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Parent(s):
Duplicate from Tune-A-Video-library/Tune-A-Video-Training-UI
Browse filesCo-authored-by: hysts <hysts@users.noreply.huggingface.co>
- .gitattributes +35 -0
- .gitignore +164 -0
- .gitmodules +3 -0
- .pre-commit-config.yaml +37 -0
- .style.yapf +5 -0
- Dockerfile +57 -0
- LICENSE +21 -0
- README.md +12 -0
- Tune-A-Video +1 -0
- app.py +84 -0
- app_inference.py +170 -0
- app_training.py +135 -0
- app_upload.py +106 -0
- constants.py +10 -0
- inference.py +109 -0
- packages.txt +1 -0
- patch +15 -0
- requirements.txt +19 -0
- style.css +3 -0
- trainer.py +166 -0
- uploader.py +44 -0
- utils.py +65 -0
.gitattributes
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*.whl filter=lfs diff=lfs merge=lfs -text
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.npz filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.wasm filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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.gitignore
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checkpoints/
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experiments/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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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/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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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.
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*.manifest
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*.spec
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+
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# Installer logs
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pip-log.txt
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+
pip-delete-this-directory.txt
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# Unit test / coverage reports
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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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# Translations
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*.mo
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*.pot
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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:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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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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# 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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# 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/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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+
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# Rope project settings
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.ropeproject
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+
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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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+
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# PyCharm
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+
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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.gitmodules
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[submodule "Tune-A-Video"]
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path = Tune-A-Video
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url = https://github.com/showlab/Tune-A-Video
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.pre-commit-config.yaml
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exclude: patch
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.2.0
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hooks:
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+
- id: check-executables-have-shebangs
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+
- id: check-json
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8 |
+
- id: check-merge-conflict
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+
- id: check-shebang-scripts-are-executable
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+
- id: check-toml
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+
- id: check-yaml
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+
- id: double-quote-string-fixer
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+
- id: end-of-file-fixer
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+
- id: mixed-line-ending
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args: ['--fix=lf']
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+
- id: requirements-txt-fixer
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+
- id: trailing-whitespace
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+
- repo: https://github.com/myint/docformatter
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rev: v1.4
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+
hooks:
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- id: docformatter
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args: ['--in-place']
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+
- repo: https://github.com/pycqa/isort
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rev: 5.12.0
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hooks:
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- id: isort
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- repo: https://github.com/pre-commit/mirrors-mypy
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rev: v0.991
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hooks:
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- id: mypy
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args: ['--ignore-missing-imports']
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additional_dependencies: ['types-python-slugify']
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- repo: https://github.com/google/yapf
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rev: v0.32.0
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hooks:
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- id: yapf
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args: ['--parallel', '--in-place']
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.style.yapf
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[style]
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based_on_style = pep8
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blank_line_before_nested_class_or_def = false
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spaces_before_comment = 2
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split_before_logical_operator = true
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Dockerfile
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FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
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ENV DEBIAN_FRONTEND=noninteractive
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RUN apt-get update && \
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apt-get upgrade -y && \
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apt-get install -y --no-install-recommends \
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git \
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git-lfs \
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wget \
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curl \
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# ffmpeg \
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ffmpeg \
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x264 \
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# python build dependencies \
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build-essential \
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+
libssl-dev \
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+
zlib1g-dev \
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+
libbz2-dev \
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+
libreadline-dev \
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+
libsqlite3-dev \
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libncursesw5-dev \
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xz-utils \
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tk-dev \
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+
libxml2-dev \
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libxmlsec1-dev \
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libffi-dev \
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liblzma-dev && \
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apt-get clean && \
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rm -rf /var/lib/apt/lists/*
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:${PATH}
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WORKDIR ${HOME}/app
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RUN curl https://pyenv.run | bash
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ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
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ENV PYTHON_VERSION=3.10.9
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RUN pyenv install ${PYTHON_VERSION} && \
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pyenv global ${PYTHON_VERSION} && \
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pyenv rehash && \
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42 |
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pip install --no-cache-dir -U pip setuptools wheel
|
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RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
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COPY --chown=1000 requirements.txt /tmp/requirements.txt
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RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
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COPY --chown=1000 . ${HOME}/app
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RUN cd Tune-A-Video && patch -p1 < ../patch
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ENV PYTHONPATH=${HOME}/app \
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PYTHONUNBUFFERED=1 \
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GRADIO_ALLOW_FLAGGING=never \
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GRADIO_NUM_PORTS=1 \
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GRADIO_SERVER_NAME=0.0.0.0 \
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GRADIO_THEME=huggingface \
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SYSTEM=spaces
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CMD ["python", "app.py"]
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LICENSE
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MIT License
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Copyright (c) 2022 hysts
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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---
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title: Tune-A-Video Training UI
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emoji: ⚡
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colorFrom: red
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colorTo: purple
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sdk: docker
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pinned: false
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license: mit
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duplicated_from: Tune-A-Video-library/Tune-A-Video-Training-UI
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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Tune-A-Video
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Subproject commit b2c8c3eeac0df5c5d9eccc4dd2153e17b83c638c
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app.py
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#!/usr/bin/env python
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from __future__ import annotations
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import os
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from subprocess import getoutput
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import gradio as gr
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import torch
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from app_inference import create_inference_demo
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from app_training import create_training_demo
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from app_upload import create_upload_demo
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from inference import InferencePipeline
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from trainer import Trainer
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TITLE = '# [Tune-A-Video](https://tuneavideo.github.io/) UI'
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ORIGINAL_SPACE_ID = 'Tune-A-Video-library/Tune-A-Video-Training-UI'
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SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
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GPU_DATA = getoutput('nvidia-smi')
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SHARED_UI_WARNING = f'''## Attention - Training doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
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<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center>
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'''
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if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
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SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'
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else:
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SETTINGS = 'Settings'
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INVALID_GPU_WARNING = f'''## Attention - the specified GPU is invalid. Training may not work. Make sure you have selected a `T4 GPU` for this task.'''
