Train Character Lora

Train a Character LoRA (local, Flux.1-dev)

Overview

The trainer runs ostris ai-toolkit's run.py inside a headless GPU Docker container, driven entirely through train_* MCP tools — you (the LLM) are the UI. You generate the dataset, launch the job, watch progress, and the finished LoRA lands in ComfyUI models/loras/ + the LoRA catalog automatically.

• Base model: FLUX.1-dev (best proven character consistency; needs ~24GB VRAM with quantization — RTX 4090 class).
• Phase-1 scope: character LoRAs only. Style/slider/edit and other bases come later.

The flow (tool sequence)

1. train_doctor — preflight once per session. Checks docker daemon, --gpus all GPU passthrough, trainer image, HF_TOKEN. If image:false → run train_build_image (one-time, several minutes — CUDA + torch + ai-toolkit). If hfTokenSet:false, warn the user: the first run downloads FLUX.1-dev (gated HF repo) and needs HF_TOKEN in the MCP server env.
2. train_prepare_dataset — stage the images. See "Dataset" below.
3. train_start — launch. Returns a job id immediately; training runs detached.
4. train_status {id} — poll progress (progress.step/totalSteps/loss, recent samples, log tail). Poll on a slow cadence (every few minutes) — a 2000-step run is roughly an hour on a 4090. Don't block on it.
5. Done — status:"completed" means the .safetensors was copied to models/loras/<name>.safetensors and upserted into the LoRA catalog (result has the paths + catalog id). Verify by loading it in a Flux workflow (LoraLoaderModelOnly, strength 1.0) with the trigger word in the prompt.

Dataset guidance

Call train_prepare_dataset with items: [{path, caption?}, ...] and a defaultCaption.

• 10–30 varied images of the subject: different angles, expressions, lighting, backgrounds, distances (close-up + half-body + full-body). Variety beats count.
• Trigger word: pick something rare and stable (e.g. ohwx, zxc_person) — NOT a real word. Use it as defaultCaption and pass it as trigger to train_start.
• Captions: describe what changes between images (pose, setting, clothing, expression); the constant identity is learned from the images themselves. Start each caption with the trigger word, e.g. ohwx person sitting in a cafe, laughing, natural light. Keep them short and factual. When in doubt, the trigger word alone (defaultCaption) is a workable baseline.
• Images are copied and renamed img_00001.<ext> etc. — source files are never modified.

Params (sane defaults — override sparingly)

Monitoring & judgement

• train_status.progress.samples are host paths — look at them. (ai-toolkit prints no saved-sample lines, so they populate at finalize from the output dir; mid-run you can look directly in the job's output/<name>/samples/ folder.) Identity should be recognizable by ~1/3 of the run; if samples stay generic past halfway, the run will likely underfit — cancel (train_cancel) and check captions/trigger.
• Loss should trend down and stabilize (~0.1–0.3); wild spikes usually mean lr too high.
• Checkpoints save every saveEvery steps under the job's output/ dir, so a cancelled run isn't a total loss.

Failure modes

• no_docker / no_image from train_start → run train_doctor, follow its hints.
• OOM / CUDA errors in the log tail → drop resolution to [512], keep quantize:true, batch stays 1.
• handoff failed in job error → training itself finished; the LoRA is still under the job's output/<name>/ dir — copy it into models/loras/ manually and upsert the catalog.
• First run is slow before step 1 — FLUX.1-dev download (~24GB) + latent caching. As long as the log tail moves, it's fine. The HF cache persists across runs.