Diffusion models have recently demonstrated strong generative capabilities, yet their potential in unsupervised image clustering remains underexplored. In this work, we propose Diffusion-driven Representation Decoupling Clustering (Diff-RDC), which reconsiders the diffusion probabilistic modeling of denoising guided jointly by semantic features and latent cluster assignments. We perform discriminative semantic inference over the entire diffusion trajectory and eliminate timestep sampling during optimization, leading to a more principled Evidence Lower Bound (ELBO) that reinforces cluster separability while preserving class-conditional generation. To implement this formulation, we design an asymmetric encoder architecture that separates discriminative and fine-grained representations, where the former initiates semantic reconstruction and the latter progressively injects fine-grained details during denoising. Without relying on explicit disentanglement constraints, our framework implicitly achieves representation decoupling through a structured decoding flow. Extensive experiments on four diverse benchmarks validate the effectiveness of our method in improving clustering performance and semantic consistency. These findings are further corroborated by ablation studies and generative visualizations.