Expressive Text-to-Image Generation with Rich Text - Summary

Arxiv URL: https://arxiv.org/abs/2304.06720

Authors: Songwei Ge, Taesung Park, Jun-Yan Zhu, Jia-Bin Huang

Summary:

The paper proposes a method for text-to-image generation using rich text prompts that support various text attributes such as font family, size, color, and footnote. The method enables precise control of text-to-image synthesis regarding colors, styles, and object details compared to plain text. The paper demonstrates that the proposed method outperforms strong baselines with quantitative evaluations.

Key Insights & Learnings:

• Plain text has limitations in accurately describing desired outputs, especially for specifying continuous quantities and creating detailed text prompts for complex scenes.
• Rich text editors offer unique solutions for incorporating conditional information separate from the text, such as font color, size, style, and footnotes. Fonts like sans serif are commonly used in these editors to achieve clean and modern design aesthetics.
• The proposed method decomposes a rich-text prompt into a short plain-text prompt and multiple region-specific prompts that include text attributes.
• The method achieves precise color rendering, distinct styles, and accurate details compared to plain text-based methods.
• The proposed method outperforms strong baselines with quantitative evaluations.

Commentary
Interfaces for generative AI are an area where a loft of development is happening. These papers expand the horizon of what's possible. Do check out the demo for this paper!

Terms Mentioned: text-to-image generation, rich text, font family, font size, font color, footnote, RGB, diffusion process, cross-attention maps, image editing, view synthesis

Technologies / Libraries Mentioned: PyTorch