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ai-toolkit vs OneTrainer for Krea 2 LoRA training
Both ai-toolkit and OneTrainer can train Krea 2 LoRAs. Here is how they compare, using each project's docs and GitHub activity.
| ai-toolkit | OneTrainer |
|---|
| Krea 2 support | supported (Raw + Turbo, incl. edit training) | supported (Krea 2 16 GB preset benchmarked in OneTrainer PR #1680; not in README list) |
|---|
| GitHub stars | 12373 | 3247 |
|---|
| Last commit | 2026-10-19 | 2026-10-19 |
|---|
| Latest release | none published | none published |
|---|
| VRAM (Krea 2) | ai-toolkit: ~20 GB at batch 1 with qfloat8; also trains on 12 GB with Krea Raw, low VRAM on, 50% transformer offload, 100% text-encoder offload, convrot 8, compile off, LoKr factor 8, 512 resolution only, and cached latents plus text embeddings. musubi: 12GB+ recommended for image training (general guidance); fp8 or --convrot_int8 halves weight VRAM |
|---|
| Overview | Ostris' trainer with a web UI and YAML configs; widest day-0 support for new image models, and the easiest option for Flux/Krea. | GUI + CLI trainer with a broad model list; good VRAM use and speed, but more settings to configure. |
|---|
ai-toolkit notes for Krea 2
- ai-toolkit downscales images larger than 1536px
- Automagic3 can give weaker LoRAs than v1/v2; pair it with LoKr factor 8 or 4 and sigmoid timesteps (that's my recipe). ai-toolkit's experimental automagic4 (Oct 23) keeps the LR from drifting to the floor
- EMA in ai-toolkit makes no noticeable difference; Automagic v3 already behaves a lot like EMA
- ai-toolkit: point to local or alternative Qwen3-VL text encoder and Qwen-Image VAE files with text_encoder_path and vae_path under model_kwargs in the YAML
- 12 GB ai-toolkit recipe (Krea Raw): low VRAM on, 50% transformer offload, 100% text-encoder offload, convrot 8 quantization, compile off, LoKr factor 8, 512 resolution only, cache latents and text embeddings. First steps can take many minutes; if the run hangs after the first step, try dropping the training adapter, a lower quant (Q6/Q4), or skipping text-encoder offload once embeddings are cached
OneTrainer notes for Krea 2
- OneTrainer preview-2 branch (not master yet): LoRA on 8-bit quantized Krea 2 trains ~1.2x faster (16 GB preset); FLUX.2 16 GB preset ~1.3x; faster 8-bit math on RTX 50-series; DoRA not sped up
Which should you pick?
If you want the quickest start with a web UI, pick ai-toolkit. If you already use a script-based trainer's ecosystem, stick with it.
Shared Krea 2 settings
- Natural-language captions, ~70-500 words; shorter captions lock features harder
- Diverse dataset (varied expressions/angles)
- Train at 1024px or higher
- Batch size >= 2 if VRAM allows
- Training at 512px can work for output up to ~2MP; add higher-res buckets gradually
- ai-toolkit downscales images larger than 1536px
- Automagic3 can give weaker LoRAs than v1/v2; pair it with LoKr factor 8 or 4 and sigmoid timesteps (that's my recipe). ai-toolkit's experimental automagic4 (Oct 23) keeps the LR from drifting to the floor
- EMA in ai-toolkit makes no noticeable difference; Automagic v3 already behaves a lot like EMA
- OneTrainer preview-2 branch (not master yet): LoRA on 8-bit quantized Krea 2 trains ~1.2x faster (16 GB preset); FLUX.2 16 GB preset ~1.3x; faster 8-bit math on RTX 50-series; DoRA not sped up
- Character captions: name, expression, pose, clothing, then scene
- Kaloscope style adapters (heathcliff01) for Krea 2 Raw and Turbo: style by artist name, weighted blends or a reference image; needs its own ComfyUI nodes
- ai-toolkit: point to local or alternative Qwen3-VL text encoder and Qwen-Image VAE files with text_encoder_path and vae_path under model_kwargs in the YAML
- Very slow steps (hundreds of seconds per step) mean the GPU is out of VRAM and swapping; lower the batch size or turn on low VRAM / offloading
- Pose control for Krea 2 Turbo: the Krea-2-PoseControl LoRA (MIT, 17-keypoint skeleton) with the ComfyUI-Krea2-Pose node pack; use release/krea2-pose-control-mix025.safetensors
- 12 GB ai-toolkit recipe (Krea Raw): low VRAM on, 50% transformer offload, 100% text-encoder offload, convrot 8 quantization, compile off, LoKr factor 8, 512 resolution only, cache latents and text embeddings. First steps can take many minutes; if the run hangs after the first step, try dropping the training adapter, a lower quant (Q6/Q4), or skipping text-encoder offload once embeddings are cached
Setup guides
Where to rent a 16 GB+ GPU
Cheapest 16 GB+ cards (2026-10-20 08:06 UTC) from RunPod and Vast.ai. Prices move constantly; see the live price table.
| GPU | VRAM | RunPod community $/hr | Vast.ai cheapest $/hr |
|---|
| Tesla V100 | 16 GB | $0.19 | $0.08 |
| RTX 3090 | 24 GB | $0.22 | $0.12 |
| RTX 5060 Ti | 16 GB | — | $0.14 |
| Tesla V100S | 32 GB | — | $0.14 |
| RTX A5000 | 24 GB | $0.16 | — |
| RTX A4000 | 16 GB | $0.17 | — |
Sources
FAQ
Is ai-toolkit or OneTrainer better for Krea 2?
Both support it. Pick based on workflow (UI vs scripts) and the notes above.
Can I train Krea 2 LoRAs with ai-toolkit?
supported (Raw + Turbo, incl. edit training)
Can I train Krea 2 LoRAs with OneTrainer?
supported (Krea 2 16 GB preset benchmarked in OneTrainer PR #1680; not in README list)
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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