multimodalart/krea2-lora-trainer

Krea 2 LoRA Trainer

Train a DreamBooth-LoRA for Krea 2 from your own images, entirely on Hugging Face infrastructure:

• —Sign in with Hugging Face — the dataset, the job, and the pushed LoRA all run under your account and billing (no pasted tokens);
• —the Space (this app, cpu-basic) collects your images + hyperparameters and submits a job;
• —training runs on HF Jobs using the diffusers Krea 2 trainer (examples/dreambooth/train_dreambooth_lora_krea2.py);
• —the LoRA is trained on Krea 2 RAW and validated / inferred on Krea 2 Turbo, then pushed to the Hub model repo you choose.

You only pay for the Job's actual GPU runtime.

How tokens are used

Three tokens, three jobs:

• —Your OAuth token (from sign-in) — your dataset repo + the pushed LoRA, under your account/billing.
• —`KREA_TOKEN` secret — downloads the gated Krea 2 weights inside the job and passes them to the trainer as local dirs, so your own token never needs Krea access and the Krea token never touches your repos.
• —`CAPTION_HF_TOKEN` secret — calls the Inference API for AI captioning on this Space only (google/gemma-4-31B-it, served with vision via the novita provider).

Set KREA_TOKEN to a token with access to krea/Krea-2-Raw + krea/Krea-2-Turbo, and CAPTION_HF_TOKEN to any token that can call Inference Providers.

Captioning

Pick whether you're training a style or an object/character — this drives both the suggested trigger and how images are captioned:

• —Style — captions describe only the content (subjects, layout, setting) and end with your style trigger phrase (e.g. heavy impasto style), so the model learns the look, not the subjects.
• —Object/character — captions describe the scene and tag the subject with a unique trigger token (e.g. b3@rcup).

✨ Suggest proposes a trigger from 2–3 of your images; ✨ Add AI captions fills every caption. Everything is editable; blank captions fall back to the trigger.

Preview gallery & README

After training, the job renders a few sample images on Krea 2 Turbo with your LoRA and pushes a model-card README to the LoRA repo where each image is captioned by its prompt. The showcase prompts are written by the LLM from your concept + trigger (or you can supply your own, one per line, using <trigger> as a placeholder). The trainer's own validation is skipped in favour of this.

diffusers version

The trainer lives in diffusers PR #14046 (branch krea2-lora). Once it is merged, set the DIFFUSERS_REF Space variable to main (or a release tag).

Usage

1. 1.Sign in with Hugging Face.
2. 2.Name your LoRA and pick what you're training — a style or an object/character.
3. 3.Upload 4–30 images, ✨ Suggest a trigger, and ✨ Add AI captions (edit anything).
4. 4.Tweak hyperparameters if you like, choose how many preview samples to render, pick a GPU flavor, and Submit training job.
5. 5.Copy the job id into the Monitor tab and Refresh to stream logs. When it finishes, the LoRA repo has the weights, a preview gallery, and a rich README.