Text to Image
Create AI images from text prompts
Alibaba/wan-v2.7-image-edit
wan-v2.7-image-edit
Free to try Alibaba Wan 2.7 API for cost-effective image editing with multi-image input, prompt-guided changes, seed control, and stable visual output. Built for free testing and stable API workflows.
Image to ImageMulti-Image
Related Wan 2.7 Image Models
Try the AI Image Generator now
Wan 2.7 Image Edit Pricing
| Parameters | Price | Original Price | Discount |
|---|
Examples
Related Models
qwen-image-2.0text-to-imageqwen-image-2.0-editimage-to-imageqwen-image-2.0-protext-to-imageqwen-image-2.0-pro-editimage-to-imageqwen-image-lora-editimage-to-imagez-imagetext-to-image
happyhorse-1.0-text-to-videotext-to-videohappyhorse-1.0-image-to-videoimage-to-videohappyhorse-1.1-text-to-videotext-to-videohappyhorse-1.1-image-to-videoimage-to-videohappyhorse-1.1-reference-to-videoreference-to-videowan-2.7-text-to-videotext-to-videowan-2.7-image-to-videoimage-to-videowan-2.7-lora-image-to-videoimage-to-videowan-2.7-video-editvideo-editwan-2.7-reference-to-videoreference-to-videowan-v2.7-imagetext-to-imagewan-v2.7-image-editimage-to-imagewan-v2.7-image-protext-to-imagewan-v2.7-image-pro-editimage-to-imagewan-2.6-text-to-videotext-to-videowan-2.6-image-to-videoimage-to-videoqwen-image-3.0text-to-imageqwen-image-3.0-editimage-to-imageqwen-image-3.0-protext-to-imageqwen-image-3.0-pro-editimage-to-imagewan-3.0-text-to-videotext-to-videowan-3.0-image-to-videoimage-to-videowan-3.0-video-editvideo-editwan-3.0-reference-to-videoreference-to-videoqwen-3.7-max-text-to-texttext-to-textqwen-3.7-plus-text-to-texttext-to-textqwen-3.7-max-web-searchweb-searchqwen-3.7-plus-web-searchweb-searchqwen-plus-charactertext-to-textqwen-flash-charactertext-to-textqwen-3.8-max-text-to-texttext-to-textqwen-3.8-max-web-searchweb-searchREADME
Fast & Affordable Wan 2.7 Image Edit API (Alibaba's Creative Image Editing Model)
Wan 2.7 Image Edit API on Flaq AI provides Alibaba image-to-image editing for developers and creative teams that need prompt-based visual transformation through a stable API route. This affordable Wan image editing API integration helps transform existing images with natural-language instructions, multi-image input support, and flexible aspect ratio output. It is built for practical design iteration, marketing refreshes, product visuals, and scalable creative operations.
Key Features of Wan 2.7 Image Edit API
- Prompt-Based Image Editing: Transform existing visuals with natural-language instructions for style changes, object adjustments, background updates, and creative refinements.
- Multi-Image Input Support: Use image inputs to guide editing workflows, visual references, and composition-aware transformations.
- Flexible Aspect Ratio Support: Create edited outputs for social, ecommerce, web, and editorial layouts through one API route.
- Cost-Effective API Access: Run high-volume editing and creative iteration through an affordable managed Wan API integration.
- Style-Preserving Transformation: Adjust images while keeping useful visual context, subject identity, and composition goals aligned.
- Developer-Friendly Workflow: Add image editing to apps, creative tools, and production systems without managing provider infrastructure.
How to Use Wan 2.7 Image Edit API for Image Editing on Flaq AI
- Input: Existing image inputs plus natural-language editing prompts describing the desired transformation.
- Output: Edited images delivered through a stable image API integration on Flaq AI.
- Aspect Ratios: Supports multiple aspect ratios for social, web, product, and editorial layouts.
- Configuration: Supports practical prompt, input image, and variation controls for creative iteration.
- Capabilities: Image-to-image editing, multi-image reference workflows, style transfer, background updates, product refreshes, and creative transformation through affordable Wan API integration.
Best Use Cases for Wan 2.7 Image Edit API Integration
- Marketing Creative Refreshes: Update campaign visuals, localize assets, and create variants without rebuilding images from scratch.
- Product Photography Optimization: Adjust backgrounds, style, layout, and presentation for ecommerce and catalog workflows.
- Social Media Variations: Turn one image into multiple platform-ready creative directions for testing and publishing.
- Design Iteration: Explore edits, references, and visual alternatives quickly for creative review cycles.
