Wan 2.2 I2V LoRA Trainer

Wan 2.2 I2V (Image-to-Video) LoRA Trainer is a specialized training service for creating custom LoRA models optimized for image-to-video generation. Train personalized models 10x faster using video datasets to achieve action and video effect generation from static images.

Training Architecture

Built on Wan 2.2's advanced MoE (Mixture of Experts) architecture, the trainer generates two specialized LoRA models:

high_noise_lora: Optimized for high-noise denoising timesteps, handling initial motion planning and temporal structure
low_noise_lora: Optimized for low-noise denoising timesteps, refining motion details and ensuring smooth transitions

This dual-model approach ensures superior image-to-video conversion quality with consistent temporal coherence.

Training Process

Video Data Upload: Upload a ZIP file containing your training video sequences
Temporal Analysis: The system analyzes motion patterns and temporal relationships
Dual Model Training: Simultaneously trains both high_noise_lora and low_noise_lora models
Motion Optimization: Fine-tunes models for smooth image-to-video transitions
Model Delivery: Receive two specialized LoRA models optimized for I2V generation

Wan 2.2 I2v Lora Trainer API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2-i2v-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 I2v Lora Trainer below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2-i2v-lora-trainer" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "trigger_word": "p3r5on",
    "steps": 100,
    "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-i2v-lora-trainer", {
        "trigger_word": "p3r5on",
        "steps": 100,
        "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-i2v-lora-trainer",
    {
    "trigger_word": "p3r5on",
    "steps": 100,
    "learning_rate": 0.0002,
    "lora_rank": 32
}
)

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

Wan 2.2 I2v Lora Trainer API — Frequently asked questions

What is the Wan 2.2 I2v Lora Trainer API?

Wan 2.2 I2v Lora Trainer is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Train custom Wan 2.2 I2V LoRA models 10x faster. Action training, motion training, video efect training. From concept to model in minutes, not hours. Upload a ZIP file containing videos to start! You can call it programmatically or try it from the playground above.

How do I call the Wan 2.2 I2v 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-i2v-lora-trainer.

How much does Wan 2.2 I2v Lora Trainer cost per run?

Wan 2.2 I2v Lora Trainer starts at $5.00 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 I2v 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-i2v-lora-trainer.

How long does Wan 2.2 I2v Lora Trainer take to generate?

Average end-to-end generation time on WaveSpeedAI is around 21404 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 I2v 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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