Remote sensing image super-resolution aims to recover fine structural and textural details from degraded low-resolution observations. However, conventional methods and early deep learning models often produce over-smoothed results and struggle to reconstruct realistic high-frequency content. Stable Diffusion-based (SD-based) methods offer a promising alternative by using strong generative priors to synthesize more natural, detail-rich super-resolved images. Although many SD-based super-resolution methods have been proposed in computer vision, their use in remote sensing imagery remains limited, and systematic comparative evaluation in this domain is still lacking, leaving insufficient empirical guidance for method development. Therefore, this paper compares four representative SD-based super-resolution methods, namely Stable Super-Resolution (StableSR), Semantics- Aware Super-Resolution (SeeSR), Different Blind Image Restoration (DiffBIR), and Pixel-Aware Stable Diffusion (PASD), on the WHU-Mix remote sensing dataset. The evaluation uses seven metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), Frechet Inception Distance (FID), CLIP Image Quality Assessment (CLIP-IQA), Multi-Scale Image Quality Transformer (MUSIQ), and Multi-Dimension Attention Network for No-Reference Image Quality Assessment (MANIQA). Quantitative results show that StableSR achieves the highest PSNR of 23.16 dB, PASD obtains the best SSIM of 0.81 and lowest LPIPS of 0.45, SeeSR achieves the best MUSIQ of 64.57 and MANIQA of 0.46, and DiffBIR achieves the best FID of 110.58 and CLIP-IQA of 0.68 but with weaker full-reference fidelity. These findings indicate that current SD-based methods favor different aspects, including fidelity preservation, perceptual quality, and generative realism, and should be selected according to the target remote sensing application.