LoRA Fine-Tuning FLUX.1-dev on Consumer GPUs with QLoRA and 4-bit Quantization

AI Impact Summary

This post demonstrates end-to-end fine-tuning of FLUX.1-dev via QLoRA on a single consumer GPU with ~10 GB VRAM, using 4-bit nf4 quantization from bitsandbytes and 8-bit AdamW to dramatically reduce memory. It trains only LoRA adapters on the FluxTransformer2DModel while keeping the CLIP/T5 text encoders and VAE frozen, and employs gradient checkpointing and latent caching to trim memory and compute. The setup demonstrates a practical path for teams to customize diffusion outputs on commodity hardware, enabling on-prem personalization for artistic styles and other domain-specific tasks.