git:20260806.68e5efc to git:20260819.9fe9ea3

---

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 entirely 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/` + 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 {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 — 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 {action:"prepare"}`** — stage the images. See "Dataset" below.

3. **`train_start {action:"start"}`** — launch. Returns a job id immediately; 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 + 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`.

- **10–30 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)

| 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/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 {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) + latent caching. As

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