Seedream 2.0: A Native Chinese-English Bilingual Image Generation
  Foundation Model
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.
67d0f422a3158b8e55d3562f
Lixue Gong
admin_assigned
2025-03-12T15:40:24.184Z
655b277023e43ac218109e08
/avatars/ee2d35942b67cbaf2c87f5300e46e685.svg
Lixue Gong
user
lixuegong
67d0f422a3158b8e55d35630
Xiaoxia Hou
67d0f422a3158b8e55d35631
Fanshi Li
67d0f422a3158b8e55d35632
Liang Li
67d0f422a3158b8e55d35633
Xiaochen Lian
67d0f422a3158b8e55d35634
Fei Liu
67d0f422a3158b8e55d35635
Liyang Liu
67d0f422a3158b8e55d35636
Wei Liu
claimed_verified
2025-03-27T09:05:14.637Z
64a50401bbc03d2b098648e6
/avatars/82a734c0da0f6245c9ee1ef9eba881fd.svg
liuwei.jikun
user
jikun63
67d0f422a3158b8e55d35637
Wei Lu
67d0f422a3158b8e55d35638
Yichun Shi
67d0f422a3158b8e55d35639
Shiqi Sun
67d0f422a3158b8e55d3563a
Yu Tian
67d0f422a3158b8e55d3563b
Zhi Tian
67d0f422a3158b8e55d3563c
Peng Wang
67d0f422a3158b8e55d3563d
Xun Wang
67d0f422a3158b8e55d3563e
Ye Wang
67d0f422a3158b8e55d3563f
Guofeng Wu
67d0f422a3158b8e55d35640
Jie Wu
claimed_verified
2025-03-12T08:40:44.088Z
6381c5d63680a7cf34e08ca9
/avatars/731467e2d80d0ae163c4a00a9e3ff9e5.svg
wujie10558@gmail.com
user
wujie10
67d0f422a3158b8e55d35641
Xin Xia
67d0f422a3158b8e55d35642
Xuefeng Xiao
67d0f422a3158b8e55d35643
Linjie Yang
67d0f422a3158b8e55d35644
Zhonghua Zhai
67d0f422a3158b8e55d35645
Xinyu Zhang
67d0f422a3158b8e55d35646
Qi Zhang
67d0f422a3158b8e55d35647
Yuwei Zhang
67d0f422a3158b8e55d35648
Shijia Zhao
67d0f422a3158b8e55d35649
Jianchao Yang
67d0f422a3158b8e55d3564a
Weilin Huang
Seedream 2.0: A Native Chinese-English Bilingual Image Generation
  Foundation Model
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.
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.
diffusion models Seedream 2.0 bilingual image generation text encoder Glyph-Aligned ByT5 Scaled ROPE SFT RLHF ELO score instruction-based image editing SeedEdit