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CUDA_NOT_AVAILABLE_WARNING = f'''## Attention - Running on CPU.
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<center>
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You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
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You can use "T4 small/medium" to run this demo.
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</center>
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'''
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HF_TOKEN_NOT_SPECIFIED_WARNING = f'''The environment variable `HF_TOKEN` is not specified. Feel free to specify your Hugging Face token with write permission if you don't want to manually provide it for every run.
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<center>
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You can check and create your Hugging Face tokens <a href="https://huggingface.co/settings/tokens" target="_blank">here</a>.
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You can specify environment variables in the "Repository secrets" section of the {SETTINGS} tab.
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</center>
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'''
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HF_TOKEN = os.getenv('HF_TOKEN')
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def show_warning(warning_text: str) -> gr.Blocks:
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with gr.Blocks() as demo:
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with gr.Box():
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gr.Markdown(warning_text)
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return demo
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pipe = InferencePipeline(HF_TOKEN)
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trainer = Trainer(HF_TOKEN)
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with gr.Blocks(css='style.css') as demo:
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if SPACE_ID == ORIGINAL_SPACE_ID:
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show_warning(SHARED_UI_WARNING)
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elif not torch.cuda.is_available():
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show_warning(CUDA_NOT_AVAILABLE_WARNING)
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elif (not 'T4' in GPU_DATA):
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show_warning(INVALID_GPU_WARNING)
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gr.Markdown(TITLE)
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with gr.Tabs():
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with gr.TabItem('Train'):
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create_training_demo(trainer, pipe)
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with gr.TabItem('Run'):
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create_inference_demo(pipe, HF_TOKEN)
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with gr.TabItem('Upload'):
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gr.Markdown('''
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- You can use this tab to upload models later if you choose not to upload models in training time or if upload in training time failed.
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''')
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create_upload_demo(HF_TOKEN)
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if not HF_TOKEN:
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show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING)
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demo.queue(max_size=1).launch(share=False)
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app_inference.py
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#!/usr/bin/env python
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from __future__ import annotations
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import enum
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import gradio as gr
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from huggingface_hub import HfApi
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from constants import MODEL_LIBRARY_ORG_NAME, UploadTarget
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from inference import InferencePipeline
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from utils import find_exp_dirs
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class ModelSource(enum.Enum):
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HUB_LIB = UploadTarget.MODEL_LIBRARY.value
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LOCAL = 'Local'
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class InferenceUtil:
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def __init__(self, hf_token: str | None):
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self.hf_token = hf_token
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def load_hub_model_list(self) -> dict:
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api = HfApi(token=self.hf_token)
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choices = [
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info.modelId
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for info in api.list_models(author=MODEL_LIBRARY_ORG_NAME)
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]
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return gr.update(choices=choices,
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value=choices[0] if choices else None)
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@staticmethod
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def load_local_model_list() -> dict:
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choices = find_exp_dirs()
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return gr.update(choices=choices,
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value=choices[0] if choices else None)
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def reload_model_list(self, model_source: str) -> dict:
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if model_source == ModelSource.HUB_LIB.value:
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return self.load_hub_model_list()
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elif model_source == ModelSource.LOCAL.value:
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return self.load_local_model_list()
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else:
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raise ValueError
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def load_model_info(self, model_id: str) -> tuple[str, str]:
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try:
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card = InferencePipeline.get_model_card(model_id, self.hf_token)
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except Exception:
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return '', ''
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base_model = getattr(card.data, 'base_model', '')
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training_prompt = getattr(card.data, 'training_prompt', '')
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return base_model, training_prompt
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def reload_model_list_and_update_model_info(
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self, model_source: str) -> tuple[dict, str, str]:
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model_list_update = self.reload_model_list(model_source)
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model_list = model_list_update['choices']
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model_info = self.load_model_info(model_list[0] if model_list else '')
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return model_list_update, *model_info
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def create_inference_demo(pipe: InferencePipeline,
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hf_token: str | None = None) -> gr.Blocks:
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app = InferenceUtil(hf_token)
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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with gr.Box():
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model_source = gr.Radio(
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label='Model Source',
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choices=[_.value for _ in ModelSource],
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value=ModelSource.HUB_LIB.value)
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reload_button = gr.Button('Reload Model List')
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model_id = gr.Dropdown(label='Model ID',
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choices=None,
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value=None)
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with gr.Accordion(
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label=
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'Model info (Base model and prompt used for training)',
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open=False):
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with gr.Row():
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base_model_used_for_training = gr.Text(
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label='Base model', interactive=False)
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prompt_used_for_training = gr.Text(
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label='Training prompt', interactive=False)
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prompt = gr.Textbox(
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label='Prompt',
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max_lines=1,
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placeholder='Example: "A panda is surfing"')
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video_length = gr.Slider(label='Video length',
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minimum=4,
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maximum=12,
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step=1,
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value=8)
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fps = gr.Slider(label='FPS',
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minimum=1,
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maximum=12,
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step=1,
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value=1)
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seed = gr.Slider(label='Seed',
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minimum=0,
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maximum=100000,
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step=1,
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value=0)
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with gr.Accordion('Other Parameters', open=False):
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num_steps = gr.Slider(label='Number of Steps',
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minimum=0,
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maximum=100,
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step=1,
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value=50)
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guidance_scale = gr.Slider(label='CFG Scale',
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minimum=0,
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maximum=50,
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step=0.1,
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value=7.5)
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run_button = gr.Button('Generate')
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gr.Markdown('''
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- After training, you can press "Reload Model List" button to load your trained model names.
