[D] In conditional GAN (cGAN) architecture, why does the discriminator need a conditional variable ?

I'm reading about conditional GAN (cGAN) architecture, what I know is that the generator creates images combining both noise vector and conditional variable, the noise vector brings in random elements like colors or shapes while conditional variable is used for maintaining the same object.

As for the discriminator, the input is an image that is either fake (generated by the generator) or real (from the dataset) combines with the conditional variable. What I don't understand is that why do we …

architecture colors gan generator images machinelearning noise random reading vector