Latent Consistency Distillation Latent Consistency Models (LCMs) are able to generate high-quality images in just a few steps, representing a big leap forward because many pipelines require at least 25+ steps. LCMs are produced by applying the latent consistency distillation method to any Stable Diffusion model. This method works by applying one-stage guided distillation to the latent space, and incorporating a skipping-step method to consistently skip timesteps to accelerate the distillation process (refer to section 4.1, 4.2, and 4.3 of the paper for more details). If you’re training on a GPU with limited vRAM, try enabling gradient_checkpointing, gradient_accumulation_steps, and mixed_precision to reduce memory-usage and speedup training. You can reduce your memory-usage even more by enabling memory-efficient attention with xFormers and bitsandbytes’ 8-bit optimizer. This guide will explore the train_lcm_distill_sd_wds.py script to help you become more familiar with it, and how you can adapt it for your own use-case. Before running the script, make sure you install the library from source:   Copied git clone https://github.com/huggingface/diffusers