#
Unconditional Image-Generation

In this section, we explain how one can train an unconditional image generation diffusion model. “Unconditional” because the model is not conditioned on any context to generate an image - once trained the model will simply generate images that resemble its training data distribution.

##
Installing the dependencies

Before running the scripts, make sure to install the library’s training dependencies:

    pip install diffusers[training] accelerate datasets

And initialize an 🤗Accelerate environment with:

    accelerate config

##
Unconditional Flowers

The command to train a DDPM UNet model on the Oxford Flowers dataset:

    accelerate launch train_unconditional.py \
      --dataset_name="huggan/flowers-102-categories" \
      --resolution=64 \
      --output_dir="ddpm-ema-flowers-64" \
      --train_batch_size=16 \
      --num_epochs=100 \
      --gradient_accumulation_steps=1 \
      --learning_rate=1e-4 \
      --lr_warmup_steps=500 \
      --mixed_precision=no \
      --push_to_hub

An example trained model: https://huggingface.co/anton-l/ddpm-ema-flowers-64

A full training run takes 2 hours on 4xV100 GPUs.

##
Unconditional Pokemon

The command to train a DDPM UNet model on the Pokemon dataset:

    accelerate launch train_unconditional.py \
      --dataset_name="huggan/pokemon" \
      --resolution=64 \
      --output_dir="ddpm-ema-pokemon-64" \
      --train_batch_size=16 \
      --num_epochs=100 \
      --gradient_accumulation_steps=1 \
      --learning_rate=1e-4 \
      --lr_warmup_steps=500 \
      --mixed_precision=no \
      --push_to_hub

An example trained model: https://huggingface.co/anton-l/ddpm-ema-pokemon-64

A full training run takes 2 hours on 4xV100 GPUs.

##
Using your own data

To use your own dataset, there are 2 ways:

- you can either provide your own folder as `--train_data_dir`
- or you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the `--dataset_name` argument.

Note: If you want to create your own training dataset please have a look at this document.

Below, we explain both in more detail.

###
Provide the dataset as a folder

If you provide your own folders with images, the script expects the following directory structure:

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

In other words, the script will take care of gathering all images inside the folder. You can then run the script like this:

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

Internally, the script will use the `ImageFolder` feature which will automatically turn the folders into 🤗 Dataset objects.

###
Upload your data to the hub, as a (possibly private) repo

It’s very easy (and convenient) to upload your image dataset to the hub using the `ImageFolder` feature available in 🤗 Datasets. Simply do the following:

    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"]}
    )

`ImageFolder` will create an `image` column containing the PIL-encoded images.

Next, push it 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)

and that’s it! You can now train your model by simply setting the `--dataset_name` argument to the name of your dataset on the hub.

More on this can also be found in this blog post.