ZeyuJiang1/OrionEditBench

--- language: - en license: apache-2.0 size_categories: - 10K<n<100K task_categories: - image-text-to-image tags: - image-editing - multimodal - diffusion configs: - config_name: image-text-to-image data_files: metadata/metadata.json --- # 🧩 OrionEditBench **OrionEditBench** is a large-scale dataset for **cross-image editing**, where each sample is structured as: > **(reference image(s), source image) → synthesis image** It is designed to support multi-image conditioned generation and editing, allowing models to integrate visual information across inputs. The dataset contains approximately **50K high-quality samples**, covering key editing scenarios including **attribute transfer**, **style alignment**, and **multi-image fusion**. We first release the core subset focusing on **attribute transfer**, including **subject replacement** and **appearance transfer**, with both single- and multi-subject settings. Additional data is under active curation and will be released in future versions. --- ## News - **2026.4.01**: The dataset has been released. - **2026.4.02**: We provide the metadata for the sub-tasks of subject replacement and appearance transfer within attribute transfer, focusing on the AI-synthesized portion. ## Overview | Data Type | Number of Samples | | :---------------------- | :----------------- | | Attribute Transfer | 10,664 | | Fusion | - | | Style Alignment | -| | **Total** | **-** | OrionEditBench focuses on **generalized cross-image editing tasks**, where models are required to: - transfer attributes from reference images - preserve structural or semantic content from source images - generate coherent target outputs The dataset consists of a mixture of: - automatically constructed samples generated using advanced multimodal models - curated samples derived from publicly available data --- ## Format Illustration To reduce training complexity and memory overhead in multi-reference-based editing, where separate branches are typically required for reference, source, and noise initialization, we pre-compose multiple reference images into a single input so that they share a unified processing branch. For clarity, we provide two representative data formats: (1) foreground-isolated references with background removed, and (2) concatenated pairs of reference images resized to match the source dimensions. <p align="center"> <img src="assets/example_foreground.png" width="40%"> <img src="assets/example_concat.png" width="40%"> </p> <p align="center"> <em>Top: foreground-isolated format. Botton: concatenated source–target format.</em> </p> ## Data Structure Each sample in the dataset is stored in JSON format: ```json { "edit_prompt": "...", "t2i_prompt": "...", "source_image": "images/source/xxx.png", "reference_image": "images/reference/xxx.png", "output_image": "images/synthesis/xxx.png", "width": 1344, "height": 768, } ``` To extract the dataset from split .tar archives: ```json cd /path/to/reference-source-synthesis cat source_part_*.tar | tar -xf - ``` After extraction, you may partition the dataset into training and testing splits based on your experimental needs. ## Resources * **GitHub**: [cityuhkai/OrionEdit](https://github.com/cityuhkai/OrionEdit) * **Model**: [OrionEdit-qwen](https://huggingface.co/ZeyuJiang1/OrionEdit-qwen) * **Paper**: [OrionEdit: Bridging Reference and Source Images for Generalized Cross-Image Editing](https://github.com/cityuhkai/OrionEdit) ## Citation If you find our dataset helpful, please consider citing our work: ``` @article{ > Our paper has been accepted to CVPR 2026. The official citation will be released upon publication. } ```--- language: - 英语 license: apache-2.0 size_categories: - 10K < 样本数量 < 100K task_categories: - 图像-文本至图像（image-text-to-image） tags: - 图像编辑（image-editing） - 多模态（multimodal） - 扩散模型（diffusion） configs: - config_name: 图像-文本至图像（image-text-to-image） data_files: metadata/metadata.json --- # 🧩 OrionEditBench **OrionEditBench** 是一款面向跨图像编辑（cross-image editing）的大规模数据集，其单条样本的结构为： > **（参考图像、源图像）→ 合成图像** 它被设计用于支持多图像条件生成与编辑任务，使模型能够整合多输入中的视觉信息。该数据集共包含约50K条高质量样本，覆盖属性迁移、风格对齐、多图像融合等核心编辑场景。我们首次发布聚焦于属性迁移的核心子集，其中包含主体替换与外观迁移两类任务，涵盖单主体与多主体两种设置。其余数据仍在积极整理中，将在后续版本中发布。 --- ## 新闻 - **2026.4.01**：本数据集正式发布。 - **2026.4.02**：我们提供了属性迁移子任务中主体替换与外观迁移的元数据，聚焦于人工智能合成部分的数据。 ## 概览 | 数据类型 | 样本数量 | | :---------------------- | :----------------- | | 属性迁移 | 10,664 | | 图像融合 | - | | 风格对齐 | -| | **总计** | **-** | OrionEditBench聚焦于通用跨图像编辑任务，要求模型完成以下操作： - 从参考图像中迁移属性特征 - 保留源图像的结构或语义内容 - 生成连贯一致的目标输出结果 该数据集由两类数据混合构建而成： - 利用先进多模态模型自动生成的样本 - 从公开可用数据源中整理得到的样本 --- ## 格式说明 为降低多参考图像编辑场景下的训练复杂度与内存开销（该场景通常需要为参考、源与噪声初始化设置独立处理分支），我们将多张参考图像预组合为单输入，使其可共享统一的处理流程。为便于理解，我们提供两种典型数据格式：(1) 移除背景的前景隔离参考图像格式，(2) 调整至与源图像尺寸匹配的拼接参考图像对格式。 <p align="center"> <img src="assets/example_foreground.png" width="40%"> <img src="assets/example_concat.png" width="40%"> </p> <p align="center"> <em>上图：前景隔离格式。下图：拼接源-目标格式。</em> </p> ## 数据结构 数据集中的每条样本均以JSON格式存储： json { "edit_prompt": "...", "t2i_prompt": "...", "source_image": "images/source/xxx.png", "reference_image": "images/reference/xxx.png", "output_image": "images/synthesis/xxx.png", "width": 1344, "height": 768, } 若需从拆分的.tar归档文件中提取数据集，请执行如下指令： bash cd /path/to/reference-source-synthesis cat source_part_*.tar | tar -xf - 解压完成后，您可根据实验需求将数据集划分为训练集与测试集。 ## 资源 * **GitHub仓库**：[cityuhkai/OrionEdit](https://github.com/cityuhkai/OrionEdit) * **模型权重**：[OrionEdit-qwen](https://huggingface.co/ZeyuJiang1/OrionEdit-qwen) * **学术论文**：[OrionEdit: Bridging Reference and Source Images for Generalized Cross-Image Editing](https://github.com/cityuhkai/OrionEdit) ## 引用 若您认为本数据集对您的研究有所帮助，请引用我们的工作： bibtex @article{ > 本文已被CVPR 2026收录。 正式引用格式将在论文正式出版后发布。 }