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Poster
in
Workshop: The 2nd Workshop on Reliable and Responsible Foundation Models

Bidding for Influence: Auction-Driven Diffusion Image Generation

Lillian Sun · Henry Huang · Fucheng Zhu · Giannis Daras · Constantinos Daskalakis


Abstract:

We introduce the first generative image auction, utilizing diffusion models to express multi-agent preferences. Our mechanism first composes advertisers’ bids and text prompts inside the diffusion model’s reverse process through a dynamic score composition. Then, it implements Monte Carlo sampling to calculate VCG-based payments and select high-welfare images. Extensive experiments on a two-agent benchmark demonstrate three core properties: (1) bid monotonicity, (2) efficiency improvement of up to 21% higher welfare than a single-winner VCG baseline, and (3) approximate incentive compatibility, with average regret achieving below 10% when deviating from truthful bidding. Crucially, these benefits are achieved while preserving high image quality. Our study establishes a principled bridge between auction theory and controllable image diffusion, laying a foundation for economically-aligned, multi-stakeholder image generation in advertising and beyond.

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