Latent SR Embeddings: Precomputed VAE Latents for Medical Image Super Resolution Precomputed VAE latent embeddings from the paper: "Domain Specific Latent Representations Improve the Fidelity of Diffusion Based Medical Image Super Resolution" Sebastian Cajas, Ashaba Judith, Rahul Gorijavolu, Sahil Kapadia, Hillary Clinton Kasimbazi, Leo Kinyera, Emmanuel Paul Kwesiga, Sri Sri Jaithra Varma Manthena, Luis Filipe Nakayama, Ninsiima Doreen, Leo Anthony Celi. arXiv:2604.12152 (2026) — under review at Nature Scientific Reports. 📄 Paper: https://arxiv.org/abs/2604.12152 💻 Code: https://github.com/sebasmos/latent sr Dataset Description Each directory contains precomputed encoder outputs (posterior mean, deterministic) for a given (VAE, dataset) pair. These are the latent inputs to the diffusion UNet — sharing them avoids re encoding during training/evaluation. Each .npy file is a single 2D latent of shape (C, H, W) , stored as float32. Directory Structure VAE × Dataset Index Directory VAE Latent shape Dataset Split sizes medvae 4 3 brats MedVAE (3×64×64) (3,64,64) BraTS 2023 brain MRI train/val/test medvae 4 3 cxr MedVAE (3×64×64) (3,64,64) MIMIC CXR chest X ray train/val/test medvae 4…
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