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While recent advances in large-scale foundational models show promisingresults, their application to the medical domain has not yet been explored indetail. In this paper, we progress into the realms of large-scale modeling inmedical synthesis by proposing Cheff - a foundational cascaded latent diffusionmodel, which generates highly-realistic chest radiographs providingstate-of-the-art quality on a 1-megapixel scale. We further propose MaCheX,which is a unified interface for public chest datasets and forms the largestopen collection of chest X-rays up to date. With Cheff conditioned onradiological reports, we further guide the synthesis process over text promptsand unveil the research area of report-to-chest-X-ray generation.

Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis