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.

翻译：近期大规模文本到图像模型的进展取得了显著成就，并在艺术领域找到了多种应用。然而，仅通过文本提示表达艺术作品的独特特征（如笔触、色调或构图）可能因语言描述的固有局限而遇到困难。为此，我们提出了DreamStyler——一个专为艺术图像合成设计的新框架，它擅长文本到图像合成与风格迁移。DreamStyler通过结合上下文感知的文本提示优化多阶段文本嵌入，从而获得突出的图像质量。此外，在内容和风格引导下，DreamStyler展现出对多种风格参考的灵活适配能力。实验结果表明，该框架在多种场景中均具有优越性能，显示出其在艺术作品创作领域的广阔潜力。