image  For both load_lora_weights() and load_attn_procs(), you can pass the cross_attention_kwargs={"scale": 0.5} parameter to adjust how much of the LoRA weights to use. A value of 0 is the same as only using the base model weights, and a value of 1 is equivalent to using the fully finetuned LoRA. To unload the LoRA weights, use the unload_lora_weights() method to discard the LoRA weights and restore the model to its original weights:   Copied pipeline.unload_lora_weights()  Load multiple LoRAs It can be fun to use multiple LoRAs together to create something entirely new and unique. The fuse_lora() method allows you to fuse the LoRA weights with the original weights of the underlying model. Fusing the weights can lead to a speedup in inference latency because you don’t need to separately load the base model and LoRA! You can save your fused pipeline with save_pretrained() to avoid loading and fusing the weights every time you want to use the model. Load an initial model:   Copied from diffusers import StableDiffusionXLPipeline, AutoencoderKL