  --mixed_precision="bf16" Most of the parameters are identical to the parameters in the Text-to-image training guide, so you’ll focus on the parameters that are relevant to training SDXL in this guide. --pretrained_vae_model_name_or_path: path to a pretrained VAE; the SDXL VAE is known to suffer from numerical instability, so this parameter allows you to specify a better VAE --proportion_empty_prompts: the proportion of image prompts to replace with empty strings --timestep_bias_strategy: where (earlier vs. later) in the timestep to apply a bias, which can encourage the model to either learn low or high frequency details --timestep_bias_multiplier: the weight of the bias to apply to the timestep --timestep_bias_begin: the timestep to begin applying the bias --timestep_bias_end: the timestep to end applying the bias --timestep_bias_portion: the proportion of timesteps to apply the bias to  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 either 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_sdxl.py \