Flux Kontext Dev

An open-weight, text-guided image editing model.

Model Overview

FLUX.1 Kontext is a cutting-edge, open-weight model designed for advanced image editing using text prompts. It allows users to modify images with high precision and creativity, offering superior control and quality compared to many other image editing models.

Best At

This model excels at a wide range of image editing tasks, including:

• Style Transfer: Applying artistic styles like watercolor, oil painting, or sketches.
• Object and Clothing Modifications: Changing hairstyles, adding accessories, or altering colors.
• Text Editing: Precisely replacing text within images (e.g., on signs or labels).
• Background Swapping: Seamlessly changing the environment while maintaining the subject's integrity.
• Character Consistency: Ensuring a subject's appearance remains consistent across multiple edits.

Limitations / Not Good At

While powerful, the model may perform less optimally with:

• Highly abstract or nonsensical prompts.
• Extremely complex scenes requiring fine-grained control over numerous small elements simultaneously.
• Generating entirely new, photorealistic images from scratch (it's primarily an editing tool).

Ideal Use Cases

• E-commerce: Modifying product images, changing backgrounds, or adding details.
• Marketing: Creating visually appealing ad creatives and social media graphics.
• Content Creation: Illustrating blog posts, articles, or presentations with unique visuals.
• Graphic Design: Iterating on designs, changing text elements, or applying artistic effects.
• Personalization: Customizing photos with specific stylistic changes or edits.

Input & Output Format

• Input: An input image (JPEG, PNG, GIF, WEBP) and a text prompt describing the desired edits. Optional parameters include aspect ratio, inference steps, guidance scale, seed, output format, output quality, and safety checker disabling.
• Output: A URI pointing to the edited image.

Performance Notes

• The go_fast parameter can speed up generation at the cost of slight quality degradation for challenging prompts.
• Offers control over num_inference_steps and guidance for balancing speed and quality.
• Setting output_quality impacts file size and fidelity for formats like JPG, but not for PNG.