AI Toolkit Trainer

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:

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.

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.