Seedream 3.0: ByteDance's High-Performance Bilingual Image Generation Model 231

What is Seedream 3.0?

Seedream 3.0 is a natively high-resolution, bilingual (Chinese and English) image generation foundation model recently released by ByteDance's Seed team. It can generate 2K resolution images in just 3 seconds, and supports small-font and multi-line text layout, with a usable character rate of 94% for both Chinese and English. Seedream 3.0 is already available on ByteDance's own platforms such as Jimeng and Doubao, and is suitable for poster design, advertising creation, graphic design, film and television scene generation, and many other scenarios.

Performance Improvements

High-resolution generation: Seedream 3.0 can natively output 2K resolution images directly, without post-processing, meeting various visual needs from mobile screens to large-format posters.

Ultra-fast generation: This model produces images extremely quickly, generating high-quality 1K resolution images in just about 3 seconds. Compared with over 10 seconds or even longer from similar industry models, and the previous text-to-image SOTA model GPT-4o's average time of 77 seconds, its advantage is obvious.

Text Processing Capabilities

High-fidelity small-font generation: It solves the industry challenge of small-font generation, capable of producing clear and accurate small-font content.

Multi-line text layout: It performs excellently in multi-line text layout, improving the naturalness of text layout and semantic coherence.

Bilingual support: It has strong support for both Chinese and English, with a usable text rate of 94% for both Chinese and English characters, essentially solving the constraint that text rendering has imposed on image generation.

Image Quality and Aesthetics

Aesthetics and structural optimization: The generated images show further improvements in aesthetics and structure, with enhanced instruction following and more impactful outputs, capable of producing high-quality compositions and color combinations.

Realistic portrait generation: It excels in portrait realism, able to generate skin textures with realistic features such as wrinkles, fine hair, and scars, with realism approaching that of professional photography.

Technical Innovations

Data optimization: Expanding the dataset through image defect perception, using a visual-semantic collaborative sampling strategy and a self-developed image-text retrieval system to improve data distribution.

Pre-training and post-training: In the pre-training stage, cross-modal rotary position embedding is used to strengthen text rendering capability; multi-resolution mixed training enables direct 2K image output, and new loss functions are adopted to improve training effectiveness. In the post-training RLHF stage, multi-granularity aesthetic descriptions are designed, the scale of the reward model is expanded, and model performance is enhanced.

Inference acceleration: A consistency noise prediction and stable sampling process are adopted, leveraging important timestep sampling to accelerate model distillation training, achieving end-to-end 1K resolution image generation in just 3 seconds.

Application Scenarios

• Professional poster design: Solves the challenges of multi-line text layout and high-fidelity small-font generation

• Film and television special effects production: Supports generation of character skin details (such as wrinkles, hair, etc.)

• Digital art creation: Capable of generating artworks with accurate colors and rich textures

Detailed introduction:https://team.doubao.com/zh/tech/seedream3_0