Cobra: Efficient Line Art COlorization with BRoAder References (2504.12240v3)

Abstract: The comic production industry requires reference-based line art colorization with high accuracy, efficiency, contextual consistency, and flexible control. A comic page often involves diverse characters, objects, and backgrounds, which complicates the coloring process. Despite advancements in diffusion models for image generation, their application in line art colorization remains limited, facing challenges related to handling extensive reference images, time-consuming inference, and flexible control. We investigate the necessity of extensive contextual image guidance on the quality of line art colorization. To address these challenges, we introduce Cobra, an efficient and versatile method that supports color hints and utilizes over 200 reference images while maintaining low latency. Central to Cobra is a Causal Sparse DiT architecture, which leverages specially designed positional encodings, causal sparse attention, and Key-Value Cache to effectively manage long-context references and ensure color identity consistency. Results demonstrate that Cobra achieves accurate line art colorization through extensive contextual reference, significantly enhancing inference speed and interactivity, thereby meeting critical industrial demands. We release our codes and models on our project page: https://zhuang2002.github.io/Cobra/.

Overview of Cobra: Efficient Line Art Colorization with Broader References

The paper "Cobra: Efficient Line Art COlorization with BRoAder References" presents a novel approach to reference-based line art colorization, addressing the challenges inherent in comic page production. The proposed method, Cobra, stands out in its ability to efficiently handle a large number of reference images while maintaining low latency, making it particularly suited for industrial applications in comic production.

Technical Contributions

Cobra introduces a method that effectively leverages extensive contextual image references to transform black-and-white line art into colored illustrations. The core innovation lies in the Causal Sparse DiT architecture. This architecture employs specialized positional encodings, causal sparse attention, and a Key-Value Cache system designed to manage long-context references and ensure consistency in color identity. This approach advances beyond the limitations seen in earlier line art colorization methods, which struggled with handling extensive reference images, ensuring contextual consistency, and enabling flexible control.

The Causal Sparse DiT architecture is significant for its computational efficiency, achieved by reducing unnecessary interactions and memory overhead associated with more traditional attention mechanisms. The paper reports how Cobra supports more than 200 reference images, a notable advancement over prior methods which typically could not exceed 12. With this capability, Cobra demonstrates significant improvements in inference speed and interactivity, which are critically demanded in industrial settings.

Results

The results outlined in the paper highlight the efficacy of Cobra in achieving accurate line art colorization. Evaluations show that Cobra surpasses existing baselines across several metrics, including image quality, color ID accuracy, and inference efficiency. These results are particularly noteworthy given the complexity involved in processing diverse objects, characters, and backgrounds on a typical comic page. By integrating extensive contextual references, Cobra provides robust solutions for high fidelity image generation.

Implications

The implications of this research are profound for the fields of Artificial Intelligence and Computer Vision, particularly in automated artistic production. Cobra's approach introduces a significant step toward fully automated comic colorization, an application that demands both precision and flexibility. Furthermore, the techniques developed could be adapted broadly for tasks involving complex image editing and colorization, beyond the field of comic art.

Future Directions

Speculating on future developments, the principles underlying Cobra may inform research into more generalized image colorization applications involving complex scene inputs, potentially extending into real-world image generation where context-awareness is critical. Additionally, the advancement in efficient attention mechanisms may contribute to further innovations in other areas requiring real-time processing of large contextual datasets, such as video processing and dynamic scene understanding.

Overall, Cobra represents a substantial contribution to improving methods for multimedia content creation, leveraging large-scale contextual data to enhance the quality and efficiency of automated processes. The paper successfully articulates how novel architectural approaches can address existing bottlenecks in computational image colorization, paving the way for future advancements in AI-driven artistic applications.