Latent Consistency Model Latent Consistency Models (LCM) enable quality image generation in typically 2-4 steps making it possible to use diffusion models in almost real-time settings. From the official website: LCMs can be distilled from any pre-trained Stable Diffusion (SD) in only 4,000 training steps (~32 A100 GPU Hours) for generating high quality 768 x 768 resolution images in 2~4 steps or even one step, significantly accelerating text-to-image generation. We employ LCM to distill the Dreamshaper-V7 version of SD in just 4,000 training iterations. For a more technical overview of LCMs, refer to the paper. LCM distilled models are available for stable-diffusion-v1-5, stable-diffusion-xl-base-1.0, and the SSD-1B model. All the checkpoints can be found in this collection. This guide shows how to perform inference with LCMs for text-to-image image-to-image combined with style LoRAs ControlNet/T2I-Adapter  Text-to-image You’ll use the StableDiffusionXLPipeline pipeline with the 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 from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler