Free to try Qwen Image 3.0 Pro Edit API by Alibaba with one to three reference images, seven flexible aspect ratios, and seed control for reproducible Alibaba image editing.
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Qwen Image 3.0 Pro Edit API (Precise Image Transformation)
Qwen Image 3.0 Pro Edit API applies natural-language instructions to existing images for controlled visual transformation. It supports image-led editing workflows with optional multiple image inputs, flexible output ratios, and seed control for creative tools and production pipelines on Flaq AI.
Key Features of Qwen Image 3.0 Pro Edit API
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Prompt-Guided Image Editing: Describe the desired changes to subjects, backgrounds, materials, lighting, style, and composition in natural language.
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Multi-Image Reference Support: Combine supported input images when an edit needs several visual sources, references, or subject relationships.
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Subject and Scene Preservation: Keep important identity, composition, and spatial relationships stable while applying targeted changes.
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Creative Transformation Workflows: Explore style changes, relighting, object adjustments, background changes, and visual refinements.
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Flexible Output Ratios: Adapt edited results to portrait, landscape, square, social, product, and editorial placements through the available ratio controls.
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Seed-Based Iteration: Reuse seed direction when a workflow needs controlled variations from a related edit request.
How to Use Qwen Image 3.0 Pro Edit API for Controlled Image Editing on Flaq AI
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Input: One or more source images together with a natural-language instruction describing the intended edit.
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Edit Direction: State what should change, what must remain unchanged, and how the final composition should look.
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Output: Edited images delivered through the Flaq AI image workflow for review and downstream design use.
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Creative Controls: Choose the available aspect-ratio and seed settings for the target composition and iteration strategy.
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Quality Review: Check subject identity, edges, text, object relationships, and unwanted changes before publishing.
Best Use Cases for Qwen Image 3.0 Pro Edit API Integration
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Product Image Refinement: Update backgrounds, lighting, materials, and composition for catalog and campaign imagery.
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Creative Versioning: Produce multiple visual treatments from a shared source image while retaining the core subject.
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Brand and Marketing Localization: Adapt campaign visuals to new layouts, environments, and audience requirements.
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Concept and Design Iteration: Explore visual directions quickly before committing to manual production work.
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Multi-Reference Compositing: Combine supported source images to guide a coherent edited scene or visual system.
Note Describe preservation requirements explicitly when identity, product shape, layout, or brand elements must remain stable. Review edited images for unintended changes before production use.
Qwen Image 3.0 Pro Edit vs. Competitors: Comparative Analysis
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Qwen Image 3.0 Pro Edit vs. Nano Banana 2 Edit: Nano Banana 2 Edit emphasizes fast semantic editing in the Gemini ecosystem. Qwen Image 3.0 Pro Edit provides an Alibaba-oriented image editing workflow with multiple input support.
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Qwen Image 3.0 Pro Edit vs. GPT Image: GPT Image supports broad image generation and editing. Qwen Image 3.0 Pro Edit is suited to applications that want controlled image transformation through Qwen integrations.
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Qwen Image 3.0 Pro Edit vs. FLUX Kontext: FLUX Kontext is designed for instruction-driven visual edits and consistency. Qwen Image 3.0 Pro Edit offers an alternative with seed and aspect-ratio controls for production iteration.
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Qwen Image 3.0 Pro Edit vs. Seedream Edit: Seedream Edit supports commercial image transformation and refinement. Qwen Image 3.0 Pro Edit focuses on prompt-led edits with optional multiple visual inputs.
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Qwen Image 3.0 Pro Edit vs. Kling Image Editing: Kling image tools are often used for creative visual transformation. Qwen Image 3.0 Pro Edit is a practical fit for teams building edits into Qwen-based content workflows.
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: 'qwen-image-3.0-pro-edit',
prompt: 'Transform this image into a professional portrait with soft studio lighting',
width: 16,
height: 9,
image_url_list: ['https://example.com/image1.jpg'],
seed: 42
})
});
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': 'qwen-image-3.0-pro-edit',
'prompt': 'Transform this image into a professional portrait with soft studio lighting',
'width': 16,
'height': 9,
'image_url_list': ['https://example.com/image1.jpg'],
'seed': 42
}
)
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": "qwen-image-3.0-pro-edit",
"prompt": "Transform this image into a professional portrait with soft studio lighting",
"width": 16,
"height": 9,
"image_url_list": ["https://example.com/image1.jpg"],
"seed": 42
}'
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"