mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
merge with main
This commit is contained in:
Lincoln Stein
commit
90333c0074
Binary file not shown.
@ -1,164 +0,0 @@
|
||||
@echo off
|
||||
|
||||
@rem This script will install git (if not found on the PATH variable)
|
||||
@rem using micromamba (an 8mb static-linked single-file binary, conda replacement).
|
||||
@rem For users who already have git, this step will be skipped.
|
||||
|
||||
@rem Next, it'll download the project's source code.
|
||||
@rem Then it will download a self-contained, standalone Python and unpack it.
|
||||
@rem Finally, it'll create the Python virtual environment and preload the models.
|
||||
|
||||
@rem This enables a user to install this project without manually installing git or Python
|
||||
|
||||
@rem change to the script's directory
|
||||
PUSHD "%~dp0"
|
||||
|
||||
set "no_cache_dir=--no-cache-dir"
|
||||
if "%1" == "use-cache" (
|
||||
set "no_cache_dir="
|
||||
)
|
||||
|
||||
echo ***** Installing InvokeAI.. *****
|
||||
@rem Config
|
||||
set INSTALL_ENV_DIR=%cd%\installer_files\env
|
||||
@rem https://mamba.readthedocs.io/en/latest/installation.html
|
||||
set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
|
||||
set RELEASE_URL=https://github.com/invoke-ai/InvokeAI
|
||||
set RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
|
||||
set PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
|
||||
set PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-x86_64-pc-windows-msvc-shared-install_only.tar.gz
|
||||
|
||||
set PACKAGES_TO_INSTALL=
|
||||
|
||||
call git --version >.tmp1 2>.tmp2
|
||||
if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% git
|
||||
|
||||
@rem Cleanup
|
||||
del /q .tmp1 .tmp2
|
||||
|
||||
@rem (if necessary) install git into a contained environment
|
||||
if "%PACKAGES_TO_INSTALL%" NEQ "" (
|
||||
@rem download micromamba
|
||||
echo ***** Downloading micromamba from %MICROMAMBA_DOWNLOAD_URL% to micromamba.exe *****
|
||||
|
||||
call curl -L "%MICROMAMBA_DOWNLOAD_URL%" > micromamba.exe
|
||||
|
||||
@rem test the mamba binary
|
||||
echo ***** Micromamba version: *****
|
||||
call micromamba.exe --version
|
||||
|
||||
@rem create the installer env
|
||||
if not exist "%INSTALL_ENV_DIR%" (
|
||||
call micromamba.exe create -y --prefix "%INSTALL_ENV_DIR%"
|
||||
)
|
||||
|
||||
echo ***** Packages to install:%PACKAGES_TO_INSTALL% *****
|
||||
|
||||
call micromamba.exe install -y --prefix "%INSTALL_ENV_DIR%" -c conda-forge %PACKAGES_TO_INSTALL%
|
||||
|
||||
if not exist "%INSTALL_ENV_DIR%" (
|
||||
echo ----- There was a problem while installing "%PACKAGES_TO_INSTALL%" using micromamba. Cannot continue. -----
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
)
|
||||
|
||||
del /q micromamba.exe
|
||||
|
||||
@rem For 'git' only
|
||||
set PATH=%INSTALL_ENV_DIR%\Library\bin;%PATH%
|
||||
|
||||
@rem Download/unpack/clean up InvokeAI release sourceball
|
||||
set err_msg=----- InvokeAI source download failed -----
|
||||
echo Trying to download "%RELEASE_URL%%RELEASE_SOURCEBALL%"
|
||||
curl -L %RELEASE_URL%%RELEASE_SOURCEBALL% --output InvokeAI.tgz
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
set err_msg=----- InvokeAI source unpack failed -----
|
||||
tar -zxf InvokeAI.tgz
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
del /q InvokeAI.tgz
|
||||
|
||||
set err_msg=----- InvokeAI source copy failed -----
|
||||
cd InvokeAI-*
|
||||
xcopy . .. /e /h
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
cd ..
|
||||
|
||||
@rem cleanup
|
||||
for /f %%i in ('dir /b InvokeAI-*') do rd /s /q %%i
|
||||
rd /s /q .dev_scripts .github docker-build tests
|
||||
del /q requirements.in requirements-mkdocs.txt shell.nix
|
||||
|
||||
echo ***** Unpacked InvokeAI source *****
|
||||
|
||||
@rem Download/unpack/clean up python-build-standalone
|
||||
set err_msg=----- Python download failed -----
|
||||
curl -L %PYTHON_BUILD_STANDALONE_URL%/%PYTHON_BUILD_STANDALONE% --output python.tgz
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
set err_msg=----- Python unpack failed -----
|
||||
tar -zxf python.tgz
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
del /q python.tgz
|
||||
|
||||
echo ***** Unpacked python-build-standalone *****
|
||||
|
||||
@rem create venv
|
||||
set err_msg=----- problem creating venv -----
|
||||
.\python\python -E -s -m venv .venv
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
call .venv\Scripts\activate.bat
|
||||
|
||||
echo ***** Created Python virtual environment *****
|
||||
|
||||
@rem Print venv's Python version
|
||||
set err_msg=----- problem calling venv's python -----
|
||||
echo We're running under
|
||||
.venv\Scripts\python --version
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
set err_msg=----- pip update failed -----
|
||||
.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location --upgrade pip wheel
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
echo ***** Updated pip and wheel *****
|
||||
|
||||
set err_msg=----- requirements file copy failed -----
|
||||
copy binary_installer\py3.10-windows-x86_64-cuda-reqs.txt requirements.txt
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
set err_msg=----- main pip install failed -----
|
||||
.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -r requirements.txt
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
echo ***** Installed Python dependencies *****
|
||||
|
||||
set err_msg=----- InvokeAI setup failed -----
|
||||
.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -e .
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
|
||||
copy binary_installer\invoke.bat.in .\invoke.bat
|
||||
echo ***** Installed invoke launcher script ******
|
||||
|
||||
@rem more cleanup
|
||||
rd /s /q binary_installer installer_files
|
||||
|
||||
@rem preload the models
|
||||
call .venv\Scripts\python ldm\invoke\config\invokeai_configure.py
|
||||
set err_msg=----- model download clone failed -----
|
||||
if %errorlevel% neq 0 goto err_exit
|
||||
deactivate
|
||||
|
||||
echo ***** Finished downloading models *****
|
||||
|
||||
echo All done! Execute the file invoke.bat in this directory to start InvokeAI
|
||||
pause
|
||||
exit
|
||||
|
||||
:err_exit
|
||||
echo %err_msg%
|
||||
pause
|
||||
exit
|
||||
@ -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 " $scriptdir/invoke.sh"
|
||||
echo "to start InvokeAI."
|
||||
read -p "Press any key to exit..."
|
||||
exit
|
||||
@ -1,36 +0,0 @@
|
||||
@echo off
|
||||
|
||||
PUSHD "%~dp0"
|
||||
call .venv\Scripts\activate.bat
|
||||
|
||||
echo Do you want to generate images using the
|
||||
echo 1. command-line
|
||||
echo 2. browser-based UI
|
||||
echo OR
|
||||
echo 3. open the developer console
|
||||
set /p choice="Please enter 1, 2 or 3: "
|
||||
if /i "%choice%" == "1" (
|
||||
echo Starting the InvokeAI command-line.
|
||||
.venv\Scripts\python scripts\invoke.py %*
|
||||
) else if /i "%choice%" == "2" (
|
||||
echo Starting the InvokeAI browser-based UI.
|
||||
.venv\Scripts\python scripts\invoke.py --web %*
|
||||
) else if /i "%choice%" == "3" (
|
||||
echo Developer Console
|
||||
echo Python command is:
|
||||
where python
|
||||
echo Python version is:
|
||||
python --version
|
||||
echo *************************
|
||||
echo You are now in the system shell, with the local InvokeAI Python virtual environment activated,
|
||||
echo so that you can troubleshoot this InvokeAI installation as necessary.
