Diffusion models (DMs) have exhibited superior performance in generating high-quality and diverse images. However, this exceptional performance comes at the cost of expensive architectural design, particularly with the attention module heavily used in leading models. Existing works mainly adopt a retraining process to enhance the efficiency, which is computationally expensive and less scalable. To this end, we introduce the Attention-driven Training-free Efficient Diffusion Model (AT-EDM), a framework that leverages attention maps to perform run-time pruning of redundant tokens during inference without retraining. Specifically, we develop a single denoising step pruning strategy, Generalized Weighted Page Rank (G-WPR), to identify redundant tokens and a similarity-based recovery method to restore tokens for convolution operation. Additionally, the Denoising-Steps-Aware Pruning (DSAP) is proposed to adjust the pruning budget across different denoising timesteps for better generation quality. Extensive evaluations show that AT-EDM performs favorably against prior arts in terms of efficiency (e.g., 38.8% FLOPs saving and up to 1.53 speedup over Stable Diffusion XL) while maintaining nearly the same FID and CLIP scores with the full model.