  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.
