We introduce MedIL, a first-of-its-kind autoencoder built for medical images with heterogeneous sizes and resolutions for image generation. Medical images are often large and heterogeneous, where fine details are of vital clinical importance. Image properties change drastically between different acquisition equipment, scan parameters, and pathologies, making realistic medical image generation challenging. Recent work in latent diffusion models (LDMs) has shown success in generating images resampled to a fixed size. However, this is a narrow subset of the native image resolutions, and resampling discards fine anatomical details. MedIL utilizes implicit neural representations to treat images as continuous signals, where encoding and decoding can be performed at arbitrary resolutions without prior resampling. We show how MedIL compresses and preserves fine details over large and multi-resolution datasets of T1w brain MRIs and lung CTs. We further demonstrate how MedIL improves the quality of images generated with a diffusion model and discuss how generative models may be improved to resemble raw clinical acquisitions.