- Added new documentation for textual inversion training process
- Move `main.py` into the deprecated scripts folder
- Fix bug in `textual_inversion.py` which was causing it to not load
the globals module correctly.
- Sort models alphabetically in console front end
- Only show diffusers models in console front end
Starting `invoke.py` with --no-xformers will disable
memory-efficient-attention support if xformers is installed.
For symmetry, `--xformers` will enable support, but this is already the
default if xformers is available.
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)
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.
This commit allows InvokeAI to store & load 🤗 models at a location set
by `XDG_CACHE_HOME` environment variable if `HF_HOME` is not set.
By integrating this commit, a user who either use `HF_HOME` or
`XDG_CACHE_HOME` environment variables in their environment can let
InvokeAI to reuse the existing cache directory used by 🤗 library by
default. I happened to benefit from this commit because I have a Jupyter
Notebook that uses 🤗 diffusers model stored at `XDG_CACHE_HOME`
directory.
Reference:
https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#xdgcachehome
Updated the link for the MS Visual C libraries - I'm not sure if MS
changed the location of the files but this new one leads right to the
file downloads.
- Migration process will not crash if duplicate model files are found,
one in legacy location and the other in new location. The model in the
legacy location will be deleted in this case.
- Added a hint to stable-diffusion-2.1 telling people it will work best
with 768 pixel images.
- Added the anything-4.0 model.
Added a --default_only argument that limits model downloads to the
single default model, for use in continuous integration.
New behavior
- switch -
--yes --default_only Behavior
----- -------------- --------
<not set> <not set> interactive download
--yes <not set> non-interactively download all
recommended models
--yes --default_only non-interactively download the
default model
