Revert "macOS GUI functionality, sub-processed GUI components"
This commit is contained in:
bmaltais
GitHub
parent
a4e34b79df
commit
369341d69f
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@ -1,243 +1,12 @@
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# Kohya_SS Specifics
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venv
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__pycache__
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cudnn_windows
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.vscode
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*.egg-info
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build
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||||
wd14_tagger_model
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.DS_Store
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locon
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gui-user.bat
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gui-user.ps1
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||||
*.whl*
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||||
.idea
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||||
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||||
# Byte-compiled / optimized / DLL files
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||||
__pycache__/
|
||||
*.py[cod]
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*$py.class
|
||||
|
||||
# C extensions
|
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*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
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build/
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||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
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||||
eggs/
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||||
.eggs/
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||||
lib/
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||||
lib64/
|
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parts/
|
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sdist/
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var/
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||||
wheels/
|
||||
share/python-wheels/
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||||
*.egg-info/
|
||||
.installed.cfg
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||||
*.egg
|
||||
MANIFEST
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||||
|
||||
# PyInstaller
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||||
# Usually these files are written by a python script from a template
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||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
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||||
|
||||
# Installer logs
|
||||
pip-log.txt
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||||
pip-delete-this-directory.txt
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||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
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||||
.tox/
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||||
.nox/
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||||
.coverage
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||||
.coverage.*
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.cache
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||||
nosetests.xml
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coverage.xml
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||||
*.cover
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||||
*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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||||
|
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# Translations
|
||||
*.mo
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||||
*.pot
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||||
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||||
# Django stuff:
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*.log
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||||
local_settings.py
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db.sqlite3
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db.sqlite3-journal
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||||
|
||||
# Flask stuff:
|
||||
instance/
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||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
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||||
|
||||
# PyBuilder
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||||
.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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||||
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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||||
# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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||||
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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||||
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
|
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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|
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# Celery stuff
|
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
|
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*.sage.py
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|
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# Environments
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.env
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.venv
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env/
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||||
venv/
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ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
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||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
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# mkdocs documentation
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||||
/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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||||
.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
|
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# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
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|
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# User-specific stuff
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.idea/**/workspace.xml
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.idea/**/tasks.xml
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.idea/**/usage.statistics.xml
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.idea/**/dictionaries
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||||
.idea/**/shelf
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||||
|
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# AWS User-specific
|
||||
.idea/**/aws.xml
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||||
|
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# Generated files
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.idea/**/contentModel.xml
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# Sensitive or high-churn files
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.idea/**/dataSources/
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.idea/**/dataSources.ids
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.idea/**/dataSources.local.xml
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.idea/**/sqlDataSources.xml
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.idea/**/dynamic.xml
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.idea/**/uiDesigner.xml
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.idea/**/dbnavigator.xml
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|
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# Gradle
|
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.idea/**/gradle.xml
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.idea/**/libraries
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# Gradle and Maven with auto-import
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# When using Gradle or Maven with auto-import, you should exclude module files,
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# since they will be recreated, and may cause churn. Uncomment if using
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# auto-import.
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# .idea/artifacts
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# .idea/compiler.xml
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# .idea/jarRepositories.xml
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# .idea/modules.xml
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# .idea/*.iml
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# .idea/modules
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# *.iml
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# *.ipr
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# CMake
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cmake-build-*/
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# Mongo Explorer plugin
|
||||
.idea/**/mongoSettings.xml
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||||
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# File-based project format
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||||
*.iws
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||||
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# IntelliJ
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out/
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||||
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||||
# mpeltonen/sbt-idea plugin
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||||
.idea_modules/
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||||
|
||||
# JIRA plugin
|
||||
atlassian-ide-plugin.xml
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||||
|
||||
# Cursive Clojure plugin
|
||||
.idea/replstate.xml
|
||||
|
||||
# SonarLint plugin
|
||||
.idea/sonarlint/
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||||
|
||||
# Crashlytics plugin (for Android Studio and IntelliJ)
|
||||
com_crashlytics_export_strings.xml
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||||
crashlytics.properties
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||||
crashlytics-build.properties
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||||
fabric.properties
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||||
|
||||
# Editor-based Rest Client
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||||
.idea/httpRequests
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||||
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||||
# Android studio 3.1+ serialized cache file
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||||
.idea/caches/build_file_checksums.ser
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library/__init__.py
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|
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@ -3,17 +3,14 @@
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# v3: Add new Utilities tab for Dreambooth folder preparation
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# v3.1: Adding captionning of images to utilities
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import argparse
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import gradio as gr
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import json
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import math
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import os
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import pathlib
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import subprocess
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import sys
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import gradio as gr
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from library.common_gui_functions import (
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import pathlib
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import argparse
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from library.common_gui import (
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get_folder_path,
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remove_doublequote,
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get_file_path,
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@ -29,89 +26,89 @@ from library.common_gui_functions import (
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gradio_source_model,
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# set_legacy_8bitadam,
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update_my_data,
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check_if_model_exist, show_message_box,
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check_if_model_exist,
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)
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from library.common_utilities import CommonUtilities
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from library.dreambooth_folder_creation_gui import (
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gradio_dreambooth_folder_creation_tab,
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)
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from library.sampler_gui import sample_gradio_config, run_cmd_sample
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from library.tensorboard_gui import (
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gradio_tensorboard,
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start_tensorboard,
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stop_tensorboard,
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)
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from library.dreambooth_folder_creation_gui import (
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gradio_dreambooth_folder_creation_tab,
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)
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from library.utilities import utilities_tab
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from library.sampler_gui import sample_gradio_config, run_cmd_sample
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from easygui import msgbox
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folder_symbol = '\U0001f4c2' # 📂
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refresh_symbol = '\U0001f504' # 🔄
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save_style_symbol = '\U0001f4be' # 💾
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document_symbol = '\U0001F4C4' # 📄
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document_symbol = '\U0001F4C4' # 📄
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def save_configuration(
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save_as,
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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epoch,
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save_every_n_epochs,
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mixed_precision,
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save_precision,
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seed,
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num_cpu_threads_per_process,
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cache_latents,
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caption_extension,
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enable_bucket,
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gradient_checkpointing,
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full_fp16,
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no_token_padding,
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stop_text_encoder_training,
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# use_8bit_adam,
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xformers,
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save_model_as,
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shuffle_caption,
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save_state,
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resume,
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prior_loss_weight,
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color_aug,
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flip_aug,
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clip_skip,
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vae,
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output_name,
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max_token_length,
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max_train_epochs,
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max_data_loader_n_workers,
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mem_eff_attn,
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gradient_accumulation_steps,
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model_list,
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keep_tokens,
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persistent_data_loader_workers,
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bucket_no_upscale,
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random_crop,
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bucket_reso_steps,
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caption_dropout_every_n_epochs,
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caption_dropout_rate,
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optimizer,
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optimizer_args,
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noise_offset,
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sample_every_n_steps,
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sample_every_n_epochs,
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sample_sampler,
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sample_prompts,
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additional_parameters,
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vae_batch_size,
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min_snr_gamma,
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save_as,
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
|
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lr_scheduler,
|
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lr_warmup,
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train_batch_size,
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epoch,
|
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save_every_n_epochs,
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mixed_precision,
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save_precision,
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seed,
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num_cpu_threads_per_process,
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cache_latents,
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caption_extension,
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enable_bucket,
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gradient_checkpointing,
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full_fp16,
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no_token_padding,
|
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stop_text_encoder_training,
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# use_8bit_adam,
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xformers,
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save_model_as,
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shuffle_caption,
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save_state,
|
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resume,
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prior_loss_weight,
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color_aug,
|
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flip_aug,
|
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clip_skip,
|
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vae,
|
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output_name,
|
||||
max_token_length,
|
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max_train_epochs,
|
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max_data_loader_n_workers,
|
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mem_eff_attn,
|
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gradient_accumulation_steps,
|
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model_list,
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keep_tokens,
|
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persistent_data_loader_workers,
|
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bucket_no_upscale,
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random_crop,
|
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bucket_reso_steps,
|
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caption_dropout_every_n_epochs,
|
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caption_dropout_rate,
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optimizer,
|
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optimizer_args,
|
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noise_offset,
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sample_every_n_steps,
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sample_every_n_epochs,
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sample_sampler,
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sample_prompts,
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additional_parameters,
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vae_batch_size,
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min_snr_gamma,
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):
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# Get list of function parameters and values
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parameters = list(locals().items())
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@ -125,12 +122,12 @@ def save_configuration(
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file_path = get_saveasfile_path(file_path)
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else:
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print('Save...')
