The existing commodity Wi-Fi based human gait recognition systems mainly focus on a single subject due to the challenges of multi-subject walking monitoring. To tackle the problem, we propose Wi-Diag, the first commodity Wi-Fi based multi-subject abnormal gait diagnosis system that leverages only one pair of off-the-shelf commercial Wi-Fi transceivers to separate each subject's gait information and maintains an excellent performance when the scenario changes. It is an intelligent multi-subject gait diagnosis system that can release an experienced doctor from heavy load work. Multi-subject abnormal gait diagnosis is modeled as a Blind Source Separation (BSS) issue, and multi-subject walking mixed signals are efficiently separated by Independent Component Analysis (ICA) approach. This fact is verified by comprehensive theoretical derivation and experimental validation. In addition, CycleGAN is leveraged to mitigate the environmental dependency so that Wi-Diag can be robust when the scenario changes. The excellent performance of Wi-Diag is verified by extensive experiments. The average mean diagnosis accuracy with a maximum group size of four and various scenarios is 87.77%.