Z Image LoRA Trainer | Custom Model Training API

Z-Image LoRA Trainer

Z-Image LoRA Trainer is a high-performance custom model training service for the Z-Image-Turbo text-to-image generation models. It allows you to train lightweight LoRA (Low-Rank Adaptation) adapters for personalized styles, characters, and concepts, while preserving the fast and high-quality generation properties of Z-Image.

Training Architecture

The trainer is designed around Z-Image’s efficient diffusion architecture and its Turbo distilled variants, and produces one or more specialized LoRA adapters depending on your configuration:

Base LoRA adapter Trains on the core Z-Image representation to capture your target style, character, or object, while keeping the base model frozen and stable.
Turbo-aware fine-tuning (optional) When used with Z-Image-Turbo, the trainer applies Turbo-compatible optimization settings (step-aware learning rate, safe rank and scaling) to maintain high image quality even at low sampling steps.

This architecture ensures that your LoRA:

Remains compact and easy to share
Is plug-and-play with supported UIs and pipelines (e.g. ComfyUI / AI Toolkit)
Preserves the speed and efficiency of Z-Image / Z-Image-Turbo

Training Process

Data Upload Prepare and upload a ZIP file containing your training images and, optionally, captions or prompts.
Automatic Preprocessing The trainer automatically:

Validates and filters your dataset
Resizes and normalizes images for Z-Image
Aligns captions / prompts (or auto-generates basic captions if enabled)

LoRA Training The system runs a tailored LoRA optimization loop for Z-Image:

Freezes the base model weights
Trains only the low-rank adapter layers
Applies Turbo-safe settings when targeting Z-Image-Turbo

Model Export After training completes, you receive:

A LoRA adapter file compatible with Z-Image / Z-Image-Turbo
Recommended loading settings (weight scale, steps, and sampling tips) for image generation

Price

You pay $1.25 for every 1,000 training steps, billed proportionally to the total number of steps in your job.

Examples

Training steps	Price (USD)
1,000	$1.25
2,000	$2.50
5,000	$6.25
10,000	$12.50

Try more trainers

Wan 2.2 Image LoRA Trainer - High-performance LoRA trainer for the Wan 2.2 image model, ideal for custom styles and characters that integrate smoothly into the Wan video/image ecosystem.
Qwen Image LoRA Trainer - Built on the Qwen image model with strong multi-language prompt support and rich semantic understanding, great for text-sensitive image customization.
Flux Dev LoRA Trainer - LoRA trainer tailored for the Flux Dev model, focusing on high-fidelity, creative visuals and experimentation with new artistic styles.

Guidance

Z Image Lora Trainer API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image-lora-trainer with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Z Image Lora Trainer below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image-lora-trainer" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "trigger_word": "p3r5on",
    "steps": 1000,
    "learning_rate": 0.0001,
    "lora_rank": 16
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("wavespeed-ai/z-image-lora-trainer", {
        "trigger_word": "p3r5on",
        "steps": 1000,
        "learning_rate": 0.0001,
        "lora_rank": 16
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "wavespeed-ai/z-image-lora-trainer",
    {
    "trigger_word": "p3r5on",
    "steps": 1000,
    "learning_rate": 0.0001,
    "lora_rank": 16
}
)

print(output["outputs"][0])  # → URL of the generated output

Z Image Lora Trainer API — Frequently asked questions

What is the Z Image Lora Trainer API?

Z Image Lora Trainer is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Z-Image-LoRA-Trainer – train custom image LoRA models from your own dataset, with zip uploads, auto-tuned defaults and fast iteration for brand, character or IP looks. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Z Image Lora Trainer API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/z-image-lora-trainer.

How much does Z Image Lora Trainer cost per run?

Z Image Lora Trainer starts at $1.25 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Z Image Lora Trainer accept?

Key inputs: `data`, `learning_rate`, `lora_rank`, `steps`, `trigger_word`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/z-image-lora-trainer.

How long does Z Image Lora Trainer take to generate?

Average end-to-end generation time on WaveSpeedAI is around 749 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Z Image Lora Trainer outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

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