Cobra用200+参考图实现快速精准漫画线稿上色，兼顾一致性与交互性。
Cobra: Efficient Line Art COlorization with BRoAder References
- 采用因果稀疏DiT架构，支持超长上下文参考
- 利用200+参考图提升颜色一致性，推理速度显著加快
- 适合需要高精度、灵活控制的工业级漫画上色场景
漫画制作行业需要高精度、高效、上下文一致且可控的基于参考的线稿上色。一张漫画页面常包含多种角色、物体和背景，使着色过程复杂化。尽管扩散模型在图像生成方面取得进展，但在线稿上色中的应用仍受限，面临处理大量参考图、推理耗时及控制灵活性不足的问题。我们研究了广泛上下文参考对线稿上色质量的影响。为此，提出Cobra方法，支持颜色提示，并可使用超过200张参考图，同时保持低延迟。Cobra的核心是因果稀疏DiT架构，通过特殊设计的位置编码、因果稀疏注意力和键值缓存，有效管理长上下文参考并确保颜色身份一致性。实验表明，Cobra通过广泛上下文参考实现了准确的线稿上色，显著提升了推理速度和交互性，满足关键工业需求。代码与模型已发布于项目主页：https://zhuang2002.github.io/Cobra/。
原文摘要 · Abstract (English)
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/.
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