  --mixed_precision="bf16" Some basic and important parameters to specify include: --dataset_name: the name of the dataset on the Hub or a local path to the dataset to train on --output_dir: where to save the trained model --push_to_hub: whether to push the trained model to the Hub --checkpointing_steps: frequency of saving a checkpoint as the model trains; this is useful if training is interrupted, you can continue training from that checkpoint by adding --resume_from_checkpoint to your training command Bring your dataset, and let the training script handle everything else!  Training script The code for preprocessing the dataset and the training loop is found in the main() function. If you need to adapt the training script, this is where you’ll need to make your changes. The train_unconditional script initializes a UNet2DModel if you don’t provide a model configuration. You can configure the UNet here if you’d like:   Copied model = UNet2DModel(