Free to try Qwen Image 3.0 Edit API by Alibaba with one to three reference images, seven flexible aspect ratios, and seed control for reproducible Alibaba image editing.
Related Qwen Image 3.0 Models
Try the AI Image Generator now
Qwen Image 3.0 Edit Pricing
| Parameters | Price | Original Price | Discount |
|---|
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
Qwen Image 3.0 Edit API for Flexible AI Image Editing
Qwen Image 3.0 Edit API brings instruction-guided image editing and visual composition to developers, creative teams, and production workflows through Flaq AI. The model combines source images with natural-language directions to refine, transform, and recompose existing visuals. Its flexible input workflow makes it suitable for practical content updates, creative iteration, and scalable image editing applications.
Key Features of Qwen Image 3.0 Edit API
-
Instruction-Guided Image Editing: Transform source images with natural-language directions for visual changes, refinements, and creative adjustments.
-
Multi-Image Composition: Use multiple source visuals in a single editing workflow to combine references, guide composition, or build a new visual result.
-
Flexible Visual Refinement: Adjust subjects, environments, styling, and overall presentation while using the original image as creative context.
-
Layout-Aware Output: Adapt edited visuals to landscape, portrait, and square compositions for different channels and publishing needs.
-
Controlled Editing Iteration: Refine instructions across repeated tasks and use generation controls when consistent comparison is important to the workflow.
-
Production-Friendly API Integration: Add image editing to applications, asset pipelines, and creative tools through Flaq AI's task-based API workflow.
How to Use Qwen Image 3.0 Edit API for Image Editing on Flaq AI
-
Input: Source image URLs together with natural-language instructions describing the requested edit, composition, style, or visual direction.
-
Output: Edited images delivered through Flaq AI's image task workflow for use in applications and production pipelines.
-
Aspect Ratios: Flexible landscape, portrait, and square formats for marketing, social media, product pages, editorial layouts, and creative assets.
-
Capabilities: Prompt-based image editing, multi-image composition, visual refinement, layout adaptation, and repeatable creative iteration.
Best Use Cases for Qwen Image 3.0 Edit API Integration
-
Marketing Asset Updates: Refresh campaign visuals, adapt existing creative, and produce coordinated variations without rebuilding every asset from scratch.
-
Product Image Refinement: Adjust presentation, surroundings, composition, and creative direction for e-commerce and catalog workflows.
-
Social Media Adaptation: Rework existing visuals for posts, stories, thumbnails, and campaign formats while retaining the source material as context.
-
Creative Composition: Combine visual references and prompt instructions to explore new layouts, concepts, and design directions.
-
Design Iteration: Test targeted changes and refine visual concepts through repeatable API-based editing workflows.
Note: Please ensure source images and prompts comply with Flaq AI's content and safety requirements. If an editing request fails, review the inputs for restricted content, revise them, and try again.
Qwen Image 3.0 Edit vs Competitors: Comparative Analysis
-
Qwen Image 3.0 Edit vs. Qwen Image 3.0 Pro Edit
Qwen Image 3.0 Edit supports flexible editing and composition workflows. Qwen Image 3.0 Pro Edit is positioned for projects that prioritize more advanced editing and professional visual requirements. -
Qwen Image 3.0 Edit vs. Nano Banana 2 Edit
Nano Banana 2 Edit operates within Google's Gemini image ecosystem. Qwen Image 3.0 Edit offers an alternative within Alibaba's Qwen family for teams comparing editing behavior and workflow fit. -
Qwen Image 3.0 Edit vs. GPT Image
GPT Image provides generation and editing capabilities for OpenAI-centered integrations. Qwen Image 3.0 Edit supplies a Qwen-based editing path through Flaq AI's unified image task API. -
Qwen Image 3.0 Edit vs. FLUX Editing Workflows
FLUX-based editing workflows offer their own visual characteristics and implementation options. Qwen Image 3.0 Edit focuses on natural-language editing and multi-image composition within the Qwen ecosystem. -
Qwen Image 3.0 Edit vs. Seedream Edit
Seedream Edit provides an alternative approach to image transformation and creative refinement. Qwen Image 3.0 Edit is suited to developers who prefer Qwen-based composition workflows and unified Flaq AI integration.
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-edit',
prompt: 'Transform the input into a polished editorial portrait with soft studio lighting',
width: 16,
height: 9,
resolution: '1k',
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-edit',
'prompt': 'Transform the input into a polished editorial portrait with soft studio lighting',
'width': 16,
'height': 9,
'resolution': '1k',
'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-edit",
"prompt": "Transform the input into a polished editorial portrait with soft studio lighting",
"width": 16,
"height": 9,
"resolution": "1k",
"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"