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FLUX.2 dev LoRA training with ai-toolkit: setup guide

Setting up ai-toolkit to train a FLUX.2 dev LoRA: support status, the settings known to work, and links to the docs. Status in ai-toolkit: supported (reference).

About ai-toolkit

Ostris' trainer with a web UI and YAML configs; widest day-0 support for new image models, and the easiest option for Flux/Krea. Sources: github.com/ostris/ai-toolkit, www.reddit.com/r/StableDiffusion/comments/1vrkd5x/which_platform_shoul

ai-toolkit-specific notes for FLUX.2 dev

• Start from ai-toolkit train_lora_flux_24gb.yaml

VRAM

80 GB recommended; ai-toolkit ships train_lora_flux_24gb.yaml

Settings that work for FLUX.2 dev (any trainer)

• 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
• 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

Compare with other trainers

• ai-toolkit vs OneTrainer for FLUX.2 dev
• ai-toolkit vs musubi-tuner for FLUX.2 dev
• ai-toolkit vs SimpleTuner for FLUX.2 dev
• FLUX.2 dev LoRA with OneTrainer
• FLUX.2 dev LoRA with musubi-tuner
• FLUX.2 dev LoRA with SimpleTuner
• FLUX.2 dev (overview)

Where to rent a 24 GB+ GPU

Cheapest 24 GB+ cards (2026-10-20 08:06 UTC) from RunPod and Vast.ai. Prices move constantly; see the live price table.

Sources

• sanj.dev/post/onetrainer-vs-kohya-ss-vs-ai-toolkit/
• github.com/nerogar/OneTrainer
• github.com/ostris/ai-toolkit#readme
• github.com/kohya-ss/musubi-tuner#readme
• github.com/bghira/SimpleTuner#readme
• github.com/Nerogar/OneTrainer/pull/1680
• github.com/huggingface/diffusers/pull/14163
• github.com/Comfy-Org/ComfyUI/pull/17196
• github.com/ostris/ai-toolkit
• www.reddit.com/r/StableDiffusion/comments/1vrkd5x/which_platform_shoul

FAQ

Does ai-toolkit support FLUX.2 dev?

Yes: supported (reference) (see sources below).

How much VRAM do I need for a FLUX.2 dev LoRA?

80 GB recommended; ai-toolkit ships train_lora_flux_24gb.yaml

Data updated 2026-10-20. Disclosure: RunPod and Vast.ai links are referral links; ImageLoRA may earn a commission at no cost to you. unknown = no data yet. · Raw data (JSON) · llms.txt