ler.step(model_output, timestep, image).prev_sample

        return scheduler_output
Cool, that’s it! 🚀 You can now run this pipeline by passing a unet and a scheduler to the init:


	Copied
from diffusers import DDPMScheduler, Unet2DModel

scheduler = DDPMScheduler()
unet = UNet2DModel()

pipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler)

output = pipeline()
But what’s even better is that you can load pre-existing weig