|
||||
echo *************************
|
||||
echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
|
||||
call cmd /k
|
||||
) else (
|
||||
echo Invalid selection
|
||||
pause
|
||||
exit /b
|
||||
)
|
||||
|
||||
deactivate
|
||||
@ -1,46 +0,0 @@
|
||||
#!/usr/bin/env sh
|
||||
|
||||
set -eu
|
||||
|
||||
. .venv/bin/activate
|
||||
|
||||
# set required env var for torch on mac MPS
|
||||
if [ "$(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
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -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.
|
||||
@ -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
|
||||
@ -43,7 +43,8 @@ socket_io = SocketIO(app)
|
||||
|
||||
# initialize config
|
||||
# this is a module global
|
||||
app_config = InvokeAIAppConfig()
|
||||
app_config = InvokeAIAppConfig.get_config()
|
||||
app_config.parse_args()
|
||||
|
||||
# Add startup event to load dependencies
|
||||
@app.on_event("startup")
|
||||
|
||||
@ -38,7 +38,7 @@ from .services.invocation_services import InvocationServices
|
||||
from .services.invoker import Invoker
|
||||
from .services.processor import DefaultInvocationProcessor
|
||||
from .services.sqlite import SqliteItemStorage
|
||||
from .services.config import get_invokeai_config
|
||||
from .services.config import InvokeAIAppConfig
|
||||
|
||||
class CliCommand(BaseModel):
|
||||
command: Union[BaseCommand.get_commands() + BaseInvocation.get_invocations()] = Field(discriminator="type") # type: ignore
|
||||
@ -197,7 +197,8 @@ logger = logger.InvokeAILogger.getLogger()
|
||||
|
||||
def invoke_cli():
|
||||
# this gets the basic configuration
|
||||
config = get_invokeai_config()
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
config.parse_args()
|
||||
|
||||
# get the optional list of invocations to execute on the command line
|
||||
parser = config.get_parser()
|
||||
|
||||
@ -51,18 +51,32 @@ in INVOKEAI_ROOT. You can replace supersede this by providing any
|
||||
OmegaConf dictionary object initialization time:
|
||||
|
||||
omegaconf = OmegaConf.load('/tmp/init.yaml')
|
||||
conf = InvokeAIAppConfig(conf=omegaconf)
|
||||
conf = InvokeAIAppConfig()
|
||||
conf.parse_args(conf=omegaconf)
|
||||
|
||||
By default, InvokeAIAppConfig will parse the contents of `sys.argv` at
|
||||
initialization time. You may pass a list of strings in the optional
|
||||
InvokeAIAppConfig.parse_args() will parse the contents of `sys.argv`
|
||||
at initialization time. You may pass a list of strings in the optional
|
||||
`argv` argument to use instead of the system argv:
|
||||
|
||||
conf = InvokeAIAppConfig(arg=['--xformers_enabled'])
|
||||
conf.parse_args(argv=['--xformers_enabled'])
|
||||
|
||||
It is also possible to set a value at initialization time. This value
|
||||
has highest priority.
|
||||
It is also possible to set a value at initialization time. However, if
|
||||
you call parse_args() it may be overwritten.
|
||||
|
||||
conf = InvokeAIAppConfig(xformers_enabled=True)
|
||||
conf.parse_args(argv=['--no-xformers'])
|
||||
conf.xformers_enabled
|
||||
# False
|
||||
|
||||
|
||||
To avoid this, use `get_config()` to retrieve the application-wide
|
||||
configuration object. This will retain any properties set at object
|
||||
creation time:
|
||||
|
||||
conf = InvokeAIAppConfig.get_config(xformers_enabled=True)
|
||||
conf.parse_args(argv=['--no-xformers'])
|
||||
conf.xformers_enabled
|
||||
# True
|
||||
|
||||
Any setting can be overwritten by setting an environment variable of
|
||||
form: "INVOKEAI_<setting>", as in:
|
||||
@ -76,18 +90,23 @@ Order of precedence (from highest):
|
||||
4) config file options
|
||||
5) pydantic defaults
|
||||
|
||||
Typical usage:
|
||||
Typical usage at the top level file:
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from invokeai.invocations.generate import TextToImageInvocation
|
||||
|
||||
# get global configuration and print its nsfw_checker value
|
||||
conf = InvokeAIAppConfig()
|
||||
conf = InvokeAIAppConfig.get_config()
|
||||
conf.parse_args()
|
||||
print(conf.nsfw_checker)
|
||||
|
||||
Typical usage in a backend module:
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
# get global configuration and print its nsfw_checker value
|
||||
conf = InvokeAIAppConfig.get_config()
|
||||
print(conf.nsfw_checker)
|
||||
|
||||
# get the text2image invocation and print its step value
|
||||
text2image = TextToImageInvocation()
|
||||
print(text2image.steps)
|
||||
|
||||
Computed properties:
|
||||
|
||||
@ -103,10 +122,11 @@ a Path object:
|
||||
lora_path - path to the LoRA directory
|
||||
|
||||
In most cases, you will want to create a single InvokeAIAppConfig
|
||||
object for the entire application. The get_invokeai_config() function
|
||||
object for the entire application. The InvokeAIAppConfig.get_config() function
|
||||
does this:
|
||||
|
||||
config = get_invokeai_config()
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
config.parse_args() # read values from the command line/config file
|
||||
print(config.root)
|
||||
|
||||
# Subclassing
|
||||
@ -140,7 +160,9 @@ two configs are kept in separate sections of the config file:
|
||||
legacy_conf_dir: configs/stable-diffusion
|
||||
outdir: outputs
|
||||
...
|
||||
|
||||
'''
|
||||
from __future__ import annotations
|
||||
import argparse
|
||||
import pydoc
|
||||
import os
|
||||
@ -155,9 +177,6 @@ INIT_FILE = Path('invokeai.yaml')
|
||||
DB_FILE = Path('invokeai.db')
|
||||
LEGACY_INIT_FILE = Path('invokeai.init')
|
||||
|
||||
# This global stores a singleton InvokeAIAppConfig configuration object
|
||||
global_config = None
|
||||
|
||||
class InvokeAISettings(BaseSettings):
|
||||
'''
|
||||
Runtime configuration settings in which default values are
|
||||
@ -330,6 +349,9 @@ the command-line client (recommended for experts only), or
|
||||
can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
|
||||
setting environment variables INVOKEAI_<setting>.
|
||||
'''
|
||||
singleton_config: ClassVar[InvokeAIAppConfig] = None
|
||||
singleton_init: ClassVar[Dict] = None
|
||||
|
||||
#fmt: off
|
||||
type: Literal["InvokeAI"] = "InvokeAI"
|
||||
host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
|
||||
@ -376,33 +398,44 @@ setting environment variables INVOKEAI_<setting>.
|
||||
log_level : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="debug", description="Emit logging messages at this level or higher", category="Logging")
|
||||
#fmt: on
|
||||
|
||||
def __init__(self, conf: DictConfig = None, argv: List[str]=None, **kwargs):
|
||||
def parse_args(self, argv: List[str]=None, conf: DictConfig = None, clobber=False):
|
||||
'''
|
||||
Initialize InvokeAIAppconfig.
|
||||
Update settings with contents of init file, environment, and
|
||||
command-line settings.
