Training-Free Text-Guided Color Editing with Multi-Modal Diffusion
  Transformer
Authors: name: Zixin Yin, name: Xili Dai, name: Ling-Hao Chen, name: Deyu Zhou, name: Jianan Wang, name: Duomin Wang, name: Gang Yu, name: Lionel M. Ni, name: Lei Zhang, name: Heung-Yeung Shum

Text-guided color editing in images and videos is a fundamental yet unsolved
problem, requiring fine-grained manipulation of color attributes, including
albedo, light source color, and ambient lighting, while preserving physical
consistency in geometry, material properties, and light-matter interactions.
Existing training-free methods offer broad applicability across editing tasks
but struggle with precise color control and often introduce visual
inconsistency in both edited and non-edited regions. In this work, we present
ColorCtrl, a training-free color editing method that leverages the attention
mechanisms of modern Multi-Modal Diffusion Transformers (MM-DiT). By
disentangling structure and color through targeted manipulation of attention
maps and value tokens, our method enables accurate and consistent color
editing, along with word-level control of attribute intensity. Our method
modifies only the intended regions specified by the prompt, leaving unrelated
areas untouched. Extensive experiments on both SD3 and FLUX.1-dev demonstrate
that ColorCtrl outperforms existing training-free approaches and achieves
state-of-the-art performances in both edit quality and consistency.
Furthermore, our method surpasses strong commercial models such as FLUX.1
Kontext Max and GPT-4o Image Generation in terms of consistency. When extended
to video models like CogVideoX, our approach exhibits greater advantages,
particularly in maintaining temporal coherence and editing stability. Finally,
our method also generalizes to instruction-based editing diffusion models such
as Step1X-Edit and FLUX.1 Kontext dev, further demonstrating its versatility.