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- It takes a few minutes to download model first.
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- Expected time to generate an 8-frame video: 70 seconds with T4, 24 seconds with A10G, (10 seconds with A100)
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''')
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with gr.Column():
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result = gr.Video(label='Result')
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model_source.change(fn=app.reload_model_list_and_update_model_info,
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inputs=model_source,
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outputs=[
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model_id,
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base_model_used_for_training,
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prompt_used_for_training,
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])
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reload_button.click(fn=app.reload_model_list_and_update_model_info,
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inputs=model_source,
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outputs=[
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model_id,
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base_model_used_for_training,
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prompt_used_for_training,
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])
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model_id.change(fn=app.load_model_info,
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inputs=model_id,
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outputs=[
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base_model_used_for_training,
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prompt_used_for_training,
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])
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inputs = [
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model_id,
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prompt,
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video_length,
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fps,
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seed,
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num_steps,
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guidance_scale,
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]
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prompt.submit(fn=pipe.run, inputs=inputs, outputs=result)
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run_button.click(fn=pipe.run, inputs=inputs, outputs=result)
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return demo
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if __name__ == '__main__':
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import os
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hf_token = os.getenv('HF_TOKEN')
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pipe = InferencePipeline(hf_token)
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demo = create_inference_demo(pipe, hf_token)
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demo.queue(max_size=10).launch(share=False)
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app_training.py
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
from __future__ import annotations
|
4 |
+
|
5 |
+
import os
|
6 |
+
|
7 |
+
import gradio as gr
|
8 |
+
|
9 |
+
from constants import MODEL_LIBRARY_ORG_NAME, SAMPLE_MODEL_REPO, UploadTarget
|
10 |
+
from inference import InferencePipeline
|
11 |
+
from trainer import Trainer
|
12 |
+
|
13 |
+
|
14 |
+
def create_training_demo(trainer: Trainer,
|
15 |
+
pipe: InferencePipeline | None = None) -> gr.Blocks:
|
16 |
+
hf_token = os.getenv('HF_TOKEN')
|
17 |
+
with gr.Blocks() as demo:
|
18 |
+
with gr.Row():
|
19 |
+
with gr.Column():
|
20 |
+
with gr.Box():
|
21 |
+
gr.Markdown('Training Data')
|
22 |
+
training_video = gr.File(label='Training video')
|
23 |
+
training_prompt = gr.Textbox(
|
24 |
+
label='Training prompt',
|
25 |
+
max_lines=1,
|
26 |
+
placeholder='A man is surfing')
|
27 |
+
gr.Markdown('''
|
28 |
+
- Upload a video and write a `Training Prompt` that describes the video.
|
29 |
+
''')
|
30 |
+
|
31 |
+
with gr.Column():
|
32 |
+
with gr.Box():
|
33 |
+
gr.Markdown('Training Parameters')
|
34 |
+
with gr.Row():
|
35 |
+
base_model = gr.Text(
|
36 |
+
label='Base Model',
|
37 |
+
value='CompVis/stable-diffusion-v1-4',
|
38 |
+
max_lines=1)
|
39 |
+
resolution = gr.Dropdown(choices=['512', '768'],
|
40 |
+
value='512',
|
41 |
+
label='Resolution',
|
42 |
+
visible=False)
|
43 |
+
|
44 |
+
input_token = gr.Text(label='Hugging Face Write Token',
|
45 |
+
placeholder='',
|
46 |
+
visible=False if hf_token else True)
|
47 |
+
with gr.Accordion('Advanced settings', open=False):
|
48 |
+
num_training_steps = gr.Number(
|
49 |
+
label='Number of Training Steps',
|
50 |
+
value=300,
|
51 |
+
precision=0)
|
52 |
+
learning_rate = gr.Number(label='Learning Rate',
|
53 |
+
value=0.000035)
|
54 |
+
gradient_accumulation = gr.Number(
|
55 |
+
label='Number of Gradient Accumulation',
|
56 |
+
value=1,
|
57 |
+
precision=0)
|
58 |
+
seed = gr.Slider(label='Seed',
|
59 |
+
minimum=0,
|
60 |
+
maximum=100000,
|
61 |
+
step=1,
|
62 |
+
randomize=True,
|
63 |
+
value=0)
|
64 |
+
fp16 = gr.Checkbox(label='FP16', value=True)
|
65 |
+
use_8bit_adam = gr.Checkbox(label='Use 8bit Adam',
|
66 |
+
value=False)
|
67 |
+
checkpointing_steps = gr.Number(
|
68 |
+
label='Checkpointing Steps',
|
69 |
+
value=1000,
|
70 |
+
precision=0)
|
71 |
+
validation_epochs = gr.Number(
|
72 |
+
label='Validation Epochs', value=100, precision=0)
|
73 |
+
gr.Markdown('''
|
74 |
+
- The base model must be a Stable Diffusion model compatible with [diffusers](https://github.com/huggingface/diffusers) library.
|
75 |
+
- Expected time to train a model for 300 steps: ~20 minutes with T4
|
76 |
+
- You can check the training status by pressing the "Open logs" button if you are running this on your Space.
