title: Seedream 2.0: A Native Chinese-English Bilingual Image Generation
    Foundation Model
summary: Rapid advancement of diffusion models has catalyzed remarkable progress in
  the field of image generation. However, prevalent models such as Flux, SD3.5
  and Midjourney, still grapple with issues like model bias, limited text
  rendering capabilities, and insufficient understanding of Chinese cultural
  nuances. To address these limitations, we present Seedream 2.0, a native
  Chinese-English bilingual image generation foundation model that excels across
  diverse dimensions, which adeptly manages text prompt in both Chinese and
  English, supporting bilingual image generation and text rendering. We develop a
  powerful data system that facilitates knowledge integration, and a caption
  system that balances the accuracy and richness for image description.
  Particularly, Seedream is integrated with a self-developed bilingual large
  language model as a text encoder, allowing it to learn native knowledge
  directly from massive data. This enable it to generate high-fidelity images
  with accurate cultural nuances and aesthetic expressions described in either
  Chinese or English. Beside, Glyph-Aligned ByT5 is applied for flexible
  character-level text rendering, while a Scaled ROPE generalizes well to
  untrained resolutions. Multi-phase post-training optimizations, including SFT
  and RLHF iterations, further improve the overall capability. Through extensive
  experimentation, we demonstrate that Seedream 2.0 achieves state-of-the-art
  performance across multiple aspects, including prompt-following, aesthetics,
  text rendering, and structural correctness. Furthermore, Seedream 2.0 has been
  optimized through multiple RLHF iterations to closely align its output with
  human preferences, as revealed by its outstanding ELO score. In addition, it
  can be readily adapted to an instruction-based image editing model, such as
  SeedEdit, with strong editing capability that balances instruction-following
  and image consistency.
paper_authors._id: 67d0f422a3158b8e55d3562f
paper_authors.name: Lixue Gong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:40:24.184Z
paper_authors._id: 655b277023e43ac218109e08
paper_authors.avatarUrl: /avatars/ee2d35942b67cbaf2c87f5300e46e685.svg
paper_authors.fullname: lixue gong
paper_authors.type: user
paper_authors.user: lixuegong
paper_authors._id: 67d0f422a3158b8e55d35630
paper_authors.name: Xiaoxia Hou
paper_authors._id: 67d0f422a3158b8e55d35631
paper_authors.name: Fanshi Li
paper_authors._id: 67d0f422a3158b8e55d35632
paper_authors.name: Liang Li
paper_authors._id: 67d0f422a3158b8e55d35633
paper_authors.name: Xiaochen Lian
paper_authors._id: 67d0f422a3158b8e55d35634
paper_authors.name: Fei Liu
paper_authors._id: 67d0f422a3158b8e55d35635
paper_authors.name: Liyang Liu
paper_authors._id: 67d0f422a3158b8e55d35636
paper_authors.name: Wei Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-27T09:05:14.637Z
paper_authors._id: 64a50401bbc03d2b098648e6
paper_authors.avatarUrl: /avatars/82a734c0da0f6245c9ee1ef9eba881fd.svg
paper_authors.fullname: liuwei.jikun
paper_authors.type: user
paper_authors.user: jikun63
paper_authors._id: 67d0f422a3158b8e55d35637
paper_authors.name: Wei Lu
paper_authors._id: 67d0f422a3158b8e55d35638
paper_authors.name: Yichun Shi
paper_authors._id: 67d0f422a3158b8e55d35639
paper_authors.name: Shiqi Sun
paper_authors._id: 67d0f422a3158b8e55d3563a
paper_authors.name: Yu Tian
paper_authors._id: 67d0f422a3158b8e55d3563b
paper_authors.name: Zhi Tian
paper_authors._id: 67d0f422a3158b8e55d3563c
paper_authors.name: Peng Wang
paper_authors._id: 67d0f422a3158b8e55d3563d
paper_authors.name: Xun Wang
paper_authors._id: 67d0f422a3158b8e55d3563e