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if file_path is None or file_path == '':
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if file_path == None or file_path == '':
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file_path = get_saveasfile_path(file_path)
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# print(file_path)
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if file_path is None or file_path == '':
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if file_path == None or file_path == '':
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return original_file_path # In case a file_path was provided and the user decide to cancel the open action
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# Return the values of the variables as a dictionary
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@ -138,10 +135,10 @@ def save_configuration(
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name: value
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for name, value in parameters # locals().items()
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if name
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not in [
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'file_path',
|
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'save_as',
|
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]
|
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not in [
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||||
'file_path',
|
||||
'save_as',
|
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]
|
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}
|
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|
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# Extract the destination directory from the file path
|
||||
|
|
@ -159,73 +156,69 @@ def save_configuration(
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|||
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|
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def open_configuration(
|
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ask_for_file,
|
||||
file_path,
|
||||
pretrained_model_name_or_path,
|
||||
v2,
|
||||
v_parameterization,
|
||||
logging_dir,
|
||||
train_data_dir,
|
||||
reg_data_dir,
|
||||
output_dir,
|
||||
max_resolution,
|
||||
learning_rate,
|
||||
lr_scheduler,
|
||||
lr_warmup,
|
||||
train_batch_size,
|
||||
epoch,
|
||||
save_every_n_epochs,
|
||||
mixed_precision,
|
||||
save_precision,
|
||||
seed,
|
||||
num_cpu_threads_per_process,
|
||||
cache_latents,
|
||||
caption_extension,
|
||||
enable_bucket,
|
||||
gradient_checkpointing,
|
||||
full_fp16,
|
||||
no_token_padding,
|
||||
stop_text_encoder_training,
|
||||
# use_8bit_adam,
|
||||
xformers,
|
||||
save_model_as,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
clip_skip,
|
||||
vae,
|
||||
output_name,
|
||||
max_token_length,
|
||||
max_train_epochs,
|
||||
max_data_loader_n_workers,
|
||||
mem_eff_attn,
|
||||
gradient_accumulation_steps,
|
||||
model_list,
|
||||
keep_tokens,
|
||||
persistent_data_loader_workers,
|
||||
bucket_no_upscale,
|
||||
random_crop,
|
||||
bucket_reso_steps,
|
||||
caption_dropout_every_n_epochs,
|
||||
caption_dropout_rate,
|
||||
optimizer,
|
||||
optimizer_args,
|
||||
noise_offset,
|
||||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
vae_batch_size,
|
||||
min_snr_gamma,
|
||||
ask_for_file,
|
||||
file_path,
|
||||
pretrained_model_name_or_path,
|
||||
v2,
|
||||
v_parameterization,
|
||||
logging_dir,
|
||||
train_data_dir,
|
||||
reg_data_dir,
|
||||
output_dir,
|
||||
max_resolution,
|
||||
learning_rate,
|
||||
lr_scheduler,
|
||||
lr_warmup,
|
||||
train_batch_size,
|
||||
epoch,
|
||||
save_every_n_epochs,
|
||||
mixed_precision,
|
||||
save_precision,
|
||||
seed,
|
||||
num_cpu_threads_per_process,
|
||||
cache_latents,
|
||||
caption_extension,
|
||||
enable_bucket,
|
||||
gradient_checkpointing,
|
||||
full_fp16,
|
||||
no_token_padding,
|
||||
stop_text_encoder_training,
|
||||
# use_8bit_adam,
|
||||
xformers,
|
||||
save_model_as,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
clip_skip,
|
||||
vae,
|
||||
output_name,
|
||||
max_token_length,
|
||||
max_train_epochs,
|
||||
max_data_loader_n_workers,
|
||||
mem_eff_attn,
|
||||
gradient_accumulation_steps,
|
||||
model_list,
|
||||
keep_tokens,
|
||||
persistent_data_loader_workers,
|
||||
bucket_no_upscale,
|
||||
random_crop,
|
||||
bucket_reso_steps,
|
||||
caption_dropout_every_n_epochs,
|
||||
caption_dropout_rate,
|
||||
optimizer,
|
||||
optimizer_args,
|
||||
noise_offset,
|
||||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
vae_batch_size,
|
||||
min_snr_gamma,
|
||||
):
|
||||
print("open_configuration called")
|
||||
print(f"locals length: {len(locals())}")
|
||||
print(f"locals: {locals()}")
|
||||
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
||||
|
|
@ -233,25 +226,18 @@ def open_configuration(
|
|||
|
||||
original_file_path = file_path
|
||||
|
||||
if ask_for_file and file_path is not None:
|
||||
print(f"File path: {file_path}")
|
||||
file_path, canceled = get_file_path(file_path=file_path, filedialog_type="json")
|
||||
if ask_for_file:
|
||||
file_path = get_file_path(file_path)
|
||||
|
||||
if canceled:
|
||||
return (None,) + (None,) * (len(parameters) - 2)
|
||||
|
||||
if not file_path == '' and file_path is not None:
|
||||
if not file_path == '' and not file_path == None:
|
||||
# load variables from JSON file
|
||||
with open(file_path, 'r') as f:
|
||||
my_data = json.load(f)
|
||||
if CommonUtilities.is_valid_config(my_data):
|
||||
print('Loading config...')
|
||||
my_data = update_my_data(my_data)
|
||||
else:
|
||||
print("Invalid configuration file.")
|
||||
my_data = {}
|
||||
show_message_box("Invalid configuration file.")
|
||||
print('Loading config...')
|
||||
# Update values to fix deprecated use_8bit_adam checkbox and set appropriate optimizer if it is set to True
|
||||
my_data = update_my_data(my_data)
|
||||
else:
|
||||
file_path = original_file_path
|
||||
file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
|
||||
my_data = {}
|
||||
|
||||
values = [file_path]
|
||||
|
|
@ -259,92 +245,90 @@ def open_configuration(
|
|||
# Set the value in the dictionary to the corresponding value in `my_data`, or the default value if not found
|
||||
if not key in ['ask_for_file', 'file_path']:
|
||||
values.append(my_data.get(key, value))
|
||||
# Print the number of returned values
|
||||
print(f"Returning: {values}")
|
||||
return tuple(values)
|
||||
|
||||
|
||||
def train_model(
|
||||
pretrained_model_name_or_path,
|
||||
v2,
|
||||
v_parameterization,
|
||||
logging_dir,
|
||||
train_data_dir,
|
||||
reg_data_dir,
|
||||
output_dir,
|
||||
max_resolution,
|
||||
learning_rate,
|
||||
lr_scheduler,
|
||||
lr_warmup,
|
||||
train_batch_size,
|
||||
epoch,
|
||||
save_every_n_epochs,
|
||||
mixed_precision,
|
||||
save_precision,
|
||||
seed,
|
||||
num_cpu_threads_per_process,
|
||||
cache_latents,
|
||||
caption_extension,
|
||||
enable_bucket,
|
||||
gradient_checkpointing,
|
||||
full_fp16,
|
||||
no_token_padding,
|
||||
stop_text_encoder_training_pct,
|
||||
# use_8bit_adam,
|
||||
xformers,
|
||||
save_model_as,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
clip_skip,
|
||||
vae,
|
||||
output_name,
|
||||
max_token_length,
|
||||
max_train_epochs,
|
||||
max_data_loader_n_workers,
|
||||
mem_eff_attn,
|
||||
gradient_accumulation_steps,
|
||||
model_list, # Keep this. Yes, it is unused here but required given the common list used
|
||||
keep_tokens,
|
||||
persistent_data_loader_workers,
|
||||
bucket_no_upscale,
|
||||
random_crop,
|
||||
bucket_reso_steps,
|
||||
caption_dropout_every_n_epochs,
|
||||
caption_dropout_rate,
|
||||
optimizer,
|
||||
optimizer_args,
|
||||
noise_offset,
|
||||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
vae_batch_size,
|
||||
min_snr_gamma,
|
||||
pretrained_model_name_or_path,
|
||||
v2,
|
||||
v_parameterization,
|
||||
logging_dir,
|
||||
train_data_dir,
|
||||
reg_data_dir,
|
||||
output_dir,
|
||||
max_resolution,
|
||||
learning_rate,
|
||||
lr_scheduler,
|
||||
lr_warmup,
|
||||
train_batch_size,
|
||||
epoch,
|
||||
save_every_n_epochs,
|
||||
mixed_precision,
|
||||
save_precision,
|
||||
seed,
|
||||
num_cpu_threads_per_process,
|
||||
cache_latents,
|
||||
caption_extension,
|
||||
enable_bucket,
|
||||
gradient_checkpointing,
|
||||
full_fp16,
|
||||
no_token_padding,
|
||||
stop_text_encoder_training_pct,
|
||||
# use_8bit_adam,
|
||||
xformers,
|
||||
save_model_as,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
color_aug,
|
||||
flip_aug,
|
||||
clip_skip,
|
||||
vae,
|
||||
output_name,
|
||||
max_token_length,
|
||||
max_train_epochs,
|
||||
max_data_loader_n_workers,
|
||||
mem_eff_attn,
|
||||
gradient_accumulation_steps,
|
||||
model_list, # Keep this. Yes, it is unused here but required given the common list used
|
||||
keep_tokens,
|
||||
persistent_data_loader_workers,
|
||||
bucket_no_upscale,
|
||||
random_crop,
|
||||
bucket_reso_steps,
|
||||
caption_dropout_every_n_epochs,
|
||||
caption_dropout_rate,
|
||||
optimizer,
|
||||
optimizer_args,
|
||||
noise_offset,
|
||||
sample_every_n_steps,
|
||||
sample_every_n_epochs,
|
||||
sample_sampler,
|
||||
sample_prompts,
|
||||
additional_parameters,
|
||||
vae_batch_size,
|
||||
min_snr_gamma,
|
||||
):
|
||||
if pretrained_model_name_or_path == '':
|
||||
show_message_box('Source model information is missing')
|
||||
msgbox('Source model information is missing')
|
||||
return
|
||||
|
||||
if train_data_dir == '':
|
||||
show_message_box('Image folder path is missing')
|
||||
msgbox('Image folder path is missing')
|
||||
return
|
||||
|
||||
if not os.path.exists(train_data_dir):
|
||||
show_message_box('Image folder does not exist')
|
||||
msgbox('Image folder does not exist')
|
||||
return
|
||||
|
||||
if reg_data_dir != '':
|
||||
if not os.path.exists(reg_data_dir):
|
||||
show_message_box('Regularisation folder does not exist')
|
||||
msgbox('Regularisation folder does not exist')
|
||||
return
|
||||
|
||||
if output_dir == '':
|
||||
show_message_box('Output folder path is missing')
|
||||
msgbox('Output folder path is missing')
|
||||
return
|
||||
|
||||
if check_if_model_exist(output_name, output_dir, save_model_as):
|
||||
|
|
@ -355,7 +339,7 @@ def train_model(
|
|||
f
|
||||
for f in os.listdir(train_data_dir)
|
||||
if os.path.isdir(os.path.join(train_data_dir, f))
|
||||
and not f.startswith('.')
|
||||
and not f.startswith('.')
