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()`](https://github.com/huggingface/diffusers/blob/aab6de22c33cc01fb7bc81c0807d6109e2c998c9/examples/t2i_adapter/train_t2i_adapter_sdxl.py#L233) 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:  
```bash
accelerate launch train_t2i_adapter_sdxl.py \
----gradient_accumulation_steps=4
```  
Many of the basic and important parameters are described in the [Text-to-image](text2image#script-parameters) training guide, so this guide just focuses on the relevant T2I-Adapter parameters:  
- `--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](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)
- `--crops_coords_top_left_h` and `--crops_coords_top_left_w`: height and width coordinates to include in SDXL's crop coordinate embeddings
- `--conditioning_image_column`: the column of the conditioning images in the dataset
- `--proportion_empty_prompts`: the proportion of image prompts to replace with empty strings
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/t2i_adapters.md