# Memory-efficient Diffusion Transformers with Quanto and Diffusers

DevFeed: [Memory-efficient Diffusion Transformers with Quanto and Diffusers](<https://devfeed.tech/articles/memory-efficient-diffusion-transformers-with-quanto-and-diffusers-7450.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/quanto-diffusers>)

Author: Sayak Paul; David Corvoysier

Published: 2024-07-30T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [diffusers](<https://devfeed.tech/topics/diffusers.md>), [diffusion-transformers](<https://devfeed.tech/topics/diffusion-transformers.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [optimum](<https://devfeed.tech/topics/optimum.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [stable-diffusion](<https://devfeed.tech/topics/stable-diffusion.md>), [text-to-image](<https://devfeed.tech/topics/text-to-image.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>)

Tags: [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [diffusion-transformers](<https://devfeed.tech/tags/diffusion-transformers.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [optimum](<https://devfeed.tech/tags/optimum.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [quality](<https://devfeed.tech/tags/quality.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [sd3](<https://devfeed.tech/tags/sd3.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>)

## AI overview

This tutorial explains how to reduce the memory requirements of Transformer-based diffusion pipelines using Quanto quantization utilities from the Diffusers library. It benchmarks FP8 quantization on PixArt-Sigma, Stable Diffusion 3, and Aura Flow, reporting memory savings with slightly higher latency and little quality degradation.

## Source excerpt

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