diffusers/examples/unconditional_image_generation/README.md

Training examples

Creating a training image set is described in a different document.

Installing the dependencies

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

Important

To make sure you can successfully run the latest versions of the example scripts, we highly recommend installing from source and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:

git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .

Then cd in the example folder and run

pip install -r requirements.txt

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 --center_crop --random_flip \
  --output_dir="ddpm-ema-flowers-64" \
  --train_batch_size=16 \
  --num_epochs=100 \
  --gradient_accumulation_steps=1 \
  --use_ema \
  --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 --center_crop --random_flip \
  --output_dir="ddpm-ema-pokemon-64" \
  --train_batch_size=16 \
  --num_epochs=100 \
  --gradient_accumulation_steps=1 \
  --use_ema \
  --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.

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.