LCM-LoRAs are similar to other Stable Diffusion LoRAs so they can be used with any DiffusionPipeline that supports LoRAs. Load the task specific pipeline and model. Set the scheduler to LCMScheduler. Load the LCM-LoRA weights for the model. Reduce the guidance_scale between [1.0, 2.0] and set the num_inference_steps between [4, 8]. Perform inference with the pipeline with the usual parameters. Let’s look at how we can perform inference with LCM-LoRAs for different tasks. First, make sure you have peft installed, for better LoRA support.   Copied pip install -U peft  Text-to-image You’ll use the StableDiffusionXLPipeline with the scheduler: LCMScheduler and then load the LCM-LoRA. Together with the LCM-LoRA and the scheduler, the pipeline enables a fast inference workflow overcoming the slow iterative nature of diffusion models.   Copied import torch