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Merge branch 'main' into diffusers-upgrade

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blessedcoolant 2023-06-09 16:27:52 +12:00 committed by GitHub
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BIN
binary_installer/WinLongPathsEnabled.reg
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binary_installer/install.bat.in
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@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

235
binary_installer/install.sh.in
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@ -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

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binary_installer/invoke.bat.in
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@ -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

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binary_installer/invoke.sh.in
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@ -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

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binary_installer/readme.txt
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@ -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.

33
binary_installer/requirements.in
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@ -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

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

220
installer/templates/invoke.sh.in
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@ -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
if [ "$0" != "bash" ]; then
while true
do
echo "Do you want to generate images using the"
echo "1. command-line interface"
echo "2. browser-based UI"
echo "3. run textual inversion training"
echo "4. merge models (diffusers type only)"
echo "5. download and install models"
echo "6. change InvokeAI startup options"
echo "7. re-run the configure script to fix a broken install"
echo "8. open the developer console"
echo "9. update InvokeAI"
echo "10. command-line help"
echo "Q - Quit"
echo ""
read -p "Please enter 1-10, Q: [2] " yn
choice=${yn:='2'}
case $choice in
1)
echo "Starting the InvokeAI command-line..."
invokeai $@
;;
2)
echo "Starting the InvokeAI browser-based UI..."
invokeai --web $@
;;
3)
echo "Starting Textual Inversion:"
invokeai-ti --gui $@
;;
4)
echo "Merging Models:"
invokeai-merge --gui $@
;;
5)
invokeai-model-install --root ${INVOKEAI_ROOT}
;;
6)
invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
;;
7)
invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
;;
8)
echo "Developer Console:"
file_name=$(basename "${BASH_SOURCE[0]}")
bash --init-file "$file_name"
;;
9)
echo "Update:"
invokeai-update
;;
10)
invokeai --help
;;
[qQ])
exit 0
;;
*)
echo "Invalid selection"
exit;;
# Primary function for the case statement to determine user input
do_choice() {
case $1 in
1)
clear
printf "Generate images with a browser-based interface\n"
invokeai-web $PARAMS
;;
2)
clear
printf "Explore InvokeAI nodes using a command-line interface\n"
invokeai $PARAMS
;;
3)
clear
printf "Textual inversion training\n"
invokeai-ti --gui $PARAMS
;;
4)
clear
printf "Merge models (diffusers type only)\n"
invokeai-merge --gui $PARAMS
;;
5)
clear
printf "Download and install models\n"
invokeai-model-install --root ${INVOKEAI_ROOT}
;;
6)
clear
printf "Change InvokeAI startup options\n"
invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
;;
7)
clear
printf "Re-run the configure script to fix a broken install\n"
invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
;;
8)
clear
printf "Open the developer console\n"
file_name=$(basename "${BASH_SOURCE[0]}")
bash --init-file "$file_name"
;;
9)
clear
printf "Update InvokeAI\n"
invokeai-update
;;
10)
clear
printf "Command-line help\n"
invokeai --help
;;
"HELP 1")
clear
printf "Command-line help\n"
invokeai --help
;;
*)
clear
printf "Exiting...\n"
exit
;;
esac
done
clear
}
# Dialog-based TUI for launcing Invoke functions
do_dialog() {
options=(
1 "Generate images with a browser-based interface"
2 "Generate images using a command-line interface"
3 "Textual inversion training"
4 "Merge models (diffusers type only)"
5 "Download and install models"
6 "Change InvokeAI startup options"
7 "Re-run the configure script to fix a broken install"
8 "Open the developer console"
9 "Update InvokeAI")
choice=$(dialog --clear \
--backtitle "\Zb\Zu\Z3InvokeAI" \
--colors \
--title "What would you like to do?" \
--ok-label "Run" \
--cancel-label "Exit" \
--help-button \
--help-label "CLI Help" \
--menu "Select an option:" \
0 0 0 \
"${options[@]}" \
2>&1 >/dev/tty) || clear
do_choice "$choice"
clear
}
# Command-line interface for launching Invoke functions
do_line_input() {
clear
printf " ** For a more attractive experience, please install the 'dialog' utility using your package manager. **\n\n"
printf "What would you like to do?\n"
printf "1: Generate images using the browser-based interface\n"
printf "2: Explore InvokeAI nodes using the command-line interface\n"
printf "3: Run textual inversion training\n"
printf "4: Merge models (diffusers type only)\n"
printf "5: Download and install models\n"
printf "6: Change InvokeAI startup options\n"
printf "7: Re-run the configure script to fix a broken install\n"
printf "8: Open the developer console\n"
printf "9: Update InvokeAI\n"
printf "10: Command-line help\n"
printf "Q: Quit\n\n"
read -p "Please enter 1-10, Q: [1] " yn
choice=${yn:='1'}
do_choice $choice
clear
}
# Main IF statement for launching Invoke with either the TUI or CLI, and for checking if the user is in the developer console
if [ "$0" != "bash" ]; then
while true; do
if $tui; then
# .dialogrc must be located in the same directory as the invoke.sh script
export DIALOGRC="./.dialogrc"
do_dialog
else
do_line_input
fi
done
else # in developer console
python --version
echo "Press ^D to exit"
printf "Press ^D to exit\n"
export PS1="(InvokeAI) \u@\h \w> "
fi

7
invokeai/app/api/dependencies.py
View File

@ -53,12 +53,11 @@ class ApiDependencies:
events = FastAPIEventService(event_handler_id)
output_folder = os.path.abspath(
os.path.join(os.path.dirname(__file__), "../../../../outputs")
)
output_folder = config.output_path
# TODO: build a file/path manager?
db_location = os.path.join(output_folder, "invokeai.db")
db_location = config.db_path
db_location.parent.mkdir(parents=True,exist_ok=True)
graph_execution_manager = SqliteItemStorage[GraphExecutionState](
filename=db_location, table_name="graph_executions"

16
invokeai/app/api_app.py
View File

@ -3,6 +3,7 @@ import asyncio
from inspect import signature
import uvicorn
from invokeai.backend.util.logging import InvokeAILogger
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
@ -11,8 +12,11 @@ from fastapi.openapi.utils import get_openapi
from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
from pathlib import Path
from pydantic.schema import schema
import invokeai.frontend.web as web_dir
from .api.dependencies import ApiDependencies
from .api.routers import sessions, models, images
from .api.sockets import SocketIO
@ -121,8 +125,7 @@ def custom_openapi():
app.openapi = custom_openapi
# Override API doc favicons
app.mount("/static", StaticFiles(directory="static/dream_web"), name="static")
app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], 'static/dream_web')), name="static")
@app.get("/docs", include_in_schema=False)
def overridden_swagger():
@ -143,10 +146,11 @@ def overridden_redoc():
# Must mount *after* the other routes else it borks em
app.mount(
"/", StaticFiles(directory="invokeai/frontend/web/dist", html=True), name="ui"
)
app.mount("/",
StaticFiles(directory=Path(web_dir.__path__[0],"dist"),
html=True
), name="ui"
)
def invoke_api():
# Start our own event loop for eventing usage

3
invokeai/app/cli_app.py
View File

@ -219,7 +219,8 @@ def invoke_cli():
if config.use_memory_db:
db_location = ":memory:"
else:
db_location = os.path.join(output_folder, "invokeai.db")
db_location = config.db_path
db_location.parent.mkdir(parents=True,exist_ok=True)
logger.info(f'InvokeAI database location is "{db_location}"')

1
invokeai/app/invocations/latent.py
View File

@ -346,7 +346,6 @@ class TextToLatentsInvocation(BaseInvocation):
model = self.get_model(context.services.model_manager)
conditioning_data = self.get_conditioning_data(context, model)
print("type of control input: ", type(self.control))
control_data = self.prep_control_data(model=model, context=context, control_input=self.control,
latents_shape=noise.shape,
do_classifier_free_guidance=(self.cfg_scale >= 1.0))

24
invokeai/app/services/config.py
View File

@ -174,6 +174,7 @@ from pydantic import BaseSettings, Field, parse_obj_as
from typing import ClassVar, Dict, List, Literal, Type, Union, get_origin, get_type_hints, get_args
INIT_FILE = Path('invokeai.yaml')
DB_FILE = Path('invokeai.db')
LEGACY_INIT_FILE = Path('invokeai.init')
class InvokeAISettings(BaseSettings):
@ -380,8 +381,10 @@ setting environment variables INVOKEAI_<setting>.
conf_path : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
embedding_dir : Path = Field(default='embeddings', description='Path to InvokeAI textual inversion aembeddings directory', category='Paths')
gfpgan_model_dir : Path = Field(default="./models/gfpgan/GFPGANv1.4.pth", description='Path to GFPGAN models directory.', category='Paths')
controlnet_dir : Path = Field(default="controlnets", description='Path to directory of ControlNet models.', category='Paths')
legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
lora_dir : Path = Field(default='loras', description='Path to InvokeAI LoRA model directory', category='Paths')
db_dir : Path = Field(default='databases', description='Path to InvokeAI databases directory', category='Paths')
outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
@ -453,6 +456,13 @@ setting environment variables INVOKEAI_<setting>.
def _resolve(self,partial_path:Path)->Path:
return (self.root_path / partial_path).resolve()
@property
def init_file_path(self)->Path:
'''
Path to invokeai.yaml
'''
return self._resolve(INIT_FILE)
@property
def output_path(self)->Path:
'''
@ -460,6 +470,13 @@ setting environment variables INVOKEAI_<setting>.
'''
return self._resolve(self.outdir)
@property
def db_path(self)->Path:
'''
Path to the invokeai.db file.
'''
return self._resolve(self.db_dir) / DB_FILE
@property
def model_conf_path(self)->Path:
'''
@ -502,6 +519,13 @@ setting environment variables INVOKEAI_<setting>.
'''
return self._resolve(self.lora_dir) if self.lora_dir else None
@property
def controlnet_path(self)->Path:
'''
Path to the controlnet models directory.
'''
return self._resolve(self.controlnet_dir) if self.controlnet_dir else None
@property
def autoconvert_path(self)->Path:
'''

