Wang and You introduce Wiener-informed Decoding (WiD), a method that leverages frozen second-order image statistics to accelerate and stabilize the training of pixel-space flow models. The approach integrates a trainable Wiener prediction path with coefficient interfaces that inject features into a native decoder and retrieve predicted coefficient corrections, initializing all components—including spectrum and coordinate transforms—using empirical data statistics. These parameters are jointly optimized under the standard pixel-space velocity objective without altering the decoder's native blocks. Evaluated on ImageNet 256×256 class-conditional generation, a PixelDiT-XL model equipped with WiD achieves an FID of 1.53 after 500 epochs, matching the quality of the baseline PixelDiT while requiring 37.5% fewer training epochs. The technique demonstrates consistent improvements across model scales, decoder architectures, and tasks including text-to-image synthesis, yielding smoother gradient trajectories and maintaining efficacy when combined with representation alignment. The results indicate that statistical denoisers facilitate pixel-space learning not only through their explicit linear predictions but also by structuring decoder computation.