DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models

Namhyuk Ahn, Junsoo Lee, Chunggi Lee, Kunhee Kim, Daesik Kim +2 more
9/13/2023
cs.CV

No official code found. Best implementation: webtoon/dreamstyler (54 stars, Apache-2.0, last commit 2024-02-16).

Abstract

Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristics of an artwork (e.g. brushwork, colortone, or composition) with text prompts alone may encounter limitations due to the inherent constraints of verbal description. To this end, we introduce DreamStyler, a novel framework designed for artistic image synthesis, proficient in both text-to-image synthesis and style transfer. DreamStyler optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality. In addition, with content and style guidance, DreamStyler exhibits flexibility to accommodate a range of style references. Experimental results demonstrate its superior performance across multiple scenarios, suggesting its promising potential in artistic product creation.

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Code Implementations(1)

Apache-2.0

Official implementation of "DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models" (AAAI24)

542PythonFeb 7, 20242 years agoApache-2.0

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Cite this paper

@article{ahn2023dreamstyler,
  title  = {DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models},
  author = {Namhyuk Ahn and Junsoo Lee and Chunggi Lee and Kunhee Kim and Daesik Kim and Seung-Hun Nam and Kibeom Hong},
  year   = {2023},
  eprint = {2309.06933},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.06933v2}
}

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