/train-character-lora

name: train-character-lora
description: Train a character/identity LoRA locally on FLUX.1-dev via the comfyui-mcp train_* tools (GPU Docker + ostris ai-toolkit). Use when the user wants to train a LoRA of a person/character from their photos on the local GPU. Covers dataset prep, launch, monitoring, and using the result in ComfyUI. For WAN/Z-Image training via the ai-toolkit UI see ai-toolkit-trainer.
globs:
  - "**/*.json"

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 through the three `train_*` MCP tools. You (the LLM) are the UI. Each takes an `action`: `train_prepare_dataset` owns the datasets, `train_start` owns the jobs, and `train_doctor` owns the trainer itself. You generate the dataset, launch the job, watch progress, and the finished LoRA lands in ComfyUI `models/loras/` and the LoRA catalog without further steps.

• Base model: FLUX.1-dev (the 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 {action:"doctor"}`. Preflight once per session. Checks docker daemon, `--gpus all` GPU passthrough, trainer image, HF_TOKEN. If `image:false`, run `train_doctor {action:"build_image"}` (one-time, several minutes, since it builds CUDA plus torch plus 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 {action:"prepare"}`. Stage the images. See "Dataset" below. 3. `train_start {action:"start"}`. Launch. Returns a job id at once; training runs detached. 4. `train_start {action:"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 and 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 {action:"prepare"}` with `name`, `items: [{path, caption?}, ...]` and a `defaultCaption`.

• 10 to 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 model learns the constant identity 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)

| Param | Default | When to change | |-------|---------|----------------| | steps | 2000 | 200 for a smoke test; 1500–3000 real runs. More ≠ better (overbake = plasticky). | | lr | 1e-4 | 5e-5 for a tighter/subtler identity. | | rank | 16 | 32 for very detailed characters. | | resolution | [512,768,1024] | [512] if VRAM-constrained. | | quantize | true | Keep true on 24GB. | | saveEvery / sampleEvery | 250 | Lower (100) to watch early progress. |

Monitoring & judgement

• `train_start {action:"status"}`'s `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_start {action:"cancel", id}`) and check captions and trigger.

• Loss should trend down and stabilize (~0.1 to 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 {action:"start"}`: run

`train_doctor {action:"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) plus latent caching. As

long as the log tail moves, it's fine. The HF cache persists across runs.

Sources

• **Official:** none found.
• **Empirical:** sampler values, wiring, and prompt notes from working graphs in `packs/` and observed renders; not a vendor prompting guide.