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How to train a FLUX.2 klein LoRA
Which trainers support FLUX.2 klein (4B), how much VRAM you need, and settings that work. Every row links to its source.
Trainer support
| Trainer | Status |
|---|
| ai-toolkit | supported (klein base 4B and 9B) |
| OneTrainer | supported |
| musubi-tuner | supported |
| kohya sd-scripts | unknown |
| SimpleTuner | unknown (Flux.2 listed; variant not stated) |
VRAM
12-16 GB
Known-good settings
- diffusers v0.42.0 (Oct 23, PR #14163) loads LoKr adapters for FLUX.2 and FLUX.2 klein
- ComfyUI master (PR #17196) runs the FLUX.2 VAE in fp16: faster, closer to fp32, and no tiled VAE node needed at high resolutions. Your existing VAE file works after updating; don't use bf16 FLUX.2 VAE files
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
- Leave out of your captions whatever you want burned into the LoRA; caption what should stay changeable
- ai-toolkit dataset folder weights let you train the same images with long, medium and short captions in separate folders
- ai-toolkit supports validation loss, which shows when a run stops improving
- In ai-toolkit, Cache text embeddings works for basically every model and frees the text encoder from VRAM and RAM
- Prefer int8 convrot over qfloat8 quantization in ai-toolkit: better quality at the same memory
- Gradient accumulation and batch size don't scale the learning rate in ai-toolkit; a bigger batch needs about 1.5-2.5x the steps
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