This paper introduces a new method for improving the training of generative adversarial networks (GANs) for image synthesis. The proposed method, called Auxiliary Classifier GAN (AC-GAN), employs label conditioning to generate high-resolution (128×128) image samples with global coherence. The model is trained on the ImageNet dataset, which contains 1000 classes, and produces samples that are more discriminable and diverse than those from lower-resolution models. The study shows that high-resolution samples are not just naive resizings of low-resolution samples, as downsampling 128×128 samples to 32×32 reduces visual discriminability by 50%. Additionally, 84.7% of the ImageNet classes have samples with diversity comparable to real ImageNet data. The AC-GAN model is trained using a specialized cost function that encourages the generator to produce samples that are both discriminable and diverse. The model is evaluated using two metrics: one to measure discriminability and another to measure diversity. The results show that the AC-GAN model achieves higher discriminability and diversity compared to previous models. The study also demonstrates that the model can generate samples from all 1000 ImageNet classes at a high resolution, and that the model's performance is not dependent on the number of classes, but rather on the diversity of the classes. The paper also discusses the challenges of training GANs on large datasets with high variability, and proposes a solution that involves splitting the dataset into smaller subsets. The study shows that training on smaller subsets improves the model's ability to generate diverse and discriminable samples. The results indicate that the AC-GAN model is effective in generating high-quality images, and that the model's performance is not limited by the number of classes. The study also highlights the importance of using a diverse set of classes for training GANs, as this leads to better performance. The paper concludes that the AC-GAN model is a promising approach for image synthesis, and that further research is needed to improve the model's performance.This paper introduces a new method for improving the training of generative adversarial networks (GANs) for image synthesis. The proposed method, called Auxiliary Classifier GAN (AC-GAN), employs label conditioning to generate high-resolution (128×128) image samples with global coherence. The model is trained on the ImageNet dataset, which contains 1000 classes, and produces samples that are more discriminable and diverse than those from lower-resolution models. The study shows that high-resolution samples are not just naive resizings of low-resolution samples, as downsampling 128×128 samples to 32×32 reduces visual discriminability by 50%. Additionally, 84.7% of the ImageNet classes have samples with diversity comparable to real ImageNet data. The AC-GAN model is trained using a specialized cost function that encourages the generator to produce samples that are both discriminable and diverse. The model is evaluated using two metrics: one to measure discriminability and another to measure diversity. The results show that the AC-GAN model achieves higher discriminability and diversity compared to previous models. The study also demonstrates that the model can generate samples from all 1000 ImageNet classes at a high resolution, and that the model's performance is not dependent on the number of classes, but rather on the diversity of the classes. The paper also discusses the challenges of training GANs on large datasets with high variability, and proposes a solution that involves splitting the dataset into smaller subsets. The study shows that training on smaller subsets improves the model's ability to generate diverse and discriminable samples. The results indicate that the AC-GAN model is effective in generating high-quality images, and that the model's performance is not limited by the number of classes. The study also highlights the importance of using a diverse set of classes for training GANs, as this leads to better performance. The paper concludes that the AC-GAN model is a promising approach for image synthesis, and that further research is needed to improve the model's performance.