Image Generation

The IONOS CLOUD AI Model Hub provides an OpenAI-compatible API that enables high-quality image generation using state-of-the-art foundation models. By inserting descriptive prompts, users can create detailed images directly through the API without the need for managing underlying hardware, or infrastructure.

Supported image generation models

AI Model Hub offers varied text-to-image models. For more information, see models list.

Overview

In this guide, you will learn how to generate images using foundation models through the IONOS API. This guide is intended for developers with basic knowledge of:

• REST APIs
• A programming language for handling REST API endpoints (Python and Bash examples are provided)

By the end, you will be able to:

1. Retrieve a list of available image generation models in the IONOS CLOUD AI Model Hub.
2. Use prompts to generate images with these models.

Getting started with image generation

To use image generation models, first set up your environment and authenticate using the OpenAI-compatible API endpoints.

Download the respective code files to use image generation-specific scripts and examples and generate the intended output:

Python Notebook

Download this Python Notebook file to use image generation-specific scripts and examples and generate the intended output.

148KB

ai-model-hub-image-generation.ipynb

Open

Python Code

Bash Code

Step 1: Retrieve Available Models

Fetch a list of models to see which are available for your use case:

Python

# Python example to retrieve available models
import requests

IONOS_API_TOKEN = "[YOUR API TOKEN HERE]"

endpoint = "https://openai.inference.de-txl.ionos.com/v1/models"

header = {
    "Authorization": f"Bearer {IONOS_API_TOKEN}", 
    "Content-Type": "application/json"
}
requests.get(endpoint, headers=header).json()

Bash

This query returns a JSON document listing each model's name, which you’ll use to specify a model for image generation in later steps.

Step 2: Generate an image using a prompt and FLUX.1-schnell

To generate an image, send a prompt to the /images/generations endpoint. Note the following parameter restrictions for FLUX.1-schnell:

• size: Must be one of "1024x1024", "1792x1024", or "1024x1792".
• n: Defaults to 1. Only 1 is accepted.

Python

# Python example for image generation
import requests

IONOS_API_TOKEN = "[YOUR API TOKEN HERE]"
MODEL_NAME = "[MODEL NAME HERE]"
PROMPT = "A futuristic cityscape at sunset, highly detailed"

endpoint = "https://openai.inference.de-txl.ionos.com/v1/images/generations"

header = {
    "Authorization": f"Bearer {IONOS_API_TOKEN}", 
    "Content-Type": "application/json"
}
body = {
    "model": MODEL_NAME,
    "prompt": PROMPT,
    "size": "1024x1024"
}
requests.post(endpoint, json=body, headers=header)

Bash

Step 3: Generate an image using a prompt and FLUX.2-klein-4B

To generate an image, send a prompt to the /images/generations endpoint. Note the following parameter restrictions for FLUX.2-klein-4B:

• size: Width and height in "widthxheight" format. Both dimensions must be multiples of 16 and between 64 and 2048. Default: "1024x1024". Example values: "1024x1024", "1536x1024", "1024x1536", "2048x2048", "2048x1152".
• n: Number of images to generate. Defaults to 1. Accepts values from 1 to 10.
• output_format: Format of the returned image. Accepted values: jpg, jpeg, png, webp.

Python

# Python example for image generation with FLUX.2-klein-4B
import requests

IONOS_API_TOKEN = "[YOUR API TOKEN HERE]"
MODEL_NAME = "black-forest-labs/FLUX.2-klein-4B"
PROMPT = "A futuristic cityscape at sunset, highly detailed"

endpoint = "https://openai.inference.de-txl.ionos.com/v1/images/generations"

header = {
    "Authorization": f"Bearer {IONOS_API_TOKEN}", 
    "Content-Type": "application/json"
}
body = {
    "model": MODEL_NAME,
    "prompt": PROMPT,
    "size": "1024x1024",
    "output_format": "png"
}
requests.post(endpoint, json=body, headers=header)

Bash

Step 4: Extract and interpret the result

The returned JSON includes several key fields, most importantly:

• data.[].b64_json: The generated image in base64 format.

What you learned

In this guide, you learned how to:

1. Access available image generation models.
2. Use descriptive prompts to generate high-quality images, ideal for applications in design, creative work, and more.

For information on text generation, see our dedicated guide on Text Generation models.