GAS: Improving Discretization of Diffusion ODEs via Generalized
  Adversarial Solver
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.
68f77d6ca7b9b96b2c13ed7d
Aleksandr Oganov
claimed_verified
2025-10-21T14:00:21.831Z
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/avatars/cffd8c2154dfa57e2ce526996992ffb8.svg
Aleksandr Oganov
user
3145tttt
68f77d6ca7b9b96b2c13ed7e
Ilya Bykov
68f77d6ca7b9b96b2c13ed7f
Eva Neudachina
68f77d6ca7b9b96b2c13ed80
Mishan Aliev
68f77d6ca7b9b96b2c13ed81
Alexander Tolmachev
68f77d6ca7b9b96b2c13ed82
Alexander Sidorov
68f77d6ca7b9b96b2c13ed83
Aleksandr Zuev
68f77d6ca7b9b96b2c13ed84
Andrey Okhotin
68f77d6ca7b9b96b2c13ed85
Denis Rakitin
68f77d6ca7b9b96b2c13ed86
Aibek Alanov
GAS: Improving Discretization of Diffusion ODEs via Generalized
  Adversarial Solver
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.
The Generalized Adversarial Solver improves diffusion model sampling efficiency and quality by combining a simple ODE solver parameterization with adversarial training.
diffusion models gradient-based optimization ODE diffusion solver function evaluations adversarial training Generalized Solver Generalized Adversarial Solver