WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing

Instruction-based image editing aims to modify specific con-tent within existing images according to user-provided instructions whilepreserving non-target regions. Beyond traditional object- and style-centricmanipulation, text-centric image editing focuses on modifying, translating,or rearranging textual elements embedded within images. However, exist-ing leading models often struggle to execute complex text editing precisely,frequently producing blurry or hallucinated characters. We attribute thesefailures primarily to the lack of specialized training paradigms tailoredfor text-centric editing, as well as the absence of large-scale datasetsand standardized benchmarks necessary for a closed-loop training andevaluation system. To address these limitations, we present WeEdit, a sys-tematic solution encompassing a scalable data construction pipeline, twobenchmarks, and a tailored two-stage training strategy. Specifically, wepropose a novel HTML-based automatic editing pipeline, which generates330K training pairs covering diverse editing operations and 15 languages,accompanied by standardized bilingual and multilingual benchmarks forcomprehensive evaluation. On the algorithmic side, we employ glyph-guided supervised fine-tuning to inject explicit spatial and content priors,followed by a multi-objective reinforcement learning stage to align genera-tion with instruction adherence, text clarity, and background preservation.Extensive experiments demonstrate that WeEdit outperforms previousopen-source models by a clear margin across diverse editing operations.