Adversarial post-training restores missing high-frequency detail in pixel diffusion, cutting DeCo FID from 33.27 to 28.59
By keeping the original diffusion or flow-matching objective and adding an adversarial loss to the predicted output at non-high-noise timesteps, this study jointly improves distribution fidelity, coverage, prompt alignment, and perceptual quality on the DeCo and PixelGen pixel backbones, attributing the gain to restored missing natural-image high-frequency power, while the same procedure yields no comparable gain in the tested latent diffusion configurations.