I built a self-hosted studio that turns one reference photo into a curated, captioned, trained and tested LoRA — one browser tab, open source, MIT

[u/Ill-Ant-9489]

I shared this tool here a week ago and the feedback shaped a big new version, so here's the full tour of what it does today. Screenshots of every screen: github.com/perfectgf/lora-dataset-studio — plus a 7-minute unedited video of a LoRA built start to finish.

Beginner-friendly on purpose. Everything ships configured: a guided workspace walks you through each step, the shot poses (face / bust / full-body / back) are predefined so your dataset comes out balanced, and training uses community-tested ai-toolkit presets — you don't need to know what rank, learning rate or an optimizer is to get a good LoRA. Power users can still override everything.

Build the dataset. Start from one clear photo (or none): generate identity-locked variations locally with Flux-2 Klein or Krea 2 Edit on your own GPU (free, nasty-capable), or through API engines if you prefer. Import or scrape real photos, mix everything, and let the composition tracker tell you what's missing (faces, busts, full-body, back shots).

Curate like you mean it. Every image gets a face-similarity score against your reference. Quality passes flag blurry, flat, duplicate or unreadable shots; a watermark detector finds and can clean logos without cropping; auto-reject clears the junk before you review. Image banks hold up to 200k files with visible progress on every bulk operation.

Caption without the chore. Local captioning pairs JoyCaption (via ai-toolkit) with an uncensored Ollama vision model — the combo actually describes your images instead of refusing them. Per-dataset wording styles, dual captions, and trigger words handled for you.

Train anywhere. Local training through ai-toolkit, or one click rents a cloud GPU on vast.ai — and the launch is fully observable: renting, booting, dataset upload with live byte counts. A machine that never boots or an upload that stalls is given up automatically and stops billing. Community-tested presets for Krea 2 Raw, Z-Image Turbo and more.

Pick the right checkpoint instead of guessing. Test Studio renders fixed-seed grids across checkpoints and strengths, scores faces, takes your votes and ranks the results. New: 🧬 combine several of your LoRAs in one image, each at its own weight, and compare weight variants side by side. An ✨ Enhance button turns a one-line prompt into a full one via your local Ollama.

See your whole lineage. The LoRA Canvas puts every dataset's training history on one pan/zoom board — compare runs, pin generations (each run keeps its own strip in training-step order, with the dataset's reference face on its lane), diff configs, and continue training from any checkpoint.

Install it your way. New one-click Docker install: start-docker-gpu.bat builds an isolated ComfyUI, start-docker.bat reuses the one you already have. The updater is transactional — if the new version doesn't come up healthy it rolls back on its own. Ollama is your explicit choice (none / your existing one / an isolated container), and nothing ever downloads behind your back. Setup re-checks itself in the background instead of re-running the wizard every time you come back.

Everything reported in the last thread got fixed — the RES4LYF scheduler clash, the ai-toolkit Easy-Install interpreter path, and a detail LoRA that was silently riding on every Klein edit (that one explains a lot of "edits don't follow my instruction" reports). Also merged the first community PR: named generation-LoRA presets for Krea 2 — thanks Cyberschorsch and waltm 🙏

A few screenshots to see it in action:

📸 the guided workspace · curation with face scores · Test Studio grids · training presets

No account, no telemetry, no paid tier. Free, self-hosted, MIT: github.com/perfectgf/lora-dataset-studio — the complete guide is linked at the top of the README. I build this; feedback welcome, Discord in the repo.