.update(cache_dir=global_cache_dir("hub"))
         if using_fp16:
             pipeline_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
@@ -549,10 +539,21 @@ class ModelManager(object):
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
 
-        print(f"  | Default image dimensions = {width} x {height}")
+        print(f"   | Default image dimensions = {width} x {height}")
 
         return pipeline, width, height, model_hash
 
+    def is_v2_config(self, config: Path) -> bool:
+        if not os.path.isabs(config):
+            config = os.path.join(Globals.root, config)
+        try:
+            mconfig = OmegaConf.load(config)
+            return (
+                mconfig["model"]["params"]["unet_config"]["params"]["context_dim"] > 768
+            )
+        except:
+            return False
+
     def model_name_or_path(self, model_name: Union[str, DictConfig]) -> str | Path:
         if isinstance(model_name, DictConfig) or isinstance(model_name, dict):
             mconfig = model_name
@@ -589,13 +590,14 @@ class ModelManager(object):
         if self._has_cuda():
             torch.cuda.empty_cache()
 
+    @classmethod
     def scan_model(self, model_name, checkpoint):
         """
         Apply picklescanner to the indicated checkpoint and issue a warning
         and option to exit if an infected file is identified.
         """
         # scan model
-        print(f">> Scanning Model: {model_name}")
+        print(f"   | Scanning Model: {model_name}")
         scan_result = scan_file_path(checkpoint)
         if scan_result.infected_files != 0:
             if scan_result.infected_files == 1:
@@ -618,13 +620,13 @@ class ModelManager(object):
                     print("### Exiting InvokeAI")
                     sys.exit()
         else:
-            print(">> Model scanned ok")
+            print("   | Model scanned ok")
 
     def import_diffuser_model(
         self,
         repo_or_path: Union[str, Path],
         model_name: str = None,
-        model_description: str = None,
+        description: str = None,
         vae: dict = None,
         commit_to_conf: Path = None,
     ) -> bool:
@@ -640,7 +642,7 @@ class ModelManager(object):
         models.yaml file.
         """
         model_name = model_name or Path(repo_or_path).stem
-        model_description = model_description or f"Imported diffusers model {model_name}"
+        model_description = description or f"Imported diffusers model {model_name}"
         new_config = dict(
             description=model_description,
             vae=vae,
@@ -724,15 +726,25 @@ class ModelManager(object):
         format. Valid return values include:
         SDLegacyType.V1
         SDLegacyType.V1_INPAINT
-        SDLegacyType.V2
+        SDLegacyType.V2     (V2 prediction type unknown)
+        SDLegacyType.V2_e   (V2 using 'epsilon' prediction type)
+        SDLegacyType.V2_v   (V2 using 'v_prediction' prediction type)
         SDLegacyType.UNKNOWN
         """
-        key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
-        if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024:
-            return SDLegacyType.V2
+        global_step = checkpoint.get("global_step")
+        state_dict = checkpoint.get("state_dict") or checkpoint
 
         try:
             state_dict = checkpoint.get("state_dict") or checkpoint
+            key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
+            if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
+                if global_step == 220000:
+                    return SDLegacyType.V2_e
+                elif global_step == 110000:
+                    return SDLegacyType.V2_v
+                else:
+                    return SDLegacyType.V2
+            # otherwise we assume a V1 file
             in_channels = state_dict[
                 "model.diffusion_model.input_blocks.0.0.weight"
             ].shape[1]
@@ -751,7 +763,9 @@ class ModelManager(object):
         convert: bool = False,
         model_name: str = None,
         description: str = None,
+        model_config_file: Path = None,
         commit_to_conf: Path = None,
+        config_file_callback: Callable[[Path], Path] = None,
     ) -> str:
         """
         Accept a string which could be:
@@ -781,30 +795,31 @@ class ModelManager(object):
         """
         model_path: Path = None
         thing = path_url_or_repo  # to save typing
+        is_temporary = False
 
         print(f">> Probing {thing} for import")
 
         if thing.startswith(("http:", "https:", "ftp:")):
-            print(f"  | {thing} appears to be a URL")
+            print(f"   | {thing} appears to be a URL")
             model_path = self._resolve_path(
                 thing, "models/ldm/stable-diffusion-v1"
             )  # _resolve_path does a download if needed
+            is_temporary = True
 
         elif Path(thing).is_file() and thing.endswith((".ckpt", ".safetensors")):
             if Path(thing).stem in ["model", "diffusion_pytorch_model"]:
                 print(
-                    f"  | {Path(thing).name} appears to be part of a diffusers model. Skipping import"
+                    f"   | {Path(thing).name} appears to be part of a diffusers model. Skipping import"
                 )
                 return
             else:
-                print(f"  | {thing} appears to be a checkpoint file on disk")
+                print(f"   | {thing} appears to be a checkpoint file on disk")
                 model_path = self._resolve_path(thing, "models/ldm/stable-diffusion-v1")
 
         elif Path(thing).is_dir() and Path(thing, "model_index.json").exists():
             print(f"  | {thing} appears to be a diffusers file on disk")
             model_name = self.import_diffuser_model(
                 thing,
-                vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
                 model_name=model_name,
                 description=description,
                 commit_to_conf=commit_to_conf,
@@ -824,7 +839,10 @@ class ModelManager(object):
                     Path(thing).rglob("*.safetensors")
                 ):
                     if model_name := self.heuristic_import(
-                        str(m), convert, commit_to_conf=commit_to_conf
+                        str(m),
+                        convert,
+                        commit_to_conf=commit_to_conf,
+                        config_file_callback=config_file_callback,
                     ):
                         print(f" >> {model_name} successfully imported")
                 return model_name
@@ -848,54 +866,86 @@ class ModelManager(object):
 
         if model_path.stem in self.config:  # already imported
             print("  | Already imported. Skipping")
-            return
+            return model_path.stem
 
