  --mixed_precision="fp16" Many of the basic and important parameters are described in the Text-to-image training guide, so this guide just focuses on the relevant parameters for ControlNet: --max_train_samples: the number of training samples; this can be lowered for faster training, but if you want to stream really large datasets, you’ll need to include this parameter and the --streaming parameter in your training command --gradient_accumulation_steps: number of update steps to accumulate before the backward pass; this allows you to train with a bigger batch size than your GPU memory can typically handle  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_controlnet.py \