  Recently, text-guided scalable vector graphics (SVGs) synthesis has shown
promise in domains such as iconography and sketch. However, existing
text-to-SVG generation methods lack editability and struggle with visual
quality and result diversity. To address these limitations, we propose a novel
text-guided vector graphics synthesis method called SVGDreamer. SVGDreamer
incorporates a semantic-driven image vectorization (SIVE) process that enables
the decomposition of synthesis into foreground objects and background, thereby
enhancing editability. Specifically, the SIVE process introduces
attention-based primitive control and an attention-mask loss function for
effective control and manipulation of individual elements. Additionally, we
propose a Vectorized Particle-based Score Distillation (VPSD) approach to
address issues of shape over-smoothing, color over-saturation, limited
diversity, and slow convergence of the existing text-to-SVG generation methods
by modeling SVGs as distributions of control points and colors. Furthermore,
VPSD leverages a reward model to re-weight vector particles, which improves
aesthetic appeal and accelerates convergence. Extensive experiments are
conducted to validate the effectiveness of SVGDreamer, demonstrating its
superiority over baseline methods in terms of editability, visual quality, and
diversity. Project page:
\href{https://ximinng.github.io/SVGDreamer-project/}{https://ximinng.github.io/SVGDreamer-project/}
