Image　Emotion　Classification　(IEC)　is　an　essential　research　area,　offering　valuable　insights　into　user　emotional　states　for　a　wide　range　of　applications,　including　opinion　mining,　recommendation　systems,　and　mental　health　treatment.　The　challenges　associated　with　IEC　are　mainly　attributed　to　the　complexity　and　ambiguity　of　human　emotions,　the　lack　of　a　universally　accepted　emotion　model,　and　excessive　dependence　on　prior　knowledge.　To　address　these　challenges,　we　propose　a　novel　Unified　Generative　framework　for　Image　Emotion　Classification　(UGRIE),　which　is　capable　of　simultaneously　modeling　various　emotion　models　and　capturing　intricate　semantic　relationships　between　emotion　labels.　Our　approach　employs　a　flexible　natural　language　template,　converting　the　IEC　task　into　a　template-filling　process　that　can　be　easily　adapted　to　accommodate　a　diverse　range　of　IEC　tasks.　To　further　enhance　the　performance,　we　devise　a　mapping　mechanism　to　seamlessly　integrate　the　multimodal　pre-training　model　CLIP　with　the　text　generation　pre-training　model　BART,　thus　leveraging　the　strengths　of　both　models.　A　comprehensive　set　of　experiments　conducted　on　multiple　public　datasets　demonstrates　that　our　proposed　method　consistently　outperforms　existing　approaches　to　a　large　margin　in　supervised　settings,　exhibits　remarkable　performance　in　low-resource　scenarios,　and　unifies　distinct　emotion　models　within　a　single,　versatile　framework.