  Generation models have shown promising performance in various tasks, making
trading around machine learning models possible. In this paper, we aim at a
novel prompt trading scenario, prompt bundle trading (PBT) system, and propose
an online pricing mechanism. Based on the combinatorial multi-armed bandit
(CMAB) and three-stage hierarchical Stackelburg (HS) game, our pricing
mechanism considers the profits of the consumer, platform, and seller,
simultaneously achieving the profit satisfaction of these three participants.
We break down the pricing issue into two steps, namely unknown category
selection and incentive strategy optimization. The former step is to select a
set of categories with the highest qualities, and the latter is to derive the
optimal strategy for each participant based on the chosen categories. Unlike
the existing fixed pricing mode, the PBT pricing mechanism we propose is more
flexible and diverse, which is more in accord with the transaction needs of
real-world scenarios. We test our method on a simulated text-to-image dataset.
The experimental results demonstrate the effectiveness of our algorithm, which
provides a feasible price-setting standard for the prompt marketplaces.
