g with more than one GPU, add the --multi_gpu parameter to the training command:   Copied 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:   Copied