Unicolor: A Unified Framework for Multi-Modal Colorization with Transformer

Zhitong Huang\(^{1 \dagger}\), Nanxuan Zhao\(^{2 \dagger}\) , Jing Liao\(^{1*}\)

\(^1\): City University of Hong Kong, Hong Kong SAR, China   \(^2\): University of Bath, Bath, United Kingdom
\(^\dagger\) : Both authors contributed equally to this research    \(^*\) : Corresponding author

Paper    Code: coming soon...

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Abstract

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We propose the first unified framework UniColor to support colorization in multiple modalities, including both unconditional and conditional ones, such as stroke, exemplar, text, and even a mix of them. Rather than learning a separate model for each type of condition, we introduce a two-stage colorization framework for incorporating various conditions into a single model. In the first stage, multi-modal conditions are converted into a common representation of hint points. Particularly, we propose a novel CLIP-based method to convert the text to hint points. In the second stage, we propose a Transformer-based network composed of Chroma-VQGAN and Hybrid-Transformer to generate diverse and high-quality colorization results conditioned on hint points. Both qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art methods in every control modality and further enables multi-modal colorization that was not feasible before. Moreover, we design an interactive interface showing the effectiveness of our unified framework in practical usage, including automatic colorization, hybrid-control colorization, local recolorization, and iterative color editing.