How do I run stable diffusion on my iPad?

To run Stable Diffusion on your iPad, you can follow these steps:

1. Model Training and Conversion:

   • Start by defining the model architecture and training the model using libraries such as PyTorch or TensorFlow on a Mac, leveraging Apple silicon and the unified memory architecture.
   • Convert the trained model into Core ML format using Core ML tools. This step includes optimizing the model representation and parameters to achieve great performance while maintaining good accuracy.

2. Model Optimization:

   • Compress the model to fit the constraints of iOS devices. For instance, applying 6-bit quantization can reduce the model size to below 2 GB, making it suitable for running on an iPad. This is crucial as larger models (e.g., 5 GB in float 16 precision) are initially too big for iPads.

3. Integration with Apple Frameworks:

   • Use the Core ML framework to load and execute the prepared model on your iPad. Core ML optimizes hardware-accelerated execution across the CPU, GPU, and neural engine, ensuring efficient performance.

4. Deployment:

   • Build your app with the Core ML model integrated and deploy it to your iPad. You can use tools like Xcode to compile and run your app on the device.

For more detailed guidance, you can refer to the following sessions from WWDC 2024:

• Explore machine learning on Apple platforms (07:32)
• Platforms State of the Union (16:37)
• Bring your machine learning and AI models to Apple silicon (04:41)

These sessions provide comprehensive insights into the process of running machine learning models, including Stable Diffusion, on Apple devices.