Wan 2.2 Image LoRA Trainer

Wan 2.2 LoRA Trainer is a high-performance custom model training service for the Wan 2.2 text-to-video generation model. Train personalized LoRA (Low-Rank Adaptation) models 10x faster than traditional methods, enabling custom styles, characters, and objects for video generation.

Training Architecture

The trainer leverages Wan 2.2's innovative MoE (Mixture of Experts) architecture, producing two specialized LoRA models:

high_noise_lora: Optimized for high-noise denoising timesteps, handling initial structure and composition
low_noise_lora: Optimized for low-noise denoising timesteps, refining details and final output quality

This dual-model approach ensures superior training efficiency and generation quality across all denoising stages.

Training Process

Data Upload: Upload a ZIP file containing your training images
Automatic Processing: The system automatically processes and optimizes your dataset
Dual Model Training: Simultaneously trains both high_noise_lora and low_noise_lora models
Model Delivery: Receive two specialized LoRA models ready for video generation

Wan 2.2 Image Lora Trainer API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2-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 Wan 2.2 Image Lora Trainer below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2-image-lora-trainer" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "trigger_word": "p3r5on",
    "steps": 1000,
    "learning_rate": 0.0002,
    "lora_rank": 32
}'

# 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/wan-2.2-image-lora-trainer", {
        "trigger_word": "p3r5on",
        "steps": 1000,
        "learning_rate": 0.0002,
        "lora_rank": 32
});

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

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "wavespeed-ai/wan-2.2-image-lora-trainer",
    {
    "trigger_word": "p3r5on",
    "steps": 1000,
    "learning_rate": 0.0002,
    "lora_rank": 32
}
)

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

Wan 2.2 Image Lora Trainer API — Frequently asked questions

What is the Wan 2.2 Image Lora Trainer API?

Wan 2.2 Image Lora Trainer is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Train custom Wan 2.2 character/style 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 Wan 2.2 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/wan-2.2-image-lora-trainer.

How much does Wan 2.2 Image Lora Trainer cost per run?

Wan 2.2 Image Lora Trainer starts at $3.00 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 Wan 2.2 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/wan-2.2-image-lora-trainer.

How long does Wan 2.2 Image Lora Trainer take to generate?

Average end-to-end generation time on WaveSpeedAI is around 1632 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Wan 2.2 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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