|
77 |
+
''')
|
78 |
+
|
79 |
+
with gr.Row():
|
80 |
+
with gr.Column():
|
81 |
+
gr.Markdown('Output Model')
|
82 |
+
output_model_name = gr.Text(label='Name of your model',
|
83 |
+
placeholder='The surfer man',
|
84 |
+
max_lines=1)
|
85 |
+
validation_prompt = gr.Text(
|
86 |
+
label='Validation Prompt',
|
87 |
+
placeholder=
|
88 |
+
'prompt to test the model, e.g: a dog is surfing')
|
89 |
+
with gr.Column():
|
90 |
+
gr.Markdown('Upload Settings')
|
91 |
+
with gr.Row():
|
92 |
+
upload_to_hub = gr.Checkbox(label='Upload model to Hub',
|
93 |
+
value=True)
|
94 |
+
use_private_repo = gr.Checkbox(label='Private', value=True)
|
95 |
+
delete_existing_repo = gr.Checkbox(
|
96 |
+
label='Delete existing repo of the same name',
|
97 |
+
value=False)
|
98 |
+
upload_to = gr.Radio(
|
99 |
+
label='Upload to',
|
100 |
+
choices=[_.value for _ in UploadTarget],
|
101 |
+
value=UploadTarget.MODEL_LIBRARY.value)
|
102 |
+
|
103 |
+
remove_gpu_after_training = gr.Checkbox(
|
104 |
+
label='Remove GPU after training',
|
105 |
+
value=False,
|
106 |
+
interactive=bool(os.getenv('SPACE_ID')),
|
107 |
+
visible=False)
|
108 |
+
run_button = gr.Button('Start Training')
|
109 |
+
|
110 |
+
with gr.Box():
|
111 |
+
gr.Markdown('Output message')
|
112 |
+
output_message = gr.Markdown()
|
113 |
+
|
114 |
+
if pipe is not None:
|
115 |
+
run_button.click(fn=pipe.clear)
|
116 |
+
run_button.click(
|
117 |
+
fn=trainer.run,
|
118 |
+
inputs=[
|
119 |
+
training_video, training_prompt, output_model_name,
|
120 |
+
delete_existing_repo, validation_prompt, base_model,
|
121 |
+
resolution, num_training_steps, learning_rate,
|
122 |
+
gradient_accumulation, seed, fp16, use_8bit_adam,
|
123 |
+
checkpointing_steps, validation_epochs, upload_to_hub,
|
124 |
+
use_private_repo, delete_existing_repo, upload_to,
|
125 |
+
remove_gpu_after_training, input_token
|
126 |
+
],
|
127 |
+
outputs=output_message)
|
128 |
+
return demo
|
129 |
+
|
130 |
+
|
131 |
+
if __name__ == '__main__':
|
132 |
+
hf_token = os.getenv('HF_TOKEN')
|
133 |
+
trainer = Trainer(hf_token)
|
134 |
+
demo = create_training_demo(trainer)
|
135 |
+
demo.queue(max_size=1).launch(share=False)
|
app_upload.py
ADDED
@@ -0,0 +1,106 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
from __future__ import annotations
|
4 |
+
|
5 |
+
import pathlib
|
6 |
+
|
7 |
+
import gradio as gr
|
8 |
+
import slugify
|
9 |
+
|
10 |
+
from constants import MODEL_LIBRARY_ORG_NAME, UploadTarget
|
11 |
+
from uploader import Uploader
|
12 |
+
from utils import find_exp_dirs
|
13 |
+
|
14 |
+
|
15 |
+
class ModelUploader(Uploader):
|
16 |
+
def upload_model(
|
17 |
+
self,
|
18 |
+
folder_path: str,
|
19 |
+
repo_name: str,
|
20 |
+
upload_to: str,
|
21 |
+
private: bool,
|
22 |
+
delete_existing_repo: bool,
|
23 |
+
input_token: str | None = None,
|
24 |
+
) -> str:
|
25 |
+
if not folder_path:
|
26 |
+
raise ValueError
|
27 |
+
if not repo_name:
|
28 |
+
repo_name = pathlib.Path(folder_path).name
|
29 |
+
repo_name = slugify.slugify(repo_name)
|
30 |
+
|
31 |
+
if upload_to == UploadTarget.PERSONAL_PROFILE.value:
|
32 |
+
organization = ''
|
33 |
+
elif upload_to == UploadTarget.MODEL_LIBRARY.value:
|
34 |
+
organization = MODEL_LIBRARY_ORG_NAME
|
35 |
+
else:
|
36 |
+
raise ValueError
|
37 |
+
|
38 |
+
return self.upload(folder_path,
|
39 |
+
repo_name,
|
40 |
+
organization=organization,
|
41 |
+
private=private,
|
42 |
+
delete_existing_repo=delete_existing_repo,
|
43 |
+
input_token=input_token)
|
44 |
+
|
45 |
+
|
46 |
+
def load_local_model_list() -> dict:
|
47 |
+
choices = find_exp_dirs()
|
48 |
+
return gr.update(choices=choices, value=choices[0] if choices else None)
|
49 |
+
|
50 |
+
|
51 |
+
def create_upload_demo(hf_token: str | None) -> gr.Blocks:
|
52 |
+
uploader = ModelUploader(hf_token)
|
53 |
+
model_dirs = find_exp_dirs()
|
54 |
+
|
55 |
+
with gr.Blocks() as demo:
|
56 |
+
with gr.Box():
|
57 |
+
gr.Markdown('Local Models')
|
58 |
+
reload_button = gr.Button('Reload Model List')
|
59 |
+
model_dir = gr.Dropdown(
|
60 |
+
label='Model names',
|
61 |
+
choices=model_dirs,
|
62 |
+
value=model_dirs[0] if model_dirs else None)
|
63 |
+
with gr.Box():
|
64 |
+
gr.Markdown('Upload Settings')
|
65 |
+
with gr.Row():
|
66 |
+
use_private_repo = gr.Checkbox(label='Private', value=True)
|
67 |
+
delete_existing_repo = gr.Checkbox(
|
68 |
+
label='Delete existing repo of the same name', value=False)
|
69 |
+
upload_to = gr.Radio(label='Upload to',
|
70 |
+
choices=[_.value for _ in UploadTarget],
|
71 |
+
value=UploadTarget.MODEL_LIBRARY.value)
|
72 |
+
model_name = gr.Textbox(label='Model Name')
|
73 |
+
input_token = gr.Text(label='Hugging Face Write Token',
|
74 |
+
placeholder='',
|
75 |
+
visible=False if hf_token else True)
|
76 |
+
upload_button = gr.Button('Upload')
|
77 |
+
gr.Markdown(f'''
|
78 |
+
- You can upload your trained model to your personal profile (i.e. https://huggingface.co/{{your_username}}/{{model_name}}) or to the public [Tune-A-Video Library](https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}) (i.e. https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}/{{model_name}}).
