# Expressive Text-to-Image Generation with Rich Text

**Authors:** Songwei Ge, Taesung Park, Jun-Yan Zhu, Jia-Bin Huang  
**arXiv:** [2304.06720](https://arxiv.org/abs/2304.06720)  
**Date:** 2023-04-13  
**Summary page:** https://www.summarizepaper.com/en/paper/2304.06720v1/

## Key points

- Rich text editors provide more formatting options than plain text for generating images from textual descriptions
- Plain text's limited customization options hinder users from accurately describing desired outputs
- Rich text editors offer various formatting options such as font style, size, color, and footnotes that make it easier to incorporate conditional information separate from the text
- Using rich text editors, we can indicate an arbitrary color using font color and define the precise color of generated objects with RGB or Hex triplets
- Font size can be used to reweight token influence and capture the artistic style of specific regions by distinguishing individual text elements' styles
- Footnotes can provide supplementary descriptions for selected words, simplifying the process of creating complex scenes
- Converting a rich-text prompt into lengthy plain text struggles to synthesize images corresponding to lengthy prompts involving multiple objects with distinct visual attributes
- The proposed method uses a region-based diffusion process that extracts each word's attributes from rich text to enable local style control, explicit token reweighting, precise color rendering, and detailed region synthesis.
- The proposed method generates more precise colors, distinct styles and accurate details compared to plain-text-based methods.
- The approach outperforms strong baselines in qualitative and quantitative evaluations.
- Code and demo are available on their project page.

## Summary

Rich text editors offer a unique solution to the limitations of plain text in text-to-image synthesis. While plain text has become a prevalent interface for generating images from textual descriptions, its limited customization options hinder users from accurately describing desired outputs. Rich text editors, on the other hand, provide various formatting options such as font style, size, color, and footnotes that make it easier to incorporate conditional information separate from the text. Using rich text editors, we can indicate an arbitrary color using font color and define the precise color of generated objects with RGB or Hex triplets. Additionally, we can use font size to reweight token influence and capture the artistic style of specific regions by distinguishing individual text elements' styles. Footnotes can also provide supplementary descriptions for selected words, simplifying the process of creating complex scenes. However, converting a rich-text prompt with detailed attributes into lengthy plain text and feeding it directly into existing methods struggles to synthesize images corresponding to lengthy prompts involving multiple objects with distinct visual attributes. To address this challenge, we propose using a region-based diffusion process that extracts each word's attributes from rich text to enable local style control, explicit token reweighting, precise color rendering, and detailed region synthesis. Our method generates more precise colors, distinct styles and accurate details compared to plain-text-based methods. We demonstrate this through qualitative and quantitative evaluations and show that our approach outperforms strong baselines. Our code and demo are available on our project page https://rich-text-to-image.github.io/. In summary, rich-text editors offer more options than plain text in incorporating conditional information in image generation tasks. Our proposed method provides precise controls over image synthesis while addressing challenges posed by lengthy prompts involving multiple objects with distinct visual attributes.