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 provides many parameters to help you customize your training run. All of the parameters and their descriptions are found in the parse_args() function. It provides default values for each parameter, such as the training batch size and learning rate, but you can also set your own values in the training command if you’d like. For example, to activate gradient accumulation, add the --gradient_accumulation_steps parameter to the training command:   Copied accelerate launch train_t2i_adapter_sdxl.py \