In　the　emerging　technology,　the　generative　aversive　networks　(GANs),　randomness,　and　unpredictability　of　inputting　noises　are　the　keys　to　the　uniqueness,　diversity,　robustness,　and　security　of　the　generated　images.　Compared　with　deterministic　software-based　noise　generation,　hardware-based　noise　generation　introduces　physical　entropy　sources,　such　as　electronic　and　photonic　noises,　to　add　unpredictability.　In　this　study,　bimode　Bi2O2Se-based　noise　generators　have　been　demonstrated　for　the　application　of　GANs.　Harnessing　its　ultrahigh　carrier　mobility,　excellent　air　stability,　marvelous　optoelectronic　performance,　as　well　as　the　unique　surface　resistive　switching　effect　and　defect　locations　in　the　energy　diagram,　Bi2O2Se　provides　a　good　material　platform　to　easily　integrate　with　multiple　device　architectures　for　generating　noises　in　different　physical　sources.　The　noise　of　the　black　current　mode　in　a　photodetector　architecture　and　the　random　telegraph　noise　in　a　memristor　mode　were　measured,　characterized,　compared,　and　analyzed.　A　method　of　Markov　chain　equipped　with　K-means　clustering　was　carried　out　to　calculate　the　discrete　noise　states　and　the　transition　probability　matrix　between　them.　To　evaluate　the　generated　properties　of　the　GANs　based　on　the　hardware　noise　source,　the　inception　score　and　Frechet　inception　distance　were　evaluated.