Once you've made all your changes or you're okay with the default configuration, you're ready to launch the training script! 🚀  
<Tip warning={true}>  
A full training run takes 2 hours on 4xV100 GPUs.  
</Tip>  
<hfoptions id="launchtraining">
<hfoption id="single GPU">  
```bash
accelerate launch train_unconditional.py \
--dataset_name="huggan/flowers-102-categories" \
--output_dir="ddpm-ema-flowers-64" \
--mixed_precision="fp16" \
--push_to_hub
```  
</hfoption>
<hfoption id="multi-GPU">  
If you're training with more than one GPU, add the `--multi_gpu` parameter to the training command:  
```bash
accelerate launch --multi_gpu train_unconditional.py \
--dataset_name="huggan/flowers-102-categories" \
--output_dir="ddpm-ema-flowers-64" \
--mixed_precision="fp16" \
--push_to_hub
```  
</hfoption>
</hfoptions>  
The training script creates and saves a checkpoint file in your repository. Now you can load and use your trained model for inference:  
```py
from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("anton-l/ddpm-butterflies-128").to("cuda")
image = pipeline().images[0]
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/training/unconditional_training.md