|
79 |
+
''')
|
80 |
+
with gr.Box():
|
81 |
+
gr.Markdown('Output message')
|
82 |
+
output_message = gr.Markdown()
|
83 |
+
|
84 |
+
reload_button.click(fn=load_local_model_list,
|
85 |
+
inputs=None,
|
86 |
+
outputs=model_dir)
|
87 |
+
upload_button.click(fn=uploader.upload_model,
|
88 |
+
inputs=[
|
89 |
+
model_dir,
|
90 |
+
model_name,
|
91 |
+
upload_to,
|
92 |
+
use_private_repo,
|
93 |
+
delete_existing_repo,
|
94 |
+
input_token,
|
95 |
+
],
|
96 |
+
outputs=output_message)
|
97 |
+
|
98 |
+
return demo
|
99 |
+
|
100 |
+
|
101 |
+
if __name__ == '__main__':
|
102 |
+
import os
|
103 |
+
|
104 |
+
hf_token = os.getenv('HF_TOKEN')
|
105 |
+
demo = create_upload_demo(hf_token)
|
106 |
+
demo.queue(max_size=1).launch(share=False)
|
constants.py
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import enum
|
2 |
+
|
3 |
+
|
4 |
+
class UploadTarget(enum.Enum):
|
5 |
+
PERSONAL_PROFILE = 'Personal Profile'
|
6 |
+
MODEL_LIBRARY = 'Tune-A-Video Library'
|
7 |
+
|
8 |
+
|
9 |
+
MODEL_LIBRARY_ORG_NAME = 'Tune-A-Video-library'
|
10 |
+
SAMPLE_MODEL_REPO = 'Tune-A-Video-library/a-man-is-surfing'
|
inference.py
ADDED
@@ -0,0 +1,109 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from __future__ import annotations
|
2 |
+
|
3 |
+
import gc
|
4 |
+
import pathlib
|
5 |
+
import sys
|
6 |
+
import tempfile
|
7 |
+
|
8 |
+
import gradio as gr
|
9 |
+
import imageio
|
10 |
+
import PIL.Image
|
11 |
+
import torch
|
12 |
+
from diffusers.utils.import_utils import is_xformers_available
|
13 |
+
from einops import rearrange
|
14 |
+
from huggingface_hub import ModelCard
|
15 |
+
|
16 |
+
sys.path.append('Tune-A-Video')
|
17 |
+
|
18 |
+
from tuneavideo.models.unet import UNet3DConditionModel
|
19 |
+
from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
|
20 |
+
|
21 |
+
|
22 |
+
class InferencePipeline:
|
23 |
+
def __init__(self, hf_token: str | None = None):
|
24 |
+
self.hf_token = hf_token
|
25 |
+
self.pipe = None
|
26 |
+
self.device = torch.device(
|
27 |
+
'cuda:0' if torch.cuda.is_available() else 'cpu')
|
28 |
+
self.model_id = None
|
29 |
+
|
30 |
+
def clear(self) -> None:
|
31 |
+
self.model_id = None
|
32 |
+
del self.pipe
|
33 |
+
self.pipe = None
|
34 |
+
torch.cuda.empty_cache()
|
35 |
+
gc.collect()
|
36 |
+
|
37 |
+
@staticmethod
|
38 |
+
def check_if_model_is_local(model_id: str) -> bool:
|
39 |
+
return pathlib.Path(model_id).exists()
|
40 |
+
|
41 |
+
@staticmethod
|
42 |
+
def get_model_card(model_id: str,
|
43 |
+
hf_token: str | None = None) -> ModelCard:
|
44 |
+
if InferencePipeline.check_if_model_is_local(model_id):
|
45 |
+
card_path = (pathlib.Path(model_id) / 'README.md').as_posix()
|
46 |
+
else:
|
47 |
+
card_path = model_id
|
48 |
+
return ModelCard.load(card_path, token=hf_token)
|
49 |
+
|
50 |
+
@staticmethod
|
51 |
+
def get_base_model_info(model_id: str, hf_token: str | None = None) -> str:
|
52 |
+
card = InferencePipeline.get_model_card(model_id, hf_token)
|
53 |
+
return card.data.base_model
|
54 |
+
|
55 |
+
def load_pipe(self, model_id: str) -> None:
|
56 |
+
if model_id == self.model_id:
|
57 |
+
return
|
58 |
+
base_model_id = self.get_base_model_info(model_id, self.hf_token)
|
59 |
+
unet = UNet3DConditionModel.from_pretrained(
|
60 |
+
model_id,
|
61 |
+
subfolder='unet',
|
62 |
+
torch_dtype=torch.float16,
|
63 |
+
use_auth_token=self.hf_token)
|
64 |
+
pipe = TuneAVideoPipeline.from_pretrained(base_model_id,
|
65 |
+
unet=unet,
|
66 |
+
torch_dtype=torch.float16,
|
67 |
+
use_auth_token=self.hf_token)
|
68 |
+
pipe = pipe.to(self.device)
|
69 |
+
if is_xformers_available():
|
70 |
+
pipe.unet.enable_xformers_memory_efficient_attention()
|
71 |
+
self.pipe = pipe
|
72 |
+
self.model_id = model_id # type: ignore
|
73 |
+
|
74 |
+
def run(
|
75 |
+
self,
|
76 |
+
model_id: str,
|
77 |
+
prompt: str,
|
78 |
+
video_length: int,
|
79 |
+
fps: int,
|
80 |
+
seed: int,
|
81 |
+
n_steps: int,
|
82 |
+
guidance_scale: float,
|
83 |
+
) -> PIL.Image.Image:
|
84 |
+
if not torch.cuda.is_available():
|
85 |
+
raise gr.Error('CUDA is not available.')
