High Noise Scheduling is a Must
Mahmut S. Gokmen Cody Bumgardner Jie Zhang Ge Wang Jin Chen
Consistency models possess high capabilities for image generation, advancing
sampling steps to a single step through their advanced techniques. Current
advancements move one step forward consistency training techniques and
eliminates the limitation of distillation training. Even though the proposed
curriculum and noise scheduling in improved training techniques yield better
results than basic consistency models, it lacks well balanced noise
distribution and its consistency between curriculum. In this study, it is
investigated the balance between high and low noise levels in noise
distribution and offered polynomial noise distribution to maintain the
stability. This proposed polynomial noise distribution is also supported with a
predefined Karras noises to prevent unique noise levels arises with Karras
noise generation algorithm. Furthermore, by elimination of learned noisy steps
with a curriculum based on sinusoidal function increase the performance of the
model in denoising. To make a fair comparison with the latest released
consistency model training techniques, experiments are conducted with same
hyper-parameters except curriculum and noise distribution. The models utilized
during experiments are determined with low depth to prove the robustness of our
proposed technique. The results show that the polynomial noise distribution
outperforms the model trained with log-normal noise distribution, yielding a
33.54 FID score after 100,000 training steps with constant discretization
steps. Additionally, the implementation of a sinusoidal-based curriculum
enhances denoising performance, resulting in a FID score of 30.48.