Open-sourcing Knowledge Distillation Code and Weights of SD-Small and SD-Tiny
Hugging Face has open-sourced knowledge distillation code and model weights for two compressed variants of Stable Diffusion: SD-Small and SD-Tiny. These distilled models are smaller and faster than the original Stable Diffusion, targeting inference efficiency. The release includes both the trained weights and the distillation training code, enabling the community to reproduce or extend the work.
Related guides (4)
Related events (8)
Diffusers welcomes Stable Diffusion 3
Hugging Face's Diffusers library adds support for Stable Diffusion 3, enabling users to run Stability AI's latest text-to-image model through the standard Diffusers API. The post covers integration details, usage patterns, and memory optimization techniques for running SD3 locally. This marks the open-weights availability of SD3 through a major ML tooling ecosystem.
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.
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.
HumP-KD: Uncertainty-aware multi-stage knowledge distillation for efficient fire classification
Researchers propose HumP-KD, a knowledge distillation framework that compresses two heterogeneous transformer teachers (Swin-Tiny and ViT-Base) into a lightweight MobileViT-S student for real-time fire classification. The student model achieves 0.9876 mean F1 on a 31K-image dataset while retaining only 4.94M parameters—a 5.7× reduction over Swin-Tiny—and runs at 37.72 CPU FPS. The framework combines hierarchical feature alignment, spatial attention masking, and progressive multi-stage distillation to maintain accuracy under degraded visual conditions.
Diffusers welcomes Stable Diffusion 3.5 Large
Hugging Face's Diffusers library has added support for Stable Diffusion 3.5 Large, Stability AI's latest image generation model. The blog post covers integration details, usage patterns, and how to run the model within the Diffusers ecosystem. This represents a standard tooling integration announcement for a recently released frontier image generation model.
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.
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.
Stable Diffusion with 🧨 Diffusers
Hugging Face published a blog post introducing Stable Diffusion integration with their Diffusers library, covering the model's architecture and how to run it using the open-source tooling. The post appeared at the time of Stable Diffusion's public release in August 2022, marking a significant moment in accessible text-to-image generation. It served as both a technical introduction and a practical guide for the community to adopt the model.