alibaba/wan-2.7/image-edit-pro
alibaba/wan-2.7/image-edit-pro
WAN 2.7 Image Edit Pro performs prompt-driven image editing with multi-image reference support and up to 2K output. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Examples
Parameters
| Name | Type | Default | Constraints | Description |
|---|---|---|---|---|
| prompt *Prompt | textarea | — | ≤ 5000 chars | The positive prompt for the generation. |
| images *Images | image_upload | — | image/* · 1–9 items | List of URLs of input images for editing (1-9 images). |
| sizeSize | text | — | ≤ 500 chars | The 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. |
Output fields
| Field | Type | Description |
|---|---|---|
| images | array<string> | Generated image URL(s). |
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-pro" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": {
"prompt": "Preserve the subject identity and composition, replace the background with a premium studio setting, and enhance fine details and lighting realism.",
"images": [
"https://assets-public.namifusion.com/marketplace/images/2026-06-12/638e1ee18ac7.jpeg"
]
}
}'
# 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-pro",
headers=HEADERS,
json={
"input": {
"prompt": "Preserve the subject identity and composition, replace the background with a premium studio setting, and enhance fine details and lighting realism.",
"images": [
"https://assets-public.namifusion.com/marketplace/images/2026-06-12/638e1ee18ac7.jpeg"
]
}
},
)
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-pro", {
method: "POST",
headers: { ...HEADERS, "Content-Type": "application/json" },
body: JSON.stringify({
"input": {
"prompt": "Preserve the subject identity and composition, replace the background with a premium studio setting, and enhance fine details and lighting realism.",
"images": [
"https://assets-public.namifusion.com/marketplace/images/2026-06-12/638e1ee18ac7.jpeg"
]
}
}),
});
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-pro
Professional prompt-driven image editing with multi-image references and up to 2K output.
alibaba/wan-2.7/image-edit-pro is a production-oriented image editing model designed for high-fidelity, controllable edits. It supports natural-language editing instructions, multiple reference images, and high-resolution output while preserving subject identity, composition, and overall scene structure. With no cold start behavior and affordable per-run pricing, it is well suited for reliable creative workflows at scale.
🚀 Key Features
- Production-grade quality: Optimized for higher-fidelity results in workflows where detail retention and visual consistency matter.
- Multi-image reference support: Use 1–9 input images for subject, style, background, or fusion guidance in more complex edits.
- Up to 2K output: Supports effective output sizes up to approximately 2048×2048, suitable for high-DPI assets and print-ready visuals.
- Natural-language editing: Describe what to change and what to preserve in plain language for intuitive editing control.
- Composition preservation: Designed to maintain subject identity, pose, and layout while applying localized or stylistic changes.
- No cold start: Ready for responsive production usage without cold-start delays.
🛠️ Technical Specifications
| Item | Details |
|---|---|
| Model architecture | WAN 2.7 Image Edit Pro(professional prompt-driven image editing model) |
| Task type | Image editing |
| Input format | Prompt + reference images(1–9 images) |
| Output format | Edited image |
| Input image count | 1–9 |
| Resolution | 512–4096 pixels per dimension; total pixels must be between 768×768 and 2048×2048 |
| Aspect Ratio | 1:8 to 8:1 |
| Default output size | If size is not specified, output typically follows the original image dimensions |
| Quality tier | Pro / high-fidelity output |
| Latency | No fixed public benchmark; higher resolutions generally take longer |
| Runtime behavior | No cold start |
Sample Prompts
- “Change the jacket to black leather, keep the face, pose, and background unchanged.”
- “Color-match the skirt in the first image to the bird in the second image, while keeping the clothing style and model unchanged.”
- “Replace the product background with a minimal studio setting, preserving the product shape, label text, and lighting direction.”
💰 Pricing
| Item | Price |
|---|---|
| Base price | $0.075 / run |
💡 Best Use Cases
- E-commerce and product photography: Retouch products, swap backgrounds, and generate high-resolution commercial assets.
- Fashion and apparel visualization: Modify clothing color, material, or style while preserving pose and identity.
- Marketing and brand creative: Produce polished campaign visuals for ads, landing pages, and social media.
- Print and high-DPI design: Create edited assets suitable for posters, packaging, and large-format display workflows.
🔗 Related Models
- Wan 2.7 Image Edit: Lower-cost standard version for everyday editing and faster iteration.
- Wan 2.6 Image Edit: Previous-generation 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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