Seedream 4.0 is a multimodal image generation and editing model developed by the ByteDance Seed team, released in September 2025. It is designed as an integrated visual creation system that unifies text-to-image synthesis and precise image editing within a single, consistent architecture. This framework allows the model to handle complex multimodal tasks such as generating high-fidelity visuals from natural language and subsequently refining them through instructions while maintaining strong feature and identity consistency.
The model is built on a Diffusion Transformer (DiT) architecture paired with an efficient Variational Autoencoder (VAE). It is characterized by high inference speeds, capable of producing 2K resolution images in approximately 1.8 seconds and supporting native output up to 4K resolution. With an architecture utilizing 12 billion parameters, Seedream 4.0 is optimized for professional-grade creative workflows, offering enhanced performance in detail retention, lighting, and composition compared to its predecessors.
Key capabilities include multi-image reference fusion, enabling the blending of up to six reference images into a single cohesive output, and advanced text rendering for both Chinese and English scripts. The model excels at layout-aware generation, making it suitable for creating posters, charts, and educational diagrams with precise whitespace and typography planning. It also incorporates in-context reasoning, which allows it to generate logically structured content like mathematical diagrams or historical timelines based on descriptive prompts.
For optimal results, users are encouraged to use descriptive, natural-language prompts. A recommended structure follows the [Subject + Action + Setting] + [Style] format to bridge the gap between initial concepts and professional outputs. Providing specific application contexts, such as "for a promotional poster" or "concept design," and utilizing reference images for style or character guidance can significantly improve the model's adherence to user intent.