|
||||
]
|
||||
|
||||
# Check if subfolders are present. If not let the user know and return
|
||||
|
|
@ -387,11 +371,11 @@ def train_model(
|
|||
[
|
||||
f
|
||||
for f, lower_f in (
|
||||
(file, file.lower())
|
||||
for file in os.listdir(
|
||||
os.path.join(train_data_dir, folder)
|
||||
)
|
||||
)
|
||||
(file, file.lower())
|
||||
for file in os.listdir(
|
||||
os.path.join(train_data_dir, folder)
|
||||
)
|
||||
)
|
||||
if lower_f.endswith(('.jpg', '.jpeg', '.png', '.webp'))
|
||||
]
|
||||
)
|
||||
|
|
@ -562,10 +546,10 @@ def train_model(
|
|||
|
||||
|
||||
def dreambooth_tab(
|
||||
train_data_dir=gr.Textbox(),
|
||||
reg_data_dir=gr.Textbox(),
|
||||
output_dir=gr.Textbox(),
|
||||
logging_dir=gr.Textbox(),
|
||||
train_data_dir=gr.Textbox(),
|
||||
reg_data_dir=gr.Textbox(),
|
||||
output_dir=gr.Textbox(),
|
||||
logging_dir=gr.Textbox(),
|
||||
):
|
||||
dummy_db_true = gr.Label(value=True, visible=False)
|
||||
dummy_db_false = gr.Label(value=False, visible=False)
|
||||
|
|
@ -848,20 +832,19 @@ def dreambooth_tab(
|
|||
]
|
||||
|
||||
button_open_config.click(
|
||||
lambda *_args, **kwargs: open_configuration(*_args, **kwargs),
|
||||
open_configuration,
|
||||
inputs=[dummy_db_true, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_load_config.click(
|
||||
lambda *args, **kwargs: (print("Lambda called"), open_configuration(*args, **kwargs)),
|
||||
inputs=[dummy_db_true, config_file_name] + settings_list,
|
||||
open_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
# Print the number of expected outputs
|
||||
print(f"Number of expected outputs: {len([config_file_name] + settings_list)}")
|
||||
|
||||
button_save_config.click(
|
||||
save_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
|
|
|
|||
10
fine_tune.py
10
fine_tune.py
|
|
@ -5,20 +5,22 @@ import argparse
|
|||
import gc
|
||||
import math
|
||||
import os
|
||||
import toml
|
||||
from multiprocessing import Value
|
||||
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
from accelerate.utils import set_seed
|
||||
import diffusers
|
||||
from diffusers import DDPMScheduler
|
||||
from tqdm import tqdm
|
||||
|
||||
import library.config_ml_util as config_util
|
||||
import library.custom_train_functions as custom_train_functions
|
||||
import library.train_util as train_util
|
||||
from library.config_ml_util import (
|
||||
import library.config_util as config_util
|
||||
from library.config_util import (
|
||||
ConfigSanitizer,
|
||||
BlueprintGenerator,
|
||||
)
|
||||
import library.custom_train_functions as custom_train_functions
|
||||
from library.custom_train_functions import apply_snr_weight
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,18 +1,17 @@
|
|||
import argparse
|
||||
import gradio as gr
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import pathlib
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from library.common_gui_functions import (
|
||||
import pathlib
|
||||
import argparse
|
||||
from library.common_gui import (
|
||||
get_folder_path,
|
||||
get_file_path,
|
||||
get_saveasfile_path,
|
||||
save_inference_file,
|
||||
gradio_advanced_training,
|
||||
run_cmd_advanced_training,
|
||||
gradio_training,
|
||||
run_cmd_advanced_training,
|
||||
gradio_config,
|
||||
|
|
@ -23,13 +22,13 @@ from library.common_gui_functions import (
|
|||
update_my_data,
|
||||
check_if_model_exist,
|
||||
)
|
||||
from library.sampler_gui import sample_gradio_config, run_cmd_sample
|
||||
from library.tensorboard_gui import (
|
||||
gradio_tensorboard,
|
||||
start_tensorboard,
|
||||
stop_tensorboard,
|
||||
)
|
||||
from library.utilities import utilities_tab
|
||||
from library.sampler_gui import sample_gradio_config, run_cmd_sample
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
refresh_symbol = '\U0001f504' # 🔄
|
||||
|
|
@ -234,7 +233,7 @@ def open_configuration(
|
|||
if ask_for_file:
|
||||
file_path = get_file_path(file_path)
|
||||
|
||||
if not file_path == '' and file_path is not None:
|
||||
if not file_path == '' and not file_path == None:
|
||||
# load variables from JSON file
|
||||
with open(file_path, 'r') as f:
|
||||
my_data = json.load(f)
|
||||
|
|
@ -800,14 +799,14 @@ def finetune_tab():
|
|||
button_run.click(train_model, inputs=settings_list)
|
||||
|
||||
button_open_config.click(
|
||||
lambda *args, **kwargs: open_configuration(),
|
||||
open_configuration,
|
||||
inputs=[dummy_db_true, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_load_config.click(
|
||||
lambda *args, **kwargs: open_configuration(),
|
||||
open_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
|
|
|
|||
13
kohya_gui.py
13
kohya_gui.py
|
|
@ -1,18 +1,17 @@
|
|||
import argparse
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
import gradio as gr
|
||||
|
||||
import os
|
||||
import argparse
|
||||
from dreambooth_gui import dreambooth_tab
|
||||
from finetune_gui import finetune_tab
|
||||
from textual_inversion_gui import ti_tab
|
||||
from library.utilities import utilities_tab
|
||||
from library.extract_lora_gui import gradio_extract_lora_tab
|
||||
from library.extract_lycoris_locon_gui import gradio_extract_lycoris_locon_tab
|
||||
from library.merge_lora_gui import gradio_merge_lora_tab
|
||||
from library.resize_lora_gui import gradio_resize_lora_tab
|
||||
from library.utilities import utilities_tab
|
||||
from lora_gui import lora_tab
|
||||
from textual_inversion_gui import ti_tab
|
||||
|
||||
|
||||
def UI(**kwargs):
|
||||
css = ''
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,8 @@
|
|||
import os
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import get_folder_path, add_pre_postfix, find_replace
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
from .common_gui import get_folder_path, add_pre_postfix, find_replace
|
||||
import os
|
||||
|
||||
|
||||
def caption_images(
|
||||
|
|
@ -18,11 +17,11 @@ def caption_images(
|
|||
):
|
||||
# Check for images_dir
|
||||
if not images_dir:
|
||||
show_message_box('Image folder is missing...')
|
||||
msgbox('Image folder is missing...')
|
||||
return
|
||||
|
||||
if not caption_ext:
|
||||
show_message_box('Please provide an extension for the caption files.')
|
||||
msgbox('Please provide an extension for the caption files.')
|
||||
return
|
||||
|
||||
if caption_text:
|
||||
|
|
@ -61,7 +60,7 @@ def caption_images(
|
|||
)
|
||||
else:
|
||||
if prefix or postfix:
|
||||
show_message_box(
|
||||
msgbox(
|
||||
'Could not modify caption files with requested change because the "Overwrite existing captions in folder" option is not selected...'
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,8 @@
|
|||
import os
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import get_folder_path, add_pre_postfix
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
import os
|
||||
from .common_gui import get_folder_path, add_pre_postfix
|
||||
|
||||
PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
|
||||
|
||||
|
|
@ -22,16 +21,16 @@ def caption_images(
|
|||
):
|
||||
# Check for caption_text_input
|
||||
# if caption_text_input == "":
|
||||
# show_message_box("Caption text is missing...")
|
||||
# msgbox("Caption text is missing...")
|
||||
# return
|
||||
|
||||
# Check for images_dir_input
|
||||
if train_data_dir == '':
|
||||
show_message_box('Image folder is missing...')
|
||||
msgbox('Image folder is missing...')
|
||||
return
|
||||
|
||||
if caption_file_ext == '':
|
||||
show_message_box('Please provide an extension for the caption files.')
|
||||
msgbox('Please provide an extension for the caption files.')
|
||||
return
|
||||
|
||||
print(f'Captioning files in {train_data_dir}...')
|
||||
|
|
|
|||
|
|
@ -1,19 +1,14 @@
|
|||
import os
|
||||
import shutil
|
||||
import subprocess
|
||||
from contextlib import contextmanager
|
||||
import tkinter as tk
|
||||
from tkinter import filedialog, Tk
|
||||
|
||||
import easygui
|
||||
from easygui import msgbox
|
||||
import os
|
||||
import gradio as gr
|
||||
|
||||
from library.common_utilities import CommonUtilities
|
||||
import easygui
|
||||
import shutil
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
refresh_symbol = '\U0001f504' # 🔄
|
||||
save_style_symbol = '\U0001f4be' # 💾
|
||||
document_symbol = '\U0001F4C4' # 📄
|
||||
document_symbol = '\U0001F4C4' # 📄
|
||||
|
||||
# define a list of substrings to search for v2 base models
|
||||
V2_BASE_MODELS = [
|
||||
|
|
@ -39,41 +34,6 @@ ALL_PRESET_MODELS = V2_BASE_MODELS + V_PARAMETERIZATION_MODELS + V1_MODELS
|
|||
FILE_ENV_EXCLUSION = ['COLAB_GPU', 'RUNPOD_POD_ID']
|
||||
|
||||
|
||||
@contextmanager
|
||||
def tk_context():
|
||||
root = tk.Tk()
|
||||
root.withdraw()
|
||||
try:
|
||||
yield root
|
||||
finally:
|
||||
root.destroy()
|
||||
|
||||
|
||||
def open_file_dialog(initial_dir, initial_file, file_types="all"):
|
||||
current_directory = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
args = ["python", f"{current_directory}/gui_subprocesses.py", "file_dialog"]
|
||||
if initial_dir:
|
||||
args.append(initial_dir)
|
||||
if initial_file:
|
||||
args.append(initial_file)
