We introduce a novel sketch-to-image tool that aligns with the iterative refinement process of artists. Our tool lets users sketch blocking strokes to coarsely represent the placement and form of objects and detail strokes to refine their shape and silhouettes. We develop a two-pass algorithm for generating high-fidelity images from such sketches at any point in the iterative process. In the first pass we use a ControlNet to generate an image that strictly follows all the strokes (blocking and detail) and in the second pass we add variation by renoising regions surrounding blocking strokes. We also present a dataset generation scheme that, when used to train a ControlNet architecture, allows regions that do not contain strokes to be interpreted as not-yet-specified regions rather than empty space. We show that this partial-sketch-aware ControlNet can generate coherent elements from partial sketches that only contain a small number of strokes. The high-fidelity images produced by our approach serve as scaffolds that can help the user adjust the shape and proportions of objects or add additional elements to the composition. We demonstrate the effectiveness of our approach with a variety of examples and evaluative comparisons.

翻译：我们提出了一种新颖的草图转图像工具，该工具与艺术家的迭代细化创作过程相契合。该工具允许用户绘制分块笔触以粗略表示物体的位置与形态，并绘制细节笔触以完善其形状与轮廓。我们开发了一种双遍算法，可在迭代过程中的任意阶段从这类草图生成高保真图像。第一遍中，我们使用ControlNet生成严格遵循所有笔触（分块与细节）的图像；第二遍中，我们通过对分块笔触周围区域进行重噪声处理来引入变化。此外，我们提出了一种数据集生成方案，当用于训练ControlNet架构时，可使未包含笔触的区域被解释为未指定区域而非空白区域。研究表明，这种部分草图感知的ControlNet能够从仅含少量笔触的局部草图中生成连贯的元素。我们方法生成的高保真图像可作为骨架，帮助用户调整物体的形状与比例，或向构图中添加额外元素。通过多种示例与对比评估，我们验证了该方法的有效性。