To evaluate Marigold quantitatively in standard leaderboards and benchmarks (such as NYU, KITTI, and other datasets), follow the evaluation protocol outlined in the paper: load the full precision fp32 model and use appropriate values for `num_inference_steps` and `ensemble_size`.
Optionally seed randomness to ensure reproducibility. Maximizing `batch_size` will deliver maximum device utilization.  
```python
import diffusers
import torch

device = "cuda"
seed = 2024
model_path = "prs-eth/marigold-v1-0"

model_paper_kwargs = {
diffusers.schedulers.DDIMScheduler: {
"num_inference_steps": 50,
"ensemble_size": 10,
},
diffusers.schedulers.LCMScheduler: {
"num_inference_steps": 4,
"ensemble_size": 10,
},
}

image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")

generator = torch.Generator(device=device).manual_seed(seed)
pipe = diffusers.MarigoldDepthPipeline.from_pretrained(model_path).to(device)
pipe_kwargs = model_paper_kwargs[type(pipe.scheduler)]

depth = pipe(image, generator=generator, **pipe_kwargs)

# evaluate metrics
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/marigold_usage.md