image = pipeline(prompt, num_inference_steps=4, guidance_scale=1.0).images[0]  LoRA LoRA is a training technique for significantly reducing the number of trainable parameters. As a result, training is faster and it is easier to store the resulting weights because they are a lot smaller (~100MBs). Use the train_lcm_distill_lora_sd_wds.py or train_lcm_distill_lora_sdxl.wds.py script to train with LoRA. The LoRA training script is discussed in more detail in the LoRA training guide.  Stable Diffusion XL Stable Diffusion XL (SDXL) is a powerful text-to-image model that generates high-resolution images, and it adds a second text-encoder to its architecture. Use the train_lcm_distill_sdxl_wds.py script to train a SDXL model with LoRA. The SDXL training script is discussed in more detail in the SDXL training guide.  Next steps Congratulations on distilling a LCM model! To learn more about LCM, the following may be helpful: Learn how to use LCMs for inference for text-to-image, image-to-image, and with LoRA checkpoints. Read the SDXL in 4 steps with Latent Consistency LoRAs blog post to learn more about SDXL LCM-LoRA’s for super fast inference, quality comparisons, benchmarks, and more.