This blog post explores how to use Ray AIR to scale and accelerate the fine-tuning process of a stable diffusion model, a type of generative AI model that can convert textual descriptions into realistic images. The authors highlight three challenges when scaling fine-tuning diffusion models: converting scripts to do distributed training, distributed data loading, and distributed orchestration. To address these challenges, they introduce Ray AIR, a native set of scalable machine libraries built on top of Ray, which simplifies distributed training for PyTorch and other common ML frameworks, and provides an interface for reading files from cloud storage and efficiently loading and sharding data into training GPUs. The authors demonstrate how to use Ray AIR to fine-tune a stable diffusion model with ease, scalability, and minimal code changes, making it possible to put a cat on the moon!