FLUX.1 Kontext [dev]

Deploy FLUX.1 Kontext [dev] model on Koyeb’s high-performance cloud infrastructure.

With one click, get a dedicated GPU-powered inference endpoint ready to handle requests with built-in autoscaling and scale-to-zero.

Overview of FLUX.1 Kontext [dev]

FLUX.1 Kontext [dev] is a 12 billion parameter model designed for editing images based on text instructions. Ideal for researchers and non-commercial users, it excels in image editing tasks requiring precise local and global edits.

FLUX.1 Kontext [dev] will be served with the vLLM inference engine, optimized for high-throughput and low-latency model serving.

The default GPU for running this model is the Nvidia RTX-A6000 instance type. You are free to adjust the GPU instance type to fit your workload requirements.

Quickstart

The FLUX.1 Kontext [dev] one-click model is served using the vLLM engine. vLLM is an advanced inference engine designed for high-throughput and low-latency model serving. Optimized for large language models, it provides efficient performance and compatibility with the OpenAI API.

After you deploy the FLUX.1 Kontext [dev] model, copy the Koyeb App public URL similar to https://<YOUR_DOMAIN_PREFIX>.koyeb.app and create a simple Python file with the following content to start interacting with the model.

import base64
from io import BytesIO

import httpx
from PIL import Image

KOYEB_URL = "https://<YOUR_DOMAIN_PREFIX>.koyeb.app"


def b64_to_pil(base64_string):
    """
    Convert a Base64 string to a PIL Image.
    :param base64_string: Base64 encoded image string
    :return: PIL Image object
    """
    # Remove the header if present
    if base64_string.startswith("data:image"):
        base64_string = base64_string.split(",")[1]

    # Decode the Base64 string
    image_data = base64.b64decode(base64_string)

    # Create a PIL Image from the decoded binary data
    return Image.open(BytesIO(image_data))


payload = {
    "prompt": "Add a hat to the cat",
    "input_image_url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png",
}

# Call the model precition endpoint
res = httpx.post(
    f"{KOYEB_URL}/predict",
    json=payload,
    timeout=60.0,
)

# Get the output image
res = res.json()
output = res.get("images")[0]

# Convert the base64 model output to an image and save it to disk
img = b64_to_pil(output)
img.save("output_image.jpg")

The snippet above showcases how to interact with the FLUX.1 Kontext [dev] model to edit an image from a text prompt and save it to disk.

Take care to replace the KOYEB_URL value in the snippet with your Koyeb App public URL.

Executing the Python script generate an image and save it to disk.

python main.py