Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing

• URL: http://arxiv.org/abs/2510.08532v1
• Date: Thu, 09 Oct 2025 17:51:03 GMT
• Title: Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing
• Authors: Rishubh Parihar, Or Patashnik, Daniil Ostashev, R. Venkatesh Babu, Daniel Cohen-Or, Kuan-Chieh Wang,
• Abstract summary: Kontinuous Kontext is an instruction-driven editing model that provides a new dimension of control over edit strength.<n>A lightweight projector network maps the input scalar and the edit instruction to coefficients in the model's modulation space.<n>For training our model, we synthesize a diverse dataset of image-edit-instruction-strength quadruplets using existing generative models.
• Score: 76.44219733285898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Instruction-based image editing offers a powerful and intuitive way to manipulate images through natural language. Yet, relying solely on text instructions limits fine-grained control over the extent of edits. We introduce Kontinuous Kontext, an instruction-driven editing model that provides a new dimension of control over edit strength, enabling users to adjust edits gradually from no change to a fully realized result in a smooth and continuous manner. Kontinuous Kontext extends a state-of-the-art image editing model to accept an additional input, a scalar edit strength which is then paired with the edit instruction, enabling explicit control over the extent of the edit. To inject this scalar information, we train a lightweight projector network that maps the input scalar and the edit instruction to coefficients in the model's modulation space. For training our model, we synthesize a diverse dataset of image-edit-instruction-strength quadruplets using existing generative models, followed by a filtering stage to ensure quality and consistency. Kontinuous Kontext provides a unified approach for fine-grained control over edit strength for instruction driven editing from subtle to strong across diverse operations such as stylization, attribute, material, background, and shape changes, without requiring attribute-specific training.

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