0
invokeai/backend/config/__init__.py → invokeai/backend/install/__init__.py
View File

186
invokeai/backend/config/invokeai_configure.py → invokeai/backend/install/invokeai_configure.py
View File

@ -12,8 +12,8 @@ print("Loading Python libraries...\n",file=sys.stderr)
import argparse
import io
import os
import re
import shutil
import textwrap
import traceback
import warnings
from argparse import Namespace
@ -38,7 +38,6 @@ from transformers import (
import invokeai.configs as configs
from invokeai.app.services.config import (
get_invokeai_config,
InvokeAIAppConfig,
)
from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
@ -46,20 +45,20 @@ from invokeai.frontend.install.widgets import (
CenteredButtonPress,
IntTitleSlider,
set_min_terminal_size,
CyclingForm,
MIN_COLS,
MIN_LINES,
)
from invokeai.backend.config.legacy_arg_parsing import legacy_parser
from invokeai.backend.config.model_install_backend import (
from invokeai.backend.install.legacy_arg_parsing import legacy_parser
from invokeai.backend.install.model_install_backend import (
default_dataset,
download_from_hf,
hf_download_with_resume,
recommended_datasets,
UserSelections,
)
from invokeai.app.services.config import InvokeAIAppConfig
warnings.filterwarnings("ignore")
transformers.logging.set_verbosity_error()
@ -70,18 +69,9 @@ config = InvokeAIAppConfig.get_config()
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"
Default_config_file = config.model_conf_path
SD_Configs = config.legacy_conf_path
Datasets = OmegaConf.load(Dataset_path)
# minimum size for the UI
MIN_COLS = 135
MIN_LINES = 45
PRECISION_CHOICES = ['auto','float16','float32','autocast']
INIT_FILE_PREAMBLE = """# InvokeAI initialization file
@ -106,7 +96,7 @@ Command-line client:
invokeai
If you installed using an installation script, run:
{config.root}/invoke.{"bat" if sys.platform == "win32" else "sh"}
{config.root_path}/invoke.{"bat" if sys.platform == "win32" else "sh"}
Add the '--help' argument to see all of the command-line switches available for use.
"""
@ -218,16 +208,11 @@ def download_realesrgan():
model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth"
wdn_model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth"
model_dest = os.path.join(
config.root, "models/realesrgan/realesr-general-x4v3.pth"
)
model_dest = config.root_path / "models/realesrgan/realesr-general-x4v3.pth"
wdn_model_dest = config.root_path / "models/realesrgan/realesr-general-wdn-x4v3.pth"
wdn_model_dest = os.path.join(
config.root, "models/realesrgan/realesr-general-wdn-x4v3.pth"
)
download_with_progress_bar(model_url, model_dest, "RealESRGAN")
download_with_progress_bar(wdn_model_url, wdn_model_dest, "RealESRGANwdn")
download_with_progress_bar(model_url, str(model_dest), "RealESRGAN")
download_with_progress_bar(wdn_model_url, str(wdn_model_dest), "RealESRGANwdn")
def download_gfpgan():
@ -246,8 +231,8 @@ def download_gfpgan():
"./models/gfpgan/weights/parsing_parsenet.pth",
],
):
model_url, model_dest = model[0], os.path.join(config.root, model[1])
download_with_progress_bar(model_url, model_dest, "GFPGAN weights")
model_url, model_dest = model[0], config.root_path / model[1]
download_with_progress_bar(model_url, str(model_dest), "GFPGAN weights")
# ---------------------------------------------
@ -256,8 +241,8 @@ def download_codeformer():
model_url = (
"https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
)
model_dest = os.path.join(config.root, "models/codeformer/codeformer.pth")
download_with_progress_bar(model_url, model_dest, "CodeFormer")
model_dest = config.root_path / "models/codeformer/codeformer.pth"
download_with_progress_bar(model_url, str(model_dest), "CodeFormer")
# ---------------------------------------------
@ -309,7 +294,7 @@ def download_vaes():
if not hf_download_with_resume(
repo_id=repo_id,
model_name=model_name,
model_dir=str(config.root / Model_dir / Weights_dir),
model_dir=str(config.root_path / Model_dir / Weights_dir),
):
raise Exception(f"download of {model_name} failed")
except Exception as e:
@ -324,24 +309,24 @@ def get_root(root: str = None) -> str:
elif os.environ.get("INVOKEAI_ROOT"):
return os.environ.get("INVOKEAI_ROOT")
else:
return config.root
return str(config.root_path)
# -------------------------------------
class editOptsForm(npyscreen.FormMultiPage):
class editOptsForm(CyclingForm, npyscreen.FormMultiPage):
# for responsive resizing - disabled
# FIX_MINIMUM_SIZE_WHEN_CREATED = False
def create(self):
program_opts = self.parentApp.program_opts
old_opts = self.parentApp.invokeai_opts
first_time = not (config.root / 'invokeai.yaml').exists()
first_time = not (config.root_path / 'invokeai.yaml').exists()
access_token = HfFolder.get_token()
window_width, window_height = get_terminal_size()
for i in [
"Configure startup settings. You can come back and change these later.",
"Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.",
"Use cursor arrows to make a checkbox selection, and space to toggle.",
]:
label = """Configure startup settings. You can come back and change these later.
Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.
Use cursor arrows to make a checkbox selection, and space to toggle.
"""
for i in textwrap.wrap(label,width=window_width-6):
self.add_widget_intelligent(
npyscreen.FixedText,
value=i,
@ -368,7 +353,7 @@ class editOptsForm(npyscreen.FormMultiPage):
self.outdir = self.add_widget_intelligent(
npyscreen.TitleFilename,
name="(<tab> autocompletes, ctrl-N advances):",
value=str(old_opts.outdir) or str(default_output_dir()),
value=str(default_output_dir()),
select_dir=True,
must_exist=False,
use_two_lines=False,
@ -391,14 +376,13 @@ class editOptsForm(npyscreen.FormMultiPage):
scroll_exit=True,
)
self.nextrely += 1
for i in [
"If you have an account at HuggingFace you may optionally paste your access token here",
'to allow InvokeAI to download restricted styles & subjects from the "Concept Library".',
"See https://huggingface.co/settings/tokens",
]:
label = """If you have an account at HuggingFace you may optionally paste your access token here
to allow InvokeAI to download restricted styles & subjects from the "Concept Library". See https://huggingface.co/settings/tokens.
"""
for line in textwrap.wrap(label,width=window_width-6):
self.add_widget_intelligent(
npyscreen.FixedText,
value=i,
value=line,
editable=False,
color="CONTROL",
)
@ -475,7 +459,7 @@ class editOptsForm(npyscreen.FormMultiPage):
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.FixedText,
value="Directories containing textual inversion and LoRA models (<tab> autocompletes, ctrl-N advances):",
value="Directories containing textual inversion, controlnet and LoRA models (<tab> autocompletes, ctrl-N advances):",
editable=False,
color="CONTROL",
)
@ -501,6 +485,17 @@ class editOptsForm(npyscreen.FormMultiPage):
begin_entry_at=32,
scroll_exit=True,
)
self.controlnet_dir = self.add_widget_intelligent(
npyscreen.TitleFilename,
name=" ControlNets:",
value=str(default_controlnet_dir()),
select_dir=True,
must_exist=False,
use_two_lines=False,
labelColor="GOOD",
begin_entry_at=32,
scroll_exit=True,
)
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
@ -511,11 +506,11 @@ class editOptsForm(npyscreen.FormMultiPage):
scroll_exit=True,
)
self.nextrely -= 1
for i in [
"BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ",
"AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSE LOCATED AT",
"https://huggingface.co/spaces/CompVis/stable-diffusion-license",
]:
label = """BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ
AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSE LOCATED AT
https://huggingface.co/spaces/CompVis/stable-diffusion-license
"""
for i in textwrap.wrap(label,width=window_width-6):
self.add_widget_intelligent(
npyscreen.FixedText,
value=i,
@ -554,7 +549,7 @@ class editOptsForm(npyscreen.FormMultiPage):
self.editing = False
else:
self.editing = True
def validate_field_values(self, opt: Namespace) -> bool:
bad_fields = []
if not opt.license_acceptance:
@ -590,6 +585,7 @@ class editOptsForm(npyscreen.FormMultiPage):
"always_use_cpu",
"embedding_dir",
"lora_dir",
"controlnet_dir",
]:
setattr(new_opts, attr, getattr(self, attr).value)
@ -617,6 +613,7 @@ class EditOptApplication(npyscreen.NPSAppManaged):
"MAIN",
editOptsForm,
name="InvokeAI Startup Options",
cycle_widgets=True,
)
if not (self.program_opts.skip_sd_weights or self.program_opts.default_only):
self.model_select = self.addForm(
@ -624,6 +621,7 @@ class EditOptApplication(npyscreen.NPSAppManaged):
addModelsForm,
name="Install Stable Diffusion Models",
multipage=True,
cycle_widgets=True,
)
def new_opts(self):
@ -638,16 +636,13 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
def default_startup_options(init_file: Path) -> Namespace:
opts = InvokeAIAppConfig.get_config()
outdir = Path(opts.outdir)
if not outdir.is_absolute():
opts.outdir = str(config.root / opts.outdir)
if not init_file.exists():
opts.nsfw_checker = True
return opts
def default_user_selections(program_opts: Namespace) -> Namespace:
return Namespace(
starter_models=default_dataset()
def default_user_selections(program_opts: Namespace) -> UserSelections:
return UserSelections(
install_models=default_dataset()
if program_opts.default_only
else recommended_datasets()
if program_opts.yes_to_all
@ -655,26 +650,27 @@ def default_user_selections(program_opts: Namespace) -> Namespace:
purge_deleted_models=False,
scan_directory=None,
autoscan_on_startup=None,
import_model_paths=None,
convert_to_diffusers=None,
)
# -------------------------------------
def initialize_rootdir(root: str, yes_to_all: bool = False):
def initialize_rootdir(root: Path, yes_to_all: bool = False):
print("** INITIALIZING INVOKEAI RUNTIME DIRECTORY **")
for name in (
"models",
"configs",
"embeddings",
"text-inversion-output",
"text-inversion-training-data",
"models",
"configs",
"embeddings",
"databases",
"loras",
"controlnets",
"text-inversion-output",
"text-inversion-training-data",
):
os.makedirs(os.path.join(root, name), exist_ok=True)
configs_src = Path(configs.__path__[0])
configs_dest = Path(root) / "configs"
configs_dest = root / "configs"
if not os.path.samefile(configs_src, configs_dest):
shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
@ -685,8 +681,17 @@ def run_console_ui(
) -> (Namespace, Namespace):
# parse_args() will read from init file if present
invokeai_opts = default_startup_options(initfile)
invokeai_opts.root = program_opts.root
set_min_terminal_size(MIN_COLS, MIN_LINES)
# The third argument is needed in the Windows 11 environment to
# launch a console window running this program.
set_min_terminal_size(MIN_COLS, MIN_LINES,'invokeai-configure')
# the install-models application spawns a subprocess to install
# models, and will crash unless this is set before running.
import torch
torch.multiprocessing.set_start_method("spawn")
editApp = EditOptApplication(program_opts, invokeai_opts)
editApp.run()
if editApp.user_cancelled:
@ -700,27 +705,32 @@ def write_opts(opts: Namespace, init_file: Path):
"""
Update the invokeai.yaml file with values from current settings.
"""
# this will load current settings
config = InvokeAIAppConfig.get_config()
new_config = InvokeAIAppConfig.get_config()
new_config.root = config.root
for key,value in opts.__dict__.items():
if hasattr(config,key):
setattr(config,key,value)
if hasattr(new_config,key):
setattr(new_config,key,value)
with open(init_file,'w', encoding='utf-8') as file:
file.write(config.to_yaml())
file.write(new_config.to_yaml())
# -------------------------------------
def default_output_dir() -> Path:
return config.root / "outputs"
return config.root_path / "outputs"
# -------------------------------------
def default_embedding_dir() -> Path:
return config.root / "embeddings"
return config.root_path / "embeddings"
# -------------------------------------
def default_lora_dir() -> Path:
return config.root / "loras"
return config.root_path / "loras"
# -------------------------------------
def default_controlnet_dir() -> Path:
return config.root_path / "controlnets"
# -------------------------------------
def write_default_options(program_opts: Namespace, initfile: Path):
@ -808,9 +818,12 @@ def main():
)
opt = parser.parse_args()
# setting a global here
global config
config.root = Path(os.path.expanduser(get_root(opt.root) or ""))
invoke_args = []
if opt.root:
invoke_args.extend(['--root',opt.root])
if opt.full_precision:
invoke_args.extend(['--precision','float32'])
config.parse_args(invoke_args)
errors = set()
@ -818,17 +831,16 @@ def main():
models_to_download = default_user_selections(opt)
# We check for to see if the runtime directory is correctly initialized.
old_init_file = Path(config.root, 'invokeai.init')
new_init_file = Path(config.root, 'invokeai.yaml')
old_init_file = config.root_path / 'invokeai.init'
new_init_file = config.root_path / 'invokeai.yaml'
if old_init_file.exists() and not new_init_file.exists():
print('** Migrating invokeai.init to invokeai.yaml')
migrate_init_file(old_init_file)
# Load new init file into config
config.parse_args(argv=[],conf=OmegaConf.load(new_init_file))
if not config.model_conf_path.exists():
initialize_rootdir(config.root, opt.yes_to_all)
initialize_rootdir(config.root_path, opt.yes_to_all)
if opt.yes_to_all:
write_default_options(opt, new_init_file)
@ -848,7 +860,7 @@ def main():
if opt.skip_support_models:
print("\n** SKIPPING SUPPORT MODEL DOWNLOADS PER USER REQUEST **")
else:
print("\n** DOWNLOADING SUPPORT MODELS **")
print("\n** CHECKING/UPDATING SUPPORT MODELS **")
download_bert()
download_sd1_clip()
download_sd2_clip()
@ -866,6 +878,8 @@ def main():
process_and_execute(opt, models_to_download)
postscript(errors=errors)
if not opt.yes_to_all:
input('Press any key to continue...')
except KeyboardInterrupt:
print("\nGoodbye! Come back soon.")

