NumeriKontrol: Adding Numeric Control to Diffusion Transformers for Instruction-based Image Editing

• URL: http://arxiv.org/abs/2511.23105v1
• Date: Fri, 28 Nov 2025 11:43:52 GMT
• Title: NumeriKontrol: Adding Numeric Control to Diffusion Transformers for Instruction-based Image Editing
• Authors: Zhenyu Xu, Xiaoqi Shen, Haotian Nan, Xinyu Zhang,
• Abstract summary: We introduce NumeriKontrol, a framework that allows users to adjust image attributes using continuous attribute values with common units.<n>Thanks to task-separated design, our approach supports zero-separated multi-condition editing.<n>We synthesize precise training data from reliable sources, including high-fidelity DSLR and DSLR cameras.
• Score: 12.728322570816248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Instruction-based image editing enables intuitive manipulation through natural language commands. However, text instructions alone often lack the precision required for fine-grained control over edit intensity. We introduce NumeriKontrol, a framework that allows users to precisely adjust image attributes using continuous scalar values with common units. NumeriKontrol encodes numeric editing scales via an effective Numeric Adapter and injects them into diffusion models in a plug-and-play manner. Thanks to a task-separated design, our approach supports zero-shot multi-condition editing, allowing users to specify multiple instructions in any order. To provide high-quality supervision, we synthesize precise training data from reliable sources, including high-fidelity rendering engines and DSLR cameras. Our Common Attribute Transform (CAT) dataset covers diverse attribute manipulations with accurate ground-truth scales, enabling NumeriKontrol to function as a simple yet powerful interactive editing studio. Extensive experiments show that NumeriKontrol delivers accurate, continuous, and stable scale control across a wide range of attribute editing scenarios. These contributions advance instruction-based image editing by enabling precise, scalable, and user-controllable image manipulation.

Related papers

• SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control [50.76070785417023]
  We introduce SliderEdit, a framework for continuous image editing with fine-grained, interpretable instruction control.<n>Given a multi-part edit instruction, SliderEdit disentangles the individual instructions and exposes each as a globally trained slider.<n>Our results pave the way for interactive, instruction-driven image manipulation with continuous and compositional control.
  arXiv  Detail & Related papers  (2025-11-12T20:21:37Z)
• Group Relative Attention Guidance for Image Editing [38.299491082179905]
  Group Relative Attention Guidance (GRAG) is a simple yet effective method that modulates the focus of the model on the input image relative to the editing instruction.<n>Our code will be released at https://www.littlemisfit.com/little-misfit/GRAG-Image-Editing.
  arXiv  Detail & Related papers  (2025-10-28T17:22:44Z)
• Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing [76.44219733285898]
  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.
  arXiv  Detail & Related papers  (2025-10-09T17:51:03Z)
• SAEdit: Token-level control for continuous image editing via Sparse AutoEncoder [52.754326452329956]
  We introduce a method for disentangled and continuous editing through token-level manipulation of text embeddings.<n>The edits are applied by manipulating the embeddings along carefully chosen directions, which control the strength of the target attribute.<n>Our method operates directly on text embeddings without modifying the diffusion process, making it model agnostic and broadly applicable to various image backbones.
  arXiv  Detail & Related papers  (2025-10-06T17:51:04Z)
• Image Editing As Programs with Diffusion Models [69.05164729625052]
  We introduce Image Editing As Programs (IEAP), a unified image editing framework built upon the Diffusion Transformer (DiT) architecture.<n>IEAP approaches instructional editing through a reductionist lens, decomposing complex editing instructions into sequences of atomic operations.<n>Our framework delivers superior accuracy and semantic fidelity, particularly for complex, multi-step instructions.
  arXiv  Detail & Related papers  (2025-06-04T16:57:24Z)
• IntrinsicEdit: Precise generative image manipulation in intrinsic space [53.404235331886255]
  We introduce a versatile, generative workflow that operates in an intrinsic-image latent space.<n>We address key challenges of identity preservation and intrinsic-channel entanglement.<n>We enable precise, efficient editing with automatic resolution of global illumination effects.
  arXiv  Detail & Related papers  (2025-05-13T18:24:15Z)
• EditGAN: High-Precision Semantic Image Editing [120.49401527771067]
  EditGAN is a novel method for high quality, high precision semantic image editing. We show that EditGAN can manipulate images with an unprecedented level of detail and freedom. We can also easily combine multiple edits and perform plausible edits beyond EditGAN training data.
  arXiv  Detail & Related papers  (2021-11-04T22:36:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.