|
||||
:param conf: alternate Omegaconf dictionary object
|
||||
:param argv: aternate sys.argv list
|
||||
:param **kwargs: attributes to initialize with
|
||||
:param clobber: ovewrite any initialization parameters passed during initialization
|
||||
'''
|
||||
super().__init__(**kwargs)
|
||||
|
||||
# Set the runtime root directory. We parse command-line switches here
|
||||
# in order to pick up the --root_dir option.
|
||||
self.parse_args(argv)
|
||||
super().parse_args(argv)
|
||||
if conf is None:
|
||||
try:
|
||||
conf = OmegaConf.load(self.root_dir / INIT_FILE)
|
||||
except:
|
||||
pass
|
||||
InvokeAISettings.initconf = conf
|
||||
|
||||
|
||||
# parse args again in order to pick up settings in configuration file
|
||||
self.parse_args(argv)
|
||||
super().parse_args(argv)
|
||||
|
||||
# restore initialization values
|
||||
hints = get_type_hints(self)
|
||||
for k in kwargs:
|
||||
setattr(self,k,parse_obj_as(hints[k],kwargs[k]))
|
||||
if self.singleton_init and not clobber:
|
||||
hints = get_type_hints(self.__class__)
|
||||
for k in self.singleton_init:
|
||||
setattr(self,k,parse_obj_as(hints[k],self.singleton_init[k]))
|
||||
|
||||
@classmethod
|
||||
def get_config(cls,**kwargs)->InvokeAIAppConfig:
|
||||
'''
|
||||
This returns a singleton InvokeAIAppConfig configuration object.
|
||||
'''
|
||||
if cls.singleton_config is None \
|
||||
or type(cls.singleton_config)!=cls \
|
||||
or (kwargs and cls.singleton_init != kwargs):
|
||||
cls.singleton_config = cls(**kwargs)
|
||||
cls.singleton_init = kwargs
|
||||
return cls.singleton_config
|
||||
|
||||
@property
|
||||
def root_path(self)->Path:
|
||||
'''
|
||||
@ -541,11 +574,8 @@ class PagingArgumentParser(argparse.ArgumentParser):
|
||||
text = self.format_help()
|
||||
pydoc.pager(text)
|
||||
|
||||
def get_invokeai_config(cls:Type[InvokeAISettings]=InvokeAIAppConfig,**kwargs)->InvokeAIAppConfig:
|
||||
def get_invokeai_config(**kwargs)->InvokeAIAppConfig:
|
||||
'''
|
||||
This returns a singleton InvokeAIAppConfig configuration object.
|
||||
Legacy function which returns InvokeAIAppConfig.get_config()
|
||||
'''
|
||||
global global_config
|
||||
if global_config is None or type(global_config)!=cls:
|
||||
global_config = cls(**kwargs)
|
||||
return global_config
|
||||
return InvokeAIAppConfig.get_config(**kwargs)
|
||||
|
||||
@ -26,7 +26,6 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
|
||||
self._table_name = table_name
|
||||
self._id_field = id_field # TODO: validate that T has this field
|
||||
self._lock = Lock()
|
||||
|
||||
self._conn = sqlite3.connect(
|
||||
self._filename, check_same_thread=False
|
||||
) # TODO: figure out a better threading solution
|
||||
|
||||
@ -6,7 +6,8 @@ be suppressed or deferred
|
||||
"""
|
||||
import numpy as np
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
class PatchMatch:
|
||||
"""
|
||||
@ -21,7 +22,6 @@ class PatchMatch:
|
||||
|
||||
@classmethod
|
||||
def _load_patch_match(self):
|
||||
config = get_invokeai_config()
|
||||
if self.tried_load:
|
||||
return
|
||||
if config.try_patchmatch:
|
||||
|
||||
@ -33,10 +33,11 @@ from PIL import Image, ImageOps
|
||||
from transformers import AutoProcessor, CLIPSegForImageSegmentation
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
|
||||
CLIPSEG_SIZE = 352
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
class SegmentedGrayscale(object):
|
||||
def __init__(self, image: Image, heatmap: torch.Tensor):
|
||||
@ -83,7 +84,6 @@ class Txt2Mask(object):
|
||||
|
||||
def __init__(self, device="cpu", refined=False):
|
||||
logger.info("Initializing clipseg model for text to mask inference")
|
||||
config = get_invokeai_config()
|
||||
|
||||
# BUG: we are not doing anything with the device option at this time
|
||||
self.device = device
|
||||
|
||||
@ -55,6 +55,8 @@ from invokeai.backend.install.model_install_backend import (
|
||||
UserSelections,
|
||||
)
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
|
||||
transformers.logging.set_verbosity_error()
|
||||
@ -62,7 +64,7 @@ transformers.logging.set_verbosity_error()
|
||||
|
||||
# --------------------------globals-----------------------
|
||||
|
||||
config = get_invokeai_config(argv=[])
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
Model_dir = "models"
|
||||
Weights_dir = "ldm/stable-diffusion-v1/"
|
||||
@ -301,7 +303,7 @@ 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:
|
||||
@ -326,7 +328,7 @@ class editOptsForm(npyscreen.FormMultiPage):
|
||||
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 [
|
||||
@ -641,7 +643,7 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
|
||||
|
||||
|
||||
def default_startup_options(init_file: Path) -> Namespace:
|
||||
opts = InvokeAIAppConfig(argv=[])
|
||||
opts = InvokeAIAppConfig.get_config()
|
||||
if not init_file.exists():
|
||||
opts.nsfw_checker = True
|
||||
return opts
|
||||
@ -709,10 +711,10 @@ def write_opts(opts: Namespace, init_file: Path):
|
||||
"""
|
||||
Update the invokeai.yaml file with values from current settings.
|
||||
"""
|
||||
|
||||
# this will load default settings
|
||||
new_config = InvokeAIAppConfig(argv=[])
|
||||
# this will load current settings
|
||||
new_config = InvokeAIAppConfig.get_config()
|
||||
new_config.root = config.root
|
||||
|
||||
for key,value in opts.__dict__.items():
|
||||
if hasattr(new_config,key):
|
||||
setattr(new_config,key,value)
|
||||
@ -722,19 +724,19 @@ def write_opts(opts: Namespace, init_file: Path):
|
||||
|
||||
# -------------------------------------
|
||||
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 / "controlnets"
|
||||
return config.root_path / "controlnets"
|
||||
|
||||
# -------------------------------------
|
||||
def write_default_options(program_opts: Namespace, initfile: Path):
|
||||
@ -748,7 +750,7 @@ def write_default_options(program_opts: Namespace, initfile: Path):
|
||||
# yaml format.
|
||||
def migrate_init_file(legacy_format:Path):
|
||||
old = legacy_parser.parse_args([f'@{str(legacy_format)}'])
|
||||
new = InvokeAIAppConfig(conf={})
|
||||
new = InvokeAIAppConfig.get_config()
|
||||
|
||||
fields = list(get_type_hints(InvokeAIAppConfig).keys())
|
||||
for attr in fields:
|
||||
@ -840,7 +842,8 @@ def main():
|
||||
if old_init_file.exists() and not new_init_file.exists():
|
||||
print('** Migrating invokeai.init to invokeai.yaml')
|
||||
migrate_init_file(old_init_file)
|
||||
config.parse_args([]) # reread defaults
|
||||
# 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)
|
||||
@ -877,7 +880,6 @@ def main():
|
||||
if opt.skip_sd_weights:
|
||||
print("\n** SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST **")
|
||||
elif models_to_download:
|
||||
print(models_to_download)
|
||||
print("\n** DOWNLOADING DIFFUSION WEIGHTS **")
|
||||
process_and_execute(opt, models_to_download)
|
||||
|
||||
|
||||
@ -20,14 +20,16 @@ from tqdm import tqdm
|
||||
|
||||
import invokeai.configs as configs
|
||||
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from ..stable_diffusion import StableDiffusionGeneratorPipeline
|
||||
from ..util.logging import InvokeAILogger
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
|
||||
# --------------------------globals-----------------------
|
||||
config = get_invokeai_config(argv=[])
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
Model_dir = "models"
|
||||
Weights_dir = "ldm/stable-diffusion-v1/"
|
||||
|
||||
|
||||
@ -26,7 +26,7 @@ import torch
|
||||
from safetensors.torch import load_file
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
from .model_manager import ModelManager, SDLegacyType
|
||||
|
||||
@ -842,7 +842,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
|
||||
|
||||
def convert_ldm_clip_checkpoint(checkpoint):
|
||||
text_model = CLIPTextModel.from_pretrained(
|
||||
"openai/clip-vit-large-patch14", cache_dir=get_invokeai_config().cache_dir
|
||||
"openai/clip-vit-large-patch14", cache_dir=InvokeAIAppConfig.get_config().cache_dir
|
||||
)
|
||||
|
||||
keys = list(checkpoint.keys())
|
||||
@ -897,7 +897,7 @@ textenc_pattern = re.compile("|".join(protected.keys()))
|
||||
|
||||
|
||||
def convert_paint_by_example_checkpoint(checkpoint):
|
||||
cache_dir = get_invokeai_config().cache_dir
|
||||
cache_dir = InvokeAIAppConfig.get_config().cache_dir
|
||||
config = CLIPVisionConfig.from_pretrained(
|
||||
"openai/clip-vit-large-patch14", cache_dir=cache_dir
|
||||
)
|
||||
@ -969,7 +969,7 @@ def convert_paint_by_example_checkpoint(checkpoint):
|
||||
|
||||
|
||||
def convert_open_clip_checkpoint(checkpoint):
|
||||
cache_dir = get_invokeai_config().cache_dir
|
||||
cache_dir = InvokeAIAppConfig.get_config().cache_dir
|
||||
text_model = CLIPTextModel.from_pretrained(
|
||||
"stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir
|
||||
)
|
||||
@ -1092,8 +1092,9 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
|
||||
:param vae: A diffusers VAE to load into the pipeline.
