d not re-download the files.
Load the cached weights into the defined model class - one of the existing model classes - and return an instance of the class.
In constrast to DiffusionPipeline.from_pretrained(), models rely on fewer files that usually don’t require a folder structure, but just a diffusion_pytorch_model.bin and config.json file.
Let’s look at an example:


	Copied
from diffusers import UNet2DConditionModel

repo_id = "runwayml/sta