flux-kontext-max is a model in the Black Forest Labs Kontext series designed for complex image editing. It can generate new images from text or edit them using existing images and modification instructions. It is suited to continued creation around an established visual: adjusting image elements, modifying local details, and aiming for overall consistency for product presentations, creative design, and marketing asset iteration.
Submit requests to the public API at api.acedata.cloud using the documented parameters, then use the results in your application.
Input parameters and result formats vary by service. Use the public API for this model and follow its guide for generation, task retrieval and editing operations.
Clarify capacity, inputs and outputs, and calling methods before choosing a model.
The above are the creation and API specifications for this platform entry. Image ratios do not correspond to fixed output pixels and do not represent the native model's resolution limit.
Learn what flux-kontext-max can bring to your work.
Kontext Max focuses on context-aware editing: using an existing image as a starting point and describing the objects or local details that need to change through text. It is suitable for tasks where the composition is already established and only further adjustments are needed. Clearly stating both the intended changes and the elements to preserve helps express creative intent more clearly.
When an image needs to balance the subject, background, and overall style, Kontext Max can be considered for complex editing. It aims to modify details while maintaining image consistency, making it suitable for further exploration of existing designs. In practice, distinguish primary changes from secondary requirements to avoid conflicting descriptions.
The same model provides both generate and edit operations. You can first create a visual draft with text, then use the selected image link for subsequent modifications. Completed results are delivered as image links and support asynchronous task processing, making it easy to connect asset generation, previewing, manual selection, and continued editing.
Start with specific tasks to identify where the model can be effective.
Provide an existing product showcase image, describe the background, scene elements, or local visual details you want to adjust, and specify the product appearance that must be preserved. The deliverable is a set of modified showcase image candidates, suitable for comparing different display approaches; before formal use, check that packaging text, logos, and product structure are accurate.
Based on an already selected advertising sketch, request specific changes, such as changing the background mood or adjusting an image element. You can organize candidate assets in landscape, square, or portrait formats, then have designers complete the layout and finalization. It is suitable for supporting visual exploration and should not replace final brand guideline checks.
Use character, scene, or illustration concept art as input, and describe the focus of this round of changes in text. Keep the original and the result each time, select an appropriate version, and continue editing to create a comparable record of the creative process. Deliverables can be used for design discussions and proposal reviews, reducing the work of rebuilding images from blank prompts each time.
Choose based on task complexity, input materials, and expected results.
flux-kontext-pro and flux-kontext-max are both designed for contextual image editing. If the task involves only a clear, single change, evaluate Pro first; if you need to account for more image relationships or have higher standards for the final visual, try Max first. When choosing, compare results using the same source image and instructions, focusing on whether the changes are properly made and the subject is preserved, rather than only on version names.
flux-kontext-max and flux-2-max are not the same model. The key consideration for Kontext Max is making contextual changes to existing images. If your primary work is creating entirely new visuals from text, you can also compare other Flux generation models. FLUX.1 Kontext [dev] is another version, and its open weights, deployment methods, or parameters should not be directly applied to Max.
From a small-scale task to formal 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.
Understand output quality and capability scope before formal use.
Answers to common questions about using flux-kontext-max.
No. It supports generate for text-to-image generation and edit for image editing. For generation, describe the image you want; for editing, provide image_url and modification instructions. Both operations should submit action, prompt, and size, and explicitly specify the model as flux-kontext-max.
First describe the object to modify, then the desired result, and add the subject, composition, or style you want to preserve. For example, adjust the background to a studio setting while keeping the product appearance unchanged. Complex tasks can be split into separate candidates for comparison to avoid putting conflicting requirements into a single instruction.
Pass the image aspect ratio through size, for example 1:1 for square images, 16:9 for landscape images, and 9:16 for portrait images. This controls the ratio, not exact pixel dimensions. Even for editing tasks, it is recommended to explicitly submit the ratio to help maintain the required delivery format.
Both are suitable for editing based on an original image and text. For routine modifications, compare Pro first; for complex editing, prioritize evaluating Max. It is best to test with the same input and observe modification accuracy and overall consistency; the name Max does not mean every original image and every instruction will necessarily produce more suitable results.
count does not apply to editing tasks; multiple editing candidates should be submitted separately. For background processing, use async=true and query the result after obtaining task_id, or receive completion notifications through callback_url. Finally, read image_url from the returned data for preview or download.
Model information · Updated: 2026-10-01. For request parameters and billing rules, see the API and pricing sections.
Choose a development approach or application suited to this model and follow its setup guide.
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