title: Cobra: Efficient Line Art COlorization with BRoAder References
summary: 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/.
paper_authors._id: 680057b49031335df49732fc
paper_authors.name: Junhao Zhuang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-17T08:04:09.222Z
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paper_authors.fullname: JunhaoZhuang
paper_authors.type: user
paper_authors.user: JunhaoZhuang
paper_authors._id: 680057b49031335df49732fd
paper_authors.name: Lingen Li
paper_authors.status: admin_assigned
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paper_authors._id: 66837d3c48edefb453b0640a
paper_authors.avatarUrl: /avatars/b16385eaa612578728e2c6460a76b38f.svg
paper_authors.fullname: Lingen Li
paper_authors.type: user
paper_authors.user: l-li
paper_authors._id: 680057b49031335df49732fe
paper_authors.name: Xuan Ju
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-17T08:09:55.870Z
paper_authors._id: 62d4577bc85b0fcf7fde39bb
paper_authors.avatarUrl: /avatars/a3a5729e33ae89ce9ba408830db3c835.svg
paper_authors.fullname: Xuan Ju
paper_authors.type: user
paper_authors.user: juxuan27
paper_authors._id: 680057b49031335df49732ff
paper_authors.name: Zhaoyang Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-20T15:03:34.890Z
paper_authors._id: 658409ceca19ccf6d9989add
paper_authors.avatarUrl: /avatars/3ac1dbd25e52435185babdeb3da28875.svg
paper_authors.fullname: Zhaoyang Zhang
paper_authors.type: user
paper_authors.user: ZyZcuhk
paper_authors._id: 680057b49031335df4973300
paper_authors.name: Chun Yuan
paper_authors._id: 680057b49031335df4973301
paper_authors.name: Ying Shan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-17T08:12:47.014Z
paper_authors._id: 63ca3ddc04c979828310bfcb
paper_authors.avatarUrl: /avatars/615e0d8622950b4408b40d550f02a894.svg
paper_authors.fullname: Ying Shan
paper_authors.type: user
paper_authors.user: yshan2u
paper_title: Cobra: Efficient Line Art COlorization with BRoAder References
paper_summary: 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/.
paper_ai_summary: Cobra, a method utilizing a Causal Sparse DiT architecture, achieves accurate, efficient, and consistent line art colorization using extensive reference images and color hints.
paper_ai_keywords: diffusion models line art colorization reference images Causal Sparse DiT positional encodings causal sparse attention Key-Value Cache color identity consistency