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 DreamStyle, a novel framework designed for artistic image synthesis, proficient in both text-to-image synthesis and style transfer.
DreamStyle optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality.
In addition, with content and style guidance, DreamStyle 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.
Project page: https://nmhkahn.github.io/dreamstyler/

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

Main Track - Computer Vision

technical paper

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Kunhee Kim

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

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