Create a dataset for training There are many datasets on the Hub to train a model on, but if you can’t find one you’re interested in or want to use your own, you can create a dataset with the 🤗 Datasets library. The dataset structure depends on the task you want to train your model on. The most basic dataset structure is a directory of images for tasks like unconditional image generation. Another dataset structure may be a directory of images and a text file containing their corresponding text captions for tasks like text-to-image generation. This guide will show you two ways to create a dataset to finetune on: provide a folder of images to the --train_data_dir argument upload a dataset to the Hub and pass the dataset repository id to the --dataset_name argument 💡 Learn more about how to create an image dataset for training in the Create an image dataset guide.  Provide a dataset as a folder For unconditional generation, you can provide your own dataset as a folder of images. The training script uses the ImageFolder builder from 🤗 Datasets to automatically build a dataset from the folder. Your directory structure should look like:   Copied data_dir/xxx.png