Flux-dev LoRA Trainer Turbo – accelerate LoRA training for FLUX with optimized pipelines, shorter epochs and rapid experiment cycles for production-ready style models.
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$1.25çalıştırma başına
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Flux Dev Lora Trainer Turbo API — Quick start
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-dev-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 Flux Dev Lora Trainer Turbo below.
HTTP example
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-dev-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/flux-dev-lora-trainer-turbo", {
"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/flux-dev-lora-trainer-turbo",
{
"trigger_word": "p3r5on",
"steps": 1000,
"learning_rate": 0.0004,
"lora_rank": 16
}
)
print(output["outputs"][0]) # → URL of the generated outputFlux Dev Lora Trainer Turbo API — Frequently asked questions
What is the Flux Dev Lora Trainer Turbo API?
Flux Dev Lora Trainer Turbo is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Flux-dev LoRA Trainer Turbo – accelerate LoRA training for FLUX with optimized pipelines, shorter epochs and rapid experiment cycles for production-ready style models. You can call it programmatically or try it from the playground above.
How do I call the Flux Dev Lora Trainer Turbo 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/flux-dev-lora-trainer-turbo.
How much does Flux Dev Lora Trainer Turbo cost per run?
Flux Dev Lora Trainer Turbo 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 Flux Dev Lora Trainer Turbo 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/flux-dev-lora-trainer-turbo.
How long does Flux Dev Lora Trainer Turbo take to generate?
Average end-to-end generation time on WaveSpeedAI is around 354 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Can I use Flux Dev Lora Trainer Turbo 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.