0
invokeai/backend/config/legacy_arg_parsing.py → invokeai/backend/install/legacy_arg_parsing.py
View File

232
invokeai/backend/config/model_install_backend.py → invokeai/backend/install/model_install_backend.py
View File

@ -6,23 +6,24 @@ import re
import shutil
import sys
import warnings
from dataclasses import dataclass,field
from pathlib import Path
from tempfile import TemporaryFile
from typing import List
from typing import List, Dict, Callable
import requests
from diffusers import AutoencoderKL
from huggingface_hub import hf_hub_url
from huggingface_hub import hf_hub_url, HfFolder
from omegaconf import OmegaConf
from omegaconf.dictconfig import DictConfig
from tqdm import tqdm
import invokeai.configs as configs
from invokeai.app.services.config import InvokeAIAppConfig
from ..model_management import ModelManager
from ..stable_diffusion import StableDiffusionGeneratorPipeline
from invokeai.app.services.config import InvokeAIAppConfig
from ..stable_diffusion import StableDiffusionGeneratorPipeline
from ..util.logging import InvokeAILogger
warnings.filterwarnings("ignore")
@ -38,6 +39,9 @@ Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
# initial models omegaconf
Datasets = None
# logger
logger = InvokeAILogger.getLogger(name='InvokeAI')
Config_preamble = """
# This file describes the alternative machine learning models
# available to InvokeAI script.
@ -48,11 +52,30 @@ Config_preamble = """
# was trained on.
"""
@dataclass
class ModelInstallList:
'''Class for listing models to be installed/removed'''
install_models: List[str] = field(default_factory=list)
remove_models: List[str] = field(default_factory=list)
@dataclass
class UserSelections():
install_models: List[str]= field(default_factory=list)
remove_models: List[str]=field(default_factory=list)
purge_deleted_models: bool=field(default_factory=list)
install_cn_models: List[str] = field(default_factory=list)
remove_cn_models: List[str] = field(default_factory=list)
install_lora_models: List[str] = field(default_factory=list)
remove_lora_models: List[str] = field(default_factory=list)
install_ti_models: List[str] = field(default_factory=list)
remove_ti_models: List[str] = field(default_factory=list)
scan_directory: Path = None
autoscan_on_startup: bool=False
import_model_paths: str=None
def default_config_file():
return config.model_conf_path
def sd_configs():
return config.legacy_conf_path
@ -60,45 +83,67 @@ def initial_models():
global Datasets
if Datasets:
return Datasets
return (Datasets := OmegaConf.load(Dataset_path))
return (Datasets := OmegaConf.load(Dataset_path)['diffusers'])
def install_requested_models(
install_initial_models: List[str] = None,
remove_models: List[str] = None,
scan_directory: Path = None,
external_models: List[str] = None,
scan_at_startup: bool = False,
precision: str = "float16",
purge_deleted: bool = False,
config_file_path: Path = None,
diffusers: ModelInstallList = None,
controlnet: ModelInstallList = None,
lora: ModelInstallList = None,
ti: ModelInstallList = None,
cn_model_map: Dict[str,str] = None, # temporary - move to model manager
scan_directory: Path = None,
external_models: List[str] = None,
scan_at_startup: bool = False,
precision: str = "float16",
purge_deleted: bool = False,
config_file_path: Path = None,
model_config_file_callback: Callable[[Path],Path] = None
):
"""
Entry point for installing/deleting starter models, or installing external models.
"""
access_token = HfFolder.get_token()
config_file_path = config_file_path or default_config_file()
if not config_file_path.exists():
open(config_file_path, "w")
# prevent circular import here
from ..model_management import ModelManager
model_manager = ModelManager(OmegaConf.load(config_file_path), precision=precision)
if controlnet:
model_manager.install_controlnet_models(controlnet.install_models, access_token=access_token)
model_manager.delete_controlnet_models(controlnet.remove_models)
if remove_models and len(remove_models) > 0:
print("== DELETING UNCHECKED STARTER MODELS ==")
for model in remove_models:
print(f"{model}...")
model_manager.del_model(model, delete_files=purge_deleted)
model_manager.commit(config_file_path)
if lora:
model_manager.install_lora_models(lora.install_models, access_token=access_token)
model_manager.delete_lora_models(lora.remove_models)
if install_initial_models and len(install_initial_models) > 0:
print("== INSTALLING SELECTED STARTER MODELS ==")
successfully_downloaded = download_weight_datasets(
models=install_initial_models,
access_token=None,
precision=precision,
) # FIX: for historical reasons, we don't use model manager here
update_config_file(successfully_downloaded, config_file_path)
if len(successfully_downloaded) < len(install_initial_models):
print("** Some of the model downloads were not successful")
if ti:
model_manager.install_ti_models(ti.install_models, access_token=access_token)
model_manager.delete_ti_models(ti.remove_models)
if diffusers:
# TODO: Replace next three paragraphs with calls into new model manager
if diffusers.remove_models and len(diffusers.remove_models) > 0:
logger.info("Processing requested deletions")
for model in diffusers.remove_models:
logger.info(f"{model}...")
model_manager.del_model(model, delete_files=purge_deleted)
model_manager.commit(config_file_path)
if diffusers.install_models and len(diffusers.install_models) > 0:
logger.info("Installing requested models")
downloaded_paths = download_weight_datasets(
models=diffusers.install_models,
access_token=None,
precision=precision,
)
successful = {x:v for x,v in downloaded_paths.items() if v is not None}
if len(successful) > 0:
update_config_file(successful, config_file_path)
if len(successful) < len(diffusers.install_models):
unsuccessful = [x for x in downloaded_paths if downloaded_paths[x] is None]
logger.warning(f"Some of the model downloads were not successful: {unsuccessful}")
# due to above, we have to reload the model manager because conf file
# was changed behind its back
@ -109,12 +154,14 @@ def install_requested_models(
external_models.append(str(scan_directory))
if len(external_models) > 0:
print("== INSTALLING EXTERNAL MODELS ==")
logger.info("INSTALLING EXTERNAL MODELS")
for path_url_or_repo in external_models:
try:
logger.debug(f'In install_requested_models; callback = {model_config_file_callback}')
model_manager.heuristic_import(
path_url_or_repo,
commit_to_conf=config_file_path,
config_file_callback = model_config_file_callback,
)
except KeyboardInterrupt:
sys.exit(-1)
@ -122,18 +169,22 @@ def install_requested_models(
pass
if scan_at_startup and scan_directory.is_dir():
argument = "--autoconvert"
print('** The global initfile is no longer supported; rewrite to support new yaml format **')
initfile = Path(config.root, 'invokeai.init')
replacement = Path(config.root, f"invokeai.init.new")
directory = str(scan_directory).replace("\\", "/")
with open(initfile, "r") as input:
with open(replacement, "w") as output:
while line := input.readline():
if not line.startswith(argument):
output.writelines([line])
output.writelines([f"{argument} {directory}"])
os.replace(replacement, initfile)
update_autoconvert_dir(scan_directory)
else:
update_autoconvert_dir(None)
def update_autoconvert_dir(autodir: Path):
'''
Update the "autoconvert_dir" option in invokeai.yaml
