It should affect at the inference time if your network has been properly trained and the architecture is not very restrictive. Plenty of them has an open size.
Think that, even if images are bigger, you are applying exactly the same kernel over them. You get bigger outputs but it shouldn’t affect convolutions. Another discussion would be if you have fixed-size latent spaces or poolings.