|
86 |
+
|
87 |
+
self.load_pipe(model_id)
|
88 |
+
|
89 |
+
generator = torch.Generator(device=self.device).manual_seed(seed)
|
90 |
+
out = self.pipe(
|
91 |
+
prompt,
|
92 |
+
video_length=video_length,
|
93 |
+
width=512,
|
94 |
+
height=512,
|
95 |
+
num_inference_steps=n_steps,
|
96 |
+
guidance_scale=guidance_scale,
|
97 |
+
generator=generator,
|
98 |
+
) # type: ignore
|
99 |
+
|
100 |
+
frames = rearrange(out.videos[0], 'c t h w -> t h w c')
|
101 |
+
frames = (frames * 255).to(torch.uint8).numpy()
|
102 |
+
|
103 |
+
out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
|
104 |
+
writer = imageio.get_writer(out_file.name, fps=fps)
|
105 |
+
for frame in frames:
|
106 |
+
writer.append_data(frame)
|
107 |
+
writer.close()
|
108 |
+
|
109 |
+
return out_file.name
|
packages.txt
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
ffmpeg
|
patch
ADDED
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
diff --git a/train_tuneavideo.py b/train_tuneavideo.py
|
2 |
+
index 66d51b2..86b2a5d 100644
|
3 |
+
--- a/train_tuneavideo.py
|
4 |
+
+++ b/train_tuneavideo.py
|
5 |
+
@@ -94,8 +94,8 @@ def main(
|
6 |
+
|
7 |
+
# Handle the output folder creation
|
8 |
+
if accelerator.is_main_process:
|
9 |
+
- now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
|
10 |
+
- output_dir = os.path.join(output_dir, now)
|
11 |
+
+ #now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
|
12 |
+
+ #output_dir = os.path.join(output_dir, now)
|
13 |
+
os.makedirs(output_dir, exist_ok=True)
|
14 |
+
OmegaConf.save(config, os.path.join(output_dir, 'config.yaml'))
|
15 |
+
|
requirements.txt
ADDED
@@ -0,0 +1,19 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
accelerate==0.15.0
|
2 |
+
bitsandbytes==0.35.4
|
3 |
+
decord==0.6.0
|
4 |
+
diffusers[torch]==0.11.1
|
5 |
+
einops==0.6.0
|
6 |
+
ftfy==6.1.1
|
7 |
+
gradio==3.16.2
|
8 |
+
huggingface-hub==0.12.0
|
9 |
+
imageio==2.25.0
|
10 |
+
imageio-ffmpeg==0.4.8
|
11 |
+
omegaconf==2.3.0
|
12 |
+
Pillow==9.4.0
|
13 |
+
python-slugify==7.0.0
|
14 |
+
tensorboard==2.11.2
|
15 |
+
torch==1.13.1
|
16 |
+
torchvision==0.14.1
|
17 |
+
transformers==4.26.0
|
18 |
+
triton==2.0.0.dev20221202
|
19 |
+
xformers==0.0.16
|
style.css
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
h1 {
|
2 |
+
text-align: center;
|
3 |
+
}
|
trainer.py
ADDED
@@ -0,0 +1,166 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from __future__ import annotations
|
2 |
+
|
3 |
+
import datetime
|
4 |
+
import os
|
5 |
+
import pathlib
|
6 |
+
import shlex
|
7 |
+
import shutil
|
8 |
+
import subprocess
|
9 |
+
import sys
|
10 |
+
|
11 |
+
import gradio as gr
|
12 |
+
import slugify
|
13 |
+
import torch
|
14 |
+
from huggingface_hub import HfApi
|
15 |
+
from omegaconf import OmegaConf
|
16 |
+
|
17 |
+
from app_upload import ModelUploader
|
18 |
+
from utils import save_model_card
|
19 |
+
|
20 |
+
sys.path.append('Tune-A-Video')
|
21 |
+
|
22 |
+
URL_TO_JOIN_MODEL_LIBRARY_ORG = 'https://huggingface.co/organizations/Tune-A-Video-library/share/YjTcaNJmKyeHFpMBioHhzBcTzCYddVErEk'
|
23 |
+
ORIGINAL_SPACE_ID = 'Tune-A-Video-library/Tune-A-Video-Training-UI'
|
24 |
+
SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
|
25 |
+
|
26 |
+
|
27 |
+
class Trainer:
|
28 |
+
def __init__(self, hf_token: str | None = None):
|
29 |
+
self.hf_token = hf_token
|
30 |
+
self.model_uploader = ModelUploader(hf_token)
|
31 |
+
|
32 |
+
self.checkpoint_dir = pathlib.Path('checkpoints')
|
33 |
+
self.checkpoint_dir.mkdir(exist_ok=True)
|
34 |
+
|
35 |
+
def download_base_model(self, base_model_id: str) -> str:
|
36 |
+
model_dir = self.checkpoint_dir / base_model_id
|
37 |
+
if not model_dir.exists():
|
38 |
+
org_name = base_model_id.split('/')[0]
|
39 |
+
org_dir = self.checkpoint_dir / org_name
|
40 |
+
org_dir.mkdir(exist_ok=True)
|
41 |
+
subprocess.run(shlex.split(
|
42 |
+
f'git clone https://huggingface.co/{base_model_id}'),
|
43 |
+
cwd=org_dir)
|
44 |
+
return model_dir.as_posix()
|
45 |
+
|
46 |
+
def join_model_library_org(self, token: str) -> None:
|