'''
invokeai_config_path = config.init_file_path
conf = OmegaConf.load(invokeai_config_path)
conf.InvokeAI.Paths.autoconvert_dir = str(autodir) if autodir else None
yaml = OmegaConf.to_yaml(conf)
tmpfile = invokeai_config_path.parent / "new_config.tmp"
with open(tmpfile, "w", encoding="utf-8") as outfile:
outfile.write(yaml)
tmpfile.replace(invokeai_config_path)
# -------------------------------------
@ -145,33 +196,21 @@ def yes_or_no(prompt: str, default_yes=True):
else:
return response[0] in ("y", "Y")
# -------------------------------------
def get_root(root: str = None) -> str:
if root:
return root
elif os.environ.get("INVOKEAI_ROOT"):
return os.environ.get("INVOKEAI_ROOT")
else:
return config.root
# ---------------------------------------------
def recommended_datasets() -> dict:
datasets = dict()
def recommended_datasets() -> List['str']:
datasets = set()
for ds in initial_models().keys():
if initial_models()[ds].get("recommended", False):
datasets[ds] = True
return datasets
datasets.add(ds)
return list(datasets)
# ---------------------------------------------
def default_dataset() -> dict:
datasets = dict()
datasets = set()
for ds in initial_models().keys():
if initial_models()[ds].get("default", False):
datasets[ds] = True
return datasets
datasets.add(ds)
return list(datasets)
# ---------------------------------------------
@ -186,14 +225,14 @@ def all_datasets() -> dict:
# look for legacy model.ckpt in models directory and offer to
# normalize its name
def migrate_models_ckpt():
model_path = os.path.join(config.root, Model_dir, Weights_dir)
model_path = os.path.join(config.root_dir, Model_dir, Weights_dir)
if not os.path.exists(os.path.join(model_path, "model.ckpt")):
return
new_name = initial_models()["stable-diffusion-1.4"]["file"]
print(
logger.warning(
'The Stable Diffusion v4.1 "model.ckpt" is already installed. The name will be changed to {new_name} to avoid confusion.'
)
print(f"model.ckpt => {new_name}")
logger.warning(f"model.ckpt => {new_name}")
os.replace(
os.path.join(model_path, "model.ckpt"), os.path.join(model_path, new_name)
)
@ -206,7 +245,7 @@ def download_weight_datasets(
migrate_models_ckpt()
successful = dict()
for mod in models:
print(f"Downloading {mod}:")
logger.info(f"Downloading {mod}:")
successful[mod] = _download_repo_or_file(
initial_models()[mod], access_token, precision=precision
)
@ -227,11 +266,10 @@ def _download_repo_or_file(
)
return path
def _download_ckpt_weights(mconfig: DictConfig, access_token: str) -> Path:
repo_id = mconfig["repo_id"]
filename = mconfig["file"]
cache_dir = os.path.join(config.root, Model_dir, Weights_dir)
cache_dir = os.path.join(config.root_dir, Model_dir, Weights_dir)
return hf_download_with_resume(
repo_id=repo_id,
model_dir=cache_dir,
@ -244,6 +282,9 @@ def _download_ckpt_weights(mconfig: DictConfig, access_token: str) -> Path:
def download_from_hf(
model_class: object, model_name: str, **kwargs
):
logger = InvokeAILogger.getLogger('InvokeAI')
logger.addFilter(lambda x: 'fp16 is not a valid' not in x.getMessage())
path = config.cache_dir
model = model_class.from_pretrained(
model_name,
@ -275,10 +316,10 @@ def _download_diffusion_weights(
**extra_args,
)
except OSError as e:
if str(e).startswith("fp16 is not a valid"):
if 'Revision Not Found' in str(e):
pass
else:
print(f"An unexpected error occurred while downloading the model: {e})")
logger.error(str(e))
if path:
break
return path
@ -286,9 +327,13 @@ def _download_diffusion_weights(
# ---------------------------------------------
def hf_download_with_resume(
repo_id: str, model_dir: str, model_name: str, access_token: str = None
repo_id: str,
model_dir: str,
model_name: str,
model_dest: Path = None,
access_token: str = None,
) -> Path:
model_dest = Path(os.path.join(model_dir, model_name))
model_dest = model_dest or Path(os.path.join(model_dir, model_name))
os.makedirs(model_dir, exist_ok=True)
url = hf_hub_url(repo_id, model_name)
@ -308,20 +353,19 @@ def hf_download_with_resume(
if (
resp.status_code == 416
): # "range not satisfiable", which means nothing to return
print(f"* {model_name}: complete file found. Skipping.")
logger.info(f"{model_name}: complete file found. Skipping.")
return model_dest
elif resp.status_code == 404:
logger.warning("File not found")
return None
elif resp.status_code != 200:
print(f"** An error occurred during downloading {model_name}: {resp.reason}")
logger.warning(f"{model_name}: {resp.reason}")
elif exist_size > 0:
print(f"* {model_name}: partial file found. Resuming...")
logger.info(f"{model_name}: partial file found. Resuming...")
else:
print(f"* {model_name}: Downloading...")
logger.info(f"{model_name}: Downloading...")
try:
if total < 2000:
print(f"*** ERROR DOWNLOADING {model_name}: {resp.text}")
return None
with open(model_dest, open_mode) as file, tqdm(
desc=model_name,
initial=exist_size,
@ -334,7 +378,7 @@ def hf_download_with_resume(
size = file.write(data)
bar.update(size)
except Exception as e:
print(f"An error occurred while downloading {model_name}: {str(e)}")
logger.error(f"An error occurred while downloading {model_name}: {str(e)}")
return None
return model_dest
@ -359,8 +403,8 @@ def update_config_file(successfully_downloaded: dict, config_file: Path):
try:
backup = None
if os.path.exists(config_file):
print(
f"** {config_file.name} exists. Renaming to {config_file.stem}.yaml.orig"
logger.warning(
f"{config_file.name} exists. Renaming to {config_file.stem}.yaml.orig"
)
backup = config_file.with_suffix(".yaml.orig")
## Ugh. Windows is unable to overwrite an existing backup file, raises a WinError 183
@ -377,16 +421,16 @@ def update_config_file(successfully_downloaded: dict, config_file: Path):
new_config.write(tmp.read())
except Exception as e:
print(f"**Error creating config file {config_file}: {str(e)} **")
logger.error(f"Error creating config file {config_file}: {str(e)}")
if backup is not None:
print("restoring previous config file")
logger.info("restoring previous config file")
## workaround, for WinError 183, see above
if sys.platform == "win32" and config_file.is_file():
config_file.unlink()
backup.rename(config_file)
return
print(f"Successfully created new configuration file {config_file}")
logger.info(f"Successfully created new configuration file {config_file}")
# ---------------------------------------------
@ -420,7 +464,7 @@ def new_config_file_contents(
stanza["height"] = mod["height"]
if "file" in mod:
stanza["weights"] = os.path.relpath(
successfully_downloaded[model], start=config.root
successfully_downloaded[model], start=config.root_dir
)
stanza["config"] = os.path.normpath(
os.path.join(sd_configs(), mod["config"])
@ -453,14 +497,14 @@ def delete_weights(model_name: str, conf_stanza: dict):
if re.match("/VAE/", conf_stanza.get("config")):
return
print(
f"\n** The checkpoint version of {model_name} is superseded by the diffusers version. Deleting the original file {weights}?"
logger.warning(
f"\nThe checkpoint version of {model_name} is superseded by the diffusers version. Deleting the original file {weights}?"
)
weights = Path(weights)
if not weights.is_absolute():
weights = Path(config.root) / weights
weights = config.root_dir / weights
try:
weights.unlink()
except OSError as e:
print(str(e))
logger.error(str(e))