|
||||
if file_types:
|
||||
args.append(file_types)
|
||||
|
||||
file_path = subprocess.check_output(args).decode("utf-8").strip()
|
||||
return file_path
|
||||
|
||||
|
||||
def show_message_box(message, title=""):
|
||||
current_directory = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
args = ["python", f"{current_directory}/gui_subprocesses.py", "msgbox", message]
|
||||
if title:
|
||||
args.append(title)
|
||||
|
||||
subprocess.run(args)
|
||||
|
||||
|
||||
def check_if_model_exist(output_name, output_dir, save_model_as):
|
||||
if save_model_as in ['diffusers', 'diffusers_safetendors']:
|
||||
ckpt_folder = os.path.join(output_dir, output_name)
|
||||
|
|
@ -127,8 +87,8 @@ def update_my_data(my_data):
|
|||
|
||||
# Update model save choices due to changes for LoRA and TI training
|
||||
if (
|
||||
(my_data.get('LoRA_type') or my_data.get('num_vectors_per_token'))
|
||||
and my_data.get('save_model_as') not in ['safetensors', 'ckpt']
|
||||
(my_data.get('LoRA_type') or my_data.get('num_vectors_per_token'))
|
||||
and my_data.get('save_model_as') not in ['safetensors', 'ckpt']
|
||||
):
|
||||
message = (
|
||||
'Updating save_model_as to safetensors because the current value in the config file is no longer applicable to {}'
|
||||
|
|
@ -142,6 +102,11 @@ def update_my_data(my_data):
|
|||
return my_data
|
||||
|
||||
|
||||
def get_dir_and_file(file_path):
|
||||
dir_path, file_name = os.path.split(file_path)
|
||||
return (dir_path, file_name)
|
||||
|
||||
|
||||
# def has_ext_files(directory, extension):
|
||||
# # Iterate through all the files in the directory
|
||||
# for file in os.listdir(directory):
|
||||
|
|
@ -152,38 +117,67 @@ def update_my_data(my_data):
|
|||
# return False
|
||||
|
||||
|
||||
def get_file_path(file_path, initial_dir=None, initial_file=None, filedialog_type="lora"):
|
||||
file_extension = os.path.splitext(file_path)[-1].lower()
|
||||
def get_file_path(
|
||||
file_path='', default_extension='.json', extension_name='Config files'
|
||||
):
|
||||
if not any(var in os.environ for var in FILE_ENV_EXCLUSION):
|
||||
current_file_path = file_path
|
||||
# print(f'current file path: {current_file_path}')
|
||||
|
||||
# Find the appropriate filedialog_type based on the file extension
|
||||
for key, extensions in CommonUtilities.file_filters.items():
|
||||
if file_extension in extensions:
|
||||
filedialog_type = key
|
||||
break
|
||||
initial_dir, initial_file = get_dir_and_file(file_path)
|
||||
|
||||
current_file_path = file_path
|
||||
# Create a hidden Tkinter root window
|
||||
root = Tk()
|
||||
root.wm_attributes('-topmost', 1)
|
||||
root.withdraw()
|
||||
|
||||
initial_dir, initial_file = os.path.split(file_path)
|
||||
result = open_file_dialog(initial_dir=initial_dir, initial_file=initial_file, file_types=filedialog_type)
|
||||
file_path, canceled = result[:2]
|
||||
return file_path, canceled
|
||||
# Show the open file dialog and get the selected file path
|
||||
file_path = filedialog.askopenfilename(
|
||||
filetypes=(
|
||||
(extension_name, f'*{default_extension}'),
|
||||
('All files', '*.*'),
|
||||
),
|
||||
defaultextension=default_extension,
|
||||
initialfile=initial_file,
|
||||
initialdir=initial_dir,
|
||||
)
|
||||
|
||||
# Destroy the hidden root window
|
||||
root.destroy()
|
||||
|
||||
# If no file is selected, use the current file path
|
||||
if not file_path:
|
||||
file_path = current_file_path
|
||||
current_file_path = file_path
|
||||
# print(f'current file path: {current_file_path}')
|
||||
|
||||
return file_path
|
||||
|
||||
|
||||
def get_any_file_path(file_path=''):
|
||||
current_file_path = file_path
|
||||
# print(f'current file path: {current_file_path}')
|
||||
if not any(var in os.environ for var in FILE_ENV_EXCLUSION):
|
||||
current_file_path = file_path
|
||||
# print(f'current file path: {current_file_path}')
|
||||
|
||||
initial_dir, initial_file = os.path.split(file_path)
|
||||
file_path = open_file_dialog(initial_dir, initial_file, "all")
|
||||
initial_dir, initial_file = get_dir_and_file(file_path)
|
||||
|
||||
if file_path == '':
|
||||
file_path = current_file_path
|
||||
root = Tk()
|
||||
root.wm_attributes('-topmost', 1)
|
||||
root.withdraw()
|
||||
file_path = filedialog.askopenfilename(
|
||||
initialdir=initial_dir,
|
||||
initialfile=initial_file,
|
||||
)
|
||||
root.destroy()
|
||||
|
||||
if file_path == '':
|
||||
file_path = current_file_path
|
||||
|
||||
return file_path
|
||||
|
||||
|
||||
def remove_doublequote(file_path):
|
||||
if file_path is not None:
|
||||
if file_path != None:
|
||||
file_path = file_path.replace('"', '')
|
||||
|
||||
return file_path
|
||||
|
|
@ -202,37 +196,62 @@ def remove_doublequote(file_path):
|
|||
# )
|
||||
|
||||
|
||||
def get_folder_path(folder_path='', filedialog_type="directory"):
|
||||
current_folder_path = folder_path
|
||||
def get_folder_path(folder_path=''):
|
||||
if not any(var in os.environ for var in FILE_ENV_EXCLUSION):
|
||||
current_folder_path = folder_path
|
||||
|
||||
initial_dir, initial_file = os.path.split(folder_path)
|
||||
file_path = open_file_dialog(initial_dir, initial_file, filedialog_type)
|
||||
initial_dir, initial_file = get_dir_and_file(folder_path)
|
||||
|
||||
if folder_path == '':
|
||||
folder_path = current_folder_path
|
||||
root = Tk()
|
||||
root.wm_attributes('-topmost', 1)
|
||||
root.withdraw()
|
||||
folder_path = filedialog.askdirectory(initialdir=initial_dir)
|
||||
root.destroy()
|
||||
|
||||
if folder_path == '':
|
||||
folder_path = current_folder_path
|
||||
|
||||
return folder_path
|
||||
|
||||
|
||||
def get_saveasfile_path(
|
||||
file_path='', filedialog_type="json"
|
||||
file_path='', defaultextension='.json', extension_name='Config files'
|
||||
):
|
||||
current_file_path = file_path
|
||||
if not any(var in os.environ for var in FILE_ENV_EXCLUSION):
|
||||
current_file_path = file_path
|
||||
# print(f'current file path: {current_file_path}')
|
||||
|
||||
initial_dir, initial_file = os.path.split(file_path)
|
||||
save_file_path = save_file_dialog(initial_dir, initial_file, filedialog_type)
|
||||
initial_dir, initial_file = get_dir_and_file(file_path)
|
||||
|
||||
if save_file_path is None:
|
||||
file_path = current_file_path
|
||||
else:
|
||||
print(save_file_path.name)
|
||||
file_path = save_file_path.name
|
||||
root = Tk()
|
||||
root.wm_attributes('-topmost', 1)
|
||||
root.withdraw()
|
||||
save_file_path = filedialog.asksaveasfile(
|
||||
filetypes=(
|
||||
(f'{extension_name}', f'{defaultextension}'),
|
||||
('All files', '*'),
|
||||
),
|
||||
defaultextension=defaultextension,
|
||||
initialdir=initial_dir,
|
||||
initialfile=initial_file,
|
||||
)
|
||||
root.destroy()
|
||||
|
||||
# print(save_file_path)
|
||||
|
||||
if save_file_path == None:
|
||||
file_path = current_file_path
|
||||
else:
|
||||
print(save_file_path.name)
|
||||
file_path = save_file_path.name
|
||||
|
||||
# print(file_path)
|
||||
|
||||
return file_path
|
||||
|
||||
|
||||
def get_saveasfilename_path(
|
||||
file_path='', extensions='*', extension_name='Config files'
|
||||
file_path='', extensions='*', extension_name='Config files'
|
||||
):
|
||||
if not any(var in os.environ for var in FILE_ENV_EXCLUSION):
|
||||
current_file_path = file_path
|
||||
|
|
@ -261,10 +280,10 @@ def get_saveasfilename_path(
|
|||
|
||||
|
||||
def add_pre_postfix(
|
||||
folder: str = '',
|
||||
prefix: str = '',
|
||||
postfix: str = '',
|
||||
caption_file_ext: str = '.caption',
|
||||
folder: str = '',
|
||||
prefix: str = '',
|
||||
postfix: str = '',
|
||||
caption_file_ext: str = '.caption',
|
||||
) -> None:
|
||||
"""
|
||||
Add prefix and/or postfix to the content of caption files within a folder.
|
||||
|
|
@ -324,10 +343,10 @@ def has_ext_files(folder_path: str, file_extension: str) -> bool:
|
|||
|
||||
|
||||
def find_replace(
|
||||
folder_path: str = '',
|
||||
caption_file_ext: str = '.caption',
|
||||
search_text: str = '',
|
||||
replace_text: str = '',
|
||||
folder_path: str = '',
|
||||
caption_file_ext: str = '.caption',
|
||||
search_text: str = '',
|
||||
replace_text: str = '',
|
||||
) -> None:
|
||||
"""
|
||||
Find and replace text in caption files within a folder.
|
||||
|
|
@ -341,7 +360,7 @@ def find_replace(
|
|||
print('Running caption find/replace')
|
||||
|
||||
if not has_ext_files(folder_path, caption_file_ext):
|
||||
show_message_box(
|
||||
msgbox(
|
||||
f'No files with extension {caption_file_ext} were found in {folder_path}...'
|
||||
)
|
||||
return
|
||||
|
|
@ -355,7 +374,7 @@ def find_replace(
|
|||
|
||||
for caption_file in caption_files:
|
||||
with open(
|
||||
os.path.join(folder_path, caption_file), 'r', errors='ignore'
|
||||
os.path.join(folder_path, caption_file), 'r', errors='ignore'
|
||||
) as f:
|
||||
content = f.read()
|
||||
|
||||
|
|
@ -367,7 +386,7 @@ def find_replace(
|
|||
|
||||
def color_aug_changed(color_aug):
|
||||
if color_aug:
|
||||
show_message_box(
|
||||
msgbox(
|
||||
'Disabling "Cache latent" because "Color augmentation" has been selected...'
