title: Seedream 3.0 Technical Report
summary: We present Seedream 3.0, a high-performance Chinese-English bilingual image
  generation foundation model. We develop several technical improvements to
  address existing challenges in Seedream 2.0, including alignment with
  complicated prompts, fine-grained typography generation, suboptimal visual
  aesthetics and fidelity, and limited image resolutions. Specifically, the
  advancements of Seedream 3.0 stem from improvements across the entire pipeline,
  from data construction to model deployment. At the data stratum, we double the
  dataset using a defect-aware training paradigm and a dual-axis collaborative
  data-sampling framework. Furthermore, we adopt several effective techniques
  such as mixed-resolution training, cross-modality RoPE, representation
  alignment loss, and resolution-aware timestep sampling in the pre-training
  phase. During the post-training stage, we utilize diversified aesthetic
  captions in SFT, and a VLM-based reward model with scaling, thereby achieving
  outputs that well align with human preferences. Furthermore, Seedream 3.0
  pioneers a novel acceleration paradigm. By employing consistent noise
  expectation and importance-aware timestep sampling, we achieve a 4 to 8 times
  speedup while maintaining image quality. Seedream 3.0 demonstrates significant
  improvements over Seedream 2.0: it enhances overall capabilities, in particular
  for text-rendering in complicated Chinese characters which is important to
  professional typography generation. In addition, it provides native
  high-resolution output (up to 2K), allowing it to generate images with high
  visual quality.
paper_authors._id: 67ff18961dc5d56fdd6ca724
paper_authors.name: Yu Gao
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paper_authors.name: Lixue Gong
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paper_authors.fullname: lixue gong
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paper_authors.user: lixuegong
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paper_authors.name: Qiushan Guo
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paper_authors.user: qiushanhku
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paper_authors.name: Xiaoxia Hou
paper_authors._id: 67ff18961dc5d56fdd6ca728
paper_authors.name: Zhichao Lai
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paper_authors.fullname: Zhichao Lai
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paper_authors.user: sgcc-chao
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paper_authors.name: Fanshi Li
paper_authors._id: 67ff18961dc5d56fdd6ca72a
paper_authors.name: Liang Li
paper_authors._id: 67ff18961dc5d56fdd6ca72b
paper_authors.name: Xiaochen Lian
paper_authors._id: 67ff18961dc5d56fdd6ca72c
paper_authors.name: Chao Liao
paper_authors._id: 67ff18961dc5d56fdd6ca72d
paper_authors.name: Liyang Liu
paper_authors._id: 67ff18961dc5d56fdd6ca72e
paper_authors.name: Wei Liu
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paper_authors.fullname: liuwei.jikun
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paper_authors.user: jikun63
paper_authors._id: 67ff18961dc5d56fdd6ca72f
paper_authors.name: Yichun Shi
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paper_authors.fullname: Shi
paper_authors.type: user
paper_authors.user: Yichun
paper_authors._id: 67ff18961dc5d56fdd6ca730
paper_authors.name: Shiqi Sun
paper_authors._id: 67ff18961dc5d56fdd6ca731
paper_authors.name: Yu Tian
paper_authors._id: 67ff18961dc5d56fdd6ca732
paper_authors.name: Zhi Tian
paper_authors._id: 67ff18961dc5d56fdd6ca733
paper_authors.name: Peng Wang
paper_authors._id: 67ff18961dc5d56fdd6ca734
paper_authors.name: Rui Wang
paper_authors._id: 67ff18961dc5d56fdd6ca735
paper_authors.name: Xuanda Wang
paper_authors._id: 67ff18961dc5d56fdd6ca736
paper_authors.name: Xun Wang
paper_authors._id: 67ff18961dc5d56fdd6ca737
paper_authors.name: Ye Wang
paper_authors._id: 67ff18961dc5d56fdd6ca738
paper_authors.name: Guofeng Wu
paper_authors._id: 67ff18961dc5d56fdd6ca739
paper_authors.name: Jie Wu
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paper_authors.fullname: wujie10558@gmail.com
paper_authors.type: user
paper_authors.user: wujie10
paper_authors._id: 67ff18961dc5d56fdd6ca73a
paper_authors.name: Xin Xia
paper_authors._id: 67ff18961dc5d56fdd6ca73b
paper_authors.name: Xuefeng Xiao
paper_authors._id: 67ff18961dc5d56fdd6ca73c
paper_authors.name: Zhonghua Zhai
paper_authors._id: 67ff18961dc5d56fdd6ca73d
paper_authors.name: Xinyu Zhang
paper_authors._id: 67ff18961dc5d56fdd6ca73e
paper_authors.name: Qi Zhang
paper_authors._id: 67ff18961dc5d56fdd6ca73f
paper_authors.name: Yuwei Zhang
paper_authors._id: 67ff18961dc5d56fdd6ca740
paper_authors.name: Shijia Zhao
paper_authors._id: 67ff18961dc5d56fdd6ca741
paper_authors.name: Jianchao Yang
paper_authors._id: 67ff18961dc5d56fdd6ca742
paper_authors.name: Weilin Huang
paper_title: Seedream 3.0 Technical Report
paper_summary: We present Seedream 3.0, a high-performance Chinese-English bilingual image
  generation foundation model. We develop several technical improvements to
  address existing challenges in Seedream 2.0, including alignment with
  complicated prompts, fine-grained typography generation, suboptimal visual
  aesthetics and fidelity, and limited image resolutions. Specifically, the
  advancements of Seedream 3.0 stem from improvements across the entire pipeline,
  from data construction to model deployment. At the data stratum, we double the
  dataset using a defect-aware training paradigm and a dual-axis collaborative
  data-sampling framework. Furthermore, we adopt several effective techniques
  such as mixed-resolution training, cross-modality RoPE, representation
  alignment loss, and resolution-aware timestep sampling in the pre-training
  phase. During the post-training stage, we utilize diversified aesthetic
  captions in SFT, and a VLM-based reward model with scaling, thereby achieving
  outputs that well align with human preferences. Furthermore, Seedream 3.0
  pioneers a novel acceleration paradigm. By employing consistent noise
  expectation and importance-aware timestep sampling, we achieve a 4 to 8 times
  speedup while maintaining image quality. Seedream 3.0 demonstrates significant
  improvements over Seedream 2.0: it enhances overall capabilities, in particular
  for text-rendering in complicated Chinese characters which is important to
  professional typography generation. In addition, it provides native
  high-resolution output (up to 2K), allowing it to generate images with high
  visual quality.
paper_ai_summary: Seedream 3.0 improves Chinese-English bilingual image generation by enhancing data training, pre-training techniques, and post-training aesthetics, resulting in higher visual quality and faster image generation.
paper_ai_keywords: defect-aware training dual-axis collaborative data-sampling mixed-resolution training cross-modality RoPE representation alignment loss resolution-aware timestep sampling aesthetic captions SFT VLM-based reward model consistent noise expectation importance-aware timestep sampling