# wavespeed-ai/wan-2.2-i2v-lora-trainer

> 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!

## Overview

- **Endpoint**: `https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2-i2v-lora-trainer`
- **Polling/result URL**: `https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result`
- **Model ID**: `wavespeed-ai/wan-2.2-i2v-lora-trainer`
- **Category**: training

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
The API is asynchronous: submit a prediction, then poll its result URL until it completes.

### Input Schema

The API accepts the following input parameters:

- **`data`** (`string`, _required_):
  To train a WAN I2V LoRA, you need to upload a zip file containing videos. In addition to videos the archive can contain text files with captions. Each text file should have the same name as the video file it corresponds to.

- **`trigger_word`** (`string`, _optional_):
  Optional trigger word. If a caption file exists, it is prepended when not already present. If no caption file exists and trigger_word is non-empty, a caption containing only the trigger word is created. Leave empty to enable no-caption training.

- **`steps`** (`integer`, _optional_):
  Number of steps to train the LoRA on.
  - Default: `100`
  - Range: `50` to `1500`

- **`learning_rate`** (`number`, _optional_):
  - Default: `0.0002`
  - Range: `0` to `1`

- **`lora_rank`** (`integer`, _optional_):
  - Default: `32`
  - Range: `1` to `128`



**Required Parameters Example**:

```json
{
  "data": "https://github.com/mdn/interactive-examples/archive/refs/heads/main.zip"
}
```

**Full Example**:

```json
{
  "data": "https://github.com/mdn/interactive-examples/archive/refs/heads/main.zip",
  "trigger_word": "example",
  "steps": 100,
  "learning_rate": 0.0002,
  "lora_rank": 32
}
```

### Result Data Schema

The `data` object returned by the API has the following fields:

- **`created_at`** (`string (date-time)`, _optional_):
  ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction, the ID of the prediction to get.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`outputs`** (`array of string | object`, _optional_):
  Array of generated outputs (empty when status is not completed). Items are usually URL strings, but may be text strings or structured result objects, depending on the model.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example `data` Object**:

```json
{
  "created_at": "example",
  "id": "example",
  "model": "example",
  "outputs": [],
  "status": "example",
  "urls": {}
}
```

## Usage Examples

The examples use `jq` to read JSON. Set your API key first:

```bash
set -euo pipefail
export WAVESPEED_API_KEY="your-api-key"
```

### 1. Submit a prediction

```bash
REQUEST_BODY=$(cat <<'JSON'
{
  "data": "https://github.com/mdn/interactive-examples/archive/refs/heads/main.zip"
}
JSON
)

SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  --request POST \
  --url https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2-i2v-lora-trainer \
  --header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  --header "Content-Type: application/json" \
  --data "${REQUEST_BODY}")

printf '%s\n' "${SUBMIT_RESPONSE}" | jq .
```

The response contains the prediction ID in `data.id`.

### 2. Poll until complete and read `outputs`

```bash
PREDICTION_ID=$(printf '%s' "${SUBMIT_RESPONSE}" | jq -r '.data.id')
if [ -z "${PREDICTION_ID}" ] || [ "${PREDICTION_ID}" = "null" ]; then
  printf 'Submission response did not contain data.id\n' >&2
  exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"

while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body \
    --request GET \
    --url "${RESULT_URL}" \
    --header "Authorization: Bearer ${WAVESPEED_API_KEY}")

  RESULT=$(printf '%s' "${RESPONSE}" | jq -e '.data')
  STATUS=$(printf '%s' "${RESULT}" | jq -er '.status')
  case "${STATUS}" in
    completed)
      # Generated files are returned in the outputs array.
      printf '%s\n' "${RESULT}" | jq '.outputs'
      break
      ;;
    failed|cancelled|timeout|deleted)
      printf '%s\n' "${RESULT}" | jq '{status, error, code}'
      exit 1
      ;;
    *)
      sleep 2
      ;;
  esac
done
```

## Additional Resources

### Documentation

- [Model Playground](https://wavespeed.ai/models/wavespeed-ai/wan-2.2-i2v-lora-trainer)
- [API Documentation](https://wavespeed.ai/docs/docs-api/wavespeed-ai/wan-2.2-i2v-lora-trainer)