
Text to Image
Create AI images from text prompts
Cost-effective image editing via Qwen Image 2.0 API. Optimized for high-concurrency stability while maintaining detail consistency at an affordable rate. Built for free testing and stable API workflows.
Try the AI Image Generator now
| Parameters | Price | Original Price | Discount |
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Qwen Image 2.0 Edit API from Alibaba delivers cost-effective, production-grade AI image editing for developers and creative teams. This affordable image editing API integration helps you transform existing visuals into professional outputs through natural-language instructions per edit. The Qwen Image 2.0 model provides deep semantic reasoning for complex edits, while the API provides stable integration for scalable editing workflows on Flaq AI.
Note Please ensure your prompts comply with Alibaba's content safety guidelines. If an error occurs, review your prompt for restricted content, adjust it, and try again.
Qwen Image 2.0 Edit vs. Qwen Image 2.0 Pro Edit Qwen Image 2.0 Pro Edit offers enhanced rendering precision and premium detail preservation for demanding commercial editing. Qwen Image 2.0 Edit provides excellent editing quality at a lower price point, making it ideal for high-volume editing workflows where cost-efficiency is essential.
Qwen Image 2.0 Edit vs. GPT-Image-1 (OpenAI) GPT-Image-1 is a broad creative image model with wide stylistic range. Qwen Image 2.0 Edit API is optimized for targeted instruction-driven edits, flexible resolution control, and stronger multilingual prompt understanding for professional editing teams through cost-effective API integration.
Qwen Image 2.0 Edit vs. FLUX.1 [dev] FLUX.1 [dev] focuses on detail-heavy technical generation workflows. Qwen Image 2.0 Edit API emphasizes semantic, instruction-driven editing with layout-aware changes and multilingual support, making it stronger for practical production editing scenarios through affordable Alibaba integration.
Qwen Image 2.0 Edit vs. Nano Banana 2 Edit Nano Banana 2 Edit leverages Google's Gemini Flash architecture for rapid editing. Qwen Image 2.0 Edit API counters with lower per-edit pricing, superior Chinese-language prompt support, and broader custom resolution options—making it versatile for diverse editing scenarios across global markets.
Qwen Image 2.0 Edit vs. Seedream Edit Seedream Edit is strong for stylized and artistic visual modifications. Qwen Image 2.0 Edit API is tuned for reliable photorealistic editing, precise composition control, and multilingual instruction handling in production pipelines with affordable pricing.
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: 'qwen-image-2.0-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;
// 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': 'qwen-image-2.0-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']
# 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": "qwen-image-2.0-edit",
"prompt": "Change the background to a beach scene",
"width": 16,
"height": 9,
"image_url_list": ["https://example.com/image1.jpg"]
}'
# 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"