|
||||
)
|
||||
return gr.Checkbox.update(value=False, interactive=False)
|
||||
|
|
@ -408,7 +427,7 @@ def save_inference_file(output_dir, v2, v_parameterization, output_name):
|
|||
|
||||
|
||||
def set_pretrained_model_name_or_path_input(
|
||||
model_list, pretrained_model_name_or_path, v2, v_parameterization
|
||||
model_list, pretrained_model_name_or_path, v2, v_parameterization
|
||||
):
|
||||
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
|
||||
if str(model_list) in V2_BASE_MODELS:
|
||||
|
|
@ -433,9 +452,9 @@ def set_pretrained_model_name_or_path_input(
|
|||
|
||||
if model_list == 'custom':
|
||||
if (
|
||||
str(pretrained_model_name_or_path) in V1_MODELS
|
||||
or str(pretrained_model_name_or_path) in V2_BASE_MODELS
|
||||
or str(pretrained_model_name_or_path) in V_PARAMETERIZATION_MODELS
|
||||
str(pretrained_model_name_or_path) in V1_MODELS
|
||||
or str(pretrained_model_name_or_path) in V2_BASE_MODELS
|
||||
or str(pretrained_model_name_or_path) in V_PARAMETERIZATION_MODELS
|
||||
):
|
||||
pretrained_model_name_or_path = ''
|
||||
v2 = False
|
||||
|
|
@ -462,11 +481,12 @@ def set_v2_checkbox(model_list, v2, v_parameterization):
|
|||
|
||||
|
||||
def set_model_list(
|
||||
model_list,
|
||||
pretrained_model_name_or_path,
|
||||
v2,
|
||||
v_parameterization,
|
||||
model_list,
|
||||
pretrained_model_name_or_path,
|
||||
v2,
|
||||
v_parameterization,
|
||||
):
|
||||
|
||||
if not pretrained_model_name_or_path in ALL_PRESET_MODELS:
|
||||
model_list = 'custom'
|
||||
else:
|
||||
|
|
@ -509,7 +529,7 @@ def gradio_config():
|
|||
|
||||
|
||||
def get_pretrained_model_name_or_path_file(
|
||||
model_list, pretrained_model_name_or_path
|
||||
model_list, pretrained_model_name_or_path
|
||||
):
|
||||
pretrained_model_name_or_path = get_any_file_path(
|
||||
pretrained_model_name_or_path
|
||||
|
|
@ -517,13 +537,13 @@ def get_pretrained_model_name_or_path_file(
|
|||
set_model_list(model_list, pretrained_model_name_or_path)
|
||||
|
||||
|
||||
def gradio_source_model(save_model_as_choices=[
|
||||
'same as source model',
|
||||
'ckpt',
|
||||
'diffusers',
|
||||
'diffusers_safetensors',
|
||||
'safetensors',
|
||||
]):
|
||||
def gradio_source_model(save_model_as_choices = [
|
||||
'same as source model',
|
||||
'ckpt',
|
||||
'diffusers',
|
||||
'diffusers_safetensors',
|
||||
'safetensors',
|
||||
]):
|
||||
with gr.Tab('Source model'):
|
||||
# Define the input elements
|
||||
with gr.Row():
|
||||
|
|
@ -628,9 +648,9 @@ def gradio_source_model(save_model_as_choices=[
|
|||
|
||||
|
||||
def gradio_training(
|
||||
learning_rate_value='1e-6',
|
||||
lr_scheduler_value='constant',
|
||||
lr_warmup_value='0',
|
||||
learning_rate_value='1e-6',
|
||||
lr_scheduler_value='constant',
|
||||
lr_warmup_value='0',
|
||||
):
|
||||
with gr.Row():
|
||||
train_batch_size = gr.Slider(
|
||||
|
|
@ -820,7 +840,7 @@ def gradio_advanced_training():
|
|||
xformers = gr.Checkbox(label='Use xformers', value=True)
|
||||
color_aug = gr.Checkbox(label='Color augmentation', value=False)
|
||||
flip_aug = gr.Checkbox(label='Flip augmentation', value=False)
|
||||
min_snr_gamma = gr.Slider(label='Min SNR gamma', value=0, minimum=0, maximum=20, step=1)
|
||||
min_snr_gamma = gr.Slider(label='Min SNR gamma', value = 0, minimum=0, maximum=20, step=1)
|
||||
with gr.Row():
|
||||
bucket_no_upscale = gr.Checkbox(
|
||||
label="Don't upscale bucket resolution", value=True
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
class CommonUtilities:
|
||||
file_filters = {
|
||||
"all": [("All files", "*.*")],
|
||||
"video": [("Video files", "*.mp4;*.avi;*.mkv;*.mov;*.flv;*.wmv")],
|
||||
"images": [("Image files", "*.jpg;*.jpeg;*.png;*.bmp;*.gif;*.tiff")],
|
||||
"json": [("JSON files", "*.json")],
|
||||
"lora": [("LoRa files", "*.ckpt;*.pt;*.safetensors")],
|
||||
"directory": [],
|
||||
}
|
||||
|
||||
def is_valid_config(self, data):
|
||||
# Check if the data is a dictionary
|
||||
if not isinstance(data, dict):
|
||||
return False
|
||||
|
||||
# Add checks for expected keys and valid values
|
||||
# For example, check if 'use_8bit_adam' is a boolean
|
||||
if "use_8bit_adam" in data and not isinstance(data["use_8bit_adam"], bool):
|
||||
return False
|
||||
|
||||
# Add more checks for other keys as needed
|
||||
|
||||
# If all checks pass, return True
|
||||
return True
|
||||
|
|
@ -1,13 +1,13 @@
|
|||
import argparse
|
||||
import functools
|
||||
import json
|
||||
import random
|
||||
from dataclasses import (
|
||||
asdict,
|
||||
dataclass,
|
||||
)
|
||||
from pathlib import Path
|
||||
import functools
|
||||
import random
|
||||
from textwrap import dedent, indent
|
||||
import json
|
||||
from pathlib import Path
|
||||
# from toolz import curry
|
||||
from typing import (
|
||||
List,
|
||||
|
|
@ -19,7 +19,6 @@ from typing import (
|
|||
|
||||
import toml
|
||||
import voluptuous
|
||||
from transformers import CLIPTokenizer
|
||||
from voluptuous import (
|
||||
Any,
|
||||
ExactSequence,
|
||||
|
|
@ -28,6 +27,7 @@ from voluptuous import (
|
|||
Required,
|
||||
Schema,
|
||||
)
|
||||
from transformers import CLIPTokenizer
|
||||
|
||||
from . import train_util
|
||||
from .train_util import (
|
||||
|
|
@ -1,29 +1,28 @@
|
|||
import gradio as gr
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
import os
|
||||
import shutil
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import get_folder_path, get_file_path
|
||||
from .common_gui import get_folder_path, get_file_path
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
refresh_symbol = '\U0001f504' # 🔄
|
||||
save_style_symbol = '\U0001f4be' # 💾
|
||||
document_symbol = '\U0001F4C4' # 📄
|
||||
document_symbol = '\U0001F4C4' # 📄
|
||||
PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
|
||||
|
||||
|
||||
def convert_model(
|
||||
source_model_input,
|
||||
source_model_type,
|
||||
target_model_folder_input,
|
||||
target_model_name_input,
|
||||
target_model_type,
|
||||
target_save_precision_type,
|
||||
source_model_input,
|
||||
source_model_type,
|
||||
target_model_folder_input,
|
||||
target_model_name_input,
|
||||
target_model_type,
|
||||
target_save_precision_type,
|
||||
):
|
||||
# Check for caption_text_input
|
||||
if source_model_type == '':
|
||||
show_message_box('Invalid source model type')
|
||||
msgbox('Invalid source model type')
|
||||
return
|
||||
|
||||
# Check if source model exist
|
||||
|
|
@ -32,14 +31,14 @@ def convert_model(
|
|||
elif os.path.isdir(source_model_input):
|
||||
print('The provided model is a folder')
|
||||
else:
|
||||
show_message_box('The provided source model is neither a file nor a folder')
|
||||
msgbox('The provided source model is neither a file nor a folder')
|
||||
return
|
||||
|
||||
# Check if source model exist
|
||||
if os.path.isdir(target_model_folder_input):
|
||||
print('The provided model folder exist')
|
||||
else:
|
||||
show_message_box('The provided target folder does not exist')
|
||||
msgbox('The provided target folder does not exist')
|
||||
return
|
||||
|
||||
run_cmd = f'{PYTHON} "tools/convert_diffusers20_original_sd.py"'
|
||||
|
|
@ -61,8 +60,8 @@ def convert_model(
|
|||
run_cmd += f' --{target_save_precision_type}'
|
||||
|
||||
if (
|
||||
target_model_type == 'diffuser'
|
||||
or target_model_type == 'diffuser_safetensors'
|
||||
target_model_type == 'diffuser'
|
||||
or target_model_type == 'diffuser_safetensors'
|
||||
):
|
||||
run_cmd += f' --reference_model="{source_model_type}"'
|
||||
|
||||
|
|
@ -72,8 +71,8 @@ def convert_model(
|
|||
run_cmd += f' "{source_model_input}"'
|
||||
|
||||
if (
|
||||
target_model_type == 'diffuser'
|
||||
or target_model_type == 'diffuser_safetensors'
|
||||
target_model_type == 'diffuser'
|
||||
or target_model_type == 'diffuser_safetensors'
|
||||
):
|
||||
target_model_path = os.path.join(
|
||||
target_model_folder_input, target_model_name_input
|
||||
|
|
@ -95,8 +94,8 @@ def convert_model(
|
|||
subprocess.run(run_cmd)
|
||||
|
||||
if (
|
||||
not target_model_type == 'diffuser'
|
||||
or target_model_type == 'diffuser_safetensors'
|
||||
not target_model_type == 'diffuser'
|
||||
or target_model_type == 'diffuser_safetensors'
|
||||
):
|
||||
|
||||
v2_models = [
|
||||
|
|
@ -180,7 +179,7 @@ def gradio_convert_model_tab():
|
|||
document_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_source_model_file.click(
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[source_model_input],
|
||||
outputs=source_model_input,
|
||||
show_progress=False,
|
||||
|
|
|
|||
|
|
@ -1,11 +1,8 @@
|
|||
import os
|
||||
import re
|
||||
|
||||
import gradio as gr
|
||||
from easygui import boolbox
|
||||
|
||||
from .common_gui_functions import get_folder_path
|
||||
|
||||
from easygui import msgbox, boolbox
|
||||
from .common_gui import get_folder_path
|
||||
|
||||
# def select_folder():
|
||||
# # Open a file dialog to select a directory
|
||||
|
|
@ -19,14 +16,14 @@ def dataset_balancing(concept_repeats, folder, insecure):
|
|||
|
||||
if not concept_repeats > 0:
|
||||
# Display an error message if the total number of repeats is not a valid integer
|
||||
show_message_box('Please enter a valid integer for the total number of repeats.')
|
||||
msgbox('Please enter a valid integer for the total number of repeats.')
|
||||
return
|
||||
|
||||
concept_repeats = int(concept_repeats)
|
||||
|
||||
# Check if folder exist
|
||||
if folder == '' or not os.path.isdir(folder):
|
||||
show_message_box('Please enter a valid folder for balancing.')
|
||||
msgbox('Please enter a valid folder for balancing.')
|
||||
return
|
||||
|
||||
pattern = re.compile(r'^\d+_.+$')
|
||||
|
|
@ -88,7 +85,7 @@ def dataset_balancing(concept_repeats, folder, insecure):
|
|||
f'Skipping folder {subdir} because it does not match kohya_ss expected syntax...'
|
||||
)
|
||||
|
||||
show_message_box('Dataset balancing completed...')
|
||||
msgbox('Dataset balancing completed...')
