  --mixed_precision="fp16" Most of the parameters are identical to the parameters in the Text-to-image training guide, so let’s get straight to a walkthrough of the Kandinsky training scripts!  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_prior.py \