Wuerstchen The Wuerstchen model drastically reduces computational costs by compressing the latent space by 42x, without compromising image quality and accelerating inference. During training, Wuerstchen uses two models (VQGAN + autoencoder) to compress the latents, and then a third model (text-conditioned latent diffusion model) is conditioned on this highly compressed space to generate an image. To fit the prior model into GPU memory and to speedup training, try enabling gradient_accumulation_steps, gradient_checkpointing, and mixed_precision respectively. This guide explores the train_text_to_image_prior.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