- this required an update to the invoke-ai fork of gfpgan
- simultaneously reverted consolidation of environment and
requirements files, as their presence in a directory
triggered setup.py to try to install a sub-package.
1. added nvidia channel to environment.yml
2. updated pytorch-cuda requirement
3. let conda figure out what version of pytorch to install
4. add conda install status checking to .bat and .sh install files
5. in preload_models.py catch and handle download/access token errors
- Faster startup for command line switch processing
- Specify configuration file to modify using --config option:
./scripts/preload_models.ply --config models/my-models-file.yaml
- Faster startup for command line switch processing
- Specify configuration file to modify using --config option:
./scripts/preload_models.ply --config models/my-models-file.yaml
- NEVER overwrite user's existing models.yaml
- Instead, merge its contents into new config file,
and rename original to models.yaml.orig (with
message)
- models.yaml has been removed from repository and renamed
models.yaml.example
- NEVER overwrite user's existing models.yaml
- Instead, merge its contents into new config file,
and rename original to models.yaml.orig (with
message)
- models.yaml has been removed from repository and renamed
models.yaml.example
- User can choose to download just recommended models, customize list to download,
or skip downloading altogether.
- Does direct download to models directory instead of to HuggingFace cache
- Able to resume interrupted downloads
- user can select which weight files to download using huggingface cache
- user must log in to huggingface, generate an access token, and accept
license terms the very first time this is run. After that, everything
works automatically.
- added placeholder for docs for installing models
- also got rid of unused config files. hopefully they weren't needed
for textual inversion, but I don't think so.
Now you can activate the Hugging Face `diffusers` library safety check
for NSFW and other potentially disturbing imagery.
To turn on the safety check, pass --safety_checker at the command
line. For developers, the flag is `safety_checker=True` passed to
ldm.generate.Generate(). Once the safety checker is turned on, it
cannot be turned off unless you reinitialize a new Generate object.
When the safety checker is active, suspect images will be blurred and
a warning icon is added. There is also a warning message printed in
the CLI, but it can be a little hard to see because of its positioning
in the output stream.
There is a slight but noticeable delay when the safety checker runs.
Note that invisible watermarking is *not* currently implemented. The
watermark code distributed by the CompViz distribution uses a library
that does not seem to be able to retrieve the watermarks it creates,
and it does not appear that Hugging Face `diffusers` or other SD
distributions are doing any watermarking.
- The directory "models" in the main InvokeAI directory was conflicting
with loading "models.clipseg". To fix this issue, I have renamed the
models.clipseg to clipseg_models.clipseg, and applied this change to
the 'models-rename' branch of invoke-ai's fork of clipseg.
- updated environment-mac.yml #932
- use the upstream GFPGAN library now that issues with color-changing fixed
and facial recognition improved #905
- preload_models fixed to download additional models needed by gfpgan
