Flux.1-Kontext-pro

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Generate and edit images through both text and image prompts. Flux.1 Kontext is a multimodal flow matching model that enables both text-to-image generation and in-context image editing. Modify images while maintaining character consistency and performing local edits up to 8x faster than other leading models.

The model provides powerful text-to-image and image-editing capabalities:

1. Change existing images based on an edit instruction.
2. Have character, style and object reference without any finetuning.
3. Robust consistency allows users to refine an image through multiple successive edits with minimal visual drift.

FLUX.1 Kontext [pro] consistently ranks among the top performers across all tasks, achieving the highest scores in text editing and character preservation while consistently outperforming competing state-of-the-art models in inference speed.

We show evaluation results across six in-context image generation tasks. FLUX.1 Kontext [pro] consistently ranks among the top performers across all tasks, achieving the highest scores in Text Editing and Character Preservation.

FLUX.1 Kontext models consistently achieve lower latencies than competing state-of-the-art models for both text-to-image generation (left) and image-editing (right)

FLUX.1 Kontext models demonstrate competitive performance across aesthetics, prompt following, typography, and realism benchmarks.

left: input image; middle: edit from input: "tilt her head towards the camera", right: "make her laugh"

left: input image; middle: edit from input: "change the 'YOU HAD ME AT BEER' to 'YOU HAD ME AT CONTEXT'", right: "change the setting to a night club"

FLUX.1 Kontext exhibits some limitations in its current implementation. Excessive multi-turn editing sessions can introduce visual artifacts that degrade image quality. The model occasionally fails to follow instructions accurately, ignoring specific prompt requirements in rare cases. World knowledge remains limited, affecting the model's ability to generate contextually accurate content. Additionally, the distillation process can introduce visual artifacts that impact output fidelity.

Illustration of a FLUX.1 Kontext failure case: After six iterative edits, the generation is visually degraded and contains visible artifacts.