DreamStyler：使用文本到圖像擴散進行風格反轉繪畫模型

摘要

最近在大規模文本到圖像模型方面取得了顯著進展，並在藝術領域中找到了各種應用。然而，僅使用文本提示來表達藝術作品的獨特特徵（例如筆觸、色調或構圖）可能會因口頭描述的固有限制而遇到限制。為此，我們介紹了DreamStyler，這是一個新穎的框架，旨在進行藝術圖像合成，擅長於文本到圖像合成和風格轉移。DreamStyler 通過具有上下文感知的文本提示來優化多階段文本嵌入，從而產生卓越的圖像質量。此外，憑藉內容和風格指導，DreamStyler展現了靈活性，以容納各種風格參考。實驗結果顯示其在多種情境下具有優越性能，表明其在藝術產品創作中具有潛在的應用價值。

English

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.

DreamStyler：使用文本到圖像擴散進行風格反轉繪畫模型

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

摘要

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