Diffusion Model Alignment

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Test-Time Alignment of Text-to-Image Diffusion Models via Null-Text Embedding Optimisation
Nov 25, 2025Taehoon Kim, Henry Gouk, Timothy HospedalesDiffusion Model Alignment Reward Hacking
School of Informatics, University of Edinburgh · 2Samsung AI Center, Cambridge
Distribution Matching Distillation Meets Reinforcement Learning
Nov 17, 2025Dengyang Jiang, Dongyang Liu, Zanyi Wang +12Diffusion Model Alignment Diffusion Model Distillation
1HKUST · 2Alibaba Group · 3SIAT, CAS +3
Advantage Weighted Matching: Aligning RL with Pretraining in Diffusion Models
Sep 29, 2025Shuchen Xue, Chongjian Ge, Shilong Zhang +2Score Matching Diffusion Model Alignment
UCAS · Adobe Research · HKU +1
MixGRPO: Unlocking Flow-based GRPO Efficiency with Mixed ODE-SDE
Jul 29, 2025Junzhe Li, Yutao Cui, Tao Huang +8Diffusion Model Alignment Flow Matching
Hunyuan, Tencent, China · School of Computer Science, Peking University, China · Computer Center, Peking University, China +2
Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model
Jul 11, 2025Fang Chen, Alex Villa, Gongbo Liang +3Diffusion Model Alignment Conditional Image Generation
University of California Merced · Texas A&M University-San Antonio · Oregon State University +1
LoRAShield: Data-Free Editing Alignment for Secure Personalized LoRA Sharing
Jul 5, 2025Jiahao Chen, Junhao Li, Yiming Wang +6Diffusion Model Alignment T2I Diffusion Models
Zhejiang University College of Computer Science and Technology Hangzhou, China · Guizhou Medical University School of Biology and Engineering Guiyang, China · Zhejiang University School of Software Technology Ningbo, China +1
Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models
Jun 12, 2025Konstantinos Vilouras, Ilias Stogiannidis, Junyu Yan +2Diffusion Model Alignment Prompt Tuning
School of Engineering, University of Edinburgh · Canon Medical Research Europe Ltd.
ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models
Jun 11, 2025Qin Zhou, Zhiyang Zhang, Jinglong Wang +5Diffusion Model Alignment Text-Image Alignment
School of Software, Beihang University, Beijing 100191, China · College of Computer and Cyber Security, Hebei Normal University, Hebei 050024, China · Department of Computer Science, The University of Hong Kong, Hong Kong
DICE: Distilling Classifier-Free Guidance into Text Embeddings
Feb 6, 2025Zhenyu Zhou, Defang Chen, Can Wang +2Diffusion Model Alignment Diffusion Model Guidance
Zhejiang University, State Key Laboratory of Blockchain and Data Security · University at Buffalo, State University of New York
Anomaly-Preference Image Generation
Date pendingFuyun Wang, Yuanzhi Wang, Xu Guo +6Diffusion Model Alignment Diffusion Model Sampling
Nanjing University of Science and Technology, Nanjing, China · Beijing Normal University, Beijing, China · China Academy of Space Technology, Beijing, China +1
TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards
Date pendingMingxuan Cui, Jingpu Yang, Fengxian Ji +7Diffusion Model Alignment Reward Modeling
Mohamed bin Zayed University of Artificial Intelligence · Chinese Academy of Sciences Institute of Automation · Northeastern University +1

Nov 25, 2025, cs.CVJ/K move · Enter open · S save

Test-Time Alignment of Text-to-Image Diffusion Models via Null-Text Embedding Optimisation

Taehoon Kim, Henry Gouk, Timothy Hospedales

School of Informatics, University of Edinburgh · 2Samsung AI Center, Cambridge

Diffusion Model Alignment Reward Hacking

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Test-time alignment (TTA) aims to adapt models to specific rewards during inference. However, existing methods tend to either under-optimise or over-optimise (reward hack) the target reward function. We propose Null-Text Test-Time Alignment (Null-TTA), which aligns diffusion models by optimising the unconditional embedding in classifier-free guidance, rather than manipulating latent or noise variables. Due to the structured semantic nature of the text embedding space, this ensures alignment occurs on a semantically coherent manifold and prevents reward hacking (exploiting non-semantic noise patterns to improve the reward). Since the unconditional embedding in classifier-free guidance serves as the anchor for the model's generative distribution, Null-TTA directly steers model's generative distribution towards the target reward rather than just adjusting the samples, even without updating model parameters. Thanks to these desirable properties, we show that Null-TTA achieves state-of-the-art target test-time alignment while maintaining strong cross-reward generalisation. This establishes semantic-space optimisation as an effective and principled novel paradigm for TTA.

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