A Fast Text-Driven Approach for Generating Artistic Content

• URL: http://arxiv.org/abs/2208.01748v1
• Date: Wed, 22 Jun 2022 14:34:59 GMT
• Title: A Fast Text-Driven Approach for Generating Artistic Content
• Authors: Marian Lupascu, Ryan Murdock, Ionut Mironic\u{a}, Yijun Li
• Abstract summary: We propose a complete framework that generates visual art. We implement an improved version that can generate a wide range of results with varying degrees of detail, style and structure. To further enhance the results, we insert an artistic super-resolution module in the generative pipeline.
• Score: 11.295288894403754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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