- This replaces the original clipseg library with the transformers
version from HuggingFace.
- This should make it possible to register InvokeAI at PyPi and do
a fully automated pip-based install.
- Minor regression: it is no longer possible to specify which device
the clipseg model will be loaded into, and it will reside in CPU.
However, performance is more than acceptable.
other changes which where required:
- move configure_invokeai.py into ldm.invoke
- update files which imported configure_invokeai to use new location:
- ldm/invoke/CLI.py
- scripts/load_models.py
- scripts/preload_models.py
- update test-invoke-pip.yml:
- remove pr type "converted_to_draft"
- remove reference to dev/diffusers
- remove no more needed requirements from matrix
- add pytorch to matrix
- install via `pip3 install --use-pep517 .`
- use the created executables
- this should also fix configure_invoke not executed in windows
To install use `pip install --use-pep517 -e .` where `-e` is optional
This commit suppresses a few irrelevant warning messages that the
diffusers module produces:
1. The warning that turning off the NSFW detector makes you an
irresponsible person.
2. Warnings about running fp16 models stored in CPU (we are not running
them in CPU, just caching them in CPU RAM)
Starting `invoke.py` with --no-xformers will disable
memory-efficient-attention support if xformers is installed.
--xformers will enable support, but this is already the
default.
- During trigger token processing, emit better status messages indicating
which triggers were found.
- Suppress message "<token> is not known to HuggingFace library, when
token is in fact a local embed.
- When a ckpt or safetensors file uses an external autoencoder and we
don't know which diffusers model corresponds to this (if any!), then
we fallback to using stabilityai/sd-vae-ft-mse
- This commit improves error reporting so that user knows what is happening.
- After successfully converting a ckt file to diffusers, model_manager
will attempt to create an equivalent 'vae' entry to the resulting
diffusers stanza.
- This is a bit of a hack, as it relies on a hard-coded dictionary
to map ckpt VAEs to diffusers VAEs. The correct way to do this
would be to convert the VAE to a diffusers model and then point
to that. But since (almost) all models are using vae-ft-mse-840000-ema-pruned,
I did it the easy way first and will work on the better solution later.
1. !import_model did not allow user to specify VAE file. This is now fixed.
2. !del_model did not offer the user the opportunity to delete the underlying
weights file or diffusers directory. This is now fixed.
