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OpenAI/gpt-image-2-edit
gpt-image-2-edit
Flexible image editing by OpenAI GPT Image 2 Edit API with multi-image input support and quality controls. Stable and affordable for scalable editing workloads. Built for free testing and stable API workflows.
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GPT Image 2 Edit Pricing
| Parameters | Price | Original Price | Discount |
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Examples
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Fast & Affordable GPT Image 2 Edit API (OpenAI's Intelligent Image Editing Integration)
GPT Image 2 Edit API from OpenAI delivers cost-effective, production-grade AI image editing for developers and creative teams. This professional image editing API integration helps you transform existing visuals through natural-language instructions while preserving scene coherence, style consistency, and design intent. Built on OpenAI's latest GPT image editing stack, GPT Image 2 Edit combines semantic multimodal reasoning with flexible quality controls and multi-image input support for scalable editing workflows on Flaq AI.
Key Features of GPT Image 2 Edit API
- Instruction-Based Professional Image Editing: Transform existing visuals with natural-language prompts while preserving unedited regions, maintaining scene coherence, and delivering precise image modifications through GPT Image 2 Edit API integration.
- Multi-Image Input Support: Edit with multiple source images in a single workflow, enabling reference-based composition, style blending, layout control, and more advanced image-to-image editing scenarios.
- Flexible Quality Controls: Choose from multiple quality modes to match editing speed and output fidelity requirements across concept development, content iteration, and professional delivery.
- Context-Aware Semantic Editing: Make targeted visual changes with strong understanding of objects, lighting, perspective, and spatial relationships through OpenAI's multimodal image reasoning.
- Style & Identity Preservation: Adjust backgrounds, details, color grading, or objects while maintaining brand consistency, character identity, and the original visual tone across edits.
- Platform-Ready Aspect Ratio Support: Output edited images in multiple aspect ratios for ads, product visuals, landing pages, and social media formats.
How to Use GPT Image 2 Edit API for Professional Image Editing on Flaq AI
- Input: Existing image(s) plus natural-language editing prompts describing the desired modifications, refinements, or transformations.
- Output: High-resolution edited images delivered via secure CDN URLs through GPT Image 2 Edit API integration.
- Aspect Ratios: Supports multiple aspect ratios for different editing and publishing scenarios.
- Quality Options: Supports multiple quality settings for different speed and output requirements.
- Image Inputs: Supports image input workflows, including multi-image editing scenarios.
- Capabilities: Prompt-based editing, multi-image editing, background replacement, object refinement, style transfer, composition adjustment, and identity-preserving transformations through OpenAI GPT Image 2 Edit API integration.
Best Use Cases for GPT Image 2 Edit API Integration
- Marketing & Branding: Refresh campaign assets, localize visuals, and adapt branded creatives with precise instruction-based image editing through GPT Image 2 Edit API workflows.
- Product Photography Optimization: Update backgrounds, props, lighting, and scene details for catalog-ready product imagery without scheduling full reshoots.
- Social Media Content Production: Create multiple edited image variants for different platforms while keeping style, tone, and brand identity consistent across outputs.
- Professional Design Iteration: Accelerate design reviews and concept refinement with controllable, prompt-driven edits that support both single-image and multi-image workflows.
- Reference-Guided Creative Editing: Blend multiple source images, preserve key visual elements, and guide composition changes with multi-image input support through scalable OpenAI editing integration.
Note Please ensure your prompts comply with OpenAI's usage policies. If an error occurs, review your prompt for restricted content, adjust it, and try again.
GPT Image 2 Edit vs Competitors: Comparative Analysis
-
GPT Image 2 Edit vs. GPT Image 2 Edit Client
GPT Image 2 Edit Client offers the same core image editing capabilities with a very affordable access path for teams optimizing around cost efficiency. GPT Image 2 Edit maintains the same strong semantic editing, multi-image workflow support, and production-ready editing fit, making both variants suitable for professional editing pipelines depending on budget priorities. -
GPT Image 2 Edit vs. Runway Gen-4 References
Runway Gen-4 References is strong for cinematic, reference-driven visual workflows. GPT Image 2 Edit API differentiates with flexible multi-image editing, dependable instruction following, and efficient API-based editing for practical production use. -
GPT Image 2 Edit vs. Stable Image Edit
Stable Image Edit offers customization flexibility and open ecosystem appeal. GPT Image 2 Edit provides lower-friction API integration, polished semantic editing behavior, and strong out-of-the-box quality for teams that want reliable editing without infrastructure overhead. -
GPT Image 2 Edit vs. Qwen Image 2.0 Edit
Qwen Image 2.0 Edit is a strong value-oriented editing model with multilingual strengths. GPT Image 2 Edit API stands out with OpenAI's refined instruction following, robust multi-image workflow support, and dependable design-oriented editing control. -
GPT Image 2 Edit vs. Nano Banana 2 Edit
Nano Banana 2 Edit emphasizes Gemini Flash speed and affordable editing throughput. GPT Image 2 Edit focuses on polished semantic editing, flexible multi-image composition, and strong OpenAI workflow integration for professional editing pipelines.
Use the GPT Image 2 Edit Model Directly in Powerful AI Tools
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.
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: 'gpt-image-2-edit',
width: 1,
height: 1,
resolution: '1k',
prompt: 'Change the jacket color to deep navy, keep lighting consistent',
quality: 'medium',
image_url_list: ['https://example.com/source.jpg']
})
});
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': 'gpt-image-2-edit',
'width': 1,
'height': 1,
'resolution': '1k',
'prompt': 'Change the jacket color to deep navy, keep lighting consistent',
'quality': 'medium',
'image_url_list': ['https://example.com/source.jpg']
}
)
task_id = response.json()['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": "gpt-image-2-edit",
"width": 1,
"height": 1,
"resolution": "1k",
"prompt": "Change the jacket color to deep navy, keep lighting consistent",
"quality": "medium",
"image_url_list": ["https://example.com/source.jpg"]
}'
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"
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