Diffusion Based Auctions for Banner Ads

We propose auctions that fractionally allocate the creation of a banner to bidders according to their preferences. The mechanism elicits bids and textual prompts from advertisers, and composes them into a score function that drives a reverse diffusion process that generates the banner. It implements Monte Carlo sampling to calculate approximate VCG-based payments to incentivize high-welfare images. Extensive experiments on a diverse 20-prompt dataset with up to 3 agents demonstrate key economic properties: bid monotonicity, an efficiency improvement of up to 21% higher welfare than a single-winner VCG baseline, and approximate incentive compatibility, with average regret below 10% when deviating from truthful bidding, while preserving high image quality.

An earlier version of this work appeared as Bidding for Influence: Auction-Driven Diffusion Image Generation at the ICML 2025 Workshop on Multi-Agent Systems in the Era of Foundation Models.