In recent years, the research of image colorization based on deep learning has made great progress. Most of the existing methods have achieved impressive colorizing performance over the entire region of a given image. However, we notice that the colorizing results of existing methods suffer from color disorder on small target region or boundary. For colorizing multi-scale targets, we propose a feature scaling network in this paper called Zoom-GAN to improve the colorizing consistency for small objects and boundary. Specifically, the Zoom-GAN proposes a zoom instance normalization layer to introduce scale information in color feature. Meanwhile, multi-scale structure is adopted in the generator and discriminator to improve the colorizing performance for various targets. Experiments on three public datasets Oxford102, Bird100 and Hero show that our Zoom-GAN achieves state-of-the-art on three subjective and objective evaluation metrics. [ABSTRACT FROM AUTHOR]