Seedream 3.0 Technical Report
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.
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Yu Gao
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Lixue Gong
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Qiushan Guo
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Xiaoxia Hou
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Zhichao Lai
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Fanshi Li
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Liang Li
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Xiaochen Lian
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Chao Liao
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Liyang Liu
67ff18961dc5d56fdd6ca72e
Wei Liu
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Yichun Shi
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Shiqi Sun
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Yu Tian
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Zhi Tian
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Peng Wang
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Rui Wang
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Xuanda Wang
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Xun Wang
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Ye Wang
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Guofeng Wu
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Jie Wu
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Xin Xia
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Xuefeng Xiao
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Zhonghua Zhai
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Xinyu Zhang
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Qi Zhang
67ff18961dc5d56fdd6ca73f
Yuwei Zhang
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Shijia Zhao
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Jianchao Yang
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Weilin Huang
Seedream 3.0 Technical Report
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.
mixed-resolution training cross-modality RoPE representation alignment loss resolution-aware timestep sampling SFT (Supervised Fine-Tuning) VLM (Vision Language Model) consistent noise expectation importance-aware timestep sampling