5
invokeai/backend/model_management/convert_ckpt_to_diffusers.py
View File

@ -1093,6 +1093,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
:param vae_path: Path to a checkpoint VAE that will be converted into diffusers and loaded into the pipeline.
"""
config = InvokeAIAppConfig.get_config()
cache_dir = config.cache_dir
with warnings.catch_warnings():
warnings.simplefilter("ignore")
verbosity = dlogging.get_verbosity()
@ -1105,7 +1107,6 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
else:
checkpoint = load_file(checkpoint_path)
cache_dir = config.cache_dir
pipeline_class = (
StableDiffusionGeneratorPipeline
if return_generator_pipeline
@ -1297,7 +1298,7 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
)
safety_checker = StableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker",
cache_dir=config.cache_dir,
cache_dir=cache_dir,
)
feature_extractor = AutoFeatureExtractor.from_pretrained(
"CompVis/stable-diffusion-safety-checker", cache_dir=cache_dir

263
invokeai/backend/model_management/model_manager.py
View File

@ -11,14 +11,16 @@ import gc
import hashlib
import os
import re
import shutil
import sys
import textwrap
import time
import traceback
import warnings
from enum import Enum, auto
from pathlib import Path
from shutil import move, rmtree
from typing import Any, Optional, Union, Callable, types
from typing import Any, Optional, Union, Callable, Dict, List, types
import safetensors
import safetensors.torch
@ -48,6 +50,10 @@ from ..stable_diffusion import (
StableDiffusionGeneratorPipeline,
)
from invokeai.app.services.config import InvokeAIAppConfig
from ..install.model_install_backend import (
Dataset_path,
hf_download_with_resume,
)
from ..util import CUDA_DEVICE, ask_user, download_with_resume
class SDLegacyType(Enum):
@ -314,7 +320,7 @@ class ModelManager(object):
models = {}
for name in sorted(self.config, key=str.casefold):
stanza = self.config[name]
# don't include VAEs in listing (legacy style)
if "config" in stanza and "/VAE/" in stanza["config"]:
continue
@ -526,7 +532,8 @@ class ModelManager(object):
**fp_args,
)
except OSError as e:
if str(e).startswith("fp16 is not a valid"):
if str(e).startswith("fp16 is not a valid") or \
'Invalid rev id: fp16' in str(e):
pass
else:
self.logger.error(
@ -773,7 +780,7 @@ class ModelManager(object):
"""
model_path: Path = None
thing = path_url_or_repo # to save typing
thing = str(path_url_or_repo) # to save typing
self.logger.info(f"Probing {thing} for import")
@ -813,7 +820,9 @@ class ModelManager(object):
Path(thing).rglob("*.safetensors")
):
if model_name := self.heuristic_import(
str(m), commit_to_conf=commit_to_conf
str(m),
commit_to_conf=commit_to_conf,
config_file_callback=config_file_callback,
):
self.logger.info(f"{model_name} successfully imported")
return model_name
@ -859,7 +868,7 @@ class ModelManager(object):
model_config_file = self.globals.legacy_conf_path / "v1-inference.yaml"
elif model_type == SDLegacyType.V1_INPAINT:
self.logger.debug("SD-v1 inpainting model detected")
model_config_file = self.globals.legacy_conf_path / "v1-inpainting-inference.yaml",
model_config_file = self.globals.legacy_conf_path / "v1-inpainting-inference.yaml"
elif model_type == SDLegacyType.V2_v:
self.logger.debug("SD-v2-v model detected")
model_config_file = self.globals.legacy_conf_path / "v2-inference-v.yaml"
@ -868,14 +877,12 @@ class ModelManager(object):
model_config_file = self.globals.legacy_conf_path / "v2-inference.yaml"
elif model_type == SDLegacyType.V2:
self.logger.warning(
f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
f"{thing} is a V2 checkpoint file, but its parameterization cannot be determined."
)
return
else:
self.logger.warning(
f"{thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
f"{thing} is a legacy checkpoint file but not a known Stable Diffusion model."
)
return
if not model_config_file and config_file_callback:
model_config_file = config_file_callback(model_path)
@ -932,34 +939,35 @@ class ModelManager(object):
from . import convert_ckpt_to_diffusers
if diffusers_path.exists():
self.logger.error(
f"The path {str(diffusers_path)} already exists. Please move or remove it and try again."
)
return
model_name = model_name or diffusers_path.name
model_description = model_description or f"Converted version of {model_name}"
self.logger.debug(f"Converting {model_name} to diffusers (30-60s)")
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 = None
if vae:
vae_model = self._load_vae(vae)
vae_path = None
convert_ckpt_to_diffusers(
ckpt_path,
diffusers_path,
extract_ema=True,
original_config_file=original_config_file,
vae=vae_model,
vae_path=vae_path,
scan_needed=scan_needed,
)
self.logger.debug(
f"Success. Converted model is now located at {str(diffusers_path)}"
)
if diffusers_path.exists():
self.logger.error(
f"The path {str(diffusers_path)} already exists. Installing previously-converted path."
)
else:
self.logger.debug(f"Converting {model_name} to diffusers (30-60s)")
# By passing the specified VAE to the conversion function, the autoencoder
# will be built into the model rather than tacked on afterward via the config file
vae_model = None
if vae:
vae_model = self._load_vae(vae)
vae_path = None
convert_ckpt_to_diffusers(
ckpt_path,
diffusers_path,
extract_ema=True,
original_config_file=original_config_file,
vae=vae_model,
vae_path=vae_path,
scan_needed=scan_needed,
)
self.logger.debug(
f"Success. Converted model is now located at {str(diffusers_path)}"
)
self.logger.debug(f"Writing new config file entry for {model_name}")
new_config = dict(
path=str(diffusers_path),
@ -971,9 +979,10 @@ class ModelManager(object):
self.add_model(model_name, new_config, True)
if commit_to_conf:
self.commit(commit_to_conf)
self.logger.debug("Conversion succeeded")
self.logger.debug(f"Model {model_name} installed")
except Exception as e:
self.logger.warning(f"Conversion failed: {str(e)}")
self.logger.warning(traceback.format_exc())
self.logger.warning(
"If you are trying to convert an inpainting or 2.X model, please indicate the correct config file (e.g. v1-inpainting-inference.yaml)"
)
@ -1314,3 +1323,185 @@ class ModelManager(object):
return (
os.getenv("HF_HOME") is not None or os.getenv("XDG_CACHE_HOME") is not None
)
def list_lora_models(self)->Dict[str,bool]:
'''Return a dict of installed lora models; key is either the shortname
defined in INITIAL_MODELS, or the basename of the file in the LoRA
directory. Value is True if installed'''
models = OmegaConf.load(Dataset_path).get('lora') or {}
installed_models = {x: False for x in models.keys()}
dir = self.globals.lora_path
installed_models = dict()
for root, dirs, files in os.walk(dir):
for name in files:
if Path(name).suffix not in ['.safetensors','.ckpt','.pt','.bin']:
continue
if name == 'pytorch_lora_weights.bin':
name = Path(root,name).parent.stem #Path(root,name).stem
else:
name = Path(name).stem
installed_models.update({name: True})
return installed_models
def install_lora_models(self, model_names: list[str], access_token:str=None):
'''Download list of LoRA/LyCORIS models'''
short_names = OmegaConf.load(Dataset_path).get('lora') or {}
for name in model_names:
name = short_names.get(name) or name
# HuggingFace style LoRA
if re.match(r"^[\w.+-]+/([\w.+-]+)$", name):
self.logger.info(f'Downloading LoRA/LyCORIS model {name}')
_,dest_dir = name.split("/")
hf_download_with_resume(
repo_id = name,
model_dir = self.globals.lora_path / dest_dir,
model_name = 'pytorch_lora_weights.bin',
access_token = access_token,
)
elif name.startswith(("http:", "https:", "ftp:")):
download_with_resume(name, self.globals.lora_path)
else:
self.logger.error(f"Unknown repo_id or URL: {name}")
def delete_lora_models(self, model_names: List[str]):
'''Remove the list of lora models'''
for name in model_names:
file_or_directory = self.globals.lora_path / name
if file_or_directory.is_dir():
self.logger.info(f'Purging LoRA/LyCORIS {name}')
shutil.rmtree(str(file_or_directory))
else:
for path in self.globals.lora_path.glob(f'{name}.*'):
self.logger.info(f'Purging LoRA/LyCORIS {name}')
path.unlink()
def list_ti_models(self)->Dict[str,bool]:
'''Return a dict of installed textual models; key is either the shortname
defined in INITIAL_MODELS, or the basename of the file in the LoRA
directory. Value is True if installed'''
models = OmegaConf.load(Dataset_path).get('textual_inversion') or {}
installed_models = {x: False for x in models.keys()}
dir = self.globals.embedding_path
for root, dirs, files in os.walk(dir):
for name in files:
if not Path(name).suffix in ['.bin','.pt','.ckpt','.safetensors']:
continue
if name == 'learned_embeds.bin':
name = Path(root,name).parent.stem #Path(root,name).stem
else:
name = Path(name).stem
installed_models.update({name: True})
return installed_models
def install_ti_models(self, model_names: list[str], access_token: str=None):
'''Download list of textual inversion embeddings'''
short_names = OmegaConf.load(Dataset_path).get('textual_inversion') or {}
for name in model_names:
name = short_names.get(name) or name
if re.match(r"^[\w.+-]+/([\w.+-]+)$", name):
self.logger.info(f'Downloading Textual Inversion embedding {name}')
_,dest_dir = name.split("/")
hf_download_with_resume(
repo_id = name,
model_dir = self.globals.embedding_path / dest_dir,
model_name = 'learned_embeds.bin',
access_token = access_token
)
elif name.startswith(('http:','https:','ftp:')):
download_with_resume(name, self.globals.embedding_path)
else:
self.logger.error(f'{name} does not look like either a HuggingFace repo_id or a downloadable URL')
def delete_ti_models(self, model_names: list[str]):
'''Remove TI embeddings from disk'''
for name in model_names:
file_or_directory = self.globals.embedding_path / name
if file_or_directory.is_dir():
self.logger.info(f'Purging textual inversion embedding {name}')
shutil.rmtree(str(file_or_directory))
else:
for path in self.globals.embedding_path.glob(f'{name}.*'):
self.logger.info(f'Purging textual inversion embedding {name}')
path.unlink()
def list_controlnet_models(self)->Dict[str,bool]:
'''Return a dict of installed controlnet models; key is repo_id or short name
of model (defined in INITIAL_MODELS), and value is True if installed'''
cn_models = OmegaConf.load(Dataset_path).get('controlnet') or {}
installed_models = {x: False for x in cn_models.keys()}
cn_dir = self.globals.controlnet_path
for root, dirs, files in os.walk(cn_dir):
for name in dirs:
if Path(root, name, '.download_complete').exists():
installed_models.update({name.replace('--','/'): True})
return installed_models
def install_controlnet_models(self, model_names: list[str], access_token: str=None):
'''Download list of controlnet models; provide either repo_id or short name listed in INITIAL_MODELS.yaml'''
short_names = OmegaConf.load(Dataset_path).get('controlnet') or {}
dest_dir = self.globals.controlnet_path
dest_dir.mkdir(parents=True,exist_ok=True)
# The model file may be fp32 or fp16, and may be either a
# .bin file or a .safetensors. We try each until we get one,
# preferring 'fp16' if using half precision, and preferring
# safetensors over over bin.
precisions = ['.fp16',''] if self.precision=='float16' else ['']
formats = ['.safetensors','.bin']
possible_filenames = list()
for p in precisions:
for f in formats:
possible_filenames.append(Path(f'diffusion_pytorch_model{p}{f}'))
for directory_name in model_names:
repo_id = short_names.get(directory_name) or directory_name
safe_name = directory_name.replace('/','--')
self.logger.info(f'Downloading ControlNet model {directory_name} ({repo_id})')
hf_download_with_resume(
repo_id = repo_id,
model_dir = dest_dir / safe_name,
model_name = 'config.json',
access_token = access_token
)
path = None
for filename in possible_filenames:
suffix = filename.suffix
dest_filename = Path(f'diffusion_pytorch_model{suffix}')
self.logger.info(f'Checking availability of {directory_name}/{filename}...')
path = hf_download_with_resume(
repo_id = repo_id,
model_dir = dest_dir / safe_name,
model_name = str(filename),
access_token = access_token,
model_dest = Path(dest_dir, safe_name, dest_filename),
)
if path:
(path.parent / '.download_complete').touch()
break
def delete_controlnet_models(self, model_names: List[str]):
'''Remove the list of controlnet models'''
for name in model_names:
safe_name = name.replace('/','--')
directory = self.globals.controlnet_path / safe_name
if directory.exists():
self.logger.info(f'Purging controlnet model {name}')
shutil.rmtree(str(directory))

