Seedream 4.0: Toward Next-generation Multimodal Image Generation

• URL: http://arxiv.org/abs/2509.20427v2
• Date: Sun, 28 Sep 2025 13:10:54 GMT
• Title: Seedream 4.0: Toward Next-generation Multimodal Image Generation
• Authors: Team Seedream, :, Yunpeng Chen, Yu Gao, Lixue Gong, Meng Guo, Qiushan Guo, Zhiyao Guo, Xiaoxia Hou, Weilin Huang, Yixuan Huang, Xiaowen Jian, Huafeng Kuang, Zhichao Lai, Fanshi Li, Liang Li, Xiaochen Lian, Chao Liao, Liyang Liu, Wei Liu, Yanzuo Lu, Zhengxiong Luo, Tongtong Ou, Guang Shi, Yichun Shi, Shiqi Sun, Yu Tian, Zhi Tian, Peng Wang, Rui Wang, Xun Wang, Ye Wang, Guofeng Wu, Jie Wu, Wenxu Wu, Yonghui Wu, Xin Xia, Xuefeng Xiao, Shuang Xu, Xin Yan, Ceyuan Yang, Jianchao Yang, Zhonghua Zhai, Chenlin Zhang, Heng Zhang, Qi Zhang, Xinyu Zhang, Yuwei Zhang, Shijia Zhao, Wenliang Zhao, Wenjia Zhu,
• Abstract summary: Seedream 4.0 is an efficient and high-performance multimodal image generation system.<n>It unifies text-to-image (T2I) synthesis, image editing, and multi-image composition within a single framework.<n>Seedream 4.0 is pretrained on billions of text-image pairs spanning diverse and knowledge-centric concepts.
• Score: 88.86697995940511
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Seedream 4.0 is now accessible on https://www.volcengine.com/experience/ark?launch=seedream.

Related papers

• Query-Kontext: An Unified Multimodal Model for Image Generation and Editing [53.765351127477224]
  Unified Multimodal Models (UMMs) have demonstrated remarkable performance in text-to-image generation (T2I) and editing (TI2I)<n>We introduce Query-Kontext, a novel approach that bridges the VLM and diffusion model via a multimodal kontext'' composed of semantic cues and coarse-grained image conditions encoded from multimodal inputs.<n> Experiments show that our approach matches strong unified baselines and even outperforms task-specific state-of-the-art methods in several cases.
  arXiv  Detail & Related papers  (2025-09-30T17:59:46Z)
• MENTOR: Efficient Multimodal-Conditioned Tuning for Autoregressive Vision Generation Models [30.494968865008513]
  Recent text-to-image models struggle with precise visual control, balancing multimodal inputs, and requiring extensive training for complex image generation.<n>We propose MENTOR, a novel framework for efficient Multimodal-conditioned Tuning for Autoregressive multimodal image generation.<n>Our method delivers superior image reconstruction fidelity, broad task adaptability, and improved training efficiency compared to diffusion-based methods.
  arXiv  Detail & Related papers  (2025-07-13T10:52:59Z)
• ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement [68.05833403672274]
  Existing unified models have struggled to handle the three fundamental capabilities in a unified model: understanding, generation, and editing.<n>ILLUME+ introduces a unified dual visual tokenizer, DualViTok, which preserves fine-grained textures and text-aligned semantics.<n>We also employ a diffusion model as the image detokenizer for enhanced generation quality and efficient super-resolution.
  arXiv  Detail & Related papers  (2025-04-02T17:45:00Z)
• Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model [69.09404597939744]
  Seedream 2.0 is a native Chinese-English bilingual image generation foundation model.<n>It adeptly manages text prompt in both Chinese and English, supporting bilingual image generation and text rendering.<n>It is integrated with a self-developed bilingual large language model as a text encoder, allowing it to learn native knowledge directly from massive data.
  arXiv  Detail & Related papers  (2025-03-10T17:58:33Z)
• X2I: Seamless Integration of Multimodal Understanding into Diffusion Transformer via Attention Distillation [7.61087111021017]
  We propose the X2I framework, which endows Diffusion Transformer (DiT) models with the capability to comprehend various modalities.<n>X2I shows a decrease in performance degradation of less than 1% while gaining various multimodal understanding abilities.
  arXiv  Detail & Related papers  (2025-03-08T09:07:45Z)
• Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation [115.63085345822175]
  We introduce Idea to Image'', a system that enables multimodal iterative self-refinement with GPT-4V(ision) for automatic image design and generation. We investigate if systems based on large multimodal models (LMMs) can develop analogous multimodal self-refinement abilities.
  arXiv  Detail & Related papers  (2023-10-12T17:34:20Z)
• GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation [143.81719619351335]
  Text-to-image (T2I) models based on diffusion processes have achieved remarkable success in controllable image generation using user-provided captions. The tight coupling between the current text encoder and image decoder in T2I models makes it challenging to replace or upgrade. We propose GlueGen, which applies a newly proposed GlueNet model to align features from single-modal or multi-modal encoders with the latent space of an existing T2I model.
  arXiv  Detail & Related papers  (2023-03-17T15:37:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.