|
||||
:param vae_path: Path to a checkpoint VAE that will be converted into diffusers and loaded into the pipeline.
|
||||
"""
|
||||
invoke_config = get_invokeai_config()
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
cache_dir = invoke_config.cache_dir
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore")
|
||||
verbosity = dlogging.get_verbosity()
|
||||
|
||||
@ -49,7 +49,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import (
|
||||
from ..stable_diffusion import (
|
||||
StableDiffusionGeneratorPipeline,
|
||||
)
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from ..install.model_install_backend import (
|
||||
Dataset_path,
|
||||
hf_download_with_resume,
|
||||
@ -104,7 +104,7 @@ class ModelManager(object):
|
||||
if not isinstance(config, DictConfig):
|
||||
config = OmegaConf.load(config)
|
||||
self.config = config
|
||||
self.globals = get_invokeai_config()
|
||||
self.globals = InvokeAIAppConfig.get_config()
|
||||
self.precision = precision
|
||||
self.device = torch.device(device_type)
|
||||
self.max_loaded_models = max_loaded_models
|
||||
@ -1063,7 +1063,7 @@ class ModelManager(object):
|
||||
"""
|
||||
# Three transformer models to check: bert, clip and safety checker, and
|
||||
# the diffusers as well
|
||||
config = get_invokeai_config()
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
models_dir = config.root_dir / "models"
|
||||
legacy_locations = [
|
||||
Path(
|
||||
@ -1293,7 +1293,7 @@ class ModelManager(object):
|
||||
|
||||
@classmethod
|
||||
def _delete_model_from_cache(cls,repo_id):
|
||||
cache_info = scan_cache_dir(get_invokeai_config().cache_dir)
|
||||
cache_info = scan_cache_dir(InvokeAIAppConfig.get_config().cache_dir)
|
||||
|
||||
# I'm sure there is a way to do this with comprehensions
|
||||
# but the code quickly became incomprehensible!
|
||||
@ -1310,7 +1310,7 @@ class ModelManager(object):
|
||||
|
||||
@staticmethod
|
||||
def _abs_path(path: str | Path) -> Path:
|
||||
globals = get_invokeai_config()
|
||||
globals = InvokeAIAppConfig.get_config()
|
||||
if path is None or Path(path).is_absolute():
|
||||
return path
|
||||
return Path(globals.root_dir, path).resolve()
|
||||
|
||||
@ -21,10 +21,12 @@ from compel.prompt_parser import (
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from ..stable_diffusion import InvokeAIDiffuserComponent
|
||||
from ..util import torch_dtype
|
||||
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
def get_uc_and_c_and_ec(prompt_string,
|
||||
model: InvokeAIDiffuserComponent,
|
||||
log_tokens=False, skip_normalize_legacy_blend=False):
|
||||
@ -37,9 +39,7 @@ def get_uc_and_c_and_ec(prompt_string,
|
||||
textual_inversion_manager=model.textual_inversion_manager,
|
||||
dtype_for_device_getter=torch_dtype,
|
||||
truncate_long_prompts=False,
|
||||
)
|
||||
|
||||
config = get_invokeai_config()
|
||||
)
|
||||
|
||||
# get rid of any newline characters
|
||||
prompt_string = prompt_string.replace("\n", " ")
|
||||
|
||||
@ -6,7 +6,7 @@ import numpy as np
|
||||
import torch
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
pretrained_model_url = (
|
||||
"https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
|
||||
@ -18,7 +18,7 @@ class CodeFormerRestoration:
|
||||
self, codeformer_dir="models/codeformer", codeformer_model_path="codeformer.pth"
|
||||
) -> None:
|
||||
|
||||
self.globals = get_invokeai_config()
|
||||
self.globals = InvokeAIAppConfig.get_config()
|
||||
codeformer_dir = self.globals.root_dir / codeformer_dir
|
||||
self.model_path = codeformer_dir / codeformer_model_path
|
||||
self.codeformer_model_exists = self.model_path.exists()
|
||||
|
||||
@ -7,11 +7,11 @@ import torch
|
||||
from PIL import Image
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
class GFPGAN:
|
||||
def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
|
||||
self.globals = get_invokeai_config()
|
||||
self.globals = InvokeAIAppConfig.get_config()
|
||||
if not os.path.isabs(gfpgan_model_path):
|
||||
gfpgan_model_path = self.globals.root_dir / gfpgan_model_path
|
||||
self.model_path = gfpgan_model_path
|
||||
|
||||
@ -6,8 +6,8 @@ from PIL import Image
|
||||
from PIL.Image import Image as ImageType
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
config = get_invokeai_config()
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
class ESRGAN:
|
||||
def __init__(self, bg_tile_size=400) -> None:
|
||||
|
||||
@ -15,9 +15,11 @@ from transformers import AutoFeatureExtractor
|
||||
|
||||
import invokeai.assets.web as web_assets
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from .util import CPU_DEVICE
|
||||
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
class SafetyChecker(object):
|
||||
CAUTION_IMG = "caution.png"
|
||||
|
||||
@ -26,7 +28,6 @@ class SafetyChecker(object):
|
||||
caution = Image.open(path)
|
||||
self.caution_img = caution.resize((caution.width // 2, caution.height // 2))
|
||||
self.device = device
|
||||
config = get_invokeai_config()
|
||||
|
||||
try:
|
||||
safety_model_id = "CompVis/stable-diffusion-safety-checker"
|
||||
|
||||
@ -17,15 +17,16 @@ from huggingface_hub import (
|
||||
hf_hub_url,
|
||||
)
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
logger = InvokeAILogger.getLogger()
|
||||
|
||||
class HuggingFaceConceptsLibrary(object):
|
||||
def __init__(self, root=None):
|
||||
"""
|
||||
Initialize the Concepts object. May optionally pass a root directory.
|
||||
"""
|
||||
self.config = get_invokeai_config()
|
||||
self.config = InvokeAIAppConfig.get_config()
|
||||
self.root = root or self.config.root
|
||||
self.hf_api = HfApi()
|
||||
self.local_concepts = dict()
|
||||
|
||||
@ -40,7 +40,7 @@ from torchvision.transforms.functional import resize as tv_resize
|
||||
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
|
||||
from typing_extensions import ParamSpec
|
||||
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from ..util import CPU_DEVICE, normalize_device
|
||||
from .diffusion import (
|
||||
AttentionMapSaver,
|
||||
@ -364,7 +364,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
|
||||
"""
|
||||
if xformers is available, use it, otherwise use sliced attention.
