DiffGraph：面向真实场景文本到图像生成的自动化智能体驱动模型合并框架
DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generation
提出基于图结构的自动化模型合并框架，通过节点注册与校准组织在线专家模型，并根据用户需求动态激活子图，为视觉生成中的模型复用与组合提供可扩展方案。
原文依据 DiffGraph dynamically activates specific subgraphs based on user needs, enabling flexible combinations of different experts
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论文本到图像（T2I）社区的快速增长催生了一个繁荣的在线专家模型生态系统，这些模型是预训练扩散模型的变体，专门用于多样化的生成能力。然而，现有的模型合并方法在充分利用丰富的在线专家资源方面仍然有限，并且难以满足真实场景中多样化的用户需求。我们提出 DiffGraph，一种新颖的自动化智能体驱动的基于图的模型合并框架，它能够自动利用在线专家模型，并针对多样化的用户需求灵活地合并它们。我们的 DiffGraph 构建了一个可扩展的图，并通过节点注册与校准将不断扩展的在线专家模型组织在其中。然后，DiffGraph 根据用户需求动态激活特定的子图，从而实现不同专家模型的灵活组合，以达到用户期望的生成效果。大量实验证明了我们方法的有效性。
英文原文摘要 918 字符
The rapid growth of the text-to-image (T2I) community has fostered a thriving online ecosystem of expert models, which are variants of pretrained diffusion models specialized for diverse generative capabilities. Yet, existing model merging methods remain limited in fully leveraging abundant online expert resources and still struggle to meet diverse in-the-wild user needs. We present DiffGraph, a novel automated agent-driven graph-based model merging framework, which automatically harnesses online experts and flexibly merges them for diverse user needs. Our DiffGraph constructs a scalable graph and organizes ever-expanding online experts within it through node registration and calibration. Then, DiffGraph dynamically activates specific subgraphs based on user needs, enabling flexible combinations of different experts to achieve user-desired generation. Extensive experiments show the efficacy of our method.