 on 4xV100 GPUs. single GPU multi-GPU    Copied 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:   Copied from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPip