duler="constant" \
  --lr_warmup_steps=0 \
  --max_train_steps=400

Training with a prior-preserving loss
	
Prior preservation is used to avoid overfitting and language-drift. Please, refer to the paper to learn more about it if you are interested. For prior preservation, we use other images of the same class as part of the training process. The nice thing is that we can generate those images using the Stable Diffusion model itself! The trainin