WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing
Instruction-based image editing aims to modify specific content within existing images according to user-provided instructions while preserving non-target regions. Beyond traditional object- and style-centric manipulation, text-centric image editing focuses on modifying, translating, or rearranging textual elements embedded within images. However, existing leading models often struggle to execute complex text editing precisely, frequently producing blurry or hallucinated characters. We attribute these failures primarily to the lack of specialized training paradigms tailored for text-centric editing, as well as the absence of large-scale datasets and standardized benchmarks necessary for a closed-loop training and evaluation system. To address these limitations, we present WeEdit, a systematic solution encompassing a scalable data construction pipeline, two benchmarks, and a tailored two-stage training strategy. Specifically, we propose a novel HTML-based automatic editing pipeline, which generates 330K training pairs covering diverse editing operations and 15 languages, accompanied by standardized bilingual and multilingual benchmarks for comprehensive 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 generation with instruction adherence, text clarity, and background preservation. Extensive experiments demonstrate that WeEdit outperforms previous open-source models by a clear margin across diverse editing operations.
69b373cc2ad6c5d4cf486a08
Hui Zhang
69b373cc2ad6c5d4cf486a09
Juntao Liu
69b373cc2ad6c5d4cf486a0a
Zongkai Liu
69b373cc2ad6c5d4cf486a0b
Liqiang Niu
admin_assigned
2026-03-13T08:56:51.049Z
635239137d071f23d083b056
/avatars/1f1a0ed38d8de499d4b78922801c6d95.svg
liqiang niu
lqniu
user
lqniu
69b373cc2ad6c5d4cf486a0c
Fandong Meng
admin_assigned
2026-03-13T08:56:45.713Z
64cb254871a7bbb60c17d5fa
/avatars/5121fd5b7b55d275eba3947f3f4c034d.svg
Fandong Meng
fandong
user
fandong
69b373cc2ad6c5d4cf486a0d
Zuxuan Wu
69b373cc2ad6c5d4cf486a0e
Yu-Gang Jiang
WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing
Instruction-based image editing aims to modify specific content within existing images according to user-provided instructions while preserving non-target regions. Beyond traditional object- and style-centric manipulation, text-centric image editing focuses on modifying, translating, or rearranging textual elements embedded within images. However, existing leading models often struggle to execute complex text editing precisely, frequently producing blurry or hallucinated characters. We attribute these failures primarily to the lack of specialized training paradigms tailored for text-centric editing, as well as the absence of large-scale datasets and standardized benchmarks necessary for a closed-loop training and evaluation system. To address these limitations, we present WeEdit, a systematic solution encompassing a scalable data construction pipeline, two benchmarks, and a tailored two-stage training strategy. Specifically, we propose a novel HTML-based automatic editing pipeline, which generates 330K training pairs covering diverse editing operations and 15 languages, accompanied by standardized bilingual and multilingual benchmarks for comprehensive 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 generation with instruction adherence, text clarity, and background preservation. Extensive experiments demonstrate that WeEdit outperforms previous open-source models by a clear margin across diverse editing operations.
WeEdit presents a systematic approach for text-centric image editing with a scalable data pipeline, multi-language benchmarks, and a two-stage training strategy combining supervised fine-tuning and reinforcement learning.
text-centric image editing HTML-based automatic editing pipeline glyph-guided supervised fine-tuning multi-objective reinforcement learning instruction adherence text clarity background preservation