Sketch-guided image editing aims to achieve local fine-tuning of the image based on the sketch information provided by the user, while maintaining the original status of the unedited areas. Due to the high cost of acquiring human sketches, previous works mostly relied on edge maps as a substitute for sketches, but sketches possess more rich structural information. In this paper, we propose a sketch generation scheme that can preserve the main contours of an image and closely adhere to the actual sketch style drawn by the user. Simultaneously, current image editing methods often face challenges such as image distortion, training cost, and loss of fine details in the sketch. To address these limitations, We propose a conditional diffusion model (SketchFFusion) based on the sketch structure vector. We evaluate the generative performance of our model and demonstrate that it outperforms existing methods.

翻译：草图引导图像编辑旨在根据用户提供的草图信息实现图像的局部微调，同时保持未编辑区域的原始状态。由于获取人工草图成本较高，此前的研究大多依赖边缘图作为草图的替代，但草图本身具有更丰富的结构信息。本文提出一种既能保留图像主要轮廓、又能紧密贴合用户实际绘制草图风格的草图生成方案。同时，现有图像编辑方法常面临图像扭曲、训练成本高及草图细节丢失等挑战。为解决这些问题，我们提出基于草图结构向量的条件扩散模型（SketchFFusion）。通过评估该模型的生成性能，证明其优于现有方法。