# Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models

arXiv: https://arxiv.org/abs/2403.06381 · HTML: https://mlsanity.com/paper/2403.06381/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 30 of its 31 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2301.13826/) (2023)
- [Compositional Visual Generation with Composable Diffusion Models](https://mlsanity.com/paper/2206.01714/) (2022)
- [Erasing Concepts from Diffusion Models](https://mlsanity.com/paper/2303.07345/) (2023)
- [Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis](https://mlsanity.com/paper/2212.05032/) (2022)
- [Scaling Open-Vocabulary Object Detection](https://mlsanity.com/paper/2306.09683/) (2023)
- [Ablating Concepts in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2303.13516/) (2023)
- [Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment](https://mlsanity.com/paper/2306.08877/) (2023)
- [Dense Text-to-Image Generation with Attention Modulation](https://mlsanity.com/paper/2308.12964/) (2023)

## Built on by (most cited first, 27 of 27)

- [Token Merging for Training-Free Semantic Binding in Text-to-Image Synthesis](https://mlsanity.com/paper/2411.07132/) (2024)
- [Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps](https://mlsanity.com/paper/2411.15236/) (2024)
- [Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation](https://mlsanity.com/paper/2411.18301/) (2024)
- [Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!](https://mlsanity.com/paper/2410.20972/) (2024)
- [The Art of Deception: Color Visual Illusions and Diffusion Models](https://mlsanity.com/paper/2412.10122/) (2024)
- [CoCoNO: Attention Contrast-and-Complete for Initial Noise Optimization in Text-to-Image Synthesis](https://mlsanity.com/paper/2411.16783/) (2024)
- [SpatialReward: Verifiable Spatial Reward Modeling for Fine-Grained Spatial Consistency in Text-to-Image Generation](https://mlsanity.com/paper/2603.22228/) (2026)
- [PGC: Peak-Guided Calibration for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2605.21207/) (2026)
- [Diffusion Beats Autoregressive: An Evaluation of Compositional Generation in Text-to-Image Models](https://mlsanity.com/paper/2410.22775/) (2024)
- [Steer Away From Mode Collisions: Improving Composition In Diffusion Models](https://mlsanity.com/paper/2509.25940/) (2025)
- [Detail++: Training-Free Detail Enhancer for T2I Diffusion Models](https://mlsanity.com/paper/2507.17853/) (2025)
- [From Noise to Nuance: Advances in Deep Generative Image Models](https://mlsanity.com/paper/2412.09656/) (2024)
- [BrandFusion: A Multi-Agent Framework for Seamless Brand Integration in Text-to-Video Generation](https://mlsanity.com/paper/2603.02816/) (2026)
- [Attention Sinks in Diffusion Transformers: A Causal Analysis](https://mlsanity.com/paper/2605.09313/) (2026)
- [Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models](https://mlsanity.com/paper/2506.23418/) (2025)
- [An h-space Based Adversarial Attack for Protection Against Few-shot Personalization](https://mlsanity.com/paper/2507.17554/) (2025)
- [Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion](https://mlsanity.com/paper/2407.17882/) (2024)
- [Sparse Fine-Tuning of Transformers for Generative Tasks](https://mlsanity.com/paper/2507.10855/) (2025)
- [EmoStory: Emotion-Aware Story Generation](https://mlsanity.com/paper/2603.10349/) (2026)
- [TWIG: Two-Step Image Generation using Segmentation Masks in Diffusion Models](https://mlsanity.com/paper/2504.14933/) (2025)
- [FashionMAC: Deformation-Free Fashion Image Generation with Fine-Grained Model Appearance Customization](https://mlsanity.com/paper/2511.14031/) (2025)
- [Human-AI Collaboration Mechanism Study on AIGC Assisted Image Production for Special Coverage](https://mlsanity.com/paper/2512.13739/) (2025)
- [PureCC: Pure Learning for Text-to-Image Concept Customization](https://mlsanity.com/paper/2603.07561/) (2026)
- [Let Triggers Control: Frequency-Aware Dropout for Effective Token Control](https://mlsanity.com/paper/2603.27199/) (2026)
- [AI-T2I: Aggregating-and-Isolating Cross-Attention to Diffusion Models for Text-to-Image Synthesis](https://mlsanity.com/paper/2605.25763/) (2026)
- [STEDiff: Strengthening Text Embedding for Text-to-Image Alignment in Diffusion Model](https://mlsanity.com/paper/2606.10653/) (2026)
- [Virtual Ring Try-On](https://mlsanity.com/paper/2606.28792/) (2026)

## Abstract (arXiv)

Recent advancements in diffusion models have notably improved the perceptual quality of generated images in text-to-image synthesis tasks. However, diffusion models often struggle to produce images that accurately reflect the intended semantics of the associated text prompts. We examine cross-attention layers in diffusion models and observe a propensity for these layers to disproportionately focus on certain tokens during the generation process, thereby undermining semantic fidelity. To address the issue of dominant attention, we introduce attention regulation, a computation-efficient on-the-fly optimization approach at inference time to align attention maps with the input text prompt. Notably, our method requires no additional training or fine-tuning and serves as a plug-in module on a model. Hence, the generation capacity of the original model is fully preserved. We compare our approach with alternative approaches across various datasets, evaluation metrics, and diffusion models. Experiment results show that our method consistently outperforms other baselines, yielding images that more faithfully reflect the desired concepts with reduced computation overhead. Code is available at https://github.com/YaNgZhAnG-V5/attention_regulation.