Abstract We introduce SketchCrafter, a training‐free framework that formulates sketch extraction as a generative process rather than relying on aggregation. SketchCrafter initiates generation with an edge‐based initialization, projecting edge features from the content image into the diffusion latent space to create a structured noise foundation. This foundation enables controlled integration of style and content, achieving high fidelity to both the source image and reference style. A dual‐control mechanism ensures coherence at multiple levels: locally, content features fuse with edge structures to preserve fine details, while globally, a content encoder reinforces structural alignment. Style information is integrated in parallel, with key and value tokens capturing fine stylistic details, supported by a global style encoder for overall consistency. Experiments show that SketchCrafter excels in content alignment and style fidelity across benchmarks, proving it as an effective solution for training‐free sketch extraction.
Long et al. (Thu,) studied this question.