2
invokeai/backend/util/logging.py
View File

@ -30,7 +30,6 @@ import invokeai.backend.util.logging as IAILogger
IAILogger.debug('this is a debugging message')
"""
import logging
import logging.handlers
import socket
import urllib.parse
@ -195,7 +194,6 @@ class InvokeAILogger(object):
@classmethod
def getLoggers(cls, config: InvokeAIAppConfig) -> list[logging.Handler]:
handler_strs = config.log_handlers
print(f'handler_strs={handler_strs}')
handlers = list()
for handler in handler_strs:
handler_name,*args = handler.split('=',2)

6
invokeai/backend/util/util.py
View File

@ -322,8 +322,8 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
logger.warning("corrupt existing file found. re-downloading")
os.remove(dest)
exist_size = 0
if resp.status_code == 416 or exist_size == content_length:
if resp.status_code == 416 or (content_length > 0 and exist_size == content_length):
logger.warning(f"{dest}: complete file found. Skipping.")
return dest
elif resp.status_code == 206 or exist_size > 0:
@ -331,7 +331,7 @@ def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path
elif resp.status_code != 200:
logger.error(f"An error occurred during downloading {dest}: {resp.reason}")
else:
logger.error(f"{dest}: Downloading...")
logger.info(f"{dest}: Downloading...")
try:
if content_length < 2000:

