mlsanityData to 7 Oct 2026

A Fast Text-Driven Approach for Generating Artistic Content

arXiv 2208.01748 ↗First posted 22 Jun 2022cs.CV cs.MMcited by 1

Not on the map. Why?

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

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  4. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  5. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  6. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
  7. 2016Generative Adversarial Text to Image Synthesiscited by 735
  8. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.