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# Unconditional image generation

Unconditional image generation is not conditioned on any text or images, unlike text- or image-to-image models. It only generates images that resemble its training data distribution.

<iframe
	src="https://stevhliu-ddpm-butterflies-128.hf.space"
	frameborder="0"
	width="850"
	height="550"
></iframe>


This guide will show you how to train an unconditional image generation model on existing datasets as well as your own custom dataset. All the training scripts for unconditional image generation can be found [here](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation) if you're interested in learning more about the training details.

Before running the script, make sure you install the library's training dependencies:

```bash
pip install diffusers[training] accelerate datasets
```

Next, initialize an 🤗 [Accelerate](https://github.com/huggingface/accelerate/) environment with:

```bash
accelerate config
```

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

```bash
accelerate config default
```

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

```bash
from accelerate.utils import write_basic_config

write_basic_config()
```

## Upload model to Hub

You can upload your model on the Hub by adding the following argument to the training script:

```bash
--push_to_hub
```

## Save and load checkpoints

It is a good idea to regularly save checkpoints in case anything happens during training. To save a checkpoint, pass the following argument to the training script:

```bash
--checkpointing_steps=500
```

The full training state is saved in a subfolder in the `output_dir` every 500 steps, which allows you to load a checkpoint and resume training if you pass the `--resume_from_checkpoint` argument to the training script:

```bash
--resume_from_checkpoint="checkpoint-1500"
```

## Finetuning

You're ready to launch the [training script](https://github.com/huggingface/diffusers/blob/main/examples/unconditional_image_generation/train_unconditional.py) now! Specify the dataset name to finetune on with the `--dataset_name` argument and then save it to the path in `--output_dir`.

<Tip>

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

</Tip>

For example, to finetune on the [Oxford Flowers](https://huggingface.co/datasets/huggan/flowers-102-categories) dataset:

```bash
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
```

<div class="flex justify-center">
    <img src="https://user-images.githubusercontent.com/26864830/180248660-a0b143d0-b89a-42c5-8656-2ebf6ece7e52.png"/>
</div>

Or if you want to train your model on the [Pokemon](https://huggingface.co/datasets/huggan/pokemon) dataset:

```bash
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
```

<div class="flex justify-center">
    <img src="https://user-images.githubusercontent.com/26864830/180248200-928953b4-db38-48db-b0c6-8b740fe6786f.png"/>
</div>

## Finetuning with your own data

There are two ways to finetune a model on your own dataset:

- provide your own folder of images to the `--train_data_dir` argument
- upload your dataset to the Hub and pass the dataset repository id to the `--dataset_name` argument.

<Tip>

💡 Learn more about how to create an image dataset for training in the [Create an image dataset](https://huggingface.co/docs/datasets/image_dataset) guide. 

</Tip>

Below, we explain both in more detail.

### Provide the dataset as a folder

If you provide your own dataset as a folder, the script expects the following directory structure:

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

Pass the path to the folder containing the images to the `--train_data_dir` argument and launch the training:

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

Internally, the script uses the [`ImageFolder`](https://huggingface.co/docs/datasets/image_load#imagefolder) to automatically build a dataset from the folder.

### Upload your data to the Hub

<Tip>

💡 For more details and context about creating and uploading a dataset to the Hub, take a look at the [Image search with 🤗 Datasets](https://huggingface.co/blog/image-search-datasets) post.

</Tip>

To upload your dataset to the Hub, you can start by creating one with the [`ImageFolder`](https://huggingface.co/docs/datasets/image_load#imagefolder) feature, which creates an `image` column containing the PIL-encoded images, from 🤗 Datasets:

```python
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 you can use the [`~datasets.Dataset.push_to_hub`] method to upload it to the Hub:

```python
# 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 train your model by simply setting the `--dataset_name` argument to the name of your dataset on the Hub.