Control Color: Multimodal Diffusion-based Interactive Image Colorization

• URL: http://arxiv.org/abs/2402.10855v1
• Date: Fri, 16 Feb 2024 17:51:13 GMT
• Title: Control Color: Multimodal Diffusion-based Interactive Image Colorization
• Authors: Zhexin Liang, Zhaochen Li, Shangchen Zhou, Chongyi Li, Chen Change Loy
• Abstract summary: Control Color (Ctrl Color) is a multi-modal colorization method that leverages the pre-trained Stable Diffusion (SD) model. We present an effective way to encode user strokes to enable precise local color manipulation. We also introduce a novel module based on self-attention and a content-guided deformable autoencoder to address the long-standing issues of color overflow and inaccurate coloring.
• Score: 81.68817300796644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the existence of numerous colorization methods, several limitations still exist, such as lack of user interaction, inflexibility in local colorization, unnatural color rendering, insufficient color variation, and color overflow. To solve these issues, we introduce Control Color (CtrlColor), a multi-modal colorization method that leverages the pre-trained Stable Diffusion (SD) model, offering promising capabilities in highly controllable interactive image colorization. While several diffusion-based methods have been proposed, supporting colorization in multiple modalities remains non-trivial. In this study, we aim to tackle both unconditional and conditional image colorization (text prompts, strokes, exemplars) and address color overflow and incorrect color within a unified framework. Specifically, we present an effective way to encode user strokes to enable precise local color manipulation and employ a practical way to constrain the color distribution similar to exemplars. Apart from accepting text prompts as conditions, these designs add versatility to our approach. We also introduce a novel module based on self-attention and a content-guided deformable autoencoder to address the long-standing issues of color overflow and inaccurate coloring. Extensive comparisons show that our model outperforms state-of-the-art image colorization methods both qualitatively and quantitatively.

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