
Text to Image
Create AI images from text prompts
Free to try Alibaba Wan 2.7 API for cost-effective image generation with flexible aspect ratios, seed control, reliable quality, and scalable output. Built for free testing and stable API workflows.
Try the AI Image Generator now
| Parameters | Price | Original Price | Discount |
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Wan 2.7 Image API on Flaq AI provides Alibaba text-to-image generation for developers, marketers, and creative teams that need flexible visual production through a stable API route. This affordable Wan image API integration turns natural-language prompts into polished visuals with multiple aspect ratio support and practical prompt control. It is built for teams that want fast creative iteration, scalable image generation, and simple production integration.
Note Please ensure prompts comply with Alibaba safety and usage guidelines. If an error occurs, refine your prompt to remove restricted content and try again.
Wan 2.7 Image vs. Wan 2.7 Image Pro
Wan 2.7 Image Pro is positioned for higher-end output and premium creative needs. Wan 2.7 Image prioritizes affordability and efficient generation for everyday production workflows.
Wan 2.7 Image vs. Qwen Image 2
Qwen Image 2 is another strong Alibaba image model for prompt-driven generation. Wan 2.7 Image offers a focused Wan route for teams that want flexible creative output and cost-effective API access.
Wan 2.7 Image vs. GPT Image
GPT Image models are known for broad OpenAI ecosystem familiarity. Wan 2.7 Image gives developers an Alibaba alternative for scalable image generation on Flaq AI.
Wan 2.7 Image vs. Nano Banana
Nano Banana models emphasize Gemini image workflows. Wan 2.7 Image provides Alibaba image generation with flexible aspect ratios and practical creative controls.
Wan 2.7 Image vs. Seedream
Seedream is strong for stylized and commercial visuals. Wan 2.7 Image is a practical choice for affordable, API-driven creative generation across common production use cases.
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.

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// 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',
prompt: 'A serene landscape with mountains and a lake at sunset',
width: 16,
height: 9
})
});
const { data } = await response.json();
const taskId = data.task_id;
// 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));
}
# 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',
'prompt': 'A serene landscape with mountains and a lake at sunset',
'width': 16,
'height': 9
}
)
result = response.json()
task_id = result['data']['task_id']
# 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)
# 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",
"prompt": "A serene landscape with mountains and a lake at sunset",
"width": 16,
"height": 9
}'
# 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"