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TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis

Kazi Mahathir Rahman, Showrin Rahman, Sharmin Sultana Srishty

TL;DR

TextDiffuser-RL tackles the inefficiency of embedding text in diffusion-generated images by decoupling layout planning from synthesis. It introduces GlyphEnv, an RL environment that optimizes non-overlapping, legible text layouts, and integrates these layouts into a diffusion-based generator to produce high-fidelity text-in-image outputs. PPO-based layout optimization delivers competitive OCR/CLIP performance while dramatically reducing runtime and memory usage, achieving around 42× speedups and CPU-friendly inference on MARIOEval. The work demonstrates a practical, resource-efficient path for robust text rendering in diffusion models, with future potential for dynamic layouts, multilingual prompts, and user-guided design controls.

Abstract

Text-embedded image generation plays a critical role in industries such as graphic design, advertising, and digital content creation. Text-to-Image generation methods leveraging diffusion models, such as TextDiffuser-2, have demonstrated promising results in producing images with embedded text. TextDiffuser-2 effectively generates bounding box layouts that guide the rendering of visual text, achieving high fidelity and coherence. However, existing approaches often rely on resource-intensive processes and are limited in their ability to run efficiently on both CPU and GPU platforms. To address these challenges, we propose a novel two-stage pipeline that integrates reinforcement learning (RL) for rapid and optimized text layout generation with a diffusion-based image synthesis model. Our RL-based approach significantly accelerates the bounding box prediction step while reducing overlaps, allowing the system to run efficiently on both CPUs and GPUs. Extensive evaluations demonstrate that our framework achieves comparable performance to TextDiffuser-2 in terms of text placement and image synthesis, while offering markedly faster runtime and increased flexibility. Our method produces high-quality images comparable to TextDiffuser-2, while being 42.29 times faster and requiring only 2 MB of CPU RAM for inference, unlike TextDiffuser-2's M1 model, which is not executable on CPU-only systems.

TextDiffuser-RL: Efficient and Robust Text Layout Optimization for High-Fidelity Text-to-Image Synthesis

TL;DR

TextDiffuser-RL tackles the inefficiency of embedding text in diffusion-generated images by decoupling layout planning from synthesis. It introduces GlyphEnv, an RL environment that optimizes non-overlapping, legible text layouts, and integrates these layouts into a diffusion-based generator to produce high-fidelity text-in-image outputs. PPO-based layout optimization delivers competitive OCR/CLIP performance while dramatically reducing runtime and memory usage, achieving around 42× speedups and CPU-friendly inference on MARIOEval. The work demonstrates a practical, resource-efficient path for robust text rendering in diffusion models, with future potential for dynamic layouts, multilingual prompts, and user-guided design controls.

Abstract

Text-embedded image generation plays a critical role in industries such as graphic design, advertising, and digital content creation. Text-to-Image generation methods leveraging diffusion models, such as TextDiffuser-2, have demonstrated promising results in producing images with embedded text. TextDiffuser-2 effectively generates bounding box layouts that guide the rendering of visual text, achieving high fidelity and coherence. However, existing approaches often rely on resource-intensive processes and are limited in their ability to run efficiently on both CPU and GPU platforms. To address these challenges, we propose a novel two-stage pipeline that integrates reinforcement learning (RL) for rapid and optimized text layout generation with a diffusion-based image synthesis model. Our RL-based approach significantly accelerates the bounding box prediction step while reducing overlaps, allowing the system to run efficiently on both CPUs and GPUs. Extensive evaluations demonstrate that our framework achieves comparable performance to TextDiffuser-2 in terms of text placement and image synthesis, while offering markedly faster runtime and increased flexibility. Our method produces high-quality images comparable to TextDiffuser-2, while being 42.29 times faster and requiring only 2 MB of CPU RAM for inference, unlike TextDiffuser-2's M1 model, which is not executable on CPU-only systems.

Paper Structure

This paper contains 29 sections, 3 equations, 13 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Environment Architecture
  5. Constraint Modeling
  6. Action and Observation Spaces
  7. Reward Formulation
  8. Overlap Metric
  9. Training a Reinforcement Learning Algorithm with a Custom Environment
  10. TextDiffuser-RL Model for Image Generation
  11. Experiments
  12. Reinforcement Learning Algorithm selection
  13. Ablation Study
  14. Rectangular Numbers and Minimum Area Selection
  15. Random Seed Robustness
  16. ...and 14 more sections

Figures (13)

  • Figure 1: Overview of the model architecture, showing the diffusion-based synthesis process with GlyphEnv optimizing non-overlapping bounding box generation, layout planning, and quality assessment based on OCR accuracy and spatial alignment.
  • Figure 2: Two-stage Workflow
  • Figure 3: Comparison of PPO training performance across episodes. Episode length comparison across PPO, showing stable and shorter episodes over time, indicating faster task completion. Mean episode reward curve consistently improves over time.
  • Figure 4: RL generated bounding box
  • Figure 5: Visual comparison of RL model performances based on bounding box layouts
  • ...and 8 more figures