Train custom Qwen-Image LoRA models 10x faster. Style training, character training, object training. From concept to model in minutes, not hours. Upload a ZIP file containing images to start!
Idle
$1per run
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Qwen Image Lora Trainer API — Quick start
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-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 Qwen Image Lora Trainer below.
HTTP example
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-image-lora-trainer" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"trigger_word": "p3r5on",
"steps": 1000,
"learning_rate": 0.0004,
"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/qwen-image-lora-trainer", {
"trigger_word": "p3r5on",
"steps": 1000,
"learning_rate": 0.0004,
"lora_rank": 16
});
console.log(result.outputs[0]); // → URL of the generated outputPython example
# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/qwen-image-lora-trainer",
{
"trigger_word": "p3r5on",
"steps": 1000,
"learning_rate": 0.0004,
"lora_rank": 16
}
)
print(output["outputs"][0]) # → URL of the generated outputQwen Image Lora Trainer API — Frequently asked questions
What is the Qwen Image Lora Trainer API?
Qwen Image Lora Trainer is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Train custom Qwen-Image LoRA models 10x faster. Style training, character training, object training. From concept to model in minutes, not hours. Upload a ZIP file containing images to start! You can call it programmatically or try it from the playground above.
How do I call the Qwen 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/qwen-image-lora-trainer.
How much does Qwen Image Lora Trainer cost per run?
Qwen Image Lora Trainer starts at $1.00 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 Qwen 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/qwen-image-lora-trainer.
How long does Qwen Image Lora Trainer take to generate?
Average end-to-end generation time on WaveSpeedAI is around 1442 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Can I use Qwen 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.