Irina Blok — accepted AI-conference papers

2023

Character-Aware Models Improve Visual Text Rendering

Rosanne Liu, Dan Garrette, Chitwan Saharia, William Chan, Adam Roberts, Sharan Narang +4

ACL 2023long

Current image generation models struggle to reliably produce well-formed visual text. In this paper, we investigate a key contributing factor: popular text-to-image models lack character-level input features, making it much harder to predict a word’s visual makeup as a series of glyphs. To quantify…

Cited by 67 Source ↗PDF ↗Scholar ↗

2023

StyleDrop: Text-to-Image Synthesis of Any Style

Kihyuk Sohn, Lu Jiang, Jarred Barber, Kimin Lee, Nataniel Ruiz, Dilip Krishnan +8

NeurIPS 2023poster

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language, and out-of-distribution effects make it hard to synthesize arbitrary image styles, leveraging a specific design pattern, texture or material…

Cited by 32 Source ↗PDF ↗code ↗Scholar ↗