# (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware

DevFeed: [(LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware](<https://devfeed.tech/articles/lora-fine-tuning-flux-1-dev-on-consumer-hardware-7203.md>)

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

Author: Derek Liu; Marc Sun; Sayak Paul; merve; Linoy Tsaban

Published: 2025-06-19T00:00:00Z

Content type: tutorial

Language: en

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

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [lora](<https://devfeed.tech/topics/lora.md>), [flux](<https://devfeed.tech/topics/flux.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [dev](<https://devfeed.tech/tags/dev.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [flux](<https://devfeed.tech/tags/flux.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [lora](<https://devfeed.tech/tags/lora.md>), [model-training](<https://devfeed.tech/tags/model-training.md>), [nvidia-rtx](<https://devfeed.tech/tags/nvidia-rtx.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

This tutorial explains how to fine-tune the FLUX.1-dev diffusion model efficiently with QLoRA on a single consumer GPU using less than about 10 GB of VRAM. It describes the model components, focuses training on the transformer while keeping the text encoders and VAE frozen, and discusses LoRA, quantization, and FP8 training for memory and speed improvements.

## Source excerpt

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