  The recently introduced Consistency models pose an efficient alternative to
diffusion algorithms, enabling rapid and good quality image synthesis. These
methods overcome the slowness of diffusion models by directly mapping noise to
data, while maintaining a (relatively) simpler training. Consistency models
enable a fast one- or few-step generation, but they typically fall somewhat
short in sample quality when compared to their diffusion origins. In this work
we propose a novel and highly effective technique for post-processing
Consistency-based generated images, enhancing their perceptual quality. Our
approach utilizes a joint classifier-discriminator model, in which both
portions are trained adversarially. While the classifier aims to grade an image
based on its assignment to a designated class, the discriminator portion of the
very same network leverages the softmax values to assess the proximity of the
input image to the targeted data manifold, thereby serving as an Energy-based
Model. By employing example-specific projected gradient iterations under the
guidance of this joint machine, we refine synthesized images and achieve an
improved FID scores on the ImageNet 64x64 dataset for both Consistency-Training
and Consistency-Distillation techniques.
