Visual text generation has significantly advanced through diffusion models aimed at producing images with readable and realistic text. Recent works primarily use a ControlNet-based framework, employing standard font text images to control diffusion models. Recognizing the critical role of control information in generating high-quality text, we investigate its influence from three perspectives: input encoding, role at different stages, and output features. Our findings reveal that: 1) Input control information has unique characteristics compared to conventional inputs like Canny edges and depth maps.

These properties limit the performance.

These properties limit the performance.

Visual text generation has significantly advanced through diffusion models aimed at producing images with readable and realistic text. Recent works primarily use a ControlNet-based framework, employing standard font text images to control diffusion models. Recognizing the critical role of control information in generating high-quality text, we investigate its influence from three perspectives: input encoding, role at different stages, and output features. Our findings reveal that: 1) Input control information has unique characteristics compared to conventional inputs like Canny edges and depth maps. Based on these insights, we propose TextGen, a novel framework designed to enhance generation quality by optimizing control information.

In July 2009, deadly riots broke out in Urumqi, the capital of Xinjiang, China. Nearly 200 people died, the majority ethnic Han Chinese, and thousands of Chinese troops were brought in to quell the riots. An information battle soon followed, as mobile phone and internet service was cut off in the entire province. For the next 10 months, web access would be almost non-existent in Xinjiang, a vast region larger than Texas with a population of over 20 million. It was one of the most widespread, longest internet shutdowns ever.

Constraint propagation algorithms implement logical infe r-ence. For efficiency, it is essential to control whether and in what order basic inference steps are taken. We provide a high-level fra mework that clearly differentiates between information needed for cont rolling propagation versus that needed for the logical semantics of complex constraints composed from primitive ones. We argue for the appropriaten ess of our controlled propagation framework by showing that it captures the underlying principles of manually designed propagation algo rithms, such as literal watching for unit clause propagation and the lexi cographic ordering constraint. We provide an implementation and benchm ark results that demonstrate the practicality and efficiency of our frame work.