Training-Free Text-Guided Color Editing with Multi-Modal Diffusion Transformer

TL;DR

ColorCtrl is a training-free method leveraging Multi-Modal Diffusion Transformers to achieve precise, consistent, and region-specific color editing in images and videos guided by text prompts.

Contribution

We introduce ColorCtrl, a novel training-free color editing technique that manipulates attention maps in diffusion transformers for accurate, consistent, and controllable color modifications.

Findings

Outperforms existing training-free methods in quality and consistency

Achieves state-of-the-art results on SD3 and FLUX.1-dev datasets

Maintains temporal coherence and stability in video editing

Abstract

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.