TBStar-Edit：从图像编辑模式迁移到一致性增强
TBStar-Edit: From Image Editing Pattern Shifting to Consistency Enhancement
AI总结：
针对通用图像编辑模型在电商场景中的一致性问题，提出TBStar-Edit模型，通过数据工程、分层架构和两阶段训练实现高保真编辑，并在自建基准上超越现有模型。
摘要：
图像生成与编辑技术的最新进展使得最先进的模型在通用领域取得了令人瞩目的成果。然而，当这些通用模型应用于电子商务场景时，常常会遇到一致性问题。为了解决这一挑战，我们提出了TBStar-Edit，一个专为电子商务领域定制的全新图像编辑模型。通过严谨的数据工程、模型架构设计和训练策略，TBStar-Edit在保持产品外观和布局完整性的同时，实现了精确且高保真的图像编辑。具体而言，在数据工程方面，我们建立了一个全面的数据构建流水线，涵盖数据收集、构建、过滤和增强，以获取高质量、遵循指令且强一致性的编辑数据来支持模型训练。在模型架构设计方面，我们设计了一个分层模型框架，由基础模型、模式迁移模块和一致性增强模块组成。在模型训练方面，我们采用两阶段训练策略以增强一致性保持：第一阶段用于编辑模式迁移，第二阶段用于一致性增强。每个阶段使用不同的数据集训练不同的模块。最后，我们在自建的电子商务基准上对TBStar-Edit进行了广泛评估，结果表明，TBStar-Edit在客观指标（VIE分数）和主观用户偏好方面均优于现有的通用领域编辑模型。
英文摘要：
Recent advances in image generation and editing technologies have enabled state-of-the-art models to achieve impressive results in general domains. However, when applied to e-commerce scenarios, these general models often encounter consistency limitations. To address this challenge, we introduce TBStar-Edit, an new image editing model tailored for the e-commerce domain. Through rigorous data engineering, model architecture design and training strategy, TBStar-Edit achieves precise and high-fidelity image editing while maintaining the integrity of product appearance and layout. Specifically, for data engineering, we establish a comprehensive data construction pipeline, encompassing data collection, construction, filtering, and augmentation, to acquire high-quality, instruction-following, and strongly consistent editing data to support model training. For model architecture design, we design a hierarchical model framework consisting of a base model, pattern shifting modules, and consistency enhancement modules. For model training, we adopt a two-stage training strategy to enhance the consistency preservation: first stage for editing pattern shifting, and second stage for consistency enhancement. Each stage involves training different modules with separate datasets. Finally, we conduct extensive evaluations of TBStar-Edit on a self-proposed e-commerce benchmark, and the results demonstrate that TBStar-Edit outperforms existing general-domain editing models in both objective metrics (VIE Score) and subjective user preference.
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。
