VIF calculation difference

Question

I have found two method to calculate VIF but through both method my end result VIF value is very different. Why?

first method - X is independent variables.

def comput_vif(considered_features):
    vif = pd.DataFrame()
    vif["Variable"] = X.columns
    vif["VIF"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]
    return vif

considered_features = list(X.columns)
compute_vif(considered_features ).sort_values('VIF', ascending=False)

Second method - considered_features is independent variables.

def compute_vif(considered_features):
    X = df[considered_features]
    X['intercept'] = 1
    vif = pd.DataFrame()
    vif["Variable"] = X.columns
    vif["VIF"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]
    vif = vif[vif['Variable']!='intercept']
    return vif

considered_features = list(df.columns)
compute_vif(considered_features).sort_values('VIF', ascending=False)