DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models
This work addresses the challenge for artists and creators in generating high-quality artistic images with specific styles using text-to-image models, though it appears incremental as it builds on existing diffusion models.
The paper tackles the problem of expressing unique artistic characteristics like brushwork and colortone using text prompts alone, which is limited by verbal descriptions, by introducing DreamStyler, a framework for artistic image synthesis and style transfer that achieves superior performance across multiple scenarios.
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.