Kontinuous Kontext: Continuous Strength Control for Instruction-based
  Image Editing
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.
68ec9b74cd07fb414898caf2
Rishubh Parihar
68ec9b74cd07fb414898caf3
Or Patashnik
claimed_verified
2025-10-16T06:42:14.155Z
62853516e483e0d37b354ce1
https://cdn-avatars.huggingface.co/v1/production/uploads/62853516e483e0d37b354ce1/t5Tyd3E07w26B9Z3XpZWI.jpeg
Or Patashnik
user
orpatashnik
68ec9b74cd07fb414898caf4
Daniil Ostashev
68ec9b74cd07fb414898caf5
R. Venkatesh Babu
68ec9b74cd07fb414898caf6
Daniel Cohen-Or
68ec9b74cd07fb414898caf7
Kuan-Chieh Wang
Kontinuous Kontext: Continuous Strength Control for Instruction-based
  Image Editing
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.
Kontinuous Kontext is an instruction-driven image editing model that introduces a scalar edit strength for fine-grained control over the extent of edits, using a lightweight projector network and a synthesized dataset of image-edit-instruction-strength quadruplets.
instruction-driven editing edit strength scalar input lightweight projector network modulation space generative models stylization attribute changes material changes background changes shape changes