  Data-driven generative models have emerged as promising approaches towards
achieving efficient mechanical inverse design. However, due to prohibitively
high cost in time and money, there is still lack of open-source and large-scale
benchmarks in this field. It is mainly the case for airfoil inverse design,
which requires to generate and edit diverse geometric-qualified and
aerodynamic-qualified airfoils following the multimodal instructions,
\emph{i.e.,} dragging points and physical parameters. This paper presents the
open-source endeavors in airfoil inverse design, \emph{AFBench}, including a
large-scale dataset with 200 thousand airfoils and high-quality aerodynamic and
geometric labels, two novel and practical airfoil inverse design tasks,
\emph{i.e.,} conditional generation on multimodal physical parameters,
controllable editing, and comprehensive metrics to evaluate various existing
airfoil inverse design methods. Our aim is to establish \emph{AFBench} as an
ecosystem for training and evaluating airfoil inverse design methods, with a
specific focus on data-driven controllable inverse design models by multimodal
instructions capable of bridging the gap between ideas and execution, the
academic research and industrial applications. We have provided baseline
models, comprehensive experimental observations, and analysis to accelerate
future research. Our baseline model is trained on an RTX 3090 GPU within 16
hours. The codebase, datasets and benchmarks will be available at
\url{https://hitcslj.github.io/afbench/}.
