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About

 

Welcome to Exp-Pi's Blog!


This blog is a personal space where I share my experiences, tutorials, and experiments related to:

  • AI and machine learning

  • Fine-tuning models like Stable Diffusion

  • macOS development (especially on Apple Silicon)

  • Mobile development for iOS and Android

  • Cloud services and automation workflows

  • Retrieval-Augmented Generation (RAG) systems

  • Model Control Protocols (MCP) and Agentic frameworks

  • Building lightweight SaaS products and developer tools


I aim to make advanced tech approachable by sharing real-world use cases and personal insights — whether you’re an indie developer, AI practitioner, or someone interested in blending cloud, local, and mobile capabilities.


Thanks for visiting — and feel free to subscribe, comment, or reach out!


Thanks for visiting!

— Jack813

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Performance Comparison of Multiple Image Generation Models on Apple Silicon MacBook Pro

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Fine-tuning Stable Diffusion XL Natively on MacBook Pro M4 Max

Fine-tuning large models is typically done on CUDA-enabled devices. Whether using consumer-grade GPUs or specialized AI accelerator cards, the cost is often high. These setups also demand substantial power and efficient cooling, which usually requires a large desktop workstation. Alternatively, you can rent cloud computing resources by the hour using platforms like Runpod or Lambda.ai. However, this still incurs significant costs and often requires considerable time to upload data from your local machine to the cloud. Since Apple introduced its Silicon chip series, PyTorch has added support for the MPS (Metal Performance Shaders) backend on M1 and later devices, significantly improving compute performance on macOS. Thanks to the unified memory architecture of Apple Silicon, it’s possible to load larger models than what most consumer GPUs can handle, reducing the constraints imposed by limited VRAM. This allows developers to fine-tune models locally while still enjoying the portability...
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