The original checkpoints can be found under the [PRS-ETH](https://huggingface.co/prs-eth/) Hugging Face organization.  
<Tip>  
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. Also, to know more about reducing the memory usage of this pipeline, refer to the ["Reduce memory usage"] section [here](../../using-diffusers/svd#reduce-memory-usage).  
</Tip>  
<Tip warning={true}>  
Marigold pipelines were designed and tested only with `DDIMScheduler` and `LCMScheduler`.
Depending on the scheduler, the number of inference steps required to get reliable predictions varies, and there is no universal value that works best across schedulers.
Because of that, the default value of `num_inference_steps` in the `__call__` method of the pipeline is set to `None` (see the API reference).
Unless set explicitly, its value will be taken from the checkpoint configuration `model_index.json`.
This is done to ensure high-quality predictions when calling the pipeline with just the `image` argument.  
</Tip>  
See also Marigold [usage examples](marigold_usage).
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/marigold.md