Load pipelines, models, and schedulers            Having an easy way to use a diffusion system for inference is essential to 🧨 Diffusers. Diffusion systems often consist of multiple components like parameterized models, tokenizers, and schedulers that interact in complex ways. That is why we designed the DiffusionPipeline to wrap the complexity of the entire diffusion system into an easy-to-use API, while remaining flexible enough to be adapted for other use cases, such as loading each component individually as building blocks to assemble your own diffusion system. Everything you need for inference or training is accessible with the from_pretrained() method. This guide will show you how to load: pipelines from the Hub and locally different components into a pipeline checkpoint variants such as different floating point types or non-exponential mean averaged (EMA) weights models and schedulers  Diffusion Pipeline 💡 Skip to the DiffusionPipeline explained section if you are interested in learning in more detail about how the DiffusionPipeline class works. The DiffusionPipeline class is the simplest and most generic way to load the latest trending diffusion model from the Hub. The DiffusionPipeline.from_pretrained() method automatically detects the correct pipeline class from the checkpoint, downloads, and caches all the required configuration and weight files, and returns a pipeline instance ready for inference.   Copied from diffusers import DiffusionPipeline