#
Unconditional image generation

Unconditional image generation models are not conditioned on text or images during training. It only generates images that resemble its training data distribution.

This guide will explore the train_unconditional.py training script to help you become familiar with it, and how you can adapt it for your own use-case.

Before running the script, make sure you install the library from source:

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

Then navigate to the example folder containing the training script and install the required dependencies:

    cd examples/unconditional_image_generation
    pip install -r requirements.txt

🤗 Accelerate is a library for helping you train on multiple GPUs/TPUs or with mixed-precision. It’ll automatically configure your training setup based on your hardware and environment. Take a look at the 🤗 Accelerate Quick tour to learn more.

Initialize an 🤗 Accelerate environment:

    accelerate config

To setup a default 🤗 Accelerate environment without choosing any configurations:

    accelerate config default

Or if your environment doesn’t support an interactive shell like a notebook, you can use:

    from accelerate.utils import write_basic_config

    write_basic_config()

Lastly, if you want to train a model on your own dataset, take a look at the Create a dataset for training guide to learn how to create a dataset that works with the training script.

##
Script parameters

The following sections highlight parts of the training script that are important for understanding how to modify it, but it doesn’t cover every aspect of the script in detail. If you’re interested in learning more, feel free to read through the script and let us know if you have any questions or concerns.

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()` 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 speedup training with mixed precision using the bf16 format, add the `--mixed_precision` parameter to the training command:

    accelerate launch train_unconditional.py \
      --mixed_precision="bf16"

Some basic and important parameters to specify include:

- `--dataset_name`: the name of the dataset on the Hub or a local path to the dataset to train on
- `--output_dir`: where to save the trained model
- `--push_to_hub`: whether to push the trained model to the Hub
- `--checkpointing_steps`: frequency of saving a checkpoint as the model trains; this is useful if training is interrupted, you can continue training from that checkpoint by adding `--resume_from_checkpoint` to your training command

Bring your dataset, and let the training script handle everything else!

##
Training script

The code for preprocessing the dataset and the training loop is found in the `main()` function. If you need to adapt the training script, this is where you’ll need to make your changes.

The `train_unconditional` script initializes a `UNet2DModel` if you don’t provide a model configuration. You can configure the UNet here if you’d like:

    model = UNet2DModel(
        sample_size=args.resolution,
        in_channels=3,
        out_channels=3,
        layers_per_block=2,
        block_out_channels=(128, 128, 256, 256, 512, 512),
        down_block_types=(
            "DownBlock2D",
            "DownBlock2D",
            "DownBlock2D",
            "DownBlock2D",
            "AttnDownBlock2D",
            "DownBlock2D",
        ),
        up_block_types=(
            "UpBlock2D",
            "AttnUpBlock2D",
            "UpBlock2D",
            "UpBlock2D",
            "UpBlock2D",
            "UpBlock2D",
        ),
    )

Next, the script initializes a scheduler and optimizer:

    # Initialize the scheduler
    accepts_prediction_type = "prediction_type" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
    if accepts_prediction_type:
        noise_scheduler = DDPMScheduler(
            num_train_timesteps=args.ddpm_num_steps,
            beta_schedule=args.ddpm_beta_schedule,
            prediction_type=args.prediction_type,
        )
    else:
        noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)

    # Initialize the optimizer
    optimizer = torch.optim.AdamW(
        model.parameters(),
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

Then it loads a dataset and you can specify how to preprocess it:

    dataset = load_dataset("imagefolder", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split="train")

    augmentations = transforms.Compose(
        [
            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
            transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
            transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
            transforms.ToTensor(),
            transforms.Normalize([0.5], [0.5]),
        ]
    )

Finally, the training loop handles everything else such as adding noise to the images, predicting the noise residual, calculating the loss, saving checkpoints at specified steps, and saving and pushing the model to the Hub. If you want to learn more about how the training loop works, check out the Understanding pipelines, models and schedulers tutorial which breaks down the basic pattern of the denoising process.

##
Launch the script

Once you’ve made all your changes or you’re okay with the default configuration, you’re ready to launch the training script! 🚀

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

single GPU
multi-GPU

    accelerate launch train_unconditional.py \
      --dataset_name="huggan/flowers-102-categories" \
      --output_dir="ddpm-ema-flowers-64" \
      --mixed_precision="fp16" \
      --push_to_hub

The training script creates and saves a checkpoint file in your repository. Now you can load and use your trained model for inference:

    from diffusers import DiffusionPipeline
    import torch

    pipeline = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128").to("cuda")
    image = pipeline().images[0]

< > Update on GitHub