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

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/

Authors

• Namhyuk Ahn NAVER WEBTOON AI
• Junsoo Lee NAVER WEBTOON AI
• Chunggi Lee NAVER WEBTOON AI Harvard University
• Kunhee Kim KAIST
• Daesik Kim NAVER WEBTOON AI
• Seung-Hun Nam NAVER WEBTOON AI
• Kibeom Hong SwatchOn

Keywords

• CV: Applications
• CV: Computational Photography, Image & Video Synthesis