Recolour What Matters: Region-Aware Colour Editing via Token-Level Diffusion

Colour is one of the most perceptually salient yet least con-trollable attributes in image generation. Although recent diffusion mod-els can modify object colours from user instructions, their results of-ten deviate from the intended hue, especially for fine-grained and localedits. Early text-driven methods rely on discrete language descriptionsthat cannot accurately represent continuous chromatic variations. Toovercome this limitation, we propose ColourCrafter, a unified diffusionframework that transforms colour editing from global tone transfer intoa structured, region-aware generation process. Unlike traditional colour-driven methods, ColourCrafter performs token-level fusion of RGB colourtokens and image tokens in latent space, selectively propagating colourinformation to semantically relevant regions while preserving structuralfidelity. A perceptual Lab-space Loss further enhances pixel-level pre-cision by decoupling luminance and chrominance and constraining ed-its within masked areas. Additionally, we build ColourfulSet, a large-scale dataset of high-quality image pairs with continuous and diversecolour variations. Extensive experiments demonstrate that ColourCrafterachieves state-of-the-art colour accuracy, controllability and perceptualfidelity in fine-grained colour editing. Our project is available at https://yangyuqi317.github.io/ColourCrafter.github.io/