Kontinuous Kontext: Continuous Strength Control for Instruction-based
  Image Editing
Authors: name: Rishubh Parihar, name: Or Patashnik, name: Daniil Ostashev, name: R. Venkatesh Babu, name: Daniel Cohen-Or, name: Kuan-Chieh Wang

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.