
Text to Image
Create AI images from text prompts
Professional image generation by Qwen Image 2.0 Pro API with enhanced realism, precise text rendering. Stable output at competitive pricing for enterprise use. 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 Pro API from Alibaba delivers premium-quality AI image generation with enhanced detail fidelity for developers and creative professionals. This high-fidelity text-to-image API integration produces studio-grade visuals from natural-language descriptions. Built on Alibaba's most advanced multimodal architecture, the Qwen Image 2.0 Pro model combines superior prompt comprehension with precise rendering control, while the API provides robust integration for demanding production 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 Pro vs. Qwen Image 2.0 Standard Qwen Image 2.0 Standard provides excellent quality at a lower price point, ideal for high-volume workflows. Qwen Image 2.0 Pro delivers enhanced detail fidelity, sharper rendering, and superior composition accuracy for demanding commercial applications where premium quality justifies the investment.
Qwen Image 2.0 Pro vs. DALL-E 3 (OpenAI) DALL-E 3 offers broad creative versatility and strong prompt adherence. Qwen Image 2.0 Pro API differentiates through superior multilingual support (especially Chinese), flexible custom resolution control from 256 to 2048 pixels, and competitive pricing for premium-tier image generation.
Qwen Image 2.0 Pro vs. Midjourney v6 Midjourney v6 excels at artistic aesthetics and community-curated visual styles. Qwen Image 2.0 Pro provides programmatic API access, predictable per-image pricing, flexible resolution control, and multilingual prompt support—making it superior for developers requiring scalable, high-fidelity image generation in production pipelines.
Qwen Image 2.0 Pro vs. Nano Banana Pro Nano Banana Pro leverages Google's Gemini architecture for advanced image generation. Qwen Image 2.0 Pro API counters with broader custom resolution options, stronger Chinese-language support, and competitive premium-tier pricing—offering a compelling alternative for teams requiring multilingual and resolution-flexible workflows.
Qwen Image 2.0 Pro vs. Stable Diffusion 3 Stable Diffusion 3 provides open-source flexibility and extensive customization through fine-tuning. Qwen Image 2.0 Pro offers hassle-free API integration, superior out-of-the-box quality, multilingual text rendering, and no infrastructure management—ideal for professional teams seeking premium, production-ready image generation.
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-pro',
prompt: 'A professional product photo with studio lighting',
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': 'qwen-image-2.0-pro',
'prompt': 'A professional product photo with studio lighting',
'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": "qwen-image-2.0-pro",
"prompt": "A professional product photo with studio lighting",
"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"