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Pre-trained diffusion models provide rich multi-scale latent features and are emerging as powerful vision backbones. While recent works such as Marigold and Lotus adapt diffusion priors for dense prediction with strong cross-domain generalization, their potential for structured outputs remains underexplored. In this paper, we propose SDPose, a fine-tuning framework built upon Stable Diffusion to fully exploit pre-trained diffusion priors for human pose estimation. First, rather than modifying cross-attention modules or introducing learnable embeddings, we directly predict keypoint heatmaps in the SD U-Net's image latent space to preserve the original generative priors. Second, we map these latent features into keypoint heatmaps through a lightweight convolutional pose head, which avoids disrupting the pre-trained backbone. Finally, to prevent overfitting and enhance out-of-distribution robustness, we incorporate an auxiliary RGB reconstruction branch that preserves domain-transferable generative semantics. To evaluate robustness under domain shift, we further construct COCO-OOD, a style-transferred variant of COCO with preserved annotations. With just one-fifth of the training schedule used by Sapiens on COCO, SDPose attains parity with Sapiens-1B/2B on the COCO validation set and establishes a new state of the art on the cross-domain benchmarks HumanArt and COCO-OOD. Extensive ablations highlight the importance of diffusion priors, RGB reconstruction, and multi-scale SD U-Net features for cross-domain generalization, and t-SNE analyses further explain SD's domain-invariant latent structure. We also show that SDPose serves as an effective zero-shot pose annotator for controllable image and video generation.