Accelerating Diffusion Models with One-to-Many Knowledge Distillation

Understand

Significant advancements in image generation have been made with diffusion models.

• Nevertheless, when contrasted with previous generative models, diffusion models face substantial computational overhead, leading to failure in real-time generation.
• Recent approaches have aimed to accelerate diffusion models by reducing the number of sampling steps through improved sampling techniques or step distillation.
• However, the methods to diminish the computational cost for each timestep remain a relatively unexplored area.