|
||||
"""
|
||||
config = get_invokeai_config()
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
if (
|
||||
torch.cuda.is_available()
|
||||
and is_xformers_available()
|
||||
|
||||
@ -10,7 +10,7 @@ from diffusers.models.attention_processor import AttentionProcessor
|
||||
from typing_extensions import TypeAlias
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
from .cross_attention_control import (
|
||||
Arguments,
|
||||
@ -72,7 +72,7 @@ class InvokeAIDiffuserComponent:
|
||||
:param model: the unet model to pass through to cross attention control
|
||||
:param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
|
||||
"""
|
||||
config = get_invokeai_config()
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
self.conditioning = None
|
||||
self.model = model
|
||||
self.is_running_diffusers = is_running_diffusers
|
||||
@ -112,23 +112,25 @@ class InvokeAIDiffuserComponent:
|
||||
# TODO resuscitate attention map saving
|
||||
# self.remove_attention_map_saving()
|
||||
|
||||
def override_cross_attention(
|
||||
self, conditioning: ExtraConditioningInfo, step_count: int
|
||||
) -> Dict[str, AttentionProcessor]:
|
||||
"""
|
||||
setup cross attention .swap control. for diffusers this replaces the attention processor, so
|
||||
the previous attention processor is returned so that the caller can restore it later.
|
||||
"""
|
||||
self.conditioning = conditioning
|
||||
self.cross_attention_control_context = Context(
|
||||
arguments=self.conditioning.cross_attention_control_args,
|
||||
step_count=step_count,
|
||||
)
|
||||
return override_cross_attention(
|
||||
self.model,
|
||||
self.cross_attention_control_context,
|
||||
is_running_diffusers=self.is_running_diffusers,
|
||||
)
|
||||
# apparently unused code
|
||||
# TODO: delete
|
||||
# def override_cross_attention(
|
||||
# self, conditioning: ExtraConditioningInfo, step_count: int
|
||||
# ) -> Dict[str, AttentionProcessor]:
|
||||
# """
|
||||
# setup cross attention .swap control. for diffusers this replaces the attention processor, so
|
||||
# the previous attention processor is returned so that the caller can restore it later.
|
||||
# """
|
||||
# self.conditioning = conditioning
|
||||
# self.cross_attention_control_context = Context(
|
||||
# arguments=self.conditioning.cross_attention_control_args,
|
||||
# step_count=step_count,
|
||||
# )
|
||||
# return override_cross_attention(
|
||||
# self.model,
|
||||
# self.cross_attention_control_context,
|
||||
# is_running_diffusers=self.is_running_diffusers,
|
||||
# )
|
||||
|
||||
def restore_default_cross_attention(
|
||||
self, restore_attention_processor: Optional["AttentionProcessor"] = None
|
||||
|
||||
@ -88,7 +88,7 @@ def save_progress(
|
||||
|
||||
|
||||
def parse_args():
|
||||
config = InvokeAIAppConfig(argv=[])
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
parser = PagingArgumentParser(
|
||||
description="Textual inversion training"
|
||||
)
|
||||
|
||||
@ -4,15 +4,15 @@ from contextlib import nullcontext
|
||||
|
||||
import torch
|
||||
from torch import autocast
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
CPU_DEVICE = torch.device("cpu")
|
||||
CUDA_DEVICE = torch.device("cuda")
|
||||
MPS_DEVICE = torch.device("mps")
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
def choose_torch_device() -> torch.device:
|
||||
"""Convenience routine for guessing which GPU device to run model on"""
|
||||
config = get_invokeai_config()
|
||||
if config.always_use_cpu:
|
||||
return CPU_DEVICE
|
||||
if torch.cuda.is_available():
|
||||
@ -32,7 +32,6 @@ def choose_precision(device: torch.device) -> str:
|
||||
|
||||
|
||||
def torch_dtype(device: torch.device) -> torch.dtype:
|
||||
config = get_invokeai_config()
|
||||
if config.full_precision:
|
||||
return torch.float32
|
||||
if choose_precision(device) == "float16":
|
||||
|
||||
@ -52,13 +52,13 @@ from invokeai.frontend.install.widgets import (
|
||||
set_min_terminal_size,
|
||||
select_stable_diffusion_config_file,
|
||||
)
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
|
||||
# minimum size for the UI
|
||||
MIN_COLS = 140
|
||||
MIN_LINES = 50
|
||||
|
||||
config = get_invokeai_config()
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
# build a table mapping all non-printable characters to None
|
||||
# for stripping control characters
|
||||
@ -679,7 +679,6 @@ class AddModelApplication(npyscreen.NPSAppManaged):
|
||||
self.user_selections = UserSelections()
|
||||
|
||||
def onStart(self):
|
||||
print('here i am')
|
||||
npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
|
||||
self.main_form = self.addForm(
|
||||
"MAIN", addModelsForm, name="Install Stable Diffusion Models", cycle_widgets=True,
|
||||
|
||||
@ -20,12 +20,12 @@ from npyscreen import widget
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
from invokeai.services.config import get_invokeai_config
|
||||
from invokeai.services.config import InvokeAIAppConfig
|
||||
from ...backend.model_management import ModelManager
|
||||
from ...frontend.install.widgets import FloatTitleSlider
|
||||
|
||||
DEST_MERGED_MODEL_DIR = "merged_models"
|
||||
config = get_invokeai_config()
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
def merge_diffusion_models(
|
||||
model_ids_or_paths: List[Union[str, Path]],
|
||||
|
||||
@ -22,7 +22,7 @@ from omegaconf import OmegaConf
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
from invokeai.app.services.config import get_invokeai_config
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from ...backend.training import (
|
||||
do_textual_inversion_training,
|
||||
parse_args
|
||||
@ -423,7 +423,7 @@ def do_front_end(args: Namespace):
|
||||
save_args(args)
|
||||
|
||||
try:
|
||||
do_textual_inversion_training(get_invokeai_config(),**args)
|
||||
do_textual_inversion_training(InvokeAIAppConfig.get_config(),**args)
|
||||
copy_to_embeddings_folder(args)
|
||||
except Exception as e:
|
||||
logger.error("An exception occurred during training. The exception was:")
|
||||
@ -436,7 +436,7 @@ def main():
|
||||
global config
|
||||
|
||||
args = parse_args()
|
||||
config = get_invokeai_config(argv=[])
|
||||
config = InvokeAIAppConfig.get_config()
|
||||
|
||||
# change root if needed
|
||||
if args.root_dir:
|
||||
|
||||
@ -46,9 +46,12 @@ const ImageDndContext = (props: ImageDndContextProps) => {
|
||||
const touchSensor = useSensor(TouchSensor, {
|
||||
activationConstraint: { distance: 15 },
|
||||
});
|
||||
const keyboardSensor = useSensor(KeyboardSensor);