         # another round of heuristics to guess the correct config file.
-        checkpoint = (
-            safetensors.torch.load_file(model_path)
-            if model_path.suffix == ".safetensors"
-            else torch.load(model_path)
-        )
-        model_type = self.probe_model_type(checkpoint)
-
-        model_config_file = None
-        if model_type == SDLegacyType.V1:
-            print("  | SD-v1 model detected")
-            model_config_file = Path(
-                Globals.root, "configs/stable-diffusion/v1-inference.yaml"
-            )
-        elif model_type == SDLegacyType.V1_INPAINT:
-            print("  | SD-v1 inpainting model detected")
-            model_config_file = Path(
-                Globals.root, "configs/stable-diffusion/v1-inpainting-inference.yaml"
-            )
-        elif model_type == SDLegacyType.V2:
-            print(
-                "  | SD-v2 model detected; model will be converted to diffusers format"
-            )
-            model_config_file = Path(
-                Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
-            )
-            convert = True
+        checkpoint = None
+        if model_path.suffix.endswith((".ckpt", ".pt")):
+            self.scan_model(model_path, model_path)
+            checkpoint = torch.load(model_path)
         else:
-            print(
-                f"** {thing} is a legacy checkpoint file but not in a known Stable Diffusion model. Skipping import"
-            )
-            return
+            checkpoint = safetensors.torch.load_file(model_path)
+        # additional probing needed if no config file provided
+        if model_config_file is None:
+            # Is there a like-named .yaml file in the same directory as the
+            # weights file? If so, we treat this as our model
+            if model_path.with_suffix(".yaml").exists():
+                model_config_file = model_path.with_suffix(".yaml")
+                print(f"   | Using config file {model_config_file.name}")
+            else:
+                model_type = self.probe_model_type(checkpoint)
+                if model_type == SDLegacyType.V1:
+                    print("   | SD-v1 model detected")
+                    model_config_file = Path(
+                        Globals.root, "configs/stable-diffusion/v1-inference.yaml"
+                    )
+                elif model_type == SDLegacyType.V1_INPAINT:
+                    print("   | SD-v1 inpainting model detected")
+                    model_config_file = Path(
+                        Globals.root,
+                        "configs/stable-diffusion/v1-inpainting-inference.yaml",
+                    )
+                elif model_type == SDLegacyType.V2_v:
+                    print("   | SD-v2-v model detected")
+                    model_config_file = Path(
+                        Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
+                    )
+                elif model_type == SDLegacyType.V2_e:
+                    print("   | SD-v2-e model detected")
+                    model_config_file = Path(
+                        Globals.root, "configs/stable-diffusion/v2-inference.yaml"
+                    )
+                elif model_type == SDLegacyType.V2:
+                    print(
+                        f"** {thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide the configuration file type or path."
+                    )
+                else:
+                    print(
+                        f"** {thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide the configuration file type or path."
+                    )
 
+            if not model_config_file and config_file_callback:
+                model_config_file = config_file_callback(model_path)
+            if not model_config_file:
+                return
+
+        if self.is_v2_config(model_config_file):
+            convert = True
+            print("   | This SD-v2 model will be converted to diffusers format for use")
+
+        if (vae_path := self._scan_for_matching_file(model_path)):
+            print(f"   | Using VAE file {vae_path.name}")
+        
         if convert:
             diffuser_path = Path(
                 Globals.root, "models", Globals.converted_ckpts_dir, model_path.stem
             )
+            vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
             model_name = self.convert_and_import(
                 model_path,
                 diffusers_path=diffuser_path,
-                vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
+                vae=vae,
+                vae_path=vae_path,
                 model_name=model_name,
                 model_description=description,
                 original_config_file=model_config_file,
                 commit_to_conf=commit_to_conf,
+                scan_needed=False,
             )
+            # in the event that this file was downloaded automatically prior to conversion
+            # we do not keep the original .ckpt/.safetensors around
+            if is_temporary:
+                model_path.unlink(missing_ok=True)
         else:
             model_name = self.import_ckpt_model(
                 model_path,
@@ -903,7 +953,8 @@ class ModelManager(object):
                 model_name=model_name,
                 model_description=description,
                 vae=str(
-                    Path(
+                    vae_path
+                    or Path(
                         Globals.root,
                         "models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt",
                     )
@@ -920,9 +971,11 @@ class ModelManager(object):
         diffusers_path: Path,
         model_name=None,
         model_description=None,
-        vae=None,
+        vae: dict = None,
+        vae_path: Path = None,
         original_config_file: Path = None,
         commit_to_conf: Path = None,
+        scan_needed: bool = True,
     ) -> str:
         """
         Convert a legacy ckpt weights file to diffuser model and import
@@ -936,7 +989,7 @@ class ModelManager(object):
 
         new_config = None
 
-        from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffuser
+        from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffusers
 
         if diffusers_path.exists():
             print(
@@ -950,18 +1003,23 @@ class ModelManager(object):
         try:
             # By passing the specified VAE to the conversion function, the autoencoder
             # will be built into the model rather than tacked on afterward via the config file
-            vae_model = self._load_vae(vae) if vae else None
-            convert_ckpt_to_diffuser(
+            vae_model=None
+            if vae:
+                vae_model=self._load_vae(vae)
+                vae_path=None
+            convert_ckpt_to_diffusers(
                 ckpt_path,
                 diffusers_path,
                 extract_ema=True,
                 original_config_file=original_config_file,
                 vae=vae_model,
+                vae_path=vae_path,
+                scan_needed=scan_needed,
             )
             print(
-                f"  | Success. Optimized model is now located at {str(diffusers_path)}"
+                f"   | Success. Optimized model is now located at {str(diffusers_path)}"
             )
-            print(f"  | Writing new config file entry for {model_name}")
+            print(f"   | Writing new config file entry for {model_name}")
             new_config = dict(
                 path=str(diffusers_path),
                 description=model_description,
@@ -1002,36 +1060,6 @@ class ModelManager(object):
 
         return search_folder, found_models
 
-    def _choose_diffusers_vae(
-        self, model_name: str, vae: str = None
-    ) -> Union[dict, str]:
-        # In the event that the original entry is using a custom ckpt VAE, we try to
-        # map that VAE onto a diffuser VAE using a hard-coded dictionary.
-        # I would prefer to do this differently: We load the ckpt model into memory, swap the
-        # VAE in memory, and then pass that to convert_ckpt_to_diffuser() so that the swapped
-        # VAE is built into the model. However, when I tried this I got obscure key errors.
-        if vae:
-            return vae
-        if model_name in self.config and (
-            vae_ckpt_path := self.model_info(model_name).get("vae", None)
-        ):
-            vae_basename = Path(vae_ckpt_path).stem
-            diffusers_vae = None
-            if diffusers_vae := VAE_TO_REPO_ID.get(vae_basename, None):
-                print(
-                    f">> {vae_basename} VAE corresponds to known {diffusers_vae} diffusers version"
-                )
-                vae = {"repo_id": diffusers_vae}
-            else:
-                print(
-                    f'** Custom VAE "{vae_basename}" found, but corresponding diffusers model unknown'
-                )
-                print(
-                    '** Using "stabilityai/sd-vae-ft-mse"; If this isn\'t right, please edit the model config'
-                )
-                vae = {"repo_id": "stabilityai/sd-vae-ft-mse"}
-        return vae
-
     def _make_cache_room(self) -> None:
         num_loaded_models = len(self.models)
         if num_loaded_models >= self.max_loaded_models:
@@ -1088,27 +1116,39 @@ class ModelManager(object):
         to the 2.3.0 "diffusers" version. This should be a one-time operation, called at
         script startup time.
         """
-        # Three transformer models to check: bert, clip and safety checker
+        # Three transformer models to check: bert, clip and safety checker, and
+        # the diffusers as well
+        models_dir = Path(Globals.root, "models")
         legacy_locations = [
             Path(
-                "CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker"
+                models_dir,
+                "CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker",
             ),
             Path("bert-base-uncased/models--bert-base-uncased"),
             Path(
                 "openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14"
             ),
         ]
-        models_dir = Path(Globals.root, "models")
+        legacy_locations.extend(list(global_cache_dir("diffusers").glob("*")))
         legacy_layout = False
         for model in legacy_locations:
-            legacy_layout = legacy_layout or Path(models_dir, model).exists()
+            legacy_layout = legacy_layout or model.exists()
         if not legacy_layout:
             return
 
