torch.Size([1, 3, 256, 256]) The less_noisy_sample can be passed to the next timestep where it’ll get even less noisy! Let’s bring it all together now and visualize the entire denoising process. First, create a function that postprocesses and displays the denoised image as a PIL.Image:   Copied >>> import PIL.Image