|
||||
// TODO: Use KeyboardSensor - needs composition of multiple collisionDetection algos
|
||||
// Alternatively, fix `rectIntersection` collection detection to work with the drag overlay
|
||||
// (currently the drag element collision rect is not correctly calculated)
|
||||
// const keyboardSensor = useSensor(KeyboardSensor);
|
||||
|
||||
const sensors = useSensors(mouseSensor, touchSensor, keyboardSensor);
|
||||
const sensors = useSensors(mouseSensor, touchSensor);
|
||||
|
||||
return (
|
||||
<DndContext
|
||||
|
||||
@ -1,23 +1,17 @@
|
||||
import {
|
||||
Box,
|
||||
Flex,
|
||||
Icon,
|
||||
IconButtonProps,
|
||||
Image,
|
||||
Text,
|
||||
} from '@chakra-ui/react';
|
||||
import { Box, Flex, Icon, IconButtonProps, Image } from '@chakra-ui/react';
|
||||
import { useDraggable, useDroppable } from '@dnd-kit/core';
|
||||
import { useCombinedRefs } from '@dnd-kit/utilities';
|
||||
import IAIIconButton from 'common/components/IAIIconButton';
|
||||
import { IAIImageFallback } from 'common/components/IAIImageFallback';
|
||||
import ImageMetadataOverlay from 'common/components/ImageMetadataOverlay';
|
||||
import { useGetUrl } from 'common/util/getUrl';
|
||||
import { AnimatePresence, motion } from 'framer-motion';
|
||||
import { AnimatePresence } from 'framer-motion';
|
||||
import { ReactElement, SyntheticEvent } from 'react';
|
||||
import { memo, useRef } from 'react';
|
||||
import { FaImage, FaTimes } from 'react-icons/fa';
|
||||
import { ImageDTO } from 'services/api';
|
||||
import { v4 as uuidv4 } from 'uuid';
|
||||
import IAIDropOverlay from './IAIDropOverlay';
|
||||
|
||||
type IAIDndImageProps = {
|
||||
image: ImageDTO | null | undefined;
|
||||
@ -138,7 +132,7 @@ const IAIDndImage = (props: IAIDndImageProps) => {
|
||||
</Box>
|
||||
)}
|
||||
<AnimatePresence>
|
||||
{active && <DropOverlay isOver={isOver} />}
|
||||
{active && <IAIDropOverlay isOver={isOver} />}
|
||||
</AnimatePresence>
|
||||
</Flex>
|
||||
)}
|
||||
@ -164,7 +158,7 @@ const IAIDndImage = (props: IAIDndImageProps) => {
|
||||
/>
|
||||
</Flex>
|
||||
<AnimatePresence>
|
||||
{active && <DropOverlay isOver={isOver} />}
|
||||
{active && <IAIDropOverlay isOver={isOver} />}
|
||||
</AnimatePresence>
|
||||
</>
|
||||
)}
|
||||
@ -173,86 +167,3 @@ const IAIDndImage = (props: IAIDndImageProps) => {
|
||||
};
|
||||
|
||||
export default memo(IAIDndImage);
|
||||
|
||||
type DropOverlayProps = {
|
||||
isOver: boolean;
|
||||
};
|
||||
|
||||
const DropOverlay = (props: DropOverlayProps) => {
|
||||
const { isOver } = props;
|
||||
return (
|
||||
<motion.div
|
||||
key="statusText"
|
||||
initial={{
|
||||
opacity: 0,
|
||||
}}
|
||||
animate={{
|
||||
opacity: 1,
|
||||
transition: { duration: 0.1 },
|
||||
}}
|
||||
exit={{
|
||||
opacity: 0,
|
||||
transition: { duration: 0.1 },
|
||||
}}
|
||||
>
|
||||
<Flex
|
||||
sx={{
|
||||
position: 'absolute',
|
||||
top: 0,
|
||||
left: 0,
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
}}
|
||||
>
|
||||
<Flex
|
||||
sx={{
|
||||
position: 'absolute',
|
||||
top: 0,
|
||||
left: 0,
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
bg: 'base.900',
|
||||
opacity: 0.7,
|
||||
borderRadius: 'base',
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
transitionProperty: 'common',
|
||||
transitionDuration: '0.1s',
|
||||
}}
|
||||
/>
|
||||
|
||||
<Flex
|
||||
sx={{
|
||||
position: 'absolute',
|
||||
top: 0,
|
||||
left: 0,
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
opacity: 1,
|
||||
borderWidth: 2,
|
||||
borderColor: isOver ? 'base.200' : 'base.500',
|
||||
borderRadius: 'base',
|
||||
borderStyle: 'dashed',
|
||||
transitionProperty: 'common',
|
||||
transitionDuration: '0.1s',
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
}}
|
||||
>
|
||||
<Text
|
||||
sx={{
|
||||
fontSize: '2xl',
|
||||
fontWeight: 600,
|
||||
transform: isOver ? 'scale(1.1)' : 'scale(1)',
|
||||
color: isOver ? 'base.100' : 'base.500',
|
||||
transitionProperty: 'common',
|
||||
transitionDuration: '0.1s',
|
||||
}}
|
||||
>
|
||||
Drop
|
||||
</Text>
|
||||
</Flex>
|
||||
</Flex>
|
||||
</motion.div>
|
||||
);
|
||||
};
|
||||
|
||||
@ -0,0 +1,91 @@
|
||||
import { Flex, Text } from '@chakra-ui/react';
|
||||
import { motion } from 'framer-motion';
|
||||
import { memo, useRef } from 'react';
|
||||
import { v4 as uuidv4 } from 'uuid';
|
||||
|
||||
type Props = {
|
||||
isOver: boolean;
|
||||
label?: string;
|
||||
};
|
||||
|
||||
export const IAIDropOverlay = (props: Props) => {
|
||||
const { isOver, label = 'Drop' } = props;
|
||||
const motionId = useRef(uuidv4());
|
||||
return (
|
||||
<motion.div
|
||||
key={motionId.current}
|
||||
initial={{
|
||||
opacity: 0,
|
||||
}}
|
||||
animate={{
|
||||
opacity: 1,
|
||||
transition: { duration: 0.1 },
|
||||
}}
|
||||
exit={{
|
||||
opacity: 0,
|
||||
transition: { duration: 0.1 },
|
||||
}}
|
||||
>
|
||||
<Flex
|
||||
sx={{
|
||||
position: 'absolute',
|
||||
top: 0,
|
||||
left: 0,
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
}}
|
||||
>
|
||||
<Flex
|
||||
sx={{
|
||||
position: 'absolute',
|
||||
top: 0,
|
||||
left: 0,
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
bg: 'base.900',
|
||||
opacity: 0.7,
|
||||
borderRadius: 'base',
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
transitionProperty: 'common',
|
||||
transitionDuration: '0.1s',
|
||||
}}
|
||||
/>
|
||||
|
||||
<Flex
|
||||
sx={{
|
||||
position: 'absolute',
|
||||
top: 0,
|
||||
left: 0,
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
opacity: 1,
|
||||
borderWidth: 2,
|
||||
borderColor: isOver ? 'base.200' : 'base.500',
|
||||
borderRadius: 'base',
|
||||
borderStyle: 'dashed',
|
||||
transitionProperty: 'common',
|
||||
transitionDuration: '0.1s',
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
}}
|
||||
>
|
||||
<Text
|
||||
sx={{
|
||||
fontSize: '2xl',
|
||||
fontWeight: 600,
|
||||
transform: isOver ? 'scale(1.1)' : 'scale(1)',
|
||||
color: isOver ? 'base.100' : 'base.500',
|
||||
transitionProperty: 'common',
|
||||
transitionDuration: '0.1s',
|
||||
}}
|
||||
>
|
||||
{label}
|
||||
</Text>
|
||||
</Flex>
|
||||
</Flex>
|
||||
</motion.div>
|
||||
);
|
||||
};
|
||||
|
||||
export default memo(IAIDropOverlay);
|
||||
@ -30,6 +30,7 @@ import {
|
||||
} from './canvasTypes';
|
||||
import { ImageDTO } from 'services/api';
|
||||
import { sessionCanceled } from 'services/thunks/session';
|
||||
import { setShouldUseCanvasBetaLayout } from 'features/ui/store/uiSlice';
|
||||
|
