AI RESEARCH
Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling
arXiv CS.LG
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ArXi:2605.17546v1 Announce Type: cross Understanding galaxy morphology evolution across cosmic time requires models that can generate realistic galaxy populations conditioned on redshift. In this work, we study efficient redshift-conditioned generative modeling for astrophysical image synthesis using diffusion models and pixel-MeanFlow. We first review the connections between score-based diffusion models, Flow Matching, one-step generative models, and modern diffusion samplers.