I present **LoRA Dataset Studio** - free, open source, self-hosted, no account and no telemetry. It trains FLUX.1 and FLUX.2 Klein LoRAs (plus Z-Image, Krea 2, SDXL, Anima) through [ai-toolkit](https://github.com/ostris/ai-toolkit): it is not a competitor to it, it **orchestrates** it - ai-toolkit is the trainer; this is everything before, around and after the run.

The whole pipeline lives in one browser tab:

**1. Decide what you are teaching.** A dataset is a **Character**, a **Concept** or a **Style**, and the choice changes real behaviour downstream: what the captions must leave implicit, whether person masks apply, what the readiness checks look for. A Character also picks a subject type (human, animal, creature, object, anime) that swaps the shot catalog and the identity protections.

**2. Fill it with images.** Five generation engines - Nano Banana Pro, gpt-image-2, OpenRouter, and local Klein / Krea 2 Edit through ComfyUI - each card stating its price per image and whether it runs on your GPU or bills an API. Or scrape a gallery URL. Or point the Image Bank at a folder of thousands: it reads it *in place* - your files are never modified, moved or renamed - and one pass measures blur, noise, near-duplicates, face clusters, framing, aesthetic and maturity, so you filter on measurements instead of on your eyes.

**3. Curate down to the keepers.** Keep/reject, crop, mirror, rotate, upscale candidates reviewed against the original, InsightFace similarity against your reference, a live composition meter. New this month: press the camera button on any kept image and **re-shoot the same scene from another camera position** - the subject stays put, the background moves with the camera, and the new view arrives with its angle already captioned (the one fact a vision model cannot reliably see, and that you know exactly because you asked for it).

**4. Caption for the model.** Prose or booru depending on the target family, written by JoyCaption or your local Ollama, with vocabulary and length dials, identity-leak checks, a Caption Lab to compare configurations before committing, and an external `.txt` round trip so you can caption elsewhere and come back.

**5. Scrub watermarks - and burned-in text.** Detect watermark boxes, redraw them, then crop or inpaint with LaMa/Klein. And since a comic page carries its dialogue and a screencap its subtitle, a CPU-only OCR pass now reads **burned-in lettering** (Latin or CJK) and feeds the same repaint funnel - with an outline-safe filler so speech bubbles keep their borders. Every edit keeps an `.orig` backup; Restore original always works.

**6. Train.** ai-toolkit locally with family-scoped presets and preflight guards - Z-Image, Krea 2, FLUX.1, FLUX.2 Klein, SDXL, Anima - or rent a vast.ai pod from the same screen, which shows the GPU, its hourly price and the estimated total *before* you click. The whole studio can also run on a rented RunPod box (contributed by a user). Generations queue instead of blocking each other, and a dock shows what the GPU is doing.

**7. Decide which checkpoint is actually good.** Test Studio runs fixed-seed checkpoint x strength grids, multi-LoRA stacks (including a downloaded LoRA next to yours, same prompt and seed), votes and Wilson ranking. The lineage graph keeps every run's frozen recipe and can diff two runs - settings AND dataset. A Gallery collects every image the app ever generated, and every render is stamped with what actually made it.

**8. Take it with you.** Standard ai-toolkit/Kohya layout ZIP, portable backup with the full history, Hugging Face publishing, or deploy the checkpoint straight into ComfyUI. Nothing locks your data in.

**Honest limits.** It is a lot of surface, so Setup exists to tell you what is missing instead of crashing - every capability degrades on its own. Local generation needs ComfyUI, the API engines need your own keys and bill you, and the video lane (cutting long footage into trainable clip folders for Wan/LTX) is young. Install is a Windows one-click ZIP, a git checkout, or Docker.

GitHub - install, docs, and a 7-minute unedited video of a full character LoRA built end to end: https://github.com/perfectgf/lora-dataset-studio

*Every person in these screenshots was generated by the app's own engines; no real individual is depicted.*


I present LoRA Dataset Studio - a free, self-hosted app that does everything around a LoRA run: dataset, triage, captions, training (local or rented GPU), then checkpoint comparison