A Fast Text-Driven Approach for Generating Artistic Content
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 8 references that are in the map. First posted on arXiv 22 Jun 2022.
Builds on
8 of 8 references.
References · 8
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2021Zero-Shot Text-to-Image Generationcited by 4,276
- 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
- 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
- 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
- 2016Generative Adversarial Text to Image Synthesiscited by 735
- 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
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Citing papers per year: 2024: 1.
1 paper.