We introduce a pipeline to address anatomical inaccuracies in Stable Diffusion generated hand images. The initial step involves constructing a specialized dataset, focusing on hand anomalies, to train our models effectively. A finetuned detection model is pivotal for precise identification of these anomalies, ensuring targeted correction. Body pose estimation aids in understanding hand orientation and positioning, crucial for accurate anomaly correction. The integration of ControlNet and InstructPix2Pix facilitates sophisticated inpainting and pixel-level transformation, respectively. This dual approach allows for high-fidelity image adjustments. This comprehensive approach ensures the generation of images with anatomically accurate hands, closely resembling real-world appearances. Our experimental results demonstrate the pipeline's efficacy in enhancing hand image realism in Stable Diffusion outputs. We provide an online demo at https://fixhand.yiqun.io

我们介绍了一种处理稳定扩散生成的手部图像中解剖学不准确性的流程。我们首先构建了一个专门针对手部异常的数据集，以有效训练我们的模型。一个经过微调的检测模型对这些异常的精确定位至关重要，确保了有针对性的修正。身体姿势估计有助于理解手部的方向和位置，对精确的异常修正非常重要。ControlNet和InstructPix2Pix的集成分别实现了复杂的修复和像素级变换。这种双重方法可以进行高保真度的图像调整。这种全面的方法确保生成的图像具有解剖学准确的手部，与真实世界的外观非常接近。我们的实验结果证明了该流程在增强Stable Diffusion输出中手部图像逼真度方面的有效性。我们在此https网址提供了在线演示。

稳定扩散生成图像中非标准手势的检测与修复