- Brand Asset Adaptation: Rework visuals while keeping brand direction, subject focus, and campaign intent consistent.
Note Please ensure prompts and uploaded images comply with Alibaba safety and usage guidelines. If an error occurs, review the input for restricted content, simplify the request, and try again.
Wan 2.7 Image Edit vs Competitors: Comparative Analysis
-
Wan 2.7 Image Edit vs. Wan 2.7 Image Pro Edit
Wan 2.7 Image Pro Edit is positioned for more premium editing output. Wan 2.7 Image Edit prioritizes affordability and efficient creative transformation for everyday production work. -
Wan 2.7 Image Edit vs. Qwen Image 2 Edit
Qwen Image 2 Edit offers Alibaba image editing with strong prompt control. Wan 2.7 Image Edit gives teams a Wan-specific editing route for flexible image-to-image workflows. -
Wan 2.7 Image Edit vs. GPT Image Edit
GPT Image Edit provides OpenAI-native editing behavior. Wan 2.7 Image Edit offers an Alibaba alternative for scalable, cost-effective visual editing on Flaq AI. -
Wan 2.7 Image Edit vs. Nano Banana Edit
Nano Banana Edit focuses on Gemini image editing. Wan 2.7 Image Edit provides Alibaba-powered editing with multi-image support and flexible production routing. -
Wan 2.7 Image Edit vs. Seedream Edit
Seedream Edit is strong for commercial visual transformation. Wan 2.7 Image Edit is useful for affordable, API-driven edits across marketing, ecommerce, and social workflows.
Use the Wan 2.7 Image Edit Model Directly in Powerful AI Tools
Explore several AI creation tools for quick image and video workflows in your browser, then scale successful ideas with Flaq AI's production-ready model APIs. Flaq AI provides a unified API layer for all models, making it easy to use and scale your workflows.
Image to Image
Create AI images from images and text prompts
Text to Video
Create AI videos from text prompts
Image to Video
Animate images into AI videos
AI Media Creator
Create AI images and videos in one unified workspace
Reference to Video
Create consistent videos from reference media
AI Canvas
Build visual AI image and video workflows on an infinite canvas
API Examples
Submit Example
// Step 1: Submit generation request
const response = await fetch('https://api.flaq.ai/api/v1/image/task', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
body: JSON.stringify({
model_name: 'wan-v2.7-image-edit',
prompt: 'Change the background to a beach scene',
width: 16,
height: 9,
image_url_list: ['https://example.com/image1.jpg']
})
});
const { data } = await response.json();
const taskId = data.task_id;
Polling Example
// Step 2: Poll for results
const taskId = data.task_id;
const pollResult = async (taskId) => {
const res = await fetch(`https://api.flaq.ai/api/v1/image/${taskId}`, {
headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
});
return res.json();
};
while (true) {
const pollResultData = await pollResult(taskId);
const status = pollResultData.data.task_status;
if (status === 'succeed') {
console.log(pollResultData.data.task_result.images[0].url);
break;
}
if (status === 'failed') {
console.error(pollResultData.data.task_status_msg);
break;
}
await new Promise(resolve => setTimeout(resolve, 10000));
}
Submit Example
# Step 1: Submit generation request
import requests
response = requests.post(
'https://api.flaq.ai/api/v1/image/task',
headers={
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
json={
'model_name': 'wan-v2.7-image-edit',
'prompt': 'Change the background to a beach scene',
'width': 16,
'height': 9,
'image_url_list': ['https://example.com/image1.jpg']
}
)
result = response.json()
task_id = result['data']['task_id']
Polling Example
# Step 2: Poll for results
task_id = response.json()['data']['task_id']
poll_url = f"https://api.flaq.ai/api/v1/image/{task_id}"
while True:
poll_result = requests.get(poll_url, headers={'Authorization': 'Bearer YOUR_API_KEY'}).json()
status = poll_result['data']['task_status']
if status == 'succeed':
print(poll_result['data']['task_result']['images'][0]['url'])
break
if status == 'failed':
print(poll_result['data']['task_status_msg'])
break
time.sleep(10)
Submit Example
# Step 1: Submit generation request
curl -X POST https://api.flaq.ai/api/v1/image/task \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model_name": "wan-v2.7-image-edit",
"prompt": "Change the background to a beach scene",
"width": 16,
"height": 9,
"image_url_list": ["https://example.com/image1.jpg"]
}'
Polling Example
# Step 2: Poll for results
# Replace {task_id} with the task_id returned from the submit response
curl -X GET "https://api.flaq.ai/api/v1/image/{task_id}" \
-H "Authorization: Bearer YOUR_API_KEY"