Inconsistencies In Consistency Models: Better ODE Solving Does Not Imply Better Samples
This is an incremental study for researchers in generative modeling, questioning the underlying mechanisms of a popular method.
The paper tackles the problem of understanding why consistency models (CMs) work well for diffusion model distillation despite not directly minimizing ODE solving error, by introducing Direct CMs that reduce this error but result in significantly worse sample quality.
Although diffusion models can generate remarkably high-quality samples, they are intrinsically bottlenecked by their expensive iterative sampling procedure. Consistency models (CMs) have recently emerged as a promising diffusion model distillation method, reducing the cost of sampling by generating high-fidelity samples in just a few iterations. Consistency model distillation aims to solve the probability flow ordinary differential equation (ODE) defined by an existing diffusion model. CMs are not directly trained to minimize error against an ODE solver, rather they use a more computationally tractable objective. As a way to study how effectively CMs solve the probability flow ODE, and the effect that any induced error has on the quality of generated samples, we introduce Direct CMs, which \textit{directly} minimize this error. Intriguingly, we find that Direct CMs reduce the ODE solving error compared to CMs but also result in significantly worse sample quality, calling into question why exactly CMs work well in the first place. Full code is available at: https://github.com/layer6ai-labs/direct-cms.