How to Generate a Consistent Series of Images with GPT-Image2: A Technical Guide

GPT-Image2 produces repeatable image series by combining a structured style library, prompt templates with series labels, and the APIMart generation client to maintain visual consistency across multiple frames.

The freestylefly/awesome-gpt-image-2 repository provides a complete framework for generating consistent image sequences using GPT-Image2. By leveraging structured templates and series-aware metadata, you can lock visual attributes across multiple frames while allowing controlled variations. This guide explains how to generate consistent series of images with GPT-Image2 using the repository's core components and JavaScript utilities.

Core Components for Consistent Generation

Style Library

The Style Library (data/style-library.json) acts as the central catalog of visual styles, tags, and categories. It drives the agent skill that selects templates and attaches consistent style metadata to every prompt, ensuring uniform aesthetics across a series.

Prompt Templates

Located in docs/templates.md, these human-readable blocks define composition, layout, visual style, and constraints. They enforce aspect ratios, text readability, and layout hierarchy before generation begins, providing the structural foundation for coherent multi-frame outputs.

Series-Aware Cases

The repository stores concrete examples in data/cases.json, including a 3×3 idol portrait series at line 7071. This example demonstrates how to lock subject, hairstyle, outfit, and lighting across nine frames using explicit series labels and grid directives.

Step-by-Step Workflow

1. Select a Template

Choose a template from docs/templates.md that matches your desired output type, whether UI screenshots, posters, or grid collages. Each template enforces specific constraints such as aspect ratio and layout hierarchy automatically.

2. Apply Series Metadata

Add a series tag (SeriesLabel, SeriesStyle) to your prompt. These metadata tags instruct GPT-Image2 to maintain identical visual attributes—such as lighting and subject appearance—across all frames while permitting minor variations in pose or background.

3. Submit via APIMart Client

Use the functions in src/apimartClient.js to submit the prompt. The submitPersonalGeneration function normalizes task IDs and automatically adds language flags before sending requests to the APIMart API endpoint.

4. Poll for Completion

Because each frame shares the same seed and style constraints, polling fetchPersonalTask returns a consistent visual set. Retrieve the image URLs once the task status returns succeeded to display them as a collage or animation sequence.

Implementation Examples

Using the CLI Skill

The agent skill defined in agents/skills/gpt-image-2-style-library/SKILL.md automates template selection and series constraint injection. Install it as the gpt-image-2-style-library npm package to generate ready-to-use prompts:

# Install the skill (once)

npm install -g gpt-image-2-style-library

# Generate a prompt for a 3×3 grid series

gpt-image-2-style-library generate \
  --category poster \
  --style "Korean Idol" \
  --series-label "Idol Portrait Series" \
  --grid 3x3 \
  --subject "young Korean female idol" \
  --out prompt.txt

Feed the generated prompt to the APIMart API:

curl -X POST https://api.apimart.ai/v1/images/generations \
  -H "Authorization: Bearer $APIMART_KEY" \
  -H "Content-Type: application/json" \
  -d @prompt.txt

Node.js Client Integration

For direct API access within a Node.js application, import the generation functions from src/apimartClient.js:

import { submitPersonalGeneration } from './src/apimartClient.js';
import fs from 'fs';

// Load the prompt generated by the skill
const prompt = fs.readFileSync('prompt.txt', 'utf8');

const apiKey = process.env.APIMART_API_KEY;
const language = 'en';

submitPersonalGeneration(prompt, apiKey, language)
  .then(({ taskId }) => console.log('Task submitted:', taskId))
  .catch(console.error);

Polling for Series Completion

Once submitted, poll the task until all frames in the series are ready:

import { fetchPersonalTask } from './src/apimartClient.js';

async function waitForSeries(taskId) {
  const apiKey = process.env.APIMART_API_KEY;
  const language = 'en';
  while (true) {
    const result = await fetchPersonalTask(taskId, apiKey, language);
    if (result.status === 'succeeded') {
      console.log('Series URLs:', result.images);
      break;
    }
    if (result.status === 'failed') throw new Error('Generation failed');
    await new Promise(r => setTimeout(r, 2000));
  }
}

Rendering the Grid

Display the resulting series as a cohesive collage:

<div style="display:grid;grid-template-columns:repeat(3,1fr);gap:8px;">
  <img src="IMAGE_URL_1" alt="frame 1">
  <img src="IMAGE_URL_2" alt="frame 2">
  <img src="IMAGE_URL_3" alt="frame 3">
  <img src="IMAGE_URL_4" alt="frame 4">
  <img src="IMAGE_URL_5" alt="frame 5">
  <img src="IMAGE_URL_6" alt="frame 6">
  <img src="IMAGE_URL_7" alt="frame 7">
  <img src="IMAGE_URL_8" alt="frame 8">
  <img src="IMAGE_URL_9" alt="frame 9">
</div>

Summary

• The Style Library (data/style-library.json) provides the metadata foundation for visual consistency across frames.
• Prompt Templates (docs/templates.md) enforce compositional constraints before generation begins.
• Series labels in data/cases.json (see line 7071) demonstrate how to lock attributes across multiple frames using explicit metadata.
• The APIMart client (src/apimartClient.js) handles submission and polling via submitPersonalGeneration and fetchPersonalTask.
• The agent skill (agents/skills/gpt-image-2-style-library/SKILL.md) automates prompt assembly and is available as the gpt-image-2-style-library npm package.

Frequently Asked Questions

What is the purpose of the series label in GPT-Image2 prompts?

The series label acts as a metadata anchor that instructs the model to maintain consistent subjects, lighting, and outfits across all frames in a generation batch, while allowing controlled variations in pose or background elements. This ensures visual coherence when displaying images as a sequence or collage.

How does the agent skill simplify series generation?

The agent skill (agents/skills/gpt-image-2-style-library/SKILL.md) automates template selection from docs/templates.md, injects series constraints from data/style-library.json, and returns a complete prompt compatible with the APIMart API. This eliminates manual formatting errors and ensures all required consistency metadata is present.

Can I generate series without using the npm package?

Yes, you can manually construct prompts using the templates in docs/templates.md and series examples in data/cases.json, then submit them directly via src/apimartClient.js using the submitPersonalGeneration function with your APIMart API key and language parameters.

Where can I find examples of multi-frame series configurations?

The repository includes a complete 3×3 grid example in data/cases.json at line 7071, demonstrating a Korean-idol portrait series that maintains consistent visual attributes across nine frames using explicit series labels and grid layout directives.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}