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Implicit Neural Representations (INRs) are a learning-based approach to accelerate Magnetic Resonance Imaging (MRI) acquisitions, particularly in scan-specific settings when only data from the under-sampled scan itself are available. Previous work has shown that INRs improve rapid MRI through inherent regularization imposed by neural network architectures. Typically parameterized by fully connected neural networks, INRs provide continuous image representations by mapping a physical coordinate location to its intensity. Prior approaches have applied unlearned regularization priors during INR training and were limited to 2D or low-resolution 3D acquisitions. Meanwhile, diffusion-based generative models have recently gained attention for learning powerful image priors independent of the measurement model. This work proposes INFusion, a technique that regularizes INR optimization from under-sampled MR measurements using pre-trained diffusion models to enhance reconstruction quality. In addition, a hybrid 3D approach is introduced, enabling INR application on large-scale 3D MR datasets. Experimental results show that in 2D settings, diffusion regularization improves INR training, while in 3D, it enables feasible INR training on matrix sizes of 256 × 256 × 80.