xed_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"

Finetuning with your own data
	
There are two ways to finetune a model