---
title: "Bringing Nunchaku 4-bit Diffusion Inference to Diffusers"
source: "Hugging Face：Blog（RSS）"
source_tier: "t1"
url: "https://huggingface.co/blog/nunchaku-diffusers"
permalink: "https://feed.astralor.com/items/itm_37c9890bc32e7cee73233632"
published: "2026-07-23T00:00:00.000Z"
score: 90
channel: "ai"
tags: ["4-bit quantization","Diffusers","Hugging Face","Nunchaku","Diffusion Models","Reasoning Acceleration"]
---

# Bringing Nunchaku 4-bit Diffusion Inference to Diffusers

## AI commentary

> This blog post is an extremely important reference for developers looking to deploy diffusion models on edge devices. Nunchaku's low memory footprint and ease of use are likely to become a hot topic in the community over the coming weeks. I strongly recommend that all teams using Stable Diffusion for products or research carefully read the integration guide.

## At a glance

Hugging Face announces the integration of the Nunchaku 4-bit quantization inference engine into the Diffusers library, enabling developers to run diffusion models (e.g., Stable Diffusion) with significantly lower memory footprint and higher inference speed. This integration greatly reduces deployment barriers, supports a wider range of hardware, and advances the practicality of diffusion models in edge and mobile scenarios.

## Key points

- Nunchaku is a post-training 4-bit quantization method designed for diffusion models, significantly compressing model size while preserving generation quality.
- After integration into Diffusers, users can directly call 4-bit quantized diffusion models via standard API without manual conversion or extra code.
- On consumer-grade GPUs, inference speed improves by about 2-3x and memory usage reduces by over 70%.
- Supports multiple diffusion model architectures, including Stable Diffusion 1.5/2.1/XL and community fine-tuned versions.

## Full text

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

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