Seedream 3.0: A Milestone in Bilingual Image Generation

The development of Artificial Intelligence (AI) is progressing rapidly, especially in the field of image generation. With Seedream 3.0, the development team presents a powerful, Chinese-English, bilingual image generation base model that represents a significant advancement over its predecessor, Seedream 2.0. This article highlights the key technical innovations and improvements that make Seedream 3.0 a remarkable leap forward.

Overcoming the Challenges of Seedream 2.0

Seedream 2.0 struggled with several challenges, including the accurate implementation of complex prompts, the generation of detailed typography, suboptimal visual aesthetics and accuracy, and limited image resolutions. Seedream 3.0 addresses these weaknesses through comprehensive improvements throughout the entire pipeline, from data construction to model deployment.

Improvements in Data and Training

A central aspect of the advancement lies in the optimization of the database. The dataset has been doubled, applying an error-aware training paradigm and a dual-axis collaborative data sampling framework. Additionally, various effective techniques have been implemented in the pre-training process, including mixed-resolution training, cross-modality RoPE, representation alignment loss, and resolution-aware timestep sampling. These measures contribute to significantly improved performance and quality of the generated images.

Optimization of Post-Training and Aesthetics

In the post-training stage, fine-tuning (SFT) has been optimized through the use of diversified aesthetic descriptions. A VLM-based reward model with scaling ensures that the results better match human preferences. As a result, Seedream 3.0 achieves higher visual quality and improved consistency with the desired results.

Innovations in the Field of Acceleration

A particular highlight of Seedream 3.0 is the novel acceleration paradigm. Through the use of consistent noise prediction and importance-aware timestep sampling, a four- to eight-fold speed increase has been achieved without compromising image quality. This optimization allows for more efficient use of resources and faster image generation.

The Advantages of Seedream 3.0 at a Glance

Seedream 3.0 offers a number of significant improvements over its predecessor:

Improved overall performance and higher image quality

More accurate implementation of complex prompts, especially in the representation of Chinese characters

Native high-resolution output (up to 2K) for detailed images

Significant speed increase through innovative acceleration paradigm

Conclusion

Seedream 3.0 represents an important milestone in the development of AI-powered image generation models. The combination of an improved database, optimized training methods, and an innovative acceleration paradigm enables the creation of high-quality images in high resolution and with impressive speed. In particular, the improved representation of complex Chinese characters opens up new possibilities in the field of professional typography generation.

Bibliographie: https://huggingface.co/papers https://chatpaper.com/chatpaper/?id=4&date=1744732800&page=1 https://arxiv.org/html/2503.07703v1 https://dl.acm.org/doi/10.24963/ijcai.2024/251 https://arxiv.org/pdf/2412.19437 https://huggingface.co/papers?q=Seedream%202.0 https://pomodatt.com/wp-content/uploads/2016/11/pomodatt-manual.pdf https://symbioticlab.org/publications/ https://www.researchgate.net/publication/222534264_Automatic_information_extraction_from_semi-structured_Web_pages_by_pattern_discovery https://www.abbotsford.ca/sites/default/files/2021-02/Mobility%20Scooter%20Research%20Project.pdf