, downloads and caches all the required configuration and weight files, and returns a pipeline instance ready for inference.


	Copied
from diffusers import DiffusionPipeline

repo_id = "runwayml/stable-diffusion-v1-5"
pipe = DiffusionPipeline.from_pretrained(repo_id)
You can also load a checkpoint with it’s specific pipeline class. The example above loaded a Stable Diffusion model; to get the same result, use the StableDiffusionPipeline class: