Latent diffusion models enable high-fidelity synthesis by operating in learned latent spaces. However, current approaches require complex multi-stage training where tokenizers must be trained separately before diffusion models. This paper proposes UNITE, an autoencoder architecture that unifies tokenization and latent diffusion through a single Generative Encoder with shared weights. The key innovation is treating tokenization and generation as the same latent inference problem under different conditioning regimes. The method introduces a single-stage training procedure that jointly optimizes both tokenization and generation tasks via two forward passes, enabling gradients to jointly shape the latent space for improved visual representation learning.