HyperAlign: Hypernetwork for Efficient Test-Time Alignment of Diffusion Models

• URL: http://arxiv.org/abs/2601.15968v1
• Date: Thu, 22 Jan 2026 13:49:47 GMT
• Title: HyperAlign: Hypernetwork for Efficient Test-Time Alignment of Diffusion Models
• Authors: Xin Xie, Jiaxian Guo, Dong Gong,
• Abstract summary: We propose a novel framework that trains a hypernetwork for efficient and effective test-time alignment.<n>Instead of modifying latent states, HyperAlign dynamically generates low-rank adaptation weights to modulate the diffusion model's generation operators.<n>It significantly outperforms existing fine-tuning and test-time scaling baselines in enhancing semantic consistency and visual appeal.
• Score: 23.070399327132737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models achieve state-of-the-art performance but often fail to generate outputs that align with human preferences and intentions, resulting in images with poor aesthetic quality and semantic inconsistencies. Existing alignment methods present a difficult trade-off: fine-tuning approaches suffer from loss of diversity with reward over-optimization, while test-time scaling methods introduce significant computational overhead and tend to under-optimize. To address these limitations, we propose HyperAlign, a novel framework that trains a hypernetwork for efficient and effective test-time alignment. Instead of modifying latent states, HyperAlign dynamically generates low-rank adaptation weights to modulate the diffusion model's generation operators. This allows the denoising trajectory to be adaptively adjusted based on input latents, timesteps and prompts for reward-conditioned alignment. We introduce multiple variants of HyperAlign that differ in how frequently the hypernetwork is applied, balancing between performance and efficiency. Furthermore, we optimize the hypernetwork using a reward score objective regularized with preference data to reduce reward hacking. We evaluate HyperAlign on multiple extended generative paradigms, including Stable Diffusion and FLUX. It significantly outperforms existing fine-tuning and test-time scaling baselines in enhancing semantic consistency and visual appeal.

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