DreamPoster：面向图像条件生成海报设计的统一框架

摘要

我们推出DreamPoster，一个文本到图像生成框架，它智能地综合用户提供的图像与文本提示，生成高质量海报，同时保持内容忠实度，并支持灵活的分辨率与布局输出。具体而言，DreamPoster基于我们的T2I模型Seedream3.0构建，统一处理各类海报生成任务。在数据集构建方面，我们提出了一套系统化的数据标注流程，精确标注海报图像中的文本内容及排版层级信息，并采用全面方法构建包含源材料（如原始图形/文本）及其对应最终海报输出的配对数据集。此外，我们实施了一种渐进式训练策略，使模型能够分层获取多任务生成能力，同时保持高质量生成。在我们的测试基准上进行的评估显示，DreamPoster相较于现有方法展现出显著优势，实现了高达88.55%的可用率，相比之下，GPT-4o为47.56%，SeedEdit3.0为25.96%。DreamPoster即将在吉梦及其他字节跳动应用上线。

English

We present DreamPoster, a Text-to-Image generation framework that intelligently synthesizes high-quality posters from user-provided images and text prompts while maintaining content fidelity and supporting flexible resolution and layout outputs. Specifically, DreamPoster is built upon our T2I model, Seedream3.0 to uniformly process different poster generating types. For dataset construction, we propose a systematic data annotation pipeline that precisely annotates textual content and typographic hierarchy information within poster images, while employing comprehensive methodologies to construct paired datasets comprising source materials (e.g., raw graphics/text) and their corresponding final poster outputs. Additionally, we implement a progressive training strategy that enables the model to hierarchically acquire multi-task generation capabilities while maintaining high-quality generation. Evaluations on our testing benchmarks demonstrate DreamPoster's superiority over existing methods, achieving a high usability rate of 88.55\%, compared to GPT-4o (47.56\%) and SeedEdit3.0 (25.96\%). DreamPoster will be online in Jimeng and other Bytedance Apps.

DreamPoster：面向图像条件生成海报设计的统一框架

DreamPoster: A Unified Framework for Image-Conditioned Generative Poster Design

摘要

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