Reproduce “SI-Edit: Toward Sketch-Instruction Guided Local Image Editing with Pixel-Level Precision” on OpenResearch
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About this paper
Weixin Ye, Wei Wang, Hongguang Zhu, Xuecheng Nie
Introduces SI-Data, a dataset combining fine-grained geometric sketches and semantic instructions, and SI-Edit, a framework for pixel-level local image editing. The system precisely deforms image regions according to both geometric and linguistic guidance, outperforming existing methods in structural fidelity and semantic adherence.
Key insights
- Achieving precise local image editing necessitates the collaborative integration of both semantic instructions (for 'what' to change) and geometric sketches (for 'where' and 'how' to change).
- A specialized, learnable task-trigger token, acting as a global semantic anchor, can effectively condition multi-modal attention mechanisms for sketch-instruction guided generation.
- Explicitly sharing positional encoding between source image latents and sketch overlay latents is crucial for maintaining pixel-level correspondence and producing coherent geometric deformations.
Summary by alphaXiv.
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