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 This is a simple app that shows how to integrate Apple's [Core ML Stable Diffusion implementation](https://github.com/apple/ml-stable-diffusion) in a native Swift UI application. It can be used for faster iteration, or as sample code for other use cases.
 
+This is what it looks like:
+![App Screenshot](screenshot.jpg)
+
 On first launch, the application downloads a zipped archive with a Core ML version of Runway's Stable Diffusion v1.5, from [this location in the Hugging Face Hub](https://huggingface.co/pcuenq/coreml-stable-diffusion/tree/main). This process takes a while, as several GB of data have to be downloaded and unarchived.
 
 For faster inference, we use a very fast scheduler: [DPM-Solver++](https://github.com/LuChengTHU/dpm-solver) that we ported to Swift. Since this scheduler is still not available in Apple's GitHub repository, the application depends on the following fork instead: https://github.com/pcuenca/ml-stable-diffusion. Our Swift port is based on [Diffusers' DPMSolverMultistepScheduler](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py), with a number of simplifications.
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