Testing suggests that the diffusers versions of Waifu-1.4 anything-v4.0
require the `sd-vae-ft-mse` to generate decent images, so the
appropriate arguments have been added to the initial model file.
- Model merging and textual inversion scripts have been moved into
`ldm/invoke`, which allows them to be installed properly by
pyproject.toml.
- As part of the pyproject install, the .py suffix is removed from the
command. I.e. use `invoke`, `configure_invokeai`, `merge_models` and
`textual_inversion`.
- GUI versions are activated by adding `--gui` to the command. Without
this, you get a classical argv-based command. Example: `merge_models
--gui`
- Fixed up the launcher scripts to accommodate new naming scheme.
- Keyboard behavior of the GUI front ends has been improved. You can now
use up and down arrow to move from field to field, in addition to <tab>
and ctrl-N/ctrl-P
So far the slider component was unable to take typed input due to a
bunch of issues that were a pain to solve. This PR fixes it.
Things to test:
- Moving the slider also updates the value in the input text box.
- Input text box next to slider can be changed in two ways: If you type
a manual value, the slider will be updated when you lose focus from the
input box. If you use the stepper icons to update the values, the slider
should update immediately.
- Make sure the reset buttons next to the slider are updating correctly
and make sure this updates both the slider and the input box values.
- Brush Size slider -> make sure the hotkeys are updating the input box
too.
- 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.
- 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.