         print(
-            "** Legacy version <= 2.2.5 model directory layout detected. Reorganizing."
+            """
+>> ALERT:
+>> The location of your previously-installed diffusers models needs to move from
+>> invokeai/models/diffusers to invokeai/models/hub due to a change introduced by
+>> diffusers version 0.14. InvokeAI will now move all models from the "diffusers" directory
+>> into "hub" and then remove the diffusers directory. This is a quick, safe, one-time
+>> operation. However if you have customized either of these directories and need to
+>> make adjustments, please press ctrl-C now to abort and relaunch InvokeAI when you are ready.
+>> Otherwise press <enter> to continue."""
         )
         print("** This is a quick one-time operation.")
+        input("continue> ")
 
         # transformer files get moved into the hub directory
         if cls._is_huggingface_hub_directory_present():
@@ -1120,33 +1160,20 @@ class ModelManager(object):
         for model in legacy_locations:
             source = models_dir / model
             dest = hub / model.stem
+            if dest.exists() and not source.exists():
+                continue
             print(f"** {source} => {dest}")
             if source.exists():
-                if dest.exists():
-                    rmtree(source)
+                if dest.is_symlink():
+                    print(f"** Found symlink at {dest.name}. Not migrating.")
+                elif dest.exists():
+                    if source.is_dir():
+                        rmtree(source)
+                    else:
+                        source.unlink()
                 else:
                     move(source, dest)
 
-        # anything else gets moved into the diffusers directory
-        if cls._is_huggingface_hub_directory_present():
-            diffusers = global_cache_dir("diffusers")
-        else:
-            diffusers = models_dir / "diffusers"
-
-        os.makedirs(diffusers, exist_ok=True)
-        for root, dirs, _ in os.walk(models_dir, topdown=False):
-            for dir in dirs:
-                full_path = Path(root, dir)
-                if full_path.is_relative_to(hub) or full_path.is_relative_to(diffusers):
-                    continue
-                if Path(dir).match("models--*--*"):
-                    dest = diffusers / dir
-                    print(f"** {full_path} => {dest}")
-                    if dest.exists():
-                        rmtree(full_path)
-                    else:
-                        move(full_path, dest)
-
         # now clean up by removing any empty directories
         empty = [
             root
@@ -1244,7 +1271,7 @@ class ModelManager(object):
             path = name_or_path
         else:
             owner, repo = name_or_path.split("/")
-            path = Path(global_cache_dir("diffusers") / f"models--{owner}--{repo}")
+            path = Path(global_cache_dir("hub") / f"models--{owner}--{repo}")
         if not path.exists():
             return None
         hashpath = path / "checksum.sha256"
@@ -1252,7 +1279,7 @@ class ModelManager(object):
             with open(hashpath) as f:
                 hash = f.read()
             return hash
-        print("  | Calculating sha256 hash of model files")
+        print("   | Calculating sha256 hash of model files")
         tic = time.time()
         sha = hashlib.sha256()
         count = 0
@@ -1264,7 +1291,7 @@ class ModelManager(object):
                         sha.update(chunk)
         hash = sha.hexdigest()
         toc = time.time()
-        print(f"  | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
+        print(f"   | sha256 = {hash} ({count} files hashed in", "%4.2fs)" % (toc - tic))
         with open(hashpath, "w") as f:
             f.write(hash)
         return hash
@@ -1294,6 +1321,22 @@ class ModelManager(object):
             f.write(hash)
         return hash
 
+    @classmethod
+    def _scan_for_matching_file(
+            self,model_path: Path,
+            suffixes: List[str]=['.vae.pt','.vae.ckpt','.vae.safetensors']
+    )->Path:
+        """
+        Find a file with same basename as the indicated model, but with one
+        of the suffixes passed.
+        """
+        # look for a custom vae
+        vae_path = None
+        for suffix in suffixes:
+            if model_path.with_suffix(suffix).exists():
+                vae_path = model_path.with_suffix(suffix)
+        return vae_path
+                               
     def _load_vae(self, vae_config) -> AutoencoderKL:
         vae_args = {}
         try:
@@ -1305,16 +1348,16 @@ class ModelManager(object):
         using_fp16 = self.precision == "float16"
 
         vae_args.update(
-            cache_dir=global_cache_dir("diffusers"),
+            cache_dir=global_cache_dir("hub"),
             local_files_only=not Globals.internet_available,
         )
 
-        print(f"  | Loading diffusers VAE from {name_or_path}")
+        print(f"   | Loading diffusers VAE from {name_or_path}")
         if using_fp16:
             vae_args.update(torch_dtype=torch.float16)
             fp_args_list = [{"revision": "fp16"}, {}]
         else:
-            print("  | Using more accurate float32 precision")
+            print("   | Using more accurate float32 precision")
             fp_args_list = [{}]
 
         vae = None
@@ -1355,7 +1398,7 @@ class ModelManager(object):
                     hashes_to_delete.add(revision.commit_hash)
         strategy = cache_info.delete_revisions(*hashes_to_delete)
         print(
-            f"** deletion of this model is expected to free {strategy.expected_freed_size_str}"
+            f"** Deletion of this model is expected to free {strategy.expected_freed_size_str}"
         )
         strategy.execute()
 

ldm/invoke/pngwriter.py

@@ -9,6 +9,8 @@ Exports function retrieve_metadata(path)
 import os
 import re
 import json
+from pathlib import Path
+from filelock import FileLock
 from PIL import PngImagePlugin, Image
 
 # -------------------image generation utils-----
@@ -19,8 +21,26 @@ class PngWriter:
         self.outdir = outdir
         os.makedirs(outdir, exist_ok=True)
 
