The ensembling mechanism built into Marigold pipelines combines multiple predictions obtained from different random latents.
As a side effect, it can be used to quantify epistemic (model) uncertainty; simply specify `ensemble_size` greater than 1 and set `output_uncertainty=True`.
The resulting uncertainty will be available in the `uncertainty` field of the output.
It can be visualized as follows:  
```python
import diffusers
import torch

pipe = diffusers.MarigoldDepthPipeline.from_pretrained(
"prs-eth/marigold-depth-lcm-v1-0", variant="fp16", torch_dtype=torch.float16
).to("cuda")

image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
depth = pipe(
image,
ensemble_size=10,  # any number greater than 1; higher values yield higher precision
output_uncertainty=True,
)

uncertainty = pipe.image_processor.visualize_uncertainty(depth.uncertainty)
uncertainty[0].save("einstein_depth_uncertainty.png")
```  
<div class="flex gap-4">
<div style="flex: 1 1 50%; max-width: 50%;">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_depth_uncertainty.png"/>
<figcaption class="mt-1 text-center text-sm text-gray-500">
Depth uncertainty
</figcaption>
</div>
<div style="flex: 1 1 50%; max-width: 50%;">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_normals_uncertainty.png"/>
<figcaption class="mt-1 text-center text-sm text-gray-500">
Surface normals uncertainty
</figcaption>
</div>
</div>  
The interpretation of uncertainty is easy: higher values (white) correspond to pixels, where the model struggles to make consistent predictions.
Evidently, the depth model is the least confident around edges with discontinuity, where the object depth changes drastically.
The surface normals model is the least confident in fine-grained structures, such as hair, and dark areas, such as the collar.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/marigold_usage.md