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 是一种新的框架，用于艺术图像合成，具备文本到图像合成和风格转换的能力，通过多阶段的文本嵌入和上下文感知的文本提示来优化图像质量，并具备适应一系列风格参考的灵活性，实验证明其在多种场景下的卓越性能，显示出在艺术产品创作方面的潜在优势。