--- 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/.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/.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 + Low = ON** | trains both experts | | Switch Every | **10** | raise to 20–50 if offload swapping is slow | | Optimizer / Quant | AdamW8bit / 4-bit ARA or float8 | fits 14B on consumer cards | ### Z-Image (Turbo & Base) Z-Image is a ~6B single-stream model with no hi/lo multi-stage. Leave Multi-stage OFF; you train one model. It is the lightest target here. The headline of the Z-Image releases is training on very low VRAM. | Param | Starting point | Notes | |-------|----------------|-------| | Linear rank / dim | **16–32** | 32 for detailed characters/styles | | Learning rate | **1e-4** | lower (5e-5) for tighter identity | | Steps | **1500–3000** | dataset-dependent | | Resolution | **768** (or 1024) | Z-Image's native range | | Multi-stage | **OFF** | single-stream model, not WAN's MoE | | Optimizer / Quant | AdamW8bit / float8 | enables sub-12GB training | > Train on Base, deploy anywhere. Z-Image Base is the finetuning-friendly model; a LoRA trained on Base generally applies to the Turbo workflow too. Use the z-image-xy-plot pack to grid-compare your trained LoRAs. > The param tables are aggregated starting points from community and training-guide sources, not read from the repo's `config/examples/*.yml`. Open the actual WAN / Z-Image example config in your clone and tune. See "Unverified". ## VRAM / GPU guidance - Z-Image image LoRA is the lightest. It trains on modest consumer GPUs with quantization (the releases describe very-low-VRAM training); a 4090 is comfortable, and smaller cards work with float8 at 512 to 768 res. - WAN image LoRA (t2i/t2v) needs 24GB+ locally with quantization. Below that, use RunPod. - WAN video LoRA, high res, or high rank is heavier. Use cloud (RTX 5090, or RTX 6000 Pro Blackwell / H100). - Memory savers: quantization, batch size 1, 512 res, and (WAN) raising Switch Every. ## Using the trained LoRA in ComfyUI 1. Copy `.safetensors` into ComfyUI `models/loras/`. 2. Load with `LoraLoaderModelOnly`: - WAN 2.2 is dual hi/lo. Apply the LoRA to both the HighNoise and LowNoise model branches (like lightning/concept LoRAs in wan-t2v-video). Typical strength 0.5 to 1.0. - Z-Image is a single model. Use one `LoraLoaderModelOnly` on the Z-Image model path (see the z-image-base / z-image-turbo packs). Strength 0.7 to 1.0. ```json { "class_type": "LoraLoaderModelOnly", "inputs": { "model": ["", 0], "lora_name": ".safetensors", "strength_model": 1.0 } } ``` 3. Prompt using the trigger word or caption style you trained with. For WAN motion LoRAs, describe the same camera or motion. ## Troubleshooting - **`No module named 'torchaudio'` when starting a job (AI-Toolkit).** The venv's Torch stack is mismatched. Activate the AI-Toolkit venv (`venv\Scripts\activate`), then `pip uninstall torch torchaudio torchvision -y` and `pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` (or your CUDA's index). This only affects the AI-Toolkit install, not ComfyUI. - **`self and mat2 must have the same dtype` (ComfyUI-WanVideoWrapper, WAN usage).** Re-clone `ComfyUI-WanVideoWrapper` in `custom_nodes/` and reinstall its `requirements.txt`, then restart ComfyUI. - **5000-series (Blackwell) onnxruntime "QuickGelu" / CUDA error.** `pip install onnxruntime==1.20.1` in the affected venv. - **Pascal/Maxwell GPUs (GTX 9xx/10xx).** Recent Torch (cu128/cu130) dropped them. Reinstall the cu126 Torch build into the venv. - **Path with spaces (Windows).** Keep the install path space-free or the build/launch fails. - **OOM during training.** Quantization (4-bit ARA / float8), 512 res, batch 1, (WAN) raise Switch Every, or a bigger RunPod GPU. - **RunPod UI won't load / asks for a password.** Confirm `AI_TOOLKIT_AUTH` is set and you're on the 8675 proxy URL. ## Unverified / verify before relying - The param tables (both WAN and Z-Image) are synthesized starting points, not read from the repo's `config/examples/*.yml`. Open the actual example config in your clone and adjust. - The release notes describe the Z-Image training VRAM floor only qualitatively ("very low VRAM"). Confirm against your card; quantization plus 512 to 768 res is the lever. - The Windows UI port is whatever the launcher binds (the installer doesn't print it; check the launcher window). RunPod 8675/8888 are per the template. - The launcher `.bat` files are downloaded from a third-party HuggingFace repo (`Aitrepreneur/FLX`); review before running on a security-sensitive machine. - Model weights are fetched at job time by AI-Toolkit/HF, not by the installer. Confirm the model selector lists your target WAN variant or Z-Image model before a long run. ## Sources - **Official:** https://github.com/ostris/ai-toolkit - **Empirical:** Windows/RunPod installer steps and VRAM notes from the pack installers, not the vendor's training guide.