Exciting updates to #stablediffusion with Core ML!
- 6-bit weight compression that yields just under 1 GB
- Up to 30% improved Neural Engine performance
- New benchmarks on iPhone, iPad and Macs
- Multilingual system text encoder support
- ControlNet github.com/apple/ml-stabl… 🧵
coremltools-7.0 introduced advanced model compression techniques. For Stable Diffusion, we demonstrate how 6-bit post-training palettization yields faster models that consume 63% less memory compared to float16. Output variance is comparable to GPU vs Neural Engine.
Dec 1, 2022 • 7 tweets • 3 min read
Delighted to share #stablediffusion with Core ML on Apple Silicon built on top of @huggingface diffusers! 🧵
Today's release of macOS Ventura 13.1 Beta 4 and iOS and iPadOS 16.2 Beta 4 include optimizations that let Stable Diffusion run with improved efficiency on the Apple Neural Engine as well as on Apple Silicon GPU
