TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Diffusion models have demonstrated exceptional capabilities in generating
high-fidelity images but typically suffer from inefficient sampling. Many
solver designs and noise scheduling strategies have been proposed to
dramatically improve sampling speeds. In this paper, we introduce a new
sampling method that is up to $186\%$ faster than the current state of the art
solver for comparative FID on ImageNet512. This new sampling method is
training-free and uses an ordinary differential equation (ODE) solver. The key
to our method resides in using higher-dimensional initial noise, allowing to
produce more detailed samples with less function evaluations from existing
pretrained diffusion models. In addition, by design our solver allows to
control the level of detail through a simple hyper-parameter at no extra
computational cost. We present how our approach leverages momentum dynamics by
establishing a fundamental equivalence between momentum diffusion models and
conventional diffusion models with respect to their training paradigms.
Moreover, we observe the use of higher-dimensional noise naturally exhibits
characteristics similar to stochastic differential equations (SDEs). Finally,
we demonstrate strong performances on a set of representative pretrained
diffusion models, including EDM, EDM2, and Stable-Diffusion 3, which cover
models in both pixel and latent spaces, as well as class and text conditional
settings. The code is available at https://github.com/apple/ml-tada.
