less at the end. This helps the model determine its position in the diffusion process, whether it is closer to the start or the end. Use the sample method to get the model output:   Copied >>> with torch.no_grad():
...     noisy_residual = model(sample=noisy_sample, timestep=2).sample To generate actual examples though, you’ll need a scheduler to guide the denoising process. In the next section, you’ll learn how to couple a model with a schedul