/ai-toolkit-trainer

name: ai-toolkit-trainer
description: Train custom LoRAs with ostris AI-Toolkit. Covers WAN 2.2/2.1 (people, styles, video motion) and Z-Image (Turbo & Base, low-VRAM image LoRAs). Use when the user wants to train a WAN or Z-Image LoRA; covers local + RunPod setup, dataset prep, key params, and using the result in a ComfyUI workflow.
globs:
  - "**/*.json"

AI-Toolkit LoRA Trainer (WAN 2.2 & Z-Image)

Overview

AI-Toolkit by ostris is an MIT-licensed trainer for finetuning diffusion models. It is a standalone trainer with its own web UI, not a ComfyUI custom node. It runs a Node.js UI front end over a Python (`run.py`) training backend and trains LoRAs for many model families. This skill covers the WAN 2.2 / 2.1 video models and Z-Image (Turbo & Base).

• Repo: `https://github.com/ostris/ai-toolkit` (cloned by the installers).
• Backend: `python run.py config/<job>.yml`. UI: a Node.js app under `ui/` that schedules and monitors jobs. You do not have to keep the UI open while a job runs.
• Output: a standard `.safetensors` LoRA you drop into ComfyUI `models/loras/` and load with `LoraLoaderModelOnly`.

Best for:

• WAN LoRAs. A person or character, an art style, or a specific camera or video motion, trained from image or video clip datasets. For *using* WAN see wan-t2v-video / wan-flf-video.
• Z-Image LoRAs. Fast, very low-VRAM image LoRAs (faces, characters, outfits, styles) on the 6B Z-Image base/turbo. For *using* Z-Image see z-image-base / z-image-turbo, and the z-image-xy-plot pack to compare trained LoRAs.

For low-VRAM anime image LoRAs on a different stack (kohya `sd-scripts`), see the sibling anima-lora-trainer.

> Two LoRA kinds for WAN. A WAN image LoRA trains on still images; it is cheaper (~24GB-class) and suits identity or style. A WAN video LoRA trains on short clips; it is heavier, best run on cloud, and suits *motion*. Z-Image is image-only.

Install

The installer comes in two generations. Both clone `ostris/ai-toolkit`, set up Torch for your GPU, and launch the web UI. Put it in a folder whose full path has no spaces (e.g. `C:\AI-Toolkit`).

• V1, `AI-TOOLKIT_AUTO_INSTALL.bat`, expects Git, Python 3.10.x, and Node 18+ already in PATH.
• V2, `AI-TOOLKIT_AUTO_INSTALL-V2.bat` (recommended), uses an embedded Python 3.10.11, auto-installs Git and Node, builds a clean PATH without your system Python, and adds aggressive pip/curl retries. It has far fewer prerequisites and fails less often. The Z-Image Turbo LoRA training release used it.

Both are CUDA-aware and select the Torch wheel by GPU generation:

| Choice | GPU | CUDA | Torch index | Torch packages | |--------|-----|------|-------------|----------------| | 1 | RTX 50-series (Blackwell) | **12.8** | `https://download.pytorch.org/whl/cu128` | `torch==2.7.0 torchvision==0.22.0` | | 2 | RTX 40 / 30 / 20 and older | **12.6** | `https://download.pytorch.org/whl/cu126` | `torch==2.7.0 torchvision==0.22.0` |

Each then clones `ostris/ai-toolkit`, downloads two launcher scripts (`LAUNCHER-TOOLKIT.bat`, `SECURE_LAUNCHER-TOOLKIT.bat`, from `https://huggingface.co/Aitrepreneur/FLX/resolve/main/`), makes the venv, installs Torch from the chosen index, runs `pip install -r requirements.txt`, then `cd ui && npm run build_and_start`.

RunPod / Linux — `AI-TOOLKIT_AUTO_INSTALL-RUNPOD.sh` (and `-V2.sh`)

Installs into the persistent volume `/workspace/ai-toolkit`. It is idempotent; a re-run just relaunches the UI. Use RunPod's PyTorch 2.8.0 template and a 100GB disk. It installs apt deps, clones the repo, makes a venv, installs Torch (`torchaudio` included), installs nvm + Node 22, then builds and starts the UI.

| Choice | GPU | Stream | Torch spec | |--------|-----|--------|-----------| | 1 | RTX 5000-series (Blackwell) | `cu128` | `torch==2.7.0+cu128 torchvision==0.22.0+cu128 torchaudio==2.7.0+cu128` | | 2 | Ada / Hopper / Ampere, older | `cu126` | `torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0` |

The UI listens on 8675 and Jupyter on 8888. Set `AI_TOOLKIT_AUTH` (UI password) before launch. Reach it at `https://${RUNPOD_POD_ID}-8675.proxy.runpod.net`. Use an RTX 4090/5090 for image (WAN t2i/t2v, Z-Image) LoRAs and an RTX 6000 Pro (Blackwell) for heavy WAN video, high-res, or high-rank jobs.

Launching the web UI

• On Windows, run `LAUNCHER-TOOLKIT.bat` (local) or `SECURE_LAUNCHER-TOOLKIT.bat` (password-protected) from the `ai-toolkit` folder.
• On RunPod, rerun the `.sh`. It detects the install and starts the UI on :8675.

In the UI, create a Job, point it at a dataset folder, pick the model (WAN variant or Z-Image), set params, and start. Jobs run in the Python backend, so you can close the browser. To bypass the UI, copy a `config/examples/*.yml`, edit it, and run `python run.py config/<job>.yml`.

Dataset preparation

AI-Toolkit pairs each sample with a same-basename `.txt` caption and auto-resizes/buckets aspect ratios (no pre-cropping).

Image LoRA (WAN identity/style, or Z-Image)

my_dataset/
  001.png  001.txt
  002.jpg  002.txt

• Captions are natural language. Include a unique trigger word for a person or character.
• Use about 15 to 40 varied images for a person, more for a broad style.

Video LoRA (WAN motion only)

Short clips plus a `.txt` per clip; caption the motion or camera move. Set per-clip frames via the job's `num_frames` (e.g. 81). This is markedly heavier, so prefer cloud GPUs.

Key training params

WAN 2.2

WAN 2.2 14B is a Mixture-of-Experts with a high-noise expert (structure/motion) and a low-noise expert (detail). AI-Toolkit trains both via Multi-stage.

| Param | Default | Notes | |-------|---------|-------| | Linear rank / dim | **16** | 16 simple; 16–32 complex/cinematic | | Learning rate | **5e-5** (identity) | 7e-5–1e-4 style; high LR → plasticky skin | | Steps | **1500–2500** | stop before overbaking | | Resolution | **512** (or 768) | bucketed; 768 costs more VRAM | | `num_frames` (video) | **81** | per-clip frame count | | Multi-stage | **High + L