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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.20427 |
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| _version_ | 1866908704138330112 |
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| author | Seedream, Team : Chen, Yunpeng Gao, Yu Gong, Lixue Guo, Meng Guo, Qiushan Guo, Zhiyao Hou, Xiaoxia Huang, Weilin Huang, Yixuan Jian, Xiaowen Kuang, Huafeng Lai, Zhichao Li, Fanshi Li, Liang Lian, Xiaochen Liao, Chao Liu, Liyang Liu, Wei Lu, Yanzuo Luo, Zhengxiong Ou, Tongtong Shi, Guang Shi, Yichun Sun, Shiqi Tian, Yu Tian, Zhi Wang, Peng Wang, Rui Wang, Xun Wang, Ye Wu, Guofeng Wu, Jie Wu, Wenxu Wu, Yonghui Xia, Xin Xiao, Xuefeng Xu, Shuang Yan, Xin Yang, Ceyuan Yang, Jianchao Zhai, Zhonghua Zhang, Chenlin Zhang, Heng Zhang, Qi Zhang, Xinyu Zhang, Yuwei Zhao, Shijia Zhao, Wenliang Zhu, Wenjia |
| author_facet | Seedream, Team : Chen, Yunpeng Gao, Yu Gong, Lixue Guo, Meng Guo, Qiushan Guo, Zhiyao Hou, Xiaoxia Huang, Weilin Huang, Yixuan Jian, Xiaowen Kuang, Huafeng Lai, Zhichao Li, Fanshi Li, Liang Lian, Xiaochen Liao, Chao Liu, Liyang Liu, Wei Lu, Yanzuo Luo, Zhengxiong Ou, Tongtong Shi, Guang Shi, Yichun Sun, Shiqi Tian, Yu Tian, Zhi Wang, Peng Wang, Rui Wang, Xun Wang, Ye Wu, Guofeng Wu, Jie Wu, Wenxu Wu, Yonghui Xia, Xin Xiao, Xuefeng Xu, Shuang Yan, Xin Yang, Ceyuan Yang, Jianchao Zhai, Zhonghua Zhang, Chenlin Zhang, Heng Zhang, Qi Zhang, Xinyu Zhang, Yuwei Zhao, Shijia Zhao, Wenliang Zhu, Wenjia |
| contents | We introduce Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image composition within a single framework. We develop a highly efficient diffusion transformer with a powerful VAE which also can reduce the number of image tokens considerably. This allows for efficient training of our model, and enables it to fast generate native high-resolution images (e.g., 1K-4K). Seedream 4.0 is pretrained on billions of text-image pairs spanning diverse taxonomies and knowledge-centric concepts. Comprehensive data collection across hundreds of vertical scenarios, coupled with optimized strategies, ensures stable and large-scale training, with strong generalization. By incorporating a carefully fine-tuned VLM model, we perform multi-modal post-training for training both T2I and image editing tasks jointly. For inference acceleration, we integrate adversarial distillation, distribution matching, and quantization, as well as speculative decoding. It achieves an inference time of up to 1.8 seconds for generating a 2K image (without a LLM/VLM as PE model). Comprehensive evaluations reveal that Seedream 4.0 can achieve state-of-the-art results on both T2I and multimodal image editing. In particular, it demonstrates exceptional multimodal capabilities in complex tasks, including precise image editing and in-context reasoning, and also allows for multi-image reference, and can generate multiple output images. This extends traditional T2I systems into an more interactive and multidimensional creative tool, pushing the boundary of generative AI for both creativity and professional applications. We further scale our model and data as Seedream 4.5. Seedream 4.0 and Seedream 4.5 are accessible on Volcano Engine https://www.volcengine.com/experience/ark?launch=seedream. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20427 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seedream 4.0: Toward Next-generation Multimodal Image Generation Seedream, Team : Chen, Yunpeng Gao, Yu Gong, Lixue Guo, Meng Guo, Qiushan Guo, Zhiyao Hou, Xiaoxia Huang, Weilin Huang, Yixuan Jian, Xiaowen Kuang, Huafeng Lai, Zhichao Li, Fanshi Li, Liang Lian, Xiaochen Liao, Chao Liu, Liyang Liu, Wei Lu, Yanzuo Luo, Zhengxiong Ou, Tongtong Shi, Guang Shi, Yichun Sun, Shiqi Tian, Yu Tian, Zhi Wang, Peng Wang, Rui Wang, Xun Wang, Ye Wu, Guofeng Wu, Jie Wu, Wenxu Wu, Yonghui Xia, Xin Xiao, Xuefeng Xu, Shuang Yan, Xin Yang, Ceyuan Yang, Jianchao Zhai, Zhonghua Zhang, Chenlin Zhang, Heng Zhang, Qi Zhang, Xinyu Zhang, Yuwei Zhao, Shijia Zhao, Wenliang Zhu, Wenjia Computer Vision and Pattern Recognition We introduce Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image composition within a single framework. We develop a highly efficient diffusion transformer with a powerful VAE which also can reduce the number of image tokens considerably. This allows for efficient training of our model, and enables it to fast generate native high-resolution images (e.g., 1K-4K). Seedream 4.0 is pretrained on billions of text-image pairs spanning diverse taxonomies and knowledge-centric concepts. Comprehensive data collection across hundreds of vertical scenarios, coupled with optimized strategies, ensures stable and large-scale training, with strong generalization. By incorporating a carefully fine-tuned VLM model, we perform multi-modal post-training for training both T2I and image editing tasks jointly. For inference acceleration, we integrate adversarial distillation, distribution matching, and quantization, as well as speculative decoding. It achieves an inference time of up to 1.8 seconds for generating a 2K image (without a LLM/VLM as PE model). Comprehensive evaluations reveal that Seedream 4.0 can achieve state-of-the-art results on both T2I and multimodal image editing. In particular, it demonstrates exceptional multimodal capabilities in complex tasks, including precise image editing and in-context reasoning, and also allows for multi-image reference, and can generate multiple output images. This extends traditional T2I systems into an more interactive and multidimensional creative tool, pushing the boundary of generative AI for both creativity and professional applications. We further scale our model and data as Seedream 4.5. Seedream 4.0 and Seedream 4.5 are accessible on Volcano Engine https://www.volcengine.com/experience/ark?launch=seedream. |
| title | Seedream 4.0: Toward Next-generation Multimodal Image Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.20427 |