47 |
+
subprocess.run(
|
48 |
+
shlex.split(
|
49 |
+
f'curl -X POST -H "Authorization: Bearer {token}" -H "Content-Type: application/json" {URL_TO_JOIN_MODEL_LIBRARY_ORG}'
|
50 |
+
))
|
51 |
+
|
52 |
+
def run(
|
53 |
+
self,
|
54 |
+
training_video: str,
|
55 |
+
training_prompt: str,
|
56 |
+
output_model_name: str,
|
57 |
+
overwrite_existing_model: bool,
|
58 |
+
validation_prompt: str,
|
59 |
+
base_model: str,
|
60 |
+
resolution_s: str,
|
61 |
+
n_steps: int,
|
62 |
+
learning_rate: float,
|
63 |
+
gradient_accumulation: int,
|
64 |
+
seed: int,
|
65 |
+
fp16: bool,
|
66 |
+
use_8bit_adam: bool,
|
67 |
+
checkpointing_steps: int,
|
68 |
+
validation_epochs: int,
|
69 |
+
upload_to_hub: bool,
|
70 |
+
use_private_repo: bool,
|
71 |
+
delete_existing_repo: bool,
|
72 |
+
upload_to: str,
|
73 |
+
remove_gpu_after_training: bool,
|
74 |
+
input_token: str,
|
75 |
+
) -> str:
|
76 |
+
if SPACE_ID == ORIGINAL_SPACE_ID:
|
77 |
+
raise gr.Error(
|
78 |
+
'This Space does not work on this Shared UI. Duplicate the Space and attribute a GPU'
|
79 |
+
)
|
80 |
+
if not torch.cuda.is_available():
|
81 |
+
raise gr.Error('CUDA is not available.')
|
82 |
+
if training_video is None:
|
83 |
+
raise gr.Error('You need to upload a video.')
|
84 |
+
if not training_prompt:
|
85 |
+
raise gr.Error('The training prompt is missing.')
|
86 |
+
if not validation_prompt:
|
87 |
+
raise gr.Error('The validation prompt is missing.')
|
88 |
+
|
89 |
+
resolution = int(resolution_s)
|
90 |
+
|
91 |
+
if not output_model_name:
|
92 |
+
timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
|
93 |
+
output_model_name = f'tune-a-video-{timestamp}'
|
94 |
+
output_model_name = slugify.slugify(output_model_name)
|
95 |
+
|
96 |
+
repo_dir = pathlib.Path(__file__).parent
|
97 |
+
output_dir = repo_dir / 'experiments' / output_model_name
|
98 |
+
if overwrite_existing_model or upload_to_hub:
|
99 |
+
shutil.rmtree(output_dir, ignore_errors=True)
|
100 |
+
output_dir.mkdir(parents=True)
|
101 |
+
|
102 |
+
if upload_to_hub:
|
103 |
+
self.join_model_library_org(
|
104 |
+
self.hf_token if self.hf_token else input_token)
|
105 |
+
|
106 |
+
config = OmegaConf.load('Tune-A-Video/configs/man-surfing.yaml')
|
107 |
+
config.pretrained_model_path = self.download_base_model(base_model)
|
108 |
+
config.output_dir = output_dir.as_posix()
|
109 |
+
config.train_data.video_path = training_video.name # type: ignore
|
110 |
+
config.train_data.prompt = training_prompt
|
111 |
+
config.train_data.n_sample_frames = 8
|
112 |
+
config.train_data.width = resolution
|
113 |
+
config.train_data.height = resolution
|
114 |
+
config.train_data.sample_start_idx = 0
|
115 |
+
config.train_data.sample_frame_rate = 1
|
116 |
+
config.validation_data.prompts = [validation_prompt]
|
117 |
+
config.validation_data.video_length = 8
|
118 |
+
config.validation_data.width = resolution
|
119 |
+
config.validation_data.height = resolution
|
120 |
+
config.validation_data.num_inference_steps = 50
|
121 |
+
config.validation_data.guidance_scale = 7.5
|
122 |
+
config.learning_rate = learning_rate
|
123 |
+
config.gradient_accumulation_steps = gradient_accumulation
|
124 |
+
config.train_batch_size = 1
|
125 |
+
config.max_train_steps = n_steps
|
126 |
+
config.checkpointing_steps = checkpointing_steps
|
127 |
+
config.validation_steps = validation_epochs
|
128 |
+
config.seed = seed
|
129 |
+
config.mixed_precision = 'fp16' if fp16 else ''
|
130 |
+
config.use_8bit_adam = use_8bit_adam
|
131 |
+
|
132 |
+
config_path = output_dir / 'config.yaml'
|
133 |
+
with open(config_path, 'w') as f:
|
134 |
+
OmegaConf.save(config, f)
|
135 |
+
|
136 |
+
command = f'accelerate launch Tune-A-Video/train_tuneavideo.py --config {config_path}'
|
137 |
+
subprocess.run(shlex.split(command))
|
138 |
+
save_model_card(save_dir=output_dir,
|
139 |
+
base_model=base_model,
|
140 |
+
training_prompt=training_prompt,
|
141 |
+
test_prompt=validation_prompt,
|
142 |
+
test_image_dir='samples')
|
143 |
+
|
144 |
+
message = 'Training completed!'