+    def unique_prefix(self)->str:
+        next_prefix_file = Path(self.outdir,'.next_prefix')
+        next_prefix_lock = Path(self.outdir,'.next_prefix.lock')
+        prefix = 0
+        with FileLock(next_prefix_lock):
+            if not next_prefix_file.exists():
+                prefix = self._unused_prefix()
+            else:
+                with open(next_prefix_file,'r') as file:
+                    prefix = 0
+                    try:
+                        prefix=int(file.readline())
+                    except (TypeError, ValueError):
+                        prefix=self._unused_prefix()
+            with open(next_prefix_file,'w') as file:
+                file.write(str(prefix+1))
+        return f'{prefix:06}'
+
     # gives the next unique prefix in outdir
-    def unique_prefix(self):
+    def _unused_prefix(self)->int:
         # sort reverse alphabetically until we find max+1
         dirlist = sorted(os.listdir(self.outdir), reverse=True)
         # find the first filename that matches our pattern or return 000000.0.png
@@ -28,8 +48,7 @@ class PngWriter:
             (f for f in dirlist if re.match('^(\d+)\..*\.png', f)),
             '0000000.0.png',
         )
-        basecount = int(existing_name.split('.', 1)[0]) + 1
-        return f'{basecount:06}'
+        return int(existing_name.split('.', 1)[0]) + 1
 
     # saves image named _image_ to outdir/name, writing metadata from prompt
     # returns full path of output
@@ -91,14 +110,12 @@ class PromptFormatter:
         switches.append(f'-H{opt.height       or t2i.height}')
         switches.append(f'-C{opt.cfg_scale    or t2i.cfg_scale}')
         switches.append(f'-A{opt.sampler_name or t2i.sampler_name}')
-# to do: put model name into the t2i object
-#        switches.append(f'--model{t2i.model_name}')
         if opt.seamless or t2i.seamless:
-            switches.append(f'--seamless')
+            switches.append('--seamless')
         if opt.init_img:
             switches.append(f'-I{opt.init_img}')
         if opt.fit:
-            switches.append(f'--fit')
+            switches.append('--fit')
         if opt.strength and opt.init_img is not None:
             switches.append(f'-f{opt.strength or t2i.strength}')
         if opt.gfpgan_strength:

ldm/invoke/training/textual_inversion.py

@@ -17,6 +17,7 @@ from pathlib import Path
 from typing import List, Tuple
 
 import npyscreen
+from diffusers.utils.import_utils import is_xformers_available
 from npyscreen import widget
 from omegaconf import OmegaConf
 
@@ -29,7 +30,7 @@ from ldm.invoke.training.textual_inversion_training import (
 TRAINING_DATA = "text-inversion-training-data"
 TRAINING_DIR = "text-inversion-output"
 CONF_FILE = "preferences.conf"
-
+XFORMERS_AVAILABLE = is_xformers_available()
 
 class textualInversionForm(npyscreen.FormMultiPageAction):
     resolutions = [512, 768, 1024]
@@ -178,7 +179,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             out_of=10000,
             step=500,
             lowest=1,
-            value=saved_args.get("max_train_steps", 3000),
+            value=saved_args.get("max_train_steps", 2500),
             scroll_exit=True,
         )
         self.train_batch_size = self.add_widget_intelligent(
@@ -187,7 +188,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
             out_of=50,
             step=1,
             lowest=1,
-            value=saved_args.get("train_batch_size", 8),
+            value=saved_args.get("train_batch_size", 8 if XFORMERS_AVAILABLE else 3),
             scroll_exit=True,
         )
         self.gradient_accumulation_steps = self.add_widget_intelligent(
@@ -225,7 +226,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
         self.enable_xformers_memory_efficient_attention = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="Use xformers acceleration",
-            value=saved_args.get("enable_xformers_memory_efficient_attention", False),
+            value=saved_args.get("enable_xformers_memory_efficient_attention", XFORMERS_AVAILABLE),
             scroll_exit=True,
         )
         self.lr_scheduler = self.add_widget_intelligent(
@@ -428,8 +429,7 @@ def do_front_end(args: Namespace):
             print(str(e))
             print("** DETAILS:")
             print(traceback.format_exc())
-
-
+            
 def main():
     args = parse_args()
     global_set_root(args.root_dir or Globals.root)

ldm/invoke/training/textual_inversion_training.py

@@ -67,7 +67,7 @@ else:
         "nearest": PIL.Image.NEAREST,
     }
 # ------------------------------------------------------------------------------
-
+XFORMERS_AVAILABLE = is_xformers_available
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.10.0.dev0")
@@ -227,7 +227,7 @@ def parse_args():
     training_group.add_argument(
         "--train_batch_size",
         type=int,
-        default=16,
+        default=8 if XFORMERS_AVAILABLE else 3,
         help="Batch size (per device) for the training dataloader.",
     )
     training_group.add_argument("--num_train_epochs", type=int, default=100)
@@ -324,6 +324,7 @@ def parse_args():
     parser.add_argument(
         "--enable_xformers_memory_efficient_attention",
         action="store_true",
+        default=XFORMERS_AVAILABLE,
         help="Whether or not to use xformers.",
     )
 
@@ -536,7 +537,7 @@ def do_textual_inversion_training(
     seed: int = None,
     resolution: int = 512,
     center_crop: bool = False,
-    train_batch_size: int = 16,
+    train_batch_size: int = 4,
     num_train_epochs: int = 100,
     max_train_steps: int = 5000,
     gradient_accumulation_steps: int = 1,
@@ -634,7 +635,7 @@ def do_textual_inversion_training(
     assert (
         pretrained_model_name_or_path
     ), f"models.yaml error: neither 'repo_id' nor 'path' is defined for {model}"
-    pipeline_args = dict(cache_dir=global_cache_dir("diffusers"))
+    pipeline_args = dict(cache_dir=global_cache_dir("hub"))
 
     # Load tokenizer
     if tokenizer_name:

ldm/models/diffusion/ddpm.py

@@ -19,7 +19,7 @@ from functools import partial
 from tqdm import tqdm
 from torchvision.utils import make_grid
 from pytorch_lightning.utilities.distributed import rank_zero_only
-from omegaconf import ListConfig
+from omegaconf import ListConfig, OmegaConf
 import urllib
 
 from ldm.modules.textual_inversion_manager import TextualInversionManager
@@ -609,6 +609,7 @@ class DDPM(pl.LightningModule):
         opt = torch.optim.AdamW(params, lr=lr)
         return opt
 
