The Core ML models and inference code don't support many of the features, options, and flexibility of 🧨 Diffusers. These are some of the limitations to keep in mind:  
- Core ML models are only suitable for inference. They can't be used for training or fine-tuning.
- Only two schedulers have been ported to Swift, the default one used by Stable Diffusion and `DPMSolverMultistepScheduler`, which we ported to Swift from our `diffusers` implementation. We recommend you use `DPMSolverMultistepScheduler`, since it produces the same quality in about half the steps.
- Negative prompts, classifier-free guidance scale, and image-to-image tasks are available in the inference code. Advanced features such as depth guidance, ControlNet, and latent upscalers are not available yet.  
Apple's [conversion and inference repo](https://github.com/apple/ml-stable-diffusion) and our own [swift-coreml-diffusers](https://github.com/huggingface/swift-coreml-diffusers) repos are intended as technology demonstrators to enable other developers to build upon.  
If you feel strongly about any missing features, please feel free to open a feature request or, better yet, a contribution PR 🙂.
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/optimization/coreml.md