Learning velocity fields directly in pixel space is difficult and slow. We show that coarse second-order image statistics can ease this task: frozen Wiener estimates improve velocity learning, with ga
Wiener-informed Decoding accelerates pixel-space velocity model training and improves image generation by integrating statistical predictions into decoder computation without altering native blocks.
Learning velocity fields directly in pixel space is difficult and slow. We show that coarse second-order image statistics can ease this task: frozen Wiener estimates improve velocity learning, with gains extending beyond the linear predictions they explicitly provide. Moreover, we observe similar but complementary gains from frozen bases and pixel decoding. These observations motivate Wiener-informed Decoding (WiD), which uses statistical knowledge both for direct prediction and to organize decoder computation without modifying native blocks. WiD combines a trainable Wiener prediction path with coefficient interfaces that inject features into the native decoder and return its predicted…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Beyond the Estimate: Wiener-Informed Decoding for Easier Training of Pixel-Space Flow Models was first seen on Hugging Face · Daily papers on Oct 28, 2026. Since then it has appeared in 20 checks on 3 different days. Its best position was #16 of 30, reached on Oct 30, 03:28 UTC. It dropped off the list after Oct 30, 07:28 UTC.
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It was first seen on Oct 28, 2026 and has been on the list on 3 days (20 checks) since.