This presentation introduces three techniques to ensure input data accurately reflects in output during conditional image generation: 1) Improving the discriminator (AC-GAN) to identify data classes; 2) Enhancing the generator architecture (U-Net in Pix2Pix) for better information flow; 3) Adding loss functions (e.g., Spade, cycle consistency) to penalize information loss. These methods are demonstrated with examples, highlighting improved image generation quality and the handling of paired and unpaired datasets.