Efficient Adversarial Consistency Training for One-step Diffusion Models

Adversarial Consistency Training (ACT) directly minimizes the Jensen-Shannon divergence between the generated and target distributions at each timestep, enabling improved generation quality and convergence with significantly less resource consumption compared to the baseline consistency training method.