DreamStyler：使用文本到图像扩散进行风格反演的绘画模型

摘要

最近在大规模文本到图像模型方面取得了显著进展，取得了卓越的成就，在艺术领域找到了各种应用。然而，仅凭文本提示来表达艺术作品的独特特征（如笔触、色调或构图）可能会受到口头描述固有限制的限制。为此，我们引入了DreamStyler，这是一个专为艺术图像合成而设计的新框架，精通文本到图像合成和风格迁移。DreamStyler通过具有上下文感知的文本提示优化多阶段文本嵌入，从而产生突出的图像质量。此外，通过内容和风格指导，DreamStyler表现出灵活性，以适应各种风格参考。实验结果表明，在多种情景下，它展现出卓越的性能，表明在艺术产品创作中具有潜在的前景。

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：使用文本到图像扩散进行风格反演的绘画模型

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

摘要

Support