You could also pass both sets of parameters to the optimizer but have the learning rate of one set be zero.  Then you could change the learning rate later on.
optimizer = SGD([{"params": model1.parameters()},
                 {"params": model2.parameters(), "lr": 0.}], lr=0.1, momentum=0.9, nesterov=False)

Here I’ve used a default learning rate for the optimizer which would be used with model1 and explicitly set the learning rate for model2.  If you had more models or more sets of parameters you could extend this.