It depends on your use case.
If you need exactly the same parameters for the new model in order to recreate some experiment, I would save and reload the state_dict as this would probably be the easiest method.
However, if you just want to train from scratch using a new model, you could just instantiate a new model, which will reset all parameters by default or use a method to initialize your parameters:
def weight_init(m):
    if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
        nn.init.xavier_uniform_(m.weight, gain=nn.init.calculate_gain('relu'))
        nn.init.zeros_(m.bias)

model.apply(weight_init)