stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True) The from_pretrained() method won’t download any files from the Hub when it detects a local path, but this also means it won’t download and cache the latest changes to a checkpoint.  Swap components in a pipeline You can customize the default components of any pipeline with another compatible component. Customization is important because: Changing the scheduler is important for exploring the trade-off between generation speed and quality. Different components of a model are typically trained independently and you can swap out a component with a better-performing one. During finetuning, usually only some components - like the UNet or text encoder - are trained. To find out which schedulers are compatible for customization, you can use the compatibles method:   Copied from diffusers import DiffusionPipeline