Optimizing Stable Diffusion Models for Core ML and Apple Silicon
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Questions and Answers

What is Core ML?

  • A machine learning library (correct)
  • An image generation tool
  • An Apple Silicon device
  • A neural network

What is the total number of parameters in the complex pipeline?

  • 100 million
  • 1.2 billion
  • 1.275 billion (correct)
  • 2.5 billion

What is the release comprised of?

  • A Python package and a Swift package (correct)
  • A Core ML package and a Swift package
  • A Python package and a Core ML package
  • A Python package and an Apple Neural Engine

Study Notes

  • Today, we are releasing optimizations to Core ML for Stable Diffusion in macOS 13.1 and iOS 16.2, as well as code to get started with deploying to Apple Silicon devices.

  • Core ML is a machine learning library that enables developers to create apps that use image generation from text prompts.

  • The community has built an expansive ecosystem of extensions and tools around this core technology in a matter of weeks.

  • Getting to a compelling result with Stable Diffusion can require a lot of time and iteration, so a core challenge with on-device deployment of the model is making sure it can generate results fast enough on device.

  • This requires executing a complex pipeline comprising 4 different neural networks totaling approximately 1.275 billion parameters.

  • To learn more about how we optimized a model of this size and complexity to run on the Apple Neural Engine, you can check out our previous article.

  • This release comprises a Python package for converting Stable Diffusion models from PyTorch to Core ML using diffusers and coremltools, as well as a Swift package to deploy the models.

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Description

Learn about the optimizations made to Core ML for Stable Diffusion in macOS 13.1 and iOS 16.2, as well as the code for deploying to Apple Silicon devices. Explore the challenges of on-device deployment and the complex neural network pipeline executed. Discover the Python and Swift packages available for converting and deploying Stable Diffusion models.

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