Setting up ai-toolkit to train a Krea 2 LoRA: support status, the settings known to work, and links to the official docs. Status in ai-toolkit: supported (Raw + Turbo, incl. edit training).
Ostris' trainer with a web UI and YAML configs; widest day-0 support for new image models. Community threads call it the easiest option for Flux/Krea. Sources: github.com/ostris/ai-toolkit, www.reddit.com/r/StableDiffusion/comments/1vrkd5x/which_platform_shoul
| Repo | ostris/ai-toolkit |
|---|---|
| GitHub stars | 12321 |
| Last commit | 2026-10-16 |
| Latest release | none published |
| Trainer | ai-toolkit |
|---|---|
| Base model | krea/Krea-2-Raw (arch krea2) |
| Network | LoKr: linear 32, alpha 32, full rank on, factor 8 |
| Optimizer / LR | automagic3, 1e-4 |
| Weight decay | 1e-5 (0.00001) |
| Schedule | flowmatch scheduler, sigmoid timesteps, content_or_style balanced |
| Differential guidance | On, scale 3 |
| Batch | 1, grad accumulation 1 |
| Memory | gradient checkpointing, bf16, qfloat8 model + text encoder, low_vram, cached latents and text embeddings |
| Resolution | 1024 |
| Captions | dropout 0.05; text encoder not trained; trigger word optional |
| Epochs | About 160 epochs (my best checkpoint: ~162 epochs on a 34-image dataset; batch 1, repeats 1, grad accumulation 1) |
| Samples | guidance 4, 30 steps |
| Note | Results may vary; the right epoch count depends on your dataset size and content. |
# Example ai-toolkit config (generic hyperparameters only)
config:
process:
- type: sd_trainer
network:
type: lokr
linear: 32
linear_alpha: 32
lokr_full_rank: true
lokr_factor: 8
train:
batch_size: 1
gradient_accumulation_steps: 1
steps: 5500 # steps = epochs x images (e.g. 162 x 34 ≈ 5,500); ~160 epochs worked best for me
train_text_encoder: false
gradient_checkpointing: true
noise_scheduler: flowmatch
timestep_type: sigmoid
content_or_style: balanced
optimizer: automagic3
lr: 1e-4
optimizer_params:
weight_decay: 1e-5 # 0.00001
dtype: bf16
cache_text_embeddings: true
do_differential_guidance: true
differential_guidance_scale: 3
datasets:
- caption_dropout_rate: 0.05
cache_latents_to_disk: true
resolution: [1024]
model:
name_or_path: krea/Krea-2-Raw
arch: krea2
quantize: true
qtype: qfloat8
quantize_te: true
qtype_te: qfloat8
low_vram: true
sample:
guidance_scale: 4
sample_steps: 30
# trigger_word: optional / not neededNot published by trainers. musubi: 12GB+ recommended for image training (general guidance); fp8 or --convrot_int8 halves weight VRAM
Cheapest 16 GB+ cards (2026-10-16 14:43 UTC) from RunPod and Vast.ai. Prices move constantly; see the live price table.
| GPU | VRAM | RunPod community $/hr | Vast.ai cheapest $/hr |
|---|---|---|---|
| RTX A4000 | 16 GB | $0.17 | $0.09 |
| Tesla V100 | 16 GB | $0.19 | $0.10 |
| RTX 3090 | 24 GB | $0.22 | $0.12 |
| RTX 5060 Ti | 16 GB | — | $0.12 |
| RTX 5070 Ti | 16 GB | — | $0.12 |
| RTX A5000 | 24 GB | $0.16 | — |
Yes: supported (Raw + Turbo, incl. edit training) (see sources below).
Not published by trainers. musubi: 12GB+ recommended for image training (general guidance); fp8 or --convrot_int8 halves weight VRAM
Data updated 2026-10-16. Disclosure: RunPod and Vast.ai links are referral links; ImageLoRA may earn a commission at no cost to you. unknown = not yet verified from a source.