Hugging Face· Platform

(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.

From the source

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.

huggingface.co

Announced

19 Jun 2025 00:00 UTC

Detected

03 Aug 2026 21:12 UTC (+410d after)