write_basic_config() Lastly, if you want to train a model on your own dataset, take a look at the Create a dataset for training guide to learn how to create a dataset that works with the training script. The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn’t cover every aspect of the script in detail. If you’re interested in learning more, feel free to read through the script and let us know if you have any questions or concerns.  Script parameters The training script has many parameters to help you tailor the training run to your needs. All of the parameters and their descriptions are listed in the parse_args() function. Where applicable, Diffusers provides default values for each parameter such as the training batch size and learning rate, but feel free to change these values in the training command if you’d like. For example, to increase the number of gradient accumulation steps above the default value of 1:   Copied accelerate launch textual_inversion.py \