ATHENA: Adaptive Test-Time Steering for Improving Count Fidelity in Diffusion Models

Mohammad Shahab Sepehri, Asal Mehradfar, Berk Tinaz, Salman Avestimehr, Mahdi Soltanolkotabi

Keywords: Text-to-Image Generation, Diffusion Models, Test-Time Scaling, Adaptive Steering, Object Counting, Numerical Control

TL;DR: We present ATHENA, a model-agnostic test-time framework for text-to-image diffusion models that adaptively steers the diffusion trajectory to improve object count fidelity in generated images.

Abstract: Text-to-image diffusion models achieve high visual fidelity but surprisingly exhibit systematic failures in numerical control when prompts specify explicit object counts. To address this limitation, we introduce ATHENA, a model-agnostic, test-time adaptive steering framework that improves object count fidelity without modifying model architectures or requiring retraining. ATHENA leverages intermediate representations during sampling to estimate object counts and applies count-aware noise corrections early in the denoising process, steering the generation trajectory before structural errors become difficult to revise. We present three progressively more advanced variants of ATHENA that trade additional computation for improved numerical accuracy, ranging from static prompt-based steering to dynamically adjusted count-aware control. Experiments on established benchmarks and a new visually and semantically complex dataset show that ATHENA consistently improves count fidelity, particularly at higher target counts, while maintaining favorable accuracy-runtime trade-offs across multiple diffusion backbones.

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Submission Number: 18