flux-kontext-pro is the image generation and editing model for Black Forest Labs FLUX.1 Kontext [pro], focused on context-aware modifications to existing images. It can both create new images from text and perform editing instructions based on the original image, making it suitable for adjusting product displays, character assets, and design drafts. On this platform, generation, editing, and result retrieval can all be completed through the same image interface.
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
The above are this platform's invocation specifications; the native capabilities of FLUX.1 Kontext [pro] and the control options that can be submitted through the interface should be understood separately.
Learn what flux-kontext-pro can bring to your work.
When editing, you do not need to describe the entire image from scratch. Instead, provide the original image and specify the objects, attributes, or areas you want to change. For example, adjust an object's color, add accessories to a person, or modify specified elements in the image. Its focus is on making changes based on the image context while maintaining consistency with the original composition and unmodified content.
The same model provides both text-to-image generation and image editing. You can first create a draft using descriptions of the scene, subject, lighting, and aspect ratio, then use the selected result as editing input and continue requesting changes. This workflow is suitable for establishing a visual direction first and then gradually refining details, rather than regenerating the entire image every time.
Results are delivered as image links, making them convenient for business systems to display, download, or send into subsequent review workflows. When backend processing is needed, you can use asynchronous task queries or configure completion callbacks. Applications can associate task identifiers with asset records, separating submission, waiting, and result reception, so users do not need to remain on the submission page.
Start with specific tasks to find where the model can be effective.
Input an existing product display image, clearly specify the colors, scene elements, or decorations that need adjustment, and state which product subject and composition should be preserved. The output can be used to compare different presentation options and help designers select a direction. When packaging text, trademarks, and product structure are involved, inspect each item before use to avoid mistaking generated changes for real product details.
Based on an existing photo or character design image, request specific changes such as adding accessories or adjusting clothing elements to obtain modified candidate assets. Compared with describing a person again for regeneration, the original image provides clear visual context. This is suitable for avatar design, event visuals, and character draft adjustments, but facial features and key identifying characteristics still require manual review.
First generate a concept image, then successively modify objects, color schemes, or scene details in the selected draft to create proposals that are easy to review. Save the input image, instructions, and results at every step to clearly show the modification process. The output is suitable as a visual proposal and reference for subsequent production, rather than a direct replacement for design files requiring precise layers, dimensions, or structure.
Choose based on task complexity, input materials, and expected results.
When the task is to preserve the main content of an image while changing only some elements, Kontext Pro's contextual editing alignment is better suited to the need. If you are simply exploring an entirely new composition from text, you can also use its generation mode; when choosing, distinguish between “creating a new image” and “modifying an existing image,” and compare results using actual materials rather than relying only on model names.
Kontext Pro is suitable as a starting point for everyday contextual editing; for complex editing requirements, include Kontext Max in the comparison and evaluate detail performance using the same source image and instructions. Kontext [dev] is another open-weight version, so do not directly apply its local deployment method, parameters, or license to Pro, and do not treat FLUX 2 capabilities as features of this model.
From a small-scale task to production integration.
Define the goal, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand output quality and the scope of capabilities.
Answers to common questions about using flux-kontext-pro.
Yes. Use the generate operation, submit a prompt and image aspect ratio size, and explicitly specify model as flux-kontext-pro. The prompt can describe the subject, scene, composition, and lighting; if the goal is to modify existing material, use edit instead and provide the original image link.
First identify the object to modify, then describe the desired change, and add what needs to be preserved. For example, “Change the bottle body to dark blue while preserving the cap, label, and background composition.” Try to avoid requesting multiple vague changes at once; complex tasks can be split into consecutive steps, checking the result after each step before continuing.
This model uses image aspect ratio to control the canvas, so size should contain ratios such as 1:1, 16:9, or 9:16 rather than pixel dimensions. Ratios express square, landscape, or portrait compositions and should not be understood as a promise of a specific fixed output pixel size.
The editing input here is the original image specified by image_url, and count is not used for editing tasks. When processing multiple assets, submit editing tasks separately and manage their results individually. Generation tasks can use count to request multiple candidates, but this is not the same feature as multi-original-image editing or multi-image reference.
You can use the image link returned from the previous round as the next image_url, then submit a new edit instruction to make step-by-step modifications. It is recommended to keep the original image and the results from each round for easier rollback and comparison; with continuous editing, you should still check changes to the subject, text, and composition rather than assuming every round will fully preserve everything else.
Model information · Updated: 2026-10-01. See the API and pricing sections for calling parameters and billing rules.
Start with a clear goal and judge from real results whether it suits your work.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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