  --mixed_precision="fp16" Most of the parameters are identical to the parameters in the Text-to-image training guide, so you’ll focus on the parameters that are relevant to latent consistency distillation in this guide. --pretrained_teacher_model: the path to a pretrained latent diffusion model to use as the teacher model --pretrained_vae_model_name_or_path: path to a pretrained VAE; the SDXL VAE is known to suffer from numerical instability, so this parameter allows you to specify an alternative VAE (like this VAE by madebyollin which works in fp16) --w_min and --w_max: the minimum and maximum guidance scale values for guidance scale sampling --num_ddim_timesteps: the number of timesteps for DDIM sampling --loss_type: the type of loss (L2 or Huber) to calculate for latent consistency distillation; Huber loss is generally preferred because it’s more robust to outliers --huber_c: the Huber loss parameter  Training script The training script starts by creating a dataset class - Text2ImageDataset - for preprocessing the images and creating a training dataset.   Copied def transform(example):