alibaba/wan-2.7/image-edit

alibaba/wan-2.7/image-edit

WAN 2.7 Image Edit performs prompt-driven image editing with support for multiple-image references. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Examples

alibaba/wan-2.7/image-edit example 1

Parameters

NameTypeDefaultConstraintsDescription
prompt *Prompttextarea≤ 5000 charsThe positive prompt for the generation.
images *Imagesimage_upload_groupimage/* · 1–9 itemsList of URLs of input images for editing (1-9 images).
sizeSizetext≤ 500 charsThe size of the generated image in pixels (width*height). Range: 512-4096 per dimension. Total pixels must be between 768*768 and 2048*2048. Aspect ratio must be between 1:8 and 8:1.

API

Call this model through one unified REST API. Get a key on the API Keys page.

cURL
# 1) Submit — returns { "task_uuid": "..." }
curl -X POST "https://www.namifusion.com/api/v1/marketplace/run/alibaba/wan-2.7/image-edit" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "input": {
    "prompt": "A cinematic portrait, dramatic rim light, shallow depth of field",
    "images": [
      "https://example.com/input.jpg"
    ]
  }
}'

# 2) Poll until status is "completed", then read the output URLs
curl "https://www.namifusion.com/api/v1/marketplace/run/tasks/TASK_UUID" \
  -H "Authorization: Bearer YOUR_API_KEY"
Python
import time, requests

API_KEY = "YOUR_API_KEY"
HEADERS = {"Authorization": f"Bearer {API_KEY}"}

# 1) Submit
resp = requests.post(
    "https://www.namifusion.com/api/v1/marketplace/run/alibaba/wan-2.7/image-edit",
    headers=HEADERS,
    json={
        "input": {
            "prompt": "A cinematic portrait, dramatic rim light, shallow depth of field",
            "images": [
                "https://example.com/input.jpg"
            ]
        }
    },
)
resp.raise_for_status()  # 401/402/429/5xx stop here instead of polling a bad task
task = resp.json()

# 2) Poll until a terminal state (completed / failed / cancelled).
#    This model is allowed up to 300s server-side.
deadline = time.time() + 360
while task.get("status") not in ("completed", "failed", "cancelled"):
    if time.time() > deadline:
        raise TimeoutError(f"still {task.get('status')} — keep the task_uuid and poll later")
    time.sleep(3)
    poll = requests.get(f"https://www.namifusion.com/api/v1/marketplace/run/tasks/{task['task_uuid']}", headers=HEADERS)
    poll.raise_for_status()
    task = poll.json()

print(task["status"], task.get("output"))
JavaScript
const API_KEY = "YOUR_API_KEY";
const HEADERS = { Authorization: `Bearer ${API_KEY}` };

// 1) Submit
const resp = await fetch("https://www.namifusion.com/api/v1/marketplace/run/alibaba/wan-2.7/image-edit", {
  method: "POST",
  headers: { ...HEADERS, "Content-Type": "application/json" },
  body: JSON.stringify({
    "input": {
      "prompt": "A cinematic portrait, dramatic rim light, shallow depth of field",
      "images": [
        "https://example.com/input.jpg"
      ]
    }
  }),
});
if (!resp.ok) throw new Error(`submit failed: ${resp.status} ${await resp.text()}`);
let task = await resp.json();

// 2) Poll until a terminal state (completed / failed / cancelled).
//    This model is allowed up to 300s server-side.
const deadline = Date.now() + 360 * 1000;
while (!["completed", "failed", "cancelled"].includes(task.status)) {
  if (Date.now() > deadline) throw new Error(`still ${task.status} — keep the task_uuid and poll later`);
  await new Promise((r) => setTimeout(r, 3000));
  const poll = await fetch(`https://www.namifusion.com/api/v1/marketplace/run/tasks/${task.task_uuid}`, { headers: HEADERS });
  if (!poll.ok) throw new Error(`poll failed: ${poll.status}`);
  task = await poll.json();
}

console.log(task.status, task.output);

Documentation

alibaba/wan-2.7/image-edit

Prompt-driven image editing with multi-image references, fast inference, and no cold starts.

alibaba/wan-2.7/image-edit is a prompt-based image editing model designed for targeted modifications to existing images. It preserves subject identity, composition, and scene structure while enabling fast creative iteration with multiple reference images, making it a strong fit for production workflows that prioritize quality, speed, and cost efficiency.

🚀 Key Features

  • Prompt-driven editing: Describe what to change and what to preserve in natural language for precise, workflow-friendly image edits.
  • Multi-image reference support: Accepts 1–9 input images for subject, style, background, or fusion-based editing tasks.
  • Composition preservation: Optimized to maintain identity, pose, layout, and overall scene structure while applying localized changes.
  • No cold starts: Ready for responsive production usage and rapid iteration without startup delays.
  • Affordable per-run pricing: Low-cost execution makes it suitable for high-volume creative and commercial editing workloads.

🛠️ Technical Specifications

ItemDetails
Modelalibaba/wan-2.7/image-edit
Model TypePrompt-driven image editing
Task Typeimage-to-image
ArchitectureConditional image editing model with multi-reference guidance
InputPrompt, 1–9 input images
OutputEdited image
Num Images1–9
ResolutionEffective output up to 2048×2048; each dimension may range from 512–4096 within the total-pixel limit
Pixel ConstraintsTotal pixels must be between 768×768 and 2048×2048
Aspect Ratio1:8 to 8:1
Default Output SizeTypically matches the original image if not specified
LatencyOptimized for fast inference; no cold starts
Output FormatImage

Sample Prompts

  • Change the jacket to black leather, keep the face and pose unchanged.
  • Replace the background with a rainy Tokyo street at night, keep the subject unchanged, cinematic lighting.
  • Change the product casing to brushed silver metal while preserving the original angle, lighting, and composition.

💰 Pricing

ItemPrice
Base price / per run$0.03

💡 Best Use Cases

  • Fashion and apparel: Swap clothing styles, colors, or materials while preserving model identity and pose.
  • Background replacement: Change scene setting or mood without rebuilding the full image.
  • Product image editing: Create color, material, and environment variants for e-commerce and marketing assets.
  • Creative style iteration: Explore visual directions quickly while keeping the original composition intact.

🔗 Related Models

  • Wan 2.7 Image Edit Pro: Higher-fidelity version with up to 2K editing output.
  • Wan 2.6 Image Edit: Previous-generation image editing model with a similar prompt-driven workflow.
  • Qwen Image Edit: General-purpose image editing model for everyday creative and product use cases.
  • Google Nano Banana Pro Edit: High-fidelity editing model with strong composition preservation and text handling.

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