FLUX.1 Krea [dev]

Overview

FLUX.1 Krea is a powerful open-weight model for text-to-image generation with high-quality outputs that break free from the typical "AI look". This model is the result of a close collaboration between Black Forest Labs and Krea – an example of what happens when two world-class research teams join forces. The model has full ecosystem compatibility with FLUX.1-dev tools and workflows.

Optimized for large-scale production-grade inference, FLUX.1 Krea [dev] runs seamlessly on the DataCrunch GPU infrastructure and inference services, ensuring low-latency and cost-efficient generation without compromising output quality.

Our secure and easy API access to FLUX.1 Krea lets startups and enterprises alike focus on crafting products – not managing infrastructure. With DataCrunch, you get high-quality image generation, minimal cold starts, and out-of-the-box elastic scaling – ready to integrate.

Getting Started

Before generating images, make sure your account is ready to use the Inference API. Follow the Getting Started guide to create an account and top up your balance.

Authorization

To access and use these API endpoints, authorization is required. Please visit our Authorization page for detailed instructions on obtaining and using a bearer token for secure API access.

Generating images

Parameters

prompt (string)

The text description to generate an image from. This is the core input that drives the output.

---

size (string)

The resolution of the output image in "width*height" format. Default: "1024*1024"

---

num_inference_steps (integer)

How many denoising steps to run during generation. More steps may improve quality, at the cost of speed. Default: 28

---

seed (integer)

A seed value for reproducibility. The same seed + prompt + model version = same image. Use -1 to randomize. Default: -1

---

guidance_scale (float)

CFG (Classifier-Free Guidance) controls how closely the image follows the prompt. Higher values = stronger prompt adherence, but may reduce creativity. Default: 3.5

---

num_images (integer)

How many images to generate per request. Default: 1

---

enable_safety_checker (boolean)

If enabled, content will be checked for safety violations. Default: true

---

output_format (string)

The file format of the generated image. Possible values: "jpeg", "png", "webp" Default: "jpeg"

---

output_quality (integer)

Applies to "jpeg" and "webp" formats. Defines compression quality. Range: 1–100 Default: 95

---

enable_base64_output (boolean)

If true, the API will return the image as a base64-encoded string in the response. Default: false

Text to image

cURL

curl --request POST "https://inference.datacrunch.io/flux-krea-dev/runsync" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer <your_api_key>" \
--data '{
    "input": {
        "prompt": "an F1 car on track in front of a fancy hotel",
        "num_inference_steps": 50,
        "enable_base64_output": true
    }
}'

Python

import requests
import os

token = "<your_api_key>"  # Replace with your actual key
bearer_token = f"Bearer {token}"

url = "https://inference.datacrunch.io/flux-krea-dev/runsync"
headers = {
    "Content-Type": "application/json",
    "Authorization": bearer_token
}
data = {
    "input": {
        "prompt": "an F1 car on track in front of a fancy hotel",
        "num_inference_steps": 50,
        "enable_base64_output": True
    }
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

JavaScript

const axios = require('axios');

const token = '<your_api_key>'; // Replace with your actual token
const url = 'https://inference.datacrunch.io/flux-krea-dev/runsync';
const headers = {
  'Content-Type': 'application/json',
  'Authorization': `Bearer ${token}`
};
const data = {
  input: {
    prompt: 'an F1 car on track in front of a fancy hotel',
    num_inference_steps: 50,
    enable_base64_output: true
  }
};

axios
  .post(url, data, { headers: headers })
  .then((response) => {
    console.log(response.data);
  })
  .catch((error) => {
    console.error('Error:', error);
  });