GPT Image 2 | OpenAI Text-to-Image Generator Online

About this model

GPT Image 2 is a strong OpenAI model for text-to-image generation and reference-based image editing when you need instruction following, readable in-image text, structured layouts, and clear quality-based credit control.

When is this model useful?

Choose GPT Image 2 when you want one OpenAI model for both generation and editing, from quick visual drafts to cleaner marketing, product, editorial, and presentation assets.

Best-fit jobs

Text-to-image generation for banners, posters, presentations, infographics, social content, product scenes, and UI mockups.
Image-to-image edits when you need to change lighting, background, style, in-image text, or fine scene details with reference images.
Creative assets with visible text: covers, ad layouts, slides, packaging, menus, posters, and simple diagrams.
Composing multiple references into one output, such as product plus background, character plus style, or a layout with updated copy.
Workflows where teams need to compare several variants while controlling cost through quality and number of images.

Main advantages

The model follows written instructions well and is useful when composition, detail, and in-image text matter.
It supports both text-to-image and image-to-image workflows, so the same model can handle generation, editing, and reference-based work.
Reference images are handled at high fidelity automatically, without a separate input fidelity control.
It can generate up to 10 images per run and export PNG, JPEG, or WEBP.
AISVIT pricing is easy to understand because credits depend on quality and number of images.

Limitations

GPT Image 2 creates static images, not video, animation, or layered design files.
AISVIT currently exposes 1:1, 3:2, and 2:3 aspect ratios for this model.
Transparent backgrounds are not supported. Use GPT Image 1.5 or a remove-background workflow when you need transparent PNG output.
Even strong text rendering can still need proofreading for spelling, small typography, numbers, and logos before publishing.
This integration does not include a layer editor or brush mask, so local edits need to be described through prompts and references.

How to use this model

Start with a clear prompt, choose aspect ratio, quality, and number of images. For editing, add reference images and explain what should change and what should stay stable.

Simple workflow

Write a prompt that describes the subject, style, lighting, composition, materials, mood, and any exact words that must appear in the image.
For visible text, put the exact phrase in quotes and describe the surface: poster, packaging, infographic, banner, slide, or UI mockup.
Choose aspect ratio: 1:1 for square posts, 3:2 for horizontal visuals, or 2:3 for vertical posters and covers.
For image-to-image, upload one or more reference images and state what to change and what to preserve.
Choose quality. Low is better for fast drafts, medium is balanced, high is for more detailed final images, and auto reserves the same credit tier as high in AISVIT.

Supported input

Required: a text prompt.
Optional: one or more input images for editing, style transfer, composition, or reference guidance.
Reliable upload formats in AISVIT: JPG, PNG, and WEBP.
GPT Image 2 does not need a separate input fidelity control; it handles reference images at high fidelity automatically.

What you get

1 to 10 generated images depending on number of images.
WEBP, PNG, or JPEG output.
Supported frame shapes: 1:1, 3:2, and 2:3.
No transparent background output. Use GPT Image 1.5 or a remove-background workflow for transparent PNG files.