+    
 
 class LatentDiffusion(DDPM):
     """main class"""
@@ -617,7 +618,7 @@ class LatentDiffusion(DDPM):
         self,
         first_stage_config,
         cond_stage_config,
-        personalization_config,
+        personalization_config=None,
         num_timesteps_cond=None,
         cond_stage_key='image',
         cond_stage_trainable=False,
@@ -675,7 +676,8 @@ class LatentDiffusion(DDPM):
         self.model.train = disabled_train
         for param in self.model.parameters():
             param.requires_grad = False
-
+            
+        personalization_config = personalization_config or self._fallback_personalization_config()
         self.embedding_manager = self.instantiate_embedding_manager(
             personalization_config, self.cond_stage_model
         )
@@ -2150,6 +2152,25 @@ class LatentDiffusion(DDPM):
 
                 self.emb_ckpt_counter += 500
 
+    @classmethod
+    def _fallback_personalization_config(self)->dict:
+        """
+        This protects us against custom legacy config files that
+        don't contain the personalization_config section.
+        """
+        return OmegaConf.create(
+            dict(
+                target='ldm.modules.embedding_manager.EmbeddingManager',
+                params=dict(
+                    placeholder_strings=list('*'),
+                    initializer_words=list('sculpture'),
+                    per_image_tokens=False,
+                    num_vectors_per_token=1,
+                    progressive_words=False,
+                )
+            )
+        )
+
 
 class DiffusionWrapper(pl.LightningModule):
     def __init__(self, diff_model_config, conditioning_key):

ldm/modules/encoders/modules.py

@@ -463,6 +463,10 @@ class FrozenCLIPEmbedder(AbstractEncoder):
     def encode(self, text, **kwargs):
         return self(text, **kwargs)
 
+    def set_textual_inversion_manager(self, manager): #TextualInversionManager):
+        # TODO all of the weighting and expanding stuff needs be moved out of this class
+        self.textual_inversion_manager = manager
+
     @property
     def device(self):
         return self.transformer.device
@@ -476,10 +480,6 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
     fragment_weights_key = "fragment_weights"
     return_tokens_key = "return_tokens"
 
-    def set_textual_inversion_manager(self, manager): #TextualInversionManager):
-        # TODO all of the weighting and expanding stuff needs be moved out of this class
-        self.textual_inversion_manager = manager
-
     def forward(self, text: list, **kwargs):
         # TODO all of the weighting and expanding stuff needs be moved out of this class
         '''

ldm/modules/textual_inversion_manager.py

@@ -1,9 +1,9 @@
-import os
 import traceback
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Optional, Union
 
+import safetensors.torch
 import torch
 from picklescan.scanner import scan_file_path
 from transformers import CLIPTextModel, CLIPTokenizer
@@ -71,21 +71,6 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         if str(ckpt_path).endswith(".DS_Store"):
             return
-
-        try:
-            scan_result = scan_file_path(str(ckpt_path))
-            if scan_result.infected_files == 1:
-                print(
-                    f"\n### Security Issues Found in Model: {scan_result.issues_count}"
-                )
-                print("### For your safety, InvokeAI will not load this embed.")
-                return
-        except Exception:
-            print(
-                f"### {ckpt_path.parents[0].name}/{ckpt_path.name} is damaged or corrupt."
-            )
-            return
-
         embedding_info = self._parse_embedding(str(ckpt_path))
 
         if embedding_info is None:
@@ -96,7 +81,7 @@ class TextualInversionManager(BaseTextualInversionManager):
             != embedding_info["token_dim"]
         ):
             print(
-                f"** Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info['token_dim']}."
+                f"   ** Notice: {ckpt_path.parents[0].name}/{ckpt_path.name} was trained on a model with an incompatible token dimension: {self.text_encoder.get_input_embeddings().weight.data[0].shape[0]} vs {embedding_info['token_dim']}."
             )
             return
 
@@ -309,92 +294,72 @@ class TextualInversionManager(BaseTextualInversionManager):
 
         return token_id
 
-    def _parse_embedding(self, embedding_file: str):
-        file_type = embedding_file.split(".")[-1]
-        if file_type == "pt":
-            return self._parse_embedding_pt(embedding_file)
-        elif file_type == "bin":
-            return self._parse_embedding_bin(embedding_file)
-        else:
-            print(f"** Notice: unrecognized embedding file format: {embedding_file}")
+    def _parse_embedding(self, embedding_file: str)->dict:
+        suffix = Path(embedding_file).suffix
+        try:
+            if suffix in [".pt",".ckpt",".bin"]:
+                scan_result = scan_file_path(embedding_file)
+                if scan_result.infected_files == 1:
+                    print(
+                        f"   ** Security Issues Found in Model: {scan_result.issues_count}"
+                    )
+                    print("   ** For your safety, InvokeAI will not load this embed.")
+                    return
+                ckpt = torch.load(embedding_file,map_location="cpu")
+            else:
+                ckpt = safetensors.torch.load_file(embedding_file)
+        except Exception as e:
+            print(f"   ** Notice: unrecognized embedding file format: {embedding_file}: {e}")
             return None
-
-    def _parse_embedding_pt(self, embedding_file):
-        embedding_ckpt = torch.load(embedding_file, map_location="cpu")
-        embedding_info = {}
-
-        # Check if valid embedding file
-        if "string_to_token" and "string_to_param" in embedding_ckpt:
-            # Catch variants that do not have the expected keys or values.
-            try:
-                embedding_info["name"] = embedding_ckpt["name"] or os.path.basename(
-                    os.path.splitext(embedding_file)[0]
-                )
-
-                # Check num of embeddings and warn user only the first will be used
-                embedding_info["num_of_embeddings"] = len(
-                    embedding_ckpt["string_to_token"]
-                )
-                if embedding_info["num_of_embeddings"] > 1:
-                    print(">> More than 1 embedding found. Will use the first one")
-
-                embedding = list(embedding_ckpt["string_to_param"].values())[0]
-            except (AttributeError, KeyError):
-                return self._handle_broken_pt_variants(embedding_ckpt, embedding_file)
-
-            embedding_info["embedding"] = embedding
-            embedding_info["num_vectors_per_token"] = embedding.size()[0]
-            embedding_info["token_dim"] = embedding.size()[1]
-
-            try:
-                embedding_info["trained_steps"] = embedding_ckpt["step"]
-                embedding_info["trained_model_name"] = embedding_ckpt[
-                    "sd_checkpoint_name"
-                ]
-                embedding_info["trained_model_checksum"] = embedding_ckpt[
-                    "sd_checkpoint"
-                ]
-            except AttributeError:
-                print(">> No Training Details Found. Passing ...")
-
-        # .pt files found at https://cyberes.github.io/stable-diffusion-textual-inversion-models/
-        # They are actually .bin files
-        elif len(embedding_ckpt.keys()) == 1:
-            embedding_info = self._parse_embedding_bin(embedding_file)
-
+        
+        # try to figure out what kind of embedding file it is and parse accordingly
+        keys = list(ckpt.keys())
+        if all(x in keys for x in ['string_to_token','string_to_param','name','step']):
+            return self._parse_embedding_v1(ckpt, embedding_file)     # example rem_rezero.pt
+        
+        elif all(x in keys for x in ['string_to_token','string_to_param']):
+            return self._parse_embedding_v2(ckpt, embedding_file)     # example midj-strong.pt
+        
+        elif 'emb_params' in keys:
+            return self._parse_embedding_v3(ckpt, embedding_file)     # example easynegative.safetensors
+        
         else:
-            print(">> Invalid embedding format")
-            embedding_info = None
+            return self._parse_embedding_v4(ckpt, embedding_file)     # usually a '.bin' file
 
