QLoRA enables fine-tuning of FLUX.1-dev diffusion models on consumer hardware with under 10GB VRAM by combining 4-bit quantization with Low-Rank Adaptation. The approach uses bitsandbytes for quantization, 8-bit AdamW optimizer, gradient checkpointing, and cached latents to dramatically reduce memory usage from ~120GB to ~9GB. Training on RTX 4090 takes 41 minutes for 700 steps, while FP8 training with torchao on H100 reduces time to 20 minutes. The technique maintains high-quality results while making advanced model customization accessible to developers without enterprise-grade hardware.
Table of contents
Table of ContentsDatasetFLUX ArchitectureQLoRA Fine-tuning FLUX.1-dev with diffusersFP8 Fine-tuning with torchaoInference with Trained LoRA AdaptersRunning on Google ColabConclusionSort: