Imaging mass spectrometry (IMS) enables untargeted, highly multiplexed mapping of molecular species in biological tissue with unparalleled chemical specificity and sensitivity. However, most IMS platforms lack microscopy-level spatial resolution and cellular morphological contrast, necessitating subsequent histochemical staining, microscopic imaging, and advanced image registration to correlate/link molecular distributions with specific tissue features and cell types. We present a diffusion model-based virtual histological staining approach that enhances spatial resolution and digitally introduces cellular morphological contrast into mass spectrometry images of label-free human tissue. Blind testing on human kidney tissue demonstrated that the virtually stained images of label-free samples closely match their histochemically stained counterparts (with periodic acid-Schiff staining), showing high concordance in identifying key renal pathology structures despite using IMS data with 10-fold larger pixel size. Additionally, our approach uses optimized noise sampling during the diffusion model's inference to achieve reliable and repeatable virtual staining. We believe this virtual staining method will open avenues for IMS-based biomedical research.