env.sh:
- move check for torch to CONVTAINER_FLAVOR detection
Dockerfile
- only mount `/var/cache/apt` for apt related steps
- remove `docker-clean` from `/etc/apt/apt.conf.d` for BuildKit cache
- remove apt-get clean for BuildKit cache
- only copy frontend to frontend-builder
- mount `/usr/local/share/.cache/yarn` in frountend-builder
- separate steps for yarn install and yarn build
- build pytorch in pyproject-builder
build.sh
- prepare for installation with extras
This change allows passing a directory with multiple models in it to be
imported.
Ensures that diffusers directories will still work.
Fixed up some minor type issues.
This allows the --log_tokenization option to be used as a command line
argument (or from invokeai.init), making it possible to view
tokenization information in the terminal when using the web interface.
- This fixes an edge case crash when the textual inversion frontend
tried to display the list of models and no default model defined
in models.yaml
Co-authored-by: Jonathan <34005131+JPPhoto@users.noreply.github.com>
This allows the --log_tokenization option to be used as a command line argument (or from invokeai.init), making it possible to view tokenization information in the terminal when using the web interface.
- Rename configure_invokeai.py to invokeai_configure.py to be consistent
with installed script name
- Remove warning message about half-precision models not being available
during the model download process.
- adjust estimated file size reported by configure
- guesstimate disk space needed for "all" models
- fix up the "latest" tag to be named 'v2.3-latest'
- To ensure a clean environment, the installer will now detect whether a
previous .venv exists in the install location, and move it to .venv-backup
before creating a fresh .venv.
- Any previous .venv-backup is deleted.
- User is informed of process.
- Rename configure_invokeai.py to invokeai_configure.py to be
consistent with installed script name
- Remove warning message about half-precision models not being
available during the model download process.
- adjust estimated file size reported by configure
- guesstimate disk space needed for "all" models
- fix up the "latest" tag to be named 'v2.3-latest'
`torch` wasn't seeing the environment variable. I suspect this is
because it was imported before the variable was set, so was running with
a different environment.
Many `torch` ops are supported on MPS so this wasn't noticed
immediately, but some samplers like k_dpm_2 still use unsupported
operations and need this fallback.
This PR forces the installer to install the official torch-cu117 wheel
from download.torch.org, rather than relying on PyPi.org to return the
correct version. It ought to correct the problems that some people have
experienced with cuda support not being installed.
