# Unconditional image generation
## 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.
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:
Copied
```
pip install diffusers[training] accelerate datasets
```
Next, initialize an 🌍 [Accelerate](https://github.com/huggingface/accelerate/) environment with:
Copied
```
accelerate config
```
To setup a default 🌍 Accelerate environment without choosing any configurations:
Copied
```
accelerate config default
```
Or if your environment doesn’t support an interactive shell like a notebook, you can use:
Copied
```
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:
Copied
```
--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:
Copied
```
--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:
Copied
```
--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`. To use your own dataset, take a look at the [Create a dataset for training](https://huggingface.co/docs/diffusers/training/create_dataset) guide.
The training script creates and saves a `diffusion_pytorch_model.bin` file in your repository.
💡 A full training run takes 2 hours on 4xV100 GPUs.
For example, to finetune on the [Oxford Flowers](https://huggingface.co/datasets/huggan/flowers-102-categories) dataset:
Copied
```
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
```
Or if you want to train your model on the [Pokemon](https://huggingface.co/datasets/huggan/pokemon) dataset:
Copied
```
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
```
#### Training with multiple GPUs
`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch) for running distributed training with `accelerate`. Here is an example command:
Copied
```
accelerate launch --mixed_precision="fp16" --multi_gpu 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="fp16" \
--logger="wandb" \
--push_to_hub
```
