A LoRA is a small add-on that teaches a base model a specific character, product, or style without retraining the whole thing. Krea 2 supports LoRAs well — but training one has a few Krea-2-specific rules that, if you miss them, produce a file that looks fine and does nothing. Here’s how to get it right.
Rule #0: Krea 2 LoRAs are architecture-specific
This trips up almost everyone. A LoRA trained on Krea 2 only works on Krea 2. Load a Krea 2 LoRA on a non-Krea-2 model (or the reverse) and it is ignored silently — the output looks identical to no LoRA at all, and the logs quietly say the LoRA keys weren’t loaded. If a LoRA “does nothing,” the cause is almost always the wrong base model or a truncated download, not a broken file. Check the base architecture first, and verify the file’s checksum/size.
Dataset: quality and size
- Target ~100 images. Smaller sets (say 40–50) can work but tend to produce a weaker, less consistent identity. Around 100 varied images is the sweet spot for a character.
- Vary everything except the subject — different outfits, poses, lighting, backgrounds, distances. You want the model to learn the subject, not a single photo’s context.
- Consistent subject, inconsistent surroundings is the goal.
Captions: prose, not tags (this matters on Krea 2)
Because Krea 2’s text encoder is a language model, caption your dataset in natural sentences, not comma tags. For a character LoRA:
- Start every caption with your trigger word (e.g.
mychar, ...). - Describe outfit, pose, lighting, background in plain prose.
- Do not describe the face or fixed features. You want those baked into the trigger word, not tied to caption words — describing them teaches the model to only produce the face when those words appear.
For a style LoRA, describe the content normally and let the consistent aesthetic across the set carry the style.
Training settings that work
A reliable Krea 2 Turbo LoRA recipe:
- Architecture:
krea2:turbo, with the Krea 2 Turbo training adapter loaded (training won’t produce a usable Krea 2 LoRA without it). - Rank / alpha: 16 / 16, training the transformer only.
- Optimizer / LR: AdamW 8-bit, learning rate ~1e-4, batch size 1.
- Precision: bf16 with qfloat8 quantisation. (Note: Krea 2 has no low-bit training path — don’t reach for 3/4-bit training quantisation here.)
- Resolution: multi-resolution buckets, e.g. 512 / 768 / 1024.
- Regularisation: ~5% caption dropout; cache the text embeddings to speed up training.
- Checkpoints: save every epoch with fixed-seed sample images so you can compare.
Epochs: aim for 20–30. Krea 2 identity LoRAs overfit if you push much past that — the model starts reproducing dataset artifacts instead of generalising. The best practice is to checkpoint every epoch and pick the winner by eye, comparing samples generated at the same prompt and seed across epochs. Tooling-wise, both ai-toolkit (headless) and OneTrainer (which ships a krea2 preset) handle this well.
Using the LoRA
Once trained, put the trigger word at the very start of the prompt, then describe the scene in prose. If you’re also using body or style sliders, keep them near neutral when a strong character LoRA is active — the LoRA already knows the subject, and stacking too many competing LoRAs muddies the identity.
The shortcut: train a Krea 2 LoRA on AIFLUX
If you’d rather skip the adapter, the CUDA setup, and the epoch-picking, AIFLUX trains Krea 2 LoRAs for you. You upload a photo set, it auto-captions and trains on a cloud GPU with the recipe above, then hands you an epoch-selection grid — same-seed previews for each checkpoint — so you pick the best version. The finished model drops straight into the generator.
👉 Train a Krea 2 model on AIFLUX
Frequently asked questions
Why does my Krea 2 LoRA do nothing? Almost always the wrong base model (Krea 2 LoRAs only work on Krea 2) or a truncated download. Verify the architecture and the file checksum.
How many images do I need to train a Krea 2 LoRA? Around 100 for a solid character. Fewer can work but yield a weaker identity.
How many epochs should I train? 20–30. Checkpoint every epoch and pick the best by comparing same-seed samples; more epochs usually overfit.
Should captions be tags or sentences? Sentences. Krea 2’s encoder is an LLM, so prose captions (starting with the trigger word) train better than tag lists.
Can I train a Krea 2 LoRA without a GPU? Yes — AIFLUX runs the training on cloud GPUs and returns an epoch picker so you choose the best checkpoint.
Train your own Krea 2 model: Start on AIFLUX →