- removed app directory (a 3.0 feature), so app tests had to go too
- fixed regular expression in the concepts lib which was causing deprecation warnings
Instructions:
1. Download LoRA .safetensors files of your choice and place in
`INVOKEAIROOT/loras`. Unlike the draft version of this, the file
names can contain underscores and alphanumerics. Names with
arbitrary unicode characters are not supported.
2. Add `withLora(lora-file-basename,weight)` to your prompt. The
weight is optional and will default to 1.0. A few examples, assuming
that a LoRA file named `loras/sushi.safetensors` is present:
```
family sitting at dinner table eating sushi withLora(sushi,0.9)
family sitting at dinner table eating sushi withLora(sushi, 0.75)
family sitting at dinner table eating sushi withLora(sushi)
```
Multiple `withLora()` prompt fragments are allowed. The weight can be
arbitrarily large, but the useful range is roughly 0.5 to 1.0. Higher
weights make the LoRA's influence stronger.
In my limited testing, I found it useful to reduce the CFG to avoid
oversharpening. Also I got better results when running the LoRA on top
of the model on which it was based during training.
Don't try to load a SD 1.x-trained LoRA into a SD 2.x model, and vice
versa. You will get a nasty stack trace. This needs to be cleaned up.
3. You can change the location of the `loras` directory by passing the
`--lora_directory` option to `invokeai.
Documentation can be found in docs/features/LORAS.md.
- Allow invokeai-update to update using a release, tag or branch.
- Allow CLI's root directory update routine to update directory
contents regardless of whether current version is released.
- In model importation routine, clarify wording of instructions when user is
asked to choose the type of model being imported.
This commit fixes bugs related to the on-the-fly conversion and loading of
legacy checkpoint models built on SD-2.0 base.
- When legacy checkpoints built on SD-2.0 models were converted
on-the-fly using --ckpt_convert, generation would crash with a
precision incompatibility error.
- In addition, broken logic was causing some 2.0-derived ckpt files to
be converted into diffusers and then processed through the legacy
generation routines - not good.
- installer now installs the pretty dialog-based console launcher
- added dialogrc for custom colors
- add updater to download new launcher when users do an update
- Imported V2 legacy models will now autoconvert into diffusers
at load time regardless of setting of --ckpt_convert.
- model manager `heuristic_import()` function now looks for
side-by-side yaml and vae files for custom configuration and VAE
respectively.
Example of this:
illuminati-v1.1.safetensors
illuminati-v1.1.vae.safetensors
illuminati-v1.1.yaml
When the user tries to import `illuminati-v1.1.safetensors`, the yaml
file will be used for its configuration, and the VAE will be used for
its VAE. Conversion to diffusers will happen if needed, and the yaml
file will be used to determine which V2 format (if any) to apply.
- Since 2.3.2 invokeai stores the next PNG file's numeric prefix in a
file named `.next_prefix` in the outputs directory. This avoids the
overhead of doing a directory listing to find out what file number
comes next.
- The code uses advisory locking to prevent corruption of this file in
the event that multiple invokeai's try to access it simultaneously,
but some users have experienced corruption of the file nevertheless.
- This PR addresses the problem by detecting a potentially corrupted
`.next_prefix` file and falling back to the directory listing method.
A fixed version of the file is then written out.
- Closes#3001
Lots of little bugs have been squashed since 2.3.2 and a new minor
point release is imminent. This PR updates the version number in
preparation for a RC.
- `invokeai-batch --invoke` was created a time-stamped logfile with colons in its
name, which is a Windows no-no. This corrects the problem by writing
the timestamp out as "13-06-2023_8-35-10"
- Closes#3005
This PR addresses issues raised by #3008.
1. Update documentation to indicate the correct maximum batch size for
TI training when xformers is and isn't used.
2. Update textual inversion code so that the default for batch size
is aware of xformer availability.
3. Add documentation for how to launch TI with distributed learning.
