DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models

Understand

We demonstrate that pre-trained text-to-image diffusion models, despite being trained on raster images, possess a remarkable capacity to guide vector sketch synthesis.

• In this paper, we introduce DiffSketcher, a novel algorithm for generating vectorized free-hand sketches directly from natural language prompts.
• Our method optimizes a set of B\'ezier curves via an extended Score Distillation Sampling (SDS) loss, successfully bridging a raster-level diffusion prior with a parametric vector generator.
• To further accelerate the generation process, we propose a stroke initialization strategy driven by the diffusion model's intrinsic attention maps.