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

- Company: Hugging Face (huggingface.co)
- Announced: 2025-06-19T00:00:00+00:00
- Subject: Platform
- Models affected: FLUX.1-dev
- Source: https://huggingface.co/blog/flux-qlora
- Record: https://forck.live/items/1674-lora-fine-tuning-flux-1-dev-on-consumer-hardware

This blog post guides users through fine-tuning the FLUX.1-dev model using QLoRA with the diffusers library, achieving peak memory usage under ~10 GB VRAM on a single GPU like the NVIDIA RTX 4090. It covers techniques such as 4-bit quantization, 8-bit AdamW optimizer, gradient checkpointing, and latent caching to enable efficient fine-tuning on consumer hardware.

## Evidence

Verbatim from https://huggingface.co/blog/flux-qlora:

> Performing inference is cool, but to make these models truly our own, we also need to be able to fine-tune them. Therefore, in this post, we tackle efficient fine-tuning of these models with peak memory use under ~10 GB of VRAM on a single GPU.

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