We propose EasyControlEdge, adapting an image-generation foundation model to edge detection. In real-world edge detection (e.g., floor-plan walls, satellite roads/buildings, and medical organ boundaries), crispness and data efficiency are crucial, yet producing crisp raw edge maps with limited training samples remains challenging. Although image-generation foundation models perform well on many downstream tasks, their pretrained priors for data-efficient transfer and iterative refinement for high-frequency detail preservation remain underexploited for edge detection. To enable crisp and data-efficient edge detection using these capabilities, we introduce an edge-specialized adaptation of image-generation foundation models. To better specialize the foundation model for edge detection, we incorporate an edge-oriented objective with an efficient pixel-space loss. At inference, we introduce guidance based on unconditional dynamics, enabling a single model to control the edge density through a guidance scale. Experiments on BSDS500, NYUDv2, BIPED, and CubiCasa compare against state-of-the-art methods and show consistent gains, particularly under no-post-processing crispness evaluation and with limited training data.

翻译：本文提出EasyControlEdge，将图像生成基础模型适配于边缘检测任务。在实际边缘检测场景中（如平面图墙体、卫星图像道路/建筑及医学器官边界），边缘清晰度与数据效率至关重要，然而在有限训练样本下生成清晰的原始边缘图仍具挑战性。尽管图像生成基础模型在众多下游任务中表现优异，但其面向数据高效迁移的预训练先验知识与面向高频细节保持的迭代优化机制在边缘检测领域尚未得到充分探索。为利用这些能力实现清晰且数据高效的边缘检测，我们提出针对边缘检测特化的图像生成基础模型适配方法。为更好地使基础模型适应边缘检测任务，我们引入面向边缘的优化目标及高效的像素空间损失函数。在推理阶段，我们提出基于无条件动力学的引导机制，使单一模型能够通过引导尺度控制边缘密度。在BSDS500、NYUDv2、BIPED和CubiCasa数据集上的实验表明，相较于现有先进方法，本方法取得稳定性能提升，尤其在无后处理的清晰度评估及有限训练数据条件下表现突出。