In this work, we propose a complete framework that generates visual art. Unlike previous stylization methods that are not flexible with style parameters (i.e., they allow stylization with only one style image, a single stylization text or stylization of a content image from a certain domain), our method has no such restriction. In addition, we implement an improved version that can generate a wide range of results with varying degrees of detail, style and structure, with a boost in generation speed. To further enhance the results, we insert an artistic super-resolution module in the generative pipeline. This module will bring additional details such as patterns specific to painters, slight brush marks, and so on.
@article{arxiv.2208.01748,
title = {A Fast Text-Driven Approach for Generating Artistic Content},
author = {Marian Lupascu and Ryan Murdock and Ionut Mironica and Yijun Li},
journal= {arXiv preprint arXiv:2208.01748},
year = {2025}
}