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豆包大模型团队:2025年Seedream 3.0 文生图模型技术报告(英文版)(22页).pdf

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1、Seedream 3.0 Technical ReportByteDance SeedAbstractWe present Seedream 3.0,a high-performance Chinese-English bilingual image generation founda-tion model.We develop several technical improvements to address existing challenges in Seedream2.0,including alignment with complicated prompts,fine-grained

2、 typography generation,suboptimalvisual aesthetics and fidelity,and limited image resolutions.Specifically,the advancements ofSeedream 3.0 stem from improvements across the entire pipeline,from data construction to modeldeployment.At the data stratum,we double the dataset using a defect-aware traini

3、ng paradigmand a dual-axis collaborative data-sampling framework.Furthermore,we adopt several effectivetechniques such as mixed-resolution training,cross-modality RoPE,representation alignmentloss,and resolution-aware timestep sampling in the pre-training phase.During the post-trainingstage,we utili

4、ze diversified aesthetic captions in SFT,and a VLM-based reward model withscaling,thereby achieving outputs that well align with human preferences.Furthermore,See-dream 3.0 pioneers a novel acceleration paradigm.By employing consistent noise expectationand importance-aware timestep sampling,we achie

5、ve a 4 to 8 times speedup while maintainingimage quality.Seedream 3.0 demonstrates significant improvements over Seedream 2.0:it enhancesoverall capabilities,in particular for text-rendering in complicated Chinese characters which isimportant to professional typography generation.In addition,it prov

6、ides native high-resolutionoutput(up to 2K),allowing it to generate images with high visual quality.Official Page:https:/ 2.0Imagen 3Ideogram 3.0Midjourney v6.1FLUX1.1 ProSeedream 3.0Figure 1Seedream 3.0 demonstrates outstanding performance across all evaluation aspects.Due to missing data,thePortra

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本文介绍了Seedream 3.0,这是一个高性能的中英双语图像生成基础模型。与Seedream 2.0相比,Seedream 3.0在多个方面进行了改进,包括数据构建、模型预训练、后训练和模型加速。具体来说,Seedream 3.0的数据集大小翻倍,采用了新的动态采样机制,并引入了多种有效的训练方法,如混合分辨率训练、跨模态RoPE、表示对齐损失和分辨率感知的时间步采样。在后训练阶段,Seedream 3.0利用多样化的审美字幕进行SFT,并采用基于VLM的奖励模型进行缩放,从而实现了与人类偏好高度一致的输出。在模型加速方面,Seedream 3.0通过一致的噪声期望和重要性感知的时间步采样,实现了4到8倍的加速,同时保持了图像质量。 Seedream 3.0在人工分析和综合评估中表现出色,特别是在密集文本渲染和逼真人像生成方面。此外,Seedream 3.0还提供了原生高分辨率输出,支持高达2K的分辨率,消除了对后处理的依赖。 总的来说,Seedream 3.0在多个维度上相比Seedream 2.0有了显著的改进,包括文本图像对齐、结构合理性、审美质量和文本渲染。这些改进使得Seedream 3.0在各种工作和生活场景中具有强大的潜力,成为提高生产力的实用工具。
Seedream 3.0如何解决图像分辨率问题? 相比Seedream 2.0,Seedream 3.0在哪些方面有显著提升? Seedream 3.0如何实现更精细的文字渲染?
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