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

翻译：我们提出了一套处理Stable Diffusion生成手部图像中解剖学不准确问题的流水线。首先，构建针对手部异常特征的专用数据集，以有效训练模型。经过微调的检测模型能够精准识别这些异常，确保后续修正的针对性。人体姿态估计技术可辅助理解手部朝向与空间位置，这对精确修正异常至关重要。结合ControlNet与InstructPix2Pix模型，分别实现高精度的图像修复与像素级变换。这种双重方法支持高保真图像调整。该综合方案可生成解剖结构准确、接近真实外观的手部图像。实验结果表明，本流水线能有效提升Stable Diffusion输出中手部图像的真实性。我们提供在线演示：https://fixhand.yiqun.io