Z Image Base LoRA Trainer | Custom Model Training API

Z-Image Base LoRA Trainer

Z-Image Base LoRA Trainer is a high-performance custom model training service for the Z-Image text-to-image generation model. It allows you to train lightweight LoRA (Low-Rank Adaptation) adapters for personalized styles, characters, and concepts — bringing your custom visuals into AI-generated images.

Why Choose This?

Efficient training Train custom adapters specifically optimized for Z-Image's fast diffusion architecture.
Compact and portable Produces lightweight LoRA files that are easy to share and deploy.
Plug-and-play compatibility Trained LoRAs work directly with Z-Image Base LoRA and Z-Image Turbo LoRA models.
Preserves base model speed Your custom styles inherit Z-Image's fast generation capabilities.

Training Process

Data Upload Prepare and upload a ZIP file containing your training images. Include 10-20 high-quality, diverse images for best results.
Configure Trigger Word Set a unique trigger word (e.g., "p3r5on") that will activate your trained style or character in prompts.
Adjust Training Parameters

steps — Total training iterations (default: 1000)
learning_rate — Training speed (default: 0.0001)
lora_rank — Adapter capacity (default: 16)

LoRA Training The system runs a tailored LoRA optimization loop:

Freezes the base model weights
Trains only the low-rank adapter layers
Applies Z-Image optimized settings

Model Export After training completes, you receive a LoRA adapter file (.safetensors) compatible with:

Parameters

Parameter	Required	Default	Description
data	Yes	—	ZIP file containing training images (min 4 images recommended)
trigger_word	No	p3r5on	Unique word to activate your trained concept
steps	No	1000	Number of training steps (500-10000)
learning_rate	No	0.0001	Training speed (lower = more stable)
lora_rank	No	16	Adapter capacity (1-64, higher = more detail)

How to Use

Prepare your images — collect 10-20 high-quality, diverse images of your subject.
Create a ZIP file — package all images into a single ZIP archive.
Upload your data — drag and drop or provide a public URL to your ZIP file.
Set trigger word — choose a unique word that won't conflict with common terms.
Adjust parameters (optional) — modify steps, learning_rate, and lora_rank as needed.
Run — submit and wait for training to complete.
Download — receive your LoRA adapter file for use with Z-Image models.

Pricing

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

Billing Rules

Base price: $1.25 per 1,000 steps
Total cost = $1.25 × (steps / 1,000)

Best Use Cases

Character LoRAs — Train on character images to maintain identity across generations.
Brand Styles — Create custom visual styles for consistent marketing imagery.
Art Styles — Capture specific artistic aesthetics for creative projects.
Product Photography — Train on product photos for consistent visual presentations.

Pro Tips

Use 10-20 high-quality, diverse images of your subject for best results.
Choose a unique trigger word that won't conflict with common words (e.g., "m1style" instead of "style").
Higher lora_rank (32-64) captures more detail but increases training time and file size.
Lower learning_rate (0.00005) is more stable but requires more steps.
Start with default settings, then adjust if needed.

Try Your LoRA

After training, use your LoRA with these models:

Z-Image Base LoRA — Base model with LoRA support.
Z-Image Turbo LoRA — Fast generation with LoRA support.

Guidance

Notes

Minimum recommended: 4 images, optimal: 10-20 images.
Training time scales with the number of steps configured.
Higher parameter values (steps, lora_rank) will increase training time and cost.
For faster iterations, start with lower settings and increase gradually.

Z Image Base Lora Trainer API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image/base-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 Base Lora Trainer below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/z-image/base-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/base-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/base-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 Base Lora Trainer API — Frequently asked questions

What is the Z Image Base Lora Trainer API?

Z Image Base Lora Trainer is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Z-Image Base 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 cold starts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Z Image Base 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-base-lora-trainer.

How much does Z Image Base Lora Trainer cost per run?

Z Image Base 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 Base 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-base-lora-trainer.

How long does Z Image Base Lora Trainer take to generate?

Average end-to-end generation time on WaveSpeedAI is around 786 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 Base 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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