We prove that the density of X 1 +···+Xn−nE[X 1 ] √ n , where {X n } n≥1 is a sequence of independent and identically distributed random variables taking values on a abstract Wiener space, converges in L 1 to the density of a certain Gaussian measure which is absolutely continuous with respect to the reference Wiener measure. The crucial feature in our investigation is that we do not require the covariance structure of {X n } n≥1 to coincide with the one of the Wiener measure. This produces a non trivial (different from the constant function one) limiting object which reflects the different covariance structures involved. The present paper generalizes the results proved in [18] and deepens the connection between local limit theorems on (infinite dimensional) Gaussian spaces and some key tools from the Analysis on the Wiener space, like the Wiener-Itô chaos decomposition, Ornstein-Uhlenbeck semigroup and Wick product. We also verify and discuss our main assumptions on some examples arising from the applications: dimension independent Berry-Esseen-type bounds and weak solutions of stochastic differential equations.Assumption 1.1 The law of the X n 's is absolutely continuous with respect to µ with a density f belonging to L 2 (W, µ) Assumption 1.1 has several important implications. First of all, it yields the finiteness of all the moments of the scalar random variable X n , ϕ for ϕ ∈ W * . In fact, for any m ∈ N and ϕ ∈ W * a simple application of the Cauchy-Schwartz inequality gives