mpt="A robot pokemon, 4k photo"
image = pipeline(prompt=prompt).images[0] For the decoder model, you can also perform inference from a saved checkpoint which can be useful for viewing intermediate results. In this case, load the checkpoint into the UNet:   Copied from diffusers import AutoPipelineForText2Image, UNet2DConditionModel

unet = UNet2DConditionModel.from_pretrained("path/to/saved/model" + "/checkpoint-<N>/unet")

pipeline = AutoPipel