For the torch and torchvision libraries **only**, the installer will now
pass the pip `--force-reinstall` option. This is intended to fix issues
with the user getting a CPU-only version of torch and then not being
able to replace it.
Previously conversions of .ckpt and .safetensors files to diffusers
models were failing with channel mismatch errors. This is corrected
with this PR.
- The model_manager convert_and_import() method now accepts the path
to the checkpoint file's configuration file, using the parameter
`original_config_file`. For inpainting files this should be set to
the full path to `v1-inpainting-inference.yaml`.
- If no configuration file is provided in the call, then the presence
of an inpainting file will be inferred at the
`ldm.ckpt_to_diffuser.convert_ckpt_to_diffUser()` level by looking
for the string "inpaint" in the path. AUTO1111 does something
similar to this, but it is brittle and not recommended.
- This PR also changes the model manager model_names() method to return
the model names in case folded sort order.
test-invoke-pip.yml:
- enable caching of pip dependencies in `actions/setup-python@v4`
- add workflow_dispatch trigger
- fix indentation in concurrency
- set env `PIP_USE_PEP517: '1'`
- cache python dependencies
- remove models cache (since we currently use 190.96 GB of 10 GB while I
am writing this)
- add step to set `INVOKEAI_OUTDIR`
- add outdir arg to invokeai
- fix path in archive results
model_manager.py:
- read files in chunks when calculating sha (windows runner is crashing
otherwise)
- help users to avoid glossing over per-platform prerequisites
- better link colouring
- update link to community instructions to install xcode command line tools
- Issue is that if insufficient diffusers models are defined in
models.yaml the frontend would ungraciously crash.
- Now it emits appropriate error messages telling user what the problem
is.
- Issue is that if insufficient diffusers models are defined in
models.yaml the frontend would ungraciously crash.
- Now it emits appropriate error messages telling user what the problem
is.
- dont build frontend since complications with QEMU
- set pip cache dir
- add pip cache to all pip related build steps
- dont lock pip cache
- update dockerignore to exclude uneeded files
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
