FLUX.1 Kontext Guide: Image Editing, Variants, and Access

FLUX.1 Kontext is Black Forest Labs' family of models for generating and editing images from text and visual context. Pro and max are hosted services; dev is a separate downloadable image-editing checkpoint. 1, 2

Its defining workflow uses an existing image and a requested change as context for producing a revised picture. 1

What are the key facts about FLUX.1 Kontext?

What kinds of image edits can FLUX.1 Kontext make?

Kontext supports edits directed by ordinary language, including changing an object, preserving a character across scenes, and applying a reference style. BFL demonstrates these tasks as core capabilities of the family. 1

The API documentation also shows text replacement inside images. It describes using visible annotation boxes to identify a region when a text edit needs more precise targeting. 6

A practical edit request should name both the intended change and the properties you want preserved. For example: “Change the jacket to dark green. Preserve the person's face, pose, and background.”

That instruction gives you an explicit review task. Inspect the jacket color, then compare the face and pose with the input rather than judging only the overall appearance.

The authors' technical report describes style reference and visual-cue editing without requiring a separate fine-tuning step for each reference. Its examples include preserving surrounding context during text changes. 7

What are the limits of FLUX.1 Kontext editing?

The technical report documents identity loss, incorrect instruction execution, and visible artifacts after repeated editing. The launch announcement also identifies limits in world knowledge. 1, 7

Use the original image as your reference throughout the process. Compare details after every accepted revision, especially when a later edit changes the face, product outline, or written information unintentionally.

BFL's current support documentation describes Kontext as a previous-generation offering that remains available. It identifies approximately one-megapixel output and points to FLUX.2 for higher-resolution and multi-reference workflows. 5

The editing API accepts an input image as an encoded image or URL. Its documented input limits are separate from the output resolution, so a large uploaded image does not imply an equally large generated file. 6

The public dev model is designed for image editing. Do not assume that every capability described for hosted pro and max is also a supported dev workflow. 2, 3

For deployment, select a variant before estimating the surrounding work. Hosted requests require service integration; dev requires a compatible runtime and compliance with its weight license. 2, 4

How do you use FLUX.1 Kontext in the Playground or API?

Choose a route

For browser use, BFL's Playground supports Kontext pro and max. Its help article describes uploading an image, entering an editing instruction, generating a result, and downloading the selected output. 8

For application integration, use the documented flux-kontext-pro endpoint. Create a BFL account and API key through its quick start, then install Python's requests package for the example below. 6, 9

Set BFL_API_KEY in your environment. Set INPUT_IMAGE_URL to an accessible source image you control; the request below submits an edit and prints the returned polling URL. 6

import os
import requests

response = requests.post(
    "https://api.bfl.ai/v1/flux-kontext-pro",
    headers={"x-key": os.environ["BFL_API_KEY"]},
    json={
        "input_image": os.environ["INPUT_IMAGE_URL"],
        "prompt": "Make the jacket dark green; preserve face, pose, and background.",
        "output_format": "png",
    },
    timeout=60,
)
response.raise_for_status()
print(response.json()["polling_url"])

Poll that returned URL until the status is Ready, then retrieve the image at result.sample. Handle terminal failure statuses, and download promptly because BFL documents a limited lifetime for the result URL. 6

Review the requested change

Place the input and output side by side. First check whether the jacket changed as requested; then inspect the face, pose, and background for changes that the brief did not authorize.

If the result fails, return to the input and simplify the instruction. Keep the failed example with a brief explanation so you can distinguish recurring limitations from one unsuccessful output.

For a label edit, transcribe the desired wording exactly in your request and check every character afterward. Decide in advance whether line breaks, typography, and placement are also acceptance requirements.

Consider local dev access

If you need downloadable weights, use the official Kontext dev model card. It provides a FluxKontextPipeline example with an input image and points to compatible ComfyUI and reference-code implementations. 3

The Kontext open-weight guide covers that route. Keep the dev license and deployment requirements distinct from a hosted API account.

How does FLUX.1 Kontext compare with Krea dev and FLUX.2?

Use Kontext when your evaluation starts from an existing image and a defined revision. Consider Krea for text-led scene creation, or FLUX.2 when multiple references are central to the task.

For the local workflow concepts behind these choices, see the ComfyUI pillar. Compare actual supported tasks rather than assuming family names imply interchangeable inputs.

What should you know before choosing a Kontext variant?

Is FLUX.1 Kontext available as open weights?

FLUX.1 Kontext dev is downloadable under a non-commercial model license. Hosted pro and max do not have open weights. 2, 4

Does Kontext generate images from text alone?

Hosted FLUX.1 Kontext pro and max support text-to-image generation as well as editing. BFL describes the dev release as focused on editing tasks. 1, 2

Can it change text inside an image?

FLUX.1 Kontext can replace text inside images, and BFL documents targeting with annotation boxes. Review the returned wording and layout because the model can still fail an instruction. 6, 7

Does it preserve a character perfectly across edits?

FLUX.1 Kontext supports character preservation across edits, but does not guarantee perfect identity consistency. The technical report includes identity degradation and other editing failures. 7

Sources

• Kontext family launch
• Public Kontext dev release
• Kontext dev model card
• Official repository and hosted model access
• BFL's current Kontext support overview
• Kontext editing API documentation
• Kontext authors' technical report
• BFL Playground documentation
• BFL API setup
• Krea dev model card
• FLUX.2 release announcement

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