).images[0]  Notice that we use only 4 steps for generation which is way less than what’s typically used for standard SDXL. You may have noticed that we set guidance_scale=1.0, which disables classifer-free-guidance. This is because the LCM-LoRA is trained with guidance, so the batch size does not have to be doubled in this case. This leads to a faster inference time, with the drawback that negative prompts don’t have any effect on the denoising process. You can also use guidance with LCM-LoRA, but due to the nature of training the model is very sensitve to the guidance_scale values, high values can lead to artifacts in the generated images. In our experiments, we found that the best values are in the range of [1.0, 2.0].  Inference with a fine-tuned model As mentioned above, the LCM-LoRA can be applied to any fine-tuned version of the model without having to distill them separately. Let’s look at how we can perform inference with a fine-tuned model. In this example, we’ll use the animagine-xl model, which is a fine-tuned version of the SDXL model for generating anime.   Copied from diffusers import DiffusionPipeline, LCMScheduler