ed >>> from accelerate import notebook_launcher

>>> args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)

>>> notebook_launcher(train_loop, args, num_processes=1) Once training is complete, take a look at the final 🦋 images 🦋 generated by your diffusion model!   Copied >>> import glob

>>> sample_images = sorted(glob.glob(f"{config.output_dir}/samples/*.png"))
>>> Image.open(sample_images[-1])   Next steps Uncondi