# Four-Step Text-Bearing Image Generation via Distillation with Local Response Regularization and Teacher-Guided Training

**Authors:** Kairui Wen, Sheng Ye, Yong-Jin Liu  
**Published:** 9 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.12042) · [PDF](https://arxiv.org/pdf/2611.12042) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.12042.html)

## Key numbers

- **3.34×**: yielding a 3.34× speedup at the same 40 GB GPU memory requirement

## Abstract

Generating text-bearing images requires coherent scenes and accurate characters. Large text-to-image models improve both capabilities through repeated denoising, at substantial sampling cost. Few-step distillation reduces this cost but requires the student to organize scenes and refine glyphs within a short trajectory. We present a four-step distillation framework combining local response regularization with teacher-guided text training. Built on distribution matching, the framework distills the GLM-Image DiT while retaining its autoregressive visual prior, glyph encoder, and VAE. A path-length penalty promotes consistent local responses of the conditional denoising field. Progressive training combines synthetic strings with teacher-generated imagery to preserve character accuracy and scene-integrated typography. The student achieves GenEval Overall 0.84 and CVTG-2K word accuracy 0.5045, compared with 0.81 and 0.1690 for four-step TDM. A coefficient study favors 10^-5, and Qwen-Image experiments show additional gains from response regularization and text supervision. Single-image inference takes 1.742 s, compared with 5.818 s for the teacher, yielding a 3.34× speedup at the same 40 GB GPU memory requirement.

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