||||
export const initialLayerState: CanvasLayerState = {
|
||||
objects: [],
|
||||
@ -851,6 +852,10 @@ export const canvasSlice = createSlice({
|
||||
state.layerState.stagingArea = initialLayerState.stagingArea;
|
||||
}
|
||||
});
|
||||
|
||||
builder.addCase(setShouldUseCanvasBetaLayout, (state, action) => {
|
||||
state.doesCanvasNeedScaling = true;
|
||||
});
|
||||
},
|
||||
});
|
||||
|
||||
|
||||
@ -60,7 +60,10 @@ const ControlNetImagePreview = (props: Props) => {
|
||||
processorType !== 'none';
|
||||
|
||||
return (
|
||||
<Box ref={containerRef} sx={{ position: 'relative', w: 'full', h: 'full' }}>
|
||||
<Box
|
||||
ref={containerRef}
|
||||
sx={{ position: 'relative', w: 'full', h: 'full', aspectRatio: '1/1' }}
|
||||
>
|
||||
<IAIDndImage
|
||||
image={controlImage}
|
||||
onDrop={handleDrop}
|
||||
|
||||
@ -51,6 +51,7 @@ const CurrentImageDisplay = () => {
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
gap: 4,
|
||||
position: 'absolute',
|
||||
}}
|
||||
>
|
||||
<CurrentImagePreview />
|
||||
|
||||
@ -72,9 +72,10 @@ const InitialImagePreview = () => {
|
||||
sx={{
|
||||
width: 'full',
|
||||
height: 'full',
|
||||
position: 'relative',
|
||||
position: 'absolute',
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
p: 4,
|
||||
}}
|
||||
>
|
||||
<IAIDndImage
|
||||
|
||||
@ -1,72 +0,0 @@
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
// import IAICanvas from 'features/canvas/components/IAICanvas';
|
||||
import { Box, Flex } from '@chakra-ui/react';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import IAICanvas from 'features/canvas/components/IAICanvas';
|
||||
import IAICanvasResizer from 'features/canvas/components/IAICanvasResizer';
|
||||
import { canvasSelector } from 'features/canvas/store/canvasSelectors';
|
||||
|
||||
import { isEqual } from 'lodash-es';
|
||||
import { useLayoutEffect } from 'react';
|
||||
import UnifiedCanvasToolbarBeta from './UnifiedCanvasToolbarBeta';
|
||||
import UnifiedCanvasToolSettingsBeta from './UnifiedCanvasToolSettingsBeta';
|
||||
import { requestCanvasRescale } from 'features/canvas/store/thunks/requestCanvasScale';
|
||||
|
||||
const selector = createSelector(
|
||||
[canvasSelector],
|
||||
(canvas) => {
|
||||
const { doesCanvasNeedScaling } = canvas;
|
||||
return {
|
||||
doesCanvasNeedScaling,
|
||||
};
|
||||
},
|
||||
{
|
||||
memoizeOptions: {
|
||||
resultEqualityCheck: isEqual,
|
||||
},
|
||||
}
|
||||
);
|
||||
|
||||
const UnifiedCanvasContentBeta = () => {
|
||||
const dispatch = useAppDispatch();
|
||||
|
||||
const { doesCanvasNeedScaling } = useAppSelector(selector);
|
||||
|
||||
useLayoutEffect(() => {
|
||||
dispatch(requestCanvasRescale());
|
||||
const resizeCallback = () => {
|
||||
dispatch(requestCanvasRescale());
|
||||
};
|
||||
|
||||
window.addEventListener('resize', resizeCallback);
|
||||
|
||||
return () => window.removeEventListener('resize', resizeCallback);
|
||||
}, [dispatch]);
|
||||
|
||||
return (
|
||||
<Box
|
||||
sx={{
|
||||
width: '100%',
|
||||
height: '100%',
|
||||
borderRadius: 'base',
|
||||
bg: 'base.850',
|
||||
}}
|
||||
>
|
||||
<Flex
|
||||
flexDirection="row"
|
||||
width="100%"
|
||||
height="100%"
|
||||
columnGap={4}
|
||||
padding={4}
|
||||
>
|
||||
<UnifiedCanvasToolbarBeta />
|
||||
<Flex width="100%" height="100%" flexDirection="column" rowGap={4}>
|
||||
<UnifiedCanvasToolSettingsBeta />
|
||||
{doesCanvasNeedScaling ? <IAICanvasResizer /> : <IAICanvas />}
|
||||
</Flex>
|
||||
</Flex>
|
||||
</Box>
|
||||
);
|
||||
};
|
||||
|
||||
export default UnifiedCanvasContentBeta;
|
||||
@ -1,34 +1,58 @@
|
||||
import { Box, Flex } from '@chakra-ui/react';
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
|
||||
import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
|
||||
import IAICanvas from 'features/canvas/components/IAICanvas';
|
||||
import IAICanvasResizer from 'features/canvas/components/IAICanvasResizer';
|
||||
import IAICanvasToolbar from 'features/canvas/components/IAICanvasToolbar/IAICanvasToolbar';
|
||||
import { canvasSelector } from 'features/canvas/store/canvasSelectors';
|
||||
import { requestCanvasRescale } from 'features/canvas/store/thunks/requestCanvasScale';
|
||||
import { isEqual } from 'lodash-es';
|
||||
import { uiSelector } from 'features/ui/store/uiSelectors';
|
||||
|
||||
import { memo, useLayoutEffect } from 'react';
|
||||
import { memo, useCallback, useLayoutEffect } from 'react';
|
||||
import UnifiedCanvasToolbarBeta from './UnifiedCanvasBeta/UnifiedCanvasToolbarBeta';
|
||||
import UnifiedCanvasToolSettingsBeta from './UnifiedCanvasBeta/UnifiedCanvasToolSettingsBeta';
|
||||
import { ImageDTO } from 'services/api';
|
||||
import { setInitialCanvasImage } from 'features/canvas/store/canvasSlice';
|
||||
import { useDroppable } from '@dnd-kit/core';
|
||||
import IAIDropOverlay from 'common/components/IAIDropOverlay';
|
||||
|
||||
const selector = createSelector(
|
||||
[canvasSelector],
|
||||
(canvas) => {
|
||||
[canvasSelector, uiSelector],
|
||||
(canvas, ui) => {
|
||||
const { doesCanvasNeedScaling } = canvas;
|
||||
const { shouldUseCanvasBetaLayout } = ui;
|
||||
return {
|
||||
doesCanvasNeedScaling,
|
||||
shouldUseCanvasBetaLayout,
|
||||
};
|
||||
},
|
||||
{
|
||||
memoizeOptions: {
|
||||
resultEqualityCheck: isEqual,
|
||||
},
|
||||
}
|
||||
defaultSelectorOptions
|
||||
);
|
||||
|
||||
const UnifiedCanvasContent = () => {
|
||||
const dispatch = useAppDispatch();
|
||||
|
||||
const { doesCanvasNeedScaling } = useAppSelector(selector);
|
||||
const { doesCanvasNeedScaling, shouldUseCanvasBetaLayout } =
|
||||
useAppSelector(selector);
|
||||
|
||||
const onDrop = useCallback(
|
||||
(droppedImage: ImageDTO) => {
|
||||
dispatch(setInitialCanvasImage(droppedImage));
|
||||
},
|
||||
[dispatch]
|
||||
);
|
||||
|
||||
const {
|
||||
isOver,
|
||||
setNodeRef: setDroppableRef,
|
||||
active,
|
||||
} = useDroppable({
|
||||
id: 'unifiedCanvas',
|
||||
data: {
|
||||
handleDrop: onDrop,
|
||||
},
|
||||
});
|
||||
|
||||
useLayoutEffect(() => {
|
||||
dispatch(requestCanvasRescale());
|
||||
@ -42,14 +66,57 @@ const UnifiedCanvasContent = () => {
|
||||
return () => window.removeEventListener('resize', resizeCallback);
|
||||
}, [dispatch]);
|
||||
|
||||
if (shouldUseCanvasBetaLayout) {
|
||||
return (
|
||||
<Box
|
||||
ref={setDroppableRef}
|
||||
tabIndex={0}
|
||||
sx={{
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
borderRadius: 'base',
|
||||
bg: 'base.850',
|
||||
p: 4,
|
||||