paper_authors.name: Ye Wang
paper_authors._id: 67d0f422a3158b8e55d3563f
paper_authors.name: Guofeng Wu
paper_authors._id: 67d0f422a3158b8e55d35640
paper_authors.name: Jie Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T08:40:44.088Z
paper_authors._id: 6381c5d63680a7cf34e08ca9
paper_authors.avatarUrl: /avatars/731467e2d80d0ae163c4a00a9e3ff9e5.svg
paper_authors.fullname: wujie10558@gmail.com
paper_authors.type: user
paper_authors.user: wujie10
paper_authors._id: 67d0f422a3158b8e55d35641
paper_authors.name: Xin Xia
paper_authors._id: 67d0f422a3158b8e55d35642
paper_authors.name: Xuefeng Xiao
paper_authors._id: 67d0f422a3158b8e55d35643
paper_authors.name: Linjie Yang
paper_authors._id: 67d0f422a3158b8e55d35644
paper_authors.name: Zhonghua Zhai
paper_authors._id: 67d0f422a3158b8e55d35645
paper_authors.name: Xinyu Zhang
paper_authors._id: 67d0f422a3158b8e55d35646
paper_authors.name: Qi Zhang
paper_authors._id: 67d0f422a3158b8e55d35647
paper_authors.name: Yuwei Zhang
paper_authors._id: 67d0f422a3158b8e55d35648
paper_authors.name: Shijia Zhao
paper_authors._id: 67d0f422a3158b8e55d35649
paper_authors.name: Jianchao Yang
paper_authors._id: 67d0f422a3158b8e55d3564a
paper_authors.name: Weilin Huang
paper_title: Seedream 2.0: A Native Chinese-English Bilingual Image Generation
    Foundation Model
paper_summary: Rapid advancement of diffusion models has catalyzed remarkable progress in
  the field of image generation. However, prevalent models such as Flux, SD3.5
  and Midjourney, still grapple with issues like model bias, limited text
  rendering capabilities, and insufficient understanding of Chinese cultural
  nuances. To address these limitations, we present Seedream 2.0, a native
  Chinese-English bilingual image generation foundation model that excels across
  diverse dimensions, which adeptly manages text prompt in both Chinese and
  English, supporting bilingual image generation and text rendering. We develop a
  powerful data system that facilitates knowledge integration, and a caption
  system that balances the accuracy and richness for image description.
  Particularly, Seedream is integrated with a self-developed bilingual large
  language model as a text encoder, allowing it to learn native knowledge
  directly from massive data. This enable it to generate high-fidelity images
  with accurate cultural nuances and aesthetic expressions described in either
  Chinese or English. Beside, Glyph-Aligned ByT5 is applied for flexible
  character-level text rendering, while a Scaled ROPE generalizes well to
  untrained resolutions. Multi-phase post-training optimizations, including SFT
  and RLHF iterations, further improve the overall capability. Through extensive
  experimentation, we demonstrate that Seedream 2.0 achieves state-of-the-art
  performance across multiple aspects, including prompt-following, aesthetics,
  text rendering, and structural correctness. Furthermore, Seedream 2.0 has been
  optimized through multiple RLHF iterations to closely align its output with
  human preferences, as revealed by its outstanding ELO score. In addition, it
  can be readily adapted to an instruction-based image editing model, such as
  SeedEdit, with strong editing capability that balances instruction-following
  and image consistency.
paper_ai_summary: Seedream 2.0, a bilingual Chinese-English image generation model, excels in text rendering and cultural nuances, employing data integration, caption balancing, and multi-phase optimizations to achieve state-of-the-art performance.
paper_ai_keywords: diffusion models Seedream 2.0 bilingual image generation text encoder Glyph-Aligned ByT5 Scaled ROPE SFT RLHF ELO score instruction-based image editing SeedEdit