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artokun/nic0le-zimage
nic0le-zimage is a text-to-image model from artokun. Use it when you need an image from a text prompt. It is set up for diffusers.
Character LoRA for Z-Image Turbo. Trigger word: nic0le.
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From the Hugging Face model README
Character LoRA for Z-Image Turbo. Trigger word: nic0le.
Trained with ostris/ai-toolkit.
Companion to artokun/nic0le-krea2, which is the same subject trained on Krea 2 Raw. They target different base models and are not interchangeable.
| File | Steps |
|---|---|
nic0le_zt_000002000.safetensors | 2000 |
nic0le_zt_000002250.safetensors | 2250 |
nic0le_zt_000002500.safetensors | 2500 |
nic0le_zt_000002750.safetensors | 2750 |
nic0le_zt.safetensors | 3000 (final) |
| Base | Tongyi-MAI/Z-Image-Turbo (arch zimage:turbo, qfloat8 quantized for training) |
| Network | LoRA, linear 32 / alpha 32 |
| Steps | 3000, batch size 1 |
| Optimizer | adamw8bit, lr 1e-4, constant |
| Scheduler | flowmatch, linear timesteps |
| Dataset | 19 images at 1024px, captioned with nic0le as the leading token |
UNETLoader z_image_turbo_bf16.safetensors → LoraLoaderModelOnly (this LoRA) → KSampler
CLIPLoader qwen_3_4b.safetensors type=qwen_image
VAELoader ae.safetensors (the Z-Image / Flux-style AE, NOT the Qwen image VAE)
Z-Image Turbo is distilled: ~6 steps at cfg 1, res_multistep / simple.
Higher cfg over-cooks it.
Beyond plain text-to-image, this works well driving a second-pass refiner or an
Impact-Pack detailer over output from another model. Without a character LoRA on
the refine pass, DetailerForEach re-renders the face with a model that has never
seen the subject, and the likeness drifts. Loading this on the refiner's model
input keeps identity through the detail pass.
If you chain Z-Image with another large model (Krea 2, Flux, etc.) in a single
graph, free VRAM between model loads. Loading a second family alongside the first
silently corrupted the second model's weights in our testing — sampling to NaN,
producing uniform black output with no error in the log. A /free call before
the run fixed it completely.