 of the timesteps array.   Copied >>> input = noise

>>> for t in scheduler.timesteps:
...     with torch.no_grad():
...         noisy_residual = model(input, t).sample
...     previous_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
...     input = previous_noisy_sample This is the entire denoising process, and you can use this same pattern to write any diffusion system. The last step is to convert the denoised output into 