We propose a framework that synthesizes artistic landscape sketches using a diffusion model-based approach. Furthermore, we suggest a three-channel perspective map (3CPM) that mimics the artistic skill used by real artists. We employ Stable Diffusion, which leads us to use ControlNet to process 3CPM in Stable Diffusion. Additionally, we adopt the Low Rank Adaptation (LoRA) method to fine-tune our framework, thereby enhancing the quality of sketch and resolving the color-remaining problem, which is a frequently observed artifact in the sketch images using diffusion models. We implement a bimodal sketch generation interface: text to sketch and image to sketch. In producing a sketch, a guide token is used so that our method synthesizes an artistic sketch in both cases. Finally, we evaluate our framework using quantitative and quantitative schemes. Various sketch images synthesized by our framework demonstrate the excellence of our study.

We present a novel sketch generation scheme from an image using the flowpath and the value through the flow (VTF). The first stage of our scheme is to produce grayscale noisy sketch using a deep learning-based approach. In the second stage, the unclear contour and unwanted noises in the grayscale noisy sketch are resolved using our flowpath and VTF-based schemes. We build a flowpath by integrating the tangent flow extracted from the input image. The integrated tangent flow produces a strong clue for the salient contour of the shape in the image. We further compute VTF by sampling values through the flowpath to extract line segments that correspond to the sketch stroke. By combining the deep learning-based approach and VTF, we can extract salient sketch strokes from various images that suppresses unwanted noises. We demonstrate the excellence of our scheme by generating sketches from various images including portrait, landscape, objects, animals, and animation scenes.