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In the context of linear inverse problems, we propose and study a general iterative regularization method allowing to consider large classes of data-fit terms and regularizers. The algorithm we propose is based on a primal-dual diagonal descent method. Our analysis establishes convergence as well as stability results. Theoretical findings are complemented with numerical experiments showing state-of-the-art performances.