gpt-image-2-api-prompts

What this skill does

```markdown
---
name: gpt-image-2-api-prompts
description: Expertise in using the GPT-Image-2 API via Evolink, prompt engineering patterns, and curated visual workflows for image generation
triggers:
  - generate an image with GPT Image 2
  - use gpt-image-2 api
  - create image generation prompts
  - text to image with openai gpt image
  - gpt image 2 api integration
  - image generation prompt patterns
  - evolink image api
  - awesome gpt image prompts
---

# GPT-Image-2 API and Prompts

> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.

## What This Project Does

**awesome-gpt-image-2-API-and-Prompts** is a curated collection of 330+ prompt patterns, reference cases, and reusable visual workflows for the GPT-Image-2 model served via the [Evolink API](https://evolink.ai). It covers:

- Text-to-image and image-to-image generation
- Domain-specific prompt patterns: e-commerce, ad creative, portrait, poster, character design, UI mockups
- A callable skill (`gpt-image-2-gen-skill`) for agent integration
- A cinematic workflow combining GPT-Image-2 with Seedance 2.0

---

## Installation & Setup

### 1. Install the Callable Skill (Node.js)

```bash
npx evolink-gpt-image -y
```

### 2. Get Your API Key

1. Go to [https://evolink.ai/dashboard](https://evolink.ai/dashboard)
2. Create an account and generate an API key
3. Store it as an environment variable:

```bash
export EVOLINK_API_KEY="your_api_key_here"
```

### 3. Python Dependencies

```bash
pip install openai requests python-dotenv
```

---

## API Reference

**Base URL:** `https://api.evolink.ai/v1`

**Image Generation Endpoint:** `POST /images/generations`

**Headers:**
- `Authorization: Bearer YOUR_API_KEY`
- `Content-Type: application/json`

**Key Parameters:**

| Parameter | Type | Description |
|-----------|------|-------------|
| `model` | string | Always `"gpt-image-2"` |
| `prompt` | string | Text description of desired image |
| `n` | integer | Number of images (default: 1) |
| `size` | string | `"1024x1024"`, `"1792x1024"`, `"1024x1792"` |
| `quality` | string | `"standard"` or `"hd"` |
| `response_format` | string | `"url"` or `"b64_json"` |

---

## Code Examples

### Basic Text-to-Image (Python, requests)

```python
import os
import requests

api_key = os.environ["EVOLINK_API_KEY"]

response = requests.post(
    "https://api.evolink.ai/v1/images/generations",
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    },
    json={
        "model": "gpt-image-2",
        "prompt": "A beautiful colorful sunset over the ocean",
        "n": 1,
        "size": "1024x1024",
    },
)

data = response.json()
image_url = data["data"][0]["url"]
print(image_url)
```

### Using the OpenAI Python Client (Compatible)

```python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["EVOLINK_API_KEY"],
    base_url="https://api.evolink.ai/v1",
)

response = client.images.generate(
    model="gpt-image-2",
    prompt="Hyper-realistic miniature diorama product advertisement, luxury skincare bottle, tiny figurine workers on scaffolding, warm beige palette, studio photography, 8K",
    n=1,
    size="1024x1024",
    quality="hd",
)

print(response.data[0].url)
```

### Download and Save Image

```python
import os
import requests
from pathlib import Path
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["EVOLINK_API_KEY"],
    base_url="https://api.evolink.ai/v1",
)

def generate_and_save(prompt: str, output_path: str, size: str = "1024x1024") -> str:
    response = client.images.generate(
        model="gpt-image-2",
        prompt=prompt,
        n=1,
        size=size,
        quality="hd",
    )
    image_url = response.data[0].url
    img_data = requests.get(image_url).content
    Path(output_path).write_bytes(img_data)
    print(f"Saved to {output_path}")
    return image_url

generate_and_save(
    prompt="Cinematic hero image of a gourmet cheeseburger on dark stone surface, glossy brioche bun, melted cheese, warm side light, shallow depth of field, premium food commercial style",
    output_path="burger_hero.jpg",
    size="1792x1024",
)
```

### Batch Generation from Prompt List

```python
import os
import json
import time
import requests
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["EVOLINK_API_KEY"],
    base_url="https://api.evolink.ai/v1",
)

def batch_generate(prompts: list[dict], delay: float = 1.0) -> list[dict]:
    """
    prompts: list of {"id": str, "prompt": str, "size": str}
    """
    results = []
    for item in prompts:
        try:
            response = client.images.generate(
                model="gpt-image-2",
                prompt=item["prompt"],
                n=1,
                size=item.get("size", "1024x1024"),
                quality=item.get("quality", "standard"),
            )
            results.append({
                "id": item["id"],
                "url": response.data[0].url,
                "status": "success",
            })
        except Exception as e:
            results.append({"id": item["id"], "status": "error", "error": str(e)})
        time.sleep(delay)
    return results

# Load prompts from the repo's JSON file
with open("gpt_image_2_prompt.json") as f:
    prompt_data = json.load(f)

results = batch_generate(prompt_data[:5])
for r in results:
    print(r)
```

### cURL Quick Test

```bash
curl --request POST \
  --url https://api.evolink.ai/v1/images/generations \
  --header "Authorization: Bearer ${EVOLINK_API_KEY}" \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "gpt-image-2",
    "prompt": "A beautiful colorful sunset over the ocean",
    "n": 1,
    "size": "1024x1024"
  }'
```

---

## Prompt Patterns by Category

### E-commerce Product Shots

```python
ECOMMERCE_TEMPLATE = """
A hyper-realistic {style} product advertisement featuring {product_description}.
{scene_elements}.
Color palette: {palette}.
Studio photography style with soft diffused lighting, clean {background} background.
Tilt-shift miniature aesthetic, ultra-detailed, commercial product photography, 8K resolution, photorealistic CGI render.
"""

prompt = ECOMMERCE_TEMPLATE.format(
    style="miniature diorama",
    product_description="oversized luxury skincare pump bottle labeled 'LUXEVEIL Skin Science' in cream/beige with polished gold pump top",
    scene_elements="Tiny figurine construction workers in yellow coveralls and white hard hats, scaffolding, tower crane, industrial tanks",
    palette="warm beige, cream, gold, and mustard yellow",
    background="beige",
)
```

### Portrait & Photography

```python
PORTRAIT_TEMPLATE = """
{subject_description}, {lighting_style} lighting, {lens_style} lens,
{mood} mood, {color_grade} color grade, {camera_style} photography,
ultra-detailed, photorealistic, professional headshot composition.
"""

prompt = PORTRAIT_TEMPLATE.format(
    subject_description="close-up portrait of a woman in her 30s with natural makeup",
    lighting_style="soft golden hour",
    lens_style="85mm f/1.4 bokeh",
    mood="serene and confident",
    color_grade="warm film",
    camera_style="editorial",
)
```

### Poster & Illustration

```python
POSTER_TEMPLATE = """
{art_style} poster design for {subject},
{color_scheme} color scheme, {typography_notes},
{visual_elements}, {composition_style} composition,
high detail, print-ready quality, {resolution}.
"""

prompt = POSTER_TEMPLATE.format(
    art_style="vintage Art Deco",
    subject="an international jazz festival",
    color_scheme="deep navy, gold, and ivory",
    typography_notes="bold serif headline with geometric decorative borders",
    visual_elements="silhouette of saxophone player, geometric patterns, starbursts",
    composition_style="symmetrical centered",
    resolution="8K",
)
```

### UI & Social Media Mockups

```python
UI_MOCKUP_TEMPLATE = """
Clean {platform} UI mockup for {app_type} app,
{design_system} design language, {color