Stable Diffusion XL on Mac with Advanced Core ML Quantization
Hugging Face details the process of running Stable Diffusion XL (SDXL) on Apple Silicon Macs using Core ML with advanced quantization techniques. The post covers how quantization reduces model size and memory requirements to make SDXL feasible on consumer Mac hardware. This represents a practical deployment advance for running large diffusion models at the edge on Apple devices.
Related guides (3)
Related events (8)
Using Stable Diffusion with Core ML on Apple Silicon
Hugging Face published a guide on running Stable Diffusion models via Apple's Core ML framework on Apple Silicon hardware. The post covers converting diffusion model weights to Core ML format and integrating them into the Diffusers library for on-device inference. This represents an early effort to enable efficient local image generation on consumer Apple hardware without requiring cloud GPU resources.
Faster Stable Diffusion with Core ML on iPhone, iPad, and Mac
Hugging Face published a blog post detailing optimizations for running Stable Diffusion models via Core ML on Apple devices including iPhone, iPad, and Mac. The post covers techniques to accelerate on-device inference using Apple's neural engine and Core ML framework. This represents progress in deploying capable diffusion models at the edge without cloud dependency.
Swift Diffusers: Fast Stable Diffusion for Mac
Hugging Face published a blog post introducing Swift Diffusers, a native macOS/iOS application for running Stable Diffusion models locally on Apple Silicon hardware. The post covers optimizations leveraging Apple's Core ML framework to accelerate inference on Mac. This represents an effort to bring on-device diffusion model inference to consumer Apple hardware without cloud dependency.
Exploring Simple Optimizations for SDXL
This Hugging Face blog post explores practical optimization techniques for Stable Diffusion XL (SDXL) inference. It covers methods to improve throughput and reduce memory usage when running SDXL, targeting practitioners deploying the model. The content is oriented toward applied inference efficiency rather than novel research.
WWDC 24: Running Mistral 7B with Core ML
This Hugging Face blog post covers running Mistral 7B on Apple devices using Core ML, likely demonstrated or announced around WWDC 2024. It addresses on-device inference of a 7B parameter open-weights model using Apple's ML framework. This represents a practical deployment pattern for running capable open-weights LLMs locally on Apple Silicon hardware.
Accelerating Stable Diffusion Inference on Intel CPUs
This Hugging Face blog post details techniques for optimizing Stable Diffusion inference on Intel CPUs, likely covering quantization, operator fusion, and Intel-specific hardware acceleration libraries. The post addresses the practical challenge of running diffusion models on CPU hardware without dedicated GPUs. This is relevant to inference economics and enterprise deployment patterns where GPU availability is constrained.
Optimizing Stable Diffusion for Intel CPUs with NNCF and Hugging Face Optimum
This Hugging Face blog post details techniques for optimizing Stable Diffusion inference on Intel CPUs using Neural Network Compression Framework (NNCF) and the Optimum library. The workflow covers quantization and other compression methods to reduce latency and memory footprint on CPU hardware. This is relevant to the inference-economics and enterprise-deployment threads as it addresses running diffusion models without dedicated GPU hardware.
Accelerating SD Turbo and SDXL Turbo Inference with ONNX Runtime and Olive
This Hugging Face blog post details how to accelerate Stable Diffusion Turbo and SDXL Turbo inference using ONNX Runtime and Microsoft's Olive optimization toolkit. The post covers the workflow for converting and optimizing diffusion models for faster deployment. This is a practical inference optimization guide targeting practitioners deploying image generation models.