cd ml-stable-diffusion And then use Apple’s command line tool, Swift Package Manager:   Copied swift run StableDiffusionSample --resource-path models/coreml-stable-diffusion-v1-4_original_compiled --compute-units all "a photo of an astronaut riding a horse on mars" You have to specify in --resource-path one of the checkpoints downloaded in the previous step, so please make sure it contains compiled Core ML bundles with the extension .mlmodelc. The --compute-units has to be one of these values: all, cpuOnly, cpuAndGPU, cpuAndNeuralEngine. For more details, please refer to the instructions in Apple’s repo.  Supported Diffusers Features 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 and our own 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 🙂.  Native Diffusers Swift app One easy way to run Stable Diffusion on your own Apple hardware is to use our open-source Swift repo, based on diffusers and Apple’s conversion and inference repo. You can study the code, compile it with Xcode and adapt it for your own needs. For your convenience, there’s also a standalone Mac app in the App Store, so you can play with it without having to deal with the code or IDE. If you are a developer and have determined that Core ML is the best solution to build your Stable Diffusion app, then you can use the rest of this guide to get started with your project. We can’t wait to see what you’ll build 🙂.