Image2 is Here - A Complete Guide to All Its Capabilities

Image2 is here.

On April 21, OpenAI quietly pushed image2 to OpenAI and Codex without a发布会, no preview, just went live directly. Within 12 hours it topped the Image Arena leaderboard with 1512 points, 242 points ahead of the second place — the largest score gap in the chart’s history.

As someone who’s been following AI image tools for a long time, I wasn’t disappointed this time. After a week of practical testing, I’ve compiled a set of effective usage methods to share with you today.

First Thing: Where’s the Entry Point

If you want to use it directly, the simplest way is through OpenAI. Click the ”+” button in the input box and select “Create Image”. Free users currently get 2-3 images per day, while Plus users can use the more powerful “Thinking Mode”.

Thinking Mode takes longer but offers higher text accuracy and more complex composition capabilities. If you have high requirements for generated results, I recommend subscribing to Plus to use Thinking Mode.

Prompt Formula: Write This Way and Won’t Fail

After a week of踩坑, I’ve summarized a image2-specific prompt formula:

【Visual Style】+【Scene Background】+【Core Subject】+【Precise Details and Text】+【Layout and Constraints】

Let me give an example. A successful product image prompt:

Cinematic-quality product photography. Scene set on a dark gray rough stone surface with a dim background showing only a small amount of smoke. The subject is a square black glass men’s perfume bottle, placed slightly tilted. Details: the front of the perfume bottle features gold English letters “SPECIAL” in a sans-serif font, with realistic tiny water droplets on the bottle surface. Constraints: right-side single light source with hard lighting, casting clear contour shadows, high contrast and cool tone throughout, no other objects besides the perfume.

The core of this formula: first set the style tone, then describe scene and subject, then use specific details to constrain results, finally use exclusion conditions to lock down what should not appear.

Text Rendering: Finally No More Failures

In the past, using AI drawing, the thing I feared most was having it write Chinese. Either there were typos, or the text turned into garbled characters.

image2 has basically solved this problem in this generation. Practical testing shows horizontal short sentences and title-style text have near-zero error rates, and long paragraphs of Chinese only occasionally have small issues with punctuation density.

Key technique: Use double quotes around text you want to render.

Whether Chinese or English, any specific text you want to appear in the image must be enclosed in double quotes in your prompt. For example:

“The sign reads ‘Open for Business’” “The T-shirt front reads ‘Happy Weekend’”

Combined with specific position descriptions like “centered” or “upper left corner”, text rendering accuracy will improve another level.

Complex Composition: Use Thinking Mode

For images containing multiple elements requiring precise spatial relationships, the normal mode tends to lose track of some elements. This is when you need to enable “Thinking Mode”.

For example, if you want to generate an image with these elements: a girl in a red dress standing on the left, an orange cat in the middle, and a line of text at the bottom. When multiple elements are constrained simultaneously, Thinking Mode can better coordinate the overall composition.

Note that Thinking Mode takes 15-30 seconds or even longer per generation, and complex scenes may require waiting over a minute. This is trading speed for quality.

Editing Feature: Make Small Changes Without Regenerating

Many people don’t know that Image2 supports partial editing, and the editing logic is very intuitive.

The method is: upload an existing image, then tell it what to “keep” and what to “change”.

For example, if you’ve generated an image and want to change the background from indoor to a seaside scene, just say “Keep the character and costume unchanged, change the background to a seaside sunset”. AI will understand your intent and only change the background without affecting the subject.

This feature is especially useful when you need a series of images but only want to adjust some elements. Instead of regenerating the entire set each time, just modify the局部 and you get a new variant.

Style Consistency: How to Make a Series Look Like a Set

When you need to generate a series of images maintaining consistent style, there’s a practical technique.

After generating the first image, you can ask AI for the “Seed” number corresponding to this image set, then add the following at the beginning of subsequent prompts:

“Maintain consistent visual style with the previous images, reference Seed number: [number], modify [specific elements] based on this”

固化 style-related modifiers into templates and bring them each time. This way, even if you operate days apart, images in the same series can maintain visual unity.

FAQ

Q: How big is the difference between free and paid versions?

Free version: 2-3 images per day, instant mode only, suitable for trial. Paid version (Plus, $20/month): can use Thinking Mode with more generous daily limits, suitable for users with batch needs.

Q: How long does it take to generate one image?

Instant mode usually takes 20-60 seconds. Thinking Mode takes 30 seconds to 2 minutes depending on complexity. May be slower during peak hours.

Q: What image sizes can be generated?

Supports various aspect ratios and sizes including square (1:1), landscape (16:9), portrait (9:16), etc. Choose the appropriate ratio based on your use case.

Q: Which scenarios are not suitable?

Complex hand movements (piano playing, knitting, etc.), dense crowds (15+ people), industrial drawings requiring strict physical logic — these scenarios still have high failure rates with current models, manual processing is recommended.

Summary

image2 is currently the AI image tool closest to “usable in actual production”. The breakthrough in text rendering finally makes Chinese scenes trustworthy, and multi-element control and editing capabilities make daily workflows more efficient.

I recommend starting with simple scenarios to get familiar with the model’s capability boundaries before attempting complex compositions. When encountering problems, iterate multiple times — in most cases, you’ll get satisfactory results.