|
||||
|
||||
|
||||
def warning(insecure):
|
||||
|
|
|
|||
|
|
@ -1,9 +1,8 @@
|
|||
import os
|
||||
import shutil
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import get_folder_path
|
||||
from easygui import diropenbox, msgbox
|
||||
from .common_gui import get_folder_path
|
||||
import shutil
|
||||
import os
|
||||
|
||||
|
||||
def copy_info_to_Folders_tab(training_folder):
|
||||
|
|
@ -40,12 +39,12 @@ def dreambooth_folder_preparation(
|
|||
|
||||
# Check for instance prompt
|
||||
if util_instance_prompt_input == '':
|
||||
show_message_box('Instance prompt missing...')
|
||||
msgbox('Instance prompt missing...')
|
||||
return
|
||||
|
||||
# Check for class prompt
|
||||
if util_class_prompt_input == '':
|
||||
show_message_box('Class prompt missing...')
|
||||
msgbox('Class prompt missing...')
|
||||
return
|
||||
|
||||
# Create the training_dir path
|
||||
|
|
|
|||
|
|
@ -1,49 +1,50 @@
|
|||
import os
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import (
|
||||
get_file_path, get_saveasfile_path,
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
import os
|
||||
from .common_gui import (
|
||||
get_saveasfilename_path,
|
||||
get_any_file_path,
|
||||
get_file_path,
|
||||
)
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
refresh_symbol = '\U0001f504' # 🔄
|
||||
save_style_symbol = '\U0001f4be' # 💾
|
||||
document_symbol = '\U0001F4C4' # 📄
|
||||
document_symbol = '\U0001F4C4' # 📄
|
||||
PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
|
||||
|
||||
|
||||
def extract_lora(
|
||||
model_tuned,
|
||||
model_org,
|
||||
save_to,
|
||||
save_precision,
|
||||
dim,
|
||||
v2,
|
||||
conv_dim,
|
||||
device,
|
||||
model_tuned,
|
||||
model_org,
|
||||
save_to,
|
||||
save_precision,
|
||||
dim,
|
||||
v2,
|
||||
conv_dim,
|
||||
device,
|
||||
):
|
||||
# Check for caption_text_input
|
||||
if model_tuned == '':
|
||||
show_message_box('Invalid finetuned model file')
|
||||
msgbox('Invalid finetuned model file')
|
||||
return
|
||||
|
||||
if model_org == '':
|
||||
show_message_box('Invalid base model file')
|
||||
msgbox('Invalid base model file')
|
||||
return
|
||||
|
||||
# Check if source model exist
|
||||
if not os.path.isfile(model_tuned):
|
||||
show_message_box('The provided finetuned model is not a file')
|
||||
msgbox('The provided finetuned model is not a file')
|
||||
return
|
||||
|
||||
if not os.path.isfile(model_org):
|
||||
show_message_box('The provided base model is not a file')
|
||||
msgbox('The provided base model is not a file')
|
||||
return
|
||||
|
||||
run_cmd = (
|
||||
f'{PYTHON} "{os.path.join("networks", "extract_lora_from_models.py")}"'
|
||||
f'{PYTHON} "{os.path.join("networks","extract_lora_from_models.py")}"'
|
||||
)
|
||||
run_cmd += f' --save_precision {save_precision}'
|
||||
run_cmd += f' --save_to "{save_to}"'
|
||||
|
|
@ -90,7 +91,7 @@ def gradio_extract_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_model_tuned_file.click(
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[model_tuned, model_ext, model_ext_name],
|
||||
outputs=model_tuned,
|
||||
show_progress=False,
|
||||
|
|
@ -105,8 +106,7 @@ def gradio_extract_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_model_org_file.click(
|
||||
lambda input1, input2, input3, *args, **kwargs:
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[model_org, model_ext, model_ext_name],
|
||||
outputs=model_org,
|
||||
show_progress=False,
|
||||
|
|
@ -121,7 +121,7 @@ def gradio_extract_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_save_to.click(
|
||||
get_saveasfile_path,
|
||||
get_saveasfilename_path,
|
||||
inputs=[save_to, lora_ext, lora_ext_name],
|
||||
outputs=save_to,
|
||||
show_progress=False,
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
import os
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import (
|
||||
get_file_path, get_saveasfile_path,
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
import os
|
||||
from .common_gui import (
|
||||
get_saveasfilename_path,
|
||||
get_any_file_path,
|
||||
get_file_path,
|
||||
)
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
|
|
@ -35,20 +36,20 @@ def extract_lycoris_locon(
|
|||
):
|
||||
# Check for caption_text_input
|
||||
if db_model == '':
|
||||
show_message_box('Invalid finetuned model file')
|
||||
msgbox('Invalid finetuned model file')
|
||||
return
|
||||
|
||||
if base_model == '':
|
||||
show_message_box('Invalid base model file')
|
||||
msgbox('Invalid base model file')
|
||||
return
|
||||
|
||||
# Check if source model exist
|
||||
if not os.path.isfile(db_model):
|
||||
show_message_box('The provided finetuned model is not a file')
|
||||
msgbox('The provided finetuned model is not a file')
|
||||
return
|
||||
|
||||
if not os.path.isfile(base_model):
|
||||
show_message_box('The provided base model is not a file')
|
||||
msgbox('The provided base model is not a file')
|
||||
return
|
||||
|
||||
run_cmd = f'{PYTHON} "{os.path.join("tools","lycoris_locon_extract.py")}"'
|
||||
|
|
@ -136,8 +137,7 @@ def gradio_extract_lycoris_locon_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_db_model_file.click(
|
||||
lambda input1, input2, input3, *args, **kwargs:
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[db_model, model_ext, model_ext_name],
|
||||
outputs=db_model,
|
||||
show_progress=False,
|
||||
|
|
@ -152,7 +152,7 @@ def gradio_extract_lycoris_locon_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_base_model_file.click(
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[base_model, model_ext, model_ext_name],
|
||||
outputs=base_model,
|
||||
show_progress=False,
|
||||
|
|
@ -167,7 +167,7 @@ def gradio_extract_lycoris_locon_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_output_name.click(
|
||||
get_saveasfile_path,
|
||||
get_saveasfilename_path,
|
||||
inputs=[output_name, lora_ext, lora_ext_name],
|
||||
outputs=output_name,
|
||||
show_progress=False,
|
||||
|
|
|
|||
|
|
@ -1,9 +1,8 @@
|
|||
import os
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import get_folder_path, add_pre_postfix
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
import os
|
||||
from .common_gui import get_folder_path, add_pre_postfix
|
||||
|
||||
PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
|
||||
|
||||
|
|
@ -20,11 +19,11 @@ def caption_images(
|
|||
):
|
||||
# Check for images_dir_input
|
||||
if train_data_dir == '':
|
||||
show_message_box('Image folder is missing...')
|
||||
msgbox('Image folder is missing...')
|
||||
return
|
||||
|
||||
if caption_ext == '':
|
||||
show_message_box('Please provide an extension for the caption files.')
|
||||
msgbox('Please provide an extension for the caption files.')
|
||||
return
|
||||
|
||||
print(f'GIT captioning files in {train_data_dir}...')
|
||||
|
|
|
|||
|
|
@ -1,86 +0,0 @@
|
|||
import os
|
||||
import pathlib
|
||||
import sys
|
||||
import tkinter as tk
|
||||
from tkinter import filedialog, messagebox
|
||||
|
||||
from library.common_gui_functions import tk_context
|
||||
from library.common_utilities import CommonUtilities
|
||||
|
||||
|
||||
class TkGui:
|
||||
def __init__(self):
|
||||
self.file_types = None
|
||||
|
||||
def open_file_dialog(self, initial_dir=None, initial_file=None, file_types="all"):
|
||||
with tk_context():
|
||||
self.file_types = file_types
|
||||
if self.file_types in CommonUtilities.file_filters:
|
||||
filters = CommonUtilities.file_filters[self.file_types]
|
||||
else:
|
||||
filters = CommonUtilities.file_filters["all"]
|
||||
|
||||
if self.file_types == "directory":
|
||||
result = filedialog.askdirectory(initialdir=initial_dir)
|
||||
else:
|
||||
result = filedialog.askopenfilename(initialdir=initial_dir, initialfile=initial_file, filetypes=filters)
|
||||
|
||||
# Return a tuple (file_path, canceled)
|
||||
# file_path: the selected file path or an empty string if no file is selected
|
||||
# canceled: True if the user pressed the cancel button, False otherwise
|
||||
return result, result == ""
|
||||
|
||||
def save_file_dialog(self, initial_dir, initial_file, file_types="all"):
|
||||
self.file_types = file_types
|
||||
|
||||
# Use the tk_context function with the 'with' statement
|
||||
with tk_context():
|
||||
if self.file_types in CommonUtilities.file_filters:
|
||||
filters = CommonUtilities.file_filters[self.file_types]
|
||||
else:
|
||||
filters = CommonUtilities.file_filters["all"]
|
||||
|
||||
save_file_path = filedialog.asksaveasfilename(initialdir=initial_dir, initialfile=initial_file,
|
||||
filetypes=filters, defaultextension=".safetensors")
|
||||
|
||||
return save_file_path
|
||||
|
||||
def show_message_box(_message, _title="Message", _level="info"):
|
||||
with tk_context():
|
||||
message_type = {
|
||||
"warning": messagebox.showwarning,
|
||||
"error": messagebox.showerror,
|
||||
"info": messagebox.showinfo,
|
||||
"question": messagebox.askquestion,
|
||||
"okcancel": messagebox.askokcancel,
|
||||
"retrycancel": messagebox.askretrycancel,
|
||||
"yesno": messagebox.askyesno,
|
||||
"yesnocancel": messagebox.askyesnocancel
|
||||
}
|
||||
|
||||
if _level in message_type:
|
||||
message_type[_level](_title, _message)
|
||||
else:
|
||||
messagebox.showinfo(_title, _message)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
try:
|
||||
mode = sys.argv[1]
|
||||
|
||||
if mode == 'file_dialog':
|
||||
starting_dir = sys.argv[2] if len(sys.argv) > 2 else None
|
||||
starting_file = sys.argv[3] if len(sys.argv) > 3 else None
|
||||
file_class = sys.argv[4] if len(sys.argv) > 4 else None # Update this to sys.argv[4]
|
||||
gui = TkGui()
|
||||
file_path = gui.open_file_dialog(starting_dir, starting_file, file_class)
|
||||
print(file_path) # Make sure to print the result
|
||||
|
||||
elif mode == 'msgbox':
|
||||
message = sys.argv[2]
|
||||
title = sys.argv[3] if len(sys.argv) > 3 else ""
|
||||
gui = TkGui()
|
||||
gui.show_message_box(message, title)
|
||||
except Exception as e:
|
||||
print(f"Error: {e}", file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
|
@ -1,10 +1,11 @@
|
|||
import os
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import (
|
||||
get_file_path, get_saveasfile_path,
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
import os
|
||||
from .common_gui import (
|
||||
get_saveasfilename_path,
|
||||
get_any_file_path,
|
||||
get_file_path,
|
||||
)
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
|
|
@ -24,20 +25,20 @@ def merge_lora(
|
|||
):
|
||||
# Check for caption_text_input
|
||||
if lora_a_model == '':
|
||||
show_message_box('Invalid model A file')
|
||||
msgbox('Invalid model A file')
|
||||
return
|
||||
|
||||
if lora_b_model == '':
|
||||
show_message_box('Invalid model B file')
|
||||
msgbox('Invalid model B file')
|
||||
return
|
||||
|
||||
# Check if source model exist
|
||||
if not os.path.isfile(lora_a_model):
|
||||
show_message_box('The provided model A is not a file')
|
||||
msgbox('The provided model A is not a file')
|
||||
return
|
||||
|
||||
if not os.path.isfile(lora_b_model):
|
||||
show_message_box('The provided model B is not a file')
|
||||
msgbox('The provided model B is not a file')
|
||||
return
|
||||
|
||||
ratio_a = ratio
|
||||
|
|
@ -81,7 +82,7 @@ def gradio_merge_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_lora_a_model_file.click(
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[lora_a_model, lora_ext, lora_ext_name],
|
||||
outputs=lora_a_model,
|
||||
show_progress=False,
|
||||
|
|
@ -96,7 +97,7 @@ def gradio_merge_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_lora_b_model_file.click(
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[lora_b_model, lora_ext, lora_ext_name],
|
||||
outputs=lora_b_model,
|
||||
show_progress=False,
|
||||
|
|
@ -121,7 +122,7 @@ def gradio_merge_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_save_to.click(
|
||||
get_saveasfile_path,
|
||||
get_saveasfilename_path,
|
||||
inputs=[save_to, lora_ext, lora_ext_name],
|
||||
outputs=save_to,
|
||||
show_progress=False,
|
||||
|
|
|
|||
|
|
@ -1,9 +1,8 @@
|
|||
import os
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import get_file_path, get_saveasfile_path
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
import os
|
||||
from .common_gui import get_saveasfilename_path, get_file_path
|
||||
|
||||
PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
|
|
@ -24,24 +23,24 @@ def resize_lora(
|
|||
):
|
||||
# Check for caption_text_input
|
||||
if model == '':
|
||||
show_message_box('Invalid model file')
|
||||
msgbox('Invalid model file')
|
||||
return
|
||||
|
||||
# Check if source model exist
|
||||
if not os.path.isfile(model):
|
||||
show_message_box('The provided model is not a file')
|
||||
msgbox('The provided model is not a file')
|
||||
return
|
||||
|
||||
if dynamic_method == 'sv_ratio':
|
||||
if float(dynamic_param) < 2:
|
||||
show_message_box(
|
||||
msgbox(
|
||||
f'Dynamic parameter for {dynamic_method} need to be 2 or greater...'
|
||||
)
|
||||
return
|
||||
|
||||
if dynamic_method == 'sv_fro' or dynamic_method == 'sv_cumulative':
|
||||
if float(dynamic_param) < 0 or float(dynamic_param) > 1:
|
||||
show_message_box(
|
||||
msgbox(
|
||||
f'Dynamic parameter for {dynamic_method} need to be between 0 and 1...'
|
||||
)
|
||||
return
|
||||
|
|
@ -96,7 +95,7 @@ def gradio_resize_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_lora_a_model_file.click(
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[model, lora_ext, lora_ext_name],
|
||||
outputs=model,
|
||||
show_progress=False,
|
||||
|
|
@ -135,7 +134,7 @@ def gradio_resize_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_save_to.click(
|
||||
get_saveasfile_path,
|
||||
get_saveasfilename_path,
|
||||
inputs=[save_to, lora_ext, lora_ext_name],
|
||||
outputs=save_to,
|
||||
show_progress=False,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import tempfile
|
||||
import os
|
||||
import gradio as gr
|
||||
from easygui import msgbox
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
refresh_symbol = '\U0001f504' # 🔄
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
import os
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import (
|
||||
get_file_path, get_saveasfile_path,
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
import os
|
||||
from .common_gui import (
|
||||
get_saveasfilename_path,
|
||||
get_any_file_path,
|
||||
get_file_path,
|
||||
)
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
|
|
@ -27,20 +28,20 @@ def svd_merge_lora(
|
|||
):
|
||||
# Check for caption_text_input
|
||||
if lora_a_model == '':
|
||||
show_message_box('Invalid model A file')
|
||||
msgbox('Invalid model A file')
|
||||
return
|
||||
|
||||
if lora_b_model == '':
|
||||
show_message_box('Invalid model B file')
|
||||
msgbox('Invalid model B file')
|
||||
return
|
||||
|
||||
# Check if source model exist
|
||||
if not os.path.isfile(lora_a_model):
|
||||
show_message_box('The provided model A is not a file')
|
||||
msgbox('The provided model A is not a file')
|
||||
return
|
||||
|
||||
if not os.path.isfile(lora_b_model):
|
||||
show_message_box('The provided model B is not a file')
|
||||
msgbox('The provided model B is not a file')
|
||||
return
|
||||
|
||||
ratio_a = ratio
|
||||
|
|
@ -87,7 +88,7 @@ def gradio_svd_merge_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_lora_a_model_file.click(
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[lora_a_model, lora_ext, lora_ext_name],
|
||||
outputs=lora_a_model,
|
||||
show_progress=False,
|
||||
|
|
@ -102,7 +103,7 @@ def gradio_svd_merge_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_lora_b_model_file.click(
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[lora_b_model, lora_ext, lora_ext_name],
|
||||
outputs=lora_b_model,
|
||||
show_progress=False,
|
||||
|
|
@ -143,7 +144,7 @@ def gradio_svd_merge_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_save_to.click(
|
||||
get_saveasfile_path,
|
||||
get_saveasfilename_path,
|
||||
inputs=[save_to, lora_ext, lora_ext_name],
|
||||
outputs=save_to,
|
||||
show_progress=False,
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
import os
|
||||
import gradio as gr
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
import time
|
||||
|
||||
import gradio as gr
|
||||
|
||||
tensorboard_proc = None # I know... bad but heh
|
||||
TENSORBOARD = 'tensorboard' if os.name == 'posix' else 'tensorboard.exe'
|
||||
|
||||
|
|
@ -13,7 +13,7 @@ def start_tensorboard(logging_dir):
|
|||
|
||||
if not os.listdir(logging_dir):
|
||||
print('Error: log folder is empty')
|
||||
show_message_box(msg='Error: log folder is empty')
|
||||
msgbox(msg='Error: log folder is empty')
|
||||
return
|
||||
|
||||
run_cmd = [f'{TENSORBOARD}', '--logdir', f'{logging_dir}']
|
||||
|
|
|
|||
|
|
@ -1,9 +1,10 @@
|
|||
import os
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import (
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
import os
|
||||
from .common_gui import (
|
||||
get_saveasfilename_path,
|
||||
get_any_file_path,
|
||||
get_file_path,
|
||||
)
|
||||
|
||||
|
|
@ -19,12 +20,12 @@ def verify_lora(
|
|||
):
|
||||
# verify for caption_text_input
|
||||
if lora_model == '':
|
||||
show_message_box('Invalid model A file')
|
||||
msgbox('Invalid model A file')
|
||||
return
|
||||
|
||||
# verify if source model exist
|
||||
if not os.path.isfile(lora_model):
|
||||
show_message_box('The provided model A is not a file')
|
||||
msgbox('The provided model A is not a file')
|
||||
return
|
||||
|
||||
run_cmd = [
|
||||
|
|
@ -68,7 +69,7 @@ def gradio_verify_lora_tab():
|
|||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
button_lora_model_file.click(
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
inputs=[lora_model, lora_ext, lora_ext_name],
|
||||
outputs=lora_model,
|
||||
show_progress=False,
|
||||
|
|
|
|||
|
|
@ -1,9 +1,8 @@
|
|||
import os
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from .common_gui_functions import get_folder_path
|
||||
from easygui import msgbox
|
||||
import subprocess
|
||||
from .common_gui import get_folder_path
|
||||
import os
|
||||
|
||||
|
||||
def replace_underscore_with_space(folder_path, file_extension):
|
||||
|
|
@ -21,16 +20,16 @@ def caption_images(
|
|||
):
|
||||
# Check for caption_text_input
|
||||
# if caption_text_input == "":
|
||||
# show_message_box("Caption text is missing...")
|
||||
# msgbox("Caption text is missing...")
|
||||
# return
|
||||
|
||||
# Check for images_dir_input
|
||||
if train_data_dir == '':
|
||||
show_message_box('Image folder is missing...')
|
||||
msgbox('Image folder is missing...')
|
||||
return
|
||||
|
||||
if caption_extension == '':
|
||||
show_message_box('Please provide an extension for the caption files.')
|
||||
msgbox('Please provide an extension for the caption files.')
|
||||
return
|
||||
|
||||
print(f'Captioning files in {train_data_dir}...')
