AI Applications

OpenAI GPT Image 2 Edit

Edit images with natural language using OpenAI GPT Image 2 Edit. Upload one or more references and generate high-fidelity transformed results.

Cost: 13 credits

Input

Upload one or more reference images

Upload source images to edit. Add clear references for better control.

Try:

Describe what to change and what to preserve. Use explicit constraints for text, style, and composition.

Select output aspect ratio; if omitted, provider may auto-detect from input image.

Higher resolution provides more detail and costs more credits.

Higher quality improves fidelity and costs more credits.

Output

Generated content will appear here

Example Results

Convert this image into a dramatic cinematic poster that merges modern visuals with historical documentary elements. Preserve the main horizon and depth, then add layered archival photo fragments, timeline cards, old newspaper textures, and museum-style annotation blocks. Include visible English title text and year labels that are clean and readable. Add premium poster typography hierarchy, subtle film grain, aged paper texture, and high-contrast color grading with deep shadows and warm highlights.

Image Edit:

Convert this image into a dramatic cinematic poster that merges modern visuals with historical documentary elements. Preserve the main horizon and depth, then add layered archival photo fragments, timeline cards, old newspaper textures, and museum-style annotation blocks. Include visible English title text and year labels that are clean and readable. Add premium poster typography hierarchy, subtle film grain, aged paper texture, and high-contrast color grading with deep shadows and warm highlights.

Frequently Asked Questions

What makes GPT Image 2 Edit different from text-to-image?
GPT Image 2 Edit starts from your reference images and applies instruction-based changes. It is ideal when you want transformation while preserving key structure or visual identity.
How does it compare to Nano Banana 2 Edit?
GPT Image 2 Edit is usually better for instruction-heavy transformations and cleaner in-image typography outcomes. Nano Banana 2 Edit is often more economical for high-volume iterative edits. Use GPT Image 2 Edit when precision and final polish matter most; use Nano Banana 2 Edit for cost-sensitive batch experimentation.
How do I avoid unwanted text artifacts in outputs?
Add explicit constraints in English such as: no readable text, no letters, no logos, no typographic signs, and no watermark.
What are good use cases for this model?
It works well for style transfer, environment replacement, cinematic recoloring, product scene restyling, and concept iterations from existing visual assets.
Can this model be used in commercial creative workflows?
Yes, many teams use it in production design and campaign ideation. Review policy and compliance requirements before public publishing.

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