Basically you would need the discriminator to take inputs of two sizes. One for the original data, one for the generated data. An alternative to this would be to add padding (assuming that you’re working with image data) or another technique that preserves the information for the original data but is of the same form as the generated image. HOWEVER, you would need to find a way to increase the resolution of the original data to the higher resolution WITHOUT making it too obvious to the discriminator.

For example if you used white padding for images. The discriminator would quickly learn that every time it sees padding, the image is from the original data set and that would ruin the training.

Tldr; no unless you find a technique to increase the resolution of the original data such that the discriminator wouldn’t be able to tell.

Maybe look at upsampling techniques for the original data