Fetal MRI is often acquired with thick slices to mitigate motion artifacts, but this leads to partial volume effects and reduced through-plane spatial resolution, limiting precise anatomical analysis. To address this, various super-resolution methods have been proposed to reconstruct high-resolution volumes from thick-slice scans. Current methods face several major challenges: 1) relying on multi-stack paired data makes arbitrary super-resolution ratios difficult to achieve; 2) lacking robustness against voxel coordinate misalignment caused by partial volume effects; 3) failing to fully utilize the high in-plane resolution of MRI images. To address these issues, we propose a dual-consistency guided curriculum learning method based on implicit neural representation, which uses single-stack inputs to achieve arbitrary super-resolution. We introduce progressive consistency and volumetric consistency to mitigate voxel misalignment caused by partial volume effects and ensure smooth transitions during the model's curriculum-based training. Additionally, we design a curriculum-aware multi-scale feature interaction block to fully leverage thick-slice MRI's high in-plane resolution. Comprehensive evaluations on three fetal MRI datasets demonstrate SOTA performance, with particularly outstanding results in high-ratio super-resolution tasks.