|
||||
|
|
|
|||
46
lora_gui.py
46
lora_gui.py
|
|
@ -3,16 +3,15 @@
|
|||
# v3: Add new Utilities tab for Dreambooth folder preparation
|
||||
# v3.1: Adding captionning of images to utilities
|
||||
|
||||
import argparse
|
||||
import gradio as gr
|
||||
import easygui
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import pathlib
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from library.common_gui_functions import (
|
||||
import pathlib
|
||||
import argparse
|
||||
from library.common_gui import (
|
||||
get_folder_path,
|
||||
remove_doublequote,
|
||||
get_file_path,
|
||||
|
|
@ -28,23 +27,24 @@ from library.common_gui_functions import (
|
|||
run_cmd_training,
|
||||
# set_legacy_8bitadam,
|
||||
update_my_data,
|
||||
check_if_model_exist, show_message_box,
|
||||
check_if_model_exist,
|
||||
)
|
||||
from library.dataset_balancing_gui import gradio_dataset_balancing_tab
|
||||
from library.dreambooth_folder_creation_gui import (
|
||||
gradio_dreambooth_folder_creation_tab,
|
||||
)
|
||||
from library.merge_lora_gui import gradio_merge_lora_tab
|
||||
from library.resize_lora_gui import gradio_resize_lora_tab
|
||||
from library.sampler_gui import sample_gradio_config, run_cmd_sample
|
||||
from library.svd_merge_lora_gui import gradio_svd_merge_lora_tab
|
||||
from library.tensorboard_gui import (
|
||||
gradio_tensorboard,
|
||||
start_tensorboard,
|
||||
stop_tensorboard,
|
||||
)
|
||||
from library.dataset_balancing_gui import gradio_dataset_balancing_tab
|
||||
from library.utilities import utilities_tab
|
||||
from library.merge_lora_gui import gradio_merge_lora_tab
|
||||
from library.svd_merge_lora_gui import gradio_svd_merge_lora_tab
|
||||
from library.verify_lora_gui import gradio_verify_lora_tab
|
||||
from library.resize_lora_gui import gradio_resize_lora_tab
|
||||
from library.sampler_gui import sample_gradio_config, run_cmd_sample
|
||||
from easygui import msgbox
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
refresh_symbol = '\U0001f504' # 🔄
|
||||
|
|
@ -359,35 +359,35 @@ def train_model(
|
|||
print_only_bool = True if print_only.get('label') == 'True' else False
|
||||
|
||||
if pretrained_model_name_or_path == '':
|
||||
show_message_box('Source model information is missing')
|
||||
msgbox('Source model information is missing')
|
||||
return
|
||||
|
||||
if train_data_dir == '':
|
||||
show_message_box('Image folder path is missing')
|
||||
msgbox('Image folder path is missing')
|
||||
return
|
||||
|
||||
if not os.path.exists(train_data_dir):
|
||||
show_message_box('Image folder does not exist')
|
||||
msgbox('Image folder does not exist')
|
||||
return
|
||||
|
||||
if reg_data_dir != '':
|
||||
if not os.path.exists(reg_data_dir):
|
||||
show_message_box('Regularisation folder does not exist')
|
||||
msgbox('Regularisation folder does not exist')
|
||||
return
|
||||
|
||||
if output_dir == '':
|
||||
show_message_box('Output folder path is missing')
|
||||
msgbox('Output folder path is missing')
|
||||
return
|
||||
|
||||
if int(bucket_reso_steps) < 1:
|
||||
show_message_box('Bucket resolution steps need to be greater than 0')
|
||||
msgbox('Bucket resolution steps need to be greater than 0')
|
||||
return
|
||||
|
||||
if not os.path.exists(output_dir):
|
||||
os.makedirs(output_dir)
|
||||
|
||||
if stop_text_encoder_training_pct > 0:
|
||||
show_message_box(
|
||||
msgbox(
|
||||
'Output "stop text encoder training" is not yet supported. Ignoring'
|
||||
)
|
||||
stop_text_encoder_training_pct = 0
|
||||
|
|
@ -402,7 +402,7 @@ def train_model(
|
|||
unet_lr = 0
|
||||
|
||||
# if (float(text_encoder_lr) == 0) and (float(unet_lr) == 0):
|
||||
# show_message_box(
|
||||
# msgbox(
|
||||
# 'At least one Learning Rate value for "Text encoder" or "Unet" need to be provided'
|
||||
# )
|
||||
# return
|
||||
|
|
@ -540,7 +540,7 @@ def train_model(
|
|||
run_cmd += f' --network_train_unet_only'
|
||||
else:
|
||||
if float(text_encoder_lr) == 0:
|
||||
show_message_box('Please input learning rate values.')
|
||||
msgbox('Please input learning rate values.')
|
||||
return
|
||||
|
||||
run_cmd += f' --network_dim={network_dim}'
|
||||
|
|
@ -1031,14 +1031,14 @@ def lora_tab(
|
|||
]
|
||||
|
||||
button_open_config.click(
|
||||
lambda *args, **kwargs: open_configuration(),
|
||||
open_configuration,
|
||||
inputs=[dummy_db_true, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list + [LoCon_row],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_load_config.click(
|
||||
lambda *args, **kwargs: open_configuration(),
|
||||
open_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list + [LoCon_row],
|
||||
show_progress=False,
|
||||
|
|
|
|||
|
|
@ -3,16 +3,14 @@
|
|||
# v3: Add new Utilities tab for Dreambooth folder preparation
|
||||
# v3.1: Adding captionning of images to utilities
|
||||
|
||||
import argparse
|
||||
import gradio as gr
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import pathlib
|
||||
import subprocess
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from library.common_gui_functions import (
|
||||
import pathlib
|
||||
import argparse
|
||||
from library.common_gui import (
|
||||
get_folder_path,
|
||||
remove_doublequote,
|
||||
get_file_path,
|
||||
|
|
@ -30,16 +28,17 @@ from library.common_gui_functions import (
|
|||
update_my_data,
|
||||
check_if_model_exist,
|
||||
)
|
||||
from library.dreambooth_folder_creation_gui import (
|
||||
gradio_dreambooth_folder_creation_tab,
|
||||
)
|
||||
from library.sampler_gui import sample_gradio_config, run_cmd_sample
|
||||
from library.tensorboard_gui import (
|
||||
gradio_tensorboard,
|
||||
start_tensorboard,
|
||||
stop_tensorboard,
|
||||
)
|
||||
from library.dreambooth_folder_creation_gui import (
|
||||
gradio_dreambooth_folder_creation_tab,
|
||||
)
|
||||
from library.utilities import utilities_tab
|
||||
from library.sampler_gui import sample_gradio_config, run_cmd_sample
|
||||
from easygui import msgbox
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
refresh_symbol = '\U0001f504' # 🔄
|
||||
|
|
@ -242,7 +241,7 @@ def open_configuration(
|
|||
if ask_for_file:
|
||||
file_path = get_file_path(file_path)
|
||||
|
||||
if not file_path == '' and file_path is not None:
|
||||
if not file_path == '' and not file_path == None:
|
||||
# load variables from JSON file
|
||||
with open(file_path, 'r') as f:
|
||||
my_data = json.load(f)
|
||||
|
|
@ -330,32 +329,32 @@ def train_model(
|
|||
min_snr_gamma,
|
||||
):
|
||||
if pretrained_model_name_or_path == '':
|
||||
show_message_box('Source model information is missing')
|
||||
msgbox('Source model information is missing')
|
||||
return
|
||||
|
||||
if train_data_dir == '':
|
||||
show_message_box('Image folder path is missing')
|
||||
msgbox('Image folder path is missing')
|
||||
return
|
||||
|
||||
if not os.path.exists(train_data_dir):
|
||||
show_message_box('Image folder does not exist')
|
||||
msgbox('Image folder does not exist')
|
||||
return
|
||||
|
||||
if reg_data_dir != '':
|
||||
if not os.path.exists(reg_data_dir):
|
||||
show_message_box('Regularisation folder does not exist')
|
||||
msgbox('Regularisation folder does not exist')
|
||||
return
|
||||
|
||||
if output_dir == '':
|
||||
show_message_box('Output folder path is missing')
|
||||
msgbox('Output folder path is missing')
|
||||
return
|
||||
|
||||
if token_string == '':
|
||||
show_message_box('Token string is missing')
|
||||
msgbox('Token string is missing')
|
||||
return
|
||||
|
||||
if init_word == '':
|
||||
show_message_box('Init word is missing')
|
||||
msgbox('Init word is missing')
|
||||
return
|
||||
|
||||
if not os.path.exists(output_dir):
|
||||
|
|
@ -673,7 +672,7 @@ def ti_tab(
|
|||
)
|
||||
weights_file_input = gr.Button('📂', elem_id='open_folder_small')
|
||||
weights_file_input.click(
|
||||
lambda *args, **kwargs: get_file_path(*args),
|
||||
get_file_path,
|
||||
outputs=weights,
|
||||
show_progress=False,
|
||||
)
|
||||
|
|
@ -899,14 +898,14 @@ def ti_tab(
|
|||
]
|
||||
|
||||
button_open_config.click(
|
||||
lambda *args, **kwargs: open_configuration(),
|
||||
open_configuration,
|
||||
inputs=[dummy_db_true, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
button_load_config.click(
|
||||
lambda *args, **kwargs: open_configuration(),
|
||||
open_configuration,
|
||||
inputs=[dummy_db_false, config_file_name] + settings_list,
|
||||
outputs=[config_file_name] + settings_list,
|
||||
show_progress=False,
|
||||
|
|
|
|||
13
train_db.py
13
train_db.py
|
|
@ -1,25 +1,28 @@
|
|||
# DreamBooth training
|
||||
# XXX dropped option: fine_tune
|
||||
|
||||
import argparse
|
||||
import gc
|
||||
import time
|
||||
import argparse
|
||||
import itertools
|
||||
import math
|
||||
import os
|
||||
import toml
|
||||
from multiprocessing import Value
|
||||
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
from accelerate.utils import set_seed
|
||||
import diffusers
|
||||
from diffusers import DDPMScheduler
|
||||
from tqdm import tqdm
|
||||
|
||||
import library.config_ml_util as config_util
|
||||
import library.custom_train_functions as custom_train_functions
|
||||
import library.train_util as train_util
|
||||
from library.config_ml_util import (
|
||||
import library.config_util as config_util
|
||||
from library.config_util import (
|
||||
ConfigSanitizer,
|
||||
BlueprintGenerator,
|
||||
)
|
||||
import library.custom_train_functions as custom_train_functions
|
||||
from library.custom_train_functions import apply_snr_weight
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,21 +1,24 @@
|
|||
import importlib
|
||||
import argparse
|
||||
import gc
|
||||
import math
|
||||
import os
|
||||
import toml
|
||||
from multiprocessing import Value
|
||||
|
||||
from tqdm import tqdm
|
||||
import torch
|
||||
from accelerate.utils import set_seed
|
||||
import diffusers
|
||||
from diffusers import DDPMScheduler
|
||||
from tqdm import tqdm
|
||||
|
||||
import library.config_ml_util as config_util
|
||||
import library.custom_train_functions as custom_train_functions
|
||||
import library.train_util as train_util
|
||||
from library.config_ml_util import (
|
||||
import library.config_util as config_util
|
||||
from library.config_util import (
|
||||
ConfigSanitizer,
|
||||
BlueprintGenerator,
|
||||
)
|
||||
import library.custom_train_functions as custom_train_functions
|
||||
from library.custom_train_functions import apply_snr_weight
|
||||
|
||||
imagenet_templates_small = [
|
||||
|
|
|
|||
Loading…
Reference in New Issue