How do I run stable diffusion?

To run Stable Diffusion on Apple devices, you can follow these steps as outlined in various WWDC sessions:

1. Model Preparation:

   • Start with a pre-trained model, such as a PyTorch model.
   • Convert the model into Core ML format using Core ML tools. This involves optimizing the model representation and parameters to achieve great performance while maintaining good accuracy. Techniques like quantization and efficient key-value caching can be used to optimize the model further (Platforms State of the Union).

2. Model Compression:

   • Apply compression techniques to reduce the model size. For example, you can use pruning and quantization to compress the model. This is particularly useful for large models like Stable Diffusion, which can initially be too large to run efficiently on devices like iPhones or iPads (Bring your machine learning and AI models to Apple silicon).

3. Integration and Execution:

   • Integrate the model into your app using the Core ML framework. This can be as simple as passing in the required input and reading the returned output. For more advanced use cases, you might need to implement additional operations to support the computation outside the model (Deploy machine learning and AI models on-device with Core ML).

4. Running the Model:

   • Use Core ML to run the model on Apple devices. Core ML optimizes hardware-accelerated execution across the CPU, GPU, and neural engine, ensuring efficient performance (Explore machine learning on Apple platforms).

For a practical demonstration, you can refer to the demo in the session "Deploy machine learning and AI models on-device with Core ML," where an open-source Stable Diffusion model is used to generate images from text (Deploy machine learning and AI models on-device with Core ML).

Relevant Sessions:

1. Deploy machine learning and AI models on-device with Core ML
2. Explore machine learning on Apple platforms
3. Bring your machine learning and AI models to Apple silicon
4. Platforms State of the Union