|
145 |
+
print(message)
|
146 |
+
|
147 |
+
if upload_to_hub:
|
148 |
+
upload_message = self.model_uploader.upload_model(
|
149 |
+
folder_path=output_dir.as_posix(),
|
150 |
+
repo_name=output_model_name,
|
151 |
+
upload_to=upload_to,
|
152 |
+
private=use_private_repo,
|
153 |
+
delete_existing_repo=delete_existing_repo,
|
154 |
+
input_token=input_token)
|
155 |
+
print(upload_message)
|
156 |
+
message = message + '\n' + upload_message
|
157 |
+
|
158 |
+
if remove_gpu_after_training:
|
159 |
+
space_id = os.getenv('SPACE_ID')
|
160 |
+
if space_id:
|
161 |
+
api = HfApi(
|
162 |
+
token=self.hf_token if self.hf_token else input_token)
|
163 |
+
api.request_space_hardware(repo_id=space_id,
|
164 |
+
hardware='cpu-basic')
|
165 |
+
|
166 |
+
return message
|
uploader.py
ADDED
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from __future__ import annotations
|
2 |
+
|
3 |
+
from huggingface_hub import HfApi
|
4 |
+
|
5 |
+
|
6 |
+
class Uploader:
|
7 |
+
def __init__(self, hf_token: str | None):
|
8 |
+
self.hf_token = hf_token
|
9 |
+
|
10 |
+
def upload(self,
|
11 |
+
folder_path: str,
|
12 |
+
repo_name: str,
|
13 |
+
organization: str = '',
|
14 |
+
repo_type: str = 'model',
|
15 |
+
private: bool = True,
|
16 |
+
delete_existing_repo: bool = False,
|
17 |
+
input_token: str | None = None) -> str:
|
18 |
+
|
19 |
+
api = HfApi(token=self.hf_token if self.hf_token else input_token)
|
20 |
+
|
21 |
+
if not folder_path:
|
22 |
+
raise ValueError
|
23 |
+
if not repo_name:
|
24 |
+
raise ValueError
|
25 |
+
if not organization:
|
26 |
+
organization = api.whoami()['name']
|
27 |
+
|
28 |
+
repo_id = f'{organization}/{repo_name}'
|
29 |
+
if delete_existing_repo:
|
30 |
+
try:
|
31 |
+
api.delete_repo(repo_id, repo_type=repo_type)
|
32 |
+
except Exception:
|
33 |
+
pass
|
34 |
+
try:
|
35 |
+
api.create_repo(repo_id, repo_type=repo_type, private=private)
|
36 |
+
api.upload_folder(repo_id=repo_id,
|
37 |
+
folder_path=folder_path,
|
38 |
+
path_in_repo='.',
|
39 |
+
repo_type=repo_type)
|
40 |
+
url = f'https://huggingface.co/{repo_id}'
|
41 |
+
message = f'Your model was successfully uploaded to <a href="{url}" target="_blank">{url}</a>.'
|
42 |
+
except Exception as e:
|
43 |
+
message = str(e)
|
44 |
+
return message
|
utils.py
ADDED
@@ -0,0 +1,65 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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1 |
+
from __future__ import annotations
|
2 |
+
|
3 |
+
import pathlib
|
4 |
+
|
5 |
+
|
6 |
+
def find_exp_dirs() -> list[str]:
|
7 |
+
repo_dir = pathlib.Path(__file__).parent
|
8 |
+
exp_root_dir = repo_dir / 'experiments'
|
9 |
+
if not exp_root_dir.exists():
|
10 |
+
return []
|
11 |
+
exp_dirs = sorted(exp_root_dir.glob('*'))
|
12 |
+
exp_dirs = [
|
13 |
+
exp_dir for exp_dir in exp_dirs
|
14 |
+
if (exp_dir / 'model_index.json').exists()
|
15 |
+
]
|
16 |
+
return [path.relative_to(repo_dir).as_posix() for path in exp_dirs]
|
17 |
+
|
18 |
+
|
19 |
+
def save_model_card(
|
20 |
+
save_dir: pathlib.Path,
|
21 |
+
base_model: str,
|
22 |
+
training_prompt: str,
|
23 |
+
test_prompt: str = '',
|
24 |
+
test_image_dir: str = '',
|
25 |
+
) -> None:
|
26 |
+
image_str = ''
|
27 |
+
if test_prompt and test_image_dir:
|
28 |
+
image_paths = sorted((save_dir / test_image_dir).glob('*.gif'))
|
29 |
+
if image_paths:
|
30 |
+
image_path = image_paths[-1]
|
31 |
+
rel_path = image_path.relative_to(save_dir)
|
32 |
+
image_str = f'''## Samples
|
33 |
+
Test prompt: {test_prompt}
|
34 |
+
|
35 |
+
![{image_path.stem}]({rel_path})'''
|
36 |
+
|
37 |
+
model_card = f'''---
|
38 |
+
license: creativeml-openrail-m
|
39 |
+
base_model: {base_model}
|
40 |
+
training_prompt: {training_prompt}
|
41 |
+
tags:
|
42 |
+
- stable-diffusion
|
43 |
+
- stable-diffusion-diffusers
|
44 |
+
- text-to-image
|
45 |
+
- diffusers
|
46 |
+
- text-to-video
|
47 |
+
- tune-a-video
|
48 |
+
inference: false
|
49 |
+
---
|
50 |
+
|
51 |
+
# Tune-A-Video - {save_dir.name}
|
52 |
+
|
53 |
+
## Model description
|
54 |
+
- Base model: [{base_model}](https://huggingface.co/{base_model})
|
55 |
+
- Training prompt: {training_prompt}
|
56 |
+
|
57 |
+
{image_str}
|
58 |
+
|
59 |
+
## Related papers:
|
60 |
+
- [Tune-A-Video](https://arxiv.org/abs/2212.11565): One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
|
61 |
+
- [Stable-Diffusion](https://arxiv.org/abs/2112.10752): High-Resolution Image Synthesis with Latent Diffusion Models
|
62 |
+
'''
|
63 |
+
|
64 |
+
with open(save_dir / 'README.md', 'w') as f:
|
65 |
+
f.write(model_card)
|