Expressive Text-to-Image Generation with Rich Text is a state-of-the-art deep learning model that allows for the generation of high-quality images from textual descriptions. The model is based on the GPT-3 architecture and incorporates a range of techniques to generate images that accurately reflect the input text.
What sets this model apart is its ability to handle rich text, including complex descriptions that contain multiple clauses, modifiers, and other linguistic structures. The model can also incorporate style and context information from the input text, allowing it to generate images that reflect the intended tone, mood, and aesthetic of the text.
One key advantage of Expressive Text-to-Image Generation with Rich Text is its versatility. The model can be trained on a range of datasets, allowing it to generate images for a wide range of applications, from storytelling and marketing to scientific visualization and more. Additionally, the model can be fine-tuned to improve its performance on specific types of text or images, making it a powerful tool for custom applications.
Overall, Expressive Text-to-Image Generation with Rich Text represents a significant advance in the field of generative AI, offering new possibilities for creating high-quality visual content from text. Whether you’re a content creator, marketer, or researcher, this model has the potential to transform the way you work with text and images.
tl;dr: We use various formatting information from rich text, including font size, color, style, and footnote, to increase control of text-to-image generation. Our method enables explicit token reweighting, precise color rendering, local style control, and detailed region synthesis.
Expressive Text-to-Image Generation with Rich Text
Songwei Ge, Taesung Park, Jun-Yan Zhu, Jia-Bin Huang
UMD, Adobe, CMU