UltraEdit：大规模基于指令的细粒度图像编辑
UltraEdit: Instruction-based Fine-Grained Image Editing at Scale
AI总结：
提出大规模自动生成数据集UltraEdit，通过真实图像与LLM创造力支持细粒度指令与区域编辑，显著提升扩散模型在MagicBrush和Emu-Edit基准上的表现。
摘要：
本文提出了UltraEdit，一个大规模（约400万个编辑样本）、自动生成的基于指令的图像编辑数据集。我们的核心思想是解决InstructPix2Pix和MagicBrush等现有图像编辑数据集的缺陷，并提供一种系统化的方法来生成海量且高质量的图像编辑样本。UltraEdit具有几个显著优势：1）通过利用大型语言模型（LLMs）的创造力以及人类评估者提供的上下文编辑示例，它包含了更广泛的编辑指令；2）其数据源基于真实图像，包括照片和艺术作品，与仅由文本到图像模型生成的数据集相比，提供了更大的多样性并减少了偏差；3）它还支持基于区域的编辑，并通过高质量、自动生成的区域标注进行增强。我们的实验表明，在UltraEdit上训练的规范基于扩散的编辑基线在MagicBrush和Emu-Edit基准上创下了新纪录。我们的分析进一步证实了真实图像锚点和基于区域的编辑数据的关键作用。数据集、代码和模型可在 https://ultra-editing.github.io 找到。
英文摘要：
This paper presents UltraEdit, a large-scale (approximately 4 million editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2Pix and MagicBrush, and provide a systematic approach to producing massive and high-quality image editing samples. UltraEdit offers several distinct advantages: 1) It features a broader range of editing instructions by leveraging the creativity of large language models (LLMs) alongside in-context editing examples from human raters; 2) Its data sources are based on real images, including photographs and artworks, which provide greater diversity and reduced bias compared to datasets solely generated by text-to-image models; 3) It also supports region-based editing, enhanced by high-quality, automatically produced region annotations. Our experiments show that canonical diffusion-based editing baselines trained on UltraEdit set new records on MagicBrush and Emu-Edit benchmarks. Our analysis further confirms the crucial role of real image anchors and region-based editing data. The dataset, code, and models can be found in https://ultra-editing.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
