ed to ~1e-6) compared to non-LoRA Dreambooth fine-tuning.
But there is no free lunch. For the given dataset and expected generation quality, you’d still need to experiment with
different hyperparameters. Here are some important ones:
Training timeLearning rate 
Number of training steps
Inference time Number of steps 
Scheduler type
Additionally, you can follow this blog that documents some of our experimental
findings for performing DreamBooth 