Bespoke Solvers for Generative Flow Models
Neta Shaul Juan Perez Ricky T. Q. Chen Ali Thabet Albert Pumarola Yaron Lipman
Diffusion or flow-based models are powerful generative paradigms that are
notoriously hard to sample as samples are defined as solutions to
high-dimensional Ordinary or Stochastic Differential Equations (ODEs/SDEs)
which require a large Number of Function Evaluations (NFE) to approximate well.
Existing methods to alleviate the costly sampling process include model
distillation and designing dedicated ODE solvers. However, distillation is
costly to train and sometimes can deteriorate quality, while dedicated solvers
still require relatively large NFE to produce high quality samples. In this
paper we introduce "Bespoke solvers", a novel framework for constructing custom
ODE solvers tailored to the ODE of a given pre-trained flow model. Our approach
optimizes an order consistent and parameter-efficient solver (e.g., with 80
learnable parameters), is trained for roughly 1% of the GPU time required for
training the pre-trained model, and significantly improves approximation and
generation quality compared to dedicated solvers. For example, a Bespoke solver
for a CIFAR10 model produces samples with Fr\'echet Inception Distance (FID) of
2.73 with 10 NFE, and gets to 1% of the Ground Truth (GT) FID (2.59) for this
model with only 20 NFE. On the more challenging ImageNet-64$\times$64, Bespoke
samples at 2.2 FID with 10 NFE, and gets within 2% of GT FID (1.71) with 20
NFE.