# Alibaba | Wan 2.7 | Pro | Image Edit

Alibaba Wan 2.7 Pro Image Edit is the professional-tier image editing model in the Wan 2.7 family, offering the highest level of edit quality, detail preservation, and semantic accuracy for instruction-guided image modification. It handles complex, multi-element edits with greater precision than the standard Wan 2.7 Image Edit variant. Best suited for high-fidelity commercial retouching, brand asset modification, and production environments where editing accuracy and output quality are critical.

## API Information

- **Model Slug:** alibaba-wan-2-7-pro-image-edit
- **Branded URL:** https://www.eachlabs.ai/alibaba/wan-2-7/alibaba-wan-2-7-pro-image-edit
- **Provider:** Alibaba
- **Category:** Image to Image
- **Output Type:** array
- **Status:** active
- **Version:** 0.0.1
- **Estimated Processing Time:** 15 seconds
- **Last Updated:** 2026-06-08
- **Interactive Demo:** https://www.eachlabs.ai/ai-models/alibaba-wan-2-7-pro-image-edit

## Pricing

Pricing information not available.

## Input Schema

| Parameter | Type | Required | Default | Constraints | Description |
|-----------|------|----------|---------|-------------|-------------|
| prompt | string | Yes | - | - | Text instruction describing the desired editing operation. Supports Chinese and English. Max 5,000 characters. |
| img_url | string | Yes | - | - | URL of the primary input image to edit. Supported formats: JPEG, JPG, PNG, BMP, WEBP. Max 20 MB. |
| img_url_2 | string | No | - | - | URL of a second reference image (optional). |
| img_url_3 | string | No | - | - | URL of a third reference image (optional). |
| img_url_4 | string | No | - | - | URL of a fourth reference image (optional). |
| size | string | No | 2K | ["1K","2K"] | Output image resolution. 1K: ~1024px. 2K: ~2048px (default). Aspect ratio matches last input image. |
| n | integer | No | 1 | - | Number of images to generate. Range: 1-4. Each image is billed separately. |
| seed | integer | No | - | - | Seed for reproducibility. Same seed produces similar results. Random if omitted. |

## Example Request

```bash
curl -X POST https://api.eachlabs.ai/v1/prediction/ \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "alibaba-wan-2-7-pro-image-edit",
  "input": {
    "prompt": "Turn image 1 into a sketch.",
    "img_url": "https://storage.googleapis.com/magicpoint/inputs/alibaba-wan-2-7-pro-image-edit-input.png"
  }
}'
```

## Output Schema

Response returned by `GET /v1/prediction/{id}` when the job completes:

```json
{
  "status": "success",
  "predictionID": "string",
  "output": "array",
  "metrics": {
    "predict_time": "number (seconds)"
  }
}
```

## Polling

```bash
curl https://api.eachlabs.ai/v1/prediction/{PREDICTION_ID} \
  -H "X-API-Key: YOUR_API_KEY"
```

| Status | Meaning |
|--------|---------|
| `processing` | Still running — poll again |
| `success` | Done — read `output` |
| `error` | Failed — read `message` / `details` |

## Webhook (alternative to polling)

Pass `"webhook_url": "https://your.host/path"` in the create request. Eachlabs POSTs this payload when the job ends:

```json
{
  "exec_id": "prediction-uuid",
  "status": "succeeded",
  "output": "https://...",
  "error": ""
}
```

`status` is `"succeeded"` or `"failed"`. `exec_id` equals the `predictionID` from create. Return 2xx within 30 seconds.

## Errors

Error body: `{ "status": "error", "message": "...", "details": "..." }`

| Code | Meaning |
|------|---------|
| `400` | Invalid input |
| `401` | Missing / invalid `X-API-Key` |
| `404` | Unknown model or prediction id |
| `429` | Rate limit — 100 creates / min, 10 concurrent per key |
| `5xx` | Retry with backoff |

## Overview

Alibaba | Wan 2.7 | Pro | Image Edit revolutionizes image-to-image workflows by enabling precise, professional-grade transformations through natural-language instructions on input images. Developed by Alibaba's Tongyi Lab as part of the advanced Wan 2.7 family, this Pro variant stands out with enhanced 4K support and superior reasoning for complex edits like style transfer, element swapping, and multi-image fusion. Unlike standard models, it processes up to 9 reference images in a structured grid, delivering consistent, high-fidelity outputs ideal for creators seeking cinematic quality without local hardware. Available via APIs on platforms like each::labs, Alibaba | Wan 2.7 | Pro | Image Edit empowers designers and developers to edit images with endpoint precision, maintaining subject consistency down to bone structure levels while integrating seamlessly into production pipelines.

## Usage Notes

- API Base URL: `https://api.eachlabs.ai/v1`
- Authentication: send `X-API-Key: YOUR_API_KEY`. Generate a key from the Eachlabs dashboard at https://www.eachlabs.ai/dashboard/api-keys.
- File-typed parameters (`*_url`, `image_url`, `video_url`, `audio_url`, etc.) accept publicly-reachable HTTPS URLs only. Upload your asset first (GCS / S3 / your CDN) and pass the resulting URL. Data-URIs and localhost URLs are rejected.
- For structured parameters (arrays / objects) send real JSON values, not stringified payloads.
- Monetary values are reported in USD; per-token / per-megapixel rates may be billed in micro-cents internally.
- Prefer `webhook_url` over polling for long-running predictions — see the Webhook Callback section.