}}
|
||||
>
|
||||
<Flex
|
||||
sx={{
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
gap: 4,
|
||||
}}
|
||||
>
|
||||
<UnifiedCanvasToolbarBeta />
|
||||
<Flex
|
||||
sx={{
|
||||
flexDir: 'column',
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
gap: 4,
|
||||
position: 'relative',
|
||||
}}
|
||||
>
|
||||
<UnifiedCanvasToolSettingsBeta />
|
||||
<Box sx={{ w: 'full', h: 'full', position: 'relative' }}>
|
||||
{doesCanvasNeedScaling ? <IAICanvasResizer /> : <IAICanvas />}
|
||||
{active && <IAIDropOverlay isOver={isOver} />}
|
||||
</Box>
|
||||
</Flex>
|
||||
</Flex>
|
||||
</Box>
|
||||
);
|
||||
}
|
||||
|
||||
return (
|
||||
<Box
|
||||
ref={setDroppableRef}
|
||||
tabIndex={-1}
|
||||
sx={{
|
||||
width: '100%',
|
||||
height: '100%',
|
||||
padding: 4,
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
borderRadius: 'base',
|
||||
bg: 'base.850',
|
||||
p: 4,
|
||||
}}
|
||||
>
|
||||
<Flex
|
||||
@ -57,8 +124,8 @@ const UnifiedCanvasContent = () => {
|
||||
flexDirection: 'column',
|
||||
alignItems: 'center',
|
||||
gap: 4,
|
||||
width: '100%',
|
||||
height: '100%',
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
}}
|
||||
>
|
||||
<IAICanvasToolbar />
|
||||
@ -68,11 +135,14 @@ const UnifiedCanvasContent = () => {
|
||||
alignItems: 'center',
|
||||
justifyContent: 'center',
|
||||
gap: 4,
|
||||
width: '100%',
|
||||
height: '100%',
|
||||
w: 'full',
|
||||
h: 'full',
|
||||
}}
|
||||
>
|
||||
{doesCanvasNeedScaling ? <IAICanvasResizer /> : <IAICanvas />}
|
||||
<Box sx={{ w: 'full', h: 'full', position: 'relative' }}>
|
||||
{doesCanvasNeedScaling ? <IAICanvasResizer /> : <IAICanvas />}
|
||||
{active && <IAIDropOverlay isOver={isOver} />}
|
||||
</Box>
|
||||
</Flex>
|
||||
</Flex>
|
||||
</Box>
|
||||
|
||||
@ -1,34 +1,16 @@
|
||||
import { Flex } from '@chakra-ui/react';
|
||||
import { memo } from 'react';
|
||||
import { createSelector } from '@reduxjs/toolkit';
|
||||
import { uiSelector } from 'features/ui/store/uiSelectors';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import UnifiedCanvasContent from './UnifiedCanvasContent';
|
||||
import UnifiedCanvasParameters from './UnifiedCanvasParameters';
|
||||
import UnifiedCanvasContentBeta from './UnifiedCanvasBeta/UnifiedCanvasContentBeta';
|
||||
import ParametersPinnedWrapper from '../../ParametersPinnedWrapper';
|
||||
|
||||
const selector = createSelector(uiSelector, (ui) => {
|
||||
const { shouldUseCanvasBetaLayout } = ui;
|
||||
|
||||
return {
|
||||
shouldUseCanvasBetaLayout,
|
||||
};
|
||||
});
|
||||
|
||||
const UnifiedCanvasTab = () => {
|
||||
const { shouldUseCanvasBetaLayout } = useAppSelector(selector);
|
||||
|
||||
return (
|
||||
<Flex sx={{ gap: 4, w: 'full', h: 'full' }}>
|
||||
<ParametersPinnedWrapper>
|
||||
<UnifiedCanvasParameters />
|
||||
</ParametersPinnedWrapper>
|
||||
{shouldUseCanvasBetaLayout ? (
|
||||
<UnifiedCanvasContentBeta />
|
||||
) : (
|
||||
<UnifiedCanvasContent />
|
||||
)}
|
||||
<UnifiedCanvasContent />
|
||||
</Flex>
|
||||
);
|
||||
};
|
||||
|
||||
@ -6,9 +6,8 @@ from omegaconf import OmegaConf
|
||||
from pathlib import Path
|
||||
|
||||
os.environ['INVOKEAI_ROOT']='/tmp'
|
||||
sys.argv = [] # to prevent config from trying to parse pytest arguments
|
||||
|
||||
from invokeai.app.services.config import InvokeAIAppConfig, InvokeAISettings
|
||||
from invokeai.app.services.config import InvokeAIAppConfig
|
||||
from invokeai.app.invocations.generate import TextToImageInvocation
|
||||
|
||||
|
||||
@ -36,48 +35,56 @@ def test_use_init():
|
||||
# note that we explicitly set omegaconf dict and argv here
|
||||
# so that the values aren't read from ~invokeai/invokeai.yaml and
|
||||
# sys.argv respectively.
|
||||
conf1 = InvokeAIAppConfig(init1,[])
|
||||
conf1 = InvokeAIAppConfig.get_config()
|
||||
assert conf1
|
||||
conf1.parse_args(conf=init1)
|
||||
assert conf1.max_loaded_models==5
|
||||
assert not conf1.nsfw_checker
|
||||
|
||||
conf2 = InvokeAIAppConfig(init2,[])
|
||||
conf2 = InvokeAIAppConfig.get_config()
|
||||
assert conf2
|
||||
conf2.parse_args(conf=init2)
|
||||
assert conf2.nsfw_checker
|
||||
assert conf2.max_loaded_models==2
|
||||
assert not hasattr(conf2,'invalid_attribute')
|
||||
|
||||
def test_argv_override():
|
||||
conf = InvokeAIAppConfig(init1,['--nsfw_checker','--max_loaded=10'])
|
||||
conf = InvokeAIAppConfig.get_config()
|
||||
conf.parse_args(conf=init1,argv=['--nsfw_checker','--max_loaded=10'])
|
||||
assert conf.nsfw_checker
|
||||
assert conf.max_loaded_models==10
|
||||
assert conf.outdir==Path('outputs') # this is the default
|
||||
|
||||
def test_env_override():
|
||||
# argv overrides
|
||||
conf = InvokeAIAppConfig(conf=init1,argv=['--max_loaded=10'])
|
||||
conf = InvokeAIAppConfig()
|
||||
conf.parse_args(conf=init1,argv=['--max_loaded=10'])
|
||||
assert conf.nsfw_checker==False
|
||||
|
||||
os.environ['INVOKEAI_nsfw_checker'] = 'True'
|
||||
conf = InvokeAIAppConfig(conf=init1,argv=['--max_loaded=10'])
|
||||
conf.parse_args(conf=init1,argv=['--max_loaded=10'])
|
||||
assert conf.nsfw_checker==True
|
||||
|
||||
# environment variables should be case insensitive
|
||||
os.environ['InvokeAI_Max_Loaded_Models'] = '15'
|
||||
conf = InvokeAIAppConfig(conf=init1)
|
||||
conf = InvokeAIAppConfig()
|
||||
conf.parse_args(conf=init1)
|
||||
assert conf.max_loaded_models == 15
|
||||
|
||||
conf = InvokeAIAppConfig(conf=init1,argv=['--no-nsfw_checker','--max_loaded=10'])
|
||||
conf = InvokeAIAppConfig()
|
||||
conf.parse_args(conf=init1,argv=['--no-nsfw_checker','--max_loaded=10'])
|
||||
assert conf.nsfw_checker==False
|
||||
assert conf.max_loaded_models==10
|
||||
|
||||
conf = InvokeAIAppConfig(conf=init1,argv=[],max_loaded_models=20)
|
||||
conf = InvokeAIAppConfig.get_config(max_loaded_models=20)
|
||||
conf.parse_args(conf=init1,argv=[])
|
||||
assert conf.max_loaded_models==20
|
||||
|
||||
def test_type_coercion():
|
||||
conf = InvokeAIAppConfig(argv=['--root=/tmp/foobar'])
|
||||
conf = InvokeAIAppConfig().get_config()
|
||||
conf.parse_args(argv=['--root=/tmp/foobar'])
|
||||
assert conf.root==Path('/tmp/foobar')
|
||||
assert isinstance(conf.root,Path)
|
||||
conf = InvokeAIAppConfig(argv=['--root=/tmp/foobar'],root='/tmp/different')
|
||||
conf = InvokeAIAppConfig.get_config(root='/tmp/different')
|
||||
conf.parse_args(argv=['--root=/tmp/foobar'])
|
||||
assert conf.root==Path('/tmp/different')
|
||||
assert isinstance(conf.root,Path)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user