GAS: Improving Discretization of Diffusion ODEs via Generalized
  Adversarial Solver
Authors: name: Aleksandr Oganov, name: Ilya Bykov, name: Eva Neudachina, name: Mishan Aliev, name: Alexander Tolmachev, name: Alexander Sidorov, name: Aleksandr Zuev, name: Andrey Okhotin, name: Denis Rakitin, name: Aibek Alanov
Organization: Bayesian Methods Research Group

While diffusion models achieve state-of-the-art generation quality, they
still suffer from computationally expensive sampling. Recent works address this
issue with gradient-based optimization methods that distill a few-step ODE
diffusion solver from the full sampling process, reducing the number of
function evaluations from dozens to just a few. However, these approaches often
rely on intricate training techniques and do not explicitly focus on preserving
fine-grained details. In this paper, we introduce the Generalized Solver: a
simple parameterization of the ODE sampler that does not require additional
training tricks and improves quality over existing approaches. We further
combine the original distillation loss with adversarial training, which
mitigates artifacts and enhances detail fidelity. We call the resulting method
the Generalized Adversarial Solver and demonstrate its superior performance
compared to existing solver training methods under similar resource
constraints. Code is available at https://github.com/3145tttt/GAS.