We introduce a novel generative framework that unifies adversarial and diffusion-based training to overcome the limitations of conventional models. Our approach, termed Generative Adversarial Diffusion (GAD), integrates an adversarial loss directly into each denoising step of a latent diffusion model. By employing a single U-Net as a unified generator and discriminator, our framework eliminates the need for a separate discriminator, thereby reducing memory overhead and mitigating common GAN issues such as mode collapse and training instability. This integrated adversarial regularizer promotes semantic information exchange across timesteps, enabling the model to better capture complex data distributions even when training data is scarce or biased. Extensive experiments on standard latent diffusion benchmarks demonstrate that GAD significantly enhances image quality and mode coverage in tasks including text-to-image and image-to-3D generation. Our results suggest that unifying adversarial and diffusion-based training in a single network offers a promising new direction for high-fidelity, stable image synthesis.