adversarial networks

Adversarial Training for Pixel Diffusionarxiv.org

Pixel diffusion models often underrepresent fine-scale image statistics. This paper demonstrates that adversarial post-training, by adding an adversarial loss to non-high-noise timesteps, effectively corrects this deficiency. It restores missing high-frequency content, jointly improving distribution fidelity, coverage, prompt alignment, and perceptual quality.