Google/nano-banana-2-edit
nano-banana-2-edit
Fast and stable image editing via Gemini 3.1 Flash API. Ideal for real-time applications requiring reliability and a low-cost API entry point. Built for free testing and stable API workflows.
Image to ImageMulti-Image
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Nano Banana 2 Edit Pricing
| Parameters | Price | Original Price | Discount |
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Examples
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Fast & Affordable Nano Banana 2 Edit API (Gemini 3.1 Flash Image Editing Integration)
Google Nano Banana 2 Edit API (powered by the Gemini 3.1 Flash Image model) delivers cost-effective, production-grade AI image editing for developers and creative teams. This affordable Gemini image editing API integration helps you transform existing visuals into professional outputs through natural-language instructions. The Gemini model provides semantic reasoning for complex edits, while the API provides stable integration for scalable workflows on Flaq AI.
Key Features of Nano Banana 2 Edit API
- High-Fidelity Image Editing: Produce crisp, production-ready edited images with fine detail, realistic textures, and clean edges through affordable Gemini image editing API integration.
- Flash-Speed Image Processing: Edit images at exceptional speed while maintaining professional quality using the Gemini 3.1 Flash Image model, enabling rapid iteration and instant creative adjustments for time-sensitive workflows.
- Context-Aware Semantic Image Editing: Edit images with natural-language prompts while preserving object relationships, lighting logic, and scene coherence using the Gemini model's advanced understanding.
- Professional Camera-Style API Controls: Adjust lens perspective, focus, depth of field (bokeh), and color grading for advanced visual direction in API-based editing pipelines.
- Complete Aspect Ratio Flexibility: API supports 1:1, 4:3, 16:9, 21:9, and 9:16 outputs, suitable for ads, social media, product pages, and cinematic layouts.
- Consistent Character & Brand Rendering: Maintain identity consistency, brand elements, and visual style across multiple edited images through cost-effective API integration.
How to Use Nano Banana 2 Edit API for Professional Image Editing on Flaq AI
- Input: Existing image(s) (JPEG/PNG) plus natural-language editing prompts
- Output: High-resolution edited images (JPEG/PNG) through Nano Banana 2 Edit API integration
- Aspect Ratios: 11 supported formats including 16:9, 9:16, 1:1, 3:2, 2:3, 4:3, 3:4, 4:5, 5:4, 21:9, and 9:21.
- Capabilities: Prompt-based editing, style transfer, smart relighting, background replacement, and object-level manipulation powered by the Gemini Flash Image model
Best Use Cases for Nano Banana 2 Edit API Integration
- Marketing & Branding: Rapidly localize and refresh campaign creatives with high-quality AI image editing API workflows.
- Product Photography Optimization: Update backgrounds, lighting, and composition for catalog-ready product visuals without full reshoots using affordable API integration.
- Social Media Content Production: Edit and create high-volume image variants with consistent style and brand alignment for multi-platform publishing through cost-effective Gemini API.
- Professional Design Iteration: Accelerate concept refinement with controlled, semantic image edits for creative and design teams.
Note Please ensure your prompts comply with Google's Safety Guidelines. If an error occurs, review your prompt for restricted content, adjust it, and try again.
Nano Banana 2 Edit vs Competitors: Comparative Analysis
- Nano Banana 2 Edit vs. FLUX.1 [dev]
FLUX.1 [dev] focuses on detail-heavy technical generation workflows. Nano Banana 2 Edit API emphasizes semantic, instruction-driven editing with layout-aware changes, making it stronger for practical production editing scenarios using affordable Gemini integration. - Nano Banana 2 Edit vs. GPT-Image-1 (OpenAI)
GPT-Image-1 is a broad creative image model with wide stylistic range. Nano Banana 2 Edit API is optimized for targeted edits, fast processing speed, and predictable output control for professional teams through cost-effective API integration. - Nano Banana 2 Edit vs. Original Nano Banana
Original Nano Banana is optimized for rapid, lightweight iterations. Nano Banana 2 Edit prioritizes flash-speed editing with better reasoning, sharper rendering, and stronger consistency across complex multi-step edits using the Gemini Flash model. - Nano Banana 2 Edit vs. Seedream
Seedream is strong for stylized, fast visual generation. Nano Banana 2 Edit API is tuned for reliable editing, photorealistic refinement, and controlled composition in production pipelines with affordable pricing. - Nano Banana 2 Edit vs. Qwen Image 2509
Qwen Image performs well in open-source and document-style contexts. Nano Banana 2 Edit API differentiates with high-fidelity editing quality and robust design control for global content operations through Gemini API 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: 'nano-banana-2-edit',
prompt: 'Add flying cars and neon lights to this cityscape',
image_url_list: ['https://example.com/input-image.jpg'],
width: 16,
height: 9,
resolution: '2k'
})
});
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': 'nano-banana-2-edit',
'prompt': 'Add flying cars and neon lights to this cityscape',
'image_url_list': ['https://example.com/input-image.jpg'],
'width': 16,
'height': 9,
'resolution': '2k'
}
)
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": "nano-banana-2-edit",
"prompt": "Add flying cars and neon lights to this cityscape",
"image_url_list": ["https://example.com/input-image.jpg"],
"width": 16,
"height": 9,
"resolution": "2k"
}'
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"