  --mixed_precision="fp16" Some basic and important parameters include: --pretrained_model_name_or_path: the name of the model on the Hub or a local path to the pretrained model --dataset_name: the name of the dataset on the Hub or a local path to the dataset to train on --image_column: the name of the image column in the dataset to train on --caption_column: the name of the text column in 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 for some reason training is interrupted, you can continue training from that checkpoint by adding --resume_from_checkpoint to your training command  Min-SNR weighting The Min-SNR weighting strategy can help with training by rebalancing the loss to achieve faster convergence. The training script supports predicting epsilon (noise) or v_prediction, but Min-SNR is compatible with both prediction types. This weighting strategy is only supported by PyTorch and is unavailable in the Flax training script. Add the --snr_gamma parameter and set it to the recommended value of 5.0:   Copied accelerate launch train_text_to_image.py \