+    def _parse_embedding_v1(self, embedding_ckpt: dict, file_path: str):
+        basename = Path(file_path).stem
+        print(f'   | Loading v1 embedding file: {basename}')
+
+        embedding_info = {}
+        embedding_info["name"] = embedding_ckpt["name"]
+                
+        # Check num of embeddings and warn user only the first will be used
+        embedding_info["num_of_embeddings"] = len(
+            embedding_ckpt["string_to_token"]
+        )
+        if embedding_info["num_of_embeddings"] > 1:
+            print("   | More than 1 embedding found. Will use the first one")
+        embedding = list(embedding_ckpt["string_to_param"].values())[0]
+        embedding_info["embedding"] = embedding
+        embedding_info["num_vectors_per_token"] = embedding.size()[0]
+        embedding_info["token_dim"] = embedding.size()[1]
+        embedding_info["trained_steps"] = embedding_ckpt["step"]
+        embedding_info["trained_model_name"] = embedding_ckpt[
+            "sd_checkpoint_name"
+        ]
+        embedding_info["trained_model_checksum"] = embedding_ckpt[
+            "sd_checkpoint"
+        ]
         return embedding_info
 
-    def _parse_embedding_bin(self, embedding_file):
-        embedding_ckpt = torch.load(embedding_file, map_location="cpu")
-        embedding_info = {}
-
-        if list(embedding_ckpt.keys()) == 0:
-            print(">> Invalid concepts file")
-            embedding_info = None
-        else:
-            for token in list(embedding_ckpt.keys()):
-                embedding_info["name"] = (
-                    token
-                    or f"<{os.path.basename(os.path.splitext(embedding_file)[0])}>"
-                )
-                embedding_info["embedding"] = embedding_ckpt[token]
-                embedding_info[
-                    "num_vectors_per_token"
-                ] = 1  # All Concepts seem to default to 1
-                embedding_info["token_dim"] = embedding_info["embedding"].size()[0]
-
-        return embedding_info
-
-    def _handle_broken_pt_variants(
-        self, embedding_ckpt: dict, embedding_file: str
+    def _parse_embedding_v2 (
+        self, embedding_ckpt: dict, file_path: str
     ) -> dict:
         """
-        This handles the broken .pt file variants. We only know of one at present.
+        This handles embedding .pt file variant #2.
         """
+        basename = Path(file_path).stem
+        print(f'   | Loading v2 embedding file: {basename}')
         embedding_info = {}
         if isinstance(
             list(embedding_ckpt["string_to_token"].values())[0], torch.Tensor
@@ -403,7 +368,7 @@ class TextualInversionManager(BaseTextualInversionManager):
                 embedding_info["name"] = (
                     token
                     if token != "*"
-                    else f"<{os.path.basename(os.path.splitext(embedding_file)[0])}>"
+                    else f"<{basename}>"
                 )
                 embedding_info["embedding"] = embedding_ckpt[
                     "string_to_param"
@@ -413,7 +378,46 @@ class TextualInversionManager(BaseTextualInversionManager):
                 ].shape[0]
                 embedding_info["token_dim"] = embedding_info["embedding"].size()[1]
         else:
-            print(">> Invalid embedding format")
+            print(f"   ** {basename}: Unrecognized embedding format")
             embedding_info = None
 
         return embedding_info
+
+    def _parse_embedding_v3(self, embedding_ckpt: dict, file_path: str):
+        """
+        Parse 'version 3' of the .pt textual inversion embedding files.
+        """
+        basename = Path(file_path).stem
+        print(f'   | Loading v3 embedding file: {basename}')
+        embedding_info = {}
+        embedding_info["name"] = f'<{basename}>'
+        embedding_info["num_of_embeddings"] = 1
+        embedding = embedding_ckpt['emb_params']
+        embedding_info["embedding"] = embedding
+        embedding_info["num_vectors_per_token"] = embedding.size()[0]
+        embedding_info["token_dim"] = embedding.size()[1]
+        return embedding_info
+    
+    def _parse_embedding_v4(self, embedding_ckpt: dict, filepath: str):
+        """
+        Parse 'version 4' of the textual inversion embedding files. This one
+        is usually associated with .bin files trained by HuggingFace diffusers.
+        """
+        basename = Path(filepath).stem
+        short_path = Path(filepath).parents[0].name+'/'+Path(filepath).name
+        
+        print(f'   | Loading v4 embedding file: {short_path}')
+        embedding_info = {}
+        if list(embedding_ckpt.keys()) == 0:
+            print(f"   ** Invalid embeddings file: {short_path}")
+            embedding_info = None
+        else:
+            for token in list(embedding_ckpt.keys()):
+                embedding_info["name"] = (
+                    token
+                    or f"<{basename}>"
+                )
+                embedding_info["embedding"] = embedding_ckpt[token]
+                embedding_info["num_vectors_per_token"] = 1  # All Concepts seem to default to 1
+                embedding_info["token_dim"] = embedding_info["embedding"].size()[0]
+        return embedding_info

ldm/util.py

@@ -329,7 +329,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
         resp = requests.get(url, headers=header, stream=True) # new request with range
 
     if exist_size > content_length:
-        print('* corrupt existing file found. re-downloading')
+        print(f'* corrupt existing file found (existing_size={exist_size}, content_length={content_length}). re-downloading')
         os.remove(dest)
         exist_size = 0
 
@@ -341,15 +341,12 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
     elif resp.status_code == 206 or exist_size > 0:
         print(f"* {dest}: partial file found. Resuming...")
     elif resp.status_code != 200:
-        print(f"** An error occurred during downloading {dest}: {resp.reason}")
+        print(f"** An error occurred while downloading {url}: {resp.reason}")
+        return None
     else:
         print(f"* {dest}: Downloading...")
 
