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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.