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

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:

data_dir/xxx.png
data_dir/xxy.png
data_dir/[...]/xxz.png

Pass the path to the dataset directory to the --train_data_dir argument, and then you can start training:

accelerate launch train_unconditional.py \
    --train_data_dir <path-to-train-directory> \
    <other-arguments>

Upload your data to the Hub

Start by creating a dataset with the ImageFolder feature, which creates an image column containing the PIL-encoded images.

You can use the data_dir or data_files parameters to specify the location of the dataset. The data_files parameter supports mapping specific files to dataset splits like train or test:

from datasets import load_dataset

# example 1: local folder
dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")

# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset("imagefolder", data_files="path_to_zip_file")

# example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)
dataset = load_dataset(
    "imagefolder",
    data_files="https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip",
)

# example 4: providing several splits
dataset = load_dataset(
    "imagefolder", data_files={"train": ["path/to/file1", "path/to/file2"], "test": ["path/to/file3", "path/to/file4"]}
)

Then use the push_to_hub method to upload the dataset to the Hub:

# assuming you have ran the huggingface-cli login command in a terminal
dataset.push_to_hub("name_of_your_dataset")

# if you want to push to a private repo, simply pass private=True:
dataset.push_to_hub("name_of_your_dataset", private=True)

Now the dataset is available for training by passing the dataset name to the --dataset_name argument:

accelerate launch --mixed_precision="fp16"  train_text_to_image.py \
  --pretrained_model_name_or_path="stable-diffusion-v1-5/stable-diffusion-v1-5" \
  --dataset_name="name_of_your_dataset" \
  <other-arguments>

Next steps

Now that you’ve created a dataset, you can plug it into the train_data_dir (if your dataset is local) or dataset_name (if your dataset is on the Hub) arguments of a training script.

For your next steps, feel free to try and use your dataset to train a model for unconditional generation or text-to-image generation!