190
invokeai/configs/INITIAL_MODELS.yaml
View File

@ -1,83 +1,107 @@
stable-diffusion-1.5:
description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
repo_id: runwayml/stable-diffusion-v1-5
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: True
default: True
sd-inpainting-1.5:
description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
repo_id: runwayml/stable-diffusion-inpainting
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: True
stable-diffusion-2.1:
description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-1
format: diffusers
recommended: True
sd-inpainting-2.0:
description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-inpainting
format: diffusers
recommended: False
analog-diffusion-1.0:
description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
repo_id: wavymulder/Analog-Diffusion
format: diffusers
recommended: false
deliberate-1.0:
description: Versatile model that produces detailed images up to 768px (4.27 GB)
format: diffusers
repo_id: XpucT/Deliberate
recommended: False
d&d-diffusion-1.0:
description: Dungeons & Dragons characters (2.13 GB)
format: diffusers
repo_id: 0xJustin/Dungeons-and-Diffusion
recommended: False
dreamlike-photoreal-2.0:
description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
format: diffusers
repo_id: dreamlike-art/dreamlike-photoreal-2.0
recommended: False
inkpunk-1.0:
description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
format: diffusers
repo_id: Envvi/Inkpunk-Diffusion
recommended: False
openjourney-4.0:
description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
format: diffusers
repo_id: prompthero/openjourney
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: False
portrait-plus-1.0:
description: An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)
format: diffusers
repo_id: wavymulder/portraitplus
recommended: False
seek-art-mega-1.0:
description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
repo_id: coreco/seek.art_MEGA
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: False
trinart-2.0:
description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
repo_id: naclbit/trinart_stable_diffusion_v2
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: False
waifu-diffusion-1.4:
description: An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)
repo_id: hakurei/waifu-diffusion
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: False
# This file predefines a few models that the user may want to install.
diffusers:
stable-diffusion-1.5:
description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
repo_id: runwayml/stable-diffusion-v1-5
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: True
default: True
sd-inpainting-1.5:
description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
repo_id: runwayml/stable-diffusion-inpainting
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: True
stable-diffusion-2.1:
description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-1
format: diffusers
recommended: True
sd-inpainting-2.0:
description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
repo_id: stabilityai/stable-diffusion-2-inpainting
format: diffusers
recommended: False
analog-diffusion-1.0:
description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
repo_id: wavymulder/Analog-Diffusion
format: diffusers
recommended: false
deliberate-1.0:
description: Versatile model that produces detailed images up to 768px (4.27 GB)
format: diffusers
repo_id: XpucT/Deliberate
recommended: False
d&d-diffusion-1.0:
description: Dungeons & Dragons characters (2.13 GB)
format: diffusers
repo_id: 0xJustin/Dungeons-and-Diffusion
recommended: False
dreamlike-photoreal-2.0:
description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
format: diffusers
repo_id: dreamlike-art/dreamlike-photoreal-2.0
recommended: False
inkpunk-1.0:
description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
format: diffusers
repo_id: Envvi/Inkpunk-Diffusion
recommended: False
openjourney-4.0:
description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
format: diffusers
repo_id: prompthero/openjourney
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: False
portrait-plus-1.0:
description: An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)
format: diffusers
repo_id: wavymulder/portraitplus
recommended: False
seek-art-mega-1.0:
description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
repo_id: coreco/seek.art_MEGA
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: False
trinart-2.0:
description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
repo_id: naclbit/trinart_stable_diffusion_v2
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: False
waifu-diffusion-1.4:
description: An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)
repo_id: hakurei/waifu-diffusion
format: diffusers
vae:
repo_id: stabilityai/sd-vae-ft-mse
recommended: False
controlnet:
canny: lllyasviel/control_v11p_sd15_canny
inpaint: lllyasviel/control_v11p_sd15_inpaint
mlsd: lllyasviel/control_v11p_sd15_mlsd
depth: lllyasviel/control_v11f1p_sd15_depth
normal_bae: lllyasviel/control_v11p_sd15_normalbae
seg: lllyasviel/control_v11p_sd15_seg
lineart: lllyasviel/control_v11p_sd15_lineart
lineart_anime: lllyasviel/control_v11p_sd15s2_lineart_anime
scribble: lllyasviel/control_v11p_sd15_scribble
softedge: lllyasviel/control_v11p_sd15_softedge
shuffle: lllyasviel/control_v11e_sd15_shuffle
tile: lllyasviel/control_v11f1e_sd15_tile
ip2p: lllyasviel/control_v11e_sd15_ip2p
textual_inversion:
'EasyNegative': https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
'ahx-beta-453407d': sd-concepts-library/ahx-beta-453407d
lora:
'LowRA': https://civitai.com/api/download/models/63006
'Ink scenery': https://civitai.com/api/download/models/83390
'sd-model-finetuned-lora-t4': sayakpaul/sd-model-finetuned-lora-t4

2
invokeai/frontend/install/invokeai_configure.py
View File

@ -1,4 +1,4 @@
"""
Wrapper for invokeai.backend.configure.invokeai_configure
"""
from ...backend.config.invokeai_configure import main
from ...backend.install.invokeai_configure import main

40
invokeai/frontend/install/invokeai_update.py
View File

@ -4,14 +4,14 @@ pip install <path_to_git_source>.
'''
import os
import platform
import pkg_resources
import psutil
import requests
from rich import box, print
from rich.console import Console, Group, group
from rich.console import Console, group
from rich.panel import Panel
from rich.prompt import Prompt
from rich.style import Style
from rich.syntax import Syntax
from rich.text import Text
from invokeai.version import __version__
@ -32,6 +32,18 @@ else:
def get_versions()->dict:
return requests.get(url=INVOKE_AI_REL).json()
def invokeai_is_running()->bool:
for p in psutil.process_iter():
try:
cmdline = p.cmdline()
matches = [x for x in cmdline if x.endswith(('invokeai','invokeai.exe'))]
if matches:
print(f':exclamation: [bold red]An InvokeAI instance appears to be running as process {p.pid}[/red bold]')
return True
except (psutil.AccessDenied,psutil.NoSuchProcess):
continue
return False
def welcome(versions: dict):
@group()
@ -60,8 +72,22 @@ def welcome(versions: dict):
)
console.line()
def get_extras():
extras = ''
try:
dist = pkg_resources.get_distribution('xformers')
extras = '[xformers]'
except pkg_resources.DistributionNotFound:
pass
return extras
def main():
versions = get_versions()
if invokeai_is_running():
print(f':exclamation: [bold red]Please terminate all running instances of InvokeAI before updating.[/red bold]')
input('Press any key to continue...')
return
welcome(versions)
tag = None
@ -78,13 +104,15 @@ def main():
elif choice=='4':
branch = Prompt.ask('Enter an InvokeAI branch name')
extras = get_extras()
print(f':crossed_fingers: Upgrading to [yellow]{tag if tag else release}[/yellow]')
if release:
cmd = f'pip install {INVOKE_AI_SRC}/{release}.zip --use-pep517 --upgrade'
cmd = f"pip install 'invokeai{extras} @ {INVOKE_AI_SRC}/{release}.zip' --use-pep517 --upgrade"
elif tag:
cmd = f'pip install {INVOKE_AI_TAG}/{tag}.zip --use-pep517 --upgrade'
cmd = f"pip install 'invokeai{extras} @ {INVOKE_AI_TAG}/{tag}.zip' --use-pep517 --upgrade"
else:
cmd = f'pip install {INVOKE_AI_BRANCH}/{branch}.zip --use-pep517 --upgrade'
cmd = f"pip install 'invokeai{extras} @ {INVOKE_AI_BRANCH}/{branch}.zip' --use-pep517 --upgrade"
print('')
print('')
if os.system(cmd)==0:

931
invokeai/frontend/install/model_install.py
View File

File diff suppressed because it is too large Load Diff

374
invokeai/frontend/install/widgets.py
View File

@ -5,35 +5,75 @@ import curses
import math
import os
import platform
import pyperclip
import struct
import subprocess
import sys
from shutil import get_terminal_size
import npyscreen
import textwrap
import npyscreen.wgmultiline as wgmultiline
from npyscreen import fmPopup
from shutil import get_terminal_size
from curses import BUTTON2_CLICKED,BUTTON3_CLICKED
# minimum size for UIs
MIN_COLS = 120
MIN_LINES = 50
# -------------------------------------
def set_terminal_size(columns: int, lines: int):
def set_terminal_size(columns: int, lines: int, launch_command: str=None):
ts = get_terminal_size()
width = max(columns,ts.columns)
height = max(lines,ts.lines)
OS = platform.uname().system
if OS == "Windows":
os.system(f"mode con: cols={columns} lines={lines}")
# The new Windows Terminal doesn't resize, so we relaunch in a CMD window.
# Would prefer to use execvpe() here, but somehow it is not working properly
# in the Windows 10 environment.
if 'IA_RELAUNCHED' not in os.environ:
args=['conhost']
args.extend([launch_command] if launch_command else [sys.argv[0]])
args.extend(sys.argv[1:])
os.environ['IA_RELAUNCHED'] = 'True'
os.execvp('conhost',args)
else:
_set_terminal_size_powershell(width,height)
elif OS in ["Darwin", "Linux"]:
import fcntl
import termios
_set_terminal_size_unix(width,height)
winsize = struct.pack("HHHH", lines, columns, 0, 0)
fcntl.ioctl(sys.stdout.fileno(), termios.TIOCSWINSZ, winsize)
sys.stdout.write("\x1b[8;{rows};{cols}t".format(rows=lines, cols=columns))
sys.stdout.flush()
def _set_terminal_size_powershell(width: int, height: int):
script=f'''
$pshost = get-host
$pswindow = $pshost.ui.rawui
$newsize = $pswindow.buffersize
$newsize.height = 3000
$newsize.width = {width}
$pswindow.buffersize = $newsize
$newsize = $pswindow.windowsize
$newsize.height = {height}
$newsize.width = {width}
$pswindow.windowsize = $newsize
'''
subprocess.run(["powershell","-Command","-"],input=script,text=True)
def _set_terminal_size_unix(width: int, height: int):
import fcntl
import termios
def set_min_terminal_size(min_cols: int, min_lines: int):
winsize = struct.pack("HHHH", height, width, 0, 0)
fcntl.ioctl(sys.stdout.fileno(), termios.TIOCSWINSZ, winsize)
sys.stdout.write("\x1b[8;{height};{width}t".format(height=height, width=width))
sys.stdout.flush()
def set_min_terminal_size(min_cols: int, min_lines: int, launch_command: str=None):
# make sure there's enough room for the ui
term_cols, term_lines = get_terminal_size()
if term_cols >= min_cols and term_lines >= min_lines:
return
cols = max(term_cols, min_cols)
lines = max(term_lines, min_lines)
set_terminal_size(cols, lines)
set_terminal_size(cols, lines, launch_command)
class IntSlider(npyscreen.Slider):
def translate_value(self):
@ -42,7 +82,23 @@ class IntSlider(npyscreen.Slider):
stri = stri.rjust(l)
return stri
# -------------------------------------
# fix npyscreen form so that cursor wraps both forward and backward
class CyclingForm(object):
def find_previous_editable(self, *args):
done = False
n = self.editw-1
while not done:
if self._widgets__[n].editable and not self._widgets__[n].hidden:
self.editw = n
done = True
n -= 1
if n<0:
if self.cycle_widgets:
n = len(self._widgets__)-1
else:
done = True
# -------------------------------------
class CenteredTitleText(npyscreen.TitleText):
def __init__(self, *args, **keywords):
@ -93,14 +149,7 @@ class FloatSlider(npyscreen.Slider):
class FloatTitleSlider(npyscreen.TitleText):
_entry_type = FloatSlider
class MultiSelectColumns(npyscreen.MultiSelect):
def __init__(self, screen, columns: int = 1, values: list = [], **keywords):
self.columns = columns
self.value_cnt = len(values)
self.rows = math.ceil(self.value_cnt / self.columns)
super().__init__(screen, values=values, **keywords)
class SelectColumnBase():
def make_contained_widgets(self):
self._my_widgets = []
column_width = self.width // self.columns
@ -150,53 +199,242 @@ class MultiSelectColumns(npyscreen.MultiSelect):
def h_cursor_line_right(self, ch):
super().h_cursor_line_down(ch)
def handle_mouse_event(self, mouse_event):
mouse_id, rel_x, rel_y, z, bstate = self.interpret_mouse_event(mouse_event)
column_width = self.width // self.columns
column_height = math.ceil(self.value_cnt / self.columns)
column_no = rel_x // column_width
row_no = rel_y // self._contained_widget_height
self.cursor_line = column_no * column_height + row_no
if bstate & curses.BUTTON1_DOUBLE_CLICKED:
if hasattr(self,'on_mouse_double_click'):
self.on_mouse_double_click(self.cursor_line)
self.display()
class TextBox(npyscreen.MultiLineEdit):
def update(self, clear=True):
if clear:
self.clear()
class MultiSelectColumns( SelectColumnBase, npyscreen.MultiSelect):
def __init__(self, screen, columns: int = 1, values: list = [], **keywords):
self.columns = columns
self.value_cnt = len(values)
self.rows = math.ceil(self.value_cnt / self.columns)
super().__init__(screen, values=values, **keywords)
HEIGHT = self.height
WIDTH = self.width
# draw box.
self.parent.curses_pad.hline(self.rely, self.relx, curses.ACS_HLINE, WIDTH)
self.parent.curses_pad.hline(
self.rely + HEIGHT, self.relx, curses.ACS_HLINE, WIDTH
)
self.parent.curses_pad.vline(
self.rely, self.relx, curses.ACS_VLINE, self.height
)
self.parent.curses_pad.vline(
self.rely, self.relx + WIDTH, curses.ACS_VLINE, HEIGHT
)
def on_mouse_double_click(self, cursor_line):
self.h_select_toggle(cursor_line)
# draw corners
self.parent.curses_pad.addch(
self.rely,
self.relx,
curses.ACS_ULCORNER,
)
self.parent.curses_pad.addch(
self.rely,
self.relx + WIDTH,
curses.ACS_URCORNER,
)
self.parent.curses_pad.addch(
self.rely + HEIGHT,
self.relx,
curses.ACS_LLCORNER,
)
self.parent.curses_pad.addch(
self.rely + HEIGHT,
self.relx + WIDTH,
curses.ACS_LRCORNER,
)
class SingleSelectWithChanged(npyscreen.SelectOne):
def __init__(self,*args,**kwargs):
super().__init__(*args,**kwargs)
# fool our superclass into thinking drawing area is smaller - this is really hacky but it seems to work
(relx, rely, height, width) = (self.relx, self.rely, self.height, self.width)
self.relx += 1
self.rely += 1
self.height -= 1
self.width -= 1
super().update(clear=False)
(self.relx, self.rely, self.height, self.width) = (relx, rely, height, width)
def h_select(self,ch):
super().h_select(ch)
if self.on_changed:
self.on_changed(self.value)
class SingleSelectColumns(SelectColumnBase, SingleSelectWithChanged):
def __init__(self, screen, columns: int = 1, values: list = [], **keywords):
self.columns = columns
self.value_cnt = len(values)
self.rows = math.ceil(self.value_cnt / self.columns)
self.on_changed = None
super().__init__(screen, values=values, **keywords)
def when_value_edited(self):
self.h_select(self.cursor_line)
def when_cursor_moved(self):
self.h_select(self.cursor_line)
def h_cursor_line_right(self,ch):
self.h_exit_down('bye bye')
class TextBoxInner(npyscreen.MultiLineEdit):
def __init__(self,*args,**kwargs):
super().__init__(*args,**kwargs)
self.yank = None
self.handlers.update({
"^A": self.h_cursor_to_start,
"^E": self.h_cursor_to_end,
"^K": self.h_kill,
"^F": self.h_cursor_right,
"^B": self.h_cursor_left,
"^Y": self.h_yank,
"^V": self.h_paste,
})
def h_cursor_to_start(self, input):
self.cursor_position = 0
def h_cursor_to_end(self, input):
self.cursor_position = len(self.value)
def h_kill(self, input):
self.yank = self.value[self.cursor_position:]
self.value = self.value[:self.cursor_position]
def h_yank(self, input):
if self.yank:
self.paste(self.yank)
def paste(self, text: str):
self.value = self.value[:self.cursor_position] + text + self.value[self.cursor_position:]
self.cursor_position += len(text)
def h_paste(self, input: int=0):
try:
text = pyperclip.paste()
except ModuleNotFoundError:
text = "To paste with the mouse on Linux, please install the 'xclip' program."
self.paste(text)
def handle_mouse_event(self, mouse_event):
mouse_id, rel_x, rel_y, z, bstate = self.interpret_mouse_event(mouse_event)
if bstate & (BUTTON2_CLICKED|BUTTON3_CLICKED):
self.h_paste()
# def update(self, clear=True):
# if clear:
# self.clear()
# HEIGHT = self.height
# WIDTH = self.width
# # draw box.
# self.parent.curses_pad.hline(self.rely, self.relx, curses.ACS_HLINE, WIDTH)
# self.parent.curses_pad.hline(
# self.rely + HEIGHT, self.relx, curses.ACS_HLINE, WIDTH
# )
# self.parent.curses_pad.vline(
# self.rely, self.relx, curses.ACS_VLINE, self.height
# )
# self.parent.curses_pad.vline(
# self.rely, self.relx + WIDTH, curses.ACS_VLINE, HEIGHT
# )
# # draw corners
# self.parent.curses_pad.addch(
# self.rely,
# self.relx,
# curses.ACS_ULCORNER,
# )
# self.parent.curses_pad.addch(
# self.rely,
# self.relx + WIDTH,
# curses.ACS_URCORNER,
# )
# self.parent.curses_pad.addch(
# self.rely + HEIGHT,
# self.relx,
# curses.ACS_LLCORNER,
# )
# self.parent.curses_pad.addch(
# self.rely + HEIGHT,
# self.relx + WIDTH,
# curses.ACS_LRCORNER,
# )
# # fool our superclass into thinking drawing area is smaller - this is really hacky but it seems to work
# (relx, rely, height, width) = (self.relx, self.rely, self.height, self.width)
# self.relx += 1
# self.rely += 1
# self.height -= 1
# self.width -= 1
# super().update(clear=False)
# (self.relx, self.rely, self.height, self.width) = (relx, rely, height, width)
class TextBox(npyscreen.BoxTitle):
_contained_widget = TextBoxInner
class BufferBox(npyscreen.BoxTitle):
_contained_widget = npyscreen.BufferPager
class ConfirmCancelPopup(fmPopup.ActionPopup):
DEFAULT_COLUMNS = 100
def on_ok(self):
self.value = True
def on_cancel(self):
self.value = False
class FileBox(npyscreen.BoxTitle):
_contained_widget = npyscreen.Filename
class PrettyTextBox(npyscreen.BoxTitle):
_contained_widget = TextBox
def _wrap_message_lines(message, line_length):
lines = []
for line in message.split('\n'):
lines.extend(textwrap.wrap(line.rstrip(), line_length))
return lines
def _prepare_message(message):
if isinstance(message, list) or isinstance(message, tuple):
return "\n".join([ s.rstrip() for s in message])
#return "\n".join(message)
else:
return message
def select_stable_diffusion_config_file(
form_color: str='DANGER',
wrap:bool =True,
model_name:str='Unknown',
):
message = "Please select the correct base model for the V2 checkpoint named {model_name}. Press <CANCEL> to skip installation."
title = "CONFIG FILE SELECTION"
options=[
"An SD v2.x base model (512 pixels; no 'parameterization:' line in its yaml file)",
"An SD v2.x v-predictive model (768 pixels; 'parameterization: \"v\"' line in its yaml file)",
"Skip installation for now and come back later",
"Enter config file path manually",
]
F = ConfirmCancelPopup(
name=title,
color=form_color,
cycle_widgets=True,
lines=16,
)
F.preserve_selected_widget = True
mlw = F.add(
wgmultiline.Pager,
max_height=4,
editable=False,
)
mlw_width = mlw.width-1
if wrap:
message = _wrap_message_lines(message, mlw_width)
mlw.values = message
choice = F.add(
SingleSelectWithChanged,
values = options,
value = [0],
max_height = len(options)+1,
scroll_exit=True,
)
file = F.add(
FileBox,
name='Path to config file',
max_height=3,
hidden=True,
must_exist=True,
scroll_exit=True
)
def toggle_visible(value):
value = value[0]
if value==3:
file.hidden=False
else:
file.hidden=True
F.display()
choice.on_changed = toggle_visible
F.editw = 1
F.edit()
if not F.value:
return None
assert choice.value[0] in range(0,4),'invalid choice'
choices = ['epsilon','v','abort',file.value]
return choices[choice.value[0]]

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