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.
