Generative Edge Detection with Stable Diffusion

• URL: http://arxiv.org/abs/2410.03080v1
• Date: Fri, 4 Oct 2024 01:52:23 GMT
• Title: Generative Edge Detection with Stable Diffusion
• Authors: Caixia Zhou, Yaping Huang, Mochu Xiang, Jiahui Ren, Haibin Ling, Jing Zhang,
• Abstract summary: Edge detection is typically viewed as a pixel-level classification problem mainly addressed by discriminative methods. We propose a novel approach, named Generative Edge Detector (GED), by fully utilizing the potential of the pre-trained stable diffusion model. We conduct extensive experiments on multiple datasets and achieve competitive performance.
• Score: 52.870631376660924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Edge detection is typically viewed as a pixel-level classification problem mainly addressed by discriminative methods. Recently, generative edge detection methods, especially diffusion model based solutions, are initialized in the edge detection task. Despite great potential, the retraining of task-specific designed modules and multi-step denoising inference limits their broader applications. Upon closer investigation, we speculate that part of the reason is the under-exploration of the rich discriminative information encoded in extensively pre-trained large models (\eg, stable diffusion models). Thus motivated, we propose a novel approach, named Generative Edge Detector (GED), by fully utilizing the potential of the pre-trained stable diffusion model. Our model can be trained and inferred efficiently without specific network design due to the rich high-level and low-level prior knowledge empowered by the pre-trained stable diffusion. Specifically, we propose to finetune the denoising U-Net and predict latent edge maps directly, by taking the latent image feature maps as input. Additionally, due to the subjectivity and ambiguity of the edges, we also incorporate the granularity of the edges into the denoising U-Net model as one of the conditions to achieve controllable and diverse predictions. Furthermore, we devise a granularity regularization to ensure the relative granularity relationship of the multiple predictions. We conduct extensive experiments on multiple datasets and achieve competitive performance (\eg, 0.870 and 0.880 in terms of ODS and OIS on the BSDS test dataset).

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