This guide provides an overview of how to train a Stable Diffusion model using Ray Train + PyTorch Lightning, including strategies for optimizing the training process, scaling the training process, and handling extensive datasets and computational demands. The guide covers key concepts such as model initialization, data loading, and training step, and provides code examples in Python. It also discusses the importance of choosing the right distributed training strategy (DDP or FSDP) based on specific requirements, and how to implement online vs. offline preprocessing using Ray Data.