  --num_train_epochs=150 \ Many of the basic and important parameters are described in the Text-to-image training guide, so this guide just focuses on the LoRA relevant parameters: --rank: the number of low-rank matrices to train --learning_rate: the default learning rate is 1e-4, but with LoRA, you can use a higher learning rate  Training script The dataset preprocessing code and training loop are found in the main() function, and if you need to adapt the training script, this is where you’ll make your changes. As with the script parameters, a walkthrough of the training script is provided in the Text-to-image training guide. Instead, this guide takes a look at the LoRA relevant parts of the script. The script begins by adding the new LoRA weights to the attention layers. This involves correctly configuring the weight size for each block in the UNet. You’ll see the rank parameter is used to create the LoRAAttnProcessor:   Copied lora_attn_procs = {}