     try:
-        if content_length < 2000:
-            print(f"*** ERROR DOWNLOADING {url}: {resp.text}")
-            return None
-
         with open(dest, open_mode) as file, tqdm(
                 desc=str(dest),
                 initial=exist_size,

mkdocs.yml

@@ -2,14 +2,14 @@
 
 # General
 site_name: InvokeAI Stable Diffusion Toolkit Docs
-site_url: https://invoke-ai.github.io/InvokeAI
+site_url: !ENV [SITE_URL, 'https://invoke-ai.github.io/InvokeAI']
 site_author: mauwii
 dev_addr: '127.0.0.1:8080'
 
 # Repository
-repo_name: 'invoke-ai/InvokeAI'
-repo_url: 'https://github.com/invoke-ai/InvokeAI'
-edit_uri: edit/main/docs/
+repo_name: !ENV [REPO_NAME, 'invoke-ai/InvokeAI']
+repo_url: !ENV [REPO_URL, 'https://github.com/invoke-ai/InvokeAI']
+edit_uri: blob/main/docs/
 
 # Copyright
 copyright: Copyright © 2022 InvokeAI Team
@@ -19,7 +19,8 @@ theme:
   name: material
   icon:
     repo: fontawesome/brands/github
-    edit: material/file-document-edit-outline
+    edit: material/pencil
+    view: material/eye
   palette:
     - media: '(prefers-color-scheme: light)'
       scheme: default
@@ -33,6 +34,11 @@ theme:
         icon: material/lightbulb-outline
         name: Switch to light mode
   features:
+    - content.action.edit
+    - content.action.view
+    - content.code.copy
+    - content.tabs.link
+    - navigation.indexes
     - navigation.instant
     - navigation.tabs
     - navigation.top
@@ -89,9 +95,9 @@ plugins:
       enable_creation_date: true
   - redirects:
       redirect_maps:
-          'installation/INSTALL_AUTOMATED.md': 'installation/010_INSTALL_AUTOMATED.md'
-          'installation/INSTALL_MANUAL.md': 'installation/020_INSTALL_MANUAL.md'
-          'installation/INSTALL_SOURCE.md': 'installation/020_INSTALL_MANUAL.md'
-          'installation/INSTALL_DOCKER.md': 'installation/040_INSTALL_DOCKER.md'
-          'installation/INSTALLING_MODELS.md': 'installation/050_INSTALLING_MODELS.md'
-          'installation/INSTALL_PATCHMATCH.md': 'installation/060_INSTALL_PATCHMATCH.md'
+        'installation/INSTALL_AUTOMATED.md': 'installation/010_INSTALL_AUTOMATED.md'
+        'installation/INSTALL_MANUAL.md': 'installation/020_INSTALL_MANUAL.md'
+        'installation/INSTALL_SOURCE.md': 'installation/020_INSTALL_MANUAL.md'
+        'installation/INSTALL_DOCKER.md': 'installation/040_INSTALL_DOCKER.md'
+        'installation/INSTALLING_MODELS.md': 'installation/050_INSTALLING_MODELS.md'
+        'installation/INSTALL_PATCHMATCH.md': 'installation/060_INSTALL_PATCHMATCH.md'

pyproject.toml

@@ -1,46 +1,40 @@
 [build-system]
-requires = ["setuptools~=65.5", "pip~=22.3", "wheel"]
 build-backend = "setuptools.build_meta"
+requires = ["setuptools ~= 67.1", "wheel"]
 
 [project]
-name = "InvokeAI"
-description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
-requires-python = ">=3.9, <3.11"
-readme = { content-type = "text/markdown", file = "README.md" }
-keywords = ["stable-diffusion", "AI"]
-dynamic = ["version"]
-license = { file = "LICENSE" }
-authors = [{ name = "The InvokeAI Project", email = "lincoln.stein@gmail.com" }]
+authors = [{name = "The InvokeAI Project", email = "lincoln.stein@gmail.com"}]
 classifiers = [
-  'Development Status :: 4 - Beta',
-  'Environment :: GPU',
-  'Environment :: GPU :: NVIDIA CUDA',
-  'Environment :: MacOS X',
-  'Intended Audience :: End Users/Desktop',
-  'Intended Audience :: Developers',
-  'License :: OSI Approved :: MIT License',
-  'Operating System :: POSIX :: Linux',
-  'Operating System :: MacOS',
-  'Operating System :: Microsoft :: Windows',
-  'Programming Language :: Python :: 3 :: Only',
-  'Programming Language :: Python :: 3.8',
-  'Programming Language :: Python :: 3.9',
-  'Programming Language :: Python :: 3.10',
-  'Topic :: Artistic Software',
-  'Topic :: Internet :: WWW/HTTP :: WSGI :: Application',
-  'Topic :: Internet :: WWW/HTTP :: WSGI :: Server',
-  'Topic :: Multimedia :: Graphics',
-  'Topic :: Scientific/Engineering :: Artificial Intelligence',
-  'Topic :: Scientific/Engineering :: Image Processing',
+  "Development Status :: 4 - Beta",
+  "Environment :: GPU :: NVIDIA CUDA",
+  "Environment :: GPU",
+  "Environment :: MacOS X",
+  "Intended Audience :: Developers",
+  "Intended Audience :: End Users/Desktop",
+  "License :: OSI Approved :: MIT License",
+  "Operating System :: MacOS",
+  "Operating System :: Microsoft :: Windows",
+  "Operating System :: POSIX :: Linux",
+  "Programming Language :: Python :: 3 :: Only",
+  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python",
+  "Topic :: Artistic Software",
+  "Topic :: Internet :: WWW/HTTP :: WSGI :: Application",
+  "Topic :: Internet :: WWW/HTTP :: WSGI :: Server",
+  "Topic :: Multimedia :: Graphics",
+  "Topic :: Scientific/Engineering :: Artificial Intelligence",
+  "Topic :: Scientific/Engineering :: Image Processing",
 ]
 dependencies = [
-  "accelerate",
+  "accelerate~=0.16",
   "albumentations",
   "click",
-  "clip_anytorch",          # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
+  "clip_anytorch",
   "compel==0.1.7",
   "datasets",
-  "diffusers[torch]~=0.13",
+  "diffusers[torch]~=0.14",
   "dnspython==2.2.1",
   "einops",
   "eventlet",
@@ -54,7 +48,7 @@ dependencies = [
   "huggingface-hub>=0.11.1",
   "imageio",
   "imageio-ffmpeg",
-  "k-diffusion",              # replacing "k-diffusion @ https://github.com/Birch-san/k-diffusion/archive/refs/heads/mps.zip",
+  "k-diffusion",
   "kornia",
   "npyscreen",
   "numpy<1.24",
@@ -62,75 +56,134 @@ dependencies = [
   "opencv-python",
   "picklescan",
   "pillow",
-  "pudb",
   "prompt-toolkit",
+  "pudb",
+  "packaging",
   "pypatchmatch",
   "pyreadline3",
   "pytorch-lightning==1.7.7",
   "realesrgan",
   "requests==2.28.2",
-  "safetensors",
+  "safetensors~=0.3.0",
   "scikit-image>=0.19",
   "send2trash",
   "streamlit",
   "taming-transformers-rom1504",
   "test-tube>=0.7.5",
-  "torch>=1.13.1",
   "torch-fidelity",
-  "torchvision>=0.14.1",
+  "torch~=1.13.1",
   "torchmetrics",
-  "transformers~=4.25",
+  "torchvision>=0.14.1",
+  "transformers~=4.26",
   "windows-curses; sys_platform=='win32'",
 ]
+description = "An implementation of Stable Diffusion which provides various new features and options to aid the image generation process"
+dynamic = ["version"]
+keywords = ["AI", "stable-diffusion"]
+license = {text = "MIT"}
+name = "InvokeAI"
+readme = {content-type = "text/markdown", file = "README.md"}
+requires-python = ">=3.9, <3.11"
 
 [project.optional-dependencies]
+"dev" = [
+  "black[jupyter]",
+  "flake8",
+  "flake8-black",
+  "flake8-bugbear",
+  "isort",
+  "pre-commit",
+]
 "dist" = ["pip-tools", "pipdeptree", "twine"]
 "docs" = [
-  "mkdocs-material<9.0",
   "mkdocs-git-revision-date-localized-plugin",
+  "mkdocs-material==9.*",
   "mkdocs-redirects==1.2.0",
 ]
-"test" = ["pytest>6.0.0", "pytest-cov"]
+"test" = ["pytest-cov", "pytest>6.0.0"]
 "xformers" = [
-	   "xformers~=0.0.16; sys_platform!='darwin'",
-	   "triton; sys_platform=='linux'",
+  "triton; sys_platform=='linux'",
+  "xformers~=0.0.16; sys_platform!='darwin'",
 ]
 
 [project.scripts]
 
 # legacy entrypoints; provided for backwards compatibility
-"invoke.py" = "ldm.invoke.CLI:main"
 "configure_invokeai.py" = "ldm.invoke.config.invokeai_configure:main"
-"textual_inversion.py" = "ldm.invoke.training.textual_inversion:main"
+"invoke.py" = "ldm.invoke.CLI:main"
 "merge_embeddings.py" = "ldm.invoke.merge_diffusers:main"
+"textual_inversion.py" = "ldm.invoke.training.textual_inversion:main"
 
 # modern entrypoints
 "invokeai" = "ldm.invoke.CLI:main"
 "invokeai-configure" = "ldm.invoke.config.invokeai_configure:main"
-"invokeai-merge" = "ldm.invoke.merge_diffusers:main"   # note name munging
-"invokeai-ti" = "ldm.invoke.training.textual_inversion:main"
 "invokeai-model-install" = "ldm.invoke.config.model_install:main"
+"invokeai-merge" = "ldm.invoke.merge_diffusers:main"
+"invokeai-ti" = "ldm.invoke.training.textual_inversion:main"
 "invokeai-update" = "ldm.invoke.config.invokeai_update:main"
+"invokeai-batch" = "ldm.invoke.dynamic_prompts:main"
+"invokeai-metadata" = "ldm.invoke.invokeai_metadata:main"
 
 [project.urls]
-"Homepage" = "https://invoke-ai.github.io/InvokeAI/"
-"Documentation" = "https://invoke-ai.github.io/InvokeAI/"
-"Source" = "https://github.com/invoke-ai/InvokeAI/"
 "Bug Reports" = "https://github.com/invoke-ai/InvokeAI/issues"
 "Discord" = "https://discord.gg/ZmtBAhwWhy"
+"Documentation" = "https://invoke-ai.github.io/InvokeAI/"
+"Homepage" = "https://invoke-ai.github.io/InvokeAI/"
+"Source" = "https://github.com/invoke-ai/InvokeAI/"
+
+[tool.setuptools]
+license-files = ["LICENSE"]
 
 [tool.setuptools.dynamic]
-version = { attr = "ldm.invoke.__version__" }
+version = {attr = "ldm.invoke.__version__"}
 
 [tool.setuptools.packages.find]
+"include" = [
+  "invokeai.assets.web",
+  "invokeai.backend*",
+  "invokeai.configs*",
+  "invokeai.frontend.dist*",
+  "ldm*",
+]
 "where" = ["."]
-"include" = ["invokeai.assets.web*", "invokeai.backend*", "invokeai.frontend.dist*", "invokeai.configs*", "ldm*"]
 
 [tool.setuptools.package-data]
 "invokeai.assets.web" = ["**.png"]
-"invokeai.backend" = ["**.png"]
-"invokeai.configs" = ["*.example", "**/*.yaml", "*.txt"]
+"invokeai.configs" = ["**.example", "**.txt", "**.yaml", "**/*.yaml"]
 "invokeai.frontend.dist" = ["**"]
 
+[tool.black]
+extend-exclude = '''
+/(
+  # skip legacy scripts
+  | scripts/orig_scripts
+)/
+'''
+line-length = 88
+target-version = ['py39']
+
+[tool.isort]
+atomic = true
+extend_skip_glob = ["scripts/orig_scripts/*"]
+filter_files = true
+line_length = 120
+profile = "black"
+py_version = 39
+remove_redundant_aliases = true
+skip_gitignore = true
+src_paths = ["installer", "invokeai", "ldm", "tests"]
+virtual_env = ".venv"
+
+[tool.coverage.run]
+branch = true
+parallel = true
+
+[tool.coverage.report]
+skip_covered = true
+skip_empty = true
+
+[tool.coverage.paths]
+source = ["invokeai/backend", "ldm/invoke"]
+
 [tool.pytest.ini_options]
-addopts = "-p pytest_cov --junitxml=junit/test-results.xml --cov-report=term:skip-covered --cov=ldm/invoke --cov=backend --cov-branch"
+addopts = ["--cov=invokeai/backend", "--cov=ldm/invoke"]

scripts/dynamic_prompts.py

@@ -0,0 +1,9 @@
+#!/usr/bin/env python
+
+"""
+Simple script to generate a file of InvokeAI prompts and settings
+that scan across steps and other parameters.
+"""
+
+import ldm.invoke.dynamic_prompts
+ldm.invoke.dynamic_prompts.main()