Marigold pipelines have a built-in ensembling mechanism combining multiple predictions from different random latents.
This is a brute-force way of improving the precision of predictions, capitalizing on the generative nature of diffusion.
The ensembling path is activated automatically when the `ensemble_size` argument is set greater than `1`.
When aiming for maximum precision, it makes sense to adjust `num_inference_steps` simultaneously with `ensemble_size`.