Seedream 3.0 Technical Report (2504.11346v3)

Abstract: 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.

Overview of Seedream 3.0 Technical Report

The "Seedream 3.0 Technical Report" by ByteDance Seed introduces an advanced bilingual image generation model that marks a distinct evolution from Seedream 2.0. Seedream 3.0 focuses on tackling significant limitations found in its predecessor, such as imperfect alignment with intricate prompts, subpar visual aesthetics, fidelity issues, and restricted image resolutions. The enhancements in Seedream 3.0 span the spectrum of data structuring to model employment, setting a new benchmark for both Chinese and English image generation.

Key Innovations and Technical Enhancements

The notable advancements in Seedream 3.0 emerge from several strategic innovations:

1. Data Strategy Improvement: The dataset employed is doubled in size compared to Seedream 2.0, using a defect-aware training framework along with a dual-axis collaborative data-sampling method. This method optimizes the representation in both visual form and semantic distribution, effectively increasing data diversity and quality.
2. Pre-training Techniques: Advanced techniques such as mixed-resolution training, cross-modality Rotary Position Embeddings (RoPE), representation alignment loss, and resolution-aware timestep sampling are implemented. These contribute to enhanced scalability and improved visual-language alignment.
3. Post-training Optimization: Diverse caption models are adopted for boosted controlled generation, supported by a VLM-based reward model with scaling to align outputs with human aesthetic preferences.
4. Model Acceleration: Seedream 3.0 leverages techniques like consistent noise expectation and importance-aware timestep sampling to achieve a remarkable 4 to 8 times speedup in image generation while maintaining high image quality.

Performance and Evaluation

Seedream 3.0 demonstrates significant performance advancements over the previous version, particularly in text rendering for complex Chinese characters, which is crucial for professional typography generation. The model supports native high-resolution outputs up to 2K, facilitating enhanced visual quality without relying on post-processing. Speed optimization techniques allow for cost-effective inference, producing 1K resolution images in approximately three seconds, underscoring its substantial efficiency.

In competitive evaluations, Seedream 3.0 is consistently ranked first among leading text-to-image models, showcasing superior performance across various dimensions such as aesthetic quality, text-image alignment, and structural fidelity. These results are supported by a blend of expert human evaluations and algorithmic metrics, affirming its improved capabilities in real-world applications.

Implications and Future Prospects

The advancements brought by Seedream 3.0 not only enhance its practical utility in professional typography and graphic design but also emphasize its academic and technological significance. Through integrating innovative data sampling, training approaches, and model acceleration strategies, Seedream 3.0 sets a precedent for future developments in bilingual image generation models. These comprehensive advancements suggest potential applications extending beyond creative industries to sectors that demand high-quality, high-resolution image generation, such as virtual reality and digital content creation.

Overall, Seedream 3.0 represents a notable stride in the evolving landscape of image generation models, pushing forward the capabilities in producing nuanced and culturally coherent bilingual image content. It serves as a foundation for further exploration into scalable, efficient models capable of nuanced and detailed image synthesis across diverse languages and cultures.