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How to train a FLUX.2 LoRA (dev)
Which trainers support FLUX.2 dev (32B), how much VRAM you need, and settings that work. Every row links to its source.
Trainer support
| Trainer | Status |
|---|
| ai-toolkit | supported (reference) |
| OneTrainer | supported |
| musubi-tuner | supported |
| kohya sd-scripts | experimental |
| SimpleTuner | supported |
VRAM
80 GB recommended; ai-toolkit ships train_lora_flux_24gb.yaml
Known-good settings
- Start from ai-toolkit train_lora_flux_24gb.yaml
- OneTrainer preview-2 branch (not master yet): LoRA on 8-bit quantized FLUX.2 trains ~1.3x faster with the 16 GB preset; DoRA not sped up
General tips
- Gated Hugging Face models: click 'Agree and access' on each model page, or you'll get 403 errors
- Steps scale with dataset size (steps = epochs x images at batch 1)
- A safe start: LoKr with auto rank (~64), batch/grad accumulation 4
Where to rent a GPU
See cheapest GPU to train a LoRA (RunPod, Vast.ai).
Sources
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Data updated 2026-10-20. RunPod and Vast.ai links are referral links. unknown = no data yet. · Raw data (JSON) · llms.txt