Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling
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. We then evaluate DDPM, DDIM, DEIS-AB2, DPM++2M, and one-step pixel-MeanFlow on the GalaxiesML-64 dataset using morphology-based metrics, including ellipticity, semi-major axis, Sérsic index, and isophotal area. Our results show a clear accuracy-efficien...
| Authors | Tianyue Yang, Sandro Tacchella, Xiao Xue |
|---|---|
| Primary category | astro-ph.IM |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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