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Z-Image LoRA training with OneTrainer: setup guide

Setting up OneTrainer to train a Z-Image LoRA: support status, the settings known to work, and links to the docs. Status in OneTrainer: supported.

About OneTrainer

GUI + CLI trainer with a broad model list; good VRAM use and speed, but more settings to configure. Sources: github.com/nerogar/OneTrainer, sanj.dev/post/onetrainer-vs-kohya-ss-vs-ai-toolkit/

VRAM

Not published. musubi: 12GB+ recommended for image training (general); --fp8_llm saves VRAM

Settings that work for Z-Image (any trainer)

• diffusers v0.42.0 (Oct 23, PR #14163) loads LoKr adapters for Z-Image

General tips

• Gated Hugging Face models: click 'Agree and access' on each model page, or you'll get 403 errors
• Steps scale with dataset size (steps = epochs x images at batch 1)
• A safe start: LoKr with auto rank (~64), batch/grad accumulation 4
• Leave out of your captions whatever you want burned into the LoRA; caption what should stay changeable
• ai-toolkit dataset folder weights let you train the same images with long, medium and short captions in separate folders
• ai-toolkit supports validation loss, which shows when a run stops improving
• In ai-toolkit, Cache text embeddings works for basically every model and frees the text encoder from VRAM and RAM
• Prefer int8 convrot over qfloat8 quantization in ai-toolkit: better quality at the same memory
• Gradient accumulation and batch size don't scale the learning rate in ai-toolkit; a bigger batch needs about 1.5-2.5x the steps

Compare with other trainers

• ai-toolkit vs OneTrainer for Z-Image
• OneTrainer vs musubi-tuner for Z-Image
• OneTrainer vs SimpleTuner for Z-Image
• Z-Image LoRA with ai-toolkit
• Z-Image LoRA with musubi-tuner
• Z-Image LoRA with SimpleTuner
• Z-Image (overview)

Where to rent a 16 GB+ GPU

Cheapest 16 GB+ cards (2026-10-20 08:06 UTC) from RunPod and Vast.ai. Prices move constantly; see the live price table.

Sources

• github.com/nerogar/OneTrainer
• www.reddit.com/r/StableDiffusion/comments/1vrkd5x/which_platform_shoul
• github.com/ostris/ai-toolkit#readme
• github.com/kohya-ss/musubi-tuner#readme
• github.com/kohya-ss/musubi-tuner/blob/main/docs/zimage.md
• github.com/bghira/SimpleTuner#readme
• github.com/huggingface/diffusers/pull/14163
• sanj.dev/post/onetrainer-vs-kohya-ss-vs-ai-toolkit/

FAQ

Does OneTrainer support Z-Image?

Yes: supported (see sources below).

How much VRAM do I need for a Z-Image LoRA?

Not published. musubi: 12GB+ recommended for image training (general); --fp8_llm saves VRAM

Data updated 2026-10-20. Disclosure: RunPod and Vast.ai links are referral links; ImageLoRA may earn a commission at no cost to you. unknown = no data yet. · Raw data (JSON) · llms.txt