Merge LoRAs It can be fun and creative to use multiple LoRAs together to generate something entirely new and unique. This works by merging multiple LoRA weights together to produce images that are a blend of different styles. Diffusers provides a few methods to merge LoRAs depending on how you want to merge their weights, which can affect image quality. This guide will show you how to merge LoRAs using the set_adapters() and ~peft.LoraModel.add_weighted_adapter methods. To improve inference speed and reduce memory-usage of merged LoRAs, you’ll also see how to use the fuse_lora() method to fuse the LoRA weights with the original weights of the underlying model. For this guide, load a Stable Diffusion XL (SDXL) checkpoint and the KappaNeuro/studio-ghibli-style and Norod78/sdxl-chalkboarddrawing-lora LoRAs with the load_lora_weights() method. You’ll need to assign each LoRA an adapter_name to combine them later.   Copied from diffusers import DiffusionPipeline