>>> sample = noisy_sample

>>> for i, t in enumerate(tqdm.tqdm(scheduler.timesteps)):
...     # 1. predict noise residual
...     with torch.no_grad():
...         residual = model(sample, t).sample

...     # 2. compute less noisy image and set x_t -> x_t-